kernel_id,kaggle_score,kaggle_comments,kaggle_upvotes,kernel_link,comp_name
23299654,0.96392,0,0,/antonsharandin/lr1-anton-sharandin,Digit Recognizer
23156584,0.96442,0,0,/aleksandrkolbin/kolbin-8305-lr1,Digit Recognizer
23095035,0.97364,3,4,/sumeetbohra/a-very-basic-neural-network,Digit Recognizer
23100959,0.96467,0,0,/denisshvetsov811/lr1-shvetsov8305,Digit Recognizer
23077471,0.09896,4,7,/mdjafrilalamshihab/digit-recognizer-using-classic-ml,Digit Recognizer
23000939,0.964,0,0,/ivansmeyukha/smeyukha-8305-lab1,Digit Recognizer
22893153,0.96192,0,1,/soloveve/telushkina-dr,Digit Recognizer
22886970,0.97092,0,1,/kirsonbrz/digit-recognizer-830804,Digit Recognizer
22847169,0.98817,0,0,/leonidspiridonov/digitrecignizer,Digit Recognizer
22839137,0.98696,5,7,/dhyeydabhi/resnet50,Digit Recognizer
22789362,0.98921,1,6,/saumilagrawal10/getting-started-with-neural-networks-keras,Digit Recognizer
22795265,0.97685,0,0,/vinayakunde/digit-recognizer-using-cnn,Digit Recognizer
22447831,0.99071,0,0,/spameron/learning-cnn,Digit Recognizer
22834546,0.99225,0,0,/leo45890/mnist-with-keras-cnn-and-gpu,Digit Recognizer
22613712,0.95028,0,0,/philippstarzacher/exercise04,Digit Recognizer
21467591,0.98442,0,0,/jaipawar/digit-recognizer,Digit Recognizer
22404806,0.93135,0,0,/patricksto/mnist,Digit Recognizer
22558489,0.99082,1,3,/sanchitpathak/digit-recognizer-using-cnn,Digit Recognizer
22544565,0.93585,0,0,/fabianstiegler/digit-recognition-mlp,Digit Recognizer
22159074,1.0,3,10,/andrej0marinchenko/digit-recognizer,Digit Recognizer
13248670,5242.189,9,41,/hamditarek/market-prediction-catboost-classifier,Jane Street Market Prediction
14498655,0.70627,8,7,/indranisen06/xgbregressor-model,Tabular Playground Series - Jan 2021
14452077,0.71745,7,9,/onurserbetci/keras-lightgbm,Tabular Playground Series - Jan 2021
14477733,0.71114,0,0,/webanalyst88/tabular-playground-fastai-v2,Tabular Playground Series - Jan 2021
14442516,0.70533,5,8,/niteshsaini/tabular-playground-series,Tabular Playground Series - Jan 2021
14178239,0.71419,0,0,/jayanthappalla/notebook01972ce568,Tabular Playground Series - Jan 2021
14403860,0.70181,0,5,/jamesmcguigan/tabular-playground-lightgbm,Tabular Playground Series - Jan 2021
14376120,0.69766,3,6,/neilcosgrove/jan-2021-tabular-playground-lgbm-xgboost,Tabular Playground Series - Jan 2021
14377327,0.69782,0,1,/dikuchan/tabular-playground-ensemble-team-cringe,Tabular Playground Series - Jan 2021
14132528,0.70142,1,1,/iainmcintosh/simple-xgboost-example-using-a-random-forest,Tabular Playground Series - Jan 2021
14403812,0.71198,0,0,/code1110/tabplay-kerastuner-starter,Tabular Playground Series - Jan 2021
14208130,0.70094,1,1,/blighpark/pycaret-regression,Tabular Playground Series - Jan 2021
14254347,0.70227,0,1,/jordankeith/tabular-playground-series-january,Tabular Playground Series - Jan 2021
14282092,0.69713,4,16,/shogosuzuki/0-69713-lightgbm-with-small-learning-rate,Tabular Playground Series - Jan 2021
14279666,0.72782,9,6,/tosinabase/jan-21-regularized-regression-ridge-and-lasso,Tabular Playground Series - Jan 2021
14262865,0.70204,0,1,/rushobd/tabularplayground,Tabular Playground Series - Jan 2021
14230649,0.9854,6,10,/mdhamani/tps-getting-better-eda-pytorch-neural-net,Tabular Playground Series - Jan 2021
14247181,0.7015,0,0,/vegorovmsk/xgb-tuning-by-analytics-vidhya,Tabular Playground Series - Jan 2021
14207193,0.70046,5,6,/essefiahlem/get-started-jan-tabular-playground-competition,Tabular Playground Series - Jan 2021
14202948,0.70218,4,7,/vegorovmsk/xgb-with-removed-outliers,Tabular Playground Series - Jan 2021
14139468,0.7003,0,0,/karimeid95/catboost,Tabular Playground Series - Jan 2021
14220134,0.69973,5,4,/biswajitghosh145/beginner-friendly-tutorial-0-69973-score,Tabular Playground Series - Jan 2021
14205960,0.71793,1,1,/luisvelasco/dnnregressor,Tabular Playground Series - Jan 2021
20150135,0.6522,6,5,/hyunhp/eda-baseline-model-by-aiswarya,New York City Taxi Trip Duration
18937160,0.68717,0,1,/sudohumberto/ny-taxis,New York City Taxi Trip Duration
8682100,0.35116,5,10,/prateekagnihotri/dfdc-lrcn-inference-kernel-1,Deepfake Detection Challenge
17910828,0.77574,0,0,/haramlee/bigdata-project-eda-fe-haram-lee,Home Credit Default Risk
17544955,0.77289,0,0,/kimyijun/bigdata-project-eda-fe,Home Credit Default Risk
16847556,0.73428,0,0,/sakibsadmanshajib/home-credit-loan,Home Credit Default Risk
17096357,0.75229,0,0,/yshiml/homecredit-lgb,Home Credit Default Risk
16366342,0.6738,0,0,/mogu4iy/home-credit-default-risk,Home Credit Default Risk
15886745,0.79977,0,3,/sangseoseo/home-credit-default-risk-prediction,Home Credit Default Risk
15025260,0.73901,0,3,/drbeanesp21/home-credit-default-risk-submission,Home Credit Default Risk
21768463,0.78708,0,0,/nisharahysmith/notebookda32490206,Titanic - Machine Learning from Disaster
21675214,0.76555,9,14,/karansehgal13/logistic-regression-on-titanic-dataset-eda,Titanic - Machine Learning from Disaster
21712413,0.75598,0,3,/rhythmcam/automl-mljar-optuna-titanic,Titanic - Machine Learning from Disaster
21322805,0.77511,0,2,/ryotaroyabe/notebook-titanic,Titanic - Machine Learning from Disaster
14247697,0.77511,0,1,/brkstat/getting-started-with-titanic,Titanic - Machine Learning from Disaster
21686340,0.76794,0,7,/rhythmcam/automl-search-best-titanic-model,Titanic - Machine Learning from Disaster
21674605,0.77751,3,14,/ehsandahesh/titanic-with-xgboost,Titanic - Machine Learning from Disaster
21541563,0.66746,0,1,/yagyeshb/base-logistic-regression-model-with-numerical-col,Titanic - Machine Learning from Disaster
21678228,0.77511,0,1,/liubei666/study1-titanic,Titanic - Machine Learning from Disaster
21660342,0.78229,0,2,/aershov/ml2021-lab-2-titanic,Titanic - Machine Learning from Disaster
21648808,0.80382,0,4,/rhythmcam/autogluon-titanic,Titanic - Machine Learning from Disaster
19078367,0.77511,0,0,/mohamedgalaleldin/getting-started-with-titanic,Titanic - Machine Learning from Disaster
21612161,0.76794,0,15,/moathmohamed/titanic-disaster-88-simple-explanation,Titanic - Machine Learning from Disaster
21632140,0.76555,0,0,/yulikawijayanti/classification-titanic-kaggle,Titanic - Machine Learning from Disaster
21030150,0.72727,0,0,/rizkynindra/titanic-extraction,Titanic - Machine Learning from Disaster
21596992,0.73684,1,3,/chaqsa/titanic-basic-solution-using-adaboost,Titanic - Machine Learning from Disaster
21374940,0.78468,4,6,/sudhakar51/titanic-rfc-some-analysis-on-ticket,Titanic - Machine Learning from Disaster
16585522,0.97015,0,8,/tt195361/ranzcr-1st-place-solution-by-tf-5-inference,RANZCR CLiP - Catheter and Line Position Challenge
15515986,0.967,0,6,/c7934597/resnet200d-public-benchmark-inference-model,RANZCR CLiP - Catheter and Line Position Challenge
15725024,0.81489,0,1,/dionysios1981/ranzcr-resnet200d,RANZCR CLiP - Catheter and Line Position Challenge
15591628,0.97287,1,37,/haqishen/ranzcr-1st-place-soluiton-inference-small-ver,RANZCR CLiP - Catheter and Line Position Challenge
15164527,0.968,0,3,/kanruwang/ranzcr-resnet200d-seresnet152d-efficientnetb5,RANZCR CLiP - Catheter and Line Position Challenge
15311429,0.5,2,4,/stormchaser/ranzcr-clip-fastai,RANZCR CLiP - Catheter and Line Position Challenge
15200959,0.966,0,3,/omarhammemi/pytorch-model,RANZCR CLiP - Catheter and Line Position Challenge
15410384,0.965,0,2,/sa2zoi/simplified-ranzcr-clip-resnet200d,RANZCR CLiP - Catheter and Line Position Challenge
15412632,0.901,0,2,/asafkeidar/densenet121-finetuning-inference,RANZCR CLiP - Catheter and Line Position Challenge
15173072,0.964,2,6,/archanabenur/resnet200d,RANZCR CLiP - Catheter and Line Position Challenge
15250275,0.956,0,5,/vickygoyal/eff7inference,RANZCR CLiP - Catheter and Line Position Challenge
15203842,0.967,1,61,/roydatascience/resnet200d-public-multi-head-private-lb-970,RANZCR CLiP - Catheter and Line Position Challenge
14767736,0.965,0,2,/ghaiyur/ranzcr-resnet,RANZCR CLiP - Catheter and Line Position Challenge
15074438,0.965,0,4,/rakshitapatil07/resnet-c12,RANZCR CLiP - Catheter and Line Position Challenge
15115184,0.77,0,3,/jainarindam/ranzcr-baseline,RANZCR CLiP - Catheter and Line Position Challenge
13297916,0.405,0,0,/suryaaseran/cassava-leaf-disease,Cassava Leaf Disease Classification
17151036,0.8824,0,0,/chaewonkimmm/cs376-team-3-cassava-leaf-training,Cassava Leaf Disease Classification
14158315,0.8747,0,0,/nanguyen/cassava-simple-baseline,Cassava Leaf Disease Classification
16391713,0.4137,0,0,/akashsdas/cassava-leaf-disease-classification-submission,Cassava Leaf Disease Classification
14113092,0.79834,0,1,/nicknosorogov/distweetrhinosceros,Natural Language Processing with Disaster Tweets
23000148,0.80815,0,3,/koan14/ktrain-disaster-tweet-model,Natural Language Processing with Disaster Tweets
23121360,0.83941,0,3,/sytuannguyen/nlp-with-bert,Natural Language Processing with Disaster Tweets
22956883,0.79957,0,1,/renraeldab/disaster-tweets,Natural Language Processing with Disaster Tweets
22336401,0.76677,4,0,/cluelessds/logistic-regression-with-threshold-tuning,Natural Language Processing with Disaster Tweets
22848179,0.75819,1,1,/mohamedmostafa335/nlp-tokenization-embedding-lstm,Natural Language Processing with Disaster Tweets
22728893,0.79589,1,3,/rjconstable/nlp-starter-spacy-binary-text-classifier,Natural Language Processing with Disaster Tweets
22626888,1.0,3,9,/andrej0marinchenko/nlp-with-disaster-tweets-new-b,Natural Language Processing with Disaster Tweets
22263006,0.82745,0,4,/phanttan/using-keywords-embedding-to-improve-bert-model,Natural Language Processing with Disaster Tweets
20205650,0.60435,6,4,/mohamedsarwat/nlp-with-disaster-tweets,Natural Language Processing with Disaster Tweets
21952415,0.78026,0,0,/benwilliams16/nlp-assignment-1,Natural Language Processing with Disaster Tweets
22312048,0.79558,3,2,/semipro/nlp-disaster-tweets-tfidf-lr,Natural Language Processing with Disaster Tweets
22200665,0.844,5,15,/brendanartley/roberta-w-tensorflow-explained-0-844,Natural Language Processing with Disaster Tweets
20035187,1.01383,0,0,/jonasfichtmller/combined-version-eda-fe-model-training,Predict Future Sales
20074105,0.90684,1,3,/realtimshady/feature-engineering-xgboost,Predict Future Sales
19895879,3.22752,0,0,/stevenylai/predictfuturesales-boosttrees,Predict Future Sales
19877342,1.87668,0,0,/santhiyar/day-16,Predict Future Sales
17678280,1.65639,0,0,/harshhzz/notebook81c77cda2a,Predict Future Sales
18265096,1.18632,2,6,/abhishekyawalkar/sales-predictions-gradient-boosting-regressor,Predict Future Sales
17825111,1.95955,2,14,/jerifate/future-sales-time-series-visualization,Predict Future Sales
17122132,2.71056,0,0,/bob871227/predict-future-sales,Predict Future Sales
17334187,1.43483,4,6,/sohaelshafey/predicting-future-sales,Predict Future Sales
16404911,1.05424,3,9,/jzeferino/predictive-sales-with-linear-regressions,Predict Future Sales
17478968,5.74201,1,3,/andadada/sales-forecasting-based-on-rf,Predict Future Sales
17186486,1.45706,3,5,/chidhvilas/sales-forecast-coursera,Predict Future Sales
17035686,1.19838,3,1,/yeeyunjie/sales-prediction,Predict Future Sales
18841125,0.78838,0,3,/tarunnagavinaysai/leaf-classification,Leaf Classification
12137366,0.59978,0,2,/leangab/tensorflow-speech-recognition-challenge,TensorFlow Speech Recognition Challenge
21332080,2073986.64465,0,4,/at3191/restaurant-revenue-prediction-trial-1,Restaurant Revenue Prediction
18104668,1710903.36576,0,0,/taigasogame/group3-mixparam,Restaurant Revenue Prediction
16677552,3061215.91449,0,6,/megha8v/p1-restaurant-revenue-prediction,Restaurant Revenue Prediction
15783175,1727330.2677,1,1,/rookgordon/notebookef8f96df00,Restaurant Revenue Prediction
18365065,1713253.58286,0,0,/yukitotu/3han-restaurant-revenue-prediction,Restaurant Revenue Prediction
21111009,0.0,0,0,/sandynigs/ensemble-models-jigsaw,Jigsaw Unintended Bias in Toxicity Classification
18051586,0.0,1,1,/afrinjubaida/toxicity,Jigsaw Unintended Bias in Toxicity Classification
17243020,0.8964,1,7,/muhakabartay/siim-isic-melanoma-classification-efficientnet,SIIM-ISIC Melanoma Classification
15456565,0.6696,0,0,/alisharifi2000/autoencoder-gpu,SIIM-ISIC Melanoma Classification
13171978,0.8056,0,5,/saschamet/siim-isic-melanoma-classification,SIIM-ISIC Melanoma Classification
17662497,0.015433,0,0,/linsistx/nfl-simple-model-using-lightgbm-from-scratch,NFL Big Data Bowl
20003291,0.16562,0,1,/manishguptads/exercise-intro-to-automl,House Prices - Advanced Regression Techniques
19847658,0.15284,2,8,/shenurisumanasekara/house-price-2,House Prices - Advanced Regression Techniques
19777303,0.18237,0,2,/blood174/predicting-house-prices-with-linear-regression,House Prices - Advanced Regression Techniques
19555124,0.17676,0,0,/lemorra8kaggle/house-price-prediction-with-ridge-regression,House Prices - Advanced Regression Techniques
19922564,0.14761,0,3,/sanjeevkumarm/house-price-prediction-using-pipeline,House Prices - Advanced Regression Techniques
19908822,0.47701,0,0,/santhiyar/house-price-prediction,House Prices - Advanced Regression Techniques
19838613,0.1531,11,12,/ahmedhaytham/new777,House Prices - Advanced Regression Techniques
18656695,0.12828,0,0,/dipesh8/house-prices-feature-engineering,House Prices - Advanced Regression Techniques
19743793,0.13641,0,1,/aeyeee/hyperparameter-optimization-using-optuna,House Prices - Advanced Regression Techniques
19295244,0.21503,0,1,/laloromero/house-prices-challenge,House Prices - Advanced Regression Techniques
19695351,0.22476,1,5,/dariocioni/house-prices,House Prices - Advanced Regression Techniques
19689053,0.14779,0,0,/yogeshwaranma/house-price,House Prices - Advanced Regression Techniques
18651914,0.20155,0,2,/muhammadshahroz/house-prices,House Prices - Advanced Regression Techniques
19586269,0.4202,25,33,/pralabhpoudel/house-price-prediction-regression-models-and-dl,House Prices - Advanced Regression Techniques
19580205,0.12721,4,8,/smartsolutaris/house-price-prediction,House Prices - Advanced Regression Techniques
18708747,0.13873,0,3,/jortam/predicting-house-prices,House Prices - Advanced Regression Techniques
14012851,0.783,10,22,/mukuldsagupta/riiid-answer-correctness-prediction-lgbm,Riiid Answer Correctness Prediction
22520881,0.9247,0,0,/amythecoolest/ww-cancer-detection-submission-v04-ensem,Histopathologic Cancer Detection
22000364,0.7971,0,1,/leopoldtchomgwi/lt-cancer-detection-cropping-layers-submission,Histopathologic Cancer Detection
16275989,0.9547,0,0,/alxthms/keras,Histopathologic Cancer Detection
13592835,0.895,0,0,/matveevayulia/cassava-leaf-disease-gpu,Cassava Leaf Disease Classification
13997153,0.733,0,1,/kei96kag/cassava-study-augmentation-and-cnn,Cassava Leaf Disease Classification
13008265,0.749,8,9,/bibhash123/cassava-classification-simple-overview-tf-keras,Cassava Leaf Disease Classification
13936057,0.886,2,7,/danurahul/data-cleaning-model-training-inference-using-tez,Cassava Leaf Disease Classification
13867500,0.448,0,3,/anantgupt/cassava-leaf-doctor-submission,Cassava Leaf Disease Classification
13786551,0.625,0,1,/ssarkar445/cassava-leaf-disease-tr-baseline,Cassava Leaf Disease Classification
14923844,0.01596,0,0,/heisenbergppt/testmlp,PUBG Finish Placement Prediction (Kernels Only)
13849542,0.0203,0,0,/nmsx2016029/nmsx2016058,PUBG Finish Placement Prediction (Kernels Only)
17364953,195.608,0,0,/khushalbr/cs535-resnet50-inference,Lyft Motion Prediction for Autonomous Vehicles
19567958,0.99446,1,1,/jenilsavani/cnn-with-99-44-accuracy,Digit Recognizer
19478081,0.99428,6,12,/mhmdsyed/digital-handwriting-recognizer,Digit Recognizer
18933780,0.96414,0,1,/caiofernandeslima/96-accuracymnist,Digit Recognizer
18907580,0.99357,0,2,/machiavelli81/minst-digit-recognizer-resnet18,Digit Recognizer
19308449,0.986,0,3,/endy3032/mnist-digit-recognizer,Digit Recognizer
19223927,0.99532,3,9,/miguelquiceno/digit-recognizer-cnn-data-augmentation,Digit Recognizer
18968817,0.99235,2,6,/shub20/digit-recognizer-using-2d-cnn,Digit Recognizer
18967724,0.9651,1,1,/patrickb1912/simplest-digit-recognizer-knn,Digit Recognizer
18871430,0.99939,0,5,/lqdisme/digit-recognizer-with-simple-cnn,Digit Recognizer
18672863,0.98989,4,8,/davidanimaddo/recognize-digits-with-tensorflow-cnn,Digit Recognizer
18794973,0.97346,3,6,/derrelldsouza/digit-recognizer-dct-pca-ann-98-accuracy,Digit Recognizer
18752947,0.99485,0,0,/katuniq/cnn-ver1,Digit Recognizer
18781375,0.97732,5,3,/ashrafr/simple-mnist-cnn-using-tensorflow,Digit Recognizer
16960601,0.99421,1,3,/aymanlafaz/mnist-resnet50-transfer-learning,Digit Recognizer
18665924,1.0,40,72,/ahmed121ashraf131/mnist-simple-cnn-knn-accuracy-100-top-1,Digit Recognizer
18757253,0.96885,0,0,/lilkaskitc/mnist-basic-neural-networks,Digit Recognizer
14782795,0.607,0,0,/alexyg/submit,Cassava Leaf Disease Classification
14771514,0.904,1,7,/jaideepvalani/with-b4-pytorch-efficientnet-baseline,Cassava Leaf Disease Classification
14754145,0.904,0,5,/markwijkhuizen/cassava-leaf-disease-inference-5-fold,Cassava Leaf Disease Classification
14743221,0.898,0,0,/flyman/se50-renext,Cassava Leaf Disease Classification
14709342,0.862,0,0,/zakariaelamim/cassava-leaf-disease-enb0,Cassava Leaf Disease Classification
14660386,0.889,1,9,/sanskarram/cassava-tta-inference,Cassava Leaf Disease Classification
14648510,0.901,0,2,/catchbelif/model-f1-469-f9-568-f4-7-f2-68,Cassava Leaf Disease Classification
14644507,0.898,0,1,/syxuming/multmodel-inference-baseline,Cassava Leaf Disease Classification
14628301,0.746,0,0,/aidtaleb/compete,Cassava Leaf Disease Classification
14587969,0.792,14,19,/mnavaidd/casava-leaf-disease-classification,Cassava Leaf Disease Classification
14562043,0.895,0,0,/kirolcheng/noisy-label-eda-with-cleanlab,Cassava Leaf Disease Classification
14547837,0.868,0,0,/zakariaelamim/cassava-leaf-disease-en-b1,Cassava Leaf Disease Classification
14524465,0.435,3,5,/saimanojakondi/inference,Cassava Leaf Disease Classification
14485909,0.905,0,0,/raipachi0704/inference-weights-optimization,Cassava Leaf Disease Classification
14477820,0.895,15,76,/telljoy/noisy-label-eda-with-cleanlab,Cassava Leaf Disease Classification
14460402,0.907,0,0,/kozistr/inference-rns50-effnet-b4,Cassava Leaf Disease Classification
14429092,0.871,31,61,/manojkumars00/casava-leaf-disease-simple-classification,Cassava Leaf Disease Classification
14353935,0.894,1,5,/durbin164/tpu-visual-transformer-vit-keras-tf-inferance,Cassava Leaf Disease Classification
22571055,2.43613,0,0,/flafuji/sf-crime-eda-model-explained,San Francisco Crime Classification
20427230,27.62709,0,0,/nikhiln2312/san-fransisco-crime,San Francisco Crime Classification
19300486,2.37129,0,4,/anshulavinashe/sanfrancisco-crime-classification,San Francisco Crime Classification
19417570,2.55949,0,1,/sohann/notebook6d330d8342,San Francisco Crime Classification
18824976,27.62709,0,0,/rajeshwaris/sanfrancisco-crime-classification,San Francisco Crime Classification
17850440,26.64021,0,0,/denimthangjam/san-francisco-crime,San Francisco Crime Classification
17775536,27.62709,0,4,/aryaadesh/sf-crime,San Francisco Crime Classification
17471337,27.62709,0,2,/chromerai/san-francisco-crime-classification,San Francisco Crime Classification
17110837,26.85629,0,0,/diyadodwad/notebook0a60656068,San Francisco Crime Classification
15942904,25.71036,0,0,/xevator/notebook3f56133c01,San Francisco Crime Classification
15168055,26.40355,0,1,/subhamsagarpaira/beginners-neural-sfcrime,San Francisco Crime Classification
17006598,0.61304,35,33,/jeongwonkim10516/eda-pred-using-roberta-emerald-color,Tweet Sentiment Extraction
10158536,0.714,1,0,/mohamedkhamis/tse-roberta-cnn,Tweet Sentiment Extraction
13954401,0.61472,2,2,/amitmehra04/sentiment-identification,Tweet Sentiment Extraction
16812286,0.9196,0,2,/jamesngoa/santander-ctp-modeling-0-917-priv-lb,Santander Customer Transaction Prediction
19038214,0.77511,0,1,/poeteller/getting-started-with-titanic,Titanic - Machine Learning from Disaster
22833988,0.77751,4,7,/dylanyves/titanic-prediction,Titanic - Machine Learning from Disaster
22913057,0.78468,0,0,/tkachevigor/ml-lab2-tkachev,Titanic - Machine Learning from Disaster
10267371,0.77511,0,0,/juliaanddragons/titanic-my-first-trial,Titanic - Machine Learning from Disaster
22892834,0.78468,0,4,/sfktrkl/titanic-votingclassifier-with-gridsearchcv,Titanic - Machine Learning from Disaster
22893677,0.78468,0,1,/oladimejialabi/oladimeji-kaggle-titanic-one,Titanic - Machine Learning from Disaster
22887181,0.77751,0,3,/rhythmcam/automl-tpot-remove-outlier,Titanic - Machine Learning from Disaster
22873841,0.77511,1,8,/sandorabad/titanic-07,Titanic - Machine Learning from Disaster
22876768,0.77511,0,0,/yantingliu01/2021-12-18-titanic,Titanic - Machine Learning from Disaster
22745657,0.78708,0,0,/juliavassilenko/laba2,Titanic - Machine Learning from Disaster
22798317,0.77033,4,18,/jaredscolaro/titanic-logistic-regression-and-decision-tree,Titanic - Machine Learning from Disaster
22812703,0.77511,0,0,/rithuraj/ttitanic-the-beginning,Titanic - Machine Learning from Disaster
22798211,0.79186,5,4,/victordonjuan/my-titanic-score-0-79186-with-random-forest,Titanic - Machine Learning from Disaster
22802293,0.77511,1,3,/juanconher/data-cleansing-exploration-voting-classifier,Titanic - Machine Learning from Disaster
22872362,0.6866,0,0,/banksn/final-neural-network-titanic-survival-challenge,Titanic - Machine Learning from Disaster
22775963,0.77511,1,4,/justinm13/titanic,Titanic - Machine Learning from Disaster
22721283,0.76315,8,13,/wonchanleee/compare-dt-lr-rf-classifier-firstsubmit,Titanic - Machine Learning from Disaster
20158673,0.69862,0,3,/divyanshtrivedi/telstra-network-disruptions,Telstra Network Disruptions
22455452,0.82869,1,1,/svnsdhananjayreddy/flower-classification-with-tpus,Flower Classification with TPUs
19201769,0.83942,1,1,/sudharshann/flowerclassification-shan,Flower Classification with TPUs
18870011,0.82686,0,0,/prathikshabr/flowerclassification-cnn-kaggleproject-9,Flower Classification with TPUs
15300756,0.83925,0,1,/nishita17/flower-classification-with-tpus,Flower Classification with TPUs
15269168,0.68839,0,0,/semenedel/flower-classification,Flower Classification with TPUs
15178347,0.73397,0,0,/subhamsagarpaira/cnn-flower-classification-with-tpus,Flower Classification with TPUs
13608334,0.9421,1,2,/phsaikiran/flower-classification-densenet201,Flower Classification with TPUs
14176535,0.72113,11,7,/agileteam/tensorflow-baseline-tutorial-starter,Tabular Playground Series - Jan 2021
14186319,0.70559,0,0,/shyam21/auto-ml-tps21,Tabular Playground Series - Jan 2021
14055870,0.69981,5,5,/rafburzy/tabular-playground-competition-xgb-gridsearch,Tabular Playground Series - Jan 2021
14013643,0.71082,0,3,/rajsengo/tps-jan-2021-stater-eda-and-modeling,Tabular Playground Series - Jan 2021
14162481,0.69876,0,0,/kengofujii/playground-lgbm-xgb-catboost,Tabular Playground Series - Jan 2021
14139026,0.71336,0,3,/code1110/tabplay-pytorch-tabnet-starter,Tabular Playground Series - Jan 2021
14139272,0.70688,0,0,/susree64/tabular-play-ground-series-jan-2021,Tabular Playground Series - Jan 2021
14135634,0.7059,1,2,/ltminseokkim/novice-s-ml-regression-analysis-korean,Tabular Playground Series - Jan 2021
14128879,0.69821,5,4,/bezul2/basic-xgbregressor-tabular-playground-jan21,Tabular Playground Series - Jan 2021
14070377,0.69989,10,9,/yevonnaelandrew/lgbm-cat-xgb-optimization-stacking,Tabular Playground Series - Jan 2021
14087727,0.70158,9,12,/mdhamani/tps-eda-super-learner-randomized-search,Tabular Playground Series - Jan 2021
14089795,0.7012,0,2,/jswxhd/xgboost-vs-lgbm-optuna,Tabular Playground Series - Jan 2021
14062235,0.71352,2,21,/sishihara/1dcnn-for-tabular-from-moa-2nd-place,Tabular Playground Series - Jan 2021
14060812,0.72782,2,3,/njelicic/pure-numpy-ols,Tabular Playground Series - Jan 2021
14049721,0.72269,0,0,/vegorovmsk/linear-models-with-gridsearchcv,Tabular Playground Series - Jan 2021
14030995,0.69835,2,7,/ernnnn4u/baseline-lgb-no-tune,Tabular Playground Series - Jan 2021
13991623,0.70046,68,70,/dwin183287/tps-jan-2021-eda-models,Tabular Playground Series - Jan 2021
14042486,0.70462,0,7,/carlmcbrideellis/tabular-playground-histogramgboost-starter-script,Tabular Playground Series - Jan 2021
14020324,0.71168,1,7,/aeryss/tabular-playground-jan-2021-tabnet-baseline,Tabular Playground Series - Jan 2021
13991572,0.70459,8,23,/hamzaghanmi/tabular-playground-series-eda-xgboost,Tabular Playground Series - Jan 2021
14016028,0.71356,0,4,/xhlulu/tps-january-train-model-in-8s-using-rapids,Tabular Playground Series - Jan 2021
13962774,0.70383,0,3,/ssarkar445/playground-jan-2021-eda-stacked,Tabular Playground Series - Jan 2021
13956292,0.7037,13,15,/chandraroy/xgb-baseline-model,Tabular Playground Series - Jan 2021
13959467,0.7002,4,8,/rmiperrier/tps-jan-eda-cleaning-xgboost,Tabular Playground Series - Jan 2021
13955851,0.70038,2,12,/drcapa/playground-series-jan-2021-xgb-tutorial,Tabular Playground Series - Jan 2021
18912448,0.745,0,0,/yongyiw/dkvmn-reproduced,Riiid Answer Correctness Prediction
18673614,0.788,0,0,/yongyiw/aikt-reproduced,Riiid Answer Correctness Prediction
21309294,0.87605,1,1,/manas13/airbnb-new-user-bookings,Airbnb New User Bookings
17296551,0.86525,0,2,/faridsharaf/airbnb-booking-recommendation-xgboost,Airbnb New User Bookings
13071854,3592.371,25,72,/mrutyunjaybiswal/jane-street-details-explained-eda-metrics,Jane Street Market Prediction
13067995,6005.582,126,413,/hamditarek/market-prediction-xgboost-with-gpu-fit-in-1min,Jane Street Market Prediction
15034769,7543.007,0,6,/faisalalsrheed/jane-street-densenet-neutralizing-features,Jane Street Market Prediction
15001866,8925.507,0,0,/prashantkikani/jane-street-simple-nn-mlp-w-purgedgroup-v2,Jane Street Market Prediction
14870275,0.0,0,0,/marouf/linear-regression,Jane Street Market Prediction
14777203,6242.629,0,1,/kwht1023/jsmp-tcn-pytorch-inferece,Jane Street Market Prediction
14758545,5041.523,0,0,/unizy22/notebookee887e2bbd,Jane Street Market Prediction
14684495,3190.24,0,0,/adithyams15/notebooke29cb459d9,Jane Street Market Prediction
14661870,10803.087,2,7,/zhangyunsheng/tf-restnet,Jane Street Market Prediction
14631257,5659.24,0,0,/jamesmccarthy65/jane-street-pytorch-lightning-nn-pgts,Jane Street Market Prediction
14618934,7370.903,0,0,/andreasthomasen/pytorch-dense-model-feature-engineering,Jane Street Market Prediction
14611852,4978.761,0,0,/kerempr/notebook29d1012638,Jane Street Market Prediction
14573931,7060.324,0,0,/andreasthomasen/pretrained-pytorch-nn-w-o-feature-reduction,Jane Street Market Prediction
14507302,7102.488,0,0,/mikelintw/compute-training-utility-score,Jane Street Market Prediction
14493148,9330.157,7,15,/samir95/keras-nn-with-features-neutralization,Jane Street Market Prediction
11186013,-6.8766,1,2,/melvin97n/a-dummies-guide-to-pulmonary-fibrosis-challenge,OSIC Pulmonary Fibrosis Progression
18665686,0.77604,0,0,/shayistamulla/notebook0069fc782d,What's Cooking?
18117806,0.77292,0,0,/sakshi0100/what-s-cooking,What's Cooking?
17420437,0.77292,0,0,/mgen2020/cooking,What's Cooking?
16654309,0.77544,0,0,/sachingupta2212/notebook52dfd2447b,What's Cooking?
14822628,0.77292,0,0,/nishita17/whats-cooking,What's Cooking?
14137093,0.77292,3,2,/subhamsagarpaira/beginners-logistic-regression-what-s-cooking,What's Cooking?
22408471,0.77292,0,0,/svnsdhananjayreddy/what-s-cooking,What's Cooking?
18183940,0.77292,0,0,/rajeshwaris/what-s-cooking,What's Cooking?
21133360,0.06522,0,4,/hyunhp/simple-xgboost-starter-written-by-anokas,Zillow Prize: Zillow’s Home Value Prediction (Zestimate)
19393871,0.06522,0,0,/kimhyelin/zillow-prize-zillow-s-home-value-prediction-hl,Zillow Prize: Zillow’s Home Value Prediction (Zestimate)
16171934,0.0651,0,1,/arthurtelders/zillow-ml-lasso,Zillow Prize: Zillow’s Home Value Prediction (Zestimate)
16167937,0.06523,0,2,/arthurtelders/zillow-ml-xgboost,Zillow Prize: Zillow’s Home Value Prediction (Zestimate)
18346280,0.84288,0,5,/stpeteishii/tps0221-xgboost-optuna,Tabular Playground Series - Feb 2021
16088708,0.71487,0,8,/kingoffitpredict/time-optimization,Tabular Playground Series - Jan 2021
14766650,0.85801,0,0,/mminguc/tabular-playground-v1,Tabular Playground Series - Feb 2021
15388495,0.84568,0,1,/muneeb2405/multiple-boosting-algorithms,Tabular Playground Series - Feb 2021
14921019,0.84224,0,0,/phuchuynguyen/tps-k-fold-cv-model-selection,Tabular Playground Series - Feb 2021
15125551,0.84184,11,31,/gaetanlopez/pretrained-single-lgbm-0-84184-public-lb,Tabular Playground Series - Feb 2021
14959902,0.87683,0,0,/bernardoco/lgbm-tuning-w-optuna,Tabular Playground Series - Feb 2021
15212114,0.84591,0,2,/vishalrossi/tabular-playground-xgboost,Tabular Playground Series - Feb 2021
15125468,0.86573,6,8,/jobyingramdodd/nn-tab,Tabular Playground Series - Feb 2021
14780622,0.896,0,0,/bmensah/feb-tps-random-forest-regressor,Tabular Playground Series - Feb 2021
14927979,0.84258,1,0,/weidongxu/tps202102-lgbm,Tabular Playground Series - Feb 2021
15117916,0.8444,3,7,/zhaonat/eda-and-optuna-lgb,Tabular Playground Series - Feb 2021
15106333,0.86261,0,3,/sureshmecad/playground-series,Tabular Playground Series - Feb 2021
15086355,0.84275,1,8,/subbugalam/tps-simplified,Tabular Playground Series - Feb 2021
15076586,0.84184,36,82,/somayyehgholami/comparative-method-tabular-feb-301,Tabular Playground Series - Feb 2021
14790614,0.84813,0,0,/manujosephv/pytorch-tabular-node,Tabular Playground Series - Feb 2021
15019145,0.8583,0,0,/madelinecaples/fastai-tabular-learner-on-tabular-playground,Tabular Playground Series - Feb 2021
15001806,0.85688,1,5,/shashankrajput9/tabularplayground-eda,Tabular Playground Series - Feb 2021
14880448,0.84209,3,14,/rmiperrier/tps-feb-step-by-step-lgb-optimization,Tabular Playground Series - Feb 2021
14915040,0.85635,11,26,/benfraser/eda-and-dnn-regression-models,Tabular Playground Series - Feb 2021
22533984,0.77511,0,0,/soloveve/titanic,Titanic - Machine Learning from Disaster
22487239,0.78229,7,16,/shlomiziskin/titanic-notebook,Titanic - Machine Learning from Disaster
22480454,0.7799,2,6,/vmindel/titanic-tutorial-vmindel,Titanic - Machine Learning from Disaster
22470622,0.78468,1,5,/crutcherdunnavant/titanic-crutcher-sequential,Titanic - Machine Learning from Disaster
22340798,0.78708,1,2,/ooolgalu/titanic-disaster,Titanic - Machine Learning from Disaster
20587754,0.57416,0,0,/pralabhpoudel/titanic-survival-prediction-using-rfclassifier,Titanic - Machine Learning from Disaster
22397220,0.77511,4,6,/kokyongteo/titanic-submissions,Titanic - Machine Learning from Disaster
17664212,0.78708,0,0,/vmindel/notebookbac9aa939b,Titanic - Machine Learning from Disaster
22400558,0.77511,0,0,/danielandzelevich/titanic-experiment,Titanic - Machine Learning from Disaster
22360789,0.78468,34,59,/sfktrkl/titanic-hyperparameter-tuning-gridsearchcv,Titanic - Machine Learning from Disaster
22357080,0.78468,0,4,/rhythmcam/keras-tuner-randomforest-titanic,Titanic - Machine Learning from Disaster
22131610,0.77511,0,0,/pasatithchantawut/invader-start-titan-ic,Titanic - Machine Learning from Disaster
22269531,0.77511,0,0,/vladrossohin/notebook82ee297449,Titanic - Machine Learning from Disaster
21307143,0.73205,0,0,/novscdr/titanic,Titanic - Machine Learning from Disaster
22184906,0.7799,0,0,/harrasnadia/titanic-com,Titanic - Machine Learning from Disaster
19127217,0.77511,0,0,/jmjonson/getting-started-with-titanic,Titanic - Machine Learning from Disaster
22077832,0.74641,0,2,/dwaynesu/mmai5000-titanic-solutions,Titanic - Machine Learning from Disaster
12846030,0.75358,0,4,/chandrahasdhiraj/titanic-the-beginners-approach,Titanic - Machine Learning from Disaster
22122443,0.72248,1,4,/pedrofurlanj/notebook453e3ca9f5,Titanic - Machine Learning from Disaster
21139957,0.77511,0,0,/bhanujoshkakarla/titanic-ml,Titanic - Machine Learning from Disaster
21908637,0.80622,27,45,/arootda/titanic-eda-modeling-for-beginners-top3,Titanic - Machine Learning from Disaster
22035816,0.77511,0,5,/iamlividguy/rms-titanic-first-attempt,Titanic - Machine Learning from Disaster
22003418,0.78708,0,5,/rhythmcam/titanic-quick-onetime-preprocess,Titanic - Machine Learning from Disaster
22062151,0.77511,2,6,/yypursuit/yypursuit,Titanic - Machine Learning from Disaster
21980531,0.77511,0,6,/thomasjonli/taitanic,Titanic - Machine Learning from Disaster
21813737,0.77511,2,7,/tigranmelikyan/getting-started-with-titanic,Titanic - Machine Learning from Disaster
21928275,0.74162,3,22,/rajendraverma4/logistic-rigression,Titanic - Machine Learning from Disaster
21931382,0.76315,0,5,/rhythmcam/autopipeline-gridsearchcv-xgb-titanic,Titanic - Machine Learning from Disaster
21928931,0.76794,0,4,/rhythmcam/gridsearchcv-pipeline-lgbm-titanic,Titanic - Machine Learning from Disaster
21959962,0.92226,0,4,/phuc16102001/plant-pathology-2020-vgg-style,Plant Pathology 2020 - FGVC7
22406089,0.9668,0,1,/werooring/ch12-plant-pathology-2020-baseline,Plant Pathology 2020 - FGVC7
21635721,0.92121,0,5,/geochatz/plant-pathology-classification-with-tensorflow,Plant Pathology 2020 - FGVC7
21266977,0.94678,0,1,/chizuchizu/train-inference-using-torchdistill,Plant Pathology 2020 - FGVC7
18604602,0.95214,0,0,/trntrungnguyn/notebook8266231ecb,Plant Pathology 2020 - FGVC7
15426783,0.48322,0,0,/kaerunantoka/plant-baseline,Plant Pathology 2020 - FGVC7
13100391,0.97646,1,0,/dahouda/plant-pathology-2020,Plant Pathology 2020 - FGVC7
15180235,0.85678,0,1,/sahilpatial/notebook532558868a,Plant Pathology 2020 - FGVC7
22546239,0.854,0,2,/kingkongs7/alaska2-efficient,ALASKA2 Image Steganalysis
22770977,0.5034,0,2,/pabloamc/santander-lightning,Santander Customer Transaction Prediction
20873518,0.71989,0,1,/wutong0218/santander-dataset-weighted-logistic-regression,Santander Customer Transaction Prediction
14764522,0.841,1,1,/fanbyprinciple/santander-fastai,Santander Customer Transaction Prediction
13849849,0.92244,0,5,/xuanzhihuang/santander-customer-transaction-prediction-lgbm,Santander Customer Transaction Prediction
16095553,0.90625,2,3,/reyvaz/severstal-inference-submission,Severstal: Steel Defect Detection
15187847,0.79028,0,0,/akhilpenta/unet-tensorflow,Severstal: Steel Defect Detection
18443938,0.85852,0,0,/zhouyifu0158/givemesomecredit-last-v1,Give Me Some Credit
18332288,0.85753,0,0,/bnucsy/notebook0fe104ab02,Give Me Some Credit
13300191,-6.9133,0,0,/khadijatulkobra/meatadata,OSIC Pulmonary Fibrosis Progression
17912494,0.73211,0,0,/vanitadiboyina/vani-tadiboyina,Forest Cover Type Prediction
16441128,0.5568,0,0,/anujkumaraj/forest-cover-project,Forest Cover Type Prediction
23408884,0.98778,1,1,/tatyy555/digit-recognizer-computer-vision,Digit Recognizer
23184748,0.98592,0,1,/drcapa/minst-fcnn-vs-cnn-pytorch-tutorial,Digit Recognizer
10649571,0.544,2,4,/yuelong/detect-birdcall,Cornell Birdcall Identification
14509998,9743.504,0,1,/hiteshkumars/own-jane-street-with-keras-nn,Jane Street Market Prediction
14531546,6150.014,8,1,/andreasthomasen/pretrained-pytorch-nn,Jane Street Market Prediction
14510354,3464.613,3,14,/code1110/janestreet-simple-lgb-with-groupkfold,Jane Street Market Prediction
14479963,5171.245,0,1,/shanemcandrew/shane,Jane Street Market Prediction
14153255,0.0,3,16,/andreasthomasen/preprocessing-and-feature-selection,Jane Street Market Prediction
14404220,4812.491,1,7,/marcodibartolo/cmu-team-pca-xgbclassifier-purgedsplit-optunacv,Jane Street Market Prediction
14395613,460.912,3,27,/carlmcbrideellis/jane-street-afternoon-shopping,Jane Street Market Prediction
14340613,4570.956,4,3,/kailassrt/lightgbm-lb-score-4570-956,Jane Street Market Prediction
14366543,7196.227,1,9,/hyperbeam/cnn-using-keras,Jane Street Market Prediction
14089259,7450.471,4,4,/magokecol/train-and-submit,Jane Street Market Prediction
14463411,9931.958,22,129,/a763337092/pytorch-resnet-starter-inference,Jane Street Market Prediction
14394549,4880.085,0,0,/cbryant/simple-xgb-no-eda-or-tuning,Jane Street Market Prediction
14296338,2826.433,2,3,/daisyrhea97/lightgbm-weight-resp,Jane Street Market Prediction
14681962,0.84648,3,9,/prokaggler/tbs-feb-2021-eda-tabnet-lgbm,Tabular Playground Series - Feb 2021
14643224,0.842,5,10,/takerumiyagawa/tabular-playground-feb-2021-very-simple-lightgbm,Tabular Playground Series - Feb 2021
14651961,0.84387,0,4,/tunguz/tps-feb-2021-with-histgradientboostingregressor,Tabular Playground Series - Feb 2021
14626230,0.84251,2,13,/shogosuzuki/optuna-lightgbm-onehotencoder-lr-0-001,Tabular Playground Series - Feb 2021
14603066,0.84535,20,37,/docxian/tabular-playground-2-let-s-go,Tabular Playground Series - Feb 2021
14613043,0.84254,2,12,/shogosuzuki/optuna-lightgbm-onehotencoder,Tabular Playground Series - Feb 2021
14603575,0.84358,2,17,/jonas0/beginner-friendly-february-tabular-tutorial,Tabular Playground Series - Feb 2021
14631863,0.84243,10,73,/tunguz/tps-02-21-feature-importance-with-xgboost-and-shap,Tabular Playground Series - Feb 2021
14605773,0.84372,0,4,/khlevnov/shortest-solution-in-30loc-catboost-with-defaults,Tabular Playground Series - Feb 2021
14609032,0.85811,0,0,/madelinecaples/tabular-playground-2-better-eda-and-random-forest,Tabular Playground Series - Feb 2021
14605859,0.84412,2,1,/shogosuzuki/lightgbm-jamessteinencoder-countencoder,Tabular Playground Series - Feb 2021
14636285,0.84257,0,0,/shogosuzuki/optuna-lightgbm-onehotencoder-ordinalencoder,Tabular Playground Series - Feb 2021
14940068,0.84192,4,16,/maostack/english-tps-feb-11th-place-solution,Tabular Playground Series - Feb 2021
17056491,0.83328,0,5,/danieldorosz/bert-feature-extraction-and-fine-tuning,Natural Language Processing with Disaster Tweets
17120655,0.82224,2,7,/sohelranaccselab/nlp-disaster-tweets-using-bert-for-beginner,Natural Language Processing with Disaster Tweets
16515934,0.84155,9,8,/hongpeiyi/roberta-with-pytorch-and-fastai,Natural Language Processing with Disaster Tweets
17012020,0.82133,2,8,/urstrulysai/nlp-disaster-tweets-bert-using-tf-hub,Natural Language Processing with Disaster Tweets
16993033,0.80539,3,3,/mohamedtaha7/baseline-nlp,Natural Language Processing with Disaster Tweets
16062656,0.77995,4,2,/kostasmar/nlp-disaster-tweets-text2emotion-vader,Natural Language Processing with Disaster Tweets
16677872,0.75513,0,1,/prawins/nlp-prediction,Natural Language Processing with Disaster Tweets
16578543,0.80416,11,10,/urstrulysai/bow-tf-idf-models-with-basic-lr-0-80-score,Natural Language Processing with Disaster Tweets
16302634,0.83634,0,1,/pavlovaivt20/nlp-getting-started,Natural Language Processing with Disaster Tweets
16264787,0.83941,6,10,/hannaliavoshka/bert-with-disaster-tweets,Natural Language Processing with Disaster Tweets
15995055,0.83818,1,2,/tarunshah/nlp-eda-cleaning-bert,Natural Language Processing with Disaster Tweets
16002240,0.78087,0,2,/erickrf/nlp-for-tweets-from-bag-of-words-to-transformers,Natural Language Processing with Disaster Tweets
16015451,0.7821,0,2,/shubh247/nlp-from-beginner-to-expert,Natural Language Processing with Disaster Tweets
15593830,0.80324,13,17,/celniker/nlp-twitter-tuned-lgbm-model-tfidf-bert,Natural Language Processing with Disaster Tweets
22072468,0.00044,3,16,/viannaandresouza/house-prices-prediction,House Prices - Advanced Regression Techniques
22029668,0.137,1,13,/teckmengwong/housing-simple-pipeline,House Prices - Advanced Regression Techniques
21959423,0.15585,6,18,/cjinquan/house-price-prediction-python,House Prices - Advanced Regression Techniques
21964176,0.40592,1,6,/xholisilemantshongo/house-prices-intel-extensions,House Prices - Advanced Regression Techniques
21669474,0.12052,1,2,/austinelledge/house-prices-top-8-modified,House Prices - Advanced Regression Techniques
21903893,0.15454,16,24,/uniquekale/predicting-sales-price,House Prices - Advanced Regression Techniques
21860330,0.11934,0,7,/maximolifer/house-prices,House Prices - Advanced Regression Techniques
21912747,0.13172,0,1,/limweixuan1994/housing-prices-predictions,House Prices - Advanced Regression Techniques
21745354,0.15068,0,7,/nandishjani/house-prices-prediction-rf-xgb,House Prices - Advanced Regression Techniques
21714295,0.12209,1,7,/carlossanchezcampos/eda-fe-house-prices-top-10,House Prices - Advanced Regression Techniques
21786141,0.1891,0,8,/jirkaborovec/house-prices-predictions-with-lightning-flash,House Prices - Advanced Regression Techniques
21779399,0.2314,1,5,/pradipwasre/house-price-regression-pipeline,House Prices - Advanced Regression Techniques
21758030,0.13647,0,11,/jhmmorgan/house-prices-advanced-regression-techniques,House Prices - Advanced Regression Techniques
21743876,0.12765,0,0,/polinamaltseva/lr-3-house-prices,House Prices - Advanced Regression Techniques
21122990,0.14742,0,3,/annabelyaeva/lab3belyaeva,House Prices - Advanced Regression Techniques
14435175,8337.589,0,0,/poojansheth/fork-of-js-cnn-3936ac,Jane Street Market Prediction
14387033,9127.894,0,0,/unizy22/submit-notebook,Jane Street Market Prediction
14324667,6632.42,0,0,/sapthrishi007/jane-lgbm-stacked-lsvc,Jane Street Market Prediction
14274758,6669.048,0,0,/poojansheth/js-cnn,Jane Street Market Prediction
14176434,5171.877,0,0,/bmustafa/jane-street-intro-notebook,Jane Street Market Prediction
13806638,8324.832,3,10,/maunish/jsmp-pytorch-bottelneck-model-inference,Jane Street Market Prediction
13594210,3267.1,17,47,/tomwarrens/stacking-xgboost-lgbm-on-gpu-optuna,Jane Street Market Prediction
15726378,0.44023,0,4,/mt77pp/mljar-automl-bnp-claims-management,BNP Paribas Cardif Claims Management
20744297,0.74291,0,0,/akornienko123/ghouls-goblins-and-ghosts-boo,"Ghouls, Goblins, and Ghosts... Boo!"
19170657,0.70132,0,0,/tejasboss/ghouls-and-goblins,"Ghouls, Goblins, and Ghosts... Boo!"
18806350,0.61625,0,0,/prathikshabr/nn-ghoulsgoblins-ghosts-kaggleproject-7,"Ghouls, Goblins, and Ghosts... Boo!"
18794174,0.7448,0,0,/konnyaku1234/boo-vote,"Ghouls, Goblins, and Ghosts... Boo!"
18666060,0.73534,0,0,/shayistamulla/ghouls-goblins-and-ghosts-boo,"Ghouls, Goblins, and Ghosts... Boo!"
18501211,0.7448,0,0,/tefusha/3g-nn-tefu,"Ghouls, Goblins, and Ghosts... Boo!"
18348948,0.7448,0,0,/konnyaku1234/notebook67fb202070,"Ghouls, Goblins, and Ghosts... Boo!"
17773897,0.6654,0,4,/aryaadesh/ggg-neuralnetwork,"Ghouls, Goblins, and Ghosts... Boo!"
17153611,0.7051,0,0,/sajjadwasti/notebookb7181cc78a,"Ghouls, Goblins, and Ghosts... Boo!"
16496750,0.67863,0,0,/mohdmaazkhan/keras-ggg,"Ghouls, Goblins, and Ghosts... Boo!"
15273392,0.65028,0,0,/nishita17/g3-notebook,"Ghouls, Goblins, and Ghosts... Boo!"
14775241,0.70699,0,0,/arko007/neural-networks-ghouls-goblins-and-ghosts,"Ghouls, Goblins, and Ghosts... Boo!"
15836464,0.69376,0,0,/xevator/neural-networks,"Ghouls, Goblins, and Ghosts... Boo!"
19608488,2632.87959,0,0,/sourav3366/randomforest,Walmart Recruiting - Store Sales Forecasting
14767113,4365.96982,0,0,/felipechupel/rf-and-prophet,Walmart Recruiting - Store Sales Forecasting
12022550,0.01868,25,103,/nayuts/moa-pytorch-nn-pca-rankgauss,Mechanisms of Action (MoA) Prediction
14109176,0.78,0,0,/jihunlorenzopark/saint-with-tags-lsi-inference,Riiid Answer Correctness Prediction
14093907,0.79,0,0,/tymurprorochenko/riiid-public-submit,Riiid Answer Correctness Prediction
14084502,0.804,0,9,/vk00st/2gbm-3nn,Riiid Answer Correctness Prediction
14084186,0.798,7,10,/marisakamozz/riiid-saint-solution,Riiid Answer Correctness Prediction
14073845,0.784,0,0,/savababin/riiid-lgbm-bagging2-1-471152-update,Riiid Answer Correctness Prediction
14070301,0.787,0,3,/takamichitoda/riiid-infer-v8-xgb-sakt,Riiid Answer Correctness Prediction
14065620,0.795,0,1,/raphael1123/single-lgbm-21-feats-private-796,Riiid Answer Correctness Prediction
14016854,0.805,1,16,/abdessalemboukil/saint-lgbm-0-805-the-simpler-the-better,Riiid Answer Correctness Prediction
13964878,0.79,0,2,/felipefonte99/attention-and-lgbm,Riiid Answer Correctness Prediction
13846640,0.766,0,3,/yohannayin/riiid-answer-question,Riiid Answer Correctness Prediction
13653125,0.766,11,70,/stevemju/riiid-simple-elo-rating,Riiid Answer Correctness Prediction
13334538,0.58,0,0,/juniroc/transformer-12-16,Riiid Answer Correctness Prediction
18734050,5.68914,0,0,/rajeshwaris/nyc-taxi-fare-prediction,New York City Taxi Fare Prediction
17068284,5.689,0,0,/denimthangjam/nyc-taxi-fare,New York City Taxi Fare Prediction
17024537,5.07016,1,0,/yeeyunjie/taxi-fare-prediction-with-neural-nets,New York City Taxi Fare Prediction
16668228,9.26488,2,11,/jeongwonkim10516/taxi-fare-prediction-advanced-regression-tech,New York City Taxi Fare Prediction
16602379,5.68914,0,0,/sajjadwasti/notebookba37f4008a,New York City Taxi Fare Prediction
15779595,3.23018,0,1,/yykt1729/nyc-randomforest,New York City Taxi Fare Prediction
15197360,5.52625,0,1,/xevator/nyc-taxi-fare-starter-kernel-simple-linear-model,New York City Taxi Fare Prediction
14246352,5.69253,0,0,/dhawalsoni/nyc-taxi-fare-starter-kernel-simple-linear-model,New York City Taxi Fare Prediction
14984513,0.968,9,35,/cdeotte/rainforest-post-process-lb-0-970,Rainforest Connection Species Audio Detection
14478286,0.521,9,15,/dimitreoliveira/rainforest-audio-classification-tf-improved,Rainforest Connection Species Audio Detection
14595988,0.849,1,15,/tomehirata/pytorch-training-rfcx-adas-optimizer-resnest,Rainforest Connection Species Audio Detection
14447601,0.59236,0,3,/skydevour/rainforest-connection-species-bronze,Rainforest Connection Species Audio Detection
14269750,0.817,0,8,/ashusma/rfcx-custom-training-with-tpu,Rainforest Connection Species Audio Detection
13923719,0.869,7,41,/kneroma/rfcx-bagging,Rainforest Connection Species Audio Detection
15659982,0.83179,0,0,/drbeanesp21/quick-draw-submit,"Quick, Draw! Doodle Recognition Challenge"
14549944,11160.505,32,220,/a763337092/blending-tensorflow-and-pytorch,Jane Street Market Prediction
13444346,5269.328,0,9,/binhlc/jane-street-light-gbm,Jane Street Market Prediction
14368842,10606.782,32,138,/code1110/jane-street-with-keras-nn-overfit,Jane Street Market Prediction
14187576,3618.109,6,24,/snippsy/bottleneck-encoder-gradientboostingclassifier,Jane Street Market Prediction
13992515,4461.486,4,24,/lgmoneda/jane-simple-lightgbm-submission-sanity-check,Jane Street Market Prediction
14018780,2925.437,6,8,/lgmoneda/jane-temporal-feature-selection-with-shap,Jane Street Market Prediction
13925315,4328.841,0,2,/batprem/market-prediction-xgboost-with-gpu-fit-in-1min,Jane Street Market Prediction
13870282,3317.758,13,33,/yifor01/tabnet-starter,Jane Street Market Prediction
13844151,4088.671,25,113,/wongguoxuan/eda-pca-xgboost-classifier-for-beginners,Jane Street Market Prediction
22564772,0.45285,0,0,/alexjorenby/notebook6212b30b54,Mercari Price Suggestion Challenge
18738224,0.53249,0,0,/otenaoki/data-science,Mercari Price Suggestion Challenge
18557279,0.48046,0,0,/maximkazantsev/mercari-price-suggestion-sgdregressor,Mercari Price Suggestion Challenge
17644237,0.46503,0,0,/nguyenkimlong/mps-nkl-18020833-ml-ridge,Mercari Price Suggestion Challenge
17576887,0.48066,0,1,/nguyenkimlong/fork-of-mps-week-5,Mercari Price Suggestion Challenge
17120258,0.45583,0,0,/phamhung0703/final-exam,Mercari Price Suggestion Challenge
17072737,0.47755,0,2,/lockieu12/mercari-draft,Mercari Price Suggestion Challenge
16989333,0.51456,0,1,/hnginhxun/mercari-price-suggestion-challenge,Mercari Price Suggestion Challenge
16591223,0.46484,0,0,/nguynththytrang/myhomework,Mercari Price Suggestion Challenge
16247112,0.44431,0,4,/ayekhine/mercari-price-rnn,Mercari Price Suggestion Challenge
16361285,0.246,0,1,/kyawkyaw/vinbigdata-x-ray-chest-abnormalities-detection,VinBigData Chest X-ray Abnormalities Detection
16138124,0.2,2,6,/morizin/fork-of-wbf-ensemble-d64644,VinBigData Chest X-ray Abnormalities Detection
15517520,0.223,0,9,/c7934597/vinbigdata-2-class-classifier-complete-pipeline,VinBigData Chest X-ray Abnormalities Detection
15811582,0.246,0,8,/vbmokin/ensemble-of-best-notebooks-tuning-by-lb-private,VinBigData Chest X-ray Abnormalities Detection
15849343,0.009,0,1,/ricafernandes/detectron-v2-inference,VinBigData Chest X-ray Abnormalities Detection
15690863,0.246,16,52,/mahmudds/vinbigdata-chest-x-ray-abnormalities-detection,VinBigData Chest X-ray Abnormalities Detection
5182119,0.9483,5,71,/roydatascience/ieee-fraud-detection-aggregating-lightgbm-models,IEEE-CIS Fraud Detection
22063323,0.36102,0,4,/uygarkk/starter-siamese-tensorflow,Quora Question Pairs
18134404,0.38355,0,0,/huikang/quora-question-pair-competition-tfidf,Quora Question Pairs
20723081,0.0,0,1,/ahmeddosama/aptos-sequential,APTOS 2019 Blindness Detection
5362687,0.563,0,0,/quickhawk/junoo,APTOS 2019 Blindness Detection
19813451,0.517,0,0,/mohammadjhanafy/don-t-overfit-mh,Don't Overfit! II
14017851,1.20542,0,0,/franckepeixoto/igti-prever-vendas-futuras-pandas-pivot-table,Predict Future Sales
22980667,1.15262,0,3,/ekaterinapolupanova/predict-future-sales-catboost,Predict Future Sales
22800294,15.59506,0,3,/vladimirzelenov/notebook,Predict Future Sales
22736707,1.01115,0,3,/masaishi/lb1-001-transition-mon-cnt-lgb-catboost-xgb,Predict Future Sales
22494236,1.03234,2,4,/masaishi/transition-features-of-cnt-month,Predict Future Sales
22160324,5.22633,1,1,/revzkaggle/futuresalesprediction-lgbmregressor,Predict Future Sales
21958606,2.44128,0,4,/samyukthamobile/sales-prediction-submission-file,Predict Future Sales
21704230,1.49008,0,3,/amareshmarekar/future-sales-prediction-using-various-algorithm,Predict Future Sales
21525894,1.20693,3,13,/esratmaria/future-sale-prediction-lgbmregressor-part-2,Predict Future Sales
21278058,0.83794,2,18,/yamqwe/feature-engineering-lgb-blend,Predict Future Sales
20608697,1.45903,0,0,/fbozoglilanian/predict-future-sales,Predict Future Sales
18864244,2.0603,0,0,/torypan/predict-future-sales,Predict Future Sales
20782181,2.56606,0,0,/atsushis/futuresales-2,Predict Future Sales
20109633,1.02655,0,0,/tracyporter/predict-sales-udemy,Predict Future Sales
16692703,0.464,0,7,/joven1997/full-data-timm-ensemble,Human Protein Atlas - Single Cell Classification
15812775,0.537,2,4,/alexanderriedel/hpa-inferencing,Human Protein Atlas - Single Cell Classification
15637602,0.322,0,0,/yuriys/hpa-fastrcnn,Human Protein Atlas - Single Cell Classification
13877484,0.861,5,10,/mistag/inference-hubmap-fpn-single-model-segmentation,HuBMAP - Hacking the Kidney
13825643,0.863,0,0,/zehuigong/hubmap-submission-withcsv,HuBMAP - Hacking the Kidney
13656764,0.919,0,1,/curiosity806/hubmap-two-stage-to-more-accurate-inference,HuBMAP - Hacking the Kidney
13292572,0.934,136,148,/vgarshin/kidney-unet-model-keras-inference,HuBMAP - Hacking the Kidney
22983300,0.32192,1,3,/danildorofeev/sber-housing,Sberbank Russian Housing Market
21538386,1.0083,2,4,/shlokramteke/sberbank-data-cleaning-and-modelling,Sberbank Russian Housing Market
14799037,0.31772,0,0,/emorkrin/baselinesber,Sberbank Russian Housing Market
22086540,0.43756,0,5,/iamhappyg/bike-sharing-demand-prediction,Bike Sharing Demand
21873001,0.39855,3,7,/hongtokki/eda-evaluate-bike-sharing,Bike Sharing Demand
21579694,0.38095,0,1,/kimjinyeon/notebookeb20a21193,Bike Sharing Demand
21287773,1.05375,0,3,/seolryeongan/bike-sharing-demand-practice,Bike Sharing Demand
20762448,0.36609,0,1,/ibrahimyassen17/final-version-notebook-bike-sharing-catboost,Bike Sharing Demand
20483668,0.39794,0,3,/werooring/ch6-bike-sharing-demand-modeling,Bike Sharing Demand
20181811,0.40898,0,0,/ahmeddmamdouhhh96/notebooke4bc3e8faf,Bike Sharing Demand
20180657,0.38892,0,1,/mahmoudabdelmohsen/bikerentals,Bike Sharing Demand
20180520,0.42507,0,0,/ahmedsh95/notebooke97169b404,Bike Sharing Demand
19912864,0.46999,0,1,/amrkasem/bikes-final,Bike Sharing Demand
19887122,0.47118,0,0,/ahmedhagras/bike-sharing-demand,Bike Sharing Demand
19276512,0.47064,0,2,/mostafaemam0/bike-sharing-randomforrest,Bike Sharing Demand
19213741,0.5469,0,1,/mohamedsalah5369/assingnment,Bike Sharing Demand
19853120,0.48422,0,0,/mohammadjhanafy/bike-sharing-prediction,Bike Sharing Demand
19751008,0.43313,1,11,/nayrouzhamdy/bike-demand-6,Bike Sharing Demand
19855652,0.41302,0,0,/mahmoudtaha3/notebookc36af73dbf,Bike Sharing Demand
19870115,3.08642,0,0,/amr611/bike-count,Bike Sharing Demand
19672581,0.43716,0,2,/azizmousa/notebook0456c78a57,Bike Sharing Demand
19022154,0.4485,3,12,/rehameltagoury/bikesharingdemand,Bike Sharing Demand
20486749,27.62709,0,0,/varunsimhareddy/sanfrancisco-crime-classification-varun,San Francisco Crime Classification
18785961,27.62709,0,0,/keerthanamreddy/san-francisco-crime-classification,San Francisco Crime Classification
16202599,2.3927,0,1,/gonzalogarciafuste/sf-crime-classification-and-nice-graphics,San Francisco Crime Classification
21912105,0.74162,0,5,/moredata/logistic-rigression,Titanic - Machine Learning from Disaster
21504934,0.77272,0,0,/rafaelsegistan/titanic-rsegista,Titanic - Machine Learning from Disaster
21840158,0.74641,10,21,/karansehgal13/knn-eda-titanic-dataset,Titanic - Machine Learning from Disaster
9566582,0.78229,0,3,/chakkmorris/titanic,Titanic - Machine Learning from Disaster
21880683,0.622,0,0,/kseona/notebookc3b3ca69ea,Titanic - Machine Learning from Disaster
21830273,0.78947,0,6,/shprit/titanic-disaster-randforestclassifier-gridscv,Titanic - Machine Learning from Disaster
20919304,0.75837,0,5,/lleomiranda/titanic-rlog-2,Titanic - Machine Learning from Disaster
21817503,0.76555,0,5,/dayatdc/practice-titanic-project-ml,Titanic - Machine Learning from Disaster
21808589,0.77751,1,7,/kseniiaplatunova/random-forest,Titanic - Machine Learning from Disaster
21750036,0.70334,0,2,/xingaku/test-ds,Titanic - Machine Learning from Disaster
14332228,0.79665,0,0,/ilyamatveichuk/titanic-competition,Titanic - Machine Learning from Disaster
21770213,0.77511,0,4,/zhenxiangpeng/getting-started-with-titanic,Titanic - Machine Learning from Disaster
21798364,0.77511,0,3,/mohdkasibsiddiqui/titanic-survival-prediction,Titanic - Machine Learning from Disaster
21793883,0.77751,0,2,/darrgebreselassie/titanic-start,Titanic - Machine Learning from Disaster
21208150,0.78708,0,0,/vrajm1209/titanic-ml-model,Titanic - Machine Learning from Disaster
21789995,0.77511,0,3,/jasmitmahajan/titanic-assignment-cs4650,Titanic - Machine Learning from Disaster
21763900,0.78468,2,9,/mgerdas/survival-prediction,Titanic - Machine Learning from Disaster
21749285,0.78708,0,0,/carlfukawa/getting-started-with-titanic,Titanic - Machine Learning from Disaster
20024469,0.77511,0,1,/alshaimaaayman/getting-started-with-titanic,Titanic - Machine Learning from Disaster
10860679,0.43658,1,23,/sarthakvajpayee/top-4-4-bert-roberta-xlnet,Google QUEST Q&A Labeling
22090473,0.86239,0,3,/mostig/starter-give-me-some-credit,Give Me Some Credit
22325796,0.86215,0,1,/yijiazhang666/notebooke5e3eefb09-yijiazhang,Give Me Some Credit
22183980,0.84662,0,0,/cordellyaagatha/give-me-some-credit,Give Me Some Credit
18459318,0.86156,0,0,/wuqiong123/givemesomecredit123,Give Me Some Credit
18488962,0.86157,0,0,/zhouyuxuan0010/give-me-some-credit,Give Me Some Credit
18441477,0.86156,0,0,/greywings/give-me-some-credit,Give Me Some Credit
18426569,0.83702,0,0,/cmpikachu/201811210138-gmsc,Give Me Some Credit
18461925,0.85485,0,0,/cuojinboshan/give-me-some-credit-2-0,Give Me Some Credit
18448683,0.83683,0,0,/zk0156/notebookcde2020160,Give Me Some Credit
18354622,0.85848,0,0,/wangzhiyan0114/final-givemesomecredit,Give Me Some Credit
18337902,0.62739,0,0,/bnugyy3/notebook60da6f1baa,Give Me Some Credit
18217360,0.86065,0,1,/decatur/ensemble-stacking,Give Me Some Credit
18130541,0.8577,0,0,/zhangzhiyi3712/give-me-some-credit-zzy,Give Me Some Credit
18119637,0.82234,0,0,/sixwater6h2o/givemesomecredit,Give Me Some Credit
17975720,0.83598,0,0,/air0612/notebook-give-me-some-credit,Give Me Some Credit
17466595,0.85252,0,0,/stereolab/notebookd2a62f797f,Give Me Some Credit
18016639,0.0,0,0,/fahimmahmood/bert-submission,Jigsaw Unintended Bias in Toxicity Classification
14887075,0.0,0,0,/mayukhdutta/pytorch-bert-inference,Jigsaw Unintended Bias in Toxicity Classification
20911016,0.37326,0,0,/operationalamplifier/quest-bert-base-tf2-0,Google QUEST Q&A Labeling
22413330,2.62382,0,3,/aschittko/movie-revenue-prediction,TMDB Box Office Prediction
20274612,2.64082,2,13,/karansehgal13/random-forest-regressor-for-tmdb-b-o-prediction,TMDB Box Office Prediction
15935410,1.81445,0,4,/adamyanayyar/advanced-features-h2o-automl-xgb-models,TMDB Box Office Prediction
16706815,0.15747,0,0,/danilorosmaninho/house-price-prediction-danilo,House Prices - Advanced Regression Techniques
21234239,0.77796,0,0,/amanjyotideka/categorical-embedding-2,Categorical Feature Encoding Challenge II
14567170,0.83512,0,2,/viroviro/detecting-disaster-tweets-fine-tuning-bert,Natural Language Processing with Disaster Tweets
14460505,0.78731,1,2,/beherenowli/baseline-svc-79,Natural Language Processing with Disaster Tweets
14426199,0.83174,1,1,/vicioussong/tweet-nlp-benchmark-2-bert-family,Natural Language Processing with Disaster Tweets
14195195,0.83328,6,19,/lvalencia/bert-exposes-fake-tweets,Natural Language Processing with Disaster Tweets
14411143,0.80937,0,0,/vicioussong/tweet-nlp-benchmark-1-cnn-rnn-with-glove,Natural Language Processing with Disaster Tweets
14308769,0.81336,0,2,/tornikeonoprishvili/dense-fig,Natural Language Processing with Disaster Tweets
14287573,0.17468,0,0,/joydattaraj/ktrain-bert-model,Natural Language Processing with Disaster Tweets
14201894,0.82531,1,3,/p77091122/nlp-bert-tf2-0-saved-model-v3,Natural Language Processing with Disaster Tweets
14175961,0.7916,0,0,/yogie25/text-classification-with-spacy,Natural Language Processing with Disaster Tweets
14059056,0.77934,0,0,/kictakimuhabirsincap/nlp-twitter,Natural Language Processing with Disaster Tweets
14032051,0.79098,2,5,/ccollado7/nlp-with-disaster-tweets-score-0-78,Natural Language Processing with Disaster Tweets
13965787,0.806,4,22,/hamzaghanmi/sentiment-analysis-using-ml-80,Natural Language Processing with Disaster Tweets
13983655,0.83236,0,1,/afridi10/disaster-tweet-01,Natural Language Processing with Disaster Tweets
13904651,0.83971,4,2,/medi52/disaster-tweets-with-roberta,Natural Language Processing with Disaster Tweets
15274484,0.6524,0,5,/gocoding/torchvision-faster-r-cnn-inference,Global Wheat Detection
18330351,0.7797,0,0,/mylnikovnikolay/cassava-leaf-pytorch,Cassava Leaf Disease Classification
17182754,0.0481,0,0,/juhoonlee/cassava-vit,Cassava Leaf Disease Classification
15684472,0.8333,0,0,/yongminghan/ensemblinginceptionresneteff,Cassava Leaf Disease Classification
15667095,0.1037,0,0,/yongminghan/class-2,Cassava Leaf Disease Classification
15618626,0.8442,0,0,/thanasisserntedakis/ml-project,Cassava Leaf Disease Classification
15301470,0.8543,0,0,/yyuan94/transfer-learning,Cassava Leaf Disease Classification
15025111,0.9023,0,0,/yanyangu/cassava-classification,Cassava Leaf Disease Classification
14983204,0.908,3,5,/chocozzz/sub4-effx3-regx3-tta-3,Cassava Leaf Disease Classification
14973010,0.906,0,0,/raipachi0704/inference-blending-2dcnn-ver3-weights,Cassava Leaf Disease Classification
14963568,0.9023,0,0,/kpriyanshu256/leaf-inference-en-b4-b4-vit-resnext,Cassava Leaf Disease Classification
14958372,0.905,0,0,/raipachi0704/inference-stacking-2dcnn-ver3,Cassava Leaf Disease Classification
14952688,0.895,0,1,/hubertwojewoda/fastai-cassava-leaves-inference,Cassava Leaf Disease Classification
14938817,0.887,0,0,/brianstumph846/effnet-b4-with-frozen-bn2d-btll-inference,Cassava Leaf Disease Classification
14916618,0.619,0,0,/rodrigocastanonm/submission-test,Cassava Leaf Disease Classification
14840092,0.8652,5,7,/jagadish13/resnet200d-reliable-pub-private-lb-and-cv,Cassava Leaf Disease Classification
14835491,0.863,1,2,/ashikshafi/cassava-leaf-disease-classification-fastai,Cassava Leaf Disease Classification
16084886,0.67304,0,0,/watanabejun/notebookmercari5,Mercari Price Suggestion Challenge
16363311,0.57198,0,0,/keiintokyo/decisiontree-0421,Bike Sharing Demand
16218354,0.66532,0,0,/keiintokyo/firsttry-tree-reg,Bike Sharing Demand
15805167,0.40877,0,0,/seojeee/bike-sharing-demand-cheese,Bike Sharing Demand
15674287,0.42647,0,0,/sungchi/bike-sharing-demand,Bike Sharing Demand
17272528,0.5301,0,0,/deniskuschevoy/notebook42cc5b2265,Instant Gratification
22772215,0.98201,0,2,/shravankumar147/bert-toxic-classifier-shravan,Toxic Comment Classification Challenge
22091890,0.9607,0,10,/lonnieqin/toxicity-classification-word2vec-tfidf,Toxic Comment Classification Challenge
21380383,0.97656,0,2,/alokevil/01-strong-baseline-model,Toxic Comment Classification Challenge
21223026,0.85586,0,0,/diasdouglas/toxic-comment-classification,Toxic Comment Classification Challenge
21202115,0.97303,0,4,/akankshasingh2001/jigsaw-toxic-comment-classification,Toxic Comment Classification Challenge
20277611,0.97028,0,2,/cheeponglee/toxic-comment-multilabel-tinybert,Toxic Comment Classification Challenge
17160868,0.97696,1,3,/praveengadiyaram/multilabel-classification-jigsaw,Toxic Comment Classification Challenge
15763166,0.84643,0,1,/santosh1974/toxic-comment-classification-nlp,Toxic Comment Classification Challenge
16155153,0.97141,1,7,/walras/nlp-toxic-comments,Toxic Comment Classification Challenge
15414242,0.5,0,7,/vippatil/toxic-comment-using-lstm,Toxic Comment Classification Challenge
14195731,0.98347,0,0,/away24/toxic-classification-gru-with-glove-and-fasttext,Toxic Comment Classification Challenge
14166374,0.9636,0,0,/ajayganti/toxic-comment-classifier-e2e,Toxic Comment Classification Challenge
13834312,0.94204,0,1,/anirbansen3027/jtcc-cnn,Toxic Comment Classification Challenge
22683495,0.12096,0,0,/omkarkhandekar/storewise-try-on-rossmann-compitition-data,Rossmann Store Sales
20127015,0.14585,0,3,/skathirmani/ml-case-study-aug-2021-session-2,Rossmann Store Sales
17673351,0.14032,0,0,/arunabh04/rossmann-store-sales,Rossmann Store Sales
16437636,0.38413,1,1,/skathirmani/data-science-course-session-26,Rossmann Store Sales
15085468,0.28612,4,12,/werooring/top-9th-lightgbm-xgboost-ensemble,Porto Seguro’s Safe Driver Prediction
10078188,0.27331,0,2,/rikdifos/lightgbm-vs-catboost-vs-xgboost,Porto Seguro’s Safe Driver Prediction
20480101,0.2262,0,0,/akhilkumarks/notebook253c63faeb,House Prices - Advanced Regression Techniques
20370501,0.13881,0,3,/shahnawaj7/house-price-main,House Prices - Advanced Regression Techniques
20369204,0.13035,0,1,/ashanpeiris/house-prices-prediction,House Prices - Advanced Regression Techniques
20223002,0.16183,0,0,/johnnynyamanaka/feature-selection-training,House Prices - Advanced Regression Techniques
19846764,9.50065,13,11,/abhisheksisodiya/house-price-randomforestregressor,House Prices - Advanced Regression Techniques
20419911,0.13708,1,1,/kmanojreddy/notebookb66727aeff,House Prices - Advanced Regression Techniques
14277873,0.14516,0,0,/anuvarshini98/house-price-prediction,House Prices - Advanced Regression Techniques
20056015,0.16895,0,0,/tracyporter/house-prices-udemy-course,House Prices - Advanced Regression Techniques
20247273,0.14473,21,40,/adityabhat/house-price-what-factors-make-people-pay-more,House Prices - Advanced Regression Techniques
20211392,0.13224,2,12,/lonnieqin/house-prices-prediction-with-xgboost,House Prices - Advanced Regression Techniques
20112721,0.13781,1,2,/fadyelkbeer/house-prices-linearregression,House Prices - Advanced Regression Techniques
20189472,0.13412,0,3,/aboudaladdin/linear-regression-with-ridge-cv,House Prices - Advanced Regression Techniques
20183204,0.3804,0,2,/amrahmedsaeed/ml1-linear-regression-house-prices,House Prices - Advanced Regression Techniques
20112483,0.13965,1,3,/vivianandrade/house-price-evaluation-xgboost-with-features,House Prices - Advanced Regression Techniques
20140791,0.1725,0,4,/akkkkii/house-price-prediction-using-linear-regression,House Prices - Advanced Regression Techniques
20089919,0.12674,2,9,/lonnieqin/house-prices-prediction-with-catboost,House Prices - Advanced Regression Techniques
14942662,0.88124,1,3,/x8x8d3n9/linear-regression-with-numeric-fes-only-baseline,Tabular Playground Series - Feb 2021
14926109,0.86322,2,5,/sanggyu3/tabular-feb,Tabular Playground Series - Feb 2021
14911902,0.84544,7,8,/tunguz/feb-21-tps-h2o-automl,Tabular Playground Series - Feb 2021
15001934,0.84193,52,106,/awwalmalhi/extreme-fine-tuning-lgbm-using-7-step-training,Tabular Playground Series - Feb 2021
14896208,0.86239,0,1,/eladwar/tabcascade,Tabular Playground Series - Feb 2021
14858835,0.84214,2,11,/heyytanay/blending-top-results,Tabular Playground Series - Feb 2021
14867457,0.86038,0,2,/shaikhaalbilais/fork-of-tps-feb-2021-rf-starter-75f892,Tabular Playground Series - Feb 2021
14851888,0.84385,1,6,/tunguz/xgb-gpu-hyperparameters-with-optuna,Tabular Playground Series - Feb 2021
14834584,0.84648,3,6,/sanjay147/lightgbm-based-kernel,Tabular Playground Series - Feb 2021
14842803,0.84207,52,84,/andreshg/tps-feb-a-complete-study,Tabular Playground Series - Feb 2021
14782058,0.84318,1,11,/harshitt21/tps-feb-2021-visualization-model,Tabular Playground Series - Feb 2021
14791572,0.8423,0,0,/sanarial/tps-feb-2021-lgbm-kfold-ensemble,Tabular Playground Series - Feb 2021
14803456,0.86316,0,1,/zenokujawa/feb-tab-pg-series-baseline,Tabular Playground Series - Feb 2021
14783747,0.84248,2,10,/sayantansadhu/ensemble-method-lgbm-xgboost-rf,Tabular Playground Series - Feb 2021
14780320,0.88047,0,3,/shaimaaljahani/tps-feb-2021-rf-starter,Tabular Playground Series - Feb 2021
14772046,0.87839,2,6,/kmldas/automl-for-beginners,Tabular Playground Series - Feb 2021
14776886,0.84777,1,1,/mauh974/kaggle-2-xgboost,Tabular Playground Series - Feb 2021
14730091,0.84245,7,10,/pratiksharm/tps-feb,Tabular Playground Series - Feb 2021
14750096,0.847,2,7,/prin543/simple-baseline-model-for-beginners,Tabular Playground Series - Feb 2021
14737248,0.86324,0,3,/josemrv/feb-tabular-opt-hyperpar-playground-competition,Tabular Playground Series - Feb 2021
14720207,0.84497,0,7,/tomohiroh/feb2021,Tabular Playground Series - Feb 2021
14651874,0.85024,0,0,/kennethr/baseline-eda-xgboost-and-cat-distributions,Tabular Playground Series - Feb 2021
10164093,0.56,47,140,/hamditarek/audio-data-analysis-using-librosa,Cornell Birdcall Identification
17281549,0.525,0,0,/darkravager/inference-bird-simple-baseline,Cornell Birdcall Identification
15148253,0.26056,0,1,/mariolu/it-5006-milestone-1,Expedia Hotel Recommendations
20954420,0.75188,0,2,/mubashir1/one-tutorial-to-understand-all-m5-forecasting,M5 Forecasting - Accuracy
21850685,0.64224,0,1,/atsushiszk/m5-forecasting-accuracy-eda-and-forecast,M5 Forecasting - Accuracy
16904803,1.11462,0,1,/tqrahman/baseline-lstm-w-dropout-3x64,M5 Forecasting - Accuracy
16640055,0.4962,0,1,/lemuz90/m5-mlforecast,M5 Forecasting - Accuracy
14993652,2.1104,4,6,/jagdmir/m5-forecasting-gru,M5 Forecasting - Accuracy
14657761,4220.402,0,0,/dalalkrish05/lightgbm-chain-classifier,Jane Street Market Prediction
14665466,0.0,0,1,/rajkumarl/tf-residual-network-on-select-features-training,Jane Street Market Prediction
14935993,11345.846,4,30,/yonikremer/pytorch-embeddingsnn-resnet-tensorflow,Jane Street Market Prediction
14930412,4540.926,4,10,/jnegrini/bayesian-optimization-for-hyperparameter-selection,Jane Street Market Prediction
14614835,11428.043,25,83,/sagarjiyani/blending-tensorflow-pytorch-th-0-4914,Jane Street Market Prediction
14618238,8246.119,0,8,/iamyajat/beginner-jane-street-market,Jane Street Market Prediction
14791352,6762.848,0,0,/sophiazamoreeva/nn-models-mp,Jane Street Market Prediction
14796533,9451.673,0,0,/marouf/tensorflow-last-one,Jane Street Market Prediction
14729220,9163.002,1,6,/mistag/jane-street-with-keras-nn-k-fold-less-overfit,Jane Street Market Prediction
14709681,6985.802,0,0,/huseyincot/resnet-keras,Jane Street Market Prediction
14343546,0.0,0,7,/yash88/bottleneck-encoder-mlp-keras-yash88,Jane Street Market Prediction
13595846,148.652,2,1,/talpinho/jsm-keras-first,Jane Street Market Prediction
14542333,6845.304,0,5,/nigelyaoj/skeleton-with-pytorch,Jane Street Market Prediction
14643441,10568.219,7,66,/mragpavank/jane-street-market-prediction,Jane Street Market Prediction
14613242,6344.221,0,9,/pyoungkangkim/densenet-batchnorm-dropout-linear-pytorch-jstreet,Jane Street Market Prediction
14605788,6697.335,0,6,/quincyqiang/tensorflow-resnet-training,Jane Street Market Prediction
15844942,0.298,0,3,/h053473666/0-284rcnn-norm-14class-1,VinBigData Chest X-ray Abnormalities Detection
15534350,0.246,1,21,/muhammad4hmed/lets-overfit-together,VinBigData Chest X-ray Abnormalities Detection
15367478,0.327,1,3,/morizin/wbf-ensemble,VinBigData Chest X-ray Abnormalities Detection
14885938,0.14,0,4,/leftyork/vinbigdata-cxr-ad-yolov5-14-class-infer-f2d2ca,VinBigData Chest X-ray Abnormalities Detection
14529941,0.091,5,8,/daiquoctran/inference-maskrcnn,VinBigData Chest X-ray Abnormalities Detection
14100954,0.053,8,41,/akhileshdkapse/vinbigdata-retinanet-detection-inference,VinBigData Chest X-ray Abnormalities Detection
13966614,0.052,4,6,/garrettwankel/chest-xray-notebook,VinBigData Chest X-ray Abnormalities Detection
13933111,0.154,15,157,/awsaf49/vinbigdata-cxr-ad-yolov5-14-class-infer,VinBigData Chest X-ray Abnormalities Detection
18456490,0.85211,0,0,/toshiakikunitomi/shelter-animal-outcomes-gw,Shelter Animal Outcomes
22939559,0.13278,0,0,/akshinguseinov/houseprcs,House Prices - Advanced Regression Techniques
22819183,0.14243,1,3,/ededhiscalifh/house-price-prediction-eda-modeling,House Prices - Advanced Regression Techniques
21353718,0.18997,0,0,/amishra527/house-prices,House Prices - Advanced Regression Techniques
22725111,0.12689,0,0,/aidachernyavskaya/house-prices,House Prices - Advanced Regression Techniques
22659515,0.20485,0,1,/nattyzepko/hw2-natty-zepko-302309513,House Prices - Advanced Regression Techniques
22676352,0.26418,1,3,/swethkm/house-price-prdeiction-analysis,House Prices - Advanced Regression Techniques
22457589,0.11876,1,11,/arnrob/house-prices-top-3,House Prices - Advanced Regression Techniques
22589533,0.18997,13,15,/ehsandahesh/house-prices-compare-all-of-regressor-algorithm,House Prices - Advanced Regression Techniques
22589620,0.13002,1,0,/anastasiyachapurina/lab-3,House Prices - Advanced Regression Techniques
22504948,0.17256,0,5,/hirokichiyoda2/01-starter-notobook,House Prices - Advanced Regression Techniques
22390923,7.0128,2,9,/kgxiao/eda-outlier-detection-model-fusion,House Prices - Advanced Regression Techniques
17480270,9.45909,0,1,/dinhkhactuananh/house-price-final-project,House Prices - Advanced Regression Techniques
22551374,0.13443,0,0,/vladislavsemenyonok/notebook338f333e5b,House Prices - Advanced Regression Techniques
22449556,0.12229,2,4,/vamsisaivanka/house-price-modeling-007,House Prices - Advanced Regression Techniques
22450556,0.12229,1,1,/shantansrivatsav/project,House Prices - Advanced Regression Techniques
21209466,0.17465,1,3,/yeswanthchode/advanced-chymt,House Prices - Advanced Regression Techniques
13826215,0.77689,0,3,/guptaaanchal/nlp-disaster-tweets,Natural Language Processing with Disaster Tweets
13420777,0.83358,0,4,/somcool/bert-tf-hub-for-disaster-tweet,Natural Language Processing with Disaster Tweets
13916455,0.03,19,22,/basu369victor/chest-x-ray-abnormalities-detection,VinBigData Chest X-ray Abnormalities Detection
14335684,0.23,56,219,/corochann/vinbigdata-2-class-classifier-complete-pipeline,VinBigData Chest X-ray Abnormalities Detection
13907537,0.052,5,44,/drcapa/chest-x-ray-starter,VinBigData Chest X-ray Abnormalities Detection
20233478,0.605,0,4,/vikassharma12911/otto-group-product,Otto Group Product Classification Challenge
22735781,0.74641,0,1,/rhythmcam/adaboost-titanic-prediction,Titanic - Machine Learning from Disaster
22165808,0.76315,0,11,/kgg1ep/titanic-survivors,Titanic - Machine Learning from Disaster
22703369,0.75358,2,5,/leonovm/titanic-data-and-choosing-model,Titanic - Machine Learning from Disaster
22701314,0.77272,1,4,/lebyby/ml2-notebook,Titanic - Machine Learning from Disaster
22735561,0.78229,1,5,/devashripatel42/notebookb5af9da972,Titanic - Machine Learning from Disaster
22598951,0.75598,1,3,/muhammadfuzail/titanic-classification,Titanic - Machine Learning from Disaster
22616167,0.75358,5,11,/subinium/data-science-tutorial-ver,Titanic - Machine Learning from Disaster
21768867,0.78947,4,10,/markbuchanan/titanic-competition,Titanic - Machine Learning from Disaster
22709736,0.78468,1,6,/tryingtraining/titanictry,Titanic - Machine Learning from Disaster
22617590,0.78468,1,4,/flafuji/titanic-eda-model-top-percentile-explained-intro,Titanic - Machine Learning from Disaster
14131271,0.79186,0,3,/mysha1rysh/fork-of-kaggle-titanic-competition,Titanic - Machine Learning from Disaster
22591503,0.76076,0,1,/rhythmcam/simple-catboost-gpu,Titanic - Machine Learning from Disaster
22533554,0.79665,7,16,/gilbertomanunza/hands-on-the-titanic-dataset,Titanic - Machine Learning from Disaster
20239546,0.77751,0,0,/somashekar1902/kaggle-titanic,Titanic - Machine Learning from Disaster
22533551,0.78229,0,8,/sumeetbohra/titanic-svc-and-randomforestclassifier,Titanic - Machine Learning from Disaster
19391443,0.79926,0,0,/nageshwari/nlp-disaster-tweets,Natural Language Processing with Disaster Tweets
19181160,0.80815,4,1,/sohaelshafey/finetune-bert-disaster-tweets-classification,Natural Language Processing with Disaster Tweets
19147392,0.8011,0,2,/chamecall/disastertweets-train,Natural Language Processing with Disaster Tweets
19079889,0.8247,1,3,/arnabs007/disaster-tweets-with-bert-on-tpu-using-keras,Natural Language Processing with Disaster Tweets
18939935,0.79405,0,0,/nitinkhandagale/disaster-tweets-classification,Natural Language Processing with Disaster Tweets
18800274,0.82684,0,0,/yuukimo/2021-07-24,Natural Language Processing with Disaster Tweets
18946719,0.83389,0,5,/abhishek/notebook160a4ac0b8,Natural Language Processing with Disaster Tweets
18820260,0.77689,0,1,/mohinitambade/tweets-classification,Natural Language Processing with Disaster Tweets
18774973,0.7634,0,2,/hyunhp/basic-eda-cleaning-and-glove-written-by-shahules,Natural Language Processing with Disaster Tweets
16186337,0.83818,0,2,/saotome/classification-bert,Natural Language Processing with Disaster Tweets
18533361,0.77505,0,0,/anvesh1997/nlp-tweets,Natural Language Processing with Disaster Tweets
17975928,0.75881,0,3,/sudohumberto/disaster-tweets,Natural Language Processing with Disaster Tweets
18595141,0.81857,0,0,/yuukimo/maeshori,Natural Language Processing with Disaster Tweets
18588011,0.8057,0,1,/ambarish/tweets-prediction-using-conv1d-and-word-embeddings,Natural Language Processing with Disaster Tweets
18470243,0.79957,1,5,/fanglidayan/4-nlp-disaster-tweets,Natural Language Processing with Disaster Tweets
18507165,0.80386,0,1,/yuukimo/bert-test,Natural Language Processing with Disaster Tweets
11896480,0.74839,1,1,/maziprimareza/sentiment-analysis,Natural Language Processing with Disaster Tweets
18435136,0.78179,0,0,/xdxdydz/nlp-application-determination-of-disaster-tweets,Natural Language Processing with Disaster Tweets
18103825,0.82715,0,5,/krishnamore/complete-eda-glove-rnn-lstm-gru-birectional-bert,Natural Language Processing with Disaster Tweets
18398566,0.82776,2,1,/jackc123/with-theory-fine-tuning-distilbert,Natural Language Processing with Disaster Tweets
18287083,0.77689,0,0,/areanullearn/notebookd67bdfa3c0,Natural Language Processing with Disaster Tweets
15202198,4.42695,0,3,/kartik18chaudhary/basic-cnn-with-keras,State Farm Distracted Driver Detection
12415076,0.92874,3,9,/jonas0/ion-switching-competition-tutorial,University of Liverpool - Ion Switching
20976328,0.1785,0,1,/meherajhossain/dog-breed-transfer-learning-combining-4-backbones,Dog Breed Identification
20427497,5.57233,0,2,/nikhiln2312/dog-breed,Dog Breed Identification
18337019,21.4453,0,1,/denimthangjam/dog-breed,Dog Breed Identification
18089636,7.53203,0,1,/sakshi0100/dog-breed,Dog Breed Identification
17809243,0.26895,1,5,/aryaadesh/dog-breed-classification,Dog Breed Identification
17260183,7.40389,0,0,/nishita17/cv-dog-breed-classification,Dog Breed Identification
15854150,0.41936,0,0,/dhawalsoni/keras,Dog Breed Identification
15619250,3.81091,0,1,/tigaxmt/dog-breed-identification-with-tensorflow-keras,Dog Breed Identification
15539934,0.41112,0,0,/jithinanievarghese/dog-breed-identification,Dog Breed Identification
15105221,5.59645,0,0,/arko007/dogbreedclassification,Dog Breed Identification
13832484,0.50039,0,1,/jackttai/dog-breed-classifier-with-pytorch-using-resnet50,Dog Breed Identification
14545058,0.176,33,50,/samusram/hpa-classifier-explainability-segmentation,Human Protein Atlas - Single Cell Classification
14634870,0.354,50,126,/dschettler8845/hpa-cellwise-classification-inference,Human Protein Atlas - Single Cell Classification
14489039,0.0,8,37,/drcapa/human-protein-atlas-starter-keras,Human Protein Atlas - Single Cell Classification
16911350,0.546,1,4,/inoueu1/hpa2021-p-all-1stx8-2ndx16-imgx16,Human Protein Atlas - Single Cell Classification
16901353,0.542,0,1,/haqishen/hpa-infer-v7-2,Human Protein Atlas - Single Cell Classification
16891875,0.477,0,6,/akimball002/hpa-2021-inference-and-submission-lb-0-47,Human Protein Atlas - Single Cell Classification
16853532,0.0,2,8,/drtausamaru/hpa-ct-ill-inference-private,Human Protein Atlas - Single Cell Classification
16840074,0.071,0,1,/philiphucklesby/dataministic-with-cellseg,Human Protein Atlas - Single Cell Classification
16264293,0.42665,0,0,/sergeyzinchenko/predicting-a-biological-response,Predicting a Biological Response
16674800,0.42,0,2,/chienhsianghung/fork-of-cellwise-infer-efnb7-classification,Human Protein Atlas - Single Cell Classification
16452150,0.431,16,44,/chienhsianghung/cellwise-infer-efnb7-classification,Human Protein Atlas - Single Cell Classification
15226157,0.364,5,51,/dragonzhang/fastai-cell-tile-prototyping-training,Human Protein Atlas - Single Cell Classification
15163123,0.061,7,9,/dschettler8845/sample-submission-on-public-test-data-only,Human Protein Atlas - Single Cell Classification
18734052,5.68914,0,0,/prathikshabr/nyctaxifare-linear-regression-kaggleproject-3,New York City Taxi Fare Prediction
15552718,5.74184,0,0,/jerinm/nyc-taxi-fare-prediction-model,New York City Taxi Fare Prediction
20464959,0.504,0,1,/smita09/beginner-don-t-overfit,Don't Overfit! II
19802479,0.508,0,2,/hazemhosny/don-t-overfit-ii,Don't Overfit! II
19745733,0.501,0,0,/amiramosa99/don-t-over-fit,Don't Overfit! II
19667492,0.504,0,1,/mohamedmostafa335/don-t-overfit,Don't Overfit! II
19805414,0.499,0,0,/omarwael/don-toverfitwithsmoteoversampling,Don't Overfit! II
19792728,0.503,0,2,/mohammedabdulnaser/dof-mo,Don't Overfit! II
19527066,0.5,0,4,/azizmousa/donot-overfit,Don't Overfit! II
19869077,0.653,0,0,/amr611/fork-of-dont-over-fit-old-data,Don't Overfit! II
19684684,0.724,0,3,/nayrouzhamdy/trial-9,Don't Overfit! II
18979592,0.5,0,2,/mohamedsalah5369/dnofkc,Don't Overfit! II
21304203,0.998,5,14,/konstantinsuloevjr/visualization-cnn-augumentation,Digit Recognizer
21281290,0.94678,0,0,/hevean110/mlp-simples-com-keras-para-iniciantes,Digit Recognizer
13169903,0.98739,3,8,/andreykozlov/pytorch-with-resnet18,Digit Recognizer
21323660,0.9816,1,3,/szymonrosaniec/mnist-competition-cnn,Digit Recognizer
21316401,0.99446,0,0,/elwinner23/mc-learn-lab1,Digit Recognizer
21256766,0.99375,0,1,/anastasiyachapurina/lab-1,Digit Recognizer
20983905,0.98717,2,3,/anivesh01/digit-recognizer,Digit Recognizer
21180568,0.98375,0,0,/alexanderbubnovman1/lab1-bubnov,Digit Recognizer
21192930,0.97903,0,2,/ngquangminh54/mnist-cnn,Digit Recognizer
21183082,0.98339,0,3,/annabelyaeva/belyaeva-lab1,Digit Recognizer
21181003,0.9615,0,2,/kseniiaplatunova/notebookb26dac83e1,Digit Recognizer
21170267,0.96535,0,1,/alexandrpanarin/digit-recognizer,Digit Recognizer
21140609,0.9836,0,5,/dmitryzaykov/sklearn-pca-svc-0-98,Digit Recognizer
21104380,1.0,0,6,/sergeyshevelev/digit-recognizer,Digit Recognizer
21097741,0.98792,0,1,/aershov/ml2021-lab-1-digit-recognizer,Digit Recognizer
21021971,0.995,0,3,/fconcas/cnn-for-the-mnist-dataset,Digit Recognizer
21012771,0.98896,0,8,/mohamedelsrogy/digit-recognition-cnn-and-svm,Digit Recognizer
20918485,0.99942,0,7,/paulplanchon/cnn-mnist-data-augmentation-99-9,Digit Recognizer
20970707,0.98285,0,9,/lonnieqin/mnist-classification-with-kerastuner-hypermodels,Digit Recognizer
12602962,0.746,0,0,/saijasthi/lr-0-01-batch-size-500000,Riiid Answer Correctness Prediction
12145569,0.5,3,17,/gunesevitan/riiid-answer-correctness-prediction-eda,Riiid Answer Correctness Prediction
16403184,2.03409,0,0,/tuhin1001/predict-the-future-an-ensemble-based-approach,Predict Future Sales
15827050,0.87408,4,7,/ep18041/predict-future-sales-lightgbm-framework,Predict Future Sales
16087220,0.86617,23,69,/werooring/top-3-5-lightgbm-with-feature-engineering,Predict Future Sales
15220096,1.00744,0,1,/brandonvoigt227/predict-future-sales,Predict Future Sales
14932237,3556.16825,17,25,/mikeka/time-series-prediction-with-arima,Predict Future Sales
13887995,0.88263,0,3,/simonhse/notebooka645461d44,Predict Future Sales
14167411,1.13273,0,1,/zeynepkaya/171307019,Predict Future Sales
14122530,1.93469,0,1,/talhaaydn/171307054-talha-aydin,Predict Future Sales
22248594,0.76494,0,0,/megannichols/dsci-598-team-3-week-6-submission,Home Credit Default Risk
22046560,0.76715,0,0,/cloycebox/default-risk-submission-notebook-week-5,Home Credit Default Risk
21809053,0.5,0,0,/megannichols/team-3-week-1-submission-dsci598,Home Credit Default Risk
20642927,0.71808,0,0,/math6010zzlll/math6010z-zhen-liu-lu-lai,Home Credit Default Risk
19576721,0.79025,1,1,/tahmidnafi/cse499-tahmid-lgbm,Home Credit Default Risk
18348927,0.80028,0,1,/pial99/home-credit-neural-log-reg,Home Credit Default Risk
17536712,0.75276,0,0,/yshiml/homecredit-lgb-xgb-logreg,Home Credit Default Risk
16720939,0.80448,0,6,/hikmetsezen/base-model-with-0-804-auc-on-home-credit,Home Credit Default Risk
16374355,0.81128,3,16,/hikmetsezen/blend-boosting-for-home-credit-default-risk,Home Credit Default Risk
8556557,0.613,5,25,/adarshsng/tweet-sentiment-extraction-analysis,Tweet Sentiment Extraction
20580257,0.71214,0,0,/afnanmousa/tweet-sentiment-extraction-using-roberta,Tweet Sentiment Extraction
16589354,0.70553,0,0,/sifatmdabdullah/distilbert-base-uncased-pytorch-2-layers,Tweet Sentiment Extraction
16339102,0.70933,0,0,/sifatmdabdullah/bert-base-uncased-pytorch-1-layer,Tweet Sentiment Extraction
17063983,4.46431,0,8,/horsek/iln-x-y-training-and-inference-part-no-pp,Indoor Location & Navigation
17054915,5.74695,6,28,/kokitanisaka/self-attentintive-lstm-by-keras,Indoor Location & Navigation
17011786,2.58,0,17,/saitodevel01/11-pseudo-labeling-from-lb-2-586-with-retry,Indoor Location & Navigation
16251515,4.527,1,31,/aristotelisch/with-magn-cost-minimization,Indoor Location & Navigation
16017458,108.122,0,2,/artemzapara/basic-eda-indoor-location-navigation,Indoor Location & Navigation
15740328,5.255,0,25,/mehrankazeminia/2-3-indoor-navigation-comparative-method,Indoor Location & Navigation
15629758,5.391,1,52,/mehrankazeminia/1-3-indoor-navigation-cost-minimization-floor,Indoor Location & Navigation
15458986,81.281,0,9,/nikitagrec/simple-eda-baseline-indoor-location,Indoor Location & Navigation
15405552,6.771,7,46,/mehrankazeminia/part-a-indoor-navigation-comparative-method,Indoor Location & Navigation
17503051,0.423487,0,0,/samuel0323/06-06,APTOS 2019 Blindness Detection
15084123,0.902,0,7,/tsuno0821/efficientnet-starter-japanese,RANZCR CLiP - Catheter and Line Position Challenge
14853826,0.904,0,6,/lokhang/ranzcr-clip,RANZCR CLiP - Catheter and Line Position Challenge
14975403,0.5,2,3,/okjinhae/pytorch-efficientnet7-starter,RANZCR CLiP - Catheter and Line Position Challenge
14675554,0.898,0,5,/apurbasarkar/rancr-final-sub-with-inceptionnetv3-trained-model,RANZCR CLiP - Catheter and Line Position Challenge
14288462,0.748,2,11,/pierretihon/a-homemade-tensorflow-model,RANZCR CLiP - Catheter and Line Position Challenge
14456355,0.962,19,74,/ammarali32/seresnet152d-inference-single-model-lb-96-2,RANZCR CLiP - Catheter and Line Position Challenge
14222175,0.93,1,3,/bindu6/final,RANZCR CLiP - Catheter and Line Position Challenge
14141912,0.696,3,5,/digvijayyadav/ranzcr-clip-exploration-with-xceptionnet,RANZCR CLiP - Catheter and Line Position Challenge
14136061,0.914,0,1,/digvijayyadav/ranzcr-clip-resnet50-submissions,RANZCR CLiP - Catheter and Line Position Challenge
20585846,0.17288,0,2,/youngguebae/yg-house-price-prediction,House Prices - Advanced Regression Techniques
19917412,0.13971,0,0,/alicewulim/6housing,House Prices - Advanced Regression Techniques
20702908,0.1375,0,2,/datarohitingole/price-prediction-using-nusvr-and-optuna,House Prices - Advanced Regression Techniques
20694289,0.12546,0,1,/maxwithrow/ds-lab-lab-3,House Prices - Advanced Regression Techniques
20598687,0.14834,2,6,/saumilagrawal10/house-price-prediction,House Prices - Advanced Regression Techniques
20617021,0.00044,2,9,/syerwin/house-prices-prediction,House Prices - Advanced Regression Techniques
20509796,0.15222,0,1,/hrishikeshbhandarkar/price-prediction-using-deep-learning-01,House Prices - Advanced Regression Techniques
20394338,0.1466,0,2,/sangyeounlee/house-price-expectation,House Prices - Advanced Regression Techniques
20445227,0.13684,2,4,/souryadipstan/house-price-prediction-xgboost-random-forest,House Prices - Advanced Regression Techniques
20433696,0.14695,1,3,/peeushthedeveloper/predict-sale-price-using-simple-regression,House Prices - Advanced Regression Techniques
20546090,0.14135,0,2,/sathishpbs/house-price-prediction,House Prices - Advanced Regression Techniques
20407724,0.13565,0,1,/zjackwang/housing-prices-regression,House Prices - Advanced Regression Techniques
20408229,0.12479,0,0,/zjackwang/house-price-regression-ridge-v-lasso,House Prices - Advanced Regression Techniques
20509225,0.19708,1,2,/xavier001/linear-regression-homework,House Prices - Advanced Regression Techniques
20429270,0.12894,2,4,/christodoulos/house-prices-ensemble-learning-w-xgboost-dnn,House Prices - Advanced Regression Techniques
20447700,0.12718,3,6,/thor1813/house-prices-ensembling-technique,House Prices - Advanced Regression Techniques
20442377,0.13841,0,0,/pradnyanandana/house-price-regression,House Prices - Advanced Regression Techniques
13497492,0.602,0,1,/cjpjg007/cassava-leaf-disease-classification,Cassava Leaf Disease Classification
13453413,0.742,0,1,/vadimtimakin/fast-automated-clean-pytorch-pipeline-inference,Cassava Leaf Disease Classification
22468758,0.75,3,1,/georgezoto/riiid-answer-correctness-prediction-eda-baseline,Riiid Answer Correctness Prediction
12836469,0.758,0,0,/srijita97/cnn-saving,Riiid Answer Correctness Prediction
13054133,0.815,0,34,/aerdem4/riiid-starter,Riiid Answer Correctness Prediction
14134128,0.817,1,19,/mamasinkgs/2nd-place-solution-for-hosts,Riiid Answer Correctness Prediction
14125508,0.78,0,0,/sapthrishi007/sakt-featureembeddings-mytrainer,Riiid Answer Correctness Prediction
14111665,0.815,4,65,/letranduckinh/riiid-model-submission-4th-place-public-version,Riiid Answer Correctness Prediction
14125477,0.789,2,2,/ttkagglett/lgbm-sakt-private-score-is-0-790,Riiid Answer Correctness Prediction
14076906,0.787,0,9,/gyanendradas/riid-0-787-0-789,Riiid Answer Correctness Prediction
14004188,0.777,0,12,/tarique7/v4-fork-of-riiid-sakt-model-full,Riiid Answer Correctness Prediction
14028641,0.781,5,6,/benoitvignaud/riiid-lgbm-bagging2-sakt-with-new-features,Riiid Answer Correctness Prediction
15953467,0.79443,0,0,/xiongzhna/u-net-resnet,TGS Salt Identification Challenge
6198645,0.79382,29,179,/subinium/11-categorical-encoders-and-benchmark,Categorical Feature Encoding Challenge
18333800,1.265,0,0,/michelezoccali/ashrae-with-fast-ai-part-3,ASHRAE - Great Energy Predictor III
17465466,0.99,0,2,/azhar2ds/mnist-tensorflow,Digit Recognizer
17391097,0.99325,0,1,/harmonialeoleo/cnn-for-mnist-chinese-notes,Digit Recognizer
17251863,0.98746,0,1,/felipehbastos/topii-2020-2-digitrecognizer-cnn,Digit Recognizer
17349889,0.98853,1,1,/harmonialeoleo/keras-2-3-1-resnet-50-chinese-notes,Digit Recognizer
17324347,0.96378,4,14,/rhul007/digit-recognizer-using-knn-beginners,Digit Recognizer
17303075,0.99417,0,0,/rozamira/mlp-digit,Digit Recognizer
17252127,0.98542,2,6,/anmolsharma00002/digit-recognition-cnn,Digit Recognizer
17270866,0.96921,1,2,/scirpus/supercool-knn,Digit Recognizer
17253784,0.91507,5,4,/williamu32/digit-recognizer-deeplearning,Digit Recognizer
17159331,0.99235,0,3,/arunpurakkatt/handwritten-digit-recognition-cnn,Digit Recognizer
17135082,0.09896,0,2,/syedshoaibabbas/mnist,Digit Recognizer
17040849,0.98921,7,7,/alekseygustov/digit-recognizer-using-tensorflow-keras,Digit Recognizer
16278398,0.90321,4,9,/stpeteishii/mnist-prediction-densenet201,Digit Recognizer
21986087,1.54093,0,0,/haritharavi/notebookfd7d225f44,Santander Value Prediction Challenge
19578670,0.18852,0,0,/tracyporter/house-prices-transform-ridgecv,House Prices - Advanced Regression Techniques
16149892,0.14565,1,3,/soumya666/house-pricing,House Prices - Advanced Regression Techniques
19106913,9.45918,2,4,/ahmedsheriif/gettingstarted-house-pricing,House Prices - Advanced Regression Techniques
19041493,0.22673,0,1,/amrkasem/houseprice-predictor,House Prices - Advanced Regression Techniques
18977369,0.17143,10,13,/lonnieqin/house-price-predictor-using-different-models,House Prices - Advanced Regression Techniques
19045972,0.18622,0,1,/navidalazim/house-prices-xgboost-rfc,House Prices - Advanced Regression Techniques
18848729,0.13797,14,12,/furkanuysl/housepriceprediction-regressiontechniques,House Prices - Advanced Regression Techniques
18879208,0.24726,1,3,/tracyporter/ames-house-prices-pca,House Prices - Advanced Regression Techniques
18832653,0.51532,0,3,/mohamedsalah5369/house-prices-linear-regression,House Prices - Advanced Regression Techniques
18804105,0.14982,0,2,/mohamedmostafa335/house-prices-prediction,House Prices - Advanced Regression Techniques
18832514,0.16802,4,3,/gaurav126/qstp-project1,House Prices - Advanced Regression Techniques
15264573,0.59613,0,0,/rmicrobe/panda-challenge-my-first-submission,Prostate cANcer graDe Assessment (PANDA) Challenge
17619507,0.9489,0,0,/c107193206/jigsaw-0812,Jigsaw Multilingual Toxic Comment Classification
16876612,0.9418,0,0,/liudmila0/notebook5223692a47,Jigsaw Multilingual Toxic Comment Classification
17267151,0.51028,0,4,/akouaorsot/santander-customer-satisfaction-classification,Santander Customer Satisfaction
15828539,0.64466,0,0,/romanabramovich/tad-lab3,Ultrasound Nerve Segmentation
15716004,0.65698,1,0,/vladislavmymlikov/lab-work-3,Ultrasound Nerve Segmentation
9076690,0.925,7,57,/frtgnn/simple-xgb-k-fold-validation-approach,University of Liverpool - Ion Switching
15141333,0.145,0,2,/abdelrahmanhabib/ensembling-approach,VinBigData Chest X-ray Abnormalities Detection
5207647,0.0,0,0,/ankurdimri/densenet201-fast-ai-ben-graham-s-preprocessing,APTOS 2019 Blindness Detection
13854406,0.93425,0,0,/wanyuaaaa/digit-recognition-xgboost,Digit Recognizer
22469110,0.98328,1,0,/veronikasmirnova/smirnovaveronika-1,Digit Recognizer
22168259,0.99021,0,5,/vesran/cnn-classifier-for-mnist-0-99-acc-in-test,Digit Recognizer
22245078,0.98578,4,8,/coldfir3/mnist-fastai-resnet,Digit Recognizer
22161338,0.97485,6,16,/suddharshan/digit-recognizer-xgboost,Digit Recognizer
22020483,0.95275,0,0,/mateusliwka/digit-recognizer,Digit Recognizer
22004970,0.97314,0,6,/nagasai524/hand-written-digit-recognition-using-mnist-dataset,Digit Recognizer
22109691,0.97107,0,3,/akshinguseinov/dgrczr,Digit Recognizer
18990193,0.99182,0,0,/chansocheattieng/digit-recognition-with-cnn,Digit Recognizer
17016126,0.0,0,2,/faridsharaf/tabular-and-sentiment-data-with-lgbm,PetFinder.my Adoption Prediction
16956835,0.0,0,1,/faridsharaf/cnn-model,PetFinder.my Adoption Prediction
16937029,0.0,0,0,/tototohpl/eda-petfinder,PetFinder.my Adoption Prediction
19419896,1.072,0,1,/patrick0302/best-single-half-half-lgbm-cleaned,ASHRAE - Great Energy Predictor III
16139191,1.45,0,2,/faridsharaf/ashrae-project,ASHRAE - Great Energy Predictor III
20983218,0.7916,0,4,/akashingoley/disaster-tweets-with-multinomialnb-and-bernoullinb,Natural Language Processing with Disaster Tweets
20927228,0.82715,1,20,/kishalmandal/disaster-tweet-bert,Natural Language Processing with Disaster Tweets
20889481,0.8014,0,3,/nandha13/beginners-nlp-preprocessing-nltk-regular-exp-tfidf,Natural Language Processing with Disaster Tweets
20626767,0.76432,2,4,/mehdisalim/nlp-desaster-tweets-with-tf,Natural Language Processing with Disaster Tweets
20756732,0.78731,0,1,/datarohitingole/disaster-tweet-classification-ridgeclassifiercv,Natural Language Processing with Disaster Tweets
20625126,0.73092,2,7,/sharifashik/nlp-for-disaster-tweets,Natural Language Processing with Disaster Tweets
20540983,0.79221,0,2,/henriqueosinski/first-time-doing-nlp,Natural Language Processing with Disaster Tweets
20407508,0.78577,0,0,/boriskaras/first-steps-in-nlp-and-keras,Natural Language Processing with Disaster Tweets
20460241,0.57033,0,0,/tejareddyd/natural-language-processing-teja-30412,Natural Language Processing with Disaster Tweets
20351891,0.80171,0,0,/franciscoxpena/nlp-evaluating-disaster-tweets,Natural Language Processing with Disaster Tweets
20170661,0.78608,0,1,/fariha23/nlp-tweets,Natural Language Processing with Disaster Tweets
20267711,0.8342,0,0,/tolgayan/bert-disaster,Natural Language Processing with Disaster Tweets
20139907,0.79926,0,6,/sidharthamohanty/getting-started-with-nlp,Natural Language Processing with Disaster Tweets
20071107,0.79619,0,3,/allyjung81/disaster-tweets-nlp-eda,Natural Language Processing with Disaster Tweets
19958768,0.84216,0,0,/tylerrosacker/bertweet-transfer-learning,Natural Language Processing with Disaster Tweets
19932865,0.77106,0,1,/bkanupam/disaster-tweets-prediction,Natural Language Processing with Disaster Tweets
19518264,0.77352,0,2,/mbalcerzak/rnn-lstm-classification-pytorch,Natural Language Processing with Disaster Tweets
19560973,0.52497,0,0,/yogeshwaranma/nlp-my-try-2,Natural Language Processing with Disaster Tweets
10634671,0.98133,1,1,/samson22/revisedbert,Toxic Comment Classification Challenge
13432352,0.0,3,35,/eudmar/jane-street-eda-pca-ensemble-methods,Jane Street Market Prediction
23023292,0.23084,0,7,/eryash15/pubg-simplest-model,PUBG Finish Placement Prediction (Kernels Only)
20813211,0.05916,0,1,/yukiyamamoto/baseline,PUBG Finish Placement Prediction (Kernels Only)
17048217,0.09123,0,0,/yihg312/final,PUBG Finish Placement Prediction (Kernels Only)
16678526,0.09443,0,2,/sivantm/pubg-win-place-percentage,PUBG Finish Placement Prediction (Kernels Only)
15176946,0.05716,0,0,/egoluback/pubg-placement-model,PUBG Finish Placement Prediction (Kernels Only)
14080678,0.06511,0,0,/danielbryan666/nmsz2016127,PUBG Finish Placement Prediction (Kernels Only)
13806137,0.04665,0,0,/liudad/pubg-homework,PUBG Finish Placement Prediction (Kernels Only)
18016278,0.96303,1,3,/umesh11/digit-recognizer,Digit Recognizer
17531578,0.99353,1,3,/michael127001/digit-recognition-with-keras,Digit Recognizer
17949778,0.97053,1,2,/andreasloeffler/digit-recognizer-sectry,Digit Recognizer
17887653,0.98825,6,5,/m3chd09/digit-recognizer,Digit Recognizer
17862039,0.99642,0,1,/minhhngchong/hand-writing,Digit Recognizer
17850788,0.99517,7,6,/satwikd/digit-recognizer-nn,Digit Recognizer
16352661,0.95871,5,10,/skiplik/another-mnist-try,Digit Recognizer
17782694,0.99671,5,3,/santosh1974/data-aug-cnn,Digit Recognizer
17531813,0.99278,1,3,/brendanartley/mnist-pytorch-cnn-99-2,Digit Recognizer
17707866,0.96378,0,3,/theblazingone/knn-implementation,Digit Recognizer
17628358,0.9745,1,5,/kmishmael/mnist-digit-recognizer,Digit Recognizer
17263076,0.99032,0,1,/miguelarribas/pytorch-cnn,Digit Recognizer
17683257,0.96882,0,0,/santhoshkakarla/notebookf6b34615f9,Digit Recognizer
17551413,0.97367,0,1,/stpeteishii/mnist-classify-xgboost,Digit Recognizer
17540997,0.96596,0,1,/shantanusoni/multiclass-classification,Digit Recognizer
17518206,0.98689,4,3,/riyadmorshedshoeb/mnist-with-convnet,Digit Recognizer
17484229,0.99346,4,17,/nettasav/mnist-digit-recognizer-top-14,Digit Recognizer
9507739,0.6605,5,14,/shebinscaria/pytorch-resnet50fpn-gwd,Global Wheat Detection
10043234,0.62443,4,4,/jswxhd/extract-tweet-with-bert,Tweet Sentiment Extraction
9738113,0.664,0,0,/prabanch/twitter-sentiment-extaction-analysis-eda-and-model,Tweet Sentiment Extraction
22572984,0.01968,0,0,/loveofpotato/mlp-tensorflow,Mechanisms of Action (MoA) Prediction
19242635,0.01968,0,0,/astsiourvas/team-3-augmented-data,Mechanisms of Action (MoA) Prediction
19170744,0.01934,0,0,/coconutforce/moa-first-submission,Mechanisms of Action (MoA) Prediction
19215370,0.01944,1,1,/gguinet/team-4-submission,Mechanisms of Action (MoA) Prediction
15171510,0.02109,0,2,/viktorurushkin/scaler-pca-cv-logistic-regression,Mechanisms of Action (MoA) Prediction
14700492,0.02027,0,0,/imanekouchaoui/mechanisms-of-action-moa-prediction,Mechanisms of Action (MoA) Prediction
14303489,0.01902,0,4,/yxohrxn/moa-cutmix,Mechanisms of Action (MoA) Prediction
14283420,0.02658,0,0,/hakkoz/ml-project-lish-moa-xgboost,Mechanisms of Action (MoA) Prediction
21106832,0.12375,7,15,/lildatascientist/eda-auto-ml-house-prices,House Prices - Advanced Regression Techniques
21001637,0.12855,0,3,/fanbyprinciple/improving-prediction-score-on-housing-prices,House Prices - Advanced Regression Techniques
12914890,0.46,0,5,/cemozgen/simple-regression-for-house-prices,House Prices - Advanced Regression Techniques
21146969,0.12833,0,7,/grigorypetrov/notebook704acc623a,House Prices - Advanced Regression Techniques
20804218,9.41299,11,19,/harshwalia/house-price-advanced-regression-with-eda,House Prices - Advanced Regression Techniques
21019479,0.11653,6,25,/filterjoe/house-price-feature-engineering-using-only-xgboost,House Prices - Advanced Regression Techniques
20984346,0.11945,2,5,/stuartsul/ames-house-price-prediction-stacked-regression,House Prices - Advanced Regression Techniques
20810470,0.23224,0,1,/udeshchathumal/house-prediction,House Prices - Advanced Regression Techniques
21017136,0.13407,0,3,/kelizatoh/house-prices-regression-lightautoml,House Prices - Advanced Regression Techniques
21016792,0.13056,0,1,/kelizatoh/house-prices-regression-tabularautoml,House Prices - Advanced Regression Techniques
21000206,0.14911,0,0,/morganamacedo/trabalho-am-predicaoprecosimoveis-dma,House Prices - Advanced Regression Techniques
20997264,0.16489,0,2,/antoninabugayova/2021-3,House Prices - Advanced Regression Techniques
20959987,0.12793,0,1,/alexandrasciocchetti/housing-prediction-w-pipeline-ensemble-methods,House Prices - Advanced Regression Techniques
20539392,2.28246,0,0,/paulamaran/simultaneous-imputation-cat-num-data,House Prices - Advanced Regression Techniques
20922162,0.00044,1,4,/luongduongminh/best-score-use-this-notebook,House Prices - Advanced Regression Techniques
20889942,0.14516,1,4,/muki2003/deeplearning-house-price-prediction,House Prices - Advanced Regression Techniques
20876931,0.13037,0,2,/mhdi1380/house-prices-xgboost-hyperparameter-tuning,House Prices - Advanced Regression Techniques
20843392,0.1312,0,3,/lonnieqin/house-prices-prediction-with-kerastuner,House Prices - Advanced Regression Techniques
20776721,0.17185,0,0,/ignacioporras/house-prices-adv-reg-tech,House Prices - Advanced Regression Techniques
20707280,9.46663,13,29,/mostafaalaa123/simple-house-prediction,House Prices - Advanced Regression Techniques
20486070,1.01043,0,0,/kelizatoh/house-prices-advanced-regression-competition,House Prices - Advanced Regression Techniques
20526803,0.80806,0,1,/werooring/ch7-categorical-feature-encoding-modeling,Categorical Feature Encoding Challenge
14519308,0.80848,4,8,/werooring/top-1st-place-solution-on-private-lb,Categorical Feature Encoding Challenge
13022518,0.899,32,70,/dimitreoliveira/cassava-leaf-disease-tpu-tensorflow-inference,Cassava Leaf Disease Classification
21462468,0.8862,1,0,/moore0403/cfdc-inference,Cassava Leaf Disease Classification
14917814,0.0,0,1,/houstonasantos/cassavaleafdisease,Cassava Leaf Disease Classification
14819527,0.7354,0,2,/rahuldshetty/cassava-leaf-disease-w-data-augmentation,Cassava Leaf Disease Classification
13318788,0.602,0,4,/suriyaks/cassava-disease-classification-normal,Cassava Leaf Disease Classification
14321589,0.901,0,6,/pupilshuo/cassava-leaf-disease-baseline,Cassava Leaf Disease Classification
14747487,0.864,0,0,/soumadiptya/cassava-disease-classification-introductory,Cassava Leaf Disease Classification
14731917,0.652,0,0,/sajalvasal/cassava-leaf-disease-classification-using-cnn,Cassava Leaf Disease Classification
14719533,0.836,2,0,/matsumuranaoki/quick-step-to-83-on-leaf-disease-classification,Cassava Leaf Disease Classification
14297923,0.872,12,14,/adityakane/inference,Cassava Leaf Disease Classification
15590260,6.062,19,194,/saitodevel01/indoor-post-processing-by-cost-minimization,Indoor Location & Navigation
15107162,9.53,0,7,/byfone/indoor-location-wi-fi-features-catboost-starter,Indoor Location & Navigation
15175829,7.745,15,113,/oxzplvifi/indoor-gbm-postprocessing-xy-prediction,Indoor Location & Navigation
14657572,10.959,2,17,/ammarali32/wifi-features-lightgbm-starter-v2,Indoor Location & Navigation
14604562,14.763,4,24,/jiweiliu/wifi-features-lightgbm-starter-simple-tuning,Indoor Location & Navigation
14564232,78.675,19,12,/ammarali32/dask-with-simple-xgb,Indoor Location & Navigation
14906288,12.981,27,246,/andradaolteanu/indoor-navigation-complete-data-understanding,Indoor Location & Navigation
16926157,7.804,0,0,/hasinireddy/lstm-with-unified-wi-fi-ids,Indoor Location & Navigation
16270949,8.251,0,1,/pfoytik/light-gbm-indoorloc,Indoor Location & Navigation
15733371,7.285,9,62,/ebinan92/time-series-rnn-xy-prediction,Indoor Location & Navigation
15664082,3.88,0,0,/nooblife/indoor-navigation-snap-to-grid-post-processing,Indoor Location & Navigation
15355221,13.136,1,10,/ktgiahieu/tabnet-baseline-wifi-features-32,Indoor Location & Navigation
15221368,7.736,0,13,/satokiogiso/ensembling-for-better-performance,Indoor Location & Navigation
20890342,0.98853,0,4,/muki2003/digit-recognizer,Digit Recognizer
20842319,0.98771,0,7,/ramkiran55devireddy/digit-recognizer-using-keras-and-matplotlib,Digit Recognizer
20847611,0.98942,0,4,/badar78/digits-recognizer-cnn,Digit Recognizer
20706284,0.97325,0,2,/stuartsul/digit-recognizer,Digit Recognizer
20685269,0.98971,0,4,/morrisbundi/notebookdadca0e2a8,Digit Recognizer
12130991,0.95021,0,1,/yongjunl/digit-recognizer,Digit Recognizer
20482624,0.9935,1,2,/furieux/extremely-simple-digit-recognizer,Digit Recognizer
20561084,0.97839,4,12,/eneszvo/pytorch-tensorflow-pytorch-baseline-tutorial,Digit Recognizer
20436440,0.98867,16,24,/vinayakshanawad/digit-recognizer-with-cnn-pooling-acc-98-867,Digit Recognizer
20402126,0.97442,4,8,/lonnieqin/catboost-mnist-classification,Digit Recognizer
20343341,0.99446,0,6,/takahiroyoshida012/efficientnetb7-baseline,Digit Recognizer
20158842,0.98782,1,2,/riyanpahuja04/digit-recognizer-using-cnn-98,Digit Recognizer
15985953,0.911,0,0,/benoitvignaud/submission-04-04,HuBMAP - Hacking the Kidney
16928194,0.0287,5,4,/sakvaua/testtifffilesubmit,HuBMAP - Hacking the Kidney
16859151,0.926,1,7,/matjes/hubmap-deepflash2-scaled-ensemble-submission,HuBMAP - Hacking the Kidney
16871190,0.919,0,2,/tlopesma/fork-of-don-t-fail-me-78bc73,HuBMAP - Hacking the Kidney
16825984,0.932,0,0,/lhagiimn/hubmap-efficient-sampling-deepflash2-sub-a8a,HuBMAP - Hacking the Kidney
15484717,0.906,1,22,/saurabhbagchi/hubmap-pytorch-with-changed-parameters,HuBMAP - Hacking the Kidney
15520201,0.922,57,111,/matjes/hubmap-efficient-sampling-deepflash2-sub,HuBMAP - Hacking the Kidney
15350289,0.85,1,7,/sa2zoi/summary-hubmap-hacking-the-kidney,HuBMAP - Hacking the Kidney
14014261,0.0,1,19,/homiarafarhana/global-mask-shift,HuBMAP - Hacking the Kidney
14704295,0.922,1,21,/graafffff/fork-of-sub-no-code,HuBMAP - Hacking the Kidney
20569894,0.9618,0,5,/tharunnayak14/tensorflow-cnn-kannada-digit-classifier,Kannada MNIST
19036424,0.71,0,0,/xiangyuhe2021/kannada-mnist-neural-network-5-hdl-and-20-neurons,Kannada MNIST
18366701,0.9804,0,0,/kaundanyachinmaya07/cnn-with-keras-it-is,Kannada MNIST
17962632,0.9848,0,1,/burakerdoan/kannada-mnist,Kannada MNIST
16641263,0.8966,0,0,/ryanmburns93/randomforest1-kannada-mnist,Kannada MNIST
14456974,0.9838,0,0,/pingan001/kannada-mnist,Kannada MNIST
10652272,0.985,1,6,/rahulbana/kannad-mnist,Kannada MNIST
20582188,0.71282,0,1,/hyunee98/210917-cat,Categorical Feature Encoding Challenge II
19210378,0.48948,0,3,/kimjunil/aiffel-yj-9-save-me,Recruit Restaurant Visitor Forecasting
13950710,0.71001,4,7,/japandata509/tabular-playground-series-simple-random-forest,Tabular Playground Series - Jan 2021
13953969,0.70775,0,3,/chandraroy/randonforest-baseline-model,Tabular Playground Series - Jan 2021
16947019,0.6953,3,6,/hikmetsezen/blend-boosting-best-score-on-tsp-jan-2021,Tabular Playground Series - Jan 2021
14331351,0.69677,49,81,/jyotmakadiya/ensemble-using-xgboost-and-lgbm-with-eda,Tabular Playground Series - Jan 2021
13374003,0.769,4,35,/gopidurgaprasad/rfcs-audio-detection-pytorch-stater,Rainforest Connection Species Audio Detection
12947769,0.461,6,31,/drcapa/species-audio-detection-starter-keras,Rainforest Connection Species Audio Detection
16826514,0.85131,0,6,/alexlwh/rfcx-fastai-localized-resnet34-tp-train,Rainforest Connection Species Audio Detection
14673411,0.94314,3,9,/vzaguskin/rfcx-complete-tpu-training-and-inference-lb-0-95,Rainforest Connection Species Audio Detection
14281690,0.44,0,0,/jainarindam/rainforest-submission-py,Rainforest Connection Species Audio Detection
14182129,0.807,9,20,/ashusma/training-rfcx-tensorflow-tpu-effnet-b2,Rainforest Connection Species Audio Detection
13459237,0.752,2,26,/truonghoang/rfcx-pannscnn14att-train-inference,Rainforest Connection Species Audio Detection
22331158,0.9356,0,1,/satya1013/jigsaw-multilingual-toxic-comment-xlm-roberta,Jigsaw Multilingual Toxic Comment Classification
19787684,0.8295,0,0,/sachin/tensorflow-distilbert,Jigsaw Multilingual Toxic Comment Classification
17294684,0.929,0,5,/bond005/multilingual-toxic-xlm-roberta,Jigsaw Multilingual Toxic Comment Classification
17505598,0.66744,0,0,/tuaaanminh/quora-insincere-classification-without-embeddings,Quora Insincere Questions Classification
17395934,0.68684,0,2,/thdtt96/super-draft,Quora Insincere Questions Classification
16971098,0.64087,0,1,/nmt1612/quora-classify-toxic-questions,Quora Insincere Questions Classification
17225310,0.58375,0,0,/thlongphm/notebookb2800739f1,Quora Insincere Questions Classification
17143087,0.60917,0,0,/longhainguyen/nh-m-1-ml-best-score,Quora Insincere Questions Classification
16928802,0.60423,0,0,/mrrob3t/toxic-question,Quora Insincere Questions Classification
16776756,0.60481,0,0,/xerenite/questions-classification-logistic-regression,Quora Insincere Questions Classification
16582889,0.585,0,0,/lequangnhat/toxic-question-logistic-regression,Quora Insincere Questions Classification
15755101,0.53101,0,0,/saket7788/baseline-model-logistic-regression,Quora Insincere Questions Classification
15190263,0.69278,0,0,/zstcode/notebook6ae81bff29,Quora Insincere Questions Classification
14774305,0.64097,1,2,/abderahman/groupe-n-4,Quora Insincere Questions Classification
14710725,0.62686,0,0,/oumaimaizem/group8-notebook,Quora Insincere Questions Classification
14578153,0.65378,0,1,/tarikikoubaane/groupe-15,Quora Insincere Questions Classification
14400477,0.63274,7,6,/saikumarkella/qiq-pretrained-embeddings-mlp-cnn,Quora Insincere Questions Classification
21839434,0.77511,0,2,/kadambaripatil/getting-started-with-titanic,Titanic - Machine Learning from Disaster
23416750,0.76315,0,0,/ivanjovanovic555/titanic-first-experience,Titanic - Machine Learning from Disaster
20728635,0.79425,0,0,/pasuvulasaikiran/titanic-sol,Titanic - Machine Learning from Disaster
23020427,0.80382,19,23,/kgxiao/top2-5-feature-processing-and-selection-model,Titanic - Machine Learning from Disaster
23228090,0.76315,5,5,/ozakiryota/titanic-dnn-classification,Titanic - Machine Learning from Disaster
23121995,0.79904,1,6,/rhythmcam/prediction-using-titanic-preprocess-py,Titanic - Machine Learning from Disaster
23116931,0.76794,0,2,/rhythmcam/import-user-defined-py-file-and-use-function,Titanic - Machine Learning from Disaster
22921498,0.69617,0,1,/vishugupta0509/titanic-predict-survivors,Titanic - Machine Learning from Disaster
23039785,0.77511,4,6,/maunilshah/titanic-ml-from-disaster,Titanic - Machine Learning from Disaster
19040128,0.77511,0,0,/sirleneandreis/titanic-notebook,Titanic - Machine Learning from Disaster
22894185,0.77033,0,0,/antonypavarin/titanic,Titanic - Machine Learning from Disaster
22925903,0.6866,7,11,/abdulrayyan/titanic-classification,Titanic - Machine Learning from Disaster
22963900,0.77511,1,6,/michaelbrendo/getting-started-with-titanic,Titanic - Machine Learning from Disaster
22953925,0.77511,3,8,/pythonash/selu-activation-function-in-dl,Titanic - Machine Learning from Disaster
22947486,0.76076,4,8,/sumeetbohra/eda-fe-ensemble-learning-curves,Titanic - Machine Learning from Disaster
22931851,0.78468,0,2,/egortuliandin/teati,Titanic - Machine Learning from Disaster
20859657,0.83062,0,3,/fanbyprinciple/pytorch-doodle-puddle,"Quick, Draw! Doodle Recognition Challenge"
17968751,0.927,2,2,/iafoss/hubmap-r2-sub,HuBMAP - Hacking the Kidney
17071232,0.9178,0,0,/shujun717/hubmap-3rd-place-fast,HuBMAP - Hacking the Kidney
16876828,0.928,2,4,/puyuzhou/test-xx,HuBMAP - Hacking the Kidney
16862256,0.938,0,0,/jihangz/hubmap-576-reduce-fp-sliding-window,HuBMAP - Hacking the Kidney
16841358,0.92,0,0,/greylord1996/resnet-34-effnetb4-original-data,HuBMAP - Hacking the Kidney
16794813,0.887,0,0,/vedenev/for-judges,HuBMAP - Hacking the Kidney
16694141,0.935,1,1,/vladimirgroza/fork-of-hubmap-submission-experts-raster-i-o-ve,HuBMAP - Hacking the Kidney
16386137,0.9386,0,3,/snowballball/hubmap-submit1,HuBMAP - Hacking the Kidney
16165148,0.919,0,0,/mountainking/hubmap-efficient-sampling-deepflash2,HuBMAP - Hacking the Kidney
15975253,0.93,0,0,/takeajioka/hubmap-pytorch-blend-submission-pl,HuBMAP - Hacking the Kidney
15798074,0.929,0,0,/takeajioka/hubmap-fastai-reduce2-1024-resnet101-elu,HuBMAP - Hacking the Kidney
15757408,0.923,0,1,/jinssaa/hubmap-512-sam-unet-effb7,HuBMAP - Hacking the Kidney
15428344,0.93,0,0,/ambitionkingo/submit-with-csv,HuBMAP - Hacking the Kidney
14332856,0.841,0,10,/koh12345/hubmap-submission,HuBMAP - Hacking the Kidney
17737986,0.87983,0,1,/tylerchenchen/predict-sales-problem-step-by-step-part2,Predict Future Sales
16445742,0.8954,0,1,/jr12der/predict-future-sales-top-12-solution,Predict Future Sales
15684584,0.0,0,0,/egerv256/notebookbb952d3786,APTOS 2019 Blindness Detection
16671160,0.9824,0,0,/dongjai04/se-net-baseline-eesm5720-inf,Kannada MNIST
18722945,0.96881,0,0,/anhvuquoc/notebook2c91a75888,Plant Pathology 2020 - FGVC7
22542933,0.913968,8,1,/flafuji/fraud-detection-eda-model-top20-explained,IEEE-CIS Fraud Detection
22222626,0.893285,0,0,/gutierrezmatias/tp3-2c2021-parte-iii-rf,IEEE-CIS Fraud Detection
21816402,0.945198,0,2,/anqiwu85/eda-and-xgboost-on-fraud-detection,IEEE-CIS Fraud Detection
20515572,0.932302,1,11,/kirshoff/fraud-detection-lightgbm-xgboost,IEEE-CIS Fraud Detection
17856919,0.93649,0,0,/lima21bd/fraud-detection-with-93-64-accuracy,IEEE-CIS Fraud Detection
17789002,0.954589,0,1,/haiduc/new-92,IEEE-CIS Fraud Detection
17421847,0.705161,1,3,/homiarafarhana/frud-detection-3,IEEE-CIS Fraud Detection
14886509,0.891478,0,0,/oleksiibernatskyi/git-fully-conected-nn-fraud,IEEE-CIS Fraud Detection
15455864,1.61637,1,5,/dmitrsl/fishes,The Nature Conservancy Fisheries Monitoring
17586312,0.81849,0,1,/nguyenquan123vn/notebook9915ffa8b5,TGS Salt Identification Challenge
18723692,0.95907,1,1,/hariprabu/digit-recognizer-ann-96-accuracy,Digit Recognizer
18626492,0.98592,5,5,/jaeyuyeh/digit-recognizer-with-keras-and-cnn,Digit Recognizer
18531555,0.98939,11,8,/atharvadumbre/mnist-cnn-99-val-acc,Digit Recognizer
18597807,0.98575,3,2,/arpitkesharwani/digit-recognizer,Digit Recognizer
18487180,0.996,0,1,/idaidai/0-996-top7-augmentation-resnet-mc-dropout,Digit Recognizer
18555598,0.99257,1,2,/advaitvagerwal/mnist-digit-recognizer-cnn,Digit Recognizer
18538105,0.99071,4,7,/raymondlo84/mnist-with-openvino-and-tensorflow-on-kaggle,Digit Recognizer
18437859,0.98375,4,8,/lasindudemel/digit-recognizer-cnn,Digit Recognizer
18394847,0.98946,2,6,/abhimanyu314/mnist-cnn,Digit Recognizer
18292541,0.98767,23,17,/bunnyistaken/cnn-model-99-accuracy-digit-recognizer,Digit Recognizer
18210859,0.98992,8,9,/zzdaodanzi/digit-recognizer-cnn,Digit Recognizer
18243332,0.9931,2,8,/atultyagi2000/mnist-digit-recognition-cnn-99,Digit Recognizer
18131450,0.95867,16,13,/thatsme123/digit-recogniser-mlp-classifier-for-beginners,Digit Recognizer
18107209,0.95892,3,5,/stpeteishii/mnist-lgbm-with-optuna-tuning,Digit Recognizer
15947575,0.7302,0,0,/takuyatone/cassava-keras-tf-baseline-inference,Cassava Leaf Disease Classification
15667029,0.1062,0,0,/yongminghan/class-1,Cassava Leaf Disease Classification
15467622,0.1394,0,1,/shivarama/leaf-disease1,Cassava Leaf Disease Classification
15052164,0.6023,0,0,/yuewangpl/team1-part1,Cassava Leaf Disease Classification
14079598,0.892,1,1,/hassanikram/efficientnet-cassava-leaf-disease-notebook,Cassava Leaf Disease Classification
14628622,0.602,0,1,/sushant097/simple-transfer-learning-with-xception,Cassava Leaf Disease Classification
14887844,0.9023,1,2,/kanruwang/cassava-ensemble-efficientnet-resnext-private0-901,Cassava Leaf Disease Classification
15066262,0.9016,1,3,/vatsalmavani/starter-cassava-trained-models,Cassava Leaf Disease Classification
15000765,0.1962,0,1,/danpotter/blind-monkey-submission-example-data2040-sp21,Cassava Leaf Disease Classification
14949467,0.899,1,5,/tunguz/ensemble-resnext50-32x4d-efficientnet-0903,Cassava Leaf Disease Classification
14264662,0.908,4,16,/imokuri/cassava-inference,Cassava Leaf Disease Classification
15007591,0.9041,0,6,/bessenyeiszilrd/final-submission-private-lb-0-901,Cassava Leaf Disease Classification
14404719,0.809,0,1,/drcodikpollonny/cassava-leaf-efficientnetb4-ns,Cassava Leaf Disease Classification
14644890,0.888,1,0,/iamprateek/cassava-keras-tf,Cassava Leaf Disease Classification
14706551,0.79,1,4,/minseokkim34/cassava-disease-resnet50-on-pytorch,Cassava Leaf Disease Classification
14506831,0.001,0,1,/oimosan/cassava-leaf-disease-r5,Cassava Leaf Disease Classification
14398182,0.9,0,0,/raipachi0704/20i-efficientnet-b3-later-cutmix-tta,Cassava Leaf Disease Classification
6149462,0.9312,0,0,/dhgupta/planet-understanding,Planet: Understanding the Amazon from Space
16724261,0.41291,0,0,/rikdifos/avazu-easy-catboost,Click-Through Rate Prediction
14401205,2.72504,0,1,/zhidkovala/2sigma,Two Sigma Connect: Rental Listing Inquiries
6713959,0.51867,0,2,/chriscc/twosigmarenthop-advanced-feature-engineering,Two Sigma Connect: Rental Listing Inquiries
17637387,0.49047,1,3,/thep200/18021206-ho-van-thep,Home Depot Product Search Relevance
23232067,0.47702,0,0,/clemaldy/home-depot-product,Home Depot Product Search Relevance
16527108,0.47988,0,0,/hoang1706/version5,Home Depot Product Search Relevance
17306075,0.034,0,0,/joey0201/centernet-baseline-with-map-valid-score,Peking University/Baidu - Autonomous Driving
16404106,0.715,0,4,/philipkd/arc-late-submission-1st-and-3rd-place-ensemble,Abstraction and Reasoning Challenge
20414075,0.79581,1,11,/carlmcbrideellis/tabular-classification-with-neural-networks-keras,Santander Customer Satisfaction
19268018,0.5,0,2,/nulikrishna/98-with-pca-randomforest,Santander Customer Satisfaction
17299702,0.83268,0,2,/lair0826/eda-for-start-base-model,Santander Customer Satisfaction
14000473,0.80454,0,1,/dogdriip/santender-pipeline-xgb-selectkbest-pca,Santander Customer Satisfaction
17638476,0.67341,0,3,/bhargavrko619/subnote,Microsoft Malware Prediction
22494142,0.22075,0,0,/iliabadekin/notebook3-house-prices-bv,House Prices - Advanced Regression Techniques
22359556,0.14234,0,0,/kremenevskiy/house-prices,House Prices - Advanced Regression Techniques
21919764,0.15838,0,3,/mrunalbhamare/house-price-prediction-linearreg-and-random-forest,House Prices - Advanced Regression Techniques
22446309,0.12149,1,0,/dineshsai6/notebook9c929aa4bc,House Prices - Advanced Regression Techniques
22328920,0.35562,0,1,/abdurrahman17/house-prices-prediction,House Prices - Advanced Regression Techniques
22359307,0.19564,0,0,/rhythmcam/keras-tuner-randomforest-decisiontree,House Prices - Advanced Regression Techniques
22223583,0.12761,0,3,/vladislavsergeev/house-prices-ensemble-0-128,House Prices - Advanced Regression Techniques
22315251,0.15267,0,0,/matyukhingeorge/notebook6f0b5f6f01,House Prices - Advanced Regression Techniques
22201805,0.15359,0,0,/aershov/ml2021-lab-3-house-prices,House Prices - Advanced Regression Techniques
22118702,0.1391,1,16,/sriju94/house-price-prediction-model-build,House Prices - Advanced Regression Techniques
21900409,0.16606,0,1,/mynamesp/data-engineer-and-random-forest,House Prices - Advanced Regression Techniques
14063996,1.15586,1,1,/emirhankprl/171307043-emirhan-k-pr-l-b-y-k-veri,Predict Future Sales
13761639,1.28792,0,1,/aurbcd/predict-future-sales-competition,Predict Future Sales
20891350,0.69163,0,8,/elenamarelmi/wsdm-kkbox-s-music-recommendation-challenge,WSDM - KKBox's Music Recommendation Challenge
22378302,0.6547,0,2,/thimoty/home-credit-timo-part-2,Home Credit Default Risk
19947580,0.75841,0,2,/hyunhp/stacking-test-written-by-eliot,Home Credit Default Risk
19576670,0.7656,0,0,/tahmidnafi/cse499-tahmid-nn,Home Credit Default Risk
18862846,0.60007,0,1,/satoshimts/home-credit-first-commit,Home Credit Default Risk
18632979,0.67041,0,1,/hyunhp/home-credit-default-risk-written-by-will,Home Credit Default Risk
18620823,0.7544,0,0,/yumamin/home-credit-default,Home Credit Default Risk
18288147,0.73872,0,0,/timvh2/fork-of-data-cleaning,Home Credit Default Risk
17915541,0.75469,0,0,/maryrose1003/bigdata-project-eda-fe-jeongyoon,Home Credit Default Risk
17915277,0.75766,0,0,/qbxkvbf/bigdata-project-eda-fe-qbxkvbf5,Home Credit Default Risk
17857459,0.76647,0,1,/yujeongella/bigdata-project-eda-fe-ella,Home Credit Default Risk
17911448,0.76291,0,0,/miyoungsong/bigdata-project-eda-fe-miyoung,Home Credit Default Risk
17905573,0.75738,0,0,/inhoocho/bigdata-project-eda-fe-inhoo-cho,Home Credit Default Risk
17896962,0.78171,0,2,/smarthan/bigdata-project-eda-fe-hr,Home Credit Default Risk
17855132,0.74635,0,0,/seoyeongpark/bigdata-project-eda-fe-seoyeong,Home Credit Default Risk
17704612,0.74552,0,0,/choiyunna/bigdata-project-eda-fe-choi-yunna,Home Credit Default Risk
17375162,0.74552,0,0,/jinyoung1212/bigdata-project-eda-fe-jinyoung1212,Home Credit Default Risk
21681698,0.56231,0,2,/geochatz/insincere-questions-binary-classification,Quora Insincere Questions Classification
21263863,0.0,0,0,/mateuslins/incinsere-questions-with-dnn-no-pretreined-embedds,Quora Insincere Questions Classification
21266418,0.36031,0,0,/ingopastl/nlp-i-guess-i-gotta-do-this-quora-edition,Quora Insincere Questions Classification
21213244,0.0,0,0,/skywolkher/quora-insincere-questions,Quora Insincere Questions Classification
20425830,0.6066,0,0,/niketanmoon/models-qiqc,Quora Insincere Questions Classification
17469823,0.60274,0,0,/nghuuloc/ml-18020779-nhl,Quora Insincere Questions Classification
16926444,0.69123,0,1,/zznuazz/ml-final,Quora Insincere Questions Classification
17100062,0.64184,0,0,/phamhaithang/18021139-final-submission-gru-kerax,Quora Insincere Questions Classification
20287499,0.99203,0,4,/lonnieqin/mnist-classifier-with-data-augmentation,Digit Recognizer
20119647,0.99339,1,7,/soumyasharma20/handwritten-digit-recognition-cnn-keras,Digit Recognizer
20154383,0.98475,0,1,/leeminhyung/mnist-dnn,Digit Recognizer
20126964,0.99121,0,0,/takeshun4418/01-digitrecognizer,Digit Recognizer
19987792,0.98989,0,1,/hemasowjanya/digit-recognizer,Digit Recognizer
19974934,0.955,0,10,/dasinenisaideepthi/digit-recognition,Digit Recognizer
19874537,0.98771,0,0,/yeonekim/mnist-densenet-xgboosting,Digit Recognizer
16163398,0.92457,0,0,/niteshsharmaiitk/digit-recognizer1,Digit Recognizer
19642755,0.98403,0,0,/sewonshin/mnist-digit-recognizer-using-cnn,Digit Recognizer
19512597,0.98164,0,0,/yavuzselimkaraman/digit-recognizer,Digit Recognizer
19868945,0.96414,0,0,/zikang/notebook52c0bcfb78,Digit Recognizer
18843631,0.96971,0,0,/iravad/digit-recognizer-tensorflow,Digit Recognizer
5681174,0.89481,0,0,/jnegrini/santander-customer-transaction,Santander Customer Transaction Prediction
15639884,0.83849,0,8,/alexryzhkov/lightautoml-starter-nlp,Natural Language Processing with Disaster Tweets
15552484,0.56297,0,2,/hyeonjin6/eda-part-nlp-with-disaster-tweets,Natural Language Processing with Disaster Tweets
15455818,0.76524,0,1,/tanviralamroni/nlp-with-disaster-tweets,Natural Language Processing with Disaster Tweets
15455647,0.79619,2,6,/ashrafulislamemon/natural-language-processing-with-disaster-tweets,Natural Language Processing with Disaster Tweets
15427694,0.74624,6,10,/lalbabusah/natural-language-processing-with-disaster-tweets,Natural Language Processing with Disaster Tweets
14591530,0.83328,2,8,/yrpang/nlp-beginer-with-bert-fine-tunning,Natural Language Processing with Disaster Tweets
15318234,0.83481,0,1,/anthonylauly/bert-model,Natural Language Processing with Disaster Tweets
15249248,0.80171,0,1,/andriik86/quick-and-simple-disaster-tweet-classification,Natural Language Processing with Disaster Tweets
15118972,1.0,0,18,/ghaiyur/ensemble-models-versiong,Natural Language Processing with Disaster Tweets
14846677,0.7824,0,1,/jatinbabbar1696/disaster-tweets-rf,Natural Language Processing with Disaster Tweets
14784149,0.78302,0,1,/leomauro/nlp-detec-o-de-tweets-de-desastres,Natural Language Processing with Disaster Tweets
14922728,0.81152,3,6,/iamabhishekdas/bert-in-pytorch-for-nlp-in-disaster-tweets,Natural Language Processing with Disaster Tweets
14958171,0.81949,0,0,/nehakumar31/nlptweets-berttransformer,Natural Language Processing with Disaster Tweets
14910610,0.81182,0,1,/larsmadsen/tf-keras-starter-0-81-score-lstm-and-word-embeddi,Natural Language Processing with Disaster Tweets
14864634,0.79436,19,29,/kppetrov/fast-svc-using-scikit-learn-intelex-for-nlp,Natural Language Processing with Disaster Tweets
14932505,0.79742,0,0,/nehakumar31/nlptweet-glove-lstm-2,Natural Language Processing with Disaster Tweets
14775201,0.78057,0,1,/nehakumar31/nlptweet-scikitnb,Natural Language Processing with Disaster Tweets
13338682,0.82837,2,2,/riziko/bert-embedding-stacking-bayesian-opt,Natural Language Processing with Disaster Tweets
14011004,0.80845,1,3,/mariuszorbik/disaster-tweets-nlp,Natural Language Processing with Disaster Tweets
22529178,0.75566,0,4,/getexcelsior/plant-seed-classification,Plant Seedlings Classification
20374243,0.9471,0,5,/akshatkhare23x3/seeding-classification,Plant Seedlings Classification
18295889,0.60453,0,1,/alankritkumar/notebookb12f2e9991,Plant Seedlings Classification
18229618,0.85768,0,0,/imanthaahangama2/nn-assignment-02-2-25a08e,Plant Seedlings Classification
15094757,0.758,0,4,/arturmrs/a-homemade-pytorch-model,RANZCR CLiP - Catheter and Line Position Challenge
14976702,0.839,0,1,/v1olet2/ranzcr-tpu,RANZCR CLiP - Catheter and Line Position Challenge
14814599,0.951,0,0,/iamprateek/ranzcr-tpu,RANZCR CLiP - Catheter and Line Position Challenge
13952043,0.934,0,3,/jasonkwm/ranzcr-efficientnetb4-submission,RANZCR CLiP - Catheter and Line Position Challenge
13823321,0.921,0,3,/bipinkrishnan/ranzcr-clip-inference-notebook,RANZCR CLiP - Catheter and Line Position Challenge
13702907,0.954,0,5,/ameya98/efficientnet-submission,RANZCR CLiP - Catheter and Line Position Challenge
14292479,0.877,0,1,/kutaykutlu/efficient7-simple-inference,Cassava Leaf Disease Classification
14182656,0.809,0,0,/iamprateek/darknet-cassava,Cassava Leaf Disease Classification
13689579,0.877,0,0,/madelinecaples/cassava-model-with-fastai-on-full-dataset,Cassava Leaf Disease Classification
14073077,0.687,0,6,/ramjib/cassava-leaf-disease-classification-model,Cassava Leaf Disease Classification
14156348,0.795,0,2,/kkmax1015/keras-cassava-efficientnetb4-for-beginner,Cassava Leaf Disease Classification
13694227,0.885,0,0,/ekaterinazhuikova/xception-cassava-leaf-disease-classification-2,Cassava Leaf Disease Classification
14022684,0.849,0,4,/tanlikesmath/cassava-pytorch-xla-tpu-starter-gpu-inference,Cassava Leaf Disease Classification
14135871,0.139,0,0,/plabannayak/cassava-classification-fastai2,Cassava Leaf Disease Classification
14105840,0.139,3,9,/legendsplay/vgg16-transfer-learning,Cassava Leaf Disease Classification
14087696,0.606,7,10,/nageshsingh/cassava-leaf-disease-classification-efficientnetb4,Cassava Leaf Disease Classification
14158600,0.896,0,0,/minhhoai1001/pytorch-efficientnet-and-visualization-tta,Cassava Leaf Disease Classification
14042172,0.162,3,4,/hazem9806/cassava-model-vgg19,Cassava Leaf Disease Classification
13085841,0.871,0,5,/aarontrefler/at-cassava-leaf-disease-fastai,Cassava Leaf Disease Classification
14064633,0.876,1,1,/user123454321/pytorch-resnet101-starter-inference,Cassava Leaf Disease Classification
14832020,11365.77,0,0,/wendelfariaslopes/simple-pytorch-tensorflow-mlp,Jane Street Market Prediction
14916621,4402.249,0,0,/davidmagny/secondneuralnetwork,Jane Street Market Prediction
14928913,3871.047,0,1,/yidancai/stock-market-prediction-test,Jane Street Market Prediction
14983321,11382.532,0,1,/lhagiimn/ensemble-submission,Jane Street Market Prediction
14950990,11428.043,0,2,/siddheshshelke/blending-tensorflow-pytorch,Jane Street Market Prediction
14778891,3417.264,0,1,/siddheshshelke/lstm-rnn-classifier,Jane Street Market Prediction
14956812,11160.505,0,1,/dongwenjian/key-notebook-blending-resnet-mlp-xgboost,Jane Street Market Prediction
14641217,8644.211,0,4,/bboltt/regression-method,Jane Street Market Prediction
14289828,6119.965,0,0,/virajkadam/jane-street-market-prediction,Jane Street Market Prediction
15073091,11473.16,3,26,/sagarjiyani/pytorch-embeddingsnn-resnet,Jane Street Market Prediction
15068247,3132.979,0,2,/ethanlhaas/xgb-mod4-predictions,Jane Street Market Prediction
15064295,4279.997,0,1,/arindam235/xgboost-stock-prediction,Jane Street Market Prediction
15052064,5826.05,0,8,/oldwine357/xgb-multilabel-part-1-purgedgrouptime-s-split,Jane Street Market Prediction
21682316,0.13722,0,2,/iravad/improve-score-by-eda-feature-engg-house-price,House Prices - Advanced Regression Techniques
21644937,0.20852,0,3,/jesse2333/neural-network-price-prediction,House Prices - Advanced Regression Techniques
21615751,0.39577,0,5,/heitornunes/a-simple-application-of-a-linear-model-first-try,House Prices - Advanced Regression Techniques
21536050,0.32174,0,0,/diegogonzlezlpez/house-pricing-with-regression,House Prices - Advanced Regression Techniques
21544315,0.12471,1,2,/juliorsleite/houseprices-xgboost-pipeline-randomscan,House Prices - Advanced Regression Techniques
21526333,0.00044,2,7,/profahmadhussein2010/house-prices1,House Prices - Advanced Regression Techniques
21342986,0.15183,0,0,/maragarcagirn/house-pricing-competition-mar-a,House Prices - Advanced Regression Techniques
21372195,0.12065,6,6,/isyedsalmanali/house-price-predictions,House Prices - Advanced Regression Techniques
21330520,0.17045,4,8,/moathmohamed/advanced-house-prices-prediction-99,House Prices - Advanced Regression Techniques
20574571,0.00044,0,0,/enesimek/house-price-prediction,House Prices - Advanced Regression Techniques
21144240,0.13933,12,22,/maricinnamon/house-prices-regression-sklearn,House Prices - Advanced Regression Techniques
20944960,0.16002,0,6,/kalpanadas/house-prices-advanced-regression-techniques,House Prices - Advanced Regression Techniques
21524760,0.28231,2,4,/noritomowatanabe/googleq-a-tfidf,Google QUEST Q&A Labeling
22811396,0.97941,0,0,/armanhak/jigsaw-toxic-comment-classification,Toxic Comment Classification Challenge
21181451,0.65633,0,0,/rodrigobrazil/toxic-comment-classification-challenge-keras,Toxic Comment Classification Challenge
13676994,0.947,0,8,/shanmukh05/ranzcr-clip-tpu-submission,RANZCR CLiP - Catheter and Line Position Challenge
13542132,0.951,14,28,/maksymshkliarevskyi/ranzcr-xception-tpu-prediction,RANZCR CLiP - Catheter and Line Position Challenge
15587798,0.969,0,4,/kozodoi/71st-place-ensembling-pipeline,RANZCR CLiP - Catheter and Line Position Challenge
15559926,0.959,0,0,/o93333/fork-of-ranzcr-split-tpu-pred-f9db06,RANZCR CLiP - Catheter and Line Position Challenge
15517725,0.969,0,0,/vladimirsydor/mixed-inference,RANZCR CLiP - Catheter and Line Position Challenge
15503424,0.929,0,1,/hedwig100/keras-submission-with-clahe,RANZCR CLiP - Catheter and Line Position Challenge
15372370,0.968,0,7,/rsinda/38th-place-solution-0-972-single-model-5-fold,RANZCR CLiP - Catheter and Line Position Challenge
15315832,0.484,0,0,/khalidanwaar/resnet-200d-timm,RANZCR CLiP - Catheter and Line Position Challenge
18574954,0.5,0,0,/foxeyjoker/springleaf-marketing-response,Springleaf Marketing Response
23463276,0.18718,4,15,/phuc16102001/house-prices-advanced-prevent-overfitting-by-fs,House Prices - Advanced Regression Techniques
22590056,0.14651,1,5,/demko1/homesales,House Prices - Advanced Regression Techniques
23342224,0.11676,8,12,/denisgu/kernel-ridge-top-3,House Prices - Advanced Regression Techniques
23072678,0.13288,0,0,/shustikovvladislav/notebook166944cce3,House Prices - Advanced Regression Techniques
23118065,0.13498,0,1,/bhavithakusam/house-price-prediction,House Prices - Advanced Regression Techniques
20854354,0.1436,1,2,/seungtaekim/house-price-advanced-regression,House Prices - Advanced Regression Techniques
22855719,0.12793,20,23,/wonchanleee/compare-lasso-ridge-elasticnet-lgbm-performance,House Prices - Advanced Regression Techniques
23011181,0.13114,0,0,/soojeonghan/submit,House Prices - Advanced Regression Techniques
22821745,0.14702,0,0,/southeslem/house-prices-taisumov,House Prices - Advanced Regression Techniques
22887322,0.1675,0,2,/chimchoppa/house-prices-8308-smi,House Prices - Advanced Regression Techniques
14666405,0.12285,0,0,/luisgarchi/house-price-analysis,House Prices - Advanced Regression Techniques
22863281,0.14272,3,6,/amogh2001pradeep/10-models-tested-top-4-mean-abs-error,House Prices - Advanced Regression Techniques
22960017,0.6083,0,5,/zwartfreak/easiest-forest-prediction-10-lines-of-code,Forest Cover Type Prediction
22386305,0.58217,0,0,/jamiesperos/g2-forestcovertype-feature-engineering-notebook,Forest Cover Type Prediction
22078163,0.74484,0,0,/mariannejoyleano/fork-of-ml-forest-cover-v03-submission,Forest Cover Type Prediction
18786351,0.72885,0,0,/vibgreon/forest,Forest Cover Type Prediction
18506296,0.72663,0,1,/keerthanamreddy/forest-cover-week-2-project,Forest Cover Type Prediction
18349135,0.58641,0,1,/tejasboss/forest-cover-pred,Forest Cover Type Prediction
17441293,0.61191,1,2,/pranjal6192/forest-cover,Forest Cover Type Prediction
17304539,0.61606,0,0,/sachingupta2212/notebook6a21b78b58,Forest Cover Type Prediction
16766427,0.63639,0,0,/rahulgujjar/notebook04765fed0e,Forest Cover Type Prediction
16311298,0.73124,0,0,/sajjadwasti/first-submisssion,Forest Cover Type Prediction
15840849,0.53134,0,1,/vishuvardhan16/forest-cover-type-prediction,Forest Cover Type Prediction
15186827,0.73114,0,0,/mgen2020/forestcoverprediction,Forest Cover Type Prediction
14114937,0.7293,0,2,/subhamsagarpaira/knn-forest-type,Forest Cover Type Prediction
10410880,0.0,1,10,/matthiasanderer/m5-alignandsubmit,Natural Language Processing with Disaster Tweets
21735904,0.99121,0,0,/diegogonzlezlpez/cnn-with-keras,Digit Recognizer
22048104,0.95378,0,1,/justynacebrat/zadanie1,Digit Recognizer
21945779,0.96442,0,0,/przemyslawpietrzak/digit-recognizer-skni-course-pp,Digit Recognizer
21942733,0.98792,12,29,/vardhansiramdasu/kaggle-digit-recognizer,Digit Recognizer
21936867,0.99246,0,0,/juliavassilenko/vassilenko-lab1,Digit Recognizer
21881595,0.99007,1,9,/mgerdas/first-attempt-at-neural-networks,Digit Recognizer
21825982,0.98728,0,2,/mokhnatkinkirill/digit-recogniser-with-cnn-keras,Digit Recognizer
21826798,0.97289,1,0,/scirpus/gp-monster,Digit Recognizer
21707701,0.9821,0,1,/veratumanova/lab2-t,Digit Recognizer
20996521,0.98582,0,1,/kevinvanbecelaere/mnist-cnn-digit-classifier,Digit Recognizer
21648756,0.99346,1,4,/olgabelitskaya/digit-recognition-models-4,Digit Recognizer
21599370,0.97867,0,6,/raqhea/digit-classification-with-a-linear-model-pytorch,Digit Recognizer
21594484,0.98867,0,1,/neerajkaroshi/digit-recognizer-draft2,Digit Recognizer
21566349,0.97814,1,2,/rushi11/mnist-digit-recognizer,Digit Recognizer
21498264,0.9776,20,35,/vandana12911/digit-recognizer-using-pytorch,Digit Recognizer
20551770,0.94971,0,3,/alexanderthomas2001/digit-recognizer-neural-network,Digit Recognizer
21052731,0.99532,0,0,/suneetsaini/beginner-s-digit-recognizer,Digit Recognizer
12050226,0.79497,0,0,/samawel97/nlp-with-disaster-tweets-svm,Natural Language Processing with Disaster Tweets
14534478,0.8611,1,3,/andreeasandu/leaf-disease-classification,Cassava Leaf Disease Classification
14334187,0.896,2,5,/botanahmad/efficientnet-89-5-tta-n-fold-cross,Cassava Leaf Disease Classification
13858447,0.048,0,0,/danilofc/cassava-project,Cassava Leaf Disease Classification
14319149,0.884,6,17,/rajeev064/cnn-for-cassava-leaf-using-efficientnet,Cassava Leaf Disease Classification
14352125,0.841,0,1,/abduljabbar110/efficientnetb0-model,Cassava Leaf Disease Classification
21905989,0.55009,0,8,/eryash15/mercedes-eda-xgboost-testline,Mercedes-Benz Greener Manufacturing
21105841,0.54818,0,4,/taranenkodaria/lama-mercedes-benz-predict,Mercedes-Benz Greener Manufacturing
16784810,0.47469,0,4,/thejas112/mercedes-benz-greener-manufacturing-20210507-v5,Mercedes-Benz Greener Manufacturing
16497332,0.55072,1,4,/santosh1974/mercedes-benz-submission,Mercedes-Benz Greener Manufacturing
15654431,0.52335,0,0,/shribhadgaonkar/mb-optimization,Mercedes-Benz Greener Manufacturing
14740270,0.55259,1,3,/podsyp/mercedes-benz-greener-manufacturing,Mercedes-Benz Greener Manufacturing
3240160,0.9999,0,6,/ananthu017/columnar-cactus-identification,Aerial Cactus Identification
22042388,0.71991,0,1,/oskarstachowski/tps-i-skni,Tabular Playground Series - Jan 2021
21318595,0.96264,0,0,/lumanlanc/regression-5,Tabular Playground Series - Jan 2021
20484376,0.72749,0,1,/smita09/tps-jan-worst-solution,Tabular Playground Series - Jan 2021
17103400,0.69763,3,1,/ktakuya864/optuna-xgboost,Tabular Playground Series - Jan 2021
15869511,0.71389,0,0,/srividhyaag/tabular-playground-competition-jan,Tabular Playground Series - Jan 2021
14396899,0.70825,0,0,/eladwar/notebooka58a105a99,Tabular Playground Series - Jan 2021
14609384,0.71129,0,2,/harshsoni/jan-tabular-2021,Tabular Playground Series - Jan 2021
14305889,0.69855,6,6,/ssooni/xgboost-lgbm-optuna,Tabular Playground Series - Jan 2021
14589590,0.69753,0,10,/davidedwards1/jan21-tabplayground-nn-final-more-features,Tabular Playground Series - Jan 2021
14064718,0.70253,0,0,/amsavchuk/tabular-playground-jan-2021-xgboost,Tabular Playground Series - Jan 2021
14579645,0.69807,3,5,/davidkatzil/sklearnpipeline-with-gridsearch-votingregressor,Tabular Playground Series - Jan 2021
14577538,0.70807,0,2,/zesulank/tabular-catboost,Tabular Playground Series - Jan 2021
14553420,0.70426,0,0,/nicholaskarlson/tps-apply-xgboost-to-data-jan2021,Tabular Playground Series - Jan 2021
14480582,1.01677,5,7,/neilgibbons/mdn-v3,Tabular Playground Series - Jan 2021
14044928,0.6981,0,4,/manujosephv/pytorch-tabular-node-lgbm-catboost,Tabular Playground Series - Jan 2021
14038334,0.70009,0,2,/radofanantenana/tabular-playground-lgbm,Tabular Playground Series - Jan 2021
14141995,0.70187,0,0,/hekkta/jan-2021-playground,Tabular Playground Series - Jan 2021
14535876,0.71289,0,2,/kingabzpro/tabnet-regressor,Tabular Playground Series - Jan 2021
14481856,0.70099,0,2,/drscarlat/playground-jan-2021-simple-nn-and-some-shallows,Tabular Playground Series - Jan 2021
15212270,1093.2,3,49,/shoheiazuma/10th-place-solution,Hungry Geese
22639248,0.9962,0,0,/helmiaziz/aerial-cactus-identification-pytorch,Aerial Cactus Identification
20464368,0.9999,0,6,/werooring/ch11-aerial-cactus-identification-modeling2,Aerial Cactus Identification
15678168,0.9928,0,0,/nmarroni/aerial-cactus-identification-cnn,Aerial Cactus Identification
17547545,0.5,0,2,/sa2zoi/dl-cactus-classification-vgg16,Aerial Cactus Identification
14421714,0.984,0,1,/maximenemo/autre-essais,Aerial Cactus Identification
20501081,0.948019,0,0,/ahnsehun/max-o-simple-lgbm,IEEE-CIS Fraud Detection
17857187,0.962081,0,0,/haiduc/xgb-fraud-with-magic-0-9600,IEEE-CIS Fraud Detection
16789799,0.927836,0,0,/adriancantu/fraud-detection-lightgbm,IEEE-CIS Fraud Detection
14566831,0.956944,1,2,/thejravichandran/fraud-detection-v16-new-pipleline-and-testing,IEEE-CIS Fraud Detection
19276687,0.01942,0,0,/esiombla/final-model,Mechanisms of Action (MoA) Prediction
15327750,0.01901,0,0,/alexandremahdhaoui/autocoordinet,Mechanisms of Action (MoA) Prediction
14019036,0.02165,0,0,/alaamohmedelbarbary/moa-logisticregression,Mechanisms of Action (MoA) Prediction
22170602,0.58,0,1,/sheromon/wheat-faster-r-cnn-with-pytorch-lightning,Global Wheat Detection
18902089,0.6495,0,0,/ecthompson99/global-wheat-detection-inference-yolov5,Global Wheat Detection
14210297,0.006,0,1,/sanchitvj/global-wheat-detection-inference,Global Wheat Detection
20409302,0.6209,0,0,/jasonhuangcn/random-forest-melanoma-malignancy-classification,SIIM-ISIC Melanoma Classification
12859418,0.952,0,4,/mnowak061/melanoma-classification-tfrec-efficientnets,SIIM-ISIC Melanoma Classification
21987054,0.78577,2,3,/onuraydere/decisiontreeandbernoullinb,Natural Language Processing with Disaster Tweets
21992662,0.79528,5,9,/samyukthamobile/nlp-beginners-level-comments-review-submission,Natural Language Processing with Disaster Tweets
21965245,0.78608,0,5,/satheeshans/nlp-tweet-mining,Natural Language Processing with Disaster Tweets
21936667,0.83236,0,5,/vinaykakara/bert-model-using-tensorflow-hub,Natural Language Processing with Disaster Tweets
21498463,0.73735,1,3,/dhruvgupta2801/nlp-using-lstm-rnn-gru,Natural Language Processing with Disaster Tweets
21691963,0.78976,0,1,/nethanael/nlp-tarea,Natural Language Processing with Disaster Tweets
21601535,0.79589,0,2,/cesarjimenezm/mla2-cjimenez-9,Natural Language Processing with Disaster Tweets
21619931,0.74103,0,1,/antoninabugayova/nlp-with-disaster-tweets-2,Natural Language Processing with Disaster Tweets
7928811,0.80661,1,3,/lkatran/disaster-tweets-svm,Natural Language Processing with Disaster Tweets
21448746,0.84033,3,1,/giovanni11/finetuning-bertweet-classification-score-85,Natural Language Processing with Disaster Tweets
21237042,0.53263,2,5,/abdulbaseetzahir/nlp-with-disaster-tweets-keras,Natural Language Processing with Disaster Tweets
21447095,0.82776,2,7,/saniyaafzal/nlp-disaster-tweets-tensorflow-spacy-bert,Natural Language Processing with Disaster Tweets
21325840,0.80508,0,1,/subhochowdhury/pretrained-bert-with-gru-text-classification,Natural Language Processing with Disaster Tweets
21338693,0.77505,0,3,/jinhyunyang/kaggle-ver1,Natural Language Processing with Disaster Tweets
21301029,0.83665,8,6,/chaki18081999/tweet-classification-using-bert-explained,Natural Language Processing with Disaster Tweets
21337527,0.77505,0,0,/youjihun/notebook5532dbe154,Natural Language Processing with Disaster Tweets
21018653,0.80447,0,0,/pedrobandeiradarocha/mnb-code,Natural Language Processing with Disaster Tweets
21068327,0.79619,10,35,/rumasinha/working-with-nlp-problem,Natural Language Processing with Disaster Tweets
21070110,0.79711,0,5,/lonnieqin/disaster-tweets-classification-transformer,Natural Language Processing with Disaster Tweets
22320571,0.8144,0,0,/leopoldtchomgwi/cancerdetection-tl-submission,Histopathologic Cancer Detection
22000205,0.7813,0,0,/leopoldtchomgwi/lt-cancer-detection-cropped-images-submission,Histopathologic Cancer Detection
21922202,0.9163,0,0,/jennaward6/team-4-cancer-detection-submit-jw-edit,Histopathologic Cancer Detection
20135675,0.90144,0,2,/sidtep/amazon-prediction,Amazon.com - Employee Access Challenge
16988348,0.92742,0,0,/gunatejas/ml-decision-tree-classifier,Loan Default Prediction - Imperial College London
20744709,0.68803,0,0,/gilbertobernal/movie-sentiment-analysis-model,Sentiment Analysis on Movie Reviews
19567201,0.57267,1,6,/shenurisumanasekara/sentiment-analysis,Sentiment Analysis on Movie Reviews
19265806,0.64463,0,0,/pranjal6192/notebook27a4cb73de,Sentiment Analysis on Movie Reviews
18765414,0.6268,0,4,/aryaadesh/sentiment-rnn,Sentiment Analysis on Movie Reviews
18118416,0.64238,0,1,/sakshi0100/sentiment-analysis-rnn,Sentiment Analysis on Movie Reviews
16960382,0.60814,2,2,/sohailadiab/movie-reviews-data-modeling,Sentiment Analysis on Movie Reviews
17091956,0.61045,0,0,/youssefsoultan/final-model,Sentiment Analysis on Movie Reviews
16017478,0.45943,1,0,/rohitjagirdar/movie-review,Sentiment Analysis on Movie Reviews
15375569,0.44469,0,2,/subhamsagarpaira/rnn-cnn-sentiment-analysis,Sentiment Analysis on Movie Reviews
14888389,0.59135,0,0,/arko007/sentiment-analysis-rnn,Sentiment Analysis on Movie Reviews
22792538,0.05553,0,0,/romanresner/cat-and-dog-classification-efficientnetb0,Dogs vs. Cats Redux: Kernels Edition
18363896,0.58025,0,0,/kohsan/notebook7dfbdb257c,Dogs vs. Cats Redux: Kernels Edition
15105665,4.20971,0,2,/digaamberdhamija/cats-vs-dogs-redux-vgg19-pretrainedbase,Dogs vs. Cats Redux: Kernels Edition
14405397,0.0917,2,3,/jackttai/cat-dog-classilfer-resnet-18-pytorch,Dogs vs. Cats Redux: Kernels Edition
13808449,0.15578,0,1,/phsaikiran/dogs-vs-cats,Dogs vs. Cats Redux: Kernels Edition
20303794,0.9978,0,0,/ilyashafeev/cnn-first-try,Aerial Cactus Identification
18786972,0.84423,0,0,/keerthanamreddy/flower-classification-tpu,Flower Classification with TPUs
18426284,0.84182,0,0,/kaundanyachinmaya07/flower-classification-with-inception-maybe,Flower Classification with TPUs
18288660,0.79129,23,30,/rawaaelghali/real-or-not-nlp-with-disaster-tweets,Natural Language Processing with Disaster Tweets
18284948,0.75329,8,9,/sid321axn/prediction-of-disaster-tweets-using-lstm,Natural Language Processing with Disaster Tweets
18259700,0.79528,14,11,/shweta2407/tweet-classification-using-machine-deep-learning,Natural Language Processing with Disaster Tweets
17733566,0.81428,0,0,/taoye0527/nlp-with-disaster-tweets,Natural Language Processing with Disaster Tweets
18006706,0.75482,4,4,/emrearuk/nlp-disaster-tweets,Natural Language Processing with Disaster Tweets
18037813,0.7919,0,0,/jnyfrtn/notebookba6150a40d,Natural Language Processing with Disaster Tweets
17691511,0.80876,15,10,/mohitnirgulkar/disaster-tweets-classification-using-deep-learning,Natural Language Processing with Disaster Tweets
18927591,7.56177,0,0,/prathikshabr/dogbreedclassification-cnn-kaggleproject-10,Dog Breed Identification
16671792,4.67411,0,0,/xevator/dog-breed,Dog Breed Identification
18572459,0.80599,0,0,/jiangtt/lightautoml-0-80599-private-lb,Categorical Feature Encoding Challenge
22754550,0.0,0,0,/matiasbalian/notebookd869a45ae3,PetFinder.my Adoption Prediction
16201528,0.0,0,0,/tiskutis/petfinder-adoption-prediction,PetFinder.my Adoption Prediction
12917326,0.766,53,207,/markwijkhuizen/riiid-training-and-prediction-using-a-state,Riiid Answer Correctness Prediction
5849655,0.77609,0,1,/claudiohfg/gallivanters-public-score-0-77609,Gallivanters
6503346,0.95925,1,5,/toedtli/numpy-knn-uebung,Schnell-Mal-Klassifizieren
12477667,0.70682,0,0,/euneun000/uci-har-raw-data-lstm,UCI-HAR
5793501,0.95,1,2,/samchatfield/cats-vs-dogs-vs-more-cnn,Cats vs Dogs vs More
7487496,0.53968,0,0,/daniil915/kernel24504cd949,Aesthetic Visual Analysis
2172406,81.90039,0,0,/oxfee1dead/samplesubmission,University of applied sciences Mannheim
3284527,0.62666,0,0,/sheebaer/text-no-text-level-5,PadhAI: Text - Non Text Classification Level 4b
3194343,0.7844399999999999,0,0,/kartikkks/attempt1-level4b,PadhAI: Text - Non Text Classification Level 4b
3290387,0.77777,0,0,/rishabmps/sigmoid-phase-4b,PadhAI: Text - Non Text Classification Level 4b
3294704,0.78666,0,3,/mohitbindra/contest-2-level-4b,PadhAI: Text - Non Text Classification Level 4b
3217707,0.6966600000000001,0,0,/akrsrivastava/level-4b,PadhAI: Text - Non Text Classification Level 4b
13165378,0.95715,0,1,/zeeniye/mlxtend-marketbasket-idndsc2020,Market Basket - ID NDSC 2020
8176725,0.54766,0,0,/kaggler85/in-class-kaggle-guide,CSM/SEM6420 workshop
10236817,21.60378,0,1,/niiivo/gradientboosting-mit-gridsearch,Machine Learning Lab - CAS Data Science FS 20
4694897,0.98454,0,0,/lordjayam/model-with-resnet-50,DSNet: fastai Hackathon
4699795,0.94807,0,1,/abhinav2907/fhxbgc,DSNet: fastai Hackathon
4698397,0.84819,1,8,/kurianbenoy/starter-kernel-fastai-discriminative-learning,DSNet: fastai Hackathon
4688634,0.82045,3,4,/lezwon/simpsons-data-analysis,DSNet: fastai Hackathon
5983656,31897.61415,0,0,/rahmaputri/day-3-final-rahmawatiputrianasari,UI DS Summer School
1908657,87008.46647999999,0,0,/mdvdaria/kernel50d6d8f15f,VSU ML 1 Regression
1683385,87008.46647999999,0,1,/sandpiturtle/sample-kernel,VSU ML 1 Regression
2751893,0.57125,1,13,/jayjay75/6th-place-final-solution,KaggleDays Paris
4477796,2.2578,2,1,/rafaelsouzarj/serpro-abalone-rafaelsouza-final,SERPRO - Abalone
6858727,0.78925,1,2,/s13658695/pytorch-kfold,2019S UTS Data Analytics Assignment 3
3536395,0.97717,1,1,/iimetra/kernel1116f3c2dc,CSC: HW4 spring19
13244797,0.95454,0,0,/crucifierbladex/notebookd143b543c1,EC524: Heart-disease classification
8314688,0.9695,0,1,/yukia18/getting-started-solution-nnabla,AILAB ML Training #0
4835694,0.99013,0,1,/tohgoroh/extra-cdnn-for-pneumonia-diagnosis,Pneumonia Diagnosis
2120941,0.62883,2,6,/jerrykuo7727/rocchio,NTUST: Information Retrieval and Applications
4060532,0.86534,0,0,/phaelpolicena/kernel403a23cc27,Predição de Churn
8470132,0.70126,0,3,/naoyamaguchi/aiacademy,Homework for Students
8683641,0.70203,0,1,/chihiroki/331final2,Homework for Students
8456461,0.69944,0,1,/yusukehasegawa/training3-for-commit,Homework for Students
8472597,0.7004100000000001,0,2,/yutaro01/kernel4265d51244,Homework for Students
8469673,0.70182,1,1,/takurof/kernel-fujita,Homework for Students
4134541,0.8272,0,0,/mateusop/mateus-oliveira,Sarcasmo
4137688,0.83849,0,0,/imfepas/am-3-sgdclassifier-and-tfidf,Sarcasmo
8794043,0.17039,0,1,/kanameseto/kernel5862b0ab3b,Used Cars Price Prediction
6527405,0.8985700000000001,0,1,/stopmosk/tensorflow-baseline,Птица или самолет?
10561288,0.79413,0,3,/sorashido/yca-cup-2nd-bert,YKC-2nd
3792876,0.3103,0,1,/hankchen/sample-code,AI for Clinical Data Analytics HW2
2360226,0.48701,0,1,/phlinhng/dm-hw2,Data Mining Lab2
1540563,0.82332,0,0,/monizearabadgi/pmr3508-2018-knn-adult,PMR3508 - Tarefa 1 - 3508 Adult Dataset
1563153,0.76113,0,0,/vhenrique21/pmr3508-tarefa-1-f4fcf75dda,PMR3508 - Tarefa 1 - 3508 Adult Dataset
1555112,0.8419,0,0,/vmbenevides/pmr3508-2018-9ec6d2de6c,PMR3508 - Tarefa 1 - 3508 Adult Dataset
1540604,0.8210200000000001,0,0,/mguinezi/pmr3508-2018-91dc8aec81pmr3508-knn-adult,PMR3508 - Tarefa 1 - 3508 Adult Dataset
5700111,0.83829,0,3,/lhsakurai/pmr3508-2019-55,PMR3508 - Tarefa 1 - 3508 Adult Dataset
1558724,0.8609399999999999,0,0,/miura99/pmr3508-tarefa-1-base-adult,PMR3508 - Tarefa 1 - 3508 Adult Dataset
2445938,0.88203,0,0,/gavrilovseva/lightgbm-model,Predicting user conversions
4148430,1.39915,0,0,/phaelpolicena/kernelf7128c07ab,Avaliação de Carros
3455154,0.10739,0,0,/mihuzz/fork-of-easy-start-with-fastai-sf-car-classificat,car-classification
5867289,0.40324,0,2,/bluexleoxgreen/tmu-5-fold-lgbm-and-simple-fe,InClass Competition at Tokyo Metropolitan Univ.
3483360,0.98495,0,1,/armanhak/bad-comments,Bad comments
12861173,0.3384,0,0,/keleas/quick-start,Хакатон от Кафедры ТПСТС МФТИ (level 1)
3196975,22.63303,0,1,/magnundenis/iesb-python-e-pandas-aula-10-2,IESB - 2019
2998906,24.60398,0,0,/anisio/competi-o-iesb-2019-nota-matematica-enem,IESB - 2019
2999075,24.07232,0,0,/josimari/elaboracao-modelo-aula-python-ml-ii,IESB - 2019
3070674,4.12678,0,0,/ricrod/trabalho-competi-o,IESB - 2019
2998747,19.45579,0,0,/lizardce/kernel7cdcca704c,IESB - 2019
2998827,24.42383,0,0,/lucas120061104/kernel346cb6c8d2,IESB - 2019
2998817,24.46305,0,0,/gleicilene304/kernel13480976ff,IESB - 2019
3204913,0.6644399999999999,0,0,/hemanthv55/text-no-text-level-4a,PadhAI: Text - Non Text Classification Level 4a
3287998,0.72555,0,0,/kavyajeet/padhai-text-classification-level4,PadhAI: Text - Non Text Classification Level 4a
3278711,0.75,0,0,/sinhanishant/text-no-text-l4a-final-submitted,PadhAI: Text - Non Text Classification Level 4a
3222799,0.66888,0,0,/ariharanv/kernel-1,PadhAI: Text - Non Text Classification Level 4a
9313344,23.24261,0,0,/mrneurongamer/solution,Предсказание положения космических объектов
9346692,23.79322,0,0,/anyago/kernel6d20e5ee62,Предсказание положения космических объектов
9345742,23.18453,0,0,/dolgayaanastasia/kernel2b59bb5638,Предсказание положения космических объектов
9340553,12.02655,0,0,/bulatko/10-np-pd-plt-4,Предсказание положения космических объектов
2915496,21.30102,0,0,/andreisaw/linearreg-primitive-without-date,House pricing
4545961,5.2414,0,2,/ndimandesibusiso18/edsa-mbti-team14-jhb,Personality Profile Prediction
4562740,5.02634,0,1,/nkamogeleng/team-11-cpt-mbti-prediction,Personality Profile Prediction
4475224,6.014069999999999,0,2,/jamieeeee/team-15-cpt-mbti-predict-svm,Personality Profile Prediction
4562860,4.81126,0,0,/niels7/group17-cpt-v5-final,Personality Profile Prediction
8806307,1.0,2,11,/whoiskk/1-0-solution-no-machine-learning,Who is a Friend?
5878601,2.36512,0,1,/claudiohfg/chh-ola-xgboost-public-score-2-36512,Chh-OLA
2971817,0.67401,0,0,/gregory711/wids19-line-counts-on-images-with-cv2,WiDS Datathon 2019
3348808,0.57919,1,0,/llewis/video-game-sales-predictions,Flatiron School
11098300,171.68941999999996,1,11,/tahsin/ieee-pes-bdc-datathon-starter-code,"IEEE PES BDC DataThon , Year-2020"
10816459,0.88682,0,6,/zunanalfikri/seleksigaib-zunan,Seleksi Calon Asisten GAIB
11008590,0.8900600000000001,0,2,/yasyfiana/mencoba,Seleksi Calon Asisten GAIB
1459805,0.2102099999999999,0,1,/yeonmin/model-base-line,Pycon Korea 2018 - Tutorial
6297722,0.9933,0,0,/dwarkanath/conocophillips-equipment-failure,Predictive Equipment Failures
4049569,0.8330299999999999,0,0,/evertoncosta/iesb-norte-igm-maio-2019,IESB Norte - IGM - Maio 2019
3968105,0.8421,0,0,/fnardotto/iesb-aula-06-igm,IESB Norte - IGM - Maio 2019
5855301,0.9054,0,2,/anonymousfive/kernal-imd,hackStat 2.0
6047347,0.88864,0,1,/asithaindrajith/kernel-test,hackStat 2.0
5142420,0.78373,0,0,/bhadreshsavani/aarya-sentimentalanalysiswithtweets,Hackathon Sentimento_v2
5174534,0.8002600000000001,0,0,/lalwaniabhishek/abhishek-lalwani-bits-twitter-text,Hackathon Sentimento_v2
6048035,0.8326600000000001,0,0,/ramachandranss/pytorch-dataloader-code-e98692,PadhAI: Tamil Vowel - Consonant Classification
4308011,0.93733,2,1,/saitharun97/tamil-vowel,PadhAI: Tamil Vowel - Consonant Classification
8337142,0.8706200000000001,1,2,/a45632/census-fastai-v10-0,ML Challenge
4697128,0.56884,0,1,/tohgoroh/cdnn-for-pneumonia-diagnosis,Pneumonia Diagnosis
4314836,0.8079999999999999,0,0,/berryzmiya/final-test,2019  ML competition with KISTI
4314877,0.792,0,0,/ohyaeryung/titanic-cp,2019  ML competition with KISTI
4315315,0.8,0,0,/jade95/titanic-kernel-ver-final,2019  ML competition with KISTI
4313087,0.8079999999999999,0,0,/sychooo/fork-of-fork-of-fork-of-fork-of-fork-of-fork-of-fo,2019  ML competition with KISTI
4344619,0.7440000000000001,0,0,/hyowonchu/kernel-hw,2019  ML competition with KISTI
4312160,0.768,0,0,/rladkfma60/yh-baseline-7a6640,2019  ML competition with KISTI
6281170,0.7,0,0,/aurigakg/2018h1030041g-lab2,Eval Lab 2 F464
6318777,0.725,0,0,/mayanksharma007/lab2-2016a8ps0388g,Eval Lab 2 F464
6318268,0.7,0,0,/f20160711/2016a3ps0711g,Eval Lab 2 F464
6316060,0.8,0,0,/f20160406/2016a8ps0406g-lab2,Eval Lab 2 F464
6291138,0.85,0,0,/nravishankar/2016b3a70184g-labeval2,Eval Lab 2 F464
7986843,0.66666,0,0,/tkmacs/diabetes,Diabetes Diagnosis
8422858,0.76041,0,0,/taku884/diabetes-diagnosis-starter,Diabetes Diagnosis
8391624,0.8125,0,0,/kyosuke380/diabetes-diagnosis,Diabetes Diagnosis
7983668,0.75,0,0,/yoshida146/kernel2b459f4d7d,Diabetes Diagnosis
13295331,0.0285699999999999,0,0,/ac6328mats/kosen-yatsushiro,ISSM2020 AI Challenge
13299801,0.90714,0,1,/koukinn/fork-of-issm-submit-e0f200,ISSM2020 AI Challenge
7988695,1.0,0,2,/awaldeep/vgg16-binary-classification,JAMP Hackathon Drive 1
8760550,0.99433,0,1,/yukia18/v3-5-ailab1-cv,AILAB ML Training #1
8513850,0.98966,0,0,/yukia18/v1-ailab1-cv,AILAB ML Training #1
2204947,0.51198,0,0,/jamesleslie/whose-line-is-it-anyway,Whose line is it anyway?
7141364,0.40692,0,2,/hrappuccino/collaborative-filtering-practice,DS特論2019年度 演習課題2
7137528,0.32098,2,1,/cashfeg/logistic-regression,DS特論2019年度 演習課題2
6504070,0.83014,0,34,/chintanchitroda/income-prediction,Predict the Income - <DECODE> WITH BOARD
6304192,0.24065,0,0,/yatin007/kernel1ef87d6244,Predict the Income - <DECODE> WITH BOARD
6022600,0.8672799999999999,0,1,/macchi57/fit-bow-kernel,Fake News e ML
2078637,0.41891,0,0,/luizffs2/regressor,Atividade_3_PMR3508
2077920,0.22393,0,0,/indiagolf99/pmr3508-2018-2341c86e07-median-value-regression,Atividade_3_PMR3508
2074747,0.43331,0,0,/pedro2318/pmr3508-2018-66f39f7a58-tarefa3,Atividade_3_PMR3508
2033454,0.3893,0,0,/henriqueyda/pmr3508-5f518d7037-atividade-3,Atividade_3_PMR3508
2077505,0.2235,0,0,/luangbbr/kernel4bccfd6d8b,Atividade_3_PMR3508
2078255,0.84862,0,0,/kikomaru/tarefa-3,Atividade_3_PMR3508
3148397,9.19623,0,2,/manoelverissimo/energy-star-with-neural-network,Competição DSA de Machine Learning
3116061,10.13278,0,0,/maicon1981/competicao-dsa-de-machine-learning-fev-2019,Competição DSA de Machine Learning
3147218,9.39247,1,3,/valencar/competi-o-dsa-modelo-xgboost,Competição DSA de Machine Learning
8006330,0.9000100000000001,0,0,/artemsolomin/eda-baseline-tfidf,OCRV Test Task
2861581,0.02736,1,0,/ananaymital/2015b4a80598g,Regression Evaluative Lab
1471667,0.7822399999999999,0,2,/mnaveenkumar2009/baseline-random-forest-bb5efc,Web Enthusiasts' Club NITK Recruitment
1460873,0.74651,0,6,/mokshjain/baseline-random-forest,Web Enthusiasts' Club NITK Recruitment
3032747,49.03342,0,1,/jarfo1/autocorrelation-baseline,Pitch estimation and voicing detection
2172412,0.99752,0,0,/gosha6037/kernel7fc501f21c,Классификация компьютерных атак
4366361,0.9117,0,0,/crystals/knn-baseline,Data Champions Android App Malware Prediction
10495314,0.50185,0,3,/nesterione/constant-predictor,EPAM: Exercise 1 -  Sentiment Analysis
2189340,0.56945,0,0,/kirilly/regression-tree-with-adaboost,ML 4 Money
2191512,0.5674399999999999,0,0,/crackoon/sklearn-forests-0-12-nulled,ML 4 Money
8033354,0.95714,0,1,/scaomath/uci-math-10-w20-final-project-starter,UC Irvine Math 10 Winter 2020
3381895,0.46662,0,0,/jwei88/xgboost-baseline,National Data Science Challenge 2019 - Advanced
3363941,0.455,2,8,/szelee/aoeul-solution-step-1-linearsvc,National Data Science Challenge 2019 - Advanced
3366028,0.46075,1,5,/ronaldcheong96/qsscode,National Data Science Challenge 2019 - Advanced
1967841,0.98181,1,0,/anant1205/bbc-tf-idf-vectorizer,AI Academy Intermediate Class Competition 1
1967694,0.98181,0,0,/bbose71/fork-of-bbc-news-classification-v2-bijoy,AI Academy Intermediate Class Competition 1
1806876,0.98181,1,9,/bbose71/bbc-news-classification,AI Academy Intermediate Class Competition 1
2688758,0.72537,0,0,/maderk/day-1-torch-tutorial,Digit Classification DL Workshop
3414410,0.9875,0,2,/manuelalb/deep-learning-solution,MLH - Pokemon Challenge
3584646,0.9835,0,1,/adrianlorenzo/mlp-gradient-boosting-mediante-bagging,MLH - Pokemon Challenge
3474428,0.983,0,4,/dotcsv/fastai-mlp,MLH - Pokemon Challenge
3390853,0.8785,0,0,/ianholing/embeddings-fc,MLH - Pokemon Challenge
3384153,0.497,4,2,/ianholing/el-kernel-m-s-peque-o-de-la-historia,MLH - Pokemon Challenge
3366037,0.951,4,1,/ianholing/ese-bosque-aleatorio,MLH - Pokemon Challenge
3369782,0.916,7,5,/pabloppp/pokemon-embeddings,MLH - Pokemon Challenge
3379888,0.9645,0,0,/ianholing/bosque-de-pokemons-tipados,MLH - Pokemon Challenge
3687657,0.8468100000000001,0,7,/protan/ignite-example,DL in NLP Spring 2019. Classification
1202586,0.7741899999999999,2,4,/gxrasool/my-final-submission,Cloud Faculty Institute Workshop
1202208,0.7741899999999999,1,1,/jrmst102/faculty-institute-gd,Cloud Faculty Institute Workshop
1201208,0.80645,5,4,/kumar234/cloudy-faculty-institute-final-sub,Cloud Faculty Institute Workshop
7739647,1.38261,0,1,/kmorihiro/1-6-6,Exam for Students20200129
7738475,1.3968200000000002,0,0,/naoabe/kernel1bef442822,Exam for Students20200129
7739354,1.6951900000000002,0,0,/manabuhirono/kernelee9cb1c11c,Exam for Students20200129
7737562,1.43761,0,0,/tomomiu3/kernel7768fa6239,Exam for Students20200129
3746703,0.7077899999999999,0,1,/thehemen/cnn-bilstm-russian-news-classifier,Sentiment Analysis in Russian
11120154,0.52153,1,7,/doctorkael/my-approach-to-peptide-binding-sites-prediction,The Kaggle Master
6216560,0.99,2,4,/djordan12/equipment-failures01,Predictive Equipment Failures
7875871,0.96089,0,1,/racastroc/lstm-benchmark,Competencia-Series-Temporales
7998777,0.22854,0,0,/plarmuseau/collaborative-filtering-a-joke,Recommender Systems
1913149,0.94736,0,0,/pmargom/first-prediction,SQL Saturday Madrid ML Challenge
5430040,0.67266,0,0,/jaspreet379/attempt1,PadhAI: Hindi Vowel - Consonant Classification
8142544,0.785,0,1,/shiva2095/vowle-consonant,PadhAI: Hindi Vowel - Consonant Classification
3286363,0.2857099999999999,0,0,/joheras/env-o,IA1819
6192602,0.8693700000000001,0,0,/wish1234/cv-example-8fb1c2,ML in biology
6081229,0.8518100000000001,0,1,/alexanderzv/example1,ML in biology
10858325,0.29913,0,0,/nigimitama/three-models-prediction-example,Property price prediction challenge
1270236,0.74545,0,0,/sasadeghi/final,NBA Rookies
1279682,0.69772,0,0,/mheidary96/majority-vote,NBA Rookies
1274847,0.725,0,1,/nasimjavanparast/nbarookie,NBA Rookies
1275111,0.71136,0,1,/laleh1374/ensemble-learning-dt-knn-rf-mlp-gbc-et-adab,NBA Rookies
1274949,0.7159,1,1,/soroushjavdan/nba-rookies-with-gradientboostingclassifier,NBA Rookies
1035453,0.65681,1,10,/sinakhorami/simple-naive-bayes,NBA Rookies
8087509,0.38017,0,0,/prkhrsrvstv1/2016b5a70438g-dm1,DM-Assignment 1
8085862,0.3851199999999999,0,0,/f20170051/2017a7ps0051g-dma1,DM-Assignment 1
3791293,0.0002399999999999,0,3,/andreab330/mysolution,Multiple regression for time series data
3788319,0.20919,0,0,/ddmitry/dummy-ok,Multiple regression for time series data
6622531,0.57757,0,3,/bobazooba/deep-average-network-submission-example,DeepNLP HSE Course
10081230,0.80047,1,5,/anzhemeng/cs506-midterm,BU CS506 Spring 2020 Midterm
9294781,157.9,0,0,/mbooth/submit-the-sample-submission,Basic Regression Competition
4522978,0.7964100000000001,0,0,/sammy3101/transfer-learning-resnet152-3,QSTP - Deep Learning 2019
4517472,0.7148899999999999,0,1,/ajinkyadandvate/template,QSTP - Deep Learning 2019
4476412,0.7178800000000001,0,0,/j105sahil/sahil-jain-qstp-dl,QSTP - Deep Learning 2019
4476055,0.59496,0,0,/nousernameforme/qstp-final-assignment,QSTP - Deep Learning 2019
3286187,0.69791,0,2,/keleas/baseline,Птица или самолет
12460077,0.84554,0,1,/snetkovr/baseline-v2,Focus start 2020
5022909,0.5213300000000001,0,0,/navinkhandeparkar/mongo-db,Hackathon Sentimento
2654685,0.98222,0,1,/raccooncoder/special-delivery-for-pavel-zakharov,Characters classification
9971592,0.8311200000000001,0,0,/pkueppers/sample-randomforest-for-age-group-predictions,Predicting Age Groups
10327193,0.5183399999999999,0,0,/namiwa/berkat-product-detecton-notebook,[Student] Shopee Code League - Product Detection
10340882,0.21905,0,10,/ffyyytt/your-first-ml-starter-code,[Student] Shopee Code League - Product Detection
10495801,0.78859,1,5,/slm37102/0-78766-image-detect-w-o-clean-data-fastai-v1,[Student] Shopee Code League - Product Detection
4078882,0.96666,0,1,/keshavramaiah/competition-q1,ML Hackathon 2019 Q1
2932642,0.52222,0,3,/studentpadhai/test1,[T] PadhAI: Text - Non Text Classification Level 1
3900565,0.76195,0,0,/hmchuong/pytorch-baseline-model,VietAI Advance Course - Retinal Disease Detection
3367243,0.23553,0,0,/rfelizomni/baseline,[ACM] Recommender System Practice
3980821,0.8021699999999999,0,0,/marcosfigueiredo/competi-o-iesb-sul-marcos,IESB Sul - IGM - Maio 2019
3980798,0.8257700000000001,0,0,/jptavares/competi-o-iesb-nota-matematica,IESB Sul - IGM - Maio 2019
3982088,0.82214,0,0,/rdewes/novokernel,IESB Sul - IGM - Maio 2019
8357201,40.32063,0,0,/safamediouni/booking-compete,Data Science - Master
7979028,0.98574,0,3,/easter3163/tutorial-for-everybody,Tobigs13_7week_competition
8134358,0.6078,0,0,/jwjohnson314/kernel2255e7273b,COMP 750/850 Project 1
2137347,0.98561,0,0,/gosha6037/kernele45fd46aa1,Data Champions Android App Malware Prediction
2083980,0.8790399999999999,0,0,/barelydedicated/average-perceptron-movie-review-classification,Classifying Movie Reviews
7030951,0.9266,1,1,/raccooncoder/bpe-lstm-lb-0-9266,Texts classification
12413028,0.95928,0,0,/leejinsoo/sklearn-logistic-regression-with-handcrafted-feat,UCI-HAR
2460835,0.82815,0,0,/flyinslowly/kernel034637d0b1,Классификация изображений
3278710,0.6066600000000001,0,0,/manojkamat/4btextnotext,PadhAI: Text - Non Text Classification Level 4b
3219666,0.60777,0,2,/awaldeep/textnotextclassification4b,PadhAI: Text - Non Text Classification Level 4b
3282077,0.60777,0,1,/ritwikdalmia/text-prediction-level-4b,PadhAI: Text - Non Text Classification Level 4b
3257973,0.77,0,0,/sinhanishant/text-no-text-l4b,PadhAI: Text - Non Text Classification Level 4b
3246665,0.62111,0,0,/ariharanv/test-1,PadhAI: Text - Non Text Classification Level 4b
13165545,1.0,3,23,/dimaskuncoro/market-basket-accuracy-1-0,Market Basket - ID NDSC 2020
10457626,0.81243,0,2,/keltingrimes/tweet-eda-and-logistic-regression,Tweet Sentiment Analysis
10023185,0.2245,0,1,/ahm6644/knn-quantile-tranform,"MLClass Dubai by ODS, Lecture 6 HW"
9343597,0.37083,0,0,/kvsnoufal/fnn-baseline-scaled-pst3-6-5,"MLClass Dubai by ODS, Lecture 6 HW"
2683869,0.85083,0,1,/kenichinakatani/make-test-submission,Fashion MNIST challenge2019
7095062,0.87544,0,0,/soyanchan/kernel39b5745177,2019 SMHRD 경진대회 ( 지능형 )
10237212,18.77379,0,1,/niiivo/lightgbm-with-data-and-hyperparameter-optimization,Machine Learning Lab - CAS Data Science FS 20
4717547,0.99048,2,8,/amankimothi100/resnet152-progressiveresizing-99-public-lb,DSNet: fastai Hackathon
4689346,0.73483,0,4,/lordjayam/resnet50-baseline,DSNet: fastai Hackathon
4698989,0.92112,0,0,/sagarsumit/simpsons-resnet34,DSNet: fastai Hackathon
5980919,47246.40615,0,0,/srarief/day-3-template-master-3-log,UI DS Summer School
1908646,87008.46647999999,0,0,/natalissss/kernel50cb8fd76e,VSU ML 1 Regression
4644690,2.1941,0,0,/agajorte/abalone-5-envio-dos-resultados,SERPRO - Abalone
3073727,0.8916,0,0,/dkghost/keras-img-gen-data-final,SkillFactory | Final hackathon
13408915,0.975,0,0,/yazans/final-notebook,SMEMI309 - Final evaluation challenge 2020
7035269,252.53581,0,0,/jacobsn/starter-submission,Predict the missing pixel value v2
789637,0.11903,0,3,/wjholst/sample-python-regression,Tracy Regression
725000,0.8376600000000001,0,1,/sharmila5656/a-starter-lenet5-dropout-data-augmentati-8907d8,notMNIST Competition
8171890,0.9899,0,2,/yukia18/getting-started-solution,AILAB ML Training #0
9800889,0.96485,0,0,/shingo405/bert-prediction,Japanese Review Rating Prediction
8650080,0.88,0,0,/larspeterss/vgg16-maxpooling-samplenormalization-7nn,SYDE 522 (Winter 2020)
7574976,285.54135,0,0,/chun1182/heroz-compe,HEROZ Internal Competition
8609337,0.6793899999999999,0,1,/hiroakiiwai/ai-academy-4,Homework for Students
8680362,0.70193,0,1,/chihiroki/331-first,Homework for Students
8686869,0.7028399999999999,0,1,/keisukekamata/ensamble1,Homework for Students
8458030,0.70116,3,1,/cubicworld/kernel62066b1043,Homework for Students
4092369,0.99988,0,0,/rogerlenke/sarcasm,Sarcasmo
8791647,0.19948,0,1,/ep17058/used-car-price-prediction,Used Cars Price Prediction
10786452,0.60787,0,0,/usin1227/demo-of-kaggle-kernel,NCTU BDALAB 2020 Onboard
10662759,0.83093,0,4,/mrkmakr/ykc-cup-2nd-nn-last-gpu,YKC-2nd
10483453,0.80605,0,2,/mrkmakr/ykc-cup-2nd-lgbm,YKC-2nd
10540707,0.06537,2,6,/code1110/ykc-cup-2nd-no-nlp-baseline,YKC-2nd
10658545,0.8202200000000001,0,3,/minoru/ykc-2nd-starter-nn,YKC-2nd
3391991,0.38793,0,0,/hli109/starter-notebook,Night at Cameo
1560747,0.8396,0,0,/indiagolf99/pmr3508-b-sico,PMR3508 - Tarefa 1 - 3508 Adult Dataset
1558400,0.83975,0,0,/fermalavasi/pmr3508-2018-0021b1a4a8,PMR3508 - Tarefa 1 - 3508 Adult Dataset
1553638,0.84459,0,0,/vkiguchi/knn24kencoded,PMR3508 - Tarefa 1 - 3508 Adult Dataset
1527751,0.84021,0,0,/joaorochalr8041800/pmr3508-adult-tarefa1,PMR3508 - Tarefa 1 - 3508 Adult Dataset
1564753,0.83668,0,0,/hgushi/pmr3508-10893812,PMR3508 - Tarefa 1 - 3508 Adult Dataset
1563038,0.83945,0,0,/gunovello/kernel0f1b6a84e9,PMR3508 - Tarefa 1 - 3508 Adult Dataset
1554855,0.84167,0,0,/galopes/pmr3508-2018-906c67c924,PMR3508 - Tarefa 1 - 3508 Adult Dataset
3345607,0.96853,1,3,/itslek/easy-start-with-fastai-sf-car-classification-v26,car-classification
3454527,0.96553,0,0,/mihuzz/easy-start-with-fastai-sf-car-classificati-d019ee,car-classification
2998829,23.88956,0,2,/manoxis/iesb-ml-competition,IESB - 2019
2998808,24.04881,0,0,/yanmendonca/iesb-2019,IESB - 2019
3070686,24.57496,0,0,/nataly302/competi-o-iesb-2019,IESB - 2019
2998814,24.16747,0,0,/adrianocastanho/competi-o-iesb-2019,IESB - 2019
2998878,24.59454,0,0,/gleidson304/kernelaf3d546681,IESB - 2019
3623786,0.78589,0,0,/qadfafasfafafafsafa/best-series-predictor-no-2-0,Time Series Classification
3194086,0.7477699999999999,0,0,/kartikkks/attempt1-level4,PadhAI: Text - Non Text Classification Level 4a
3290213,0.7611100000000001,0,0,/rishabmps/sigmoid-phase-4a,PadhAI: Text - Non Text Classification Level 4a
3219007,0.8566600000000001,1,1,/ektaraj11/level4a,PadhAI: Text - Non Text Classification Level 4a
9321148,22.64724,0,0,/herceg/sputnik-prediction,Предсказание положения космических объектов
9330843,16.66415,0,0,/dmitrykhutornoy/khutornoy,Предсказание положения космических объектов
9155318,25.17802,0,0,/bichaev/kernel5082383345,Предсказание положения космических объектов
9340374,25.79265,0,0,/why1me/tripleexp,Предсказание положения космических объектов
9360802,7.09709,0,0,/tiptopsx/spootnik-2-1,Предсказание положения космических объектов
9323611,14.751220000000002,0,0,/irrabukreeva/kernelfc85eef78b,Предсказание положения космических объектов
9349306,25.43664,0,0,/bob000011/kernel575db9517f,Предсказание положения космических объектов
9285121,23.79322,0,0,/timoninads/fork-of-fork-of-kernel77c4b20a4a,Предсказание положения космических объектов
2639474,3295.57189,0,1,/jbredbull/python-tensorflow-test,compass-canada
1015888,1.14252,1,16,/kashnitsky/pytorch-simple-convnet-baseline-gpu,ods_class_cs231n
4527609,0.3422699999999999,0,3,/tanyapaquet/connoisseurs-foodies-notebook,Personality Profile Prediction
4499708,4.56432,0,1,/thenjiwe1996/mbticlassification-team-5-jhb,Personality Profile Prediction
4367841,8.03737,0,0,/mrflamboyyandt/mbti-classification,Personality Profile Prediction
4474914,4.90685,0,0,/riaanswanepoel/new-t20,Personality Profile Prediction
4547245,5.042269999999999,0,1,/victor094/team-04-jhb,Personality Profile Prediction
4441481,0.3352,0,0,/tshego/mbti-classification-edsa-team-19,Personality Profile Prediction
4563028,5.06616,0,0,/thapelo99/team-18-cpt,Personality Profile Prediction
4212394,5.96629,0,0,/sisofano/mbti-project-notebook,Personality Profile Prediction
9151992,164.03389,0,0,/shainy/time-series-with-prophet-model,Predice el futuro
3792105,0.17728,0,0,/kevingeorgejoe/starter-kernel,ACM Summer'19 Inclass-1
2953299,0.90275,1,3,/unilageni/masun,WiDS Datathon 2019
5014907,0.91772,0,1,/samarthsarin/wids-model,WiDS Datathon 2019
2796615,0.99138,28,60,/tcapelle/fastai-starter,WiDS Datathon 2019
11044490,0.8850399999999999,0,6,/stanleyyoga/seleksigaib-stefanus-stanely-yoga-setiawan,Seleksi Calon Asisten GAIB
10769412,0.8718100000000001,0,6,/marsathoriq/baseline,Seleksi Calon Asisten GAIB
6309305,0.95053,0,2,/kieghv/kernel62dd63221d-a69510,Predictive Equipment Failures
6310211,0.99526,0,0,/geebadan/sleep-dev-v4,Predictive Equipment Failures
3967972,0.84029,0,0,/medeiros1526/iesb-norte-aula-6-trabalho-medeiros-v2,IESB Norte - IGM - Maio 2019
3967957,0.8058,0,0,/rcbrock/roger-trabalho-1-miner-2,IESB Norte - IGM - Maio 2019
9821154,0.8897200000000001,0,0,/ashishkumarpanigrahy/hacktest,hackStat 2.0
5145795,0.73033,0,0,/ronitmankad/sentiment-analysis-bert-pytorch,Hackathon Sentimento_v2
4334448,0.773,0,0,/pshivani07/kernelf176391841,PadhAI: Tamil Vowel - Consonant Classification
8411498,0.87429,0,2,/andreyraav/simplenn-uncommented-model,ML Challenge
4925202,0.00704,0,3,/shivus/gladiators-car-classification,Hackathon Auto_matic
1620397,0.95294,0,4,/vaibhavgeek/benchmark-output,ACM Machine Learning (SVNIT)
10751654,0.30126,0,2,/getdna/sirawich-s-notebook-test,HTA Tagging
4266971,0.8240000000000001,0,0,/leechoheui/kisti-titanic-competition,2019  ML competition with KISTI
4274061,0.784,0,0,/skj8109/sue-jung-05,2019  ML competition with KISTI
4314052,0.784,0,0,/sychooo/fork-of-fork-of-fork-of-fork-of-fork-of-for-94ff24,2019  ML competition with KISTI
4216606,0.792,0,0,/sbb2002/kernelcc829b0ac4,2019  ML competition with KISTI
4218093,0.76,0,0,/jxcross/titanic-kisti-201906,2019  ML competition with KISTI
4218144,0.752,0,0,/sarich/titanic-with-kisti,2019  ML competition with KISTI
6320180,0.725,0,0,/h20180056/2018h1030056g,Eval Lab 2 F464
6318877,0.775,0,0,/f20160364/2016a8ps0364,Eval Lab 2 F464
6286632,0.75,0,0,/etimishra/2018h1030049,Eval Lab 2 F464
6283129,0.775,0,0,/ritvikraj1998/2016b2a70544g-eval-lab-2-1,Eval Lab 2 F464
7985645,0.58333,0,1,/kkkojima/diabetes-pre,Diabetes Diagnosis
8345165,0.77083,0,0,/ohbatomoaki/diabetes-diagnosis-ohba,Diabetes Diagnosis
8002324,0.55729,0,0,/tohgoroh/diabetes-diagnosis-starter,Diabetes Diagnosis
13300934,0.77142,0,0,/tsubasafujimoto/semvgg16-tsubasa,ISSM2020 AI Challenge
13266032,0.85,0,4,/xuankejiang/s-notebook,ISSM2020 AI Challenge
4168390,1.0,0,1,/metriczulu/data650-second-entry-keras,UMUC DATA 650 Summer 2019 Competition
8524773,0.992,0,0,/arinko/fork-of-getting-started-cross-validation,AILAB ML Training #1
8640886,0.99333,0,0,/yukia18/v2-ailab1-inference,AILAB ML Training #1
2336315,0.1960599999999999,0,0,/marzipanz/fork-of-edsa-president-challenge-sally-1-4,Whose line is it anyway?
2279813,0.57448,0,1,/tmarakalla/thapelo-wliia,Whose line is it anyway?
2288256,0.369,0,0,/tshepom09/hackathon,Whose line is it anyway?
7141338,0.43817,0,0,/cashfeg/1000-times-logistic-regression,DS特論2019年度 演習課題2
6124336,0.8395,0,1,/macchi57/ulmfit,Fake News e ML
2549587,0.33708,0,0,/albertoparravicini/ppi-classifier-example,Oracle Graph ML Contest at Polimi
2077105,0.37354,0,0,/gabrieloliva18/tarefa-3-f7551d72e4,Atividade_3_PMR3508
2076170,0.39142,0,1,/otaviomserra/2bc56bd2c1-regress-o,Atividade_3_PMR3508
2047549,0.22704,0,0,/felipegdm/californianhouses,Atividade_3_PMR3508
1903921,0.22139,0,0,/rafabl/kernel575d8146ee,Atividade_3_PMR3508
2077825,0.303,0,0,/guilhermecmarques/california-house-values,Atividade_3_PMR3508
2077355,0.48686,0,0,/monizearabadgi/pmr3508-2018-41b596d861-regression,Atividade_3_PMR3508
2076536,0.22891,0,0,/aezequieljr/pmr3508-2018-b3f8134e02-t03,Atividade_3_PMR3508
11109286,0.5312100000000001,0,0,/mosnoiion/prezicerea-popularitatii-fara-ml-d,GirlsGoIT competition 2020
3083736,10.19506,0,0,/leonaralves/competi-o-fevereiro,Competição DSA de Machine Learning
1881540,0.76255,0,1,/valeriagomes/auc-0-76255-vegas-fia-third-submission,FIA ML T5
2912677,0.02055,0,0,/f20160081/2016a7ps0081g,Regression Evaluative Lab
1474203,0.7625,0,0,/vithikshah/vithikshah,Web Enthusiasts' Club NITK Recruitment
11380854,0.5985199999999999,0,0,/shun2741/base-submission-bikesharing,Bike Sharing Demand for Education()
2891933,0.62222,0,0,/nilsbauer/mlpc-750-epochs-pca-cross-validation,IES Data Mining(WS 18/19)
3535794,0.93736,0,0,/eudaldsans/n-gram-test-original-parameters,Language Identification
2900468,1.0,0,0,/gourishhegde/apo-challenge-task,Challenge GH
5077885,0.98577,0,0,/tonygu/5-fold-cv-for-lightgbm,Python Class - Practice
2193622,0.54348,0,5,/ernestglukhov/first-one,ML 4 Money
2186697,0.57423,3,1,/strawberrypie/baseline-solution,ML 4 Money
2187059,0.56806,0,0,/kirilly/regressiontree,ML 4 Money
1243079,1.0,0,0,/wcukierski/test-notebook-nothing-to-see-here,Test Competition Please Ignore
3370998,0.4660399999999999,0,8,/diansheng/top10-score-0-4646-private-lb-no-neural-nets,National Data Science Challenge 2019 - Advanced
3365221,0.24269,0,1,/randname/git-push-f,National Data Science Challenge 2019 - Advanced
1810405,0.98181,0,1,/akamath091/kernele201a06752,AI Academy Intermediate Class Competition 1
1967780,0.98181,1,1,/nxtasha/bbc-news-classification-natasha,AI Academy Intermediate Class Competition 1
2644935,0.1144599999999999,0,0,/maderk/using-basic-machine-learning-models,Digit Classification DL Workshop
3743226,0.99,1,4,/ancaco12/mi-soluci-n-con-lightgbm,MLH - Pokemon Challenge
3579281,0.9395,0,1,/manuelalb/speed-speed-speed,MLH - Pokemon Challenge
3466902,0.9845,2,8,/ancaco12/soluci-n-con-gradientboosting,MLH - Pokemon Challenge
3369583,0.9225,1,3,/dotcsv/los-embeddings-de-pablo-pero-en-tf-as-que-mejor,MLH - Pokemon Challenge
3373796,0.954,3,3,/manuelalb/ejemplo-de-pandas-red-neuronal,MLH - Pokemon Challenge
3658219,0.85005,2,19,/kashnitsky/clickbait-news-detection-ulmfit-starter,DL in NLP Spring 2019. Classification
1224911,0.6935399999999999,0,2,/buntyshah/cloud-faculty-institute-workshop-dnn,Cloud Faculty Institute Workshop
1201516,0.7741899999999999,1,1,/huiyang94116/gradientboostingclassifier-svm,Cloud Faculty Institute Workshop
1202378,0.7580600000000001,0,1,/jrmst102/faculty-institute-diabetes,Cloud Faculty Institute Workshop
7737613,1.39078,0,1,/utataneyuki/first-run,Exam for Students20200129
7738874,5.682919999999998,0,0,/kirim2/kernel2e24876f69,Exam for Students20200129
7737513,1.54225,0,0,/hiroyukikimura/kernel5e7afdec33,Exam for Students20200129
7738104,1.41576,0,0,/yusukeichimura/kernel4574723830,Exam for Students20200129
7738320,1.3965299999999998,0,0,/kentamatsui/kernel476652c43e,Exam for Students20200129
7737721,1.6150799999999998,0,0,/naaaas/kernel7ace2b8628,Exam for Students20200129
951820,0.7159,0,1,/ziliwang/nbsvm,Sentiment Analysis in Russian
4066959,0.5513600000000001,0,0,/si13kaggle/movie-genre-embedding,Movie Genre Classification
9796323,2078.22753,0,0,/plarmuseau/cabbage-regressions,kaggle18011884
8271718,0.60939,4,1,/plarmuseau/implicit-joke-recommender,Recommender Systems
8280477,0.64737,0,0,/plarmuseau/fork-of-implicit-joke-recommender,Recommender Systems
4916936,0.64917,0,0,/bencrabbe/sick-corrig-du-prof,Sentence Relatedness
4028285,0.8246600000000001,0,0,/hs366399/hindi-vowel-consonant-classifier,PadhAI: Hindi Vowel - Consonant Classification
4106195,0.81266,2,1,/saitharun97/hindi-vowel-part-2,PadhAI: Hindi Vowel - Consonant Classification
4458086,0.62766,0,3,/shwet31/kernel7b5120f957,PadhAI: Hindi Vowel - Consonant Classification
10131846,0.72733,0,0,/abhishekbm/hindi-consonant-vowelclass,PadhAI: Hindi Vowel - Consonant Classification
2842103,5876.51527,0,2,/khandalaryan/base-line-code,Team ISTE's Datathon
2673313,0.72727,0,0,/sreemae/diabetes,Diabetes Classification
6575113,0.86936,0,0,/alexandrboytsov/cv-example,ML in biology
2971346,0.30537,0,2,/kentay/i-wanted-5000-choen-s-challenge,Property price prediction challenge
1278958,0.725,0,1,/armanelmos/classifier-ensemble-with-majority-voting,NBA Rookies
1279617,0.7318100000000001,0,0,/ensiyeh/randomforest,NBA Rookies
1036151,0.71136,0,0,/morimorteza/ensemble-test,NBA Rookies
1275169,0.69545,0,1,/kamyar12/ensemble-method,NBA Rookies
1094729,0.71363,0,1,/fazaeefar/nba-rookies-classification-using-ensemble-methods,NBA Rookies
1279889,0.71136,0,0,/ensiyeh/nba-rookies-classification-using-ensemble-methods,NBA Rookies
7966421,0.78861,0,0,/tonygeefus/kernel39e21dee5b,Anokha AI Adept
7647759,0.4885899999999999,0,0,/chun1182/extra-heroz,HEROZ Internal Competition Extra2
8026421,0.34769,0,0,/fitzchivalry509/2017a7ps0033g,DM-Assignment 1
7995555,0.40529,0,0,/peaceagent/dm-assignment-1-2017a7ps0137g,DM-Assignment 1
8084563,0.26974,0,0,/f20170117/2017a7ps0117g-dm1,DM-Assignment 1
3791823,0.01052,0,0,/nikitosoleil/gru-nn,Multiple regression for time series data
3496534,47.64623,0,0,/kpotoh/model-improving-final-score,Задержка рейса самолета
12472616,0.98468,0,0,/niiivo/hs-20-outlier-pipeline,Machine Learning Lab - CAS Data Science HS 20
4311040,58.66378,0,3,/yurilla/lgbm-xgb-ensemble,Python for Data science ITEA
4157782,58.77301,0,5,/kirichenko17roman/lightgbm-grouped-cv-with-new-features,Python for Data science ITEA
9306731,72.1,0,0,/mbooth/applying-decision-tree-approach,Basic Regression Competition
4610626,0.85104,0,8,/peaceagent/resnext101-32x16d,QSTP - Deep Learning 2019
4479682,0.6641,0,1,/benzybit/qstp-kernel,QSTP - Deep Learning 2019
4486754,0.7481800000000001,1,0,/ameyalaad/alaad-qstp-dl-resnext101expt,QSTP - Deep Learning 2019
4506878,0.69782,0,0,/perseusdg/template-notebook-0edca3,QSTP - Deep Learning 2019
4570819,0.72556,0,0,/smiteshp/qstp-resnext-model,QSTP - Deep Learning 2019
4560680,0.5975199999999999,0,0,/mahakk/template-notebook-70f007,QSTP - Deep Learning 2019
3949616,0.0966,0,0,/saoodmohd/starter,Digit recognition
4172389,0.93888,0,0,/kpotoh/keras-for-binary-classification,Птица или самолет
2338372,0.04901,0,0,/oxfee1dead/submission-example,ClassificationOFShields
5106161,0.90088,0,1,/ronitmankad/sentiment-analysis-eda-and-model-creation,Hackathon Sentimento
7752323,0.76785,0,0,/seraphwedd18/comp-int-practice-code,Computational Intelligence Project
2594810,0.98342,0,2,/gotocoding/simplecnn,Characters classification
2624223,0.75651,0,1,/temilolu/kernel03774f7109,Technidus machine learning competition 2
10472430,0.8469200000000001,2,21,/ilosvigil/scl2020-2-5b-model-4th-place,[Student] Shopee Code League - Product Detection
1699161,43677.68719,0,0,/saivipul/dac-iitpkd-houseprice,House Price Prediction
1690624,35484.388719999995,0,4,/amitkvikram/dac-house-prices,House Price Prediction
3451511,0.99969,0,6,/seefun/simple-eda-of-fieldguide-challenge,Fieldguide Challenge: Moths & Butterflies
3358512,0.34841,0,0,/rfelizomni/singular-value-decomposition,[ACM] Recommender System Practice
3980808,0.82032,0,0,/amarantevitor/iesb-competi-o-ml-ii,IESB Sul - IGM - Maio 2019
3980785,0.8294,0,0,/wdiego/competi-o-iesb-miner-ii-nota-matem-tica,IESB Sul - IGM - Maio 2019
3980807,0.8185100000000001,0,0,/sidiclei/aula06-ml2-competi-o,IESB Sul - IGM - Maio 2019
5263249,62.82545,0,0,/amirhmi/tap30-challenge-solution-with-blending-model,Tap30 Challenge
2355692,0.86944,0,0,/barelydedicated/logistic-regression-movie-review-classifier,Classifying Movie Reviews
2360657,0.80848,0,0,/barelydedicated/naive-bayes-movie-review-classifier,Classifying Movie Reviews
6837450,0.93122,1,3,/drjerk/bonus-task-train-lstm,Texts classification
11256514,2304.95179,0,12,/sshikamaru/nmlo-covid-regression-3rd-place-solution,NMLO Contest 3 - Regression
5908144,0.9288,0,4,/toedtli/scikit-learn-kaggle-einfuehrung,Schnell-Mal-Klassifizieren
2428418,0.55486,0,0,/arvalon/kernelfd8e34d61e,Классификация изображений
4135791,0.93333,2,2,/luis17teodoro/02-iris-modelagem-preditiva,SERPRO - Iris
3957617,0.93333,0,1,/agajorte/iris-4-envio-dos-resultados,SERPRO - Iris
3280506,0.69333,0,0,/praveenthealpha/sigmoidneuron,PadhAI: Text - Non Text Classification Level 4b
3204918,0.63888,0,0,/hemanthv55/text-no-text-level-4b,PadhAI: Text - Non Text Classification Level 4b
3236580,0.60777,0,0,/vaibhavkumar049/first-version-level4b,PadhAI: Text - Non Text Classification Level 4b
3297408,0.84555,1,2,/srinivasand12395/level-4b-padhai-text-or-notext,PadhAI: Text - Non Text Classification Level 4b
3229249,0.6433300000000001,0,0,/cynosuremishra01/text-no-text-level-44b,PadhAI: Text - Non Text Classification Level 4b
3284529,0.52888,0,0,/hs366399/kernel3fdbbb7e19,PadhAI: Text - Non Text Classification Level 4b
13185381,1.0,0,7,/yogie25/market-basket-analysis-100-accuracy,Market Basket - ID NDSC 2020
4701260,0.98454,2,7,/nvsabhilash/dsnet-fastai,DSNet: fastai Hackathon
4673536,0.70828,0,13,/sidujjain/starter-kernel-dsnet-fastai-hackathon,DSNet: fastai Hackathon
4674412,0.5362600000000001,0,16,/init27/don-t-copy-blindly,DSNet: fastai Hackathon
5983674,33771.8776,0,1,/delisabeths/de-day-3-final-fix-banget-pliss-terakhir,UI DS Summer School
5983691,18519.8847,0,0,/feraldolim/day-3-feraldo-lim,UI DS Summer School
4649806,2.2909900000000003,2,0,/luis17teodoro/abalone-mod-preditiva-com-regressao,SERPRO - Abalone
3510573,0.98342,0,1,/northphoenix/hw10-finals,CSC: HW4 spring19
11151721,0.81481,0,4,/elijah981/starter-model,Heart Disease Prediction
692418,0.8433299999999999,1,1,/jwjohnson314/a-starter-lenet5-dropout-data-augmentation,notMNIST Competition
6213200,2.67796,0,5,/zelcookie/constant-submission,Анализ потребительской корзины
8606738,0.69835,0,1,/shuntakinami/lightgbm2,Homework for Students
8687713,0.70113,0,1,/naoyamaguchi/aiacademy-20200331,Homework for Students
8470244,0.6996399999999999,0,1,/ykobari/kernel20e58e446aaa,Homework for Students
8677047,0.7014199999999999,0,1,/you2okada/basecustome-tue,Homework for Students
8644478,0.69799,2,1,/suzukimotoshige/kernelaa1f06e770,Homework for Students
4110699,0.83026,0,0,/rafaelrabetti/kernelfc0b613a90,Sarcasmo
4111545,0.8380799999999999,0,0,/thiagonf/trabalho,Sarcasmo
3921389,0.82791,0,0,/liock15/kernel7df60a130c,Sarcasmo
8003147,0.12888,0,0,/yoshida146/kernel50f7b0f940,Used Cars Price Prediction
2058516,1429945.66891,1,3,/kanav0183/how-to-use-kaggle-kernel-starter-code,Kharagpur Data Analytics Group
10590805,0.8137800000000001,0,3,/gpiyama2119/nn-lightgbm-svm-rf-with-fasttext-and-tf-idf,YKC-2nd
10608044,0.79164,0,2,/code1110/ykc-2nd-neuralnet-starter,YKC-2nd
10447924,0.7448600000000001,0,6,/mrkmakr/ykc-2nd-starter,YKC-2nd
10657676,0.81507,0,2,/minoru/ykc-2nd-starter-lgb,YKC-2nd
5609763,0.84267,0,1,/lucasnseq97/pmr3508-2019-56-knn-classifier-adult-census,PMR3508 - Tarefa 1 - 3508 Adult Dataset
1544504,0.8518100000000001,0,1,/rejaili/pmr3508-adultclassifier,PMR3508 - Tarefa 1 - 3508 Adult Dataset
1564665,0.77242,0,0,/felipegdm/pmr3508-adults,PMR3508 - Tarefa 1 - 3508 Adult Dataset
1534640,0.8326100000000001,0,0,/kikomaru/pmr3508-2018-tarefa-1-1d6797eadf,PMR3508 - Tarefa 1 - 3508 Adult Dataset
1516216,0.8330700000000001,0,0,/henriqueyda/tarefa-1-base-adult,PMR3508 - Tarefa 1 - 3508 Adult Dataset
10171304,0.81587,1,3,/sohamtiwari/car-classfication,Car Classification(Project Vision)
3456161,0.10739,0,0,/mihuzz/fork-of-fork-of-easy-start-with-fastai-sf-car-cla,car-classification
3212506,23.70565,0,0,/nobary/teste9,IESB - 2019
3224581,24.38972,0,1,/werikrafael/iesb-2019,IESB - 2019
2998901,24.85303,0,1,/weuller/competi-o-igm,IESB - 2019
2999057,3.24585,0,0,/martinsegmj/competi-o-iesb,IESB - 2019
2998917,23.66315,0,0,/giorgimedeiros/competi-o-iesb-2019,IESB - 2019
2998812,22.42075,0,0,/luciojunior/lucio,IESB - 2019
3070939,21.83253,0,1,/fatimaliras/kerneld6adf9d04a,IESB - 2019
3213327,20.63678,0,0,/suellenss/kernel79ee9e543b,IESB - 2019
3231902,0.6611100000000001,0,0,/vaibhavkumar049/first-notebook-level4a,PadhAI: Text - Non Text Classification Level 4a
3282034,0.65888,0,1,/ritwikdalmia/text-prediction-level-4a,PadhAI: Text - Non Text Classification Level 4a
3294552,0.79666,0,1,/mohitbindra/contest-2-level-4a,PadhAI: Text - Non Text Classification Level 4a
3284267,0.58555,0,0,/hs366399/contest-1-4a,PadhAI: Text - Non Text Classification Level 4a
9360689,26.49308,0,0,/pet3tyak/shoty,Предсказание положения космических объектов
9363538,35.01591,0,0,/danroor/roor-timeseries,Предсказание положения космических объектов
9327656,23.7808,0,0,/pornprofessor/kernel1e6f243e46,Предсказание положения космических объектов
9253134,14.19683,1,0,/gripara/parastaev,Предсказание положения космических объектов
9078365,23.18691,0,0,/yurchenko/kernel638cde9a43,Предсказание положения космических объектов
9350100,6.737189999999999,0,0,/nasnas/kernel3a13dffc30,Предсказание положения космических объектов
9364557,16.67851,0,0,/borisgruzdev/gruzdev,Предсказание положения космических объектов
7140426,60.4475,0,1,/patrick0302/great-energy-predictor-shootout-i-lgbm-solution,Great Energy Predictor Shootout I
2788462,20.68791,0,0,/litekiwiyoghurt/randomsubmissionwhodis,House pricing
2108474,0.54108,0,4,/kmader/texture-analysis-submission-overview,Pneumonia Texture Analysis
4498126,5.38478,0,0,/kimp1995/mbti-classification,Personality Profile Prediction
4564645,10.49868,0,1,/trevorsenyane/team6-jhb,Personality Profile Prediction
4563033,5.19361,0,1,/katlegomfx/personality-profile-team-10,Personality Profile Prediction
4460213,5.41667,0,0,/jomoon/team-11-jhb-mbti,Personality Profile Prediction
4490621,4.99447,0,0,/njabulo016/edsa-jhb-team-10-final-notebook,Personality Profile Prediction
4521543,0.66691,0,0,/keletsodthb/team-17-notebook,Personality Profile Prediction
4513178,0.39464,0,0,/shivaansook/team-8-classification,Personality Profile Prediction
4563839,4.82719,0,1,/retham/team-8-edsa-jhb,Personality Profile Prediction
4564171,4.97853,0,0,/motsamai/team17-final-submission,Personality Profile Prediction
4563644,7.312489999999999,0,0,/generalzaad/kernel6758feb115,Personality Profile Prediction
2915353,0.8788299999999999,0,2,/unilageni/wids-kera,WiDS Datathon 2019
3788557,0.83667,0,2,/serivan/how-to-submit-prediction-to-kaggle-in-python,MLDM Classification Competition
10906825,0.87555,1,9,/williamong/gaib-notebook-william-13518138,Seleksi Calon Asisten GAIB
11052887,0.8745700000000001,0,0,/ramunechoco/seleksi-asisten-ca-gaib,Seleksi Calon Asisten GAIB
1463038,0.15277,0,0,/yeonmin/stacking,Pycon Korea 2018 - Tutorial
8989531,0.0,0,0,/junbumlee/baseline-charcnn-baseline-pytorch,Korean Gender Bias Detection
6309626,0.99294,0,6,/poojachourey/predictive-equipment-failures,Predictive Equipment Failures
6310245,0.99526,0,1,/eosaten/sleep-dev-v5,Predictive Equipment Failures
6309581,0.995,0,0,/eosaten/sleep-dev-v3,Predictive Equipment Failures
4049596,0.8294,0,0,/lvanine/kernelda8baf116a,IESB Norte - IGM - Maio 2019
3967929,0.8366600000000001,0,0,/gleicecosta/igm-maio-2019,IESB Norte - IGM - Maio 2019
5857863,0.89459,0,1,/asithaindrajith/kernel-kavinda,hackStat 2.0
6047642,0.8897200000000001,0,1,/asithaindrajith/kernel-test2,hackStat 2.0
2842020,31.90586,0,1,/toedtli/demo-kaggle-submission-kernel,Einfhrung in Kaggle InClass Competitions</title>
4925354,0.00373,0,1,/hitoidas/deep-learning-strike-team-cars,Hackathon Auto_matic
4921665,0.07918,0,3,/anitho2910/spai-racer-auto-hackathon,Hackathon Auto_matic
4918009,0.3043099999999999,1,2,/shubhendumishra/solo-cars-classification-using-vgg16-on-pytorch,Hackathon Auto_matic
4920059,0.01616,1,2,/lalwaniabhishek/automatic-submission-by-abhishek-lalwani,Hackathon Auto_matic
4241170,0.8,0,0,/bogyeom/in-cabin,2019  ML competition with KISTI
4289069,0.8240000000000001,0,0,/syntn00/final-try,2019  ML competition with KISTI
4218991,0.7759999999999999,0,0,/sonsuki/suki-3,2019  ML competition with KISTI
4314632,0.792,0,0,/victoryeun/fork-of-fork-of-fork-of-fork-of-fork-of-reeeeee-17,2019  ML competition with KISTI
4266453,0.7759999999999999,0,0,/seoseng/20190612-kisti-titanic-competiton,2019  ML competition with KISTI
4216411,0.72,5,12,/youhanlee/yh-baseline,2019  ML competition with KISTI
6309564,0.725,0,0,/richidubey/2017a7ps0099g-lab-2,Eval Lab 2 F464
8290500,0.78645,0,0,/onizukaharuto/diabetes-diagnosis,Diabetes Diagnosis
8424709,0.6927,0,0,/ep17058/diabetes-deagnosis,Diabetes Diagnosis
8434872,0.70833,0,0,/kanameseto/kernel17b33c54f1,Diabetes Diagnosis
13300852,0.31428,0,2,/kazuhirofuruhashi/kazuhiro-furuhashi-issm2020ai-ipynb,ISSM2020 AI Challenge
4152914,0.98679,0,1,/metriczulu/data650-first-entry,UMUC DATA 650 Summer 2019 Competition
5806549,40.01524000000001,0,3,/vladimirsydor/no-images-only-statistic-but-bad,GL Hack: Landmarks
2297927,0.57191,0,2,/jamesleslie/tfidf-neural-network,Whose line is it anyway?
2300805,0.61301,0,3,/roxzanne/donut-squad1,Whose line is it anyway?
7140987,0.36293,0,0,/cashfeg/keras-sample,DS特論2019年度 演習課題2
5850509,0.67283,0,1,/macchi57/kernel-de-exemplo,Fake News e ML
2076816,0.52955,0,0,/mguinezi/pmr3508-2018-91dc8aec81-chvregressions,Atividade_3_PMR3508
2078427,0.2450099999999999,0,0,/miura99/tarefa-3-dfc4b955cd,Atividade_3_PMR3508
2037279,0.29095,0,0,/rejaili/californiahousing-pmr3508-2018-e32fc8f41f,Atividade_3_PMR3508
2056678,0.34135,0,0,/vkiguchi/housepredict,Atividade_3_PMR3508
2062116,0.21549,0,0,/dueiras/pmr3508-2018-57d6882489-tarefa-3,Atividade_3_PMR3508
2075963,0.2293099999999999,0,0,/gyborges/kernele03df4063a,Atividade_3_PMR3508
2064089,0.2021,0,0,/vmbenevides/pmr3508-2018-9ec6d2de6c-tarefa-3,Atividade_3_PMR3508
7617603,0.52979,0,0,/hireme/kaggle-starterkit-decision-trees,[DM&PR WS19/20] Machine learning competition
3077407,13.34782,0,1,/wentzforte/nyc-building-energy-benchmarking,Competição DSA de Machine Learning
3148490,9.53231,0,0,/cidsant/dsa-fev-energy-star-score,Competição DSA de Machine Learning
8127368,0.90111,1,0,/xxxsaq/ocrv-test-task-final,OCRV Test Task
1497226,0.7769699999999999,0,0,/chetlurkrishna/baseline-random-forest-8321e7,Web Enthusiasts' Club NITK Recruitment
1478686,0.77872,0,0,/anumeha29/submission-for-wc-contest,Web Enthusiasts' Club NITK Recruitment
4462704,0.13132,1,1,/tahsin/ifood-2019-fast-ai-implementation,iFood - 2019 at FGVC6
9718266,0.4708,0,0,/jganzabal/regresi-n-log-stica-con-keras,Fashion MNIST-ITBA-LAB 2020
2656103,0.46111,0,0,/redw0lf/simple-knn-classifier,IES Data Mining(WS 18/19)
3368239,0.9367,0,2,/jarfo1/char-rnn-baseline,Language Identification
9309686,0.02165,0,0,/vukw11/kernel3cebe4485d,finec-1941-hw6
2189923,0.56265,0,4,/meehey/lgbm-by-asset,ML 4 Money
2190123,0.56625,0,0,/crackoon/7-regressors-3-features,ML 4 Money
2674506,140844.81197,4,9,/gpreda/house-sales-eda-and-prediction,House Sales 
3331405,0.46533,1,1,/szelee/aoeul-solution-step-2-deep-learning-model,National Data Science Challenge 2019 - Advanced
3374206,0.46823,0,0,/szelee/aoeul-solution-step-4-matching-test-to-train,National Data Science Challenge 2019 - Advanced
3370257,0.4681399999999999,2,0,/szelee/aoeul-solution-step-3-linearsvc-dl-model,National Data Science Challenge 2019 - Advanced
1967701,0.98181,0,0,/anushkasv225/bbc-news-classification-anushka-v2,AI Academy Intermediate Class Competition 1
1967574,0.98181,0,0,/apelyushenko/bbc-news-classification-alex-pelyushenko,AI Academy Intermediate Class Competition 1
3617197,0.9835,0,0,/juacasbu/red-neuronal-sencilla,MLH - Pokemon Challenge
3345263,0.9865,0,1,/victorsalgado/mlh-pokemon-challenge,MLH - Pokemon Challenge
3607275,0.8825,0,0,/waltterval/cargar-y-limpiar-datos-regresion-lineal-mod,MLH - Pokemon Challenge
3424796,0.962,2,1,/skalextric/lightgbm-oda-a-santi-iglesias,MLH - Pokemon Challenge
3414688,0.9725,0,0,/ianholing/ligthgbm-a-pelo,MLH - Pokemon Challenge
3369289,0.879,1,10,/rhortelanos/cargar-y-limpiar-datos-regresion-lineal,MLH - Pokemon Challenge
3653410,0.8606799999999999,1,13,/mamamot/ulmfit-the-first-place-solution,DL in NLP Spring 2019. Classification
3653417,0.82615,0,11,/kashnitsky/eda-logit-tf-idf-starter,DL in NLP Spring 2019. Classification
7374566,0.187,0,2,/jarfo1/cbow-training-1,Word vectors
1205032,0.80645,0,1,/jrmst102/cloudy-faculty-institute-final-sub,Cloud Faculty Institute Workshop
1201660,0.80645,1,1,/noahgift/final-submission-great-job-kumar,Cloud Faculty Institute Workshop
1201614,0.80645,1,1,/jrmst102/faculty-institute-diabetes-jose-mendoza,Cloud Faculty Institute Workshop
1175067,0.6774100000000001,0,11,/paultimothymooney/predict-diabetes-with-python-starter-kernel,Cloud Faculty Institute Workshop
7737967,1.40722,0,1,/yukimizumachi/mizumachi,Exam for Students20200129
7737519,1.37991,0,1,/takahiromatsumoto/aiacademy-exam,Exam for Students20200129
7739603,1.48364,0,0,/satoshisasahara/kernel16acdb8614,Exam for Students20200129
7739474,2.0304900000000004,0,0,/hikarufujii/kernelf11d3ce3bd,Exam for Students20200129
7739602,1.40631,0,0,/uchu1905/kernel122d6bb760,Exam for Students20200129
7737495,1.41098,0,0,/shinichitakikawa/kernel65253ff744,Exam for Students20200129
7738634,1.48444,0,0,/kawamin/fork-of-kernelba9f8f77b7-3,Exam for Students20200129
807552,0.71753,0,0,/ziliwang/baseline-upsampling-balanced-softmax-regression,Sentiment Analysis in Russian
6036503,0.7196600000000001,0,1,/lizhi1104/sample-decision-tree-classifier,DMA Kaggle Challenge
6279152,0.99053,0,0,/txkitty/failuretest,Predictive Equipment Failures
6208181,0.96464,0,1,/coildriller/sunday-evening,Predictive Equipment Failures
8014799,0.38587,0,0,/plarmuseau/collaborative-filtering-a-joke-2-0,Recommender Systems
10577007,0.01,0,9,/aditi81k/vowel-consonant-classification,PadhAI: Hindi Vowel - Consonant Classification
4487451,0.67433,0,0,/niteshbisht26/vowelconsclassification-resnet50,PadhAI: Hindi Vowel - Consonant Classification
6065101,0.011,0,1,/artikwh/padhai-hindi-vowel-consonant,PadhAI: Hindi Vowel - Consonant Classification
4355359,87257.6711,0,1,/stillsut/myfirstsubmission,DSI-US-8 Project 2 Regression Challenge
4003798,0.99494,1,2,/okhinko/kekas-cleaner,Cleaned vs Dirty
2858051,0.7987,0,0,/earthshaker/diabetes,Diabetes Classification
7591901,0.8579899999999999,0,0,/josoga2/example1-lgbm,ML in biology
7241665,0.8246600000000001,0,3,/twoepochs/2epochsfinal,Penyisihan Datavidia 2019
2967696,0.3024,0,1,/cashfeg/one-day-challenge-harada,Property price prediction challenge
2614791,2.43372,0,1,/haritoshi/submit,Property price prediction challenge
4859866,0.855,0,0,/kenichinakatani/makesubmission-note,Fashion MNIST challenge201907
4082352,0.90909,0,4,/keshavramaiah/fruit-classification-using-cnn,ML Hackathon 2019 Q2
1278740,0.72954,0,1,/asiyehbahaloo/asiyeh-bahaloo,NBA Rookies
1277153,0.725,0,0,/sanazsn/nba-rookies-prediction-using-voting-classifier,NBA Rookies
1274999,0.70681,0,3,/nganji1993/ensemble-tree-knn-mlp-ran-lg,NBA Rookies
1279815,0.7,0,0,/ensiyeh/ensemble-method,NBA Rookies
3468960,47.71768,0,3,/kpotoh/simple-catboost-regression,Задержка рейса самолета
4146470,94.47937,0,2,/kirichenko17roman/knn-example,Python for Data science ITEA
6442152,-0.00072,0,0,/jandiers/prognose-auf-den-testdaten-abgeben,Data-Driven Business Analytics
9294887,71.2,0,0,/mbooth/starting-with-a-kneighborsregressor,Basic Regression Competition
4472348,0.7469,0,0,/sammy3101/transfer-learning-vgg19,QSTP - Deep Learning 2019
4562098,0.7358,0,1,/oytrik/kernel12940141ea,QSTP - Deep Learning 2019
4460251,0.61203,0,0,/arshika/qstp-assignment-16,QSTP - Deep Learning 2019
4519429,0.71702,0,0,/perseusdg/template-notebook-b8b4f1,QSTP - Deep Learning 2019
4552365,0.66794,0,0,/adithya99/qstp-assignment,QSTP - Deep Learning 2019
2346624,0.23529,0,0,/oxfee1dead/beginner-pipeline,ClassificationOFShields
8087124,0.8348700000000001,0,0,/gordeevaln/cv-hw3-net,Traffic signs classification
5024653,0.87955,0,3,/bhadreshsavani/aarya-sentimentalanalysis,Hackathon Sentimento
5033120,0.8931100000000001,0,0,/ronitmankad/ronit-mankad-sentiment-analysis,Hackathon Sentimento
10994069,0.8413799999999999,0,2,/davydev/shopee-product-detection-challenge,[Student] Shopee Code League - Product Detection
10311510,0.02586,1,16,/huikang/sample-submission-please-pad-zeroes,[Student] Shopee Code League - Product Detection
2236557,2.33359,0,0,/rciphertext/xxasaaasa,IIITB ML Project: SFO Crime Classification
4095355,0.76344,0,4,/hmchuong/keras-baseline-model,VietAI Advance Course - Retinal Disease Detection
5053880,3.18075,0,0,/tohgoroh/lstm-for-temperature-prediction,Temperature Forecasting
3360572,0.36054,0,0,/rfelizomni/association-rule-mining,[ACM] Recommender System Practice
4508556,0.8166899999999999,0,0,/barroswilson2012/competi-o-carlos-w-g-barros,IESB Sul - IGM - Maio 2019
4056612,0.81488,0,0,/eduardokental/kental01,IESB Sul - IGM - Maio 2019
4038190,0.8166899999999999,0,0,/tiagopeliciari/competi-o,IESB Sul - IGM - Maio 2019
4498684,0.81488,0,0,/gustavo9101/gustavo-santos,IESB Sul - IGM - Maio 2019
2498539,0.51015,0,0,/hireme/kaggle-starter-kit-using-decision-trees,[DM&PR WS18/19] Machine learning competition
4847020,0.87613,0,0,/bencrabbe/corrig-du-prof-bag-of-words,AS-bow-2019-2020
736747,60.93799,0,0,/amingolzari/features,Tap30 Challenge
2102533,0.88256,0,0,/barelydedicated/svm-movie-review-classifier,Classifying Movie Reviews
2364388,0.856,0,0,/barelydedicated/neural-net-movie-review-classifier,Classifying Movie Reviews
1849774,0.18688,0,0,/jannesklaas/flowers-comp-starter,Oxford Fast AI Week 2
3255751,0.9953,0,0,/andreiv4/cnn-cells-image-classification-32x32x3,Aerial Cactus Identification
3815027,1.0,0,1,/sdoctor86/fast-ai-densenet,Aerial Cactus Identification
3800912,0.9998,0,0,/spsayakpaul/aerial-cactus-identification-using-fastai,Aerial Cactus Identification
3786377,0.9987,0,0,/rolandkopka/aerial-cactus-identification,Aerial Cactus Identification
3765191,0.9999,0,2,/dcahn12/cuctus-kaggle,Aerial Cactus Identification
3684914,0.9716,0,1,/khiwila/kernel9871eaa9f8,Aerial Cactus Identification
3672389,0.9062,0,2,/zhengze94/multiple-instance-learning-as-pooling-layer-in-cnn,Aerial Cactus Identification
3590261,0.9972,1,0,/toobadimnotgood/cactus-identification-cnn,Aerial Cactus Identification
3643890,0.9998,0,3,/aminta/areial-cactus-tensoflow-cs230-stanford-method,Aerial Cactus Identification
3617831,1.0,0,0,/abhaysidhwani/kernelda16ec6028,Aerial Cactus Identification
3617730,0.9834,0,1,/cheyuriy/yet-another-keras-example-densenet,Aerial Cactus Identification
3594030,0.9999,1,4,/petersonkt23/fastai-densetnet,Aerial Cactus Identification
3504588,0.894,0,0,/parikshit14074/linear-svm,Aerial Cactus Identification
3503175,0.9984,0,1,/zhuhai/imagegenerator,Aerial Cactus Identification
3461932,0.9997,0,10,/artgor/cactalyst,Aerial Cactus Identification
3441173,0.9992,2,5,/twhitehurst3/aerial-cactus-identification-keras-cnn-comp,Aerial Cactus Identification
3438280,0.586,1,0,/debanjan02/kernela1d744c1c6,Aerial Cactus Identification
3368472,0.9897,0,0,/gauravadhikari/playing-with-the-cactus-dataset,Aerial Cactus Identification
3376183,0.9995,0,0,/elvinmirze/fork-of-simple-deep-cnn-different-batch-size-and-e,Aerial Cactus Identification
3331670,0.9999,0,1,/mariammohamed/simple-ensemble,Aerial Cactus Identification
3222673,0.995,0,0,/mohanamurali/fast-ai,Aerial Cactus Identification
3283695,0.9351,1,1,/sujoykg/keras-cnn-with-grayscale-images,Aerial Cactus Identification
3273853,0.9993,0,0,/bachrr/cactus-identification-with-fastai-resnet-34,Aerial Cactus Identification
3267749,0.9938,0,0,/jacky5112/aerial-cactus-identification-v1,Aerial Cactus Identification
3235867,0.9881,1,3,/thorgas1988/ensemble-of-3-fastai-models,Aerial Cactus Identification
3190861,0.9998,24,65,/artgor/detecting-cactus-with-kekas,Aerial Cactus Identification
3202295,0.9782,0,4,/masonblier/aerial-cactus-simple-cnn,Aerial Cactus Identification
6927693,0.0,0,0,/marcelorbsousa/kernel7b6a84a711,Aerial Cactus Identification
3756803,8.82249,0,0,/seungwanryu/2019-04-30,New York City Taxi Fare Prediction
3393706,3.08025,0,0,/kumaml/kuma-ny-taxi-fare-predict,New York City Taxi Fare Prediction
3223192,3.3504400000000003,0,0,/neumatron11/taxi-weather-holidays-kmeans-neighborhoods-lgb,New York City Taxi Fare Prediction
1879182,4.76952,0,0,/valentinricher/visualization-and-prediction-of-nyc-taxi-data,New York City Taxi Fare Prediction
2571714,5.74184,0,0,/nikhilmittal/new-york-city-taxi-fare-prediction-0,New York City Taxi Fare Prediction
2358946,4.16223,0,0,/maheshas88/nyc-taxi-fare-prediction-using-deep-learning,New York City Taxi Fare Prediction
2188648,2.99931,0,4,/linhndg/kernel189dc31ddd,New York City Taxi Fare Prediction
2018933,5.07942,0,0,/dangizzi/predicting-fare-price-with-random-forest,New York City Taxi Fare Prediction
1413593,3.1154,0,1,/mahtieu/nyc-taxi-fare-prediction-data-expl-xgboost,New York City Taxi Fare Prediction
1396367,3.30872,0,1,/nocturnaltribe/newyork-city-taxi-fare-prediction,New York City Taxi Fare Prediction
1409066,4.07608,0,1,/upperdomain/ml-spark-alpha-group,New York City Taxi Fare Prediction
1486818,3.67623,0,0,/rishabh254/nyc-ola,New York City Taxi Fare Prediction
1571922,3.29707,0,0,/nanometers/pipeline-testing-for-nyc-taxis,New York City Taxi Fare Prediction
1689063,3.76827,0,3,/suniliitb96/nyc-taxi-fare-prediction,New York City Taxi Fare Prediction
1616835,3.49615,0,0,/mohit2508/mohit-choudhary-nyctaxi,New York City Taxi Fare Prediction
1705503,3.1074,0,1,/tuliocasagrande/nyc-taxi-fare-xgboost,New York City Taxi Fare Prediction
1696384,4.03603,0,2,/easter3163/simple-analysis-of-new-york-city-fare,New York City Taxi Fare Prediction
3388242,0.5589999999999999,0,1,/nikitaomare/kernela4cb13745e,Santander Customer Transaction Prediction
4117582,0.76993,0,2,/shotaku/pca-ica-xgboost,Santander Customer Transaction Prediction
3297578,0.603,0,0,/aritrase/pytorch-ff-nn,Santander Customer Transaction Prediction
3967453,0.85085,0,2,/danielviturrate/red-neuronal-keras-b883cf,Santander Customer Transaction Prediction
3887762,0.8529100000000001,0,1,/zoomelectrico/red-neuronal-keras,Santander Customer Transaction Prediction
3948785,0.50698,0,0,/ikunobu/santander-01,Santander Customer Transaction Prediction
3954615,0.8543299999999999,0,0,/diabliyo12/red-neuronal-keras,Santander Customer Transaction Prediction
3813147,0.72251,0,1,/niyipop/simple-xgboost-model,Santander Customer Transaction Prediction
3672061,0.8609600000000001,0,1,/askolkova/customer-transaction-prediction-a-s,Santander Customer Transaction Prediction
3609800,0.86092,0,0,/mcclymont/santander-neural-net,Santander Customer Transaction Prediction
3216641,0.92285,4,4,/aawanghui/santander-nn,Santander Customer Transaction Prediction
3163498,0.92362,4,32,/gunesevitan/santander-customer-transaction-eda-fe-lgb,Santander Customer Transaction Prediction
3557757,0.92214,25,95,/felipemello/step-by-step-guide-to-the-magic-lb-0-922,Santander Customer Transaction Prediction
3567533,0.92198,2,9,/nagiss/9-solution-nagiss-part-1-2-2step-lgbm,Santander Customer Transaction Prediction
3020511,0.853,1,13,/naraque/red-neuronal-keras,Santander Customer Transaction Prediction
3520294,0.773,0,1,/matheuspush/santander-imbalanced-random-forest,Santander Customer Transaction Prediction
3556087,0.92259,1,11,/ilu000/simplistic-magic-lgbm,Santander Customer Transaction Prediction
3524143,0.91453,0,7,/aawanghui/catboostalsoworks,Santander Customer Transaction Prediction
14417318,0.12399,22,16,/winternguyen/eda-prediction-of-house-price,House Prices - Advanced Regression Techniques
14535894,0.1214599999999999,8,11,/vorobevaleksandr/house-prices-prediction,House Prices - Advanced Regression Techniques
14306299,9.45864,1,2,/pandekp/house-price-prediction-analysis,House Prices - Advanced Regression Techniques
13322883,0.1273,0,1,/ranaelzahy/house-prices,House Prices - Advanced Regression Techniques
8909961,0.20418,0,0,/yadavhimanshu/housing-prediction,House Prices - Advanced Regression Techniques
14335856,0.13762,0,0,/rainbowhyena/housepricescorrect,House Prices - Advanced Regression Techniques
14331613,0.24514,2,2,/sarthak218/house-prices-adaboostregressor,House Prices - Advanced Regression Techniques
14504713,0.00044,0,0,/abdallamahmoudamin/notebooka45aab4f47,House Prices - Advanced Regression Techniques
14272465,0.14592,1,3,/hananxx/house-price-predication,House Prices - Advanced Regression Techniques
12588669,0.635,3,7,/rajgandhi/riiid-answer-correctness-prediction,Riiid Answer Correctness Prediction
12977106,0.746,5,16,/sahilmaheshwari/what-we-know-about-riids-so-far,Riiid Answer Correctness Prediction
13057566,0.757,5,30,/shinomoriaoshi/riiid-catboost-baseline,Riiid Answer Correctness Prediction
13057532,0.672,0,1,/chaochaoma/notebook8923e6a4fc,Riiid Answer Correctness Prediction
13007204,0.5,0,7,/brubian/riiid-explain-the-compete-in-japanese,Riiid Answer Correctness Prediction
12814361,0.69,10,60,/chumajin/eda-for-biginner,Riiid Answer Correctness Prediction
12839274,0.754,2,7,/beable/lgbmclassifier-submission,Riiid Answer Correctness Prediction
12682124,0.5,18,50,/tomooinubushi/inference-must-be-0-55-sec-iter,Riiid Answer Correctness Prediction
12668001,0.752,0,3,/sergei416/hyperparameter-grid-search-lgbm,Riiid Answer Correctness Prediction
12622889,0.721,6,19,/arpitsolanki14/rapids-exploratory-analysis-xgboost-prediction,Riiid Answer Correctness Prediction
12620413,0.706,0,7,/legend507/riiid-answer-correctness-prediction-eda-model,Riiid Answer Correctness Prediction
12364699,0.518,0,2,/supetronix/riiid-skorch-rnn-embedding,Riiid Answer Correctness Prediction
12553191,0.7509999999999999,16,36,/gilfernandes/fastai-single-nn,Riiid Answer Correctness Prediction
12566517,0.7509999999999999,0,2,/kelink/riiid-lgbm-starter,Riiid Answer Correctness Prediction
10568707,0.6986,0,1,/abqyum/xgboost-calibratedclassifiercv-power-of-tpu,SIIM-ISIC Melanoma Classification
10803232,0.9361,6,53,/tunguz/melanoma-with-h2o-automl-4,SIIM-ISIC Melanoma Classification
10778968,0.8784,5,8,/wittmannf/eda-and-base-model-in-keras-with-resnet50-awari,SIIM-ISIC Melanoma Classification
10776455,0.8534,1,9,/amneves/tensorflow-inception-v3-transfer-learning,SIIM-ISIC Melanoma Classification
10759060,0.9512,0,20,/kmldas/new-basline-np-log2-ensemble-top-10,SIIM-ISIC Melanoma Classification
10709427,0.9244,46,85,/vishnus/a-simple-pytorch-starter-code-single-fold-93,SIIM-ISIC Melanoma Classification
10717427,0.8879,7,27,/anyexiezouqu/big-transfer-only-one-epoch-0-88-score,SIIM-ISIC Melanoma Classification
10706765,0.7288,0,2,/priteshshrivastava/melanoma-tabular-data-xgboost,SIIM-ISIC Melanoma Classification
10705225,0.8116,0,1,/priteshshrivastava/melanoma-keras-vgg,SIIM-ISIC Melanoma Classification
10664980,0.8428,0,24,/fireheart7/melanoma-mobilenetv2,SIIM-ISIC Melanoma Classification
14662305,0.154,0,0,/leftyork/vinbigdata-2-class-filter,Predict Future Sales
14524087,0.079,1,3,/basu369victor/chest-x-ray-abnormality-detection-yolo-v3-infer,Predict Future Sales
14163962,0.221,26,69,/corochann/vinbigdata-detectron2-prediction,Predict Future Sales
14142601,0.211,22,72,/awsaf49/vinbigdata-2-class-filter,Predict Future Sales
14135066,0.099,0,7,/basu369victor/chest-x-ray-abnormalities-detection-submission,Predict Future Sales
14019243,0.027,14,6,/backtracking/efficientdet-inference,Predict Future Sales
8694584,0.94322,0,3,/ozgurb/pfs-v3,Predict Future Sales
7949631,1.15363,0,1,/siriusmhlee/predict-future-sales-competition-keras-lstm,Predict Future Sales
8113223,0.8966799999999999,0,4,/wangqiyuan/xgb-baseline-advanced-feature-engineering,Predict Future Sales
7161697,0.9324,0,0,/dimonrtm/first-look-on-data,Predict Future Sales
7646763,0.91429,4,5,/pintowar/sales-predictions-feature-engineering-lgbm,Predict Future Sales
7714562,0.90315,1,7,/meliao/lgb-baseline-top8-clear-code-50-faster,Predict Future Sales
7522061,2.1836,1,1,/biswajit1998/kernel4fa130305d,Predict Future Sales
7243012,2.46647,0,1,/obiaf88/simple-linear-model-for-sales-predictions,Predict Future Sales
4830420,0.752,0,1,/manishkumarnaik999/aptos-2019-manish-keras,APTOS 2019 Blindness Detection
5288539,0.804,2,0,/haydenmuscat/dense-submission,APTOS 2019 Blindness Detection
5692577,0.8315229999999999,1,8,/quandapro/aptos-efficientnetb3,APTOS 2019 Blindness Detection
7932848,0.146615,0,0,/nids24/from-resnet-and-cropping,APTOS 2019 Blindness Detection
5621180,0.792,0,1,/amishsethi/more-efficient-efficientnet-for-0-792,APTOS 2019 Blindness Detection
5012488,0.613,0,0,/debangshu16/densenet-class-weights-tta,APTOS 2019 Blindness Detection
6776871,0.794219,0,0,/justinaskazanavicius/cappgle2-0,APTOS 2019 Blindness Detection
5721356,0.711,0,0,/gauripradhan/aptos-19-tta-tl-dnn,APTOS 2019 Blindness Detection
5293148,0.792,0,0,/filemide/branched-of-aptos-vote,APTOS 2019 Blindness Detection
6594802,0.686394,0,2,/flyingmuttus/fast-ai-fundus-eye,APTOS 2019 Blindness Detection
6595611,0.6311939999999999,0,0,/topsarun/aptos-1,APTOS 2019 Blindness Detection
4941706,0.7340000000000001,2,0,/agayev169/aptos-2019-best,APTOS 2019 Blindness Detection
5267645,0.759,0,1,/buntyke/efficientnet-submission,APTOS 2019 Blindness Detection
5654058,0.7929999999999999,0,2,/rsinda/enb52,APTOS 2019 Blindness Detection
5474364,0.812,0,1,/nwzish/aptos-submission-kernel,APTOS 2019 Blindness Detection
4810107,0.7290000000000001,0,1,/chinta/kfold-5epochs,APTOS 2019 Blindness Detection
5195880,0.027,0,0,/abhishek24021996/aptos-keras-dataugmentation,APTOS 2019 Blindness Detection
4565612,0.379,0,0,/ibrahimpasha/blind-or-not-keras,APTOS 2019 Blindness Detection
5869053,0.239171,0,2,/julienpascal/ensemble-cnn,APTOS 2019 Blindness Detection
5666617,0.728,1,1,/yeayates21/densenet-keras-starter-fork-v2,APTOS 2019 Blindness Detection
2103463,0.0202,0,0,/chuckshaw/ensemble-baseline,PUBG Finish Placement Prediction (Kernels Only)
2942003,0.04712,0,0,/amoswish/pubg-prediction-1,PUBG Finish Placement Prediction (Kernels Only)
1887302,0.04467,0,0,/kpriyanshu256/pubg-winner,PUBG Finish Placement Prediction (Kernels Only)
2805965,1407.55026,0,2,/ivangord/pubg-s-kernel-new-one,PUBG Finish Placement Prediction (Kernels Only)
2874742,0.1290099999999999,0,1,/nannjanannja/pubg-practice,PUBG Finish Placement Prediction (Kernels Only)
2192594,0.01828,4,17,/batalov/hardest-way-to-get-a-t-shirt-4th-place-solution,PUBG Finish Placement Prediction (Kernels Only)
2211178,0.021,0,9,/mgiraygokirmak/lightgbm-with-gridsearch-and-feat-importance-att,PUBG Finish Placement Prediction (Kernels Only)
2735232,0.05695,0,0,/damienpark/keras-prediction-for-pubg,PUBG Finish Placement Prediction (Kernels Only)
2745785,0.0572,1,5,/liyunzhejack/random-forest,PUBG Finish Placement Prediction (Kernels Only)
2400118,0.0248,0,0,/sniper1824/radhe,PUBG Finish Placement Prediction (Kernels Only)
2681387,0.0918,0,0,/amoswish/pubg-predict-of-amoswish,PUBG Finish Placement Prediction (Kernels Only)
2661002,0.0548,0,0,/samiamami/one-match-at-a-time,PUBG Finish Placement Prediction (Kernels Only)
2648582,0.0583,0,0,/lavinov/optimizationmethodsproject-pubg,PUBG Finish Placement Prediction (Kernels Only)
2600273,0.0606,0,10,/priteshshrivastava/pubg-rf-with-model-interpretation-fast-ai,PUBG Finish Placement Prediction (Kernels Only)
2531335,0.0323,0,0,/pavelvpster/pubg-linear-regression-3,PUBG Finish Placement Prediction (Kernels Only)
2379194,0.0621,0,0,/siftsingh/random-forest-optimised,PUBG Finish Placement Prediction (Kernels Only)
2409269,0.0218,0,4,/jotaro/pubg-eda-and-cheater-exclusion-lgb-training,PUBG Finish Placement Prediction (Kernels Only)
2433265,0.0246,0,0,/bivek2211/player-unknown-battleground,PUBG Finish Placement Prediction (Kernels Only)
2410934,0.0575,0,1,/vaseline555/catboost-regressor-baseline,PUBG Finish Placement Prediction (Kernels Only)
2390571,0.0445,0,0,/yansun1996/baseline-linearregression,PUBG Finish Placement Prediction (Kernels Only)
2396996,0.0675,0,0,/praneethvarmaalluri/baseline-model,PUBG Finish Placement Prediction (Kernels Only)
2394116,0.0566,0,0,/mirosh111/pubg-lightgbm,PUBG Finish Placement Prediction (Kernels Only)
14247441,0.405,18,41,/dimitreoliveira/rainforest-audio-classification-tensorflow-starter,Rainforest Connection Species Audio Detection
13965071,0.845,4,19,/prvnkmr/baseline-implementation-resnet34,Rainforest Connection Species Audio Detection
13934254,0.879,18,29,/saurabhbagchi/rfcx-bagging-with-different-weights-0-879-score,Rainforest Connection Species Audio Detection
13868855,0.8759999999999999,19,43,/mehrankazeminia/automl-inference-audio-detection-soliset,Rainforest Connection Species Audio Detection
13495416,0.845,40,98,/khoongweihao/resnet34-more-augmentations-mixup-tta-inference,Rainforest Connection Species Audio Detection
13332593,0.616,0,9,/kanruwang/simple-tabular-fft-xgboost-gpu,Rainforest Connection Species Audio Detection
13072549,0.552,0,7,/riadalmadani/random-forest-sklearn,Rainforest Connection Species Audio Detection
10243132,0.7851600000000001,2,5,/bulbulbhati/tweets-true-or-not,Natural Language Processing with Disaster Tweets
10214391,0.79895,4,8,/saraivaufc/real-or-not-using-lstm-for-disaster-tweets,Natural Language Processing with Disaster Tweets
10225902,0.82255,0,1,/srjony/roberta-based-model-starting-kernel,Natural Language Processing with Disaster Tweets
10157262,0.80999,0,6,/hoangpham51/text-classification-pytorch-torchtext-lstm,Natural Language Processing with Disaster Tweets
10105619,0.75298,0,1,/raj26000/tweet-classification-with-bi-lstm,Natural Language Processing with Disaster Tweets
10103840,0.78394,0,1,/jswxhd/ml-svm-nltk-preprocessing-keyword,Natural Language Processing with Disaster Tweets
10038839,0.84186,10,54,/tuckerarrants/disaster-tweets-eda-glove-rnns-bert,Natural Language Processing with Disaster Tweets
9998024,0.8262299999999999,0,2,/jswxhd/keras-tensorflow-sklearn-bert,Natural Language Processing with Disaster Tweets
9869475,0.7955800000000001,4,8,/danoozy44/disaster-tweets-nlp-for-beginners,Natural Language Processing with Disaster Tweets
9872535,0.78179,0,1,/colinnicotina/disaster-prediction,Natural Language Processing with Disaster Tweets
9516555,0.80784,0,3,/littleraj30/how-to-train-your-bert-model-huggingface,Natural Language Processing with Disaster Tweets
9641083,0.82562,0,6,/heyytanay/fake-tweet-classification-eda,Natural Language Processing with Disaster Tweets
9447545,0.78057,0,1,/rishabhgarg1023/nlp-with-disaster-tweets,Natural Language Processing with Disaster Tweets
9572554,0.79681,0,0,/danielbilitewski/bag-of-words-and-naive-bayes,Natural Language Processing with Disaster Tweets
9430865,0.73256,4,14,/robertburbidge/state-space-model,M5 Forecasting - Accuracy
9287954,0.6936100000000001,22,61,/bountyhunters/baseline-lstm-with-keras-0-7,M5 Forecasting - Accuracy
9300504,5.44561,11,7,/digvijayyadav/m5-accuracy-model,M5 Forecasting - Accuracy
9293457,0.8377,1,3,/syoheihoh/simple-submission,M5 Forecasting - Accuracy
9260240,0.47506,1,7,/shantanu1118/m5-shades-of-dark-magic,M5 Forecasting - Accuracy
8431166,0.46938,0,0,/kamalnaithani/m5-eda-and-forecasting-model,M5 Forecasting - Accuracy
8922622,0.8377,0,0,/lucacarotenuto/kernel-last28days,M5 Forecasting - Accuracy
10712817,2.75018,0,4,/makhloufsabir/higgs-boson-classification-physics,Higgs Boson Machine Learning Challenge
10379298,0.85,0,23,/micheomaano/pandas-42x256x256x3-inference,Prostate cANcer graDe Assessment (PANDA) Challenge
10220788,0.87,6,45,/raininbox/blend-different-models-with-different-n-tile,Prostate cANcer graDe Assessment (PANDA) Challenge
9952136,0.87,0,9,/santosh8896/panda-inference-w-36-tiles-256,Prostate cANcer graDe Assessment (PANDA) Challenge
9847551,0.87,13,125,/haqishen/panda-inference-w-36-tiles-256,Prostate cANcer graDe Assessment (PANDA) Challenge
9527297,0.52,0,0,/sumitjha19/gleason-to-isup-score-resnet50,Prostate cANcer graDe Assessment (PANDA) Challenge
2300066,3.692,40,139,/ashishpatel26/lightgbm-goss-dart-parameter-tuning,Elo Merchant Category Recommendation
2312072,0.604,0,2,/tboyle10/cnn-google-news-vectors,Quora Insincere Questions Classification
2258547,0.5870000000000001,0,0,/tboyle10/embedding-cnn,Quora Insincere Questions Classification
2531809,0.6609999999999999,0,0,/krithi07/learning-about-word-embeddings-i,Quora Insincere Questions Classification
2545084,0.616,0,0,/xsakix/cnn-base-classifier-fold-unfreeze-avg-weights,Quora Insincere Questions Classification
2522722,0.6559999999999999,0,1,/hsankesara/quora-embedding-analysis,Quora Insincere Questions Classification
2058476,0.662,2,3,/abtexp/quora-qs,Quora Insincere Questions Classification
2495420,0.688,1,9,/bkkaggle/skip-rnn-meta-features-pseudo-labeling,Quora Insincere Questions Classification
2387193,0.6679999999999999,0,0,/strifonov/avg-cnn-predictions,Quora Insincere Questions Classification
2522607,0.545,0,0,/xsakix/base-classifier-fold,Quora Insincere Questions Classification
2430305,0.659,4,11,/tundraman/hierarchical-attention-network-cnn-on-qiqc,Quora Insincere Questions Classification
2436528,0.622,0,0,/dex314/simple-nlp-and-practice-v3,Quora Insincere Questions Classification
2491351,0.185,0,0,/lalitapatel/starter-without-nlp-frameworks,Quora Insincere Questions Classification
2477722,0.621,0,0,/neopalmas/test-submit,Quora Insincere Questions Classification
2463141,0.69,0,1,/johnfly/baseline-for-text-classification,Quora Insincere Questions Classification
2437698,0.696,28,164,/spirosrap/bilstm-attention-kfold-clr-extra-features-capsule,Quora Insincere Questions Classification
2413892,0.6779999999999999,0,2,/raghav3490/bidirectional-gru-with-unweighted-mean-embeddings,Quora Insincere Questions Classification
2465225,0.643,0,0,/xsakix/glove-filter-bilstm-att,Quora Insincere Questions Classification
2465234,0.63,0,0,/xsakix/para-filter-bilstm-att,Quora Insincere Questions Classification
2346767,0.376,0,0,/muzaffar21/kernelbc89cfa558,Quora Insincere Questions Classification
2464363,0.634,0,0,/xsakix/filter-bilstm-att-base,Quora Insincere Questions Classification
2204966,0.6459999999999999,0,2,/strifonov/combined-with-nlp,Quora Insincere Questions Classification
2388651,0.685,8,102,/zikazika/natural-language-processing-theory-and-practice,Quora Insincere Questions Classification
2349209,0.6609999999999999,0,0,/anurag16ph20003/quora-insincere-questions-classification,Quora Insincere Questions Classification
2395941,0.319,0,0,/alexandruuu/glove-tree-model,Quora Insincere Questions Classification
2402154,0.6859999999999999,0,5,/mlwhiz/deterministic-nn-pytorch-with-clr-and-save,Quora Insincere Questions Classification
8889210,0.10035,0,0,/nilanshk/covid-19-w4-2,COVID19 Global Forecasting (Week 4)
8859867,0.13888,0,0,/akashsuper2000/using-xgboost-week-4,COVID19 Global Forecasting (Week 4)
8860340,0.4785,0,1,/lorenzorota23/predicting-covid-19-with-lstm,COVID19 Global Forecasting (Week 4)
8834871,2.21627,0,0,/algonell/covid-19-series-poly-fit-w4,COVID19 Global Forecasting (Week 4)
8789095,0.03138,0,7,/vlomme/russian-baseline,COVID19 Global Forecasting (Week 4)
8846103,1.70355,0,1,/divyacn/covid-19-deathprediction-arima,COVID19 Global Forecasting (Week 4)
8835094,0.2183,0,2,/tmathieu/which-countries-s-cases-are-hard-to-predict,COVID19 Global Forecasting (Week 4)
8839588,0.13848,0,2,/kirderf/ridgecv-ns-7ave-ns-7avev2noswal15-week4,COVID19 Global Forecasting (Week 4)
8869502,0.03592,0,0,/yustasalex/covid19-gf-week4-sarimax-with-bec,COVID19 Global Forecasting (Week 4)
8833004,0.7316600000000001,2,2,/ranjithks/ran-covid-19-week4,COVID19 Global Forecasting (Week 4)
8832610,0.8600200000000001,0,0,/amithsbhat/kernelb66b21982f,COVID19 Global Forecasting (Week 4)
8849478,3.82883,0,0,/mustaphayinkayusuf/kernel745a756388,COVID19 Global Forecasting (Week 4)
8606971,1.0860299999999998,8,20,/sadiakhalil/covid-19-global-eda-forecast-2,COVID19 Global Forecasting (Week 4)
8620830,1.09011,0,0,/sklasfeld/covid19-forescasting-week4-holt-v1,COVID19 Global Forecasting (Week 4)
12343822,0.0,0,0,/allenzhang20/simple-covid19-week-4-prediction-with-xgbregressor,COVID19 Global Forecasting (Week 4)
9206325,0.50565,0,0,/kirderf/ensemble-short-long-term-models-lock-pp-weight-5,COVID19 Global Forecasting (Week 4)
9188007,0.50565,0,0,/kirderf/ensemble-short-long-term-models-indeweight-lock-p,COVID19 Global Forecasting (Week 4)
8951322,0.0586799999999999,0,0,/robikscube/xgboost-algorithm-covid-19-week-4,COVID19 Global Forecasting (Week 4)
8950098,0.03439,0,0,/benbla/fork-of-covid-19-linear-regression-and-arima,COVID19 Global Forecasting (Week 4)
8949558,0.03614,0,0,/ashimak01/fork-of-trmfrun,COVID19 Global Forecasting (Week 4)
8948941,0.0394399999999999,1,0,/appian/covid19-week4-1,COVID19 Global Forecasting (Week 4)
8942874,0.0343399999999999,0,0,/szukiyu/covid-19-sarima-week4,COVID19 Global Forecasting (Week 4)
8936829,0.0340699999999999,0,0,/janetht/linearregressionmodel,COVID19 Global Forecasting (Week 4)
8897926,0.21043,0,0,/menglu/covid-19-prediction-week4,COVID19 Global Forecasting (Week 4)
13343285,0.79133,0,0,/juliazhuravleva/notebook925d82a8be,What's Cooking? (Kernels Only)
7439307,0.82109,0,0,/vilceanumihnea97/kernel2833eb6e1f,What's Cooking? (Kernels Only)
6625784,0.72858,0,0,/zupepoto/preprocess-and-random-forest,What's Cooking? (Kernels Only)
5585965,0.78308,0,0,/stasler/nom-nom-nom,What's Cooking? (Kernels Only)
4559160,0.8245100000000001,0,0,/nazakathussain/what-s-cooking-script,What's Cooking? (Kernels Only)
3930946,0.73521,0,3,/iavinas/what-s-cooking-working-with-text-tfidfvectorizer,What's Cooking? (Kernels Only)
3558319,0.77745,0,0,/chaitali17/hey-what-s-cooking,What's Cooking? (Kernels Only)
1656930,0.8037,0,0,/faustbeirdo/what-s-cooking-ann,What's Cooking? (Kernels Only)
3441167,0.7580399999999999,0,0,/hrush777/simple-lstm,What's Cooking? (Kernels Only)
2841250,0.7791600000000001,0,1,/justk1/let-s-cook-wait-what-can-we-cook,What's Cooking? (Kernels Only)
2922683,0.8280299999999999,2,2,/oracool/natty-svc-better-score-than-the-first-place,What's Cooking? (Kernels Only)
2830460,0.78992,2,2,/shiyugong/what-s-cooking-eda-and-multi-class-classification,What's Cooking? (Kernels Only)
1517441,0.78338,0,0,/ericxu10101/tfidf-gbdt,What's Cooking? (Kernels Only)
2396077,0.8055100000000001,0,0,/nmquach/simple-try,What's Cooking? (Kernels Only)
2199127,0.80681,0,1,/rootofallevil/what-s-cooking,What's Cooking? (Kernels Only)
2113906,0.76639,1,1,/noachr/rnn-in-pytorch,What's Cooking? (Kernels Only)
2020978,0.7882100000000001,0,0,/kinoet/what-s-cooking,What's Cooking? (Kernels Only)
1780130,0.7872,0,0,/aliciadeng24/linear-svc-detect-cuisine,What's Cooking? (Kernels Only)
1624891,0.7087600000000001,0,0,/kukulkan/cook-prediction-cnn,What's Cooking? (Kernels Only)
1525592,0.80812,0,0,/grafiszti/multiple-model-with-feature-engineering,What's Cooking? (Kernels Only)
1603893,0.7867,0,0,/saychelsea11/cuisine-prediction-tfidf-and-logistic-regression,What's Cooking? (Kernels Only)
1665990,0.7711100000000001,0,0,/ritesaluja/cooking-it,What's Cooking? (Kernels Only)
1683432,0.7866,0,1,/pearl2201/cocking-recipe,What's Cooking? (Kernels Only)
1667283,0.78117,0,5,/bhasha4995dushara/delicious-food-cooking-model,What's Cooking? (Kernels Only)
1585160,0.7858,0,1,/drkalko/simple-eda-and-logistic-starter,What's Cooking? (Kernels Only)
82623,0.81932,0,0,/sanjulademel/animal-outcome-6,Shelter Animal Outcomes
72555,0.75305,3,0,/edwardelson/separate-classifier-for-cats-ad-dogs,Shelter Animal Outcomes
64670,0.8882200000000001,0,0,/ymcdull/shelter-animal-first-test,Shelter Animal Outcomes
51110,0.96967,0,0,/juandoso/simple-try-with-randomforest,Shelter Animal Outcomes
48852,1.58052,0,4,/rplaca/wasting-time-on-kaggle-competitions,Shelter Animal Outcomes
2672318,0.7,22,191,/jannen/reaching-0-7-fork-from-bilstm-attention-kfold,Quora Insincere Questions Classification
2655599,0.659,0,0,/dilapsky/decrease-lr-local-f1-0-7432,Quora Insincere Questions Classification
2674341,0.667,0,0,/xsakix/torch-cnn-lstm,Quora Insincere Questions Classification
2672853,0.6759999999999999,0,0,/xsakix/torch-ensemble,Quora Insincere Questions Classification
2580001,0.6990000000000001,0,13,/chenshengabc/fork-bilstm-attention-kfold-clr-extra-features,Quora Insincere Questions Classification
2637990,0.6990000000000001,6,81,/garydf/fork-from-bilstm-attention-kfold-0115,Quora Insincere Questions Classification
2599256,0.644,0,2,/tboyle10/lstm-google-news-vectors,Quora Insincere Questions Classification
2067527,0.068,0,6,/anebzt/quora-eda,Quora Insincere Questions Classification
2255463,0.524,0,0,/tboyle10/baseline-models-with-upsampling,Quora Insincere Questions Classification
2067248,0.67,0,1,/rajashri/inceptioncnn-with-flip,Quora Insincere Questions Classification
2613038,0.6890000000000001,0,0,/konohayui/cv-v-s-lb,Quora Insincere Questions Classification
2610177,0.113,0,16,/hamishdickson/submission-distributions,Quora Insincere Questions Classification
2615665,0.6709999999999999,0,0,/xsakix/torch-lstm-2,Quora Insincere Questions Classification
2591826,0.696,37,159,/bminixhofer/a-validation-framework-impact-of-the-random-seed,Quora Insincere Questions Classification
2604000,0.492,2,3,/jialinzhang/xgboost-quora-question,Quora Insincere Questions Classification
2577105,0.379,1,4,/dmitriyvaletov/log-reg-on-word-tfidf,Quora Insincere Questions Classification
2565136,0.69523,11,81,/mlwhiz/third-place-model-for-toxic-comments-in-pytorch,Quora Insincere Questions Classification
2594361,0.6970000000000001,4,33,/mikexia/kernela488168da3,Quora Insincere Questions Classification
2153236,0.552,0,0,/kathy0603/quora-insincere-questions-classification,Quora Insincere Questions Classification
2466999,0.669,2,9,/n2cholas/preprocessing-lstm-in-tensorflow,Quora Insincere Questions Classification
2496011,0.669,0,0,/strifonov/simple-rnn,Quora Insincere Questions Classification
2582122,0.5760000000000001,0,0,/xsakix/torch-with-validation-and-test,Quora Insincere Questions Classification
2557075,0.6779999999999999,0,0,/xsakix/bilstm-base-classifier-fold-unfreeze-meta-v2,Quora Insincere Questions Classification
2542942,0.6920000000000001,0,2,/zwscuter/pytorch-starter,Quora Insincere Questions Classification
2632150,3.692,0,9,/alajangi/combining-your-model-with-a-model-without-o-0d991a,Elo Merchant Category Recommendation
2613081,3.692,0,8,/asrinivasan16/combining-your-model-with-a-model-without-o-0d991a,Elo Merchant Category Recommendation
2309738,3.798,0,0,/ronniemiller/bgu-dl-assignmnt2,Elo Merchant Category Recommendation
2409202,3.702,21,43,/tunguz/elo-with-h2o-automl,Elo Merchant Category Recommendation
2334218,3.687,22,97,/ashishpatel26/lb-3-687-truncated-mean,Elo Merchant Category Recommendation
8903899,0.5532600000000001,6,12,/vgarshin/m5-catboost,M5 Forecasting - Accuracy
8780873,0.4887399999999999,154,717,/kneroma/m5-first-public-notebook-under-0-50,M5 Forecasting - Accuracy
8743800,0.5102899999999999,48,270,/kneroma/m5-forecast-v2-python,M5 Forecasting - Accuracy
8711917,5.44561,8,9,/samiranand14/m5-forecasting-accuracy,M5 Forecasting - Accuracy
8739643,0.51577,7,24,/servietsky/m5-magic-blending,M5 Forecasting - Accuracy
8640594,0.5443100000000001,24,122,/kyakovlev/m5-dark-magic,M5 Forecasting - Accuracy
8608171,0.54156,2,9,/nxrprime/darker-magic,M5 Forecasting - Accuracy
8674743,1.06406,0,0,/eligeti/quick-look-and-baseline-model-moving-average,M5 Forecasting - Accuracy
8547625,0.60869,11,99,/rohitsingh9990/m5-lgbm-fe,M5 Forecasting - Accuracy
8379698,1.17258,3,1,/hiteshsom/m5-accuracy-feature-engg-lightgbm,M5 Forecasting - Accuracy
8515195,1.07118,5,10,/rohitsingh9990/m5-forecasting-eda-feature-engineering,M5 Forecasting - Accuracy
9438589,0.17,0,1,/nobletp/keras-vgg16-vgg19-inceptionv3-resnet50,Prostate cANcer graDe Assessment (PANDA) Challenge
9490022,0.17,2,3,/indranilbhattacharya/image-stats-submission-trial,Prostate cANcer graDe Assessment (PANDA) Challenge
9153730,0.6,5,11,/kaushal2896/panda-densenet121-tta-inference,Prostate cANcer graDe Assessment (PANDA) Challenge
9173924,0.79,11,156,/iafoss/panda-concat-tile-pooling-starter-inference,Prostate cANcer graDe Assessment (PANDA) Challenge
9159906,0.57,11,34,/yeayates21/panda-densenet-keras-starter-gpu,Prostate cANcer graDe Assessment (PANDA) Challenge
9162025,0.63,0,11,/debanga/pytorchcv-efficientnetb3-baseline,Prostate cANcer graDe Assessment (PANDA) Challenge
9051948,0.34,6,37,/frlemarchand/high-res-samples-into-multi-input-cnn-keras,Prostate cANcer graDe Assessment (PANDA) Challenge
9050123,0.03,42,402,/rohitsingh9990/panda-eda-better-visualization-simple-baseline,Prostate cANcer graDe Assessment (PANDA) Challenge
9069000,0.51,2,14,/greatgamedota/panda-baseline-classifier-5fold-inference,Prostate cANcer graDe Assessment (PANDA) Challenge
9055601,0.64,8,34,/rohitsingh9990/panda-resnext-inference,Prostate cANcer graDe Assessment (PANDA) Challenge
10686495,0.79681,12,25,/friskycodeur/nlp-w-disaster-tweets-explained,Natural Language Processing with Disaster Tweets
7409740,0.8329700000000001,0,0,/mikeleske/disaster-with-bert,Natural Language Processing with Disaster Tweets
10644856,0.83849,2,15,/gordotron85/nlp-text-classification-linear-models-vs-bert,Natural Language Processing with Disaster Tweets
10721032,0.78087,0,0,/hirotakakawachi/kernel1b9f62fb43,Natural Language Processing with Disaster Tweets
10573285,0.53509,5,10,/harshaggarwal7/disaster-tweet-eda-tfidf-bag-of-words,Natural Language Processing with Disaster Tweets
10670092,0.78087,0,0,/kaggluserjp/kernelc5dbd4458c,Natural Language Processing with Disaster Tweets
10592431,0.78455,0,1,/shivleon/real-or-not-analysis,Natural Language Processing with Disaster Tweets
10474150,0.79282,0,0,/sodiumlz/real-or-not-02,Natural Language Processing with Disaster Tweets
10541799,0.7992600000000001,1,4,/shrikantkulkarni/real-or-fake-tweets,Natural Language Processing with Disaster Tweets
10556663,0.8194899999999999,0,0,/baniyamabanio/kernel421bd84c12,Natural Language Processing with Disaster Tweets
10466695,0.81918,0,1,/braddyhe/identify-disaster-tweets,Natural Language Processing with Disaster Tweets
9148670,0.80845,0,2,/rakkaalhazimi/nlp-disaster-classification,Natural Language Processing with Disaster Tweets
10165272,0.8378700000000001,0,6,/shirishsharma/nlp-from-embeddings-and-rnns-to-bert,Natural Language Processing with Disaster Tweets
10397900,0.7919,0,3,/onurakkse/a-simple-tutorial,Natural Language Processing with Disaster Tweets
10362714,0.77689,0,0,/yuritakaki/nlp-01,Natural Language Processing with Disaster Tweets
1435038,0.516,0,0,/maezono/recruit-challenge,Recruit Restaurant Visitor Forecasting
5712669,0.7240000000000001,0,0,/rpeer333/aptos-pre-processing-and-pytorch-efficientnet,APTOS 2019 Blindness Detection
5607294,0.142,0,3,/vinayak123tyagi/blindness-detection,APTOS 2019 Blindness Detection
5697153,0.8029999999999999,0,1,/fanconic/blending-kernel-top3-model-2-public-gm,APTOS 2019 Blindness Detection
5214059,0.7829999999999999,1,2,/jtbontinck/cnn-xgb-end-to-end-1-54,APTOS 2019 Blindness Detection
5540502,0.762,0,0,/mayank17/efficientnet-old-data,APTOS 2019 Blindness Detection
5703482,0.647,0,3,/shujunge/aptos-2019-blindness-detection,APTOS 2019 Blindness Detection
5233592,0.7120000000000001,0,1,/chrisfs/vgg19-bn-base-model,APTOS 2019 Blindness Detection
5606493,0.623,0,0,/fliske/resnet50-on-aptos,APTOS 2019 Blindness Detection
5431919,0.7979999999999999,6,51,/xwxw2929/starter-kernel-for-0-79,APTOS 2019 Blindness Detection
4588271,0.715,0,0,/sbolaris/keras-blindness-detector,APTOS 2019 Blindness Detection
5613377,0.715,0,0,/bhargav5040/aptos-fastai-0p713,APTOS 2019 Blindness Detection
5587929,0.7490000000000001,0,3,/phantomakame/efficient-net-3-models-keras-radam-tta,APTOS 2019 Blindness Detection
5008696,0.762,0,0,/mbolaris/keras-efficientnet-submitter,APTOS 2019 Blindness Detection
5560169,0.772,0,9,/supportvectordevin/fast-ai-starter-with-resnet-50,APTOS 2019 Blindness Detection
5576974,0.762,0,2,/rajnishe/efficenet-b5,APTOS 2019 Blindness Detection
5510349,0.511,0,3,/drcapa/aptos-2019-blindness-image-engineering-vgg19,APTOS 2019 Blindness Detection
5554813,0.25,1,3,/morganzhou/image-clean-and-randomforestclassifier,APTOS 2019 Blindness Detection
5440759,0.151,0,1,/kakirastern/kstern-aptos-2019,APTOS 2019 Blindness Detection
5438653,0.767,6,20,/chopinforest/efficientnetb4-fastai-blindness-detection,APTOS 2019 Blindness Detection
5429710,0.733,0,5,/kunal281187/aptos-fastai-resnet152,APTOS 2019 Blindness Detection
5094271,0.713,3,12,/sarthakbatra/blindness-detection-fastai,APTOS 2019 Blindness Detection
7093657,0.05685,1,1,/nmsf1916036/nmsf1916036-pubg-prediction,PUBG Finish Placement Prediction (Kernels Only)
7243125,0.0445,0,1,/marksmirnov/kernel1310d6c120,PUBG Finish Placement Prediction (Kernels Only)
7494984,0.0565,0,0,/renguowei/kernel17ecb74d2b,PUBG Finish Placement Prediction (Kernels Only)
7508323,0.05705,0,0,/menlonbun/kernel48abeb4dc7,PUBG Finish Placement Prediction (Kernels Only)
7520616,0.0564299999999999,0,0,/nmst1916003/kernel4ffe78f965,PUBG Finish Placement Prediction (Kernels Only)
7451737,0.04451,0,2,/wangsky/kernel7d8b594486,PUBG Finish Placement Prediction (Kernels Only)
6962712,0.05969,0,0,/nik7even/simple-eda,PUBG Finish Placement Prediction (Kernels Only)
5956786,0.0588,0,2,/nitishgupta0/pubg-rank,PUBG Finish Placement Prediction (Kernels Only)
5789085,0.05642,0,0,/rashmiranjan00/pubg-placement-prediction,PUBG Finish Placement Prediction (Kernels Only)
5201776,0.08186,0,1,/mahendrabishnoi2/pubg-placement-prediction,PUBG Finish Placement Prediction (Kernels Only)
4972012,0.1613099999999999,0,0,/sunamyagupta/kernelebb39f78c3,PUBG Finish Placement Prediction (Kernels Only)
4519368,0.01978,0,1,/bilalahmed5/pubg12,PUBG Finish Placement Prediction (Kernels Only)
4259098,0.05984,0,0,/akumaldo/pubg-prediction-lgb-model,PUBG Finish Placement Prediction (Kernels Only)
3625975,0.0595799999999999,0,0,/mahajanhm4/randomforestsimple,PUBG Finish Placement Prediction (Kernels Only)
3905217,0.06302,0,0,/reijerknol/pubg-data,PUBG Finish Placement Prediction (Kernels Only)
3775593,0.07649,0,0,/hrush777/pubg-nn,PUBG Finish Placement Prediction (Kernels Only)
3471021,0.02005,0,0,/tanyeejet/pubg-finish-placement-prediction-ensemble,PUBG Finish Placement Prediction (Kernels Only)
3514706,0.17548,0,1,/mikulskibartosz/poz-pubg-competition,PUBG Finish Placement Prediction (Kernels Only)
1855886,0.0363,0,0,/dnik007/fork-of-chicken-dinner-lgbmregression,PUBG Finish Placement Prediction (Kernels Only)
3451245,0.2838,1,5,/scsaurabh/pubg-eda,PUBG Finish Placement Prediction (Kernels Only)
3423659,0.02026,0,1,/fajb420/kernelcbc17636ff,PUBG Finish Placement Prediction (Kernels Only)
2222399,0.0475,0,0,/mattburt07/pubg-data-analysis-xgboost,PUBG Finish Placement Prediction (Kernels Only)
3220809,0.2671699999999999,3,1,/jd81197/pubg-kernel,PUBG Finish Placement Prediction (Kernels Only)
666110,0.0,11,51,/gpreda/google-landmark-recogn-challenge-data-exploration,Google Landmark Recognition Challenge
6230884,0.95338,2,19,/kawakeee/feature-engineering-xgboost,Predict Future Sales
5584725,0.899,1,12,/zengyaner/predict-future-sales-2-0,Predict Future Sales
4413460,1.16658,0,0,/tushar97/future-sales-using-lstm,Predict Future Sales
12456053,0.723,0,2,/kotarosatoh/riiid-lightgbm-model-ver1,Riiid Answer Correctness Prediction
12459039,0.754,3,30,/johannesbruch/focus-on-important-features,Riiid Answer Correctness Prediction
12374753,0.71,39,206,/andradaolteanu/answer-correctness-rapids-xgb-lgbm,Riiid Answer Correctness Prediction
12490769,0.6559999999999999,0,1,/huangheqing/notebookfd4c3c016e,Riiid Answer Correctness Prediction
12259184,0.754,21,44,/dwit392/lgbm-iii,Riiid Answer Correctness Prediction
12406877,0.5,0,1,/saijasthi/notebook-12406877,Riiid Answer Correctness Prediction
12326481,0.74,13,118,/rohanrao/riiid-ftrl-ftw,Riiid Answer Correctness Prediction
12337739,0.753,2,9,/code1110/riiid-lgb-inference-from-pretrained-model,Riiid Answer Correctness Prediction
12340105,0.748,1,6,/ulrich07/riiid-keras-nnet-on-full-dataset-inference,Riiid Answer Correctness Prediction
12245922,0.5,0,33,/yihdarshieh/riiid-verifying-private-test-dataset-properties,Riiid Answer Correctness Prediction
12245559,0.748,2,6,/lgreig/riiid-pydatatable-starter,Riiid Answer Correctness Prediction
12226108,0.752,3,15,/salimkhazem/tuned-lgbm,Riiid Answer Correctness Prediction
12180190,0.703,0,2,/yutohisamatsu/riiid-eda-baseline,Riiid Answer Correctness Prediction
12214766,0.75,4,24,/ilialar/riiid-5-folds-double-validation,Riiid Answer Correctness Prediction
12216765,0.6890000000000001,3,5,/samihadouaj/simple-model-for-begginers,Riiid Answer Correctness Prediction
12168955,0.753,24,134,/dwit392/expanding-on-simple-lgbm,Riiid Answer Correctness Prediction
10492694,0.933,0,1,/akashsuper2000/incredible-tpus-finetune-effnetb0-b6-at-once,SIIM-ISIC Melanoma Classification
10458076,0.9226,0,0,/akashsuper2000/melanoma-detection-enet-on-tpus,SIIM-ISIC Melanoma Classification
10615154,0.928,0,0,/andyden/resize-and-ensemble,SIIM-ISIC Melanoma Classification
10508569,0.941,3,38,/ragnar123/ligthgbm-stack-groupkfold,SIIM-ISIC Melanoma Classification
9760768,0.906,0,0,/akashsuper2000/siim-isic-multiple-model-training-inference,SIIM-ISIC Melanoma Classification
9823449,0.926,4,15,/volcanoflash/melanoma-siim-isic-2020-fast-ai-efficientnetb0,SIIM-ISIC Melanoma Classification
10487342,0.857,1,6,/mushaya/melanoma-class-2,SIIM-ISIC Melanoma Classification
10425935,0.866,7,39,/dimitreoliveira/melanoma-classification-shap-model-explained,SIIM-ISIC Melanoma Classification
10370435,0.8220000000000001,2,11,/adityapatil673/neural-network-for-dummies-pytorch-melanoma,SIIM-ISIC Melanoma Classification
8099112,0.8876799999999999,1,2,/darwinwin/automl-easy-with-h2o-santander-transaction-v3,Santander Customer Transaction Prediction
3523830,0.653,0,0,/offirinbar/santander-kaggle,Santander Customer Transaction Prediction
6600263,0.6622100000000001,0,0,/kavinder31phogat/kernel39443b1018,Santander Customer Transaction Prediction
6261856,0.8977,0,0,/lagostra/cnn-2,Santander Customer Transaction Prediction
2981223,0.898,0,0,/neelambuj717/santander-basic-model-score-0-898,Santander Customer Transaction Prediction
6095063,0.8502,0,1,/ivanpbf/red-neuronal-keras,Santander Customer Transaction Prediction
5755945,0.7769,0,1,/marlonferrari/competition-santander-transactions,Santander Customer Transaction Prediction
5524176,0.52181,0,2,/arvindkt/santander-customer-transaction-prediction,Santander Customer Transaction Prediction
3462416,0.888,0,0,/satyamsanu/santander-customer-transaction,Santander Customer Transaction Prediction
3400231,0.84,0,0,/lopost/santander-nn-v2,Santander Customer Transaction Prediction
4558496,0.76889,0,0,/liux3021/santander-ml,Santander Customer Transaction Prediction
4274350,0.64836,0,4,/geochatz/santander-transactions-classification-with-pca,Santander Customer Transaction Prediction
4457975,0.76648,0,0,/fullomen/a-simple-bayesian-inference-approach,Santander Customer Transaction Prediction
5455920,0.9996,0,0,/faizu07/aerial-cactus-identifier,Aerial Cactus Identification
4722040,0.9906,0,0,/flowy98/cactus-cnn,Aerial Cactus Identification
4321941,0.9935,0,0,/parvez218/pytorchdensenet-539843,Aerial Cactus Identification
4108379,1.0,0,0,/akashdeepjassal/solving-aerial-cactus-challenge-using-fast-ai,Aerial Cactus Identification
3385601,0.9999,0,0,/asura93/simple-cnn-keras,Aerial Cactus Identification
1469586,9.30446,7,5,/ottpeterr/what-s-a-ride-cost-2-of-2-neural-net,New York City Taxi Fare Prediction
1590565,3.13751,15,102,/willkoehrsen/a-walkthrough-and-a-challenge,New York City Taxi Fare Prediction
1441105,3.04742,12,20,/amar09/fare-prediction-stacked-ensemble-xgboost-lgbm,New York City Taxi Fare Prediction
1476169,3.04651,1,0,/morningbitefan/taxi-fare-prediction,New York City Taxi Fare Prediction
1477050,4.05769,0,5,/pragyanbo/finding-the-fare-of-taxi-rides,New York City Taxi Fare Prediction
1354347,5.74184,0,1,/talismanic/nyc-taxi-fare-starter-kernel-mehedi-s-model,New York City Taxi Fare Prediction
1347209,5.51414,0,3,/jamebondnguyen/simple-nyc-taxi-fare-starter,New York City Taxi Fare Prediction
1368012,3.64145,1,13,/breemen/nyc-taxi-fare-evaluating-models,New York City Taxi Fare Prediction
1362527,3.03992,16,49,/gunbl4d3/xgboost-ing-taxi-fares,New York City Taxi Fare Prediction
1360145,6.85141,0,0,/nebilekodaz/xgbregressor-for-nyc-taxi-by-a-beginner,New York City Taxi Fare Prediction
1352786,8.39274,0,0,/mohitkhanna/get-the-cost-of-your-next-ride-in-new-york,New York City Taxi Fare Prediction
1355063,3.7603800000000014,0,4,/akshayb7/nyc-cab-fare-prediction-fastai-rf,New York City Taxi Fare Prediction
1345626,3.51162,0,8,/donniedarko/deep-haversine,New York City Taxi Fare Prediction
1345978,3.3393900000000003,0,0,/junchengzhou/ny-city-taxi-fare-prediction,New York City Taxi Fare Prediction
14442918,3.20608,0,3,/marinovik/xgboost-new-york-taxi-fares-prediction,New York City Taxi Fare Prediction
14124715,2085.39277,0,0,/subhamsagarpaira/taxi-fair-prediction,New York City Taxi Fare Prediction
13944137,5.57615,0,0,/sanskrutighadipatil/taxi-fare-f,New York City Taxi Fare Prediction
12336826,3.5664300000000004,0,0,/achyar/regressor-stacking-pipeline,New York City Taxi Fare Prediction
12070356,2.92883,1,4,/deepdivelm/nyc-taxi-fares-eda-modelling-2-93,New York City Taxi Fare Prediction
11839301,5.7445900000000005,0,2,/sudhirmundhra/nyc-taxi-fare-starter-kernel-simple-linear-model,New York City Taxi Fare Prediction
10645935,5.57587,0,1,/julianbenny/nyc-taxi-fare-linearregression,New York City Taxi Fare Prediction
10626351,5.45383,0,0,/teramera/nyc-taxi-fare-starter-kernel-simple-linear-model,New York City Taxi Fare Prediction
4811061,4.19455,0,0,/dhruvgupta2801/taxi-price-prediction,New York City Taxi Fare Prediction
7606141,4.59286,0,0,/asya666/kernel7a7ad51b1e,New York City Taxi Fare Prediction
6752871,3.32161,0,8,/ravijoe/data-cleaning-eda-modelling,New York City Taxi Fare Prediction
5599154,3.46767,0,1,/omarelejla/newyyork-taxi-fare-xgboosting-by-omar,New York City Taxi Fare Prediction
5418567,5.71362,0,0,/brij1823/second-attempt,New York City Taxi Fare Prediction
5431716,3.3967400000000003,1,4,/jaylaksh94/ensemble-model,New York City Taxi Fare Prediction
4930911,3.40466,0,1,/mitramishra93/taxifareprediction,New York City Taxi Fare Prediction
1416932,3.3612300000000004,0,0,/erncyp/nyc-taxi-random-forests,New York City Taxi Fare Prediction
4117929,5.8395800000000015,0,0,/ma7555/simple-linear-regression-model,New York City Taxi Fare Prediction
14243816,0.15077,3,2,/bhaskar714/house-sale-price-prediction-using-xgregressor,House Prices - Advanced Regression Techniques
13138245,0.74949,2,2,/onurserbetci/end-to-end-project-automated-eda-f-engineering,House Prices - Advanced Regression Techniques
14274616,0.15313,0,0,/rat360/notebook76a585504b,House Prices - Advanced Regression Techniques
14134561,0.24514,0,1,/tracyporter/house-prices-adaboostregressor,House Prices - Advanced Regression Techniques
13383352,0.14468,3,8,/javierorman/housing-eda-feat-selection-pipeline-models,House Prices - Advanced Regression Techniques
14044448,0.13605,35,32,/thiagopanini/pycomp-exploring-and-modeling-housing-prices,House Prices - Advanced Regression Techniques
13973893,0.13772,3,12,/blackhurt/my-approach-to-be-in-top-40,House Prices - Advanced Regression Techniques
13981911,0.19935,0,4,/klmsathishkumar/house-price-prediction,House Prices - Advanced Regression Techniques
13987270,0.80703,0,0,/sanskrutighadipatil/saleprice,House Prices - Advanced Regression Techniques
13773765,0.11968,0,0,/nishantdeshmukh/regression-a-43,House Prices - Advanced Regression Techniques
13841903,9.14137,1,3,/renzophellan/initial-steps-neural-network,House Prices - Advanced Regression Techniques
13949471,0.107,0,5,/user123454321/fasterrcnn-starter,VinBigData Chest X-ray Abnormalities Detection
13908573,0.052,12,26,/sakuraandblackcat/chest-x-ray-knowledges-for-the-14-abnormalities,VinBigData Chest X-ray Abnormalities Detection
13920297,0.052,1,13,/digvijayyadav/exploring-vinbigdata-groupkfold,VinBigData Chest X-ray Abnormalities Detection
9930403,0.894,36,143,/ibtesama/melanoma-classification-with-attention,SIIM-ISIC Melanoma Classification
9988408,0.8462,1,7,/aditya23071991/melanoma-classification-pytorch,SIIM-ISIC Melanoma Classification
9950462,0.79,2,9,/zzy990106/lgb-meta-data-image-size,SIIM-ISIC Melanoma Classification
9924185,0.86,0,4,/soumikrakshit/efficientnet-b6-with-tensorflow,SIIM-ISIC Melanoma Classification
9902625,0.927,9,113,/shonenkov/inference-single-model-melanoma-starter,SIIM-ISIC Melanoma Classification
9940633,0.773,0,3,/tunguz/melanoma-svc-baseline-32x32,SIIM-ISIC Melanoma Classification
9935834,0.877,0,1,/mohamedtayser/siim-isic-melanoma-classification,SIIM-ISIC Melanoma Classification
9828665,0.893,39,204,/abhishek/melanoma-detection-with-pytorch,SIIM-ISIC Melanoma Classification
9801544,0.908,22,83,/reighns/groupkfold-and-stratified-groupkfold-efficientnet,SIIM-ISIC Melanoma Classification
9826696,0.8637,2,13,/truonghoang/fork-of-siim-isic-melanoma-starter,SIIM-ISIC Melanoma Classification
9784612,0.872,2,9,/aviralpamecha/efficientnetb7-eda-data-processing-training,SIIM-ISIC Melanoma Classification
9747168,0.845,10,34,/tarunpaparaju/siim-isic-melanoma-eda-pytorch-baseline,SIIM-ISIC Melanoma Classification
9756012,0.828,15,102,/ibtesama/siim-baseline-keras-vgg16,SIIM-ISIC Melanoma Classification
9806476,0.728,0,8,/phoenix9032/siim-melanomas-pytorch-simple-multi-input-method,SIIM-ISIC Melanoma Classification
9785018,0.866,0,11,/ajaykumar7778/inceptionresnetv2-tpu,SIIM-ISIC Melanoma Classification
9750707,0.884,7,65,/yasufuminakama/tpu-siim-isic-efficientnetb3-inference,SIIM-ISIC Melanoma Classification
13091449,0.626,0,0,/niaibrahim/karas-nn-epoch-30-data-20-and-lr-0001,Riiid Answer Correctness Prediction
12923704,0.593,0,0,/niaibrahim/fork-of-basic-karas-nn-made-epoch-small-for-cdfda5,Riiid Answer Correctness Prediction
12728111,0.5379999999999999,0,0,/saijasthi/basic-karas-nn-made-epoch-small-for-save-074eb7,Riiid Answer Correctness Prediction
12583160,0.7509999999999999,0,0,/saijasthi/baseline,Riiid Answer Correctness Prediction
12425324,0.5579999999999999,0,0,/niaibrahim/notebook-12406877,Riiid Answer Correctness Prediction
12115301,0.728,76,411,/isaienkov/riiid-answer-correctness-prediction-eda-modeling,Riiid Answer Correctness Prediction
9128353,1.09545,0,0,/urayukitaka/lgbm-time-series-prediction,Predict Future Sales
89658,0.929809,0,0,/goelhardik/keras-starter,Predicting Red Hat Business Value
14310493,0.679537,0,0,/yizhoumaxzhang/practicelearning-yzhang,APTOS 2019 Blindness Detection
5714678,0.7829999999999999,0,1,/ramjib/aptos-blindness-detection,APTOS 2019 Blindness Detection
13496334,0.806107,0,1,/robert780612/deeplearner,APTOS 2019 Blindness Detection
13336875,0.833723,0,1,/bohau0856095/notebooka4cf7f3c4d,APTOS 2019 Blindness Detection
13296055,0.063777,0,1,/wmloh98/notebooka0eeed4bad,APTOS 2019 Blindness Detection
6929005,0.796612,0,0,/ctrasd123/kernel5915277570,APTOS 2019 Blindness Detection
10242425,0.6669039999999999,0,0,/shidqiet/non-linear-model,APTOS 2019 Blindness Detection
5261910,0.208,0,0,/govindasawa/dataaug,APTOS 2019 Blindness Detection
5023864,0.629,0,0,/sarkarpranab66/drishti-daan-keras-master,APTOS 2019 Blindness Detection
4736435,0.7340000000000001,1,2,/itsmuriuki/aptos-blindness-detection-2019-fastai-v1-0,APTOS 2019 Blindness Detection
13055801,0.613,11,31,/slawekbiel/minimal-fastai-solution-score-0-61,Rainforest Connection Species Audio Detection
12996463,0.307,0,8,/riadalmadani/keras-model-with-fft-features,Rainforest Connection Species Audio Detection
12944534,0.525,12,83,/titericz/0-525-tabular-xgboost-gpu-fft-gpu-cuml-fast,Rainforest Connection Species Audio Detection
12944466,0.447,6,60,/tunguz/rainforest-rapids-baseline,Rainforest Connection Species Audio Detection
12941700,0.309,0,51,/titericz/0-309-baseline-logisticregression-using-fft,Rainforest Connection Species Audio Detection
12942669,0.236,1,13,/rohitsingh9990/baseline-submission,Rainforest Connection Species Audio Detection
124077,0.03601,1,0,/tobikaggle/fork-of-nn-through-keras-copied-mod,Leaf Classification
11650463,0.87525,0,3,/spears27/panda-final-inference,Prostate cANcer graDe Assessment (PANDA) Challenge
11466498,0.67418,0,1,/spears27/prostate-cancer-efnetb3-fastai-custom-datablock,Prostate cANcer graDe Assessment (PANDA) Challenge
11638109,0.87849,0,0,/aathiraks/exp5-inference-notebook-d766a4,Prostate cANcer graDe Assessment (PANDA) Challenge
10336988,0.91435,0,0,/iafoss/panda-128-tiles,Prostate cANcer graDe Assessment (PANDA) Challenge
10782848,0.921,0,4,/rvslight/mean-ensemble-asnell-and-shujun-by-jjshadow,Prostate cANcer graDe Assessment (PANDA) Challenge
10501444,0.87,1,4,/c7934597/prostate-cancer-grade-inference-panda,Prostate cANcer graDe Assessment (PANDA) Challenge
10808081,0.8881100000000001,0,6,/kyunghoonhur/efficientnet-b0-b1-1-1-ensemble-0-92664,Prostate cANcer graDe Assessment (PANDA) Challenge
10289924,0.8889100000000001,0,2,/vainof/panda-inference-w-36-tiles-256,Prostate cANcer graDe Assessment (PANDA) Challenge
10837921,0.89095,0,2,/coreacasa/12th-place-solution-quick-save-inference,Prostate cANcer graDe Assessment (PANDA) Challenge
10810118,0.8759999999999999,9,23,/rsinda/panda-inference-efficientnet-b1,Prostate cANcer graDe Assessment (PANDA) Challenge
10818268,0.8759999999999999,0,1,/utkarshtripathi/panda-inference-efficientnet-b1,Prostate cANcer graDe Assessment (PANDA) Challenge
10821935,0.8759999999999999,0,0,/koukou1/prostate-cancer,Prostate cANcer graDe Assessment (PANDA) Challenge
10778977,0.875,0,16,/vineeth1999/prostate-cancer-grade-assessment-panda-challenge,Prostate cANcer graDe Assessment (PANDA) Challenge
10437185,0.32,0,4,/timsthebomb/fastaiv2-resnet34-dynamic-unet,Prostate cANcer graDe Assessment (PANDA) Challenge
10547089,0.05,0,1,/timsthebomb/test-submission,Prostate cANcer graDe Assessment (PANDA) Challenge
10486240,0.87,6,21,/mahmudds/prostate-cancer-grade-assessment-panda-challenge,Prostate cANcer graDe Assessment (PANDA) Challenge
9806113,0.4816,0,0,/gabzchua/m5-accuracy-poisson-tweedie,M5 Forecasting - Accuracy
8909595,1.29713,0,0,/harupy/predict-day-by-day,M5 Forecasting - Accuracy
8316990,0.68577,0,0,/chariots17/ensemble-starter,M5 Forecasting - Accuracy
3279820,0.67984,0,4,/andrelmfarias/bi-gru-with-self-attention-and-statistical-feat,Quora Insincere Questions Classification
3305501,0.52154,0,0,/krithika11/quora-questions-classification,Quora Insincere Questions Classification
2540174,0.34,0,1,/sreekarchidurala/text-classification-ml,Quora Insincere Questions Classification
3265862,0.6079100000000001,0,3,/andrelmfarias/simple-1-layer-gru,Quora Insincere Questions Classification
3240560,0.64522,0,0,/statseon/gensim-final,Quora Insincere Questions Classification
2829516,0.652,0,0,/adityajitta/pytorch-bilstm-selfattention-es10,Quora Insincere Questions Classification
2360909,0.675,0,1,/hsankesara/han-model-experiments,Quora Insincere Questions Classification
2746907,0.337,0,0,/hariprasathkag/quora-insincere-questions,Quora Insincere Questions Classification
2985458,0.54059,0,1,/sameerdev7/quora-classification-cnn-lstm,Quora Insincere Questions Classification
2438358,0.6519199999999999,0,0,/ayush07/quora-insincere-questions-classifier-using-rnns,Quora Insincere Questions Classification
2953736,0.7086600000000001,8,61,/jiangm/5th-place-solution,Quora Insincere Questions Classification
2942733,0.69958,0,21,/xiaobai1123q/15th-place-solution,Quora Insincere Questions Classification
2356456,0.70026,0,1,/shaz13/deterministic-neural-networks-using-pytorch-re,Quora Insincere Questions Classification
2764313,0.76267,0,0,/waseemkhalifa/what-s-cooking-simple-pandas-keras-ann-approach,What's Cooking? (Kernels Only)
1386767,0.80661,0,0,/vijaykris/prediction-using-svm-rbf-grid-search,What's Cooking? (Kernels Only)
1291251,0.67497,0,0,/adityajn105/whats-cooking-countvectorizer-tfidf-and-xgboost,What's Cooking? (Kernels Only)
8946881,0.04638,0,0,/dmitri9149/kernel6e5cd78771-logistic,COVID19 Global Forecasting (Week 4)
8949081,0.0364,0,0,/jiashun/week4mitigation,COVID19 Global Forecasting (Week 4)
8884681,0.08485,0,0,/pritha21/covid-19-week-4,COVID19 Global Forecasting (Week 4)
8844837,0.22735,0,2,/zhengli0817/lroger-covid19-global-forecasting-week-4-lgbm,COVID19 Global Forecasting (Week 4)
8936151,0.03639,1,1,/manojankk/xgboost-with-tuning,COVID19 Global Forecasting (Week 4)
8966307,0.0364,0,0,/henrychinaski/kernel5395dd0f14,COVID19 Global Forecasting (Week 4)
8850071,0.35763,0,0,/anirudhhatiwari/ml-sp20-anirudhhamahant-wk4,COVID19 Global Forecasting (Week 4)
8937445,0.1522099999999999,0,0,/rushisavulge/stay-safe,COVID19 Global Forecasting (Week 4)
8947906,0.03391,0,0,/drcyle/kernel209fec2b31,COVID19 Global Forecasting (Week 4)
8895998,0.43899,0,0,/williamsabodunrin/kernel6c0cb2513b,COVID19 Global Forecasting (Week 4)
8919450,0.128,4,7,/manovirat/covid-19-analysis-timeseriesprediction,COVID19 Global Forecasting (Week 4)
8910937,0.72825,0,1,/gluzman/covid-global-forecast-sir-model-ml-regressions,COVID19 Global Forecasting (Week 4)
8930779,0.07827,0,1,/mikeberhane9/kernel35153fd214,COVID19 Global Forecasting (Week 4)
8932683,0.95282,0,0,/pstarszyk/week4sub,COVID19 Global Forecasting (Week 4)
8946719,3.24121,0,0,/rubben/kernel269a6b06e6,COVID19 Global Forecasting (Week 4)
8937596,0.06311,0,0,/akshitsharma206/covid-19-week-4-another-attempt,COVID19 Global Forecasting (Week 4)
8928786,6.583310000000001,0,0,/samarendra109/kernel30b955016d,COVID19 Global Forecasting (Week 4)
8951201,0.11141,0,0,/therealroman/kernel496d42ca7-4,COVID19 Global Forecasting (Week 4)
8926910,0.74766,0,1,/gluzman/covid19-explained-through-visualizations,COVID19 Global Forecasting (Week 4)
8881863,0.4105,2,2,/bitsnpieces/covid19-forecast-wk4-lightgbm,COVID19 Global Forecasting (Week 4)
8950056,0.03507,0,0,/petersorensen360/kernel5515af9c10,COVID19 Global Forecasting (Week 4)
8938024,0.03639,0,0,/milantripathi/covid19-forecastingfinetuningpycarat,COVID19 Global Forecasting (Week 4)
8900842,0.03438,0,0,/afshiin/covid-19-forecasting-using-ensemble-learning,COVID19 Global Forecasting (Week 4)
8857484,0.41778,0,0,/bernhardklinger/kernel352b1d581a,COVID19 Global Forecasting (Week 4)
8911326,0.0788,0,2,/bisharp/first-try-just-learning,COVID19 Global Forecasting (Week 4)
8914040,0.108,0,1,/akshitsharma206/covid-19-week-4-xgboost,COVID19 Global Forecasting (Week 4)
8838500,0.03362,12,28,/aestheteaman01/covtan-covid-19-timeseries-analysis-notebook,COVID19 Global Forecasting (Week 4)
8909598,0.03907,0,0,/binhlc/sars-cov-2-week-4,COVID19 Global Forecasting (Week 4)
8896038,2.4312400000000003,0,0,/menglu/covid19week4,COVID19 Global Forecasting (Week 4)
8950200,3.2859199999999995,0,0,/jesucristo/w4-lgb-mad,COVID19 Global Forecasting (Week 4)
8899155,0.40901,2,2,/ekzemplaro/covid19-week04-apr15,COVID19 Global Forecasting (Week 4)
8932786,0.07322,0,0,/xscripter/kerneld70131c818,COVID19 Global Forecasting (Week 4)
8951230,0.03864,0,0,/mystery/kernel71d51fae35,COVID19 Global Forecasting (Week 4)
8894358,0.32228,0,2,/ffares/less-than-0-3-using-one-single-feature,COVID19 Global Forecasting (Week 4)
8949221,0.13882,0,0,/buitri91/kernel3b4f6b7d47,COVID19 Global Forecasting (Week 4)
8875937,0.03734,15,22,/mielek/covid19-forecasting-xgboost,COVID19 Global Forecasting (Week 4)
8913821,0.7629199999999999,0,0,/kinkpunk/my-forecasting-covid19-ver-3-week-4,COVID19 Global Forecasting (Week 4)
8902906,0.5403899999999999,0,0,/chandelmannu7/kernel493b5dcc7e,COVID19 Global Forecasting (Week 4)
8880752,0.06104,0,0,/cseamaoo/i-hope-these-predicts-are-wrong,COVID19 Global Forecasting (Week 4)
8880180,4.2394,0,1,/akshitsharma206/covid-19-week-4-eda,COVID19 Global Forecasting (Week 4)
8834175,0.01135,65,155,/soham1024/covid-19-india-visualization-forecasting,COVID19 Global Forecasting (Week 4)
8874412,0.03358,0,0,/kjm1559/predict-gru-attention-4-week,COVID19 Global Forecasting (Week 4)
8882596,1.91047,0,0,/sagihaider/eda-prediction,COVID19 Global Forecasting (Week 4)
8867294,0.03639,4,8,/khotijahs1/covid19-global-forecasting-week-4,COVID19 Global Forecasting (Week 4)
8875137,0.45337,0,1,/gabrielmilan/baselining-w-week-2-notebook,COVID19 Global Forecasting (Week 4)
8836230,0.91802,0,1,/begehr/covid19-week4-sir-model-leocorona,COVID19 Global Forecasting (Week 4)
8878269,3.78277,0,0,/nishantpatyal/covid-19-with-the-help-of-gbclassifier,COVID19 Global Forecasting (Week 4)
8871927,0.10035,1,1,/kumarandatascientist/covid19-observations-and-forecasting,COVID19 Global Forecasting (Week 4)
8835600,0.04633,28,41,/madz2000/simple-covid19-week-4-prediction-with-xgbregressor,COVID19 Global Forecasting (Week 4)
8859913,0.03907,0,4,/akashsuper2000/sarimax-model-for-week-4,COVID19 Global Forecasting (Week 4)
8912300,2.42841,0,0,/shakshisharma/kernel7ac1e9133f,COVID19 Global Forecasting (Week 4)
8845452,0.4201399999999999,0,2,/gabrielmilan/baselining-w-week-3-notebook,COVID19 Global Forecasting (Week 4)
8852907,0.45216,2,4,/casras/covid19-week4-exponential-curve-fit,COVID19 Global Forecasting (Week 4)
1616805,0.6791,0,0,/paraplu/plain-one-hot-encoding-and-random-forest,What's Cooking? (Kernels Only)
1492982,0.77031,0,0,/timothycwillard/tuning-a-ridge-classifier,What's Cooking? (Kernels Only)
1563512,0.82491,5,24,/rejasupotaro/let-s-cook-model,What's Cooking? (Kernels Only)
1555800,0.71872,0,0,/uds5501/hey-girls-what-s-cookin,What's Cooking? (Kernels Only)
1543892,0.19267,0,0,/nuthanreddy/first-submission,What's Cooking? (Kernels Only)
1533372,0.77695,1,6,/gcmartinelli/cooking-with-neural-nets,What's Cooking? (Kernels Only)
1471264,0.80611,0,10,/sathyz/simple-nn-approach-kfold,What's Cooking? (Kernels Only)
1495821,0.77996,0,0,/janeyb/what-s-cooking-kernel-logistic-regression,What's Cooking? (Kernels Only)
1455965,0.66401,0,0,/jnsmorinigo/linearsvc-fcyt,What's Cooking? (Kernels Only)
1397607,0.68503,0,1,/aalchemist/tfidr-bayesian-optimization,What's Cooking? (Kernels Only)
1394728,0.81868,2,8,/zdeutsch/recipes-basic-visualizations-ml-models,What's Cooking? (Kernels Only)
1375701,0.77493,0,0,/plarmuseau/embed-logistic,What's Cooking? (Kernels Only)
1383381,0.37801,0,2,/suyashgulati/n-grams-of-cnn-whats-cooking,What's Cooking? (Kernels Only)
1379876,0.78268,0,0,/djsaunde/what-s-cooking-logistic-regression-on-count-data,What's Cooking? (Kernels Only)
1307552,0.79032,0,0,/skooch/one-vs-all-extra-trees,What's Cooking? (Kernels Only)
1334415,0.81999,0,5,/saifadeeb/what-s-cooking-kernel,What's Cooking? (Kernels Only)
1318821,0.81134,0,1,/dzkaggle/re-lemmatize-tfidf-svm-pretty-good-score,What's Cooking? (Kernels Only)
1304753,0.7753399999999999,0,3,/pkarkh/tfidf-peethon,What's Cooking? (Kernels Only)
1278112,0.7974600000000001,0,0,/mehulgupta2016154/groupdclassifiers,What's Cooking? (Kernels Only)
1290055,0.64018,0,0,/plarmuseau/cosinesimilarity,What's Cooking? (Kernels Only)
1298837,0.7867,0,0,/ibraheemmoosa/logistic-regression,What's Cooking? (Kernels Only)
1274525,0.79555,0,1,/ruthwikmasina/a-ratatouille-receipe,What's Cooking? (Kernels Only)
1259630,0.7900200000000001,0,13,/tejaeduc/whats-cooking-neural-nets-log-reg-svc,What's Cooking? (Kernels Only)
1242591,0.78117,1,1,/alfonsogarcia/nn-aproximation,What's Cooking? (Kernels Only)
1230776,0.78761,0,5,/paultimothymooney/predict-cuisine-type-from-recipe-ingredients,What's Cooking? (Kernels Only)
1193438,0.78509,9,38,/nicapotato/this-model-is-bland-simple-logistic-starter,What's Cooking? (Kernels Only)
7238576,1.03583,0,0,/philboaz/predict-sales-price-using-xgboost-the-best,Predict Future Sales
4800169,1.03583,0,0,/taka0830/predict-sales-price,Predict Future Sales
2840695,0.705,1,7,/mchahhou/fork-of-quora-lb-no-cv-2,Quora Insincere Questions Classification
2826540,0.695,0,9,/leighplt/glove-wiki-gnews-full-set,Quora Insincere Questions Classification
2919747,0.6943199999999999,2,7,/mlwhiz/third-place-model-for-toxic-spatial-dropout,Quora Insincere Questions Classification
2802695,0.672,0,0,/infoabhitech/quora-submission-sequential-nn-tune-word-embed,Quora Insincere Questions Classification
2775220,0.66,0,0,/shubhamkanodia/quora-diff-embeddings,Quora Insincere Questions Classification
2792536,0.7020000000000001,2,2,/econdata/quora-insincere-questions-sorting,Quora Insincere Questions Classification
2725247,0.669,0,0,/ericma/lstm-embeddings,Quora Insincere Questions Classification
2781130,0.7,0,1,/johnkyon/fork-of-gamma-bianli-feature-1-1-i-16,Quora Insincere Questions Classification
2817913,0.633,0,0,/michelkouassi/quora-challenge,Quora Insincere Questions Classification
2813958,0.623,0,0,/ousou214738/logistic-regression-and-bag-of-embeddings,Quora Insincere Questions Classification
2785081,0.573,0,8,/maksad/quora-insincere-question-classif-with-fasttext,Quora Insincere Questions Classification
2809029,0.669,0,2,/econdata/quora3,Quora Insincere Questions Classification
2787361,0.621,0,0,/praxitelisk/quora-insincere-questions-classification-dl,Quora Insincere Questions Classification
2746970,0.695,0,3,/sfzero/focal-loss-holdout-gamma-0-99999,Quora Insincere Questions Classification
2724999,0.66,0,1,/robindong/pooling-is-all-you-need,Quora Insincere Questions Classification
2753260,0.496,0,0,/shreyansh20/quora-answers-analyzed,Quora Insincere Questions Classification
2760636,0.685,0,0,/xsakix/pytorch-bilstm-meta-v2,Quora Insincere Questions Classification
2724915,0.7,1,38,/peining/about-the-variance-and-the-cv-result,Quora Insincere Questions Classification
2742417,0.6920000000000001,0,1,/mailyousufkhan/quora-insincere-questions-classification,Quora Insincere Questions Classification
2733974,0.19,0,0,/highkaggle/qa-classify-with-several-features,Quora Insincere Questions Classification
2728060,0.696,0,2,/nicke1/bilstm-attention-2dcnn-kfold,Quora Insincere Questions Classification
2707453,0.649,0,6,/ruijiezheng2022/single-layer-lstm,Quora Insincere Questions Classification
2698898,0.662,0,1,/xsakix/torch-ensemble-in-one-model,Quora Insincere Questions Classification
534209,0.097916,0,3,/kmader/dnn-on-fft-for-camera-detection,IEEE's Signal Processing Society - Camera Model Identification
505052,0.129166,4,34,/CVxTz/keras-simple-cnn-starter,IEEE's Signal Processing Society - Camera Model Identification
8303068,0.6211300000000001,65,243,/harupy/m5-baseline,M5 Forecasting - Accuracy
8300626,1.40742,2,12,/tnmasui/m5-forecasting-lstm-w-custom-data-generator,M5 Forecasting - Accuracy
8232430,1.08216,62,661,/tarunpaparaju/m5-competition-eda-models,M5 Forecasting - Accuracy
8243436,0.77927,24,169,/ratan123/m5-forecasting-lightgbm-with-timeseries-splits,M5 Forecasting - Accuracy
8238776,2.46103,3,12,/kaushal2896/m5-preparing-training-ready-data-lightgbm,M5 Forecasting - Accuracy
8226214,1.07118,76,752,/robikscube/m5-forecasting-starter-data-exploration,M5 Forecasting - Accuracy
8266469,0.8377,1,2,/graymant/baseline-score-using-last-28-days,M5 Forecasting - Accuracy
8266685,2.79766,0,0,/zollkron/same-day-means-no-statistical-model,M5 Forecasting - Accuracy
8252094,1.06111,7,8,/nicapotato/kiss-simple-heuristics-only,M5 Forecasting - Accuracy
8230756,2.22978,0,1,/khoongweihao/m5-forecasting-eda-gaussian-kde-baseline-model,M5 Forecasting - Accuracy
8228740,0.75541,0,2,/ranjithks/ran-m5-forecasting-accuracy-28d-baseline-model,M5 Forecasting - Accuracy
8229825,1.05659,2,7,/sidharthkumar/ma-model-new-baseline-0-86331,M5 Forecasting - Accuracy
8232893,5.44561,0,4,/tenajima/evaluation-of-sample-submission-don-t-effect-lb,M5 Forecasting - Accuracy
8098228,3.69782,0,2,/darwinwin/elo-with-h2o-automl,Elo Merchant Category Recommendation
2860261,3.73194,0,1,/mks2192/features-from-old-model,Elo Merchant Category Recommendation
2478198,3.686,29,90,/frtgnn/easy-blend-post-processing,Elo Merchant Category Recommendation
3057792,3.728,0,1,/tommycpp/elo-rf,Elo Merchant Category Recommendation
716075,0.71654,0,1,/rajathmc/west-nile-virus-prediction,West Nile Virus Prediction
3332078,0.695,0,0,/elecshiba/vsb-data-augmentation,VSB Power Line Fault Detection
2783686,0.6513800000000001,0,2,/jagannathrk/5-fold-lstm-attention-fully-commented,VSB Power Line Fault Detection
3169687,0.4529999999999999,0,0,/kmekar/vsb-fault-detection-dwt-denoising,VSB Power Line Fault Detection
3372661,0.67173,6,6,/qinhui1999/handmade-features-0685-private-score,VSB Power Line Fault Detection
3343223,0.698,0,11,/hengzheng/vsb-attention-capsule-cnn,VSB Power Line Fault Detection
3055873,0.64827,0,3,/sheriytm/catboost-with-handmade-features,VSB Power Line Fault Detection
3260001,0.67,2,2,/yatzhash/fixed-vsb-competition-base-neural-network-045804,VSB Power Line Fault Detection
2893904,0.662,6,18,/vhessel/5-fold-lstm-attention-keras-stateful-metrics,VSB Power Line Fault Detection
2738487,0.6940000000000001,75,430,/braquino/5-fold-lstm-attention-fully-commented-0-694,VSB Power Line Fault Detection
2692495,0.377,4,25,/pnussbaum/vsb-power-using-autoencoding-v09,VSB Power Line Fault Detection
2555605,0.486,1,3,/takafumitakizawa/cnn-lstm-model,VSB Power Line Fault Detection
2483736,0.158,0,8,/delayedkarma/vsb-h2o-automl-baseline-lb-0-158-mcc-0-60,VSB Power Line Fault Detection
2451064,0.183,4,33,/bluexleoxgreen/simple-feature-lightgbm-baseline,VSB Power Line Fault Detection
14159992,0.86321,0,0,/hidekiizumi/submit-best,Recruit Restaurant Visitor Forecasting
582222,0.498,0,7,/gopisaran/sql-data-profiling-feature-imp,Recruit Restaurant Visitor Forecasting
562648,0.479,13,27,/aharless/exclude-same-wk-res-from-nitin-s-surpriseme2-w-nn,Recruit Restaurant Visitor Forecasting
548489,0.48,1,2,/chenpu/surprise-me-2-neural-networks,Recruit Restaurant Visitor Forecasting
503883,0.588,2,8,/vlasoff/freak-separate-modelling-for-timelines,Recruit Restaurant Visitor Forecasting
493112,0.5,3,5,/noklamchan/baseline-3-month-mean-time-series,Recruit Restaurant Visitor Forecasting
465088,0.497,0,5,/hashizu/recruit,Recruit Restaurant Visitor Forecasting
462440,0.495,10,22,/cadong/weighted-average-on-four-kernels-lb-0-495,Recruit Restaurant Visitor Forecasting
460165,0.542,0,8,/armamut/prophet-baseline,Recruit Restaurant Visitor Forecasting
13934599,0.01757,0,0,/a0224898l/notebookfe4281c0be,PUBG Finish Placement Prediction (Kernels Only)
7436554,0.02033,0,0,/nm1916050/kernel283d1b3c2a,PUBG Finish Placement Prediction (Kernels Only)
13213859,0.09785,0,0,/blackhurt/notebooka3ec5cf63f,PUBG Finish Placement Prediction (Kernels Only)
12103817,0.02702,0,0,/ankitsharmax/bengal-ne-jhar-orissa-ii-team-2,PUBG Finish Placement Prediction (Kernels Only)
12227711,0.0600399999999999,0,0,/huaruilu/hxuaruilu,PUBG Finish Placement Prediction (Kernels Only)
12112919,0.05654,4,4,/oneplustricks/pubg-predict3-delhi-team3,PUBG Finish Placement Prediction (Kernels Only)
12076600,0.05952,0,3,/tathagatabardhan/delhi-ncr-haryana-uttarakhand-team-2,PUBG Finish Placement Prediction (Kernels Only)
11311780,0.09122,2,5,/hegab7/pubg-note,PUBG Finish Placement Prediction (Kernels Only)
11308355,0.08716,0,4,/omermo/pubg-finish-placement-prediction,PUBG Finish Placement Prediction (Kernels Only)
10375759,0.26737,0,1,/rohitsaka/pubg-finish-predictions,PUBG Finish Placement Prediction (Kernels Only)
2709662,0.0447,0,0,/bhaskarboora/pubg-prediction,PUBG Finish Placement Prediction (Kernels Only)
8590565,0.07145,0,0,/tomoki86/kernel3dbaaee967,PUBG Finish Placement Prediction (Kernels Only)
7290415,0.0285,1,3,/chimiro/battlegrounds,PUBG Finish Placement Prediction (Kernels Only)
7545316,0.06043,0,0,/kamalboulahya/nmsl1919012,PUBG Finish Placement Prediction (Kernels Only)
7528390,0.0575799999999999,0,0,/nmsf1916014/nmsf1916014,PUBG Finish Placement Prediction (Kernels Only)
90818,0.967507,0,0,/aakashnain/redhatxgbnew,Predicting Red Hat Business Value
90394,0.961774,0,0,/aakashnain/redhatxgb,Predicting Red Hat Business Value
10352072,7.4256,0,3,/mertcankaraku/160202038-b-y-k-veri-final-devi,Predict Future Sales
10295594,2.99529,0,6,/eddam123/kou-b-y-k-veri-final-160202061,Predict Future Sales
10329554,16.092270000000006,0,5,/emrekara98/kou-buyuk-veri-160202094-m-emre-kara,Predict Future Sales
10236324,0.9212,0,3,/shubhamrgandhi/feature-engineering-ensembling,Predict Future Sales
10258460,1.4014799999999998,0,1,/murataltnay/b-y-k-veri-final-170202109,Predict Future Sales
10180508,1.20423,1,10,/danoozy44/future-sales-practical-eda-for-dummies,Predict Future Sales
10105611,1.06293,1,2,/jesvinho/fork-of-coursera-1,Predict Future Sales
10057541,1.36437,0,6,/sureshmecad/predict-future-sales-playground,Predict Future Sales
10388945,0.875,1,6,/shivyshiv/pytorch-efficientnet-gridmask-inference-training,SIIM-ISIC Melanoma Classification
10328022,0.9251,4,28,/akensert/siim-isic-distributed-tf2-2-single-multi-gpu,SIIM-ISIC Melanoma Classification
10284810,0.8981,6,24,/niteshx2/improve-blending-using-rankdata-ensemble,SIIM-ISIC Melanoma Classification
10257064,0.946,15,53,/ragnar123/rank-then-blend,SIIM-ISIC Melanoma Classification
9961356,0.902,0,2,/zzy990106/pytorch-efficientnet-b2-resnext50,SIIM-ISIC Melanoma Classification
10248175,0.9201,3,18,/shaitender/melanoma-with-efficientnet-keras,SIIM-ISIC Melanoma Classification
10296295,0.8156,0,2,/morenovanton/melanoma-classification-xgboost,SIIM-ISIC Melanoma Classification
9894385,0.941,110,329,/agentauers/incredible-tpus-finetune-effnetb0-b6-at-once,SIIM-ISIC Melanoma Classification
10145737,0.916,6,8,/fredericods/from-efficientnetb0-to-b7-melanoma-classification,SIIM-ISIC Melanoma Classification
10012995,0.888,0,6,/tt195361/splitting-tensorflow-dataset-for-validation,SIIM-ISIC Melanoma Classification
10069382,0.94,17,74,/redwankarimsony/power-of-metadata-xgboost-cnn-ensemble,SIIM-ISIC Melanoma Classification
10102763,0.675,0,1,/iamprateek/melanoma-classification-with-catboost,SIIM-ISIC Melanoma Classification
10062794,0.848,1,1,/samuelvedrik/tf-keras-efficientnet-classifier,SIIM-ISIC Melanoma Classification
12185810,0.7509999999999999,1,4,/yamsam/riiid-lgbm-simple-hpo,Riiid Answer Correctness Prediction
12162914,0.7490000000000001,4,25,/hiro5299834/riiid-lgbm-starter-ii,Riiid Answer Correctness Prediction
12141153,0.738,1,41,/lgreig/simple-lgbm-baseline,Riiid Answer Correctness Prediction
12125655,0.541,4,33,/nareshbhat/riiid-catboost-lgbm-model-ensembling,Riiid Answer Correctness Prediction
12121933,0.745,6,49,/artgor/riiid-eda-feature-engineering-and-models,Riiid Answer Correctness Prediction
12122431,0.74,5,40,/pavelvpster/riiid-target-encoding-sgd,Riiid Answer Correctness Prediction
12114769,0.5,17,63,/piantic/riiid-answer-correctness-prediction-basic-eda,Riiid Answer Correctness Prediction
12116223,0.725,5,19,/kamalnaithani/riiid-correctness,Riiid Answer Correctness Prediction
14082678,0.71,0,0,/trilabs/rst-mod,Riiid Answer Correctness Prediction
14047495,0.7859999999999999,0,0,/wojiaoyibaqiang/3-lgbm-bagging-280,Riiid Answer Correctness Prediction
13729172,0.733,0,0,/rohithansdah/riiid-answer-correctness-submission-rohit,Riiid Answer Correctness Prediction
13475215,0.7709999999999999,0,0,/zekunn/771-sakt-with-randomization-state-updates,Riiid Answer Correctness Prediction
13386492,0.762,0,0,/pandaman817/riiid-submit,Riiid Answer Correctness Prediction
13730849,0.16118,0,0,/cesarfernandez/house-prices-project,House Prices - Advanced Regression Techniques
13673815,0.11617,9,13,/glushko/house-prices-regression-modelling-part-ii,House Prices - Advanced Regression Techniques
13600179,0.14664,0,0,/natchimuthun/house-price-prediction-gbr-xgb-pipeline,House Prices - Advanced Regression Techniques
13593707,0.42577,2,2,/itokianarafidinarivo/houses-prices-baseline-model,House Prices - Advanced Regression Techniques
13528224,0.14759,0,2,/howeverforever/house-price-dnn-using-tensorflow,House Prices - Advanced Regression Techniques
11894950,0.12479,0,0,/mahmoudiamir/house-pricing-data-analysis,House Prices - Advanced Regression Techniques
12787595,0.15479,0,0,/nikosraftogiannis/house-prices-regression-with-tensorflow-2,House Prices - Advanced Regression Techniques
12626024,0.14792,4,4,/mr11235/house-prices-eda-pipelines-gridsearchcv,House Prices - Advanced Regression Techniques
13444812,0.14808,0,1,/howeverforever/house-price-xgb-baseline,House Prices - Advanced Regression Techniques
13375554,0.61337,1,14,/daotan/houseprice-using-linearregression,House Prices - Advanced Regression Techniques
13380656,0.12209,0,1,/rahulmumbai/house-price-competition,House Prices - Advanced Regression Techniques
1904836,0.35427,0,1,/joelmasters/housing-prices-kernel,House Prices - Advanced Regression Techniques
14644011,0.8996,0,0,/narayanareddych/customer-transaction-prediction,Santander Customer Transaction Prediction
14139646,0.84849,0,0,/jclangner/santander-customer-prediction-fastai,Santander Customer Transaction Prediction
13504935,0.92063,0,3,/arozrl/accelerated-training-using-snap-ml,Santander Customer Transaction Prediction
3091179,0.894,0,0,/pathofdata/lightgbm-out-of-the-box,Santander Customer Transaction Prediction
11377788,0.79796,0,1,/venkateshthota/santander-customer-transaction,Santander Customer Transaction Prediction
10059398,0.9000799999999999,0,0,/shanu11/santander-through-gpreda,Santander Customer Transaction Prediction
9789943,0.85019,0,0,/alejandrovelazco/red-neuronal-keras,Santander Customer Transaction Prediction
8655854,0.8884700000000001,0,0,/philieseg/shallow-nn-prediction-keras,Santander Customer Transaction Prediction
8368014,0.83721,0,0,/daniyarkyzyrov/midtermdan,Santander Customer Transaction Prediction
8380265,0.5002800000000001,0,0,/meruertadilgazy/meruert-adilgazinamid,Santander Customer Transaction Prediction
8370542,0.8872700000000001,0,1,/arshtematida/da4-midterm1,Santander Customer Transaction Prediction
8369916,0.7700199999999999,0,0,/kossaibek/ml-mid,Santander Customer Transaction Prediction
8354341,0.88826,0,2,/ho1zlar/azamatmidterm,Santander Customer Transaction Prediction
7439533,0.341,0,1,/manyregression/fastai-google-quest-classifier-q-and-a-models,Google QUEST Q&A Labeling
7694549,0.385,0,7,/khoongweihao/google-quest-bert-base-tf2-0-inference,Google QUEST Q&A Labeling
7606235,0.3879999999999999,7,43,/nxrprime/jigsaw-google-q-a-eda-ii,Google QUEST Q&A Labeling
7301640,0.373,1,2,/pavelvpster/google-q-a-labeling-bert,Google QUEST Q&A Labeling
7300656,0.222,0,1,/rishisinha19/google-quest-q-a-labeling-v2,Google QUEST Q&A Labeling
7197992,0.226,0,0,/buntyshah/google-quest-q-a-labeling-embeddings-glove,Google QUEST Q&A Labeling
7365874,0.37,0,1,/myphung/bertuned-multilabel-stratified,Google QUEST Q&A Labeling
7281226,0.377,0,1,/ayushyajnik/google-kaggle,Google QUEST Q&A Labeling
7206876,0.3389999999999999,2,7,/arvissu/simple-pytorch-lstm,Google QUEST Q&A Labeling
7116641,-0.002,0,0,/stitch/elmo-offline-with-keras-baseline,Google QUEST Q&A Labeling
7047974,0.327,6,20,/bags8040/xlnet-classification-demo,Google QUEST Q&A Labeling
7025525,0.352,28,64,/melissarajaram/roberta-fastai-huggingface-transformers,Google QUEST Q&A Labeling
7021092,0.26,0,0,/yukitakezawa/tfidf-doc2vec-pytorch,Google QUEST Q&A Labeling
6918032,0.372,6,16,/chenshengabc/from-quest-encoding-ensemble-a-little-bit-differen,Google QUEST Q&A Labeling
6929054,0.364,0,5,/meghakapoor/fastai-bert-universal-encoder-tfidf,Google QUEST Q&A Labeling
6866685,0.306,1,3,/aninda/thibaut-aninda-fastai-v2,Google QUEST Q&A Labeling
6818901,0.3279999999999999,7,28,/konradb/one-column-one-model,Google QUEST Q&A Labeling
5457349,0.9943,0,0,/bhabie/cactusnotebook,Aerial Cactus Identification
5788484,0.9974,0,0,/mahedihasanriday/aerial-cactus-identification-using-pytorch,Aerial Cactus Identification
5761035,0.9983,0,0,/alexanderdbooth/identify-a-cactus-w-deep-learning,Aerial Cactus Identification
5576217,0.9846,0,0,/sergei416/cactus-cnn,Aerial Cactus Identification
5640147,0.9999,0,3,/phamtrongthang123/simple-fastai-exercise-with-resnet,Aerial Cactus Identification
5234336,0.9875,0,0,/zhudongxiao/cnn-soluation,Aerial Cactus Identification
4611530,0.9999,0,0,/darthgera/transfer-learning-with-densenet-and-fastai,Aerial Cactus Identification
4688994,0.9565,0,0,/sohaibanwaar1203/pre-trained-resnet-py-torch,Aerial Cactus Identification
4857710,0.9855,0,2,/louplus/aerial-cactus-identification-used-auto-keras,Aerial Cactus Identification
4829265,0.9994,1,0,/kasumil5x/kernelba61e95ea2,Aerial Cactus Identification
4749999,0.5,0,1,/andyandy/cactus-classifier,Aerial Cactus Identification
4720184,0.9965,0,2,/ahkhalwai55/simple-fastai-exercise-alexnet,Aerial Cactus Identification
4721961,0.9999,0,1,/ahkhalwai55/simple-fastai-exercise-densenet201,Aerial Cactus Identification
4698755,0.9835,1,3,/apresswala52/aerial-cactus-identification,Aerial Cactus Identification
4666566,0.9935,0,0,/kakooyang/kerneld87ba2b3f5,Aerial Cactus Identification
4573863,0.9661,12,12,/akhileshrai/viz-agg-clustering-qda-lda-cnn,Aerial Cactus Identification
4569147,0.9999,1,5,/aayush26/getting-started-fast-ai-transferlearning-resnet18,Aerial Cactus Identification
3269529,1.0,1,10,/jiajunc/aerial-cactus-identification-v12,Aerial Cactus Identification
4673345,0.9998,0,1,/sarzhan/cactus-pytorch-implementation,Aerial Cactus Identification
4566166,0.9999,1,5,/phoenix9032/fastai-ensemble-tutorial-aerial-cactus,Aerial Cactus Identification
4562899,0.9958,0,1,/semlan/cactus-kernel,Aerial Cactus Identification
4565445,0.9703,3,7,/okeaditya/aerial-cactus-keras-cnn,Aerial Cactus Identification
4545209,0.9992,0,1,/piyush28/first-rodeo-with-fastai,Aerial Cactus Identification
4193484,0.9805,0,1,/elgatodelbosque/aerial-cactus-svm-sklearn,Aerial Cactus Identification
4484386,0.9985,0,1,/kocayinana/cactus-id-with-resnet-and-two-plain-cnn,Aerial Cactus Identification
3426008,0.86,5,23,/abdouljalil/auc-0-86-with-any-ml-model-just-maths,Santander Customer Transaction Prediction
3119498,0.899,11,109,/blackblitz/gaussian-mixture-naive-bayes,Santander Customer Transaction Prediction
3424321,0.6779999999999999,0,2,/bejeweled/h2o-santander-gridsearch,Santander Customer Transaction Prediction
3409588,0.899,0,0,/samfiddis/lgb-model,Santander Customer Transaction Prediction
3344239,0.8590000000000001,0,1,/rajeshcv/tensorflow-models,Santander Customer Transaction Prediction
3369591,0.8959999999999999,1,3,/mahyaret/santander-eda-using-xgboost-with-gpu-support,Santander Customer Transaction Prediction
3376371,0.899,2,3,/mitjasha/lightgbm-with-blender-and-augment,Santander Customer Transaction Prediction
3370402,0.9,9,56,/b5strbal/lightgbm-naive-bayes-santander-0-900,Santander Customer Transaction Prediction
3376225,0.889,0,1,/ttomas91/satander4-2-2-lgb-fake-train-aug,Santander Customer Transaction Prediction
3363340,0.894,5,32,/hatunina/no-training-challenge,Santander Customer Transaction Prediction
3222488,0.9003200000000001,0,0,/adarsh415/solving-santander-customer-transaction-problem,Santander Customer Transaction Prediction
3331476,0.872,16,50,/jiazhuang/a-proof-of-synthetic-data,Santander Customer Transaction Prediction
3380443,0.848,0,0,/arunsing/fork-of-santandar-eda-and-prediction,Santander Customer Transaction Prediction
3260936,0.901,118,542,/jiweiliu/lgb-2-leaves-augment,Santander Customer Transaction Prediction
10997479,0.00044,0,6,/mekhdigakhramanian/first-place-top-1-with-leakage,House Prices - Advanced Regression Techniques
12067697,0.12295,0,0,/iamandrewliao/ames-housing-prices,House Prices - Advanced Regression Techniques
12076079,0.14394,0,1,/pierretoussing/predicting-house-prices,House Prices - Advanced Regression Techniques
12114962,0.20163,0,0,/tessamendoza/submission,House Prices - Advanced Regression Techniques
11808128,0.14739,5,10,/prakharprasad/ames-house-prices-full-project,House Prices - Advanced Regression Techniques
12026403,0.13193,5,12,/nehalbandal/housing-price-prediction-model-stacking,House Prices - Advanced Regression Techniques
12031464,0.33753,0,3,/jonathanburritt/kaggle-competition-predict-house-prices,House Prices - Advanced Regression Techniques
12060723,0.12889,0,0,/tracyporter/ames-house-prices-catboost,House Prices - Advanced Regression Techniques
11987361,0.11885,2,4,/shashinkumarsachan/house-price-prediction-ensemble,House Prices - Advanced Regression Techniques
11214667,0.1338099999999999,0,1,/rohandesai24/housing-price-prediction-regularization,House Prices - Advanced Regression Techniques
11922239,0.12184,1,3,/vytautasv/house-prices,House Prices - Advanced Regression Techniques
11863638,0.18879,1,6,/jonathanpaserman/iowa-house-pricing,House Prices - Advanced Regression Techniques
11906921,0.13338,0,0,/nahumsa/prediction-using-xgboost,House Prices - Advanced Regression Techniques
803783,0.973,24,123,/aharless/simple-linear-stacking-lb-9730,TalkingData AdTracking Fraud Detection Challenge
801081,0.9563,10,34,/tetyanayatsenko/prepare-data-form-features-find-their-importance,TalkingData AdTracking Fraud Detection Challenge
753241,0.9688,0,2,/submarineering/submarineering-average,TalkingData AdTracking Fraud Detection Challenge
726779,0.9421,0,5,/aishanhang/fraud-gbdt,TalkingData AdTracking Fraud Detection Challenge
709294,0.9649,0,5,/iamprateek/detecting-the-user-click-as-fraud-or-not,TalkingData AdTracking Fraud Detection Challenge
758271,0.9577,1,0,/alex5009/kernel715d04d507,TalkingData AdTracking Fraud Detection Challenge
31085,26.452140000000004,5,2,/gustavodemari/san-francisco-crime-classification,San Francisco Crime Classification
29599,2.45952,0,0,/thibaut95k/knn-test,San Francisco Crime Classification
14010456,0.758,0,0,/southsakura/notebooka6b67fa973,Riiid Answer Correctness Prediction
13396114,0.745,0,0,/zekun98/blend-fold,Riiid Answer Correctness Prediction
14058610,0.722,0,0,/ginger88895/lgbm-constant-content-avg,Riiid Answer Correctness Prediction
13346742,0.774,4,3,/brendanartley/riiid-lgb-model-attempt,Riiid Answer Correctness Prediction
13833998,0.722,0,0,/nicolaswattiez/riid-competition-rapids-part-ii-fe-and-training,Riiid Answer Correctness Prediction
13305283,0.76,0,1,/zekun98/lgbm-with-feature-engineering,Riiid Answer Correctness Prediction
12322170,0.6990000000000001,0,1,/tylerpike/riid-submission-notebook-simple-ensemble,Riiid Answer Correctness Prediction
13804556,0.7759999999999999,1,7,/zekun98/sometune-about-riiid-self-attention-transformer,Riiid Answer Correctness Prediction
13486314,0.5,0,0,/amitush/riiid-eda-and-thoughts,Riiid Answer Correctness Prediction
13394203,0.759,0,0,/zekun98/riid-modelingbayes,Riiid Answer Correctness Prediction
13887331,0.773,0,0,/merrybad/riiid-model-lgbm,Riiid Answer Correctness Prediction
13736485,0.742,0,1,/jwilliamhughdore/collaborative-filetering-with-fast-ai-nn,Riiid Answer Correctness Prediction
13677940,0.7759999999999999,16,99,/gilfernandes/riiid-self-attention-transformer,Riiid Answer Correctness Prediction
13667636,0.518,0,1,/pauldiee/riid-notebook,Riiid Answer Correctness Prediction
13743765,0.716,0,0,/sindhuj/ridd-answers-correction,Riiid Answer Correctness Prediction
13725753,0.7809999999999999,1,11,/pandaman817/riiid-lgbm-bagging2-sakt-0-781,Riiid Answer Correctness Prediction
13747993,0.7040000000000001,0,0,/rohithansdah/riiid-competition,Riiid Answer Correctness Prediction
11330921,0.9196,0,4,/doncalculator/tensorflow-vs-pytorch-part-2-pytorch,SIIM-ISIC Melanoma Classification
11323491,0.892,0,7,/kozodoi/pre-training-on-full-data-with-surrogate-labels,SIIM-ISIC Melanoma Classification
11086826,0.9619,0,1,/akashsuper2000/minmax-highest-public,SIIM-ISIC Melanoma Classification
11259553,0.9577,27,126,/cdeotte/forward-selection-oof-ensemble-0-942-private,SIIM-ISIC Melanoma Classification
10517231,0.9427,0,1,/darshanpatel11/xgb-metadata,SIIM-ISIC Melanoma Classification
11233897,0.9623,1,11,/vatsalparsaniya/oof-pseudo-labeling-stacking-with-metadata,SIIM-ISIC Melanoma Classification
11268495,0.9575,1,5,/projdev/chris-submission-oof-weighted-ensemble-prlb-0-9427,SIIM-ISIC Melanoma Classification
11203220,0.9625,1,22,/roydatascience/silver-medal-solution-private-lb-9445,SIIM-ISIC Melanoma Classification
10922762,0.9454,0,0,/akashsuper2000/kfold-with-tfrecord-upsample-and-coarse-dropout,SIIM-ISIC Melanoma Classification
11229900,0.9512,3,14,/mpsampat/simple-oof-ensembling-methods-6-models,SIIM-ISIC Melanoma Classification
10532179,0.9514,0,3,/teyang/melanoma-detection-using-effnet-and-meta-data,SIIM-ISIC Melanoma Classification
11176177,0.8797,0,12,/jagdmir/vgg16-model,SIIM-ISIC Melanoma Classification
11234580,0.9646,2,11,/deepakd14/efficient-ensembling-highest-public-lb-0-9646,SIIM-ISIC Melanoma Classification
11099212,0.9381,1,5,/vadimtimakin/multisession-k-fold-af-dataset,SIIM-ISIC Melanoma Classification
11204995,0.8708,0,1,/krisho007/melanoma-with-pylightning-192-b0,SIIM-ISIC Melanoma Classification
11158399,0.9492,2,6,/aakashveera/ensemble-512-with-dropout,SIIM-ISIC Melanoma Classification
10995289,0.9464,0,0,/akashsuper2000/stacking-ensemble,SIIM-ISIC Melanoma Classification
11455362,62.503,0,5,/leoisleo1/multi-mode-models-ensemble-0e6557,Lyft Motion Prediction for Autonomous Vehicles
11454322,55.412,13,40,/tuckerarrants/lyft-ensembling-raster-sizes,Lyft Motion Prediction for Autonomous Vehicles
11407164,312.311,5,54,/ryches/lyft-constant-velocity-extrapolation-baseline,Lyft Motion Prediction for Autonomous Vehicles
11417949,300.9120000000001,1,11,/rhtsingh/lyft-gpu-inference-torch-baseline,Lyft Motion Prediction for Autonomous Vehicles
11356823,7406.135999999999,2,13,/pestipeti/sample-submissions,Lyft Motion Prediction for Autonomous Vehicles
11372828,7350.5430000000015,0,2,/paulorzp/installing-l5kit-offline,Lyft Motion Prediction for Autonomous Vehicles
13480028,188.412,0,0,/hbparman/alt-pixel-size,Lyft Motion Prediction for Autonomous Vehicles
13091375,10.385,0,0,/zaburo/private-submit-without-kernel-inference-02a197,Lyft Motion Prediction for Autonomous Vehicles
12949510,7350.4,0,0,/kropisartem/lyft-ensembling-raster-sizes,Lyft Motion Prediction for Autonomous Vehicles
12280835,23.58,0,0,/akashsuper2000/lyft-complete-train-and-prediction-pipeline,Lyft Motion Prediction for Autonomous Vehicles
11972118,134.091,0,0,/shams1/save-your-time-submit-without-kernel-inference,Lyft Motion Prediction for Autonomous Vehicles
11457486,62.503,0,0,/akashsuper2000/multi-mode-models-ensemble,Lyft Motion Prediction for Autonomous Vehicles
5386269,0.726,0,2,/geekforever/densenet-121-b5-transfer-learning-adam,APTOS 2019 Blindness Detection
5355531,0.735,0,7,/geekforever/densenet-169-b5-pre-process-aug-adamopt,APTOS 2019 Blindness Detection
5355662,0.718,1,3,/yosefardhito/pb0-87-pytorch-ordinal-regression-w-spacecutter,APTOS 2019 Blindness Detection
5269621,0.5660000000000001,0,0,/sergei416/x-ray-data-resnet,APTOS 2019 Blindness Detection
5244178,0.7120000000000001,13,17,/harendrap/fastai-densenet,APTOS 2019 Blindness Detection
5172149,0.594,0,1,/onu6024/keras-resnet-test-time-augmentation,APTOS 2019 Blindness Detection
5057167,0.4629999999999999,0,1,/rishabha/aptos-blindness,APTOS 2019 Blindness Detection
5261190,0.74,0,0,/haowumelbourne/resnet50-starter,APTOS 2019 Blindness Detection
5241819,0.722,0,2,/raghavgupta5/kernelf656d1ecce,APTOS 2019 Blindness Detection
5236837,0.7490000000000001,1,2,/aayushkt/xceptionnet,APTOS 2019 Blindness Detection
4993634,0.74,5,12,/lhohoz/aptos-fastai-resnet50-with-previous-data,APTOS 2019 Blindness Detection
5204458,0.738,0,0,/kcaravapalli/blindness-detection-resnet34,APTOS 2019 Blindness Detection
4981213,0.42,0,0,/deeplearn1/aptos-pytorch-resnet152-learning,APTOS 2019 Blindness Detection
5017226,0.067,0,1,/itsshubham/blindness-detection,APTOS 2019 Blindness Detection
5028690,0.723,0,7,/modojj/densenet121-and-cropping-aptos-2019,APTOS 2019 Blindness Detection
5023411,0.625,0,15,/bonhart/inceptionv3-tta-grad-cam-pytorch,APTOS 2019 Blindness Detection
5088174,0.0069999999999999,0,1,/andyandy/vgg19-batch-train-baseline,APTOS 2019 Blindness Detection
4970949,0.078,6,6,/hitoidas/a-simple-fastai-ensemble-training-kernel-0-60,APTOS 2019 Blindness Detection
4912485,0.787,1,21,/solomonk/ensemble-8-x-tta-2-models-008,APTOS 2019 Blindness Detection
4971652,0.741,16,20,/raimonds1993/aptos19-inference-efficientnet-keras-regression,APTOS 2019 Blindness Detection
4972789,0.345,0,1,/ryanallen/keras-inception-resnet-v2,APTOS 2019 Blindness Detection
4969807,-0.004,0,0,/snakayama/resnext50-pytorch,APTOS 2019 Blindness Detection
1843295,0.0819999999999999,0,0,/mapharazzo/kernelf1a1176982,PUBG Finish Placement Prediction (Kernels Only)
1993653,0.0566,0,7,/akshaysiras/pubg-prediction,PUBG Finish Placement Prediction (Kernels Only)
1995714,0.027,1,5,/nathanbruzat/pubg-prediction-groupby-matchtype,PUBG Finish Placement Prediction (Kernels Only)
1959188,0.0527,0,1,/huzefalw/pubg-position-prediction-xgboost,PUBG Finish Placement Prediction (Kernels Only)
1993156,0.0218,0,9,/zhongzisen/kernelf7bacfbf00,PUBG Finish Placement Prediction (Kernels Only)
1977272,0.0601,0,1,/sachinjchorge/fork-of-pubg-neural-networks-part-2,PUBG Finish Placement Prediction (Kernels Only)
1957460,0.1058,0,1,/sachinjchorge/pubg-neural-networks,PUBG Finish Placement Prediction (Kernels Only)
1951372,0.0565,0,0,/jagratkhandelwal/exploration,PUBG Finish Placement Prediction (Kernels Only)
1913774,0.5266,0,0,/bashamsc/prediction-using-lda,PUBG Finish Placement Prediction (Kernels Only)
1854334,0.071,0,8,/bhasha4995dushara/pubg-placement-prediction,PUBG Finish Placement Prediction (Kernels Only)
1933334,0.0567,2,3,/srcecde/model-stacking-blending,PUBG Finish Placement Prediction (Kernels Only)
1925080,0.0569,0,0,/corvuslee/pubg-random-forest-practice-baseline,PUBG Finish Placement Prediction (Kernels Only)
1922433,0.0251,3,3,/modmari/deep-neural-network-predicting-pubg-winners,PUBG Finish Placement Prediction (Kernels Only)
1908270,0.0314,0,3,/tarunpaparaju/pubg-placement-prediction-nn-regression-v3,PUBG Finish Placement Prediction (Kernels Only)
1887569,0.0562,122,254,/carlolepelaars/pubg-data-exploration-rf-funny-gifs,PUBG Finish Placement Prediction (Kernels Only)
1898952,0.092,0,2,/loxaaa/pubg-predictions,PUBG Finish Placement Prediction (Kernels Only)
1890420,0.0426,0,1,/tarunpaparaju/pubg-placement-prediction-catboost-regression-v2,PUBG Finish Placement Prediction (Kernels Only)
1878181,0.0565,5,11,/snakayama/pubg-finish-placement-prediction-nn,PUBG Finish Placement Prediction (Kernels Only)
1859666,0.0674,0,4,/ramkish/ramki-pubg-competition,PUBG Finish Placement Prediction (Kernels Only)
1862266,0.0398,0,2,/tarunpaparaju/pubg-placement-prediction-catboost-regression,PUBG Finish Placement Prediction (Kernels Only)
1861374,0.0397,0,0,/eduardok/submit-test2,PUBG Finish Placement Prediction (Kernels Only)
181548,0.01458,0,1,/udaysa/notebook9c51422f6f,Leaf Classification
177302,0.02283,0,1,/miyadi/tensorflow-example,Leaf Classification
176216,0.6894600000000001,3,2,/hugorgsilva/leaf-or-leave,Leaf Classification
149613,0.02668,0,1,/wordsforthewise/neural-network-through-keras,Leaf Classification
11101888,0.02275,0,0,/israakhalil/fork-of-leaf-classification-v3,Leaf Classification
10765260,0.65328,0,0,/maimahdi/leafclassification,Leaf Classification
9314253,0.49586,0,0,/iamprateek/sales-forecasting-lgbm,M5 Forecasting - Accuracy
10276250,0.6299600000000001,0,26,/jagdmir/m5-forecasting-part-two-lgbm-regressor,M5 Forecasting - Accuracy
9832018,0.69725,0,13,/gromml/m5-linear-regression-for-each-item,M5 Forecasting - Accuracy
10029346,0.0,4,5,/nagayamacho3/m5-forecasting-accuracy-simple-forcasting,M5 Forecasting - Accuracy
10256442,0.48257,0,4,/shaitender/optimum-weighted-average,M5 Forecasting - Accuracy
9873226,0.5482,0,2,/mrcooperr/daemencloudt-submission-notebook,M5 Forecasting - Accuracy
8800016,0.50981,0,0,/akashsuper2000/m5-forecast-v2-python,M5 Forecasting - Accuracy
9863365,0.55194,0,1,/fbergh/best-model-parameter-tuning,M5 Forecasting - Accuracy
9438239,0.0,0,17,/rodrigoignacioperez/optimum-weighted-average,M5 Forecasting - Accuracy
9781155,0.54597,0,0,/fbergh/rausnaus-lgbm-ensemble,M5 Forecasting - Accuracy
9946472,0.55194,0,0,/fbergh/single-model-evaluation-submission,M5 Forecasting - Accuracy
11903799,0.30125,0,2,/hirazawahiroshi/openvaccine-simple-lgb-use-of-error-information,OpenVaccine: COVID-19 mRNA Vaccine Degradation Prediction
11901383,0.26086,15,21,/daishu/why-keras-is-better-than-pytorch,OpenVaccine: COVID-19 mRNA Vaccine Degradation Prediction
11856502,0.2516,57,281,/its7171/gru-lstm-with-feature-engineering-and-augmentation,OpenVaccine: COVID-19 mRNA Vaccine Degradation Prediction
11849315,0.26783,3,7,/fernandoramacciotti/tpu-self-attention-lstm-bpp-sequences,OpenVaccine: COVID-19 mRNA Vaccine Degradation Prediction
11845493,0.27032,2,8,/shadowburning/tcn-lstm-gru,OpenVaccine: COVID-19 mRNA Vaccine Degradation Prediction
11806031,0.25811,6,25,/dinhthilan/open-vaccine-simple-ensemble,OpenVaccine: COVID-19 mRNA Vaccine Degradation Prediction
11806403,0.38697,0,5,/fernandoramacciotti/resnet-bpp-paired-unpaired-feature-maps,OpenVaccine: COVID-19 mRNA Vaccine Degradation Prediction
11765853,0.26592,10,80,/vudangthinh/openvaccine-gcn-graphsage-gru-kfold,OpenVaccine: COVID-19 mRNA Vaccine Degradation Prediction
11725624,0.34396,10,76,/jameschapman19/openvaccine-gcn,OpenVaccine: COVID-19 mRNA Vaccine Degradation Prediction
11749426,0.2606,10,80,/ragnar123/wavenet-gru-baseline,OpenVaccine: COVID-19 mRNA Vaccine Degradation Prediction
11711273,0.3069699999999999,1,4,/rhtsingh/mrna-vaccine-degradation-prediction-in-pytorch,OpenVaccine: COVID-19 mRNA Vaccine Degradation Prediction
11761430,0.62322,1,6,/mannsingh/cnn-for-npy-files,OpenVaccine: COVID-19 mRNA Vaccine Degradation Prediction
11713677,0.27409,3,9,/barteksadlej123/lstm-vs-gru-with-cross-validation,OpenVaccine: COVID-19 mRNA Vaccine Degradation Prediction
11707726,0.27092,8,42,/alelafe/openvaccine-gru-lstm-noiselevel,OpenVaccine: COVID-19 mRNA Vaccine Degradation Prediction
11711959,0.2715599999999999,1,2,/ajaykumar7778/openvaccine-gru-lstm-noiselevel-d0987a,OpenVaccine: COVID-19 mRNA Vaccine Degradation Prediction
11679100,0.31323,11,107,/t88take/openvaccine-simple-lgb-baseline,OpenVaccine: COVID-19 mRNA Vaccine Degradation Prediction
11687179,0.30567,1,12,/kaushal2896/openvaccine-neural-network-baseline,OpenVaccine: COVID-19 mRNA Vaccine Degradation Prediction
11494113,0.774,0,0,/flyingmuttus/panda-independence-eds-inference,Prostate cANcer graDe Assessment (PANDA) Challenge
10692851,0.89,0,0,/darraghdog/lstm-effnetb2-1507v33a,Prostate cANcer graDe Assessment (PANDA) Challenge
10188492,0.0,0,0,/naomidd/panda-stain-norm-downsample12x64x64-inference,Prostate cANcer graDe Assessment (PANDA) Challenge
9771789,0.01,0,0,/santosh8896/panda-eda-better-visualization-simple-baseline,Prostate cANcer graDe Assessment (PANDA) Challenge
13621681,0.66602,0,0,/caiqingyang/quroa-nlp,Quora Insincere Questions Classification
13177643,0.5996199999999999,0,0,/dpipi17/demetre-pipia,Quora Insincere Questions Classification
13099633,0.67874,0,0,/bignottirom/advanced-insincereclassification,Quora Insincere Questions Classification
12734276,0.54986,0,4,/shivkumarganesh/quora-insincere-questions,Quora Insincere Questions Classification
12191586,0.6701,0,0,/maverickmonk94/qqic-dl-notebook,Quora Insincere Questions Classification
10881903,0.59844,2,2,/palash97/quora-insincere-questions,Quora Insincere Questions Classification
9797062,0.68114,0,0,/fredthered2/bi-dir-lstm-with-convolution-layer,Quora Insincere Questions Classification
9674444,0.55057,1,4,/xiu0714/quora-insincere-questions-smote,Quora Insincere Questions Classification
9540844,0.6704600000000001,0,1,/halftru/kernel49eb0bd25f,Quora Insincere Questions Classification
9370121,0.60627,0,3,/xiu0714/logisticregression-base-model,Quora Insincere Questions Classification
2721503,0.693,0,0,/viabar/submission-1-gru-capsule-attention,Quora Insincere Questions Classification
8920790,0.63284,0,0,/canirudh/qisc-final,Quora Insincere Questions Classification
7413930,0.65993,5,6,/yankarto/bidirectional-lstm-cnn-withembeddings,Quora Insincere Questions Classification
11208248,0.40986,0,0,/luyoucong/rfmodel-imporved-week4,COVID19 Global Forecasting (Week 4)
10696526,0.61254,0,0,/luyoucong/prmodle-week4,COVID19 Global Forecasting (Week 4)
10499318,0.0410899999999999,0,0,/aakashveera/covid-week4-overall,COVID19 Global Forecasting (Week 4)
9497409,0.64379,0,0,/phakphumj/5904640876,COVID19 Global Forecasting (Week 4)
9454772,2.2236,1,4,/tmchls/covid-19-week-4-world-analysis,COVID19 Global Forecasting (Week 4)
9476313,0.10277,0,0,/trisornt/ts-for-am,COVID19 Global Forecasting (Week 4)
9485033,0.0,0,0,/pradeepkumarrajkumar/m3-xgb-djp-a,COVID19 Global Forecasting (Week 4)
9485961,0.19614,0,0,/pradeepkumarrajkumar/m2-poly-djp,COVID19 Global Forecasting (Week 4)
9326369,0.19925,0,0,/hackboom/covid,COVID19 Global Forecasting (Week 4)
9126371,0.1768,0,0,/hotstaff/regional-differences-in-the-virus-growth-rate,COVID19 Global Forecasting (Week 4)
8876438,3.56521,0,0,/omezario/covid-prediction-training,COVID19 Global Forecasting (Week 4)
2044261,0.608,0,7,/rooshroosh/lightgmb-tfidf-benchmark,Quora Insincere Questions Classification
2044422,0.644,0,7,/gpreda/baseline-using-embeddings-forked,Quora Insincere Questions Classification
2038592,0.0,0,14,/tunguz/sample-submission-starter,Quora Insincere Questions Classification
2041536,0.639,1,1,/rajashri/beginner-nlp-notebook,Quora Insincere Questions Classification
6793726,0.65595,0,0,/mafesantacoloma/proyecto-3,Quora Insincere Questions Classification
4835572,0.63234,0,0,/quasikris/fork-of-question-classification-quora-870bf0,Quora Insincere Questions Classification
4147544,0.45092,0,0,/cbiancaniello/compressing-a-binary-submission-within-a-s-86d75d,Quora Insincere Questions Classification
3972964,0.53465,0,0,/ranjan2612/quora-insincere-questions-classification,Quora Insincere Questions Classification
3246629,0.66089,0,0,/noexittv/embeddings-keras-v02,Quora Insincere Questions Classification
2723254,0.094,0,0,/cicipoo/quora-first-pass,Quora Insincere Questions Classification
2546938,0.3389999999999999,0,0,/srujanperam/basic-text-mining,Quora Insincere Questions Classification
2428789,0.613,0,0,/xsakix/baseline-2,Quora Insincere Questions Classification
2402226,0.6629999999999999,0,0,/xsakix/bilstm-att-base-classifier-all-emb-2-train-on-acc,Quora Insincere Questions Classification
2323809,0.688,0,0,/daobuliao/gru-capsule,Quora Insincere Questions Classification
2288206,0.662,1,0,/robertke94/pytorch-textcnn-relu,Quora Insincere Questions Classification
5629183,0.076,0,0,/cyannani123/keras-cellular-image-classification,Recursion Cellular Image Classification
5947241,0.258,2,8,/davidalami/mode-stacking-1-5-x,Recursion Cellular Image Classification
5077275,0.006,0,2,/anthonyemeka12/cellularimages,Recursion Cellular Image Classification
5298188,0.247,3,29,/roydatascience/cellular-stacking-1-5,Recursion Cellular Image Classification
5025042,0.231,15,110,/zaharch/keras-model-boosted-with-plates-leak,Recursion Cellular Image Classification
4837800,0.193,10,79,/tanlikesmath/rcic-fastai-starter,Recursion Cellular Image Classification
4619270,0.0,1,5,/hmendonca/sample-submission,Recursion Cellular Image Classification
2106886,0.654,3,10,/satian/finding-insincerity-with-dpcnn-glove-embeddings,Quora Insincere Questions Classification
2087840,0.684,38,260,/shujian/different-embeddings-with-attention-fork-fork,Quora Insincere Questions Classification
2093232,0.452,7,31,/evaart/nlp-with-ml,Quora Insincere Questions Classification
2052016,0.664,0,1,/jlochter/insincere-embeddings-mgnc-cnn-and-mv-cnn,Quora Insincere Questions Classification
2080597,0.665,0,1,/abhibisht89/pay-some-attention,Quora Insincere Questions Classification
2081669,0.677,4,52,/danofer/different-embeddings-with-attention-fork,Quora Insincere Questions Classification
2072532,0.488,0,17,/kuldeep7688/simple-rnn-using-glove-embeddings-in-pytorch,Quora Insincere Questions Classification
2085610,0.593,1,6,/jazivxt/where-d-you-get-the-beauty-scar-tough-guy,Quora Insincere Questions Classification
2040096,0.6679999999999999,5,37,/tunguz/lstm-gru-why-not-both,Quora Insincere Questions Classification
2046388,0.672,11,78,/suicaokhoailang/lstm-attention-baseline-0-672-lb,Quora Insincere Questions Classification
2072892,0.626,0,0,/tejakallakuri1714/quora-classify-v1,Quora Insincere Questions Classification
2068852,0.5770000000000001,4,16,/thebrownviking20/n-gram-multichannel-bilstm-cnn-embed1,Quora Insincere Questions Classification
2043602,0.674,108,1027,/sudalairajkumar/a-look-at-different-embeddings,Quora Insincere Questions Classification
2057526,0.66,6,46,/strideradu/word2vec-and-gensim-go-go-go,Quora Insincere Questions Classification
2051936,0.622,1,28,/mihaskalic/dynamic-lstm-pytorch-starter,Quora Insincere Questions Classification
2064507,0.049,1,1,/shravankoninti/eda-only,Quora Insincere Questions Classification
2063618,0.652,1,3,/dkmerona/simple-parallele-cnn-with-2-epoch,Quora Insincere Questions Classification
2041511,0.61,51,182,/artgor/eda-and-lstm-cnn,Quora Insincere Questions Classification
2060481,0.627,0,5,/georsara1/no-fancy-stuff-just-a-bi-lstm,Quora Insincere Questions Classification
2054848,0.057,3,9,/karthik7395/attention-based-bidi-lstm-attention-is-needed,Quora Insincere Questions Classification
2046751,0.62,0,11,/youhanlee/cnn1d-lstm,Quora Insincere Questions Classification
2040852,0.65,2,27,/applecer/use-f1-to-select-model-lstm-based,Quora Insincere Questions Classification
2046280,0.534,2,6,/karthik7395/baseline-svc,Quora Insincere Questions Classification
3507540,0.3329999999999999,5,16,/chewzy/gru-w-attention-baseline-model-curated,Freesound Audio Tagging 2019
3490639,0.122,1,46,/shujian/keras-cnn-starter-curated-training-data-only,Freesound Audio Tagging 2019
3580805,0.034,0,0,/starxcf/sample-submission-all-random,Freesound Audio Tagging 2019
11691258,0.29752,0,5,/parmarsuraj99/neural-covid-vaccine,OpenVaccine: COVID-19 mRNA Vaccine Degradation Prediction
11687132,0.47352,3,15,/mannsingh/openvaccine-lgbm,OpenVaccine: COVID-19 mRNA Vaccine Degradation Prediction
11679254,0.44608,1,7,/jjinho/preliminary-eda-and-predicted-loop-mean-model,OpenVaccine: COVID-19 mRNA Vaccine Degradation Prediction
11670772,0.3815199999999999,0,5,/arnabark/covid19-feature-engineering-xgboost,OpenVaccine: COVID-19 mRNA Vaccine Degradation Prediction
12121399,0.2465099999999999,0,0,/akashsuper2000/covid-ae-pretrain-gnn-attn-cnn,OpenVaccine: COVID-19 mRNA Vaccine Degradation Prediction
9910392,0.47506,0,0,/ajinkyaindulkar/mlipioneers-ensemblelearning-m5-accuracy,M5 Forecasting - Accuracy
9778557,0.67715,8,10,/anlgrbz/adaptation-of-favorita-competition-winner-approach,M5 Forecasting - Accuracy
9597361,0.6413,4,21,/timetraveller98/m5-accuracy-pytorch-deep-neural-net-dnn,M5 Forecasting - Accuracy
9569732,0.73945,7,34,/urayukitaka/m5-forecasting-eda-and-lgbm-prediction,M5 Forecasting - Accuracy
193574,0.0133099999999999,0,0,/auygur/nn-through-keras-copied-mod,Leaf Classification
4918830,0.034,20,147,/taindow/be-careful-what-you-train-on,APTOS 2019 Blindness Detection
4944547,0.649,0,4,/hirotaka0122/feature-extraction-by-resnet,APTOS 2019 Blindness Detection
4903170,0.7829999999999999,77,275,/drhabib/starter-kernel-for-0-79,APTOS 2019 Blindness Detection
4881150,0.737,19,78,/ratan123/aptos-keras-efficientnet-with-attention-baseline,APTOS 2019 Blindness Detection
4906333,0.6970000000000001,0,1,/jinbao/resnet50-baseline-submit-jinbao,APTOS 2019 Blindness Detection
4869486,0.7,1,3,/nzxwang/aptos2019-fastai-resnet50-starter,APTOS 2019 Blindness Detection
4865849,0.713,1,4,/benoitcharmettant/resnet50-attention-layer,APTOS 2019 Blindness Detection
4798137,0.6659999999999999,0,0,/shahad9544/aptos-take1-with-data-augment1,APTOS 2019 Blindness Detection
4843517,0.706,1,2,/aditya100/aptos-2019-blindness-detection,APTOS 2019 Blindness Detection
4828750,0.7190000000000001,6,18,/raimonds1993/aptos19-densenet-inference-old-new-data,APTOS 2019 Blindness Detection
4783961,0.711,0,15,/manojprabhaakr/vgg19-bn-base-model,APTOS 2019 Blindness Detection
4774959,-0.035,1,0,/rahul845/blindness-aptos,APTOS 2019 Blindness Detection
4709256,0.0,1,2,/hitoidas/my-pytorch-fastai-kernel-0-62-valid-accuracy,APTOS 2019 Blindness Detection
4721418,0.703,0,2,/aninda/blindness-detection-fastai,APTOS 2019 Blindness Detection
4742757,0.609,0,3,/venkat555/aptos-pytorch-starter,APTOS 2019 Blindness Detection
4736970,0.5760000000000001,4,6,/jtbontinck/cnn-features-extraction-xgb-submission,APTOS 2019 Blindness Detection
4576962,0.4629999999999999,3,8,/joycechidi/aptos-blindness-detection-with-resnet50-pytorch,APTOS 2019 Blindness Detection
4668525,0.7390000000000001,4,27,/axel81/fastai-ensembler,APTOS 2019 Blindness Detection
4613319,0.455,0,0,/xcz12138/pytorch-vgg16-train,APTOS 2019 Blindness Detection
2283314,0.0258,0,2,/xbcccr/pubg-placement-pred-nn-baseline-lightgbm,PUBG Finish Placement Prediction (Kernels Only)
2284686,0.0578,0,1,/kamalkishor1991/exploring-decision-trees-using-pubg-data,PUBG Finish Placement Prediction (Kernels Only)
2329017,0.0506,0,1,/danielmartinezb/pubg-finish-placement-prediction,PUBG Finish Placement Prediction (Kernels Only)
2353619,0.0562,0,0,/nikkisharma536/pubg-finale,PUBG Finish Placement Prediction (Kernels Only)
2349772,0.0918,0,0,/aykhansh/kernelf3e43d63fc,PUBG Finish Placement Prediction (Kernels Only)
2298579,0.027,0,0,/chittu27/chaitanya,PUBG Finish Placement Prediction (Kernels Only)
1905419,0.0371,0,0,/takanobu0210/pubg-eda-lightgbm,PUBG Finish Placement Prediction (Kernels Only)
2310567,0.0238,0,0,/woocheol/kernel63c8135642,PUBG Finish Placement Prediction (Kernels Only)
2123415,0.0914,0,0,/ymmtyy/pubg1-linearreg-delete-object-mean-interpolate,PUBG Finish Placement Prediction (Kernels Only)
2123255,0.1319,0,0,/beaubellamy/pubg-feature-engineering-modelling,PUBG Finish Placement Prediction (Kernels Only)
2186866,0.0605,0,1,/snakayama/pugb-first-nn-v2,PUBG Finish Placement Prediction (Kernels Only)
2210701,0.0979,0,0,/rahul1932/fork-of-kernelb08666c708-16a7f4,PUBG Finish Placement Prediction (Kernels Only)
1965500,0.0576,0,0,/wdnam1066/comp-woodo-draft-20181031,PUBG Finish Placement Prediction (Kernels Only)
2171070,0.0605,2,2,/specbug/pubg-finish-placement-with-eda,PUBG Finish Placement Prediction (Kernels Only)
2192488,0.3319,0,0,/beaubellamy/pubg-predictions-gradient-boost,PUBG Finish Placement Prediction (Kernels Only)
2186064,0.0369,0,0,/sachinjchorge/fork-of-pubg-light-gbm-part-3-729a2e,PUBG Finish Placement Prediction (Kernels Only)
1894042,0.0552,0,0,/santhoshbala18/dinner,PUBG Finish Placement Prediction (Kernels Only)
2110540,0.0603,0,2,/ashishkhuraishy/the-pubg-challenge-advanced-regression,PUBG Finish Placement Prediction (Kernels Only)
2076013,0.0604,0,3,/mirosh111/pubg-regression,PUBG Finish Placement Prediction (Kernels Only)
14445592,0.7006100000000001,57,53,/andreshg/xgboost-optuna-hyperparameter-tunning,Tabular Playground Series - Jan 2021
14193890,0.90183,8,21,/homiarafarhana/predict-future-sales,Predict Future Sales
14623291,0.8699100000000001,4,3,/dkomyagin/predict-future-sales-lightgbm-framework,Predict Future Sales
13938092,0.8538899999999999,15,24,/deepdivelm/feature-engineering-lightgbm-exploring-performance,Predict Future Sales
13558924,1.09167,3,5,/howeverforever/future-sales-xgb-lgbm-lstm,Predict Future Sales
13459956,1.0300200000000002,10,15,/bhavikjain/predict-future-sales,Predict Future Sales
12815684,0.90097,0,1,/minmyk/sales-xgb,Predict Future Sales
136470,0.8912959999999999,0,0,/sharmin/final,Predicting Red Hat Business Value
13285838,0.7440000000000001,0,0,/niaibrahim/fork-of-cnn-reduced-learning-rate-b78e2e,Riiid Answer Correctness Prediction
13345166,0.745,2,22,/ash1706/riiid-lgbm-catboost-baseline-weights-optimization,Riiid Answer Correctness Prediction
13276975,0.58,18,25,/zcatherine/riiid-mlp,Riiid Answer Correctness Prediction
12132344,0.7509999999999999,0,0,/aman2114/riid-education,Riiid Answer Correctness Prediction
12952261,0.748,5,11,/sreejaej/riid-correctness-of-fe-ensures-better-score,Riiid Answer Correctness Prediction
13499110,0.774,15,88,/manikanthr5/riiid-sakt-model-inference-public,Riiid Answer Correctness Prediction
12310562,0.625,2,10,/shyjohn/shy-john-riid-submission,Riiid Answer Correctness Prediction
13274898,0.7440000000000001,3,21,/rendalemarktaas/riiid-answer-correctness-submission,Riiid Answer Correctness Prediction
13417120,0.7709999999999999,18,155,/leadbest/sakt-with-randomization-state-updates,Riiid Answer Correctness Prediction
13439765,0.682,0,2,/arjunalbert/lgbm-voting-with-tags-1000,Riiid Answer Correctness Prediction
13363550,0.75,5,23,/optimo/tabnet-with-loop-feature-engineering-explained,Riiid Answer Correctness Prediction
13401496,0.76,9,6,/manikanthr5/riiid-lgbm-single-model-ensembling-scoring,Riiid Answer Correctness Prediction
13291353,0.7490000000000001,0,1,/gilfernandes/riiid-ignite-starter,Riiid Answer Correctness Prediction
13328301,0.746,13,39,/ldevyataykina/riiid-exploratory-data-analysis-baseline,Riiid Answer Correctness Prediction
12239442,0.7490000000000001,0,8,/nastarcookies7/riiid-test-answer-prediction,Riiid Answer Correctness Prediction
13231340,0.512,0,3,/melissarasmussen/first-try,Riiid Answer Correctness Prediction
13235078,0.6559999999999999,0,1,/angstykid92/test-model,Riiid Answer Correctness Prediction
12901904,0.622,0,2,/braysen/ridd-baseline,Riiid Answer Correctness Prediction
9925465,0.884,0,3,/truonghoang/siim-isic-melanoma-lightgbm-cpu,SIIM-ISIC Melanoma Classification
11017385,0.9606,0,11,/ajaykumar7778/fork-of-ensemble-melanoma-ac9964,SIIM-ISIC Melanoma Classification
11107451,0.948,2,6,/aakashveera/efficient-ensemble-with-all-data,SIIM-ISIC Melanoma Classification
11108729,0.9603,3,19,/servietsky/dark-magic-blending-0-9603,SIIM-ISIC Melanoma Classification
11114115,0.9362,0,0,/yeon1234/train-cv-068ec0,SIIM-ISIC Melanoma Classification
10631545,0.8917,4,6,/marcoalexandreramos/melanoma-cnn-with-pytorch-using-efficientnet,SIIM-ISIC Melanoma Classification
11044912,0.9478,1,12,/samklein/submission-and-tta-exploration,SIIM-ISIC Melanoma Classification
11009490,0.7773,0,8,/zainahmad/kfold-with-efficientnet-and-image-augumentation,SIIM-ISIC Melanoma Classification
10974166,0.9577,40,194,/datafan07/eda-modelling-of-the-external-data-inc-ensemble,SIIM-ISIC Melanoma Classification
10624248,0.9456,0,8,/ajaykumar7778/triple-stratified-kfold-with-tfrecords,SIIM-ISIC Melanoma Classification
10847145,0.9477,6,43,/abiolatti/train-cv,SIIM-ISIC Melanoma Classification
10940475,0.5,0,1,/jumpingmandt/skin-cancer-melanoma-classification-study,SIIM-ISIC Melanoma Classification
10408297,0.5725,0,0,/rishika2095/transfer-learning-resnet,SIIM-ISIC Melanoma Classification
10884661,0.6718,0,7,/fireheart7/melanoma-resnet50,SIIM-ISIC Melanoma Classification
5185623,2.2215700000000003,0,0,/sjun4530/sf-crime-classification-hyper,San Francisco Crime Classification
3930967,1533169.02553,0,0,/bivek2211/tsp-solver-greedy-and-concorde,Traveling Santa 2018 - Prime Paths
2576913,1514320.92,3,99,/bicotsp/pmtest1,Traveling Santa 2018 - Prime Paths
2608619,1515413.15,0,10,/theoviel/from-public-kernels-to-bronze,Traveling Santa 2018 - Prime Paths
2604521,1515564.01,4,10,/blacksix/dp-shuffle-strikes-back,Traveling Santa 2018 - Prime Paths
2604002,1515557.76,0,2,/zfturbo/reversing-and-shifting,Traveling Santa 2018 - Prime Paths
2553645,1515561.85,0,1,/zfturbo/dp-shuffle,Traveling Santa 2018 - Prime Paths
2568622,1533418.47,0,4,/rdpharr/traveling-santa-prob-ortools-concorde-n-neigh,Traveling Santa 2018 - Prime Paths
2534679,1515602.88,5,67,/kostyaatarik/not-a-5-and-5-halves-opt,Traveling Santa 2018 - Prime Paths
2476583,1515905.08,18,54,/kostyaatarik/better-input-for-aka-2-opt,Traveling Santa 2018 - Prime Paths
2302918,2208262.1,0,4,/dan3dewey/all-the-carrots-traveling-santa-2018,Traveling Santa 2018 - Prime Paths
2405513,1516404.85,16,97,/kostyaatarik/close-ends-chunks-optimization-aka-2-opt,Traveling Santa 2018 - Prime Paths
2331447,1533213.8,0,1,/jasonduncanwilson/run-run-rudolph-tsp-solver-path-flip,Traveling Santa 2018 - Prime Paths
2280051,1516867.85,8,33,/steubk/concorde-monitoring-batch,Traveling Santa 2018 - Prime Paths
2267169,1516897.14,14,27,/byfone/riffling-for-fine-selection,Traveling Santa 2018 - Prime Paths
2218288,1516912.37,10,101,/blacksix/concorde-for-5-hours,Traveling Santa 2018 - Prime Paths
2211176,1524601.2,0,8,/blacksix/concorde-solver-with-scaling,Traveling Santa 2018 - Prime Paths
2191153,446884407.52,0,0,/pochiblack/submission-test,Traveling Santa 2018 - Prime Paths
3469005,0.92,0,10,/naivelamb/multibranch-nn-baseline-magic,Santander Customer Transaction Prediction
3552467,0.92021,0,9,/kelexu/fork-of-pytorch-nn-cyclelr-k-fold-0-920-with-aug,Santander Customer Transaction Prediction
3553381,0.91608,0,6,/adish333/now-you-see-me-private-lb-914,Santander Customer Transaction Prediction
3209110,0.902,0,3,/super13579/lgbm-model-catboost,Santander Customer Transaction Prediction
3551764,0.77065,0,2,/arnabdan/nn-dropouts-early-stopping-xgboost-ensemble,Santander Customer Transaction Prediction
3127163,0.897,0,0,/apurvasinha2003/light-gbm-with-eda,Santander Customer Transaction Prediction
3524307,0.901,6,9,/sandeepkumar121995/santander-customer-transaction-prediction,Santander Customer Transaction Prediction
3461343,0.637,1,3,/adrianlievano/light-gbm-with-stratified-kfold,Santander Customer Transaction Prediction
3512444,0.8909999999999999,7,25,/stevexyu/kfold-convolutional-neural-network,Santander Customer Transaction Prediction
3511568,0.5,1,0,/maheshak04/simpl,Santander Customer Transaction Prediction
3324473,0.899,0,2,/samueltommzy/santander-customerprediction-model,Santander Customer Transaction Prediction
3463756,0.857,2,3,/dnik007/santander-challenge,Santander Customer Transaction Prediction
3460093,0.8959999999999999,10,24,/neibyr/simple-use-mean-target-encoding,Santander Customer Transaction Prediction
3306670,0.901,1,1,/youlei0106/santander-customer-transaction-prediction-f2,Santander Customer Transaction Prediction
3456814,0.5589999999999999,2,0,/stevexyu/decision-tree-baseline-for-santander,Santander Customer Transaction Prediction
3426730,0.9,7,39,/robikscube/comparing-each-feature-vs-normal-distribution,Santander Customer Transaction Prediction
3450398,0.8640000000000001,0,0,/coolaks/pytorch-complete-nn-santander-comp,Santander Customer Transaction Prediction
12219965,0.1296,2,9,/aastha124/top-33-house-price-prediction-using-catboost,House Prices - Advanced Regression Techniques
8911795,0.12059,0,0,/rakkaalhazimi/house-price-adaboost-with-feature-creation,House Prices - Advanced Regression Techniques
12304337,0.13142,0,0,/nandhakumar01/house-prices-advanced-regression-techniques,House Prices - Advanced Regression Techniques
12374017,0.11635,3,15,/mviola/house-prices-eda-lasso-lightgbm-0-11635,House Prices - Advanced Regression Techniques
12375983,0.1201,0,1,/marcogorelli/cln-stacked-regressions-top-4-on-leaderboard,House Prices - Advanced Regression Techniques
11853302,0.12129,0,0,/urayukitaka/ensemble-prediction-pseudo-labeling,House Prices - Advanced Regression Techniques
12420745,0.1468,0,0,/nguynthnhtuyt/house-prices,House Prices - Advanced Regression Techniques
12234238,0.1405799999999999,0,1,/artemnaumchak/notebook73592dce74,House Prices - Advanced Regression Techniques
12330095,0.12095,0,2,/danielj6/stacked-housing,House Prices - Advanced Regression Techniques
12382256,0.1233099999999999,0,0,/waq0000/notebook1132edfa1e,House Prices - Advanced Regression Techniques
12288625,0.15023,0,1,/damoonshahhosseini/functional-neural-networks,House Prices - Advanced Regression Techniques
12270921,0.15497,0,4,/raufsafar/house-price-models,House Prices - Advanced Regression Techniques
12164911,0.13656,0,1,/amintavakkolnia/home-price-competition,House Prices - Advanced Regression Techniques
12228542,0.1856,0,0,/brettwhite/house-prices,House Prices - Advanced Regression Techniques
4454963,0.9726,0,1,/sohaibanwaar1203/dense-net-image-classification,Aerial Cactus Identification
4429213,0.9912,0,0,/nikhiltheallknowing/ariel-cactus-identification,Aerial Cactus Identification
4404912,0.9999,1,2,/saripudin/aerial-cactus-identification-densenet-201,Aerial Cactus Identification
4378912,0.9996,0,0,/calder10/aerial-cactus-identification-using-cnn,Aerial Cactus Identification
4305761,0.9998,0,0,/srisravya484/aerial-cactus-identification-using-resnet50,Aerial Cactus Identification
4315344,0.9513,4,86,/ateplyuk/pytorch-efficientnet,Aerial Cactus Identification
4144849,0.9839,0,1,/kotlanikhil222/kernel95b45cc5ce,Aerial Cactus Identification
4142204,0.9993,0,2,/hutmacher303/convnet-without-pretrained-network,Aerial Cactus Identification
4252989,0.9753,0,0,/kalikichandu/aerial-cactus-identification,Aerial Cactus Identification
4245712,0.8833,0,2,/christianwallenwein/fastai-baseline-model-aerial-cactus,Aerial Cactus Identification
4111909,1.0,0,3,/abyaadrafid/cactus-finder-perfect-score-with-fastai-vision,Aerial Cactus Identification
4202027,0.9995,0,4,/mkl62unh/keras-classifier-for-32x32-grayscale-images,Aerial Cactus Identification
3445752,1.0,0,4,/khursani8/fastai-effnetb3,Aerial Cactus Identification
3792211,0.9975,0,0,/commandercool/cactus-identify,Aerial Cactus Identification
4173015,0.998,0,0,/benjaminassel/a-simple-cnn-model,Aerial Cactus Identification
4104515,0.9996,0,2,/bgmello/transfer-learning-with-keras,Aerial Cactus Identification
4129682,0.9967,0,0,/gramgram123/aerial-cactus-keras-resnet50,Aerial Cactus Identification
4055899,0.9887,0,2,/aditya100/aerial-cactus-identification-cnn-with-keras,Aerial Cactus Identification
4050358,0.9513,0,2,/akumaldo/resnet-5-layers-keras-0-95lb-for-fun,Aerial Cactus Identification
4025894,0.9999,0,3,/filipmg/cactus-identification-densenet,Aerial Cactus Identification
4043095,0.9761,0,0,/snaily16/cactus-identification-keras,Aerial Cactus Identification
4010162,0.9833,0,0,/seedlite/aerial-cactus-identification-using-keras,Aerial Cactus Identification
3985517,0.9935,0,1,/pdx250697/simple-cnn-with-keras,Aerial Cactus Identification
3990638,0.9999,0,0,/aniruddhas435/fastai-densenet161,Aerial Cactus Identification
3950731,0.9982,0,2,/divyam17/aerial-cactus-detection-using-cnns,Aerial Cactus Identification
3938929,0.9997,0,1,/anirudhchak/cnn-using-keras,Aerial Cactus Identification
3876224,0.9788,0,0,/rabbitmai87/cnn-model-on-cactus-detections,Aerial Cactus Identification
3872741,0.9658,0,2,/atrisaxena/pytorch-simple-model-iscactus-classification,Aerial Cactus Identification
3849216,0.9976,1,2,/chitra14/simple-cnn-implementation-fit-generator,Aerial Cactus Identification
3867612,0.938,0,1,/luisda2994/densenet169-transfer-learning,Aerial Cactus Identification
6835069,0.362,11,84,/idv2005/pytorch-bert-baseline,Google QUEST Q&A Labeling
6843186,0.291,2,8,/leighplt/pytorch-torchtext,Google QUEST Q&A Labeling
6892454,0.185,4,7,/babubhai/ananya-google-quest,Google QUEST Q&A Labeling
6819448,0.196,6,23,/tunguz/quest-simple-eda,Google QUEST Q&A Labeling
6792261,0.342,1,14,/ragnar123/simple-lgbm-solution-baseline,Google QUEST Q&A Labeling
6783603,0.302,1,35,/abhishek/bert-s-the-word-distillbert,Google QUEST Q&A Labeling
6739631,0.356,22,113,/artgor/pytorch-approach,Google QUEST Q&A Labeling
6763636,0.153,0,1,/petebleackley/doc2vec-and-keras,Google QUEST Q&A Labeling
6752543,0.35,4,34,/chanhu/updated-quest-lstm-word2sent-handmade-features,Google QUEST Q&A Labeling
6729731,0.289,20,68,/hamditarek/get-started-with-nlp-lda-lsa,Google QUEST Q&A Labeling
6736570,0.29,8,36,/tunguz/quest-logistic-regression-with-word-char-ngrams,Google QUEST Q&A Labeling
6732760,0.325,4,17,/abhikjha/fastai-google-quest,Google QUEST Q&A Labeling
6729668,0.301,5,21,/ryches/tfidf-bov-meta-features-simple-model,Google QUEST Q&A Labeling
6728979,0.165,0,14,/ryches/mean-of-categories-benchmark,Google QUEST Q&A Labeling
6728358,-0.003,0,7,/mashlyn/sample-submission-benchmark-lb-0-003,Google QUEST Q&A Labeling
8928454,0.36869,0,0,/changshengyan/googlequest-bert-final,Google QUEST Q&A Labeling
7786792,0.3779999999999999,0,0,/dmytruto/kernel7cd206963d,Google QUEST Q&A Labeling
7355936,-0.011,0,0,/stitch/albert-huggingface-internet-off-i,Google QUEST Q&A Labeling
7951873,0.05479,0,0,/belugazk/kernel-from-newbie-to-newbies,Open Images 2019 - Object Detection
5414953,0.07624,2,0,/harshitholmes/comp-4-od,Open Images 2019 - Object Detection
4940917,0.07727,7,8,/akashdeepjassal/inception-resnet-tf-hub-submission,Open Images 2019 - Object Detection
4583310,0.05479,5,11,/bitumok/kernel-from-newbie-to-newbies,Open Images 2019 - Object Detection
4236014,0.20119,36,230,/xhlulu/intro-to-tf-hub-for-object-detection,Open Images 2019 - Object Detection
10120934,0.5466,0,1,/dhsong13/model-v1-mf-hyper-parameter-adjustment,WSDM - KKBox's Music Recommendation Challenge
380720,0.60531,1,0,/srini1304/music-recommender-firstcut,WSDM - KKBox's Music Recommendation Challenge
13311292,0.9941,0,0,/clmentdauvilliers/cactus-notebook-2,Aerial Cactus Identification
14229079,0.9987,0,0,/jacobmorrison213/simple-cnn-using-keras,Aerial Cactus Identification
13482548,0.986,0,0,/danielverdu/aerial-cactus-identification-pytorch,Aerial Cactus Identification
12903455,0.9726,0,1,/yohanseo/1116-cactus-test,Aerial Cactus Identification
12415979,0.9182,0,1,/royceda/aerial-cactus-less-is-better-sometimes,Aerial Cactus Identification
3748408,0.9863,3,9,/shivam2811/aerial-cactus-identification-98-63,Aerial Cactus Identification
11457447,0.9986,2,4,/awaawa/simple-starter-kit-with-tensorflow2-0,Aerial Cactus Identification
10480893,0.9803,0,3,/moisesamadojr/cnn-senac-2020,Aerial Cactus Identification
10298363,0.9977,0,0,/danielqdm/cnns-exerc-cio,Aerial Cactus Identification
10369295,0.9918,1,2,/luisfelipeseabra/cnn-vgg16,Aerial Cactus Identification
9080361,0.9881,0,0,/thanhhungnguyen/cactus-identification-ver-1,Aerial Cactus Identification
8554078,0.987,0,0,/sleepybatman42/aerial-cactus-3,Aerial Cactus Identification
7973270,0.9999,0,1,/tonytonyl/cactus-preprocessed,Aerial Cactus Identification
3632482,0.9998,0,1,/swapnilpote/aerial-cactus-identification,Aerial Cactus Identification
7604856,0.9953,0,0,/alekseykobylin/aerialcactusidentification,Aerial Cactus Identification
7059593,0.9933,0,1,/marcelorbsousa/kernel4f62727778,Aerial Cactus Identification
6916681,0.9981,0,1,/alexrios/jat-team-vers-o-final-cactus,Aerial Cactus Identification
6915183,0.0,0,1,/renatovaladares/kernel13d8e6f04f,Aerial Cactus Identification
4335164,0.982,0,0,/kabilan45/aerial-cactus-classification,Aerial Cactus Identification
6612553,0.9648,0,0,/aabhi202/experiment-by-switching-optimizers-using-monk,Aerial Cactus Identification
3599227,0.5106,1,1,/ratnesh88/predict-presence-of-cactus,Aerial Cactus Identification
6161138,0.5119,0,0,/chirodiplodh/cactus-challenge-by-chirodip,Aerial Cactus Identification
6008778,0.9567,0,3,/aroonkp/arial-cactus,Aerial Cactus Identification
14346797,0.0098,0,0,/ryotaiijima/fork-of-23th-place-solusion-35a1e7,Google QUEST Q&A Labeling
7815020,0.429,0,0,/bluexleoxgreen/google-quest-inference-groupcv,Google QUEST Q&A Labeling
12226808,0.29124,0,0,/alessandrosolbiati/gpu-bert-linear-regression-baseline,Google QUEST Q&A Labeling
7174018,0.301,0,0,/emanlapponi/qa-labels,Google QUEST Q&A Labeling
8476497,0.21511,0,0,/ruibin/ray-edition,Google QUEST Q&A Labeling
7670859,0.39358,0,0,/nizamuddin/qa-augur,Google QUEST Q&A Labeling
7673368,0.372,0,0,/ruhong/google-quest-challenge,Google QUEST Q&A Labeling
9669868,0.0391699999999999,0,0,/rajprakhar/google-quest-architecture-1,Google QUEST Q&A Labeling
7676170,0.22873,0,1,/atuage/google-quest-bow-approach,Google QUEST Q&A Labeling
8556117,0.37001,0,0,/amogh05/bert-base,Google QUEST Q&A Labeling
7052814,0.207,0,1,/sasidharturaga/google-quest,Google QUEST Q&A Labeling
7600580,0.365,0,0,/sugh93/pytorch-bert-baseline-training,Google QUEST Q&A Labeling
7832450,0.391,0,1,/subediaarjun/bert-base-pretrained-models-custom1,Google QUEST Q&A Labeling
7924093,0.45763,12,34,/shuheigoda/23th-place-solusion,Google QUEST Q&A Labeling
7918788,0.45,14,30,/jionie/models-with-optimization-v5,Google QUEST Q&A Labeling
7889682,0.452,1,5,/bookerd/google-quest-final,Google QUEST Q&A Labeling
7365402,0.385,0,1,/tangchengshun/lightgbm-distilbert-tensorflow2-0-pytorch,Google QUEST Q&A Labeling
7883396,0.39,0,1,/tangchengshun/bert-base-pytorch-inference-1-0,Google QUEST Q&A Labeling
7915281,0.442,0,10,/markpeng/ensemble-5models-v4-v7-magic,Google QUEST Q&A Labeling
7920703,0.36244,0,2,/afajohn/use-tfidf-crazynn-different-approach,Google QUEST Q&A Labeling
7767905,0.3979999999999999,0,0,/vinaydoshi/tfbert-ensemble-preprocess-with-2-hidden-layers,Google QUEST Q&A Labeling
7873872,0.32736,0,0,/xianglinguo/bert-mlp1,Google QUEST Q&A Labeling
6923941,0.006,0,0,/s2chandel/kernel1564a3b078,Google QUEST Q&A Labeling
7240226,0.386,0,1,/tangchengshun/bert-tensorflow2-0-10,Google QUEST Q&A Labeling
7871709,0.381,3,0,/takiyu/bert-base-tf2-0-add-features,Google QUEST Q&A Labeling
12598493,0.95522,0,0,/georgezoto/talkingdata-adtracking-competition-baseline-model,TalkingData AdTracking Fraud Detection Challenge
6925826,0.63531,0,0,/atashnezhad/ann-and-talking-data-fraud-detection,TalkingData AdTracking Fraud Detection Challenge
3367644,0.93061,0,1,/yuriarthf/talkingdata-adtracking-fraud-detection-challenge,TalkingData AdTracking Fraud Detection Challenge
6287117,0.97997,0,7,/fangkun119/competition-talkingdata-adtracking-frauddetection,TalkingData AdTracking Fraud Detection Challenge
904327,0.9631,0,1,/theexpertahmeed/kernelf2bec992d4-94466c,TalkingData AdTracking Fraud Detection Challenge
1339340,0.952813,0,2,/josephpope/talkingdata-classification,TalkingData AdTracking Fraud Detection Challenge
826552,0.959,0,1,/gpreda/talkingdata-adtracking-eda,TalkingData AdTracking Fraud Detection Challenge
910345,0.9539,4,15,/wesamelshamy/click-farm-features-oversampling-lgbm,TalkingData AdTracking Fraud Detection Challenge
909140,0.9728,0,5,/asraful70/talkingdata-xgboost-implementation,TalkingData AdTracking Fraud Detection Challenge
892905,0.9786,11,35,/asraful70/notebook-version-of-talkingdata-lb-0-9786,TalkingData AdTracking Fraud Detection Challenge
889771,0.9143,2,1,/ianchute/naive-bayes-model-on-eight-basic-features,TalkingData AdTracking Fraud Detection Challenge
13266705,0.19184,0,0,/semenedel/notebookd73981d422,House Prices - Advanced Regression Techniques
12954731,0.63541,0,2,/dhanyajayan/house-price,House Prices - Advanced Regression Techniques
13023839,0.11638,0,4,/hazimmir/advaced-stacking-blending-houseprice-regression,House Prices - Advanced Regression Techniques
12623017,0.15735,0,2,/jagdmir/house-price-prediction-random-forest,House Prices - Advanced Regression Techniques
12936288,0.12579,12,22,/mathchi/predict-sales-prices-and-practice-feature-engineer,House Prices - Advanced Regression Techniques
12936227,0.1251799999999999,0,0,/letmewin97/t-sne-catboost-and-more-than-enough,House Prices - Advanced Regression Techniques
11232903,0.12395,0,0,/kamleshshimpi78/xgboost-house-price-prediction-submission,House Prices - Advanced Regression Techniques
12959869,0.14429,0,0,/donaldcth/linerregression-ver-2-2,House Prices - Advanced Regression Techniques
12913200,0.20016,0,2,/wilexroger/house-price-solution,House Prices - Advanced Regression Techniques
12961338,0.20059,2,2,/fwziayasser/notebookd681e0aec3,House Prices - Advanced Regression Techniques
3277695,0.901,42,181,/roydatascience/eda-pca-simple-lgbm-on-kfold-technique,Santander Customer Transaction Prediction
3313102,0.88,1,0,/jacky5112/santander-customer-transaction-prediction-cnn,Santander Customer Transaction Prediction
3280853,0.901,9,42,/roydatascience/santander-transaction-stacking-1-0,Santander Customer Transaction Prediction
3277182,0.8540000000000001,9,12,/mitjasha/854-easy-nn-for-santander,Santander Customer Transaction Prediction
3325972,0.879,0,0,/returnofsputnik/frequentist-inference-approach-to-santander-round0,Santander Customer Transaction Prediction
3267777,0.9,6,19,/sarmat/eda-and-preprocessing-example,Santander Customer Transaction Prediction
3259609,0.9,5,26,/ranjoranjan/kernel83f071aec1,Santander Customer Transaction Prediction
3233321,0.901,42,98,/roydatascience/eda-pca-lgbm-santander-transactions,Santander Customer Transaction Prediction
3254005,0.895,2,17,/xubujie/hint-for-categorize-features,Santander Customer Transaction Prediction
3262296,0.899,0,0,/karangautam/correcting-mistake,Santander Customer Transaction Prediction
3234113,0.9,5,39,/sandeepkumar121995/magic-parameters,Santander Customer Transaction Prediction
3212328,0.901,23,201,/jesucristo/santander-magic-lgb-0-901,Santander Customer Transaction Prediction
3233931,0.898,1,8,/adnaiksachin25/santander-lgbm-216-features,Santander Customer Transaction Prediction
3190433,0.9,10,61,/tezdhar/getting-started-santander,Santander Customer Transaction Prediction
3215323,0.8490000000000001,2,1,/swarnim97/simple-neural-network,Santander Customer Transaction Prediction
3219642,0.89,0,0,/tandonarpit6/santander-customer-lgb-neural-network,Santander Customer Transaction Prediction
13419457,0.9118,0,0,/b10617046/my-final,SIIM-ISIC Melanoma Classification
13016006,0.7259,0,0,/annasofielunde/machinelearning-tdt4173-cnn-model1,SIIM-ISIC Melanoma Classification
12663870,0.4998,0,6,/sd4321/fastai-v2-training-inference-prediction,SIIM-ISIC Melanoma Classification
10921773,0.4403,0,2,/erikcabeza/data-analysis-and-mobilenet,SIIM-ISIC Melanoma Classification
11213310,0.9463,0,4,/c7934597/triple-stratified-kfold-with-tfrecords,SIIM-ISIC Melanoma Classification
11318813,0.9329,13,46,/datafan07/final-melanoma-model-16th-place-solution-light-v,SIIM-ISIC Melanoma Classification
14515572,0.748,0,0,/phucpx/riiid-akt-nr-model,Riiid Answer Correctness Prediction
12132703,0.701,0,0,/binman159/riiid,Riiid Answer Correctness Prediction
13728448,0.754,0,0,/erivanoliveirajr/predi-o-de-acertos,Riiid Answer Correctness Prediction
14088929,0.792,1,10,/bowaka/riid-lgb-single-model-0-793-full-summary,Riiid Answer Correctness Prediction
12351481,0.792,0,4,/doctorkael/riiid-lgb-ftrl-data-gen-and-training-logic,Riiid Answer Correctness Prediction
14051621,0.782,0,1,/narendra/riiiid-saint-elapsed-lag-submission,Riiid Answer Correctness Prediction
13428887,0.7879999999999999,0,3,/ammarnassanalhajali/only-14-features-lb-0-789,Riiid Answer Correctness Prediction
14073839,0.795,2,2,/guoyonfan/lgbm-model-vv0,Riiid Answer Correctness Prediction
14079766,0.5,0,3,/yrquni/uninet,Riiid Answer Correctness Prediction
14072771,0.79,0,4,/superchenhao/single-lgbm-with-only-17-features,Riiid Answer Correctness Prediction
12395424,26.755,0,1,/ak0210/inference-resnet-adamw,Lyft Motion Prediction for Autonomous Vehicles
12523003,23.58,0,1,/c7934597/lyft-complete-train-and-prediction-pipeline,Lyft Motion Prediction for Autonomous Vehicles
13427239,159.643,0,0,/kristiinakeps/combined-version,Lyft Motion Prediction for Autonomous Vehicles
12068607,27.129,0,0,/mad6068/lyft-net,Lyft Motion Prediction for Autonomous Vehicles
13013832,21.401,0,0,/sietseschrder/predict-multiple-models,Lyft Motion Prediction for Autonomous Vehicles
12789632,23.58,2,24,/mekhdigakhramanian/lyft-complete-prediction-pipelin-ensamble,Lyft Motion Prediction for Autonomous Vehicles
12690643,23.622,0,9,/aikhmelnytskyy/lyft-complete-tr-and-pred-on-colab-or-kaggle,Lyft Motion Prediction for Autonomous Vehicles
12634309,6515.023,0,0,/louis925/constant-prediction-baseline,Lyft Motion Prediction for Autonomous Vehicles
12452105,546.779,2,5,/nxrprime/lyft-vision-transformer-inference,Lyft Motion Prediction for Autonomous Vehicles
12153116,195.395,0,4,/etareduce/lyft-av-py-lightning-resnet-eval-baseline,Lyft Motion Prediction for Autonomous Vehicles
12020193,42.644,0,0,/srinivasgopalkrishna/lyft-submission-1,Lyft Motion Prediction for Autonomous Vehicles
11898335,33.953,9,31,/ilu000/lyft-multi-mode-448px-inference,Lyft Motion Prediction for Autonomous Vehicles
11570632,25.742,24,114,/corochann/lyft-prediction-with-multi-mode-confidence,Lyft Motion Prediction for Autonomous Vehicles
11584830,356.084,0,8,/satorushibata/lgbm-on-lyft-tabular-data-inference-tuning,Lyft Motion Prediction for Autonomous Vehicles
94609,0.967419,0,0,/tanlikesmath/xredboost-cv,Predicting Red Hat Business Value
11415734,0.8826299999999999,0,1,/leoisleo1/future-sales-3,Predict Future Sales
11271013,0.94457,0,3,/otonyeamietubodie/future-sales,Predict Future Sales
10929504,0.92638,0,1,/swatisk2702/predict-shop-sales-xgboost,Predict Future Sales
10963422,1.48738,0,0,/catgigi/kernel73322eb19e,Predict Future Sales
10863292,0.91403,1,12,/dhiiyaur/predict-future-sales-lgbm-hyperparameter-optuna,Predict Future Sales
10751660,8.58933,0,1,/asiaahmedabushawish/predict-future-sales,Predict Future Sales
10088450,2.34117,0,3,/iekaterina/random-forest-regression,Predict Future Sales
10601240,0.99263,0,0,/rooney5/kernel548280cc46,Predict Future Sales
10556990,1.06842,0,0,/silvermino/kernel2134d6a27c,Predict Future Sales
1856617,0.442,0,0,/boomberung/first-try,PUBG Finish Placement Prediction (Kernels Only)
1827160,0.0787,0,1,/viniciusbbizarri/winner-winner-machine-dinner,PUBG Finish Placement Prediction (Kernels Only)
1838611,0.1292,1,2,/sachinjchorge/pubg-linear-regression,PUBG Finish Placement Prediction (Kernels Only)
1828818,0.0375,2,17,/amoeba3215/keras-nn-mlp,PUBG Finish Placement Prediction (Kernels Only)
1851583,0.0376,1,0,/afgonczol/pubg-group-level-match-stats,PUBG Finish Placement Prediction (Kernels Only)
1823241,0.0653,5,6,/sayangoswami/chicken-dinner,PUBG Finish Placement Prediction (Kernels Only)
1802784,0.0246,59,121,/chocozzz/pubg-data-description-a-to-z-fe-with-python,PUBG Finish Placement Prediction (Kernels Only)
1813797,0.3452,2,2,/tarunpaparaju/pubg-random-placement-predictor,PUBG Finish Placement Prediction (Kernels Only)
1811563,0.0621,1,2,/royalbhati/eda-and-modelling,PUBG Finish Placement Prediction (Kernels Only)
1806159,0.0823,1,2,/tarunpaparaju/pubg-placement-predictor-dnns-and-random-forests,PUBG Finish Placement Prediction (Kernels Only)
1807893,0.1007,0,0,/kunal17/pubg-winner-prediction,PUBG Finish Placement Prediction (Kernels Only)
1797694,0.0651,0,3,/suttergustavo/simple-xgboost-baseline,PUBG Finish Placement Prediction (Kernels Only)
1813497,0.0845,0,0,/tarunpaparaju/pubg-placement-predictor-random-forest-regressor,PUBG Finish Placement Prediction (Kernels Only)
13753155,0.15043,0,0,/gaothis/pubg-finish-placement-prediction,PUBG Finish Placement Prediction (Kernels Only)
7461314,0.05855,0,0,/nmsx1916016/pubg-random-forest,PUBG Finish Placement Prediction (Kernels Only)
3583800,0.05948,1,0,/srivastava4/pubg-sid,PUBG Finish Placement Prediction (Kernels Only)
2631900,0.182,0,0,/akshitgupta98/pubg-ann-analysisng,PUBG Finish Placement Prediction (Kernels Only)
2426613,0.0253,0,0,/amaruak00/pubg-finish-placement-prediction-playground,PUBG Finish Placement Prediction (Kernels Only)
2397503,0.0372,0,0,/yansun1996/gbr-ipynb,PUBG Finish Placement Prediction (Kernels Only)
4098933,0.31761,1,1,/arjunrao2000/cnnkeras-ipynb,Humpback Whale Identification Challenge
3483794,0.00093,0,0,/kmader/whale-pca-logistic-regression-as-notebook,Humpback Whale Identification Challenge
532251,0.0273,0,14,/andersy005/getting-started,Humpback Whale Identification Challenge
5017599,0.688,0,0,/yeayates21/densenet-customaug-v4,APTOS 2019 Blindness Detection
64590,0.49654,0,0,/cutddm/leakage-solution,Expedia Hotel Recommendations
10346021,0.64363,0,0,/qizhengqi/m5-forecasting-accuracy-qizhengqi,M5 Forecasting - Accuracy
13195103,1.12873,0,1,/saneyukimakino/m5-forecasting-lstm,M5 Forecasting - Accuracy
12925570,0.0,0,0,/tomokinishida/m5-forecasting-accuracy,M5 Forecasting - Accuracy
12124225,0.76599,1,1,/marisakamozz/m5-exponential-smoothing,M5 Forecasting - Accuracy
8274886,1.39525,0,0,/alafan/walmart,M5 Forecasting - Accuracy
9894353,0.79799,0,1,/annahulita13/m5-forecast,M5 Forecasting - Accuracy
9837922,0.99189,0,0,/tehutahu/nn-model-by-feature-first-under-0-50,M5 Forecasting - Accuracy
10456388,5.44561,0,10,/mothermoto/25th-place-solution,M5 Forecasting - Accuracy
10341204,0.0,0,2,/akashsuper2000/m5-forecasting-accuracy-moving-average-lightgbm,M5 Forecasting - Accuracy
11979442,0.24981,0,4,/c7934597/covid-ae-pretrain-gnn-attn-cnn,OpenVaccine: COVID-19 mRNA Vaccine Degradation Prediction
12282552,0.23162,5,37,/group16/covid-19-mrna-4th-place-solution,OpenVaccine: COVID-19 mRNA Vaccine Degradation Prediction
11700020,0.26145,0,0,/arunamenon/openvaccine-lstm-gru-overall-features,OpenVaccine: COVID-19 mRNA Vaccine Degradation Prediction
11688005,0.2340699999999999,2,10,/wimwim/covid19-cnn-transformer,OpenVaccine: COVID-19 mRNA Vaccine Degradation Prediction
11718324,0.26925,0,1,/aeryss/openvaccine-gru-lstm-attention,OpenVaccine: COVID-19 mRNA Vaccine Degradation Prediction
12129379,0.23851,2,11,/theoviel/open-vaccine-pl,OpenVaccine: COVID-19 mRNA Vaccine Degradation Prediction
12101236,0.2377699999999999,1,7,/underwearfitting/final-post-processing,OpenVaccine: COVID-19 mRNA Vaccine Degradation Prediction
12084799,0.2425199999999999,0,2,/liaowenxiong/open-vaccine-pytorch-ii,OpenVaccine: COVID-19 mRNA Vaccine Degradation Prediction
11831120,0.29109,0,0,/neithermannormachine/openvaccine-sequence-models,OpenVaccine: COVID-19 mRNA Vaccine Degradation Prediction
12121374,0.25921,0,0,/akashsuper2000/open-vaccine-pytorch-v,OpenVaccine: COVID-19 mRNA Vaccine Degradation Prediction
11966632,0.2460099999999999,0,2,/akghyd/covid-ae-pretrain-gnn-attn-cnn,OpenVaccine: COVID-19 mRNA Vaccine Degradation Prediction
12002585,0.25824,7,46,/daishu/answer-why-is-keras-better-than-pytorch,OpenVaccine: COVID-19 mRNA Vaccine Degradation Prediction
12010847,0.25275,3,27,/mathurinache/gru-lstm-with-48k-augmentation,OpenVaccine: COVID-19 mRNA Vaccine Degradation Prediction
12020909,0.27024,0,1,/shams1/openvaccine-simple-gru-model,OpenVaccine: COVID-19 mRNA Vaccine Degradation Prediction
11953652,0.26075,4,15,/hirayukis/pytorch-lstm-ensemble,OpenVaccine: COVID-19 mRNA Vaccine Degradation Prediction
10993625,0.05486,0,0,/doaajaber/leaf-classification-cnnn,Leaf Classification
11213660,0.8784700000000001,0,4,/mikhailg0/leaf-classification-solution,Leaf Classification
10918128,0.10023,0,0,/fathialasali/leaf-classification-comp,Leaf Classification
10908340,0.67291,0,1,/mahmoudalhallaq/leaf-mahmoud,Leaf Classification
10889834,4.8369,0,5,/abdalazez/leaf-classification,Leaf Classification
10804240,0.85429,0,0,/maimahdi/xgboostleafclass,Leaf Classification
10756965,0.70023,0,1,/islammohaisen/leaf-classification-lda,Leaf Classification
6641449,0.01991,0,3,/saeedtqp/leaf-classification,Leaf Classification
5095755,0.0454899999999999,0,0,/zhudongxiao/cnn-w-o-image-data,Leaf Classification
5021099,2.32036,1,0,/ahkhalwai55/classifier-showdown,Leaf Classification
3604880,0.02878,0,0,/jerrypan001/kernelc607afd0d6,Leaf Classification
590654,0.5009600000000001,0,0,/akhileshjoshi/ann-keras-dropout-99-6-accuracy,Leaf Classification
229903,0.25553,0,1,/darrellulm/leaf-basic-attempt-fork-and-test,Leaf Classification
7243712,0.48944,0,1,/a6015a/kernel17f49e3279,Quora Insincere Questions Classification
7149377,0.0,0,0,/alexejdrosdov/lb6nlp-2,Quora Insincere Questions Classification
6820920,0.6322800000000001,0,0,/sebastianalcala/hep-py,Quora Insincere Questions Classification
6811553,0.60254,0,0,/ixcheldelsol/proyecto-3-emergente,Quora Insincere Questions Classification
6800400,0.67506,0,0,/manuelojeda432/p3-manuel-ojeda,Quora Insincere Questions Classification
2505701,0.679,0,0,/thtonmoy/insincere-questions-gru-with-attention-mechanism,Quora Insincere Questions Classification
6531716,0.69743,0,2,/abyaadrafid/qiqc-final-submission,Quora Insincere Questions Classification
6458859,0.67528,0,1,/dipta007/only-glove,Quora Insincere Questions Classification
2402770,0.6709999999999999,0,1,/magnusmcm/kenet,Quora Insincere Questions Classification
6057922,0.20019,0,10,/jsvishnuj/nlp-using-deep-learning-for-beginners,Quora Insincere Questions Classification
5934730,0.6967,0,0,/therabiulawal/insincere-approach-1,Quora Insincere Questions Classification
5313358,0.43361,0,0,/abhisheksurana/kernel4d305f1b3d,Quora Insincere Questions Classification
4636104,0.66461,0,0,/plasticgrammer/quora-insincere-questions-playground,Quora Insincere Questions Classification
4940430,0.4397399999999999,0,0,/johnies/quora-insincere,Quora Insincere Questions Classification
2357531,0.65,0,0,/shwksl101/baseline,Quora Insincere Questions Classification
8979881,0.03639,0,0,/pritaman/covid19-global-forecasting-week-4,COVID19 Global Forecasting (Week 4)
9030702,0.03025,0,3,/woshifym/time-series-lstm-forecasting-without-external-data,COVID19 Global Forecasting (Week 4)
9037541,4.56131,0,3,/kartikay99k/covid-19,COVID19 Global Forecasting (Week 4)
9091029,0.11388,0,0,/wuhong60909/logistic-growth-model-2020-04-22,COVID19 Global Forecasting (Week 4)
8965017,3.31585,0,1,/bhumiharrakeshsharma/covid-19-week-4,COVID19 Global Forecasting (Week 4)
8992221,0.01135,1,5,/cpmpml/covid-19-week-with-extra-day,COVID19 Global Forecasting (Week 4)
8943935,0.07053,0,1,/denychaen/covid-19-marspline-model-w4,COVID19 Global Forecasting (Week 4)
8914966,0.03275,0,1,/ashora/lgbm-baseline-short-elegant,COVID19 Global Forecasting (Week 4)
8929289,0.31655,0,0,/ilu000/arima-week-4,COVID19 Global Forecasting (Week 4)
8889295,0.0969899999999999,0,0,/takiyu/arima-week4,COVID19 Global Forecasting (Week 4)
8951169,0.20897,0,0,/caracena/submission-week-4,COVID19 Global Forecasting (Week 4)
8932921,0.03632,0,0,/shashankponugoti/csce-5300-covid,COVID19 Global Forecasting (Week 4)
8848626,0.0947799999999999,0,0,/ruzgol/covid-19-lgbm,COVID19 Global Forecasting (Week 4)
8940761,0.04633,0,0,/ee257sp20darshan/darshan-s-r-sp20-ee257-p,COVID19 Global Forecasting (Week 4)
8828686,0.24752,0,1,/johnprasanth/sarima-forecasting-week4,COVID19 Global Forecasting (Week 4)
8947410,0.0364,0,1,/yogeshpalrecha/covid19-week4-challenge,COVID19 Global Forecasting (Week 4)
8927187,2.89652,0,0,/jonathanbilstein/covid-19-forecast-of-infections-and-fatalities,COVID19 Global Forecasting (Week 4)
8950720,2.33407,0,1,/andnov/zand-covid19-gf-w4-catboost,COVID19 Global Forecasting (Week 4)
8900801,0.06104,0,0,/sophiasusanraju/covid-prediction-week4-v1,COVID19 Global Forecasting (Week 4)
8905476,0.53409,0,0,/razasaleemi/covid19-global-forecasting-week4,COVID19 Global Forecasting (Week 4)
8847829,0.32465,0,0,/gunavardhanjakkidi/covid19week4,COVID19 Global Forecasting (Week 4)
8930820,0.03639,0,0,/ee257sp20madhuarjun/randomforest,COVID19 Global Forecasting (Week 4)
8926007,0.06157,0,1,/digimagi/covid19-forecasting-xgboost,COVID19 Global Forecasting (Week 4)
8925916,0.9267,0,0,/sebastianpine/covid19-forecast-week4,COVID19 Global Forecasting (Week 4)
8862021,0.04024,0,0,/alviishmam/covid-19-week-4,COVID19 Global Forecasting (Week 4)
8907929,0.06104,0,0,/alexkhrystoforov/covid-19,COVID19 Global Forecasting (Week 4)
8947715,0.0298899999999999,0,10,/kazanova/script-with-commented-train-counts-for-zeros,COVID19 Global Forecasting (Week 4)
8934662,0.0427899999999999,0,0,/liangpang/prediction-week4,COVID19 Global Forecasting (Week 4)
8833286,0.03447,0,0,/andrewkagel/covid19-week4-1,COVID19 Global Forecasting (Week 4)
8841871,0.14874,0,0,/tigeriv/covidweek4deeplearning,COVID19 Global Forecasting (Week 4)
8949326,0.03208,0,12,/cpmpml/covid-19-week-4,COVID19 Global Forecasting (Week 4)
8949969,0.67848,0,0,/oksanasem/some-xgb-with-logfit,COVID19 Global Forecasting (Week 4)
8933759,0.06423,0,1,/ffares/xgboost2,COVID19 Global Forecasting (Week 4)
8847892,0.24693,0,1,/diamondsnake/covid-19-logistic-curve-fitting-week-4,COVID19 Global Forecasting (Week 4)
8911578,0.03325,0,2,/titericz/week-3-blend,COVID19 Global Forecasting (Week 4)
8947969,0.03354,0,5,/ddanevskyi/autoregressive-lgbm,COVID19 Global Forecasting (Week 4)
8948409,0.03295,1,2,/kirderf/all-in-super-average-over-900-models-v2,COVID19 Global Forecasting (Week 4)
8861687,2.26216,8,8,/omegaji/covid-19-india-maps-eda-xgboost,COVID19 Global Forecasting (Week 4)
8839901,0.03319,0,1,/steffanobrito/ponto-futuro-modelo-decay-fatalities-w4-v5,COVID19 Global Forecasting (Week 4)
8926233,0.0590299999999999,0,0,/kowjan1/nn-cig-alco-nolog1p-tail50,COVID19 Global Forecasting (Week 4)
8942449,0.15896,0,1,/mathurinache/mathurin-week4-prevision3,COVID19 Global Forecasting (Week 4)
8848976,0.3432699999999999,0,0,/praveenadepu/w4-001,COVID19 Global Forecasting (Week 4)
8950459,0.03377,0,3,/abhijithchandradas/eda-and-forcast-polynomial-linear-regression,COVID19 Global Forecasting (Week 4)
8916385,0.0571,0,2,/taohoang/lstm-with-multistep-prediction,COVID19 Global Forecasting (Week 4)
8887307,0.45853,0,1,/amezet/covid-19-lightgbm-2nd-place-of-w1-math-w4,COVID19 Global Forecasting (Week 4)
8943962,0.03645,0,0,/hikne707/covid-19-global-forecasting-week-4,COVID19 Global Forecasting (Week 4)
8903901,0.07483,0,1,/super13579/covid-19-global-forecast-seir-visualize-w4,COVID19 Global Forecasting (Week 4)
8832363,4.59195,0,1,/cmanning/covid-ets-wk4,COVID19 Global Forecasting (Week 4)
8753994,0.0343,0,1,/ngyibin/covid19-global-forecasting-linear-regression,COVID19 Global Forecasting (Week 4)
8887277,0.3463,0,0,/amezet/arima-influenza-mathematical-solution-w4,COVID19 Global Forecasting (Week 4)
8950160,0.11753,0,1,/nxpnsv/tbtb-w4,COVID19 Global Forecasting (Week 4)
8950377,0.09652,0,1,/carolzhang/week4-logistic-exponential-linear-models,COVID19 Global Forecasting (Week 4)
8921145,3.2859199999999995,0,0,/caodai/week4-1,COVID19 Global Forecasting (Week 4)
8833754,1.49118,0,0,/nicksadjoli/covid19-projections-w-interpretml,COVID19 Global Forecasting (Week 4)
4666536,0.6739,0,0,/richardeascanio/lstm-p3,Quora Insincere Questions Classification
4750068,0.63579,0,0,/vmmq94/ptresvvc,Quora Insincere Questions Classification
4662135,0.6693,1,2,/alemvangrieken/proyecto-3-emergente-rnn-marcano-suarez-graterol,Quora Insincere Questions Classification
4552004,0.71239,0,1,/taziz437/tariqaziz,Quora Insincere Questions Classification
2424688,0.667,0,0,/navneetkr123/quoratry1,Quora Insincere Questions Classification
4025527,0.58309,0,0,/divyanshrai/quora,Quora Insincere Questions Classification
3200145,0.67342,0,2,/ilazarevsky/quora-experiments,Quora Insincere Questions Classification
3943000,0.14761,0,0,/rakeshm6295/qiqc-model-on-glove-embedding,Quora Insincere Questions Classification
3972999,0.53465,0,0,/swathiperuvaje/kerneld73e1265ac,Quora Insincere Questions Classification
3746938,0.61919,0,0,/asifhashmi/quoraclassification,Quora Insincere Questions Classification
3751649,0.4515399999999999,0,0,/ankitj03/kernel12f3ae755d,Quora Insincere Questions Classification
3518655,0.62148,0,0,/return19/kernela7fdf4ba3a,Quora Insincere Questions Classification
3633607,0.61123,0,0,/xfffrank/fasttextclassification,Quora Insincere Questions Classification
3571156,0.66062,0,0,/aadilsrivastava01/qiqc-preprocessing-for-dl-with-embeddings,Quora Insincere Questions Classification
3569651,0.67792,0,0,/xfffrank/gru-with-preprocessed-embeddings,Quora Insincere Questions Classification
3547037,0.64056,0,0,/samarthsarin/keras-gru-with-glove-embedding,Quora Insincere Questions Classification
3506301,0.64041,0,0,/hrush777/lstm-with-glove-embedding,Quora Insincere Questions Classification
3382263,0.67293,0,0,/lundet/baseclassifier-neural-network,Quora Insincere Questions Classification
2412996,0.68579,0,1,/amitdoda/quora-amit-doda,Quora Insincere Questions Classification
3389823,0.56855,0,0,/lundet/baseclassifier-tfidf-svm,Quora Insincere Questions Classification
3411546,0.44456,0,0,/locnguyen14/naive-bayes,Quora Insincere Questions Classification
3375100,0.60823,2,14,/advaitsave/lstm-using-tensorflow-2-with-embeddings,Quora Insincere Questions Classification
4057461,0.38,0,0,/amirnasir/kernel1,Freesound Audio Tagging 2019
4171524,0.442,0,1,/amirnasir/kernel2,Freesound Audio Tagging 2019
4135579,0.693,1,1,/sabamotto/fat19-fastai-mixup-multipreprodataset,Freesound Audio Tagging 2019
4070280,0.667,2,22,/vinayaks/2d-cnn-high-score,Freesound Audio Tagging 2019
3718271,0.625,4,24,/vinayaks/simple-changes-significant-improvement-in-lb,Freesound Audio Tagging 2019
3676586,0.5870000000000001,0,19,/daisukelab/cnn-2d-basic-3-using-simple-model,Freesound Audio Tagging 2019
3677461,0.418,0,3,/wrosinski/baseline-resnet-like-keras,Freesound Audio Tagging 2019
3594454,0.5710000000000001,5,29,/hung96ad/naive-ensemble-model,Freesound Audio Tagging 2019
3615137,0.321,3,8,/titericz/lightgbm-lb-0-321,Freesound Audio Tagging 2019
3534292,0.413,20,93,/carlolepelaars/bidirectional-lstm-for-audio-labeling-with-keras,Freesound Audio Tagging 2019
3500121,0.497,46,182,/daisukelab/cnn-2d-basic-solution-powered-by-fast-ai,Freesound Audio Tagging 2019
62610,0.2177599999999999,0,0,/gautamsihag/withallatempt1,Expedia Hotel Recommendations
60527,0.3034,1,0,/praveen24kumar/predict-hotel-type-with-pandas,Expedia Hotel Recommendations
57737,0.30337,0,0,/drasko/predict-hotel-type-with-pandas,Expedia Hotel Recommendations
57202,0.30341,27,117,/dvasyukova/predict-hotel-type-with-pandas,Expedia Hotel Recommendations
2275980,0.0366,0,0,/takanobu0210/pubg-eda-feature-engineering-lightgbm,PUBG Finish Placement Prediction (Kernels Only)
2105288,0.2565,0,0,/lawngaiyan/fork-of-eda-pubg-yluo-part-1,PUBG Finish Placement Prediction (Kernels Only)
1874012,0.0386,0,0,/nikhil04/pubg-data-description-a-to-z-fe-with-python,PUBG Finish Placement Prediction (Kernels Only)
1858755,0.065,0,0,/tarunpaparaju/pubg-placement-predictor-catboost-v2,PUBG Finish Placement Prediction (Kernels Only)
1833614,0.088,0,0,/abhishek3100/pubg-finish,PUBG Finish Placement Prediction (Kernels Only)
1812520,0.069,0,0,/tarunpaparaju/pubg-placement-predictor-with-dnns-v2,PUBG Finish Placement Prediction (Kernels Only)
4563626,0.693,0,1,/aqeelnawaz44/pytorch-inference-kernel,APTOS 2019 Blindness Detection
4590369,0.034,17,40,/nivedas/aptos-blindness-detection-basic-cnn,APTOS 2019 Blindness Detection
4564103,0.075,0,0,/iwilldo/second-time-predict,APTOS 2019 Blindness Detection
4613857,0.71,9,12,/demonplus/fast-ai-starter-with-resnet-152,APTOS 2019 Blindness Detection
4566187,0.768,18,70,/hmendonca/multitask-efficientnetb4-ignite-amp-clr-aptos19,APTOS 2019 Blindness Detection
4583464,0.5539999999999999,0,0,/sohaibanwaar1203/drd-wihtout-outkiers,APTOS 2019 Blindness Detection
4569238,0.7,20,34,/kageyama/fork-of-fastai-blindness-detection-resnet34,APTOS 2019 Blindness Detection
4556507,0.589,5,82,/artgor/basic-eda-and-baseline-pytorch-model,APTOS 2019 Blindness Detection
4574511,0.04,0,4,/mehtad/base-model,APTOS 2019 Blindness Detection
4560016,0.6659999999999999,0,22,/seefun/basic-eda-and-pytorch-resnext-model,APTOS 2019 Blindness Detection
4556680,0.364,6,22,/khursani8/fast-ai-starter-resnet34,APTOS 2019 Blindness Detection
4556323,0.695,4,19,/thedocs/fast-ai-starter-800x800,APTOS 2019 Blindness Detection
4564531,0.006,0,1,/mustaffxx/aptos-2019-keras-cnn,APTOS 2019 Blindness Detection
5655675,0.8109999999999999,0,0,/atmadeepb/best-submission,APTOS 2019 Blindness Detection
13014913,1.0881299999999998,2,9,/ezeanyi/predicting-future-sales-with-neural-networks,Predict Future Sales
12805722,4.01993,1,5,/erelin6613/predict-future-sales,Predict Future Sales
11870893,1.00573,0,1,/sjaddya/xgbmodel-with-feature-selection,Predict Future Sales
12244404,9.17353,0,0,/anujsoni/random-forest-ensemble-learning,Predict Future Sales
12147444,1.02655,2,0,/tracyporter/predict-sales-ada-boost,Predict Future Sales
12074954,0.89935,0,0,/taidopurason/xgboost-model,Predict Future Sales
11950392,1.23327,0,4,/amanooo/future-sales-pred-by-the-approximate-expression,Predict Future Sales
11289713,1.16632,6,16,/yashudua/sales-viz-prediction-private-0-86289,Predict Future Sales
11658762,1.15357,1,0,/nishant483/time-series-analysis-using-lstm,Predict Future Sales
13468451,25.53827,0,0,/semenedel/notebook5982a0b143,Lyft Motion Prediction for Autonomous Vehicles
7908790,2.26562,0,0,/vh1981/sf-crime-with-lgbm-bo,San Francisco Crime Classification
11070170,32.89183,0,0,/teramera/kernel6716e7e815,San Francisco Crime Classification
4947125,2.2463900000000003,0,0,/sjun4530/kernelae032cdfc5,San Francisco Crime Classification
7928250,2.45572,0,0,/doublepoi/a-nn-with-residual-v4,San Francisco Crime Classification
7600561,2.79213,0,1,/qiaoqiao123/have-try,San Francisco Crime Classification
6414280,2.43048,1,7,/guidosalimbeni/random-forest-crime-classification,San Francisco Crime Classification
4920991,2.49728,0,0,/fycher/sf-crimes-hackatinho-imers-o-em-ci-ncia-de-dados,San Francisco Crime Classification
4389603,2.25078,0,0,/godeepdeep/clclclcl123,San Francisco Crime Classification
3174623,2.2501,0,4,/junheo/sf-crime-rate-prediction,San Francisco Crime Classification
2558858,2.25639,12,48,/yannisp/sf-crime-analysis-prediction,San Francisco Crime Classification
2567173,2.68015,0,0,/yannisp/sf-crime-analysis-prediction-naive-prediction,San Francisco Crime Classification
1264319,25.36539,0,1,/labibchowdhury/san-francisco-crime-classification,San Francisco Crime Classification
12634290,0.13037,0,6,/eduardorenz/house-prices-competition-step-by-step,House Prices - Advanced Regression Techniques
12300170,0.24835,0,0,/nickolayshklyaruk/finalscore,House Prices - Advanced Regression Techniques
12883128,0.17906,0,1,/jaeyeonwon/notebookda900237a7,House Prices - Advanced Regression Techniques
12802419,0.12448,0,0,/kelvinchow1979/voteregression,House Prices - Advanced Regression Techniques
12640935,0.12102,1,2,/randommmjy/data-analysis-house-price-prediction,House Prices - Advanced Regression Techniques
12623751,0.141,0,0,/grigorelucian/hyperparameter-tuning-on-house-prices,House Prices - Advanced Regression Techniques
12754247,0.20062,0,0,/dwz9406/house-prices-v2-lr-no-std,House Prices - Advanced Regression Techniques
12345662,0.00044,0,0,/gizemcemileelik/house-price-project-copied,House Prices - Advanced Regression Techniques
12672191,0.15047,0,2,/carlmcbrideellis/xgboost-benchmark,House Prices - Advanced Regression Techniques
12685267,1.16539,0,1,/umarkhancodes/ames-house-price,House Prices - Advanced Regression Techniques
12581297,0.66961,0,0,/katelynschreyer/data-201-lab,House Prices - Advanced Regression Techniques
12395499,0.1193,0,1,/pavelbulgakov/melody-of-the-magical-forest-top-6,House Prices - Advanced Regression Techniques
12479286,0.12575,0,9,/erkanhatipoglu/house-prices-using-pipelines,House Prices - Advanced Regression Techniques
12682506,0.92967,0,0,/deztrucktor/planet-k009-resnet50,Planet: Understanding the Amazon from Space
4212546,0.67484,0,0,/prateekjha/amazon-transfer-learning,Planet: Understanding the Amazon from Space
3087866,0.93094,2,6,/liuyd2018/planet-multi-label-image-classification,Planet: Understanding the Amazon from Space
1625508,0.44611,0,0,/ambarish/amazon-from-space-image-analysis,Planet: Understanding the Amazon from Space
3192730,0.807,1,3,/arunsing/santandar-eda-and-prediction,Santander Customer Transaction Prediction
3164086,0.901,0,5,/ankitdhall97/basic-models,Santander Customer Transaction Prediction
3181692,0.897,2,5,/huangzhiyong/a-simple-lgb-base-on-undersampler,Santander Customer Transaction Prediction
3108886,0.895,0,0,/viewside/kernel1480a081ac,Santander Customer Transaction Prediction
3160355,0.8540000000000001,3,4,/t26wtnb/adabound-optimizer-dnn-v-s-adam-experiment,Santander Customer Transaction Prediction
3058623,0.9,0,0,/rishrk007/santander-transaction,Santander Customer Transaction Prediction
3136254,0.898,0,3,/tomehta/feature-engineering-eda-and-lgbm,Santander Customer Transaction Prediction
3131672,0.898,7,30,/silverstone1903/xgboost-baseline,Santander Customer Transaction Prediction
3134932,0.888,6,13,/shrutimechlearn/santander-customer-transaction-pca-and-nb,Santander Customer Transaction Prediction
3097597,0.901,0,17,/deepak525/best-parameters-lb-0-900,Santander Customer Transaction Prediction
3126980,0.86,1,4,/kaggle2007/simple-nn-with-accuracy-and-loss-plotting,Santander Customer Transaction Prediction
3099957,0.86,0,4,/chiripurapu/nn-rlr-es-checkpoint-adam-std-regularize,Santander Customer Transaction Prediction
3051996,0.8590000000000001,0,0,/mchatham/logisticregressioncv-varselection-resampling,Santander Customer Transaction Prediction
3073729,0.895,4,6,/jatinmittal0001/santander-binary-classification,Santander Customer Transaction Prediction
3074473,0.86,2,3,/egregori/handle-imbalance-data-with-neural-network,Santander Customer Transaction Prediction
3097436,0.8640000000000001,0,2,/viswajithkn/santander-unbalanced-data,Santander Customer Transaction Prediction
3002729,0.898,0,0,/nazirashaikh/santander-customer-prediction,Santander Customer Transaction Prediction
3038236,0.898,38,191,/mathormad/knowledge-distillation-with-nn-rankgauss,Santander Customer Transaction Prediction
3111227,0.888,1,0,/jialinzhang/xgb-oversampling,Santander Customer Transaction Prediction
3101640,0.894,1,0,/missionagain/kernel5183d25362,Santander Customer Transaction Prediction
2167716,1678994.23,15,31,/hirune924/random-insertion-no-concord-solver-solution,Traveling Santa 2018 - Prime Paths
2158674,1656703.12,25,103,/jpmiller/google-or-tools-w-clusters,Traveling Santa 2018 - Prime Paths
2169971,1533242.06,0,2,/wouterlefever/concorde-solver,Traveling Santa 2018 - Prime Paths
2166075,1812550.5,2,4,/nonreviad/rudolph-the-slightly-less-greedy-reindeer,Traveling Santa 2018 - Prime Paths
2470550,1515656.79,0,0,/zfturbo/not-a-3-and-3-halves-opt,Traveling Santa 2018 - Prime Paths
12376036,0.97396,1,1,/marcogorelli/cln-introduction-to-cnn-keras-0-997-top-6,Digit Recognizer
12297004,0.99346,0,0,/omkarkonduskar/digit-recognizor-keras-cnn,Digit Recognizer
12275253,0.99425,1,1,/olgabelitskaya/recipes-of-classification,Digit Recognizer
12158968,0.97828,0,2,/mdhamani/digitrecognizer-pytorch-ann,Digit Recognizer
12233585,0.98525,1,3,/tharhtetsan/getting-start-with-tensorflow-cnn,Digit Recognizer
12185623,0.93203,0,0,/paulpernalon/mnist-dl,Digit Recognizer
12120426,0.99553,1,5,/leangab/cnn-keras-0-995,Digit Recognizer
12181202,0.91614,0,3,/gauravduttakiit/digit-recognizer-using-xgbclassifier,Digit Recognizer
12180959,0.93707,0,2,/gauravduttakiit/digit-recognizer-using-baggingclassifier,Digit Recognizer
11955886,0.99392,1,6,/bhaskar47/score-0-9927-on-mnist-with-indepth-detailed-analys,Digit Recognizer
12060595,0.99417,0,7,/tfukuda675/mnist-pytorch,Digit Recognizer
10452658,0.98782,0,0,/t4t5u0/kernel56763ee529,Digit Recognizer
13755992,0.893,0,4,/shubham108/ensemble-effecientnet-resnet-pytorch-tf2,Cassava Leaf Disease Classification
13724678,0.8909999999999999,1,33,/shaolihuang/training-with-snapmix,Cassava Leaf Disease Classification
13703884,0.897,0,0,/sneky369/cassava-leaf-disease-tpu-tensorflow-inference,Cassava Leaf Disease Classification
13629501,0.89,0,0,/wangyuf/notebook7b31fc3dbf,Cassava Leaf Disease Classification
13722010,0.879,0,0,/ekshusingh/torch-se,Cassava Leaf Disease Classification
13697112,0.205,0,1,/abhinay71/cassava-leaf-disease-prediction,Cassava Leaf Disease Classification
13571499,0.8390000000000001,0,0,/marcosoliveirajr/nb-cassava-marcos-submission,Cassava Leaf Disease Classification
13417467,0.898,0,0,/zekun98/pytorch-efficientnet-baseline-inference-tta,Cassava Leaf Disease Classification
13659105,0.5820000000000001,0,1,/ricardoaraujo/cassava-submission-template,Cassava Leaf Disease Classification
13670385,0.604,0,0,/vinciusalveshax/cassava-submission-template,Cassava Leaf Disease Classification
13609476,0.866,0,3,/abdulhadialshareef/effecientnet-b5,Cassava Leaf Disease Classification
13587519,0.86,0,0,/talonolasco/notebookae18fe1df1,Cassava Leaf Disease Classification
13541756,0.742,0,2,/wagnerloch/notebook0ab49faa31,Cassava Leaf Disease Classification
13532680,0.602,0,0,/zainahmedsharif/cassanava22efficientnetb3,Cassava Leaf Disease Classification
13464807,0.887,0,3,/tianyu5/tpus-cassava-leaf-disease-infer,Cassava Leaf Disease Classification
13296423,0.8959999999999999,0,1,/dongyingwang/cassava-leaf-disease-classification-predictions,Cassava Leaf Disease Classification
13446743,0.887,5,5,/keimoriyama/my-baseline,Cassava Leaf Disease Classification
13515105,0.139,0,0,/garibaldisilveira/cassava-garibaldd,Cassava Leaf Disease Classification
11269476,0.78057,2,10,/dmkravtsov/6-0-cats-challenge,Categorical Feature Encoding Challenge II
10899172,0.78137,0,0,/viktorpopov/cfec-ii,Categorical Feature Encoding Challenge II
9953097,0.78437,0,0,/aku0810/categorical-features,Categorical Feature Encoding Challenge II
7950910,0.7859,0,2,/lavanyashukla01/practical-guide-to-finding-the-best-ml-model,Categorical Feature Encoding Challenge II
8773664,0.74997,0,3,/gowrishankarin/learn-tensorflow2-feature-engg-metrics-0-7805,Categorical Feature Encoding Challenge II
8687105,0.78561,1,4,/tymurprorochenko/simplest-lr-12-lines,Categorical Feature Encoding Challenge II
8659952,0.76768,0,1,/venmani27/catboost-model-0-76788,Categorical Feature Encoding Challenge II
8635633,0.7391300000000001,0,0,/egolinko/cat-deux,Categorical Feature Encoding Challenge II
8524038,0.77515,0,0,/ajisamudra/experimenting-with-categorical-encoding,Categorical Feature Encoding Challenge II
8509310,0.78281,0,0,/fkdplc/cat-2-target-snapshot-ensemble,Categorical Feature Encoding Challenge II
8395192,0.78574,0,5,/gpamoukoff/using-knime-in-kernel-complete-pipeline-glm-lgb,Categorical Feature Encoding Challenge II
7895620,0.77074,0,0,/jaquelinelu/onehotencode-catboost-beginner-friendly,Categorical Feature Encoding Challenge II
8177275,0.78004,0,0,/nwolpert/cfec-ii-ensemble-model,Categorical Feature Encoding Challenge II
8149577,0.78567,1,1,/hotton/cat2te3,Categorical Feature Encoding Challenge II
7784974,0.7801,0,0,/ckanth090/cat-booost-the-data,Categorical Feature Encoding Challenge II
7361564,0.64342,0,2,/shrutimechlearn/purrfect-start,Categorical Feature Encoding Challenge II
7988709,0.78435,2,4,/vovkaperm/simple-stacking-catboost,Categorical Feature Encoding Challenge II
4836150,0.8809999999999999,0,1,/ranjitkumar1/kin-detection,Northeastern SMILE Lab - Recognizing Faces in the Wild
3966082,0.7040000000000001,1,0,/keltingrimes/feature-generation-and-analysis,Northeastern SMILE Lab - Recognizing Faces in the Wild
4614576,0.882,2,9,/tenffe/vggface-cv-focal-loss,Northeastern SMILE Lab - Recognizing Faces in the Wild
4949830,0.8959999999999999,4,7,/vaishvik25/smile,Northeastern SMILE Lab - Recognizing Faces in the Wild
4809341,0.878,0,3,/tenffe/blending-models-for-smile,Northeastern SMILE Lab - Recognizing Faces in the Wild
4577053,0.769,0,4,/flpvvvv/eda-cv-facenet-fe-lr,Northeastern SMILE Lab - Recognizing Faces in the Wild
4295667,0.855,1,3,/arjunrao2000/kinship-detection-with-vgg16,Northeastern SMILE Lab - Recognizing Faces in the Wild
4199520,0.754,0,59,/ateplyuk/vggface-baseline-in-keras,Northeastern SMILE Lab - Recognizing Faces in the Wild
3905750,0.5429999999999999,8,26,/thanatoz/one-shot-method-to-tackle-kinship-problem-keras,Northeastern SMILE Lab - Recognizing Faces in the Wild
3907286,0.754,0,3,/sanikamal/recognizing-faces-quick-eda-vggface,Northeastern SMILE Lab - Recognizing Faces in the Wild
5505420,0.7490000000000001,0,0,/teguhbds/teguhfacenetkeras,Northeastern SMILE Lab - Recognizing Faces in the Wild
598199,0.45773,0,1,/arunrajagopalan/mercari-predictions-ridge-ftrl-and-lgbm,Mercari Price Suggestion Challenge
635098,0.73056,2,2,/neetisinghal/xgboost-02,Mercari Price Suggestion Challenge
626884,0.42295,0,13,/dromosys/associated-model-rnn-ridge-1e0c6e,Mercari Price Suggestion Challenge
535372,0.44531,2,5,/rahullalu/mercari-price-suggestion-system-0-446125129352,Mercari Price Suggestion Challenge
625487,0.82478,0,0,/hg285808684/yyallday-s-work,Mercari Price Suggestion Challenge
593912,0.53614,0,1,/thec03u5/basic-random-forest,Mercari Price Suggestion Challenge
559604,0.55183,0,4,/n0rdic/mercari-xgb-simple-solution,Mercari Price Suggestion Challenge
528717,0.71502,0,0,/suchethrp/test-try1-sucheth,Mercari Price Suggestion Challenge
564958,0.59883,0,0,/lucenya/mercari-xgboost,Mercari Price Suggestion Challenge
570262,0.54593,0,4,/thomastrenner/mercari-baseline,Mercari Price Suggestion Challenge
558571,0.55013,0,0,/pavel26/notebookb2e0ebdd78,Mercari Price Suggestion Challenge
554659,0.46633,0,1,/b3lial/rnn-solution-with-keras,Mercari Price Suggestion Challenge
551069,0.8246600000000001,0,1,/improvnps/not-5-alphanumric-characters,Mercari Price Suggestion Challenge
543952,0.4325,0,4,/puks11/lerenbeslissen17-19-01,Mercari Price Suggestion Challenge
542026,0.5918899999999999,3,2,/mbkinaci/ann-with-keras-2-hidden-layers,Mercari Price Suggestion Challenge
543378,0.47336,0,0,/markmaxf/notebook4190ae9f24,Mercari Price Suggestion Challenge
519799,0.4569399999999999,2,21,/alaeddineayadi/neural-net-solution-with-keras,Mercari Price Suggestion Challenge
506453,0.56497,4,16,/justjun0321/products-eda-random-forest-regressor,Mercari Price Suggestion Challenge
507165,0.8105600000000001,0,0,/chase0213/the-first-note,Mercari Price Suggestion Challenge
505367,0.60159,0,1,/nchernetsov/mercari-notebook,Mercari Price Suggestion Challenge
484359,0.4504,13,33,/luisgarcia/keras-nn-with-parallelized-batch-training,Mercari Price Suggestion Challenge
68482,0.86554,0,0,/rsbkr333/feature-importance-w-xgboost,Airbnb New User Bookings
30537,0.86521,0,0,/chiuyeelau/airbnbbookings,Airbnb New User Bookings
5802028,0.9413,0,0,/utiancarter/feature-engineered-lightgbm,IEEE-CIS Fraud Detection
5823071,0.9506,12,117,/whitebird/a-method-to-valid-offline-lb-9506,IEEE-CIS Fraud Detection
5824311,0.948,0,5,/xwxw2929/lgb-kfold-newfeatures,IEEE-CIS Fraud Detection
5795002,0.9473,5,9,/ragnar123/e-d-a-and-baseline-mix-lgbm,IEEE-CIS Fraud Detection
5823473,0.6932,0,0,/deeplearn1/logistic-learning,IEEE-CIS Fraud Detection
5840206,0.9384,0,0,/denkimagic/kernel2320531053,IEEE-CIS Fraud Detection
5805686,0.9323,1,1,/xwxw2929/which-one-is-the-best,IEEE-CIS Fraud Detection
5794691,0.9389,0,1,/xwxw2929/xgb-kfold,IEEE-CIS Fraud Detection
5745220,0.9266,0,1,/chdhatri/cis-fraud-detection,IEEE-CIS Fraud Detection
5739650,0.8582,0,0,/nickthegreek82/fraud-detection,IEEE-CIS Fraud Detection
5749368,0.9176,4,9,/a03102030/eda-for-train-data-and-lgbm-lr,IEEE-CIS Fraud Detection
5171412,0.9399,0,1,/zzerozz/folk-ieee-lgb-bayesian-opt,IEEE-CIS Fraud Detection
5667054,0.9027,0,2,/dunaizhuan/test-on-timesplit,IEEE-CIS Fraud Detection
5250847,0.9464,28,147,/ysjf13/cis-fraud-detection-visualize-feature-engineering,IEEE-CIS Fraud Detection
5618684,0.9407,11,60,/kyakovlev/ieee-catboost-baseline-with-groupkfold-cv,IEEE-CIS Fraud Detection
5594631,0.9485,36,183,/kyakovlev/ieee-lgbm-with-groupkfold-cv,IEEE-CIS Fraud Detection
5545872,0.9216,3,4,/drexpz/ieee-xgboost-baseline-feature-import-0-92x,IEEE-CIS Fraud Detection
10348258,0.58096,1,5,/arindambaruah/amzn-access-predictions-beginner,Amazon.com - Employee Access Challenge
3402586,0.8914799999999999,0,31,/dmitrylarko/kaggledays-sf-2-amazon-unsupervised-encoding,Amazon.com - Employee Access Challenge
3258062,0.335,0,0,/praxitelisk/petfinder-adoption-prediction-eda-xgboost,PetFinder.my Adoption Prediction
3342592,0.2735599999999999,0,0,/akanumur/adoption-speed-based-on-non-image-data,PetFinder.my Adoption Prediction
3251560,0.3389999999999999,1,1,/jshen97/a-baseline-submission-with-random-forest-model,PetFinder.my Adoption Prediction
3151030,0.102,2,3,/shikha130vv/pet-finder-using-pytorch-and-bert-api,PetFinder.my Adoption Prediction
3163084,0.451,14,96,/reppy4620/xgboost,PetFinder.my Adoption Prediction
3152799,0.442,1,21,/ranjoranjan/stacking-kernels-lb-0-442,PetFinder.my Adoption Prediction
3116610,0.441,9,45,/bibek777/light-check,PetFinder.my Adoption Prediction
2630549,0.421,1,4,/skooch/corrected-catboostregressor,PetFinder.my Adoption Prediction
3060337,0.432,2,13,/ranjoranjan/baselinemodeling-keown,PetFinder.my Adoption Prediction
3103005,0.362,8,12,/zaharch/minimizing-qwk-directly-with-nn,PetFinder.my Adoption Prediction
2988686,0.428,2,11,/cdhimmel/baselinemodel-segmented,PetFinder.my Adoption Prediction
2874594,0.3279999999999999,0,0,/kqkaggle/first-kernel-0adb76,PetFinder.my Adoption Prediction
2968733,0.32,0,0,/sungdoo/petfinder-my-practice,PetFinder.my Adoption Prediction
2921010,0.344,4,7,/ksaaskil/feature-importance-exploration-and-baseline,PetFinder.my Adoption Prediction
2825008,0.208,0,1,/rodasoares/neural-network-genetic-weigths-evolution,PetFinder.my Adoption Prediction
14479967,0.69752,3,35,/tunguz/tps-01-21-feature-importance-with-xgboost-and-shap,Tabular Playground Series - Jan 2021
14532547,0.7024,38,28,/scarecrow2020/whole-pipeline-eda-lgbm-xgb-stackingreg,Tabular Playground Series - Jan 2021
13952385,0.6965399999999999,5,14,/gunesevitan/tabular-playground-series-jan-2021-models,Tabular Playground Series - Jan 2021
14517964,0.6976,3,14,/maunish/tps-simple-stacking,Tabular Playground Series - Jan 2021
14455624,0.7096,0,10,/tunguz/tps-rapids-baseline,Tabular Playground Series - Jan 2021
14388243,0.70426,4,8,/jamesmcguigan/tabular-playground-xgboost,Tabular Playground Series - Jan 2021
14421266,0.70236,1,3,/maostack/english-for-beginner-how-to-xgboost,Tabular Playground Series - Jan 2021
14479626,0.72836,4,2,/danevans/jan-playground-v2,Tabular Playground Series - Jan 2021
14395147,0.70853,0,3,/chaudharypriyanshu/kfold-optuna-xgboost,Tabular Playground Series - Jan 2021
14565931,0.7137600000000001,0,1,/sdtrklse/tabular-playground-jan21,Tabular Playground Series - Jan 2021
14520972,0.72218,1,1,/nguyncaoduy/fastai-tabular-regression-model-tabnet,Tabular Playground Series - Jan 2021
14591678,0.69784,0,0,/kennethr/jan-eda-xgb-optuna-hyperparam-tuning,Tabular Playground Series - Jan 2021
12604803,0.93463,1,4,/divyansh22/catboost-classifier-10-groupkfold-ion-switch,University of Liverpool - Ion Switching
12566545,0.9342,1,3,/divyansh22/lightning-fast-xgboost-regressor-with-rapids,University of Liverpool - Ion Switching
10044957,0.94249,1,6,/rakeshptiwari/liverpool-wavenet,University of Liverpool - Ion Switching
8592606,0.944,0,1,/anaidashaginian/eda-fft-qwk,University of Liverpool - Ion Switching
10147890,0.91801,0,1,/anermakov/ion-switching-signal-transformation-to-hit-0-92,University of Liverpool - Ion Switching
9847715,0.93903,0,1,/titericz/shifted-rfc-pipeline-using-cuml-rf-gpu,University of Liverpool - Ion Switching
9753268,0.92045,8,13,/group16/private-0-9688-a-better-but-useless-solution,University of Liverpool - Ion Switching
9830432,0.93504,0,0,/neomatrix369/90-features-model-clean-datasets,University of Liverpool - Ion Switching
9728632,0.94673,4,16,/meminozturk/into-the-wild-wavenet,University of Liverpool - Ion Switching
9736098,0.94686,4,10,/meminozturk/into-the-wild-xgb-submission,University of Liverpool - Ion Switching
9413672,0.94682,0,36,/khahuras/1st-place-non-leak-solution,University of Liverpool - Ion Switching
9212166,0.944,1,12,/khyeh0719/wavenet-with-augmentation-2,University of Liverpool - Ion Switching
9041448,0.8957299999999999,8,9,/divyansh22/xgb-regressor-on-ion-switch-4-fold-cv-strategy,University of Liverpool - Ion Switching
9480901,0.9428,5,7,/ks2019/hmm-posteriordecoder-without-prior,University of Liverpool - Ion Switching
9205393,0.92832,0,1,/neomatrix369/one-feature-model-clean-datasets-refactored,University of Liverpool - Ion Switching
9639810,0.932,0,0,/kazuhito00/lgbm-using-pycaret-0-932,University of Liverpool - Ion Switching
9561041,0.94,3,2,/lsw1993/strange-behaviors-of-proba-data-or-i-am-wrong,University of Liverpool - Ion Switching
9104766,0.42,0,8,/khotijahs1/identify-the-number-of-channels-open-at-each-time,University of Liverpool - Ion Switching
9449095,0.94,3,8,/pathofdata/wavelet-transfrom-wavenet,University of Liverpool - Ion Switching
9433903,0.922,1,5,/suparjo/training-by-segmenting-simple-split,University of Liverpool - Ion Switching
9426491,0.349,5,3,/k123vinod/when-you-try-traditional-machine-learning,University of Liverpool - Ion Switching
2020631,0.599,0,0,/abimannan/airship-deep-detection,Airbus Ship Detection Challenge
14487226,0.77604,0,0,/dhawalsoni/whatscooking,What's Cooking?
14124039,0.77453,0,0,/sanskrutighadipatil/what-s-cooking-logistic-regression,What's Cooking?
12523503,0.8106300000000001,3,8,/anmoltripathi/what-s-cooking-top-7-solution,What's Cooking?
12144887,0.74778,2,3,/cristianfat/what-do-we-have-for-dinner,What's Cooking?
10703448,0.7761399999999999,0,2,/julianbenny/whatscooking-logistic-regression,What's Cooking?
9648511,0.7729199999999999,0,0,/dhruvgupta2801/logistic-regression,What's Cooking?
4242357,0.78509,2,1,/shadylpstan/kernel1ceb500d89,What's Cooking?
3264023,0.7753399999999999,0,2,/aakashgoel12/cooking-kaggle-v1,What's Cooking?
1454062,0.66401,0,0,/jnsmorinigo/linearsvc,What's Cooking?
980607,0.78368,0,3,/limitpointinf0/analysis-of-cuisine-and-classification-modeling,What's Cooking?
92301,0.7881100000000001,2,4,/apapiu/linear-svc-on-bag-of-words,What's Cooking?
679647,0.976,0,0,/matheusbaldi/toxic-comments-clasification,Toxic Comment Classification Challenge
827973,0.9705,0,0,/sonuchhabra/toxic-comment-classification-challenge-c,Toxic Comment Classification Challenge
1091437,0.9813,0,0,/oo7feynman/toxic-comment-benchmarking-models,Toxic Comment Classification Challenge
1084489,0.9542,0,0,/joydeep29/embedding-lstm-cnn-with-rmsprop-loss-0-07,Toxic Comment Classification Challenge
1030686,0.9785,0,3,/rajat12345/lstm-embeddedmatrix,Toxic Comment Classification Challenge
955717,0.9478,3,7,/omkarsabnis/toxic-comment-classification,Toxic Comment Classification Challenge
556471,0.9739,0,2,/saxinou/toxiccomment-simple-regression-with-tf-idf,Toxic Comment Classification Challenge
592131,0.9825,0,1,/submarineering/submarineering-blending,Toxic Comment Classification Challenge
750089,0.9666,1,3,/grandmoon/random-forest-classification,Toxic Comment Classification Challenge
698960,0.9826,0,2,/ritam3144/cnn-bidirectional-lstm-with-fasttext,Toxic Comment Classification Challenge
737448,0.9804,2,7,/shanth84/tc-ensemble-3-models,Toxic Comment Classification Challenge
731211,0.9865,15,59,/reppic/lazy-ensembling-algorithm,Toxic Comment Classification Challenge
716611,0.9713,0,3,/andrewrib/modeling-online-toxicity-with-lstms,Toxic Comment Classification Challenge
710423,0.9824,0,6,/mahmudulhasanshauqi/toxic-with-nltk-fasttext-and-gru,Toxic Comment Classification Challenge
625378,0.9741,0,3,/laurenz900/toxic-comment-classification,Toxic Comment Classification Challenge
683482,0.9749,0,8,/sudhirnl7/logistic-regression-with-hashing-vectorizer,Toxic Comment Classification Challenge
8639132,0.0,5,40,/abhishek/tweet-text-extraction-roberta-infer,Tweet Sentiment Extraction
8613998,0.63,15,92,/abhishek/text-extraction-using-bert-w-sentiment-inference,Tweet Sentiment Extraction
8608169,0.698,30,101,/cheongwoongkang/roberta-baseline-starter-simple-postprocessing,Tweet Sentiment Extraction
8617858,0.589,14,20,/jonathanbesomi/0-573-lb-score-in-10-lines-of-code,Tweet Sentiment Extraction
8556989,0.664,104,983,/tanulsingh077/twitter-sentiment-extaction-analysis-eda-and-model,Tweet Sentiment Extraction
8596027,0.581,6,10,/nandhuelan/bert-pytorch-starter-v2,Tweet Sentiment Extraction
8560446,0.0,43,128,/ratan123/sentiment-extraction-understanding-metric-eda,Tweet Sentiment Extraction
8552167,0.271,1,2,/takamichitoda/tweet-simplebaseline,Tweet Sentiment Extraction
8573414,0.589,1,6,/aerdem4/tweet-sentiment-whole-text-baseline,Tweet Sentiment Extraction
8577276,0.589,1,1,/khulapkosv/baseline-sentiment,Tweet Sentiment Extraction
8566291,0.0,3,4,/rohitsingh9990/tweetsentimentextraction-eda-fe-simplebaseline,Tweet Sentiment Extraction
8554593,0.0,3,6,/zzy990106/tweet-sentiment-extraction-baseline,Tweet Sentiment Extraction
8554858,0.0,2,3,/grapestone5321/tweet-sentiment-extraction-sample-submission,Tweet Sentiment Extraction
12987934,0.7056100000000001,0,0,/nidhialipuria3369/nidhi-submission,Tweet Sentiment Extraction
10370911,0.69797,0,0,/natemare/eda-cnn-tsever-1,Tweet Sentiment Extraction
9978176,0.551,0,0,/myho63/testing,Tweet Sentiment Extraction
9835705,0.708,0,0,/sunnyville01/tensorflow-roberta-0-705,Tweet Sentiment Extraction
9239758,0.6990000000000001,0,0,/akashsuper2000/tensorflow-roberta-0-705,Tweet Sentiment Extraction
8857082,0.594,0,0,/phearos/start-from-here-complete-eda-baseline-sub,Tweet Sentiment Extraction
14055799,0.78708,0,1,/nainye/notebookc6e702ec37,Titanic - Machine Learning from Disaster
14169888,0.7751100000000001,0,1,/sominathavhad21/titanic-survival-prediction-using-logistic-regr,Titanic - Machine Learning from Disaster
14262317,0.7799,3,5,/noureddynmaousse/titanic-data-science-solutions-beginners,Titanic - Machine Learning from Disaster
12639892,0.7105199999999999,0,2,/priyammehta/titanic-analysis-by-beginner-for-begineers,Titanic - Machine Learning from Disaster
14257711,0.78708,3,3,/chienhsianghung/titanic-top-13-ensemble-randomforest-tuned,Titanic - Machine Learning from Disaster
10612785,0.7822899999999999,0,3,/erdemyilmazz/erdemtitanic-kernel-eda,Titanic - Machine Learning from Disaster
14169066,0.78468,0,16,/topptheeralerttham/titanic-beginner-s-simple-guide-w-random-forest,Titanic - Machine Learning from Disaster
14142946,0.7488,0,0,/ankit4371/titanic-survive-model,Titanic - Machine Learning from Disaster
7959703,1.0,33,253,/tarunpaparaju/arc-competition-eda-pytorch-cnn,Abstraction and Reasoning Challenge
7988657,1.0,7,80,/yakuben/basic-cnn-approach,Abstraction and Reasoning Challenge
7977914,1.0,0,1,/jt120lz/just-for-fun,Abstraction and Reasoning Challenge
9169975,0.98,0,0,/ashora/ensemble-simple-decision-tree,Abstraction and Reasoning Challenge
8919557,0.99,0,0,/akashsuper2000/xgbclassifier-for-arc-challenge,Abstraction and Reasoning Challenge
13467725,0.6528,0,0,/chia56028/pytorch-rcnn,Global Wheat Detection
12319360,0.6621,0,0,/tomchaniii/ai-fasterrcnn-inference,Global Wheat Detection
11038439,0.4305,0,0,/rishabhthakur07/fork-of-pages-2-0,Global Wheat Detection
10996330,0.7219,0,0,/zhang007bond/efficientdethhh,Global Wheat Detection
10962315,0.7442,0,0,/ufownl/global-wheat-detection-pseudo-labaling-608x608,Global Wheat Detection
10676990,0.7729,0,0,/genvsdis/yolov5-pseudo-labeling-oof-evaluation,Global Wheat Detection
9886464,0.6847,0,0,/nealys1049/test-pytorch-faster-r-cnn-with-resnet152-backbone,Global Wheat Detection
9577532,0.6857,15,42,/yashchoudhary/gwd-fasterrcnn-with-augmentation-train-inference,Global Wheat Detection
9510350,0.7451,43,137,/ufownl/global-wheat-detection-pseudo-labaling,Global Wheat Detection
9498754,0.6213,2,4,/marcomichelotti/efficientdet-d4-62,Global Wheat Detection
9386416,0.6569,7,37,/nvnnghia/yolov4-inference,Global Wheat Detection
9351797,0.7129,1,8,/ufownl/global-wheat-detection-inference,Global Wheat Detection
9322682,0.7077,26,188,/nvnnghia/fasterrcnn-pseudo-labeling,Global Wheat Detection
9356807,0.5998,4,10,/labintsevai/kerasretinanet-wheat-submission,Global Wheat Detection
9299343,0.6687,38,220,/pestipeti/pytorch-starter-fasterrcnn-inference,Global Wheat Detection
9324431,0.6728,0,22,/artgor/object-detection-with-pytorch-lightning-inference,Global Wheat Detection
9306194,0.6713,2,10,/arunmohan003/inferance-kernel-fasterrcnn,Global Wheat Detection
12041179,0.66962,0,2,/chitramdasgupta/kobe-bryant-shot-analysis,Kobe Bryant Shot Selection
6991790,0.81481,0,1,/anilkay/kobeshot,Kobe Bryant Shot Selection
5613476,0.60001,1,2,/riemann1859/shots-of-kobe-random-forest,Kobe Bryant Shot Selection
3255059,0.60787,0,0,/himselfthedecker/aam-atividade-final-avalia-o-space-jaml,Kobe Bryant Shot Selection
1349586,0.60063,0,2,/rchitic17/xgb-basket-or-not,Kobe Bryant Shot Selection
1182566,0.61258,0,0,/myxue4869/kobe-neural-network,Kobe Bryant Shot Selection
808208,0.6023,1,4,/plasticgrammer/kobe-shot-selection-training,Kobe Bryant Shot Selection
202167,0.60125,0,0,/kevins/fork-of-notebook55e4f6ba6a,Kobe Bryant Shot Selection
8534412,2.94732,0,0,/mustaphayinkayusuf/kernel11b6a7f215,COVID19 Global Forecasting (Week 1)
8487019,2.66933,0,0,/grapestone5321/covid19-global-forecasting-w1-sample-submission,COVID19 Global Forecasting (Week 1)
8583960,0.7026600000000001,0,0,/dott1718/cv19-by-growth-rate-v5-03-per-5,COVID19 Global Forecasting (Week 1)
8574092,0.68218,0,0,/akashsuper2000/lets-try-xgboost-simple-w-added-features,COVID19 Global Forecasting (Week 1)
8530053,1.02903,0,0,/yatinece/exp-model-to-state-of-city-or-country-test-7days,COVID19 Global Forecasting (Week 1)
13816070,0.78947,67,66,/riteshpatil8998/top-11-leaderboard-titanic-data-analysis,Titanic - Machine Learning from Disaster
14143711,0.77751,0,0,/aytumamkolu/titanic-solution,Titanic - Machine Learning from Disaster
14119657,0.7799,1,2,/dkumar12/titanic-using-knn,Titanic - Machine Learning from Disaster
14116304,0.76076,3,11,/dkumar12/titanic-using-random-forest,Titanic - Machine Learning from Disaster
14089630,0.7799,0,1,/ani24794/titanic-complete-machine-learning-solution,Titanic - Machine Learning from Disaster
12887739,0.76794,1,2,/amankumardwivedi/notebook3b3040ae3b,Titanic - Machine Learning from Disaster
14118328,0.61722,0,0,/goldmin9/titanium,Titanic - Machine Learning from Disaster
14057944,0.77751,0,0,/merakit/titanic-sample-notebook,Titanic - Machine Learning from Disaster
14088670,1.0,10,8,/hackspyder/easiest-way-to-get-top-score-100,Titanic - Machine Learning from Disaster
14160344,0.78947,0,0,/manoranjankrthakur/notebook5fb0e7184d,Titanic - Machine Learning from Disaster
13689598,0.76076,0,0,/naveenbhuvaneswaran/titanic-passenger-surviving-predictions,Titanic - Machine Learning from Disaster
14079362,0.76555,0,0,/devhjjoo/titanic,Titanic - Machine Learning from Disaster
13632440,0.7751100000000001,0,0,/laurenestrada/beginning-challenge-titanic,Titanic - Machine Learning from Disaster
8967354,0.71,6,44,/abhishek/roberta-inference-of-tpu-model-8-folds,Tweet Sentiment Extraction
8988645,0.687,0,0,/sandeepshaw/st-nlp-9,Tweet Sentiment Extraction
8963586,0.652,0,1,/nelepie/v1-nltk,Tweet Sentiment Extraction
8941299,0.7040000000000001,0,0,/xu1993/bert-base-with-tf2-1-mixed-precision,Tweet Sentiment Extraction
8877635,0.6409999999999999,0,3,/gaya33/nlp-m3-j007-j031-j046,Tweet Sentiment Extraction
8810818,0.7020000000000001,22,88,/enzoamp/commented-bert-base-uncased-using-pytorch,Tweet Sentiment Extraction
8876922,0.649,0,0,/aadiharan99/nlpm3-j008-j014,Tweet Sentiment Extraction
8879918,0.472,0,0,/manastokale/kernel16c154bb62,Tweet Sentiment Extraction
8753451,0.69,1,5,/bunnyyy/distillbert-for-sentiment-analysis,Tweet Sentiment Extraction
8714622,0.7090000000000001,1,28,/zzy990106/single-roberta-large,Tweet Sentiment Extraction
8749069,0.705,7,36,/cheongwoongkang/distilbert-qa-starter-cross-validation,Tweet Sentiment Extraction
8733808,0.586,0,5,/shinsei66/xlnet-baseline-v1,Tweet Sentiment Extraction
8683551,0.703,60,87,/akensert/tweet-bert-base-with-tf2-1-mixed-precision,Tweet Sentiment Extraction
8638246,0.6940000000000001,44,154,/jonathanbesomi/question-answering-starter-pack,Tweet Sentiment Extraction
8690096,0.6609999999999999,0,0,/bendigallardo/kernel70fedbd463,Tweet Sentiment Extraction
680354,0.9789,1,2,/thec03u5/tfidf-and-lr,Toxic Comment Classification Challenge
668823,0.9754,0,1,/zurman/blstm-glove,Toxic Comment Classification Challenge
658230,0.9629,1,2,/isahibzada/toxic-comment-classification-logistic-regression,Toxic Comment Classification Challenge
625382,0.9753,0,1,/kevinjordan/analysis-using-tfidfvectorizer,Toxic Comment Classification Challenge
605036,0.9788,2,9,/piumallick/toxic-comments-sentiment-analysis,Toxic Comment Classification Challenge
597929,0.9476,0,2,/ritam3144/random-forest-with-count-vectorizer,Toxic Comment Classification Challenge
575247,0.9643,0,2,/sart123/rnn-for-classification-toxic,Toxic Comment Classification Challenge
541402,0.9707,6,17,/sarvajna/keras-sequential-model-lb-0-052,Toxic Comment Classification Challenge
511197,0.9771,45,342,/jhoward/improved-lstm-baseline-glove-dropout,Toxic Comment Classification Challenge
6770603,-2.21933,0,0,/hpc8899/mpnn-skip-2,Predicting Molecular Properties
5581863,-1.7105099999999998,0,0,/choihao/distance-qm9-giba,Predicting Molecular Properties
5507194,0.446,0,0,/ruhong/champs-scalar-coupling-eda-baseline,Predicting Molecular Properties
1579821,0.847,5,34,/julian3833/4-exploring-public-models,Airbus Ship Detection Challenge
1599713,1.0,6,24,/julian3833/5-submitting-the-test-file-1-0-public-lb,Airbus Ship Detection Challenge
1576974,0.889,2,7,/nikhilroxtomar/unet34-submission,Airbus Ship Detection Challenge
1448168,0.847,0,13,/ashishpatel26/u-net-model-with-abstract-layer-submission,Airbus Ship Detection Challenge
8418847,0.4340199999999999,0,0,/blainerothrock/u-net-model-with-dropout,Airbus Ship Detection Challenge
2781652,0.335,0,0,/wizarddata/multiple-lgbm-tabular-data-only,PetFinder.my Adoption Prediction
2730880,0.347,0,6,/ericbae731/feature-importance-of-gradient-boosting-simple,PetFinder.my Adoption Prediction
2739578,0.287,4,3,/jazivxt/pound-puppies,PetFinder.my Adoption Prediction
2708682,0.008,0,0,/hmchuong/simple-neural-network-approach,PetFinder.my Adoption Prediction
2573770,0.411,2,13,/skooch/petfinder-simple-catboost-baseline,PetFinder.my Adoption Prediction
2648407,0.344,0,5,/mawuliadjei/random-forest-and-xgboost,PetFinder.my Adoption Prediction
2618178,0.344,7,11,/orange90/all-features-added-solution-of-petfinder-my,PetFinder.my Adoption Prediction
2676776,0.391,3,6,/econdata/petfinder-lgbm,PetFinder.my Adoption Prediction
2676994,0.319,0,0,/dhc1995/data-exploration-with-raw-data,PetFinder.my Adoption Prediction
2613618,0.257,1,0,/vodacthe/petfinder-sklearn-test-prediction,PetFinder.my Adoption Prediction
2608162,0.4039999999999999,2,3,/ludovicoristori/pet-adoption-simple-part-ii-data-merge-lgbm,PetFinder.my Adoption Prediction
2643272,0.011,1,1,/bashamsc/pet-adoption-prediction-using-gbc,PetFinder.my Adoption Prediction
2517517,0.412,15,83,/skooch/petfinder-simple-lgbm-baseline,PetFinder.my Adoption Prediction
2595144,0.054,0,0,/plarmuseau/sack-knife-predict-cat-dog-do-we-need-a-cleanup,PetFinder.my Adoption Prediction
2576110,0.326,2,7,/ludovicoristori/pets-adoption-simple-pandas-random-forest,PetFinder.my Adoption Prediction
2505876,0.315,0,7,/dochad/pet-adoption-starter-kernel-tutorial,PetFinder.my Adoption Prediction
2528816,0.366,129,606,/artgor/exploration-of-data-step-by-step,PetFinder.my Adoption Prediction
2502908,0.319,0,0,/ulissesdias/xgboost-all-data-hyperopt-parameter-tuning,PetFinder.my Adoption Prediction
2516887,0.321,2,8,/wizmik12/take-more-photos-of-old-pets,PetFinder.my Adoption Prediction
2504117,0.289,0,6,/ashishkhuraishy/adoption-prediction-beginners-guide,PetFinder.my Adoption Prediction
2505275,0.397,20,33,/tunguz/annoying-ab-shreck-and-bluetooth,PetFinder.my Adoption Prediction
11821783,0.555,0,4,/lisosia/mean-baseline-fixed,RSNA STR Pulmonary Embolism Detection
11674129,0.434,21,48,/redwankarimsony/rsna-str-3d-stacking-3d-plot-segmentation,RSNA STR Pulmonary Embolism Detection
11665462,0.503,36,119,/seraphwedd18/pe-detection-with-keras-model-creation,RSNA STR Pulmonary Embolism Detection
11663849,0.434,17,43,/redwankarimsony/ct-scans-dicom-files-windowing-explained,RSNA STR Pulmonary Embolism Detection
11648638,0.527,3,21,/redwankarimsony/rsna-str-pulmonary-embolism-dummy-sub,RSNA STR Pulmonary Embolism Detection
11656870,0.503,0,7,/paulorzp/sample-submission,RSNA STR Pulmonary Embolism Detection
12510561,0.555,0,0,/lisosia/consistency-gdcm-b3-monai-pos-3f-ens,RSNA STR Pulmonary Embolism Detection
12273840,0.325,0,0,/akashsuper2000/baseline-with-no-image,RSNA STR Pulmonary Embolism Detection
5349579,0.9286,0,1,/prasadkevin/extensive-eda-and-modeling-xgb-hyperopt,IEEE-CIS Fraud Detection
5563055,0.9485,6,5,/igauty/gmean-of-light-gbm-models-lb-0-9484,IEEE-CIS Fraud Detection
5524800,0.9233,1,8,/timdzh93/catboost-baseline-with-correlated-features-filter,IEEE-CIS Fraud Detection
5196040,0.884,0,0,/knightwisdom/ieee-simple-eda,IEEE-CIS Fraud Detection
5470928,0.9475,9,120,/kyakovlev/ieee-cv-options,IEEE-CIS Fraud Detection
5469356,0.9382,3,3,/yuewangmoophy/fraud-detection-xgboost,IEEE-CIS Fraud Detection
5480006,0.9196,3,30,/ryches/keras-nn-autoencoder,IEEE-CIS Fraud Detection
5478723,0.7026,0,0,/yuewangmoophy/fraud-detection-majority-voting,IEEE-CIS Fraud Detection
5487485,0.9333,0,2,/swarupghad/kernel5baa1a445e,IEEE-CIS Fraud Detection
5343476,0.6874,0,1,/himnashukumar199795/cis-fraud-detection,IEEE-CIS Fraud Detection
5372311,0.9513,27,95,/paulorzp/gmean-of-light-gbm-models-lb-0-951x,IEEE-CIS Fraud Detection
5244347,0.9357,0,1,/jolly2136/fe-xgb,IEEE-CIS Fraud Detection
5153294,0.9471,18,116,/roydatascience/light-gbm-with-complete-eda,IEEE-CIS Fraud Detection
5317263,0.9441,9,51,/kyakovlev/ieee-ground-baseline-make-amount-useful-again,IEEE-CIS Fraud Detection
5316920,0.9425,2,25,/kyakovlev/ieee-ground-baseline-deeper-learning,IEEE-CIS Fraud Detection
5247025,0.9449,63,265,/davidcairuz/feature-engineering-lightgbm,IEEE-CIS Fraud Detection
5239790,0.5589999999999999,0,1,/nanditab35/ieee-fraud-detection-feature-reduction,IEEE-CIS Fraud Detection
482186,0.58222,0,1,/takemoto/linear-regression,Mercari Price Suggestion Challenge
479402,0.5101,0,2,/kevingriest/simple-ridge-script,Mercari Price Suggestion Challenge
476017,0.56418,0,0,/swapkh91/mercari-first-try,Mercari Price Suggestion Challenge
458698,0.42755,5,33,/iamprateek/submission-to-mercari-price-suggestion-challenge,Mercari Price Suggestion Challenge
464602,0.5777,6,1,/toqoz403/first-challenge,Mercari Price Suggestion Challenge
465944,0.56657,2,3,/metadist/does-shipping-price-matter-spoiler-no,Mercari Price Suggestion Challenge
464162,0.88331,2,3,/dayemsidiqi/dayem-s-first-notebook,Mercari Price Suggestion Challenge
454023,0.55014,8,16,/viveknium/dynamic-pricing-with-feature-engineering,Mercari Price Suggestion Challenge
452888,0.47172,2,18,/tarobxl/a-simple-nn-solution-with-keras-0-48611-p-25fa17,Mercari Price Suggestion Challenge
448095,0.56107,0,3,/amlanpraharaj/xgboost-k-fold,Mercari Price Suggestion Challenge
705893,0.4609399999999999,0,0,/alex5009/for-jet-nn-xgboost,Mercari Price Suggestion Challenge
1814363,1.36845,0,0,/mikulskibartosz/poz-randomforest-with-datetime-max-50,Bike Sharing Demand
234719,1.05731,0,0,/qweasddd/bike-rental-predictions-using-lr-rf-gbr,Bike Sharing Demand
13354287,0.7340000000000001,0,1,/saurabhmaydeo/data-security-image-st,ALASKA2 Image Steganalysis
10654535,0.925,0,7,/ahmedhamada0/train-inference-gpu-baseline-tta-148,ALASKA2 Image Steganalysis
9840198,0.915,0,1,/akashsuper2000/train-inference-gpu-baseline,ALASKA2 Image Steganalysis
10799088,0.94,2,15,/tunguz/best-b4-inference,ALASKA2 Image Steganalysis
10596648,0.922,1,7,/vineeth1999/gpu-baseline-ensemble-baseline,ALASKA2 Image Steganalysis
10550230,0.922,7,30,/roydatascience/gpu-ensemble-baseline,ALASKA2 Image Steganalysis
10458918,0.843,20,32,/hooong/tpu-on-all-300k-images-without-crashing,ALASKA2 Image Steganalysis
10177773,0.816,5,12,/dimakyn/keras-efficientnetb3-efficientnetb7,ALASKA2 Image Steganalysis
9721049,0.8759999999999999,3,12,/yhn112/alaska2-cnn-multiclass-classifier-with-catalyst,ALASKA2 Image Steganalysis
9386549,0.888,36,135,/meaninglesslives/alaska2-cnn-multiclass-classifier,ALASKA2 Image Steganalysis
9277047,0.804,18,42,/khoongweihao/alaska2-blending-efficientnets-on-tpus,ALASKA2 Image Steganalysis
136744,2.27186,0,2,/xieyufish/neural-network-on-talkingdata,TalkingData Mobile User Demographics
12075272,0.99117,0,1,/yaogengqi/mnist-by-lenet,Digit Recognizer
12000878,0.98635,0,1,/anujsoni/notebooka8ebb2330b,Digit Recognizer
12061993,0.9921,0,0,/ruandiassantana/digit-recognizer,Digit Recognizer
12001799,0.9945,0,8,/shivamjohri/mnist-with-cnn-and-data-augmentation,Digit Recognizer
11983532,0.99432,11,13,/rude009/mnist-with-keras-cnn-model-99-5,Digit Recognizer
11991339,0.98467,0,0,/alexinicab/keras-convnet,Digit Recognizer
11828766,0.99503,0,1,/indermohanbains/digit-recogniser,Digit Recognizer
11950572,0.99292,1,3,/angadp/simple-cnn-to-predict-digits,Digit Recognizer
11949316,0.974,0,0,/yash92328/mnist-digit-recognizer,Digit Recognizer
4651196,0.98414,0,0,/jobzhf88/mnist-myself,Digit Recognizer
11881198,0.977,2,6,/alanchn31/mnist-fastai-v2-cnn,Digit Recognizer
11852236,0.17014,0,4,/bryangray/diginetx2,Digit Recognizer
11870532,0.99439,0,4,/arunjathari/digit-recognition,Digit Recognizer
11859937,0.97425,0,2,/cauthur/basic-deep-learning-with-pytorch,Digit Recognizer
11487173,0.96046,0,1,/arpitbajpai771/digit-recognizer-using-cnn,Digit Recognizer
11828923,0.97671,0,1,/rog007/digit-recognizer,Digit Recognizer
13947825,0.894,0,4,/orion29/cassava-leaves-inference,Cassava Leaf Disease Classification
14044490,0.888,0,0,/daveccampbell/cassava-bitempered-logistic-loss-inference,Cassava Leaf Disease Classification
12995571,0.843,0,0,/prakashpvss/pytorch-lightning-inf,Cassava Leaf Disease Classification
13427958,0.887,0,1,/leewhieldon/cassava-leaf-keras-efficientnetb-prediction,Cassava Leaf Disease Classification
14035136,0.897,0,0,/safonenkomax/cassava-leaf-disease-tpu-v2-pods-inference,Cassava Leaf Disease Classification
13953114,0.88,0,4,/electro/keras-baseline-0-88-quickstart-ii-submission,Cassava Leaf Disease Classification
13953970,0.8959999999999999,0,2,/jangwonoh/pytorch-efficientnet-baseline-inference-tta,Cassava Leaf Disease Classification
13975541,0.718,0,0,/ysunnyc/cassava-torchxla2-inf,Cassava Leaf Disease Classification
13906362,0.879,0,5,/prvnkmr/efficientnet-simple-baseline-implementation,Cassava Leaf Disease Classification
13901109,0.857,0,1,/houssemayed/cnn-inceptionnet-model,Cassava Leaf Disease Classification
13978536,0.873,0,0,/rajanlagah/noob-programmer-s-code,Cassava Leaf Disease Classification
13912009,0.851,0,4,/qinhui1999/pytorch-tpu-infer-v1-lb-0-851,Cassava Leaf Disease Classification
13837878,0.882,0,2,/makawamevy/cassava-disease-classification-first-attempt,Cassava Leaf Disease Classification
7915822,0.78332,2,5,/siavrez/singlelayerperceptron,Categorical Feature Encoding Challenge II
7913790,0.78408,0,2,/tunguz/cat-ii-et-histgradientboostingreg-baseline-sklearn,Categorical Feature Encoding Challenge II
7899934,0.7756,4,5,/scirpus/handle-missing-values-not,Categorical Feature Encoding Challenge II
7871907,0.75797,2,5,/siavrez/auto-encoder-model,Categorical Feature Encoding Challenge II
7692887,0.7270399999999999,1,9,/abhishek/clean-inference-kernel,Categorical Feature Encoding Challenge II
7744179,0.7852,5,39,/tunguz/cats-ii-with-rapids-ridge-regression,Categorical Feature Encoding Challenge II
7735005,0.7859,4,5,/superant/oh-my-plain-logreg,Categorical Feature Encoding Challenge II
7706103,0.7863399999999999,11,48,/siavrez/deepfm-model,Categorical Feature Encoding Challenge II
7695526,0.78522,2,19,/tunguz/cats-ii-with-h2o-automl,Categorical Feature Encoding Challenge II
7667432,0.7647,0,6,/erelin6613/cat-in-the-dat-imbalance-handeling,Categorical Feature Encoding Challenge II
7623943,0.7851,0,1,/bluewizard/catboost-model-w-dummy-target-encoders,Categorical Feature Encoding Challenge II
7572745,0.7853100000000001,11,13,/amoghjrules/intro-to-stacking-averaging-base-models,Categorical Feature Encoding Challenge II
7484744,0.78104,11,16,/ganeshmundra/prediction-using-6-diff-model,Categorical Feature Encoding Challenge II
7439704,0.77967,12,21,/caesarlupum/learn-logistic-regression-with-random-cats,Categorical Feature Encoding Challenge II
7473052,0.7468600000000001,0,1,/cyannani123/kernel4c60139451,Categorical Feature Encoding Challenge II
7412178,0.76966,0,8,/tunguz/cat-ii-histgradientboostingclassifier-baseline,Categorical Feature Encoding Challenge II
7368035,0.78149,25,92,/vikassingh1996/don-t-underestimate-the-power-of-a-logistic-reg,Categorical Feature Encoding Challenge II
7391222,0.7855,1,3,/ryomakawata/skf-lightgbm,Categorical Feature Encoding Challenge II
7341918,0.78586,1,13,/lucamassaron/catboost-beats-auto-ml,Categorical Feature Encoding Challenge II
7317387,0.78488,5,38,/lucamassaron/categorical-feature-encoding-with-tensorflow,Categorical Feature Encoding Challenge II
7332909,0.78586,10,31,/pavelvpster/cat-in-dat-2-embeddings-target-keras,Categorical Feature Encoding Challenge II
7333933,0.7788,4,4,/kulkarnivishwanath/categorical-feature-encoding-challenge-ii,Categorical Feature Encoding Challenge II
7322001,0.77861,0,7,/kaushal2896/cat-in-dat-ii-simple-logistic-regression,Categorical Feature Encoding Challenge II
14364582,0.7863100000000001,0,0,/alandata666/learn-deepfm-model,Categorical Feature Encoding Challenge II
13213584,0.878,0,1,/aziz69/all-in-one-cassava-inference,Cassava Leaf Disease Classification
13602318,0.883,0,0,/garibalddd/cassava-garibaldd,Cassava Leaf Disease Classification
13435582,0.882,1,1,/bilibi/retrievalsubmission,Cassava Leaf Disease Classification
13468551,0.889,0,0,/mittsommer/cassav-submission,Cassava Leaf Disease Classification
13467168,0.879,0,0,/leandrocamargo/cassava-leaf-classification,Cassava Leaf Disease Classification
13329699,0.8809999999999999,5,32,/eceifter/xception-cassava-leaf-disease-classification,Cassava Leaf Disease Classification
13089738,0.831,0,3,/carloseduardosilvabh/cassava-transfer-learning-tensorflow-predict,Cassava Leaf Disease Classification
13439440,0.885,0,0,/satorushibata/cassava-leaf-disease-best-keras-cnn-tuning,Cassava Leaf Disease Classification
13377745,0.139,0,0,/varlou23/how-to-become-leaf-doctor-with-deep-learning,Cassava Leaf Disease Classification
13187843,0.84,0,0,/songyi1999/classificat-leaf,Cassava Leaf Disease Classification
13315645,0.7929999999999999,0,0,/kabhinay/pretrained-xception-model,Cassava Leaf Disease Classification
13284813,0.9,15,110,/szuzhangzhi/vit-cuda-as-usual-ensemble-inference,Cassava Leaf Disease Classification
13274520,0.8420000000000001,2,8,/shanmukh05/cassava-leaf-diseases,Cassava Leaf Disease Classification
12748509,0.98903,0,1,/joseerlang/pytorch-ligthning,Digit Recognizer
10345485,0.9946,0,0,/hamiddd/test-cnn,Digit Recognizer
12713765,0.9736,0,0,/viacheeselove/voting-mnist,Digit Recognizer
12686477,0.99257,0,1,/tianpang/pytorch-very-simple-yet-accurate-tutorial-99,Digit Recognizer
12670856,0.72853,0,0,/gabrielmilan/wisard-baseline,Digit Recognizer
12577732,0.93592,0,0,/aniroxx/mnist-data-kaggle,Digit Recognizer
12432184,0.97575,0,0,/jsdae1/mnist-sklearn-tutorial,Digit Recognizer
12457916,0.97007,0,0,/israasaleh/notebooke5f5f7d047,Digit Recognizer
12354348,0.99642,2,6,/nickuzmenkov/digit-recognition-with-cnn-advanced-way,Digit Recognizer
13974231,0.4538899999999999,0,3,/dogdriip/mercari-vectorizer-labelbinarizer-lgbmregressor,Mercari Price Suggestion Challenge
12933072,0.6560699999999999,0,0,/namiki1984/ds-stdy-mercari-namiki,Mercari Price Suggestion Challenge
11578897,0.49237,0,0,/ilyasedelnikov/20200905-mercaribilstm,Mercari Price Suggestion Challenge
9984631,0.63112,0,2,/smallhand/mercari-smallhand-randomforest-mlpregressor,Mercari Price Suggestion Challenge
6699397,0.56193,1,3,/ilya37/catboost-tfidf,Mercari Price Suggestion Challenge
6075341,0.4253399999999999,0,2,/jubergandharv/fm-ftrl-with-wordbatch-and-lgb,Mercari Price Suggestion Challenge
5014253,0.41684,0,1,/conformal/combinemodel,Mercari Price Suggestion Challenge
4885795,0.42558,0,1,/conformal/embeddingnn,Mercari Price Suggestion Challenge
1007788,0.79298,0,0,/ratnesh88/predict-price,Mercari Price Suggestion Challenge
2618633,0.53895,0,0,/abecadel/mercari-1,Mercari Price Suggestion Challenge
2265203,0.59035,0,0,/aschukin/lgb-bagging,Mercari Price Suggestion Challenge
2071488,0.69571,0,0,/elevenlines/wsd2mercari,Mercari Price Suggestion Challenge
1595821,0.5537,0,0,/philippschwarz/mercari-1-submission,Mercari Price Suggestion Challenge
561594,0.4698699999999999,0,0,/kuntalcse006/mercari-price-suggestion-challenge,Mercari Price Suggestion Challenge
471761,0.47413,0,1,/archelunch/naive-catboost,Mercari Price Suggestion Challenge
651838,0.44818,0,2,/michaelapers/mercari-rough3,Mercari Price Suggestion Challenge
700293,0.4140899999999999,0,0,/shanth84/0-414-stage2data-rnn-wbatch,Mercari Price Suggestion Challenge
647822,0.41717,0,1,/mohshawky/3-nn-ensemble-top-3,Mercari Price Suggestion Challenge
11091931,0.0,0,5,/chandanverma/pytorch-converted-tf-model,Google Landmark Retrieval 2020
10905505,0.0279999999999999,0,10,/chandanverma/landmark-retrieval-efficientnet,Google Landmark Retrieval 2020
10558002,0.008,21,101,/mayukh18/creating-submission-from-your-own-model,Google Landmark Retrieval 2020
4868332,0.9054,8,8,/makalesta2/fraud-detection-randomforest,IEEE-CIS Fraud Detection
4836706,0.9401,207,1024,/artgor/eda-and-models,IEEE-CIS Fraud Detection
4855615,0.9382,2,7,/virajbagal/ieee-eda-dropping-cols-and-xgb-with-earlystopping,IEEE-CIS Fraud Detection
4865344,0.9326,5,44,/vincentlugat/ieee-catboost-gpu-baseline-5-kfold,IEEE-CIS Fraud Detection
4883881,0.9254,0,5,/snakayama/xgboost-using-optuna,IEEE-CIS Fraud Detection
4835353,0.9381,27,213,/xhlulu/ieee-fraud-xgboost-with-gpu-fit-in-40s,IEEE-CIS Fraud Detection
4837510,0.9389,1,6,/takanobu0210/ieee-visualization-and-xgboost-model,IEEE-CIS Fraud Detection
4874025,0.9345,2,2,/om1042/ieee-lgb-bayesian-opt-pca,IEEE-CIS Fraud Detection
4835515,0.9367,1,12,/stocks/can-we-beat-it,IEEE-CIS Fraud Detection
4836759,0.4934,4,2,/virajbagal/ieee-complete-eda-of-all-features,IEEE-CIS Fraud Detection
4835928,0.5,0,1,/shivamanhar/fraud-detection,IEEE-CIS Fraud Detection
5978638,0.8845,0,0,/prasadkevin/ieee-fraud-detection,IEEE-CIS Fraud Detection
5795851,0.9323,0,0,/xwxw2929/catboost-kfold,IEEE-CIS Fraud Detection
5286714,0.9413,0,0,/nicapotato/gpyopt-gpu-xgb,IEEE-CIS Fraud Detection
1617863,12.23412,0,0,/ambarish/dogs-cats-fujisan-s-invasive-species-3,Dogs vs. Cats Redux: Kernels Edition
1569652,0.42828,0,0,/vinitkumargtech/dogs-and-cats-final-kernel-resnet50-submission,Dogs vs. Cats Redux: Kernels Edition
1481612,0.50165,0,0,/rohankuntoji/catvsdog1,Dogs vs. Cats Redux: Kernels Edition
1402147,0.69314,0,0,/aamnafea/vgg19-fine-tuning,Dogs vs. Cats Redux: Kernels Edition
1361826,0.4276899999999999,0,1,/rajats1992/catvsdog,Dogs vs. Cats Redux: Kernels Edition
1368625,0.07005,2,1,/tcvieira/using-fastai-in-kaggle-kernel,Dogs vs. Cats Redux: Kernels Edition
1260698,11.35716,0,0,/abdelrahmanmsalim/dogs-vs-cats-111,Dogs vs. Cats Redux: Kernels Edition
1043778,1.6522400000000002,1,0,/rajatranjan/no-cats-only-dogs,Dogs vs. Cats Redux: Kernels Edition
595311,0.7141,0,3,/stahamtan/logistic-regression-to-tell-cat-or-dog,Dogs vs. Cats Redux: Kernels Edition
511717,0.51342,0,1,/moseswong33/full-classification-example-with-convnet-eb8756,Dogs vs. Cats Redux: Kernels Edition
1922754,0.63713,0,1,/hdj0401/submission-with-platform-features-removed,Outbrain Click Prediction
12636844,0.0,1,0,/nickel/modelos-de-base,PetFinder.my Adoption Prediction
9571303,0.0,0,0,/debskilukasz/petfinder-pet-adoption-prediction,PetFinder.my Adoption Prediction
8324861,0.0,0,0,/smirnovivan/pet-adoption,PetFinder.my Adoption Prediction
8099303,0.0,0,2,/darwinwin/petfinder-h2o-automl-combined,PetFinder.my Adoption Prediction
4753058,0.0,0,0,/donatastamosauskas/petfinder-vilnius-school-of-ai,PetFinder.my Adoption Prediction
6481344,0.0,1,2,/saranyashalya/eda-baseline-analysis,PetFinder.my Adoption Prediction
2795895,0.33,0,0,/chrisevans/playing-around-with-the-data,PetFinder.my Adoption Prediction
3655122,0.0,0,1,/aruchomu/petfinder-my-adoption-prediction-solution,PetFinder.my Adoption Prediction
3401140,0.442,0,3,/enisimsar/22nd-place-solution,PetFinder.my Adoption Prediction
3408604,0.316,0,0,/jshen97/petfinder-final-submission,PetFinder.my Adoption Prediction
3409277,0.46153,10,65,/wuyhbb/final-small,PetFinder.my Adoption Prediction
3375041,0.476,0,16,/baomengjiao/fork-of-v5-fork-v4-change-gpu-add-name-feature,PetFinder.my Adoption Prediction
3348838,0.4539999999999999,0,1,/hidehisaarai1213/pet-category-embedding-image-size-te,PetFinder.my Adoption Prediction
3048021,0.414,0,0,/bravenoob/cas-pml-hs18-modeling,PetFinder.my Adoption Prediction
3065681,0.457,0,0,/tuanflash/petfinder-simple-xgb-with-magic-features,PetFinder.my Adoption Prediction
3358989,0.16,0,0,/jatindholakia/notebook,PetFinder.my Adoption Prediction
14327988,0.70117,0,1,/chandraroy/lgb-grid-search-regressor-baseline-model,Tabular Playground Series - Jan 2021
14040607,0.70287,0,0,/black9t/jan-tab-data2,Tabular Playground Series - Jan 2021
14192473,0.70502,10,11,/krishna1997gopal/just-start-this-year-with-regression-lr-xgb,Tabular Playground Series - Jan 2021
14256122,0.70815,2,3,/tosinabase/jan-21-random-forest-with-gridsearchcv,Tabular Playground Series - Jan 2021
14278248,0.70468,0,1,/eladwar/generic-starter,Tabular Playground Series - Jan 2021
14253457,0.70227,2,2,/jswxhd/regression-with-h2o-beginner,Tabular Playground Series - Jan 2021
14203167,0.71358,5,16,/frankmollard/rapids-knregressor-rank-gauss-eda,Tabular Playground Series - Jan 2021
14185943,0.69924,3,11,/jonas0/beginner-friendly-tutorial-0-69924-score,Tabular Playground Series - Jan 2021
14163466,0.69971,20,28,/mdhamani/tps-getting-better-eda-lgbm-optuna,Tabular Playground Series - Jan 2021
14151081,0.69782,10,26,/chrisbradley/tab-playground-predicting-bimodal-distribution,Tabular Playground Series - Jan 2021
14142855,0.71978,0,0,/aeryss/tabular-playground-jan-2021-neural-network,Tabular Playground Series - Jan 2021
14175346,0.7148800000000001,2,1,/fanbyprinciple/fastai-v4-using-tabular-learner,Tabular Playground Series - Jan 2021
14115408,0.69652,118,131,/somayyehgholami/results-driven-tabular-playground-series-201,Tabular Playground Series - Jan 2021
14110546,0.69706,11,27,/shkanda/random-seed-averaging-lgb-xgb,Tabular Playground Series - Jan 2021
14191427,0.70038,0,0,/nyk510/vivid-lightgbm-parameter-tuning-using-optuna,Tabular Playground Series - Jan 2021
14050492,0.70001,0,0,/sanjay147/xgb-based-regressor-model,Tabular Playground Series - Jan 2021
14184008,0.71682,0,0,/ekozyreff/regression-with-higher-order-terms,Tabular Playground Series - Jan 2021
6420986,1.19,0,33,/vbmokin/very-significant-safe-memory-lightgbm,ASHRAE - Great Energy Predictor III
6346841,1.24,0,1,/jiaofenx/ashrae-great-energy-predictor-iii,ASHRAE - Great Energy Predictor III
6384816,1.22,8,32,/kyakovlev/ashrae-lgbm-simple-fe,ASHRAE - Great Energy Predictor III
6359536,2.13,22,53,/carlolepelaars/understanding-the-metric-rmsle,ASHRAE - Great Energy Predictor III
6385382,1.878,0,1,/luisfer/mia-ashrae,ASHRAE - Great Energy Predictor III
6331833,1.23,19,86,/hmendonca/shapley-values-for-feature-selection-ashrae,ASHRAE - Great Energy Predictor III
6289268,1.12,47,127,/kaushal2896/ashrae-eda-fe-lightgbm-1-12,ASHRAE - Great Energy Predictor III
6291204,1.12,0,6,/viswajithkn/great-energy-prediction,ASHRAE - Great Energy Predictor III
6251114,1.24,30,219,/hmendonca/starter-eda-and-feature-selection-ashrae3,ASHRAE - Great Energy Predictor III
6243079,1.36,7,80,/jesucristo/starter-great-energy-predictor,ASHRAE - Great Energy Predictor III
6249602,1.36,0,30,/kyakovlev/ashrae-baseline-lgbm,ASHRAE - Great Energy Predictor III
6240852,3.22,1,14,/drcapa/ashrae-datagenerator-lstm,ASHRAE - Great Energy Predictor III
6244543,2.222,2,12,/ihelon/simple-xgboost,ASHRAE - Great Energy Predictor III
6244139,1.35,8,31,/bejeweled/ashrae-catboost-regressor,ASHRAE - Great Energy Predictor III
6261443,1.78,1,3,/bhavesh09/energy-predictor-starter,ASHRAE - Great Energy Predictor III
8744615,0.93,0,0,/yilmazalp/ion-switching,University of Liverpool - Ion Switching
9143731,0.94,0,2,/gyanaluckydas/understanding-ion-switching-with-modeling,University of Liverpool - Ion Switching
8375731,0.935,2,9,/nickfleece/liverpool-ions,University of Liverpool - Ion Switching
9020836,0.927,0,1,/elmahy/ion-switching-with-keras,University of Liverpool - Ion Switching
8932764,0.93657,1,16,/tosinabase/boundary-classifier-using-quadratic-equations,University of Liverpool - Ion Switching
8810727,0.94,2,12,/binaicrai/wavenet-on-drift-removed-data,University of Liverpool - Ion Switching
8884595,0.41,1,10,/mdmahmudferdous/1d-cnn-for-ion-switching-classification,University of Liverpool - Ion Switching
8866631,0.939,0,7,/q839651612/simple-model-based-on-signals-distribution,University of Liverpool - Ion Switching
8789588,0.927,4,64,/miklgr500/ghost-drift-and-outliers,University of Liverpool - Ion Switching
8741378,0.941,15,98,/ragnar123/wavenet-with-1-more-feature,University of Liverpool - Ion Switching
8585128,0.94,4,33,/khalildmk/simple-two-layer-bidirectional-lstm-with-pytorch,University of Liverpool - Ion Switching
8669125,0.94,12,29,/siavrez/different-model-for-different-signal-types,University of Liverpool - Ion Switching
8639664,0.912,0,9,/code1110/ion-stratifiedgroupkfold-example,University of Liverpool - Ion Switching
8351841,0.935,0,1,/nikitaalbert/a-quicker-way-to-train-neural-net,University of Liverpool - Ion Switching
8579452,0.94,8,81,/teejmahal20/single-model-lgbm-kalman-filter,University of Liverpool - Ion Switching
8429536,0.94,10,48,/rohitsingh9990/lgb-featureengineering-lb-0-940,University of Liverpool - Ion Switching
8543620,0.932,2,20,/teejmahal20/the-viterbi-algorithm-kalman-filtering,University of Liverpool - Ion Switching
8441722,0.939,12,74,/teejmahal20/a-signal-processing-approach-low-pass-filtering,University of Liverpool - Ion Switching
8416874,0.938,0,2,/jagannathrk/liverpool-ion-switching-rapids-knn,University of Liverpool - Ion Switching
6513540,0.9702,0,0,/mahmoudvaziri/kannadamnist,Kannada MNIST
6521180,0.9832,8,14,/martingorner/kannada-martin-keras-with-tf-data-augmentation,Kannada MNIST
5891570,0.9852,10,45,/abhinand05/k-mnist-ensemble-of-4-models-lb-0-985-starterkit,Kannada MNIST
6353957,0.9862,0,3,/ajithvallabai/kaggle-kannada-mnist-made-simple,Kannada MNIST
6482081,0.9744,0,5,/ngupta23/kannada-mnist-digit-recognition-using-fast-ai,Kannada MNIST
6242967,0.9878,0,2,/rhysie/cnn-ensemble-98-6,Kannada MNIST
6478093,0.8658,3,6,/airk0126/cnn-implementation-in-keras-for-beginners,Kannada MNIST
6476749,0.9724,0,2,/poornachandvanga/kannada-mnist-cnn-accuracy-99,Kannada MNIST
6466486,0.9826,0,3,/nikhil2381/kannadamnist-fast-ai,Kannada MNIST
6243210,0.9742,0,1,/aleksimu/kannada,Kannada MNIST
6298247,0.99,0,7,/xiaohuangji/kaggledays-kannada-mnist,Kannada MNIST
6390712,0.9562,4,8,/polyzer/very-easy-kannada-mnist-dense-net,Kannada MNIST
6314629,0.9782,0,3,/kocayinana/solution-with-resnet-50,Kannada MNIST
6263838,0.9672,0,1,/mateusz8005/kannada-digits-recognition-using-cnn-keras,Kannada MNIST
6298731,0.9686,0,0,/dingding123/kaggledays-china-novice-comp1,Kannada MNIST
6233369,0.9764,0,4,/nicapotato/pytorch-resnet-kanada,Kannada MNIST
6239040,0.977,0,1,/tiannnn/keras-starter-kit-kanada-mnist,Kannada MNIST
6220163,0.9854,0,2,/gearlem/kannada-mnist-gear,Kannada MNIST
6208427,0.9846,0,3,/ashwin89872/kannada-mnist-in-keras,Kannada MNIST
6045171,0.9842,0,6,/drcapa/kannada-mnist-cnn,Kannada MNIST
5953601,0.9664,0,1,/gray98/kannada-pytorch,Kannada MNIST
6113474,0.9836,8,13,/jakelj/kannada-mnist-beginner-to,Kannada MNIST
6128202,0.9774,0,2,/ananthreddy/kannada,Kannada MNIST
6116874,0.9562,0,1,/ramanarvind/kannada-mnist,Kannada MNIST
6047540,0.987,0,3,/ankur1401/kannada-digit-recognizer,Kannada MNIST
6094515,0.9818,0,3,/iamsiddhant/kannada-mnist-dataset,Kannada MNIST
9774437,0.696,0,0,/josealways123/bertweet-from-colab,Tweet Sentiment Extraction
9766682,0.708,0,3,/myh0307/keyword-using-roberta,Tweet Sentiment Extraction
9890219,0.7114699999999999,7,13,/rsmits/tensorflow-roberta-qa-model,Tweet Sentiment Extraction
8888493,0.713,0,1,/drhouse3/robertaonsteroids-finetuning,Tweet Sentiment Extraction
9892190,0.7120000000000001,0,1,/kishor1210/tse2020-roberta-with-cnn-head,Tweet Sentiment Extraction
9575178,0.7020000000000001,0,1,/ameysharma/twitter-sentiment-prediction,Tweet Sentiment Extraction
9821455,0.71555,40,182,/vbmokin/tse2020-roberta-0-7175-prlb-outlier-analysis,Tweet Sentiment Extraction
9840101,0.664,0,1,/senritu/twitter-sentiment-extaction-eda-and-model,Tweet Sentiment Extraction
9778477,0.647,3,14,/anil4274/sentimental-analysis,Tweet Sentiment Extraction
9710337,0.711,4,48,/louise2001/roberta-cnn-data-augmentation,Tweet Sentiment Extraction
9555190,0.5820000000000001,1,3,/anasofiauzsoy/tweet-sentiment-extraction-1,Tweet Sentiment Extraction
9221679,0.708,2,4,/vinaybhupalam/cycliclearningrate,Tweet Sentiment Extraction
9717411,0.655,0,1,/peterye/roberta-qa-baseline,Tweet Sentiment Extraction
9718973,0.7120000000000001,4,27,/davelo12/bertweet,Tweet Sentiment Extraction
9193615,0.654,2,6,/slavaz/spacy-ner-ensemble,Tweet Sentiment Extraction
9567617,0.7120000000000001,2,4,/iamprateek/sentiment-analysis-using-roberta,Tweet Sentiment Extraction
9545100,0.581,0,0,/taohoang/tweetblob,Tweet Sentiment Extraction
9758230,0.003,0,0,/kstatistics/test-submission,Tweet Sentiment Extraction
9655741,0.705,0,0,/xsslin/trybert1,Tweet Sentiment Extraction
627745,0.8609,0,0,/saurabhg476/finding-toxic-comments,Toxic Comment Classification Challenge
13922415,0.7751100000000001,0,0,/sumukh21/titanic-ml-file,Titanic - Machine Learning from Disaster
13906035,0.75837,0,0,/martinjeannot/titanic,Titanic - Machine Learning from Disaster
7000072,0.81339,0,0,/xuzihan/master,Titanic - Machine Learning from Disaster
13857871,0.76076,3,5,/rohitw/titanic-v1,Titanic - Machine Learning from Disaster
13832044,0.78468,0,1,/salhimohamednadjib/titanic-classificationn,Titanic - Machine Learning from Disaster
13502313,0.78708,0,0,/linealbertsen/portfolio-1-b,Titanic - Machine Learning from Disaster
13841655,0.7751100000000001,0,0,/kyleli8/titanic-first-model,Titanic - Machine Learning from Disaster
13833817,0.77751,1,1,/physis/titanic-first-try,Titanic - Machine Learning from Disaster
13610042,0.7751100000000001,0,0,/amenislama/getting-started-with-titanic,Titanic - Machine Learning from Disaster
13783440,0.75598,8,10,/dataandmaths/1st-notebook-you-re-here-there-s-nothing-i-fear,Titanic - Machine Learning from Disaster
13851164,0.77272,0,0,/hrishikeshdeshmukh06/notebook4b7a124957,Titanic - Machine Learning from Disaster
13808150,0.76555,0,1,/nadish/titanic-predictions-using-logistic-and-trees,Titanic - Machine Learning from Disaster
13692206,0.76555,0,0,/shahriyarmammadli/titanic-ml-competitions,Titanic - Machine Learning from Disaster
13748437,0.7751100000000001,0,0,/philklein/second-attempt-ensembles,Titanic - Machine Learning from Disaster
13809905,0.42344,0,0,/praveenchary/titanic-ml,Titanic - Machine Learning from Disaster
9878940,0.95,1,14,/user189546/5-crop-tasks-by-brute-force,Abstraction and Reasoning Challenge
9793122,0.941,0,8,/phunghieu/yukikubo123-s-dsl-with-alignment,Abstraction and Reasoning Challenge
9751578,0.931,1,14,/alexfritz/genetic-dsl-part-from-10th-place-python-version,Abstraction and Reasoning Challenge
9755548,0.921,0,15,/golubev/arc-8solved-3grid-5tiles-no-ml,Abstraction and Reasoning Challenge
9743308,0.98,1,12,/muhakabartay/soup-ensemble-of-5-approachs-c-included,Abstraction and Reasoning Challenge
9749875,0.99,0,5,/allvor/my-simplest-0-990-grid-downsample,Abstraction and Reasoning Challenge
9230923,1.0,0,2,/jwelliav/arc-list-method,Abstraction and Reasoning Challenge
9275906,1.0,0,0,/thissentenseisfalse/my-arc-approach,Abstraction and Reasoning Challenge
9371826,1.0,0,6,/mehrankazeminia/binary-images-and-data-augmentation,Abstraction and Reasoning Challenge
9222868,1.0,0,0,/mercury01/etwas-f-r-acr,Abstraction and Reasoning Challenge
9160014,1.0,0,0,/isikkuntay/using-image-difference-as-second-target,Abstraction and Reasoning Challenge
8726681,0.99,10,66,/szabo7zoltan/colorandcountingmoduloq,Abstraction and Reasoning Challenge
8700915,0.99,1,4,/captainflemme/kernel451f29dac4,Abstraction and Reasoning Challenge
8477603,0.99,0,21,/nxrprime/grid-search-with-xgboost-and-cv,Abstraction and Reasoning Challenge
8451729,0.99,1,24,/meaninglesslives/stacking-models-and-new-features-for-arc,Abstraction and Reasoning Challenge
8378025,1.0,3,6,/anushakarthik1991/arc-competition-starter-notebook,Abstraction and Reasoning Challenge
736027,0.586688,0,8,/circle811/simple-logistic-regression,Google Cloud & NCAA® ML Competition 2018-Men's
674330,0.045511,12,19,/eaturner/logistics-and-basic-stats-lb-public-0-046,Google Cloud & NCAA® ML Competition 2018-Men's
54439,0.60854,18,52,/arjoonn/preliminary-exploration,Kobe Bryant Shot Selection
10488608,0.0009,0,2,/nipaanjum/first-submission,Global Wheat Detection
10518293,0.1698,2,8,/ravi02516/tensorflow-efficientdet-d3-with-default-parameters,Global Wheat Detection
10500456,0.5226,0,0,/abhishek4273/detection-using-monk-ai-toolkit,Global Wheat Detection
10496979,0.7257,0,3,/ricardozi/ricardo-train-weights,Global Wheat Detection
10483083,0.6757,0,2,/ricardozi/submission-file,Global Wheat Detection
9869295,0.6747,0,1,/jianiwu/fasterrcnn-train-inference,Global Wheat Detection
9959215,0.7203,0,5,/marcelosanchezortega/wbf-over-tta-single-model-efficientdet,Global Wheat Detection
10292728,0.0009,0,20,/asepsaputra/exploratory-data-analysis-for-beginners,Global Wheat Detection
10230713,0.6459,0,2,/jonykarki/simplefastercnn-inference,Global Wheat Detection
10243291,0.631,0,7,/mayukh18/keras-yolo3-inference,Global Wheat Detection
10214429,0.0009,0,2,/eladsoll/gwd-es,Global Wheat Detection
10240095,0.7138,0,1,/ufownl/global-wheat-detection-k-fold,Global Wheat Detection
9788297,0.6029,0,3,/mayukh18/keras-yolov2-inference,Global Wheat Detection
9934954,0.6951,0,2,/jerry42tw/fasterrcnn,Global Wheat Detection
9561908,0.7065,0,1,/akashsuper2000/global-wheat-detection-pseudo-labaling,Global Wheat Detection
10154466,0.4573,4,7,/vishnukv64/yolov3-inference,Global Wheat Detection
10105983,0.6701,0,0,/mahendrachouhanml/fast-rcnn-inference,Global Wheat Detection
10044294,0.6769,0,10,/julienbeaulieu/detectron2-wheat-detection-eda-training-eval,Global Wheat Detection
10008808,0.4747,0,2,/ximtecs/yolo-wheat,Global Wheat Detection
9700522,0.6168,3,5,/tiandaye/cascade-rcnn-with-mmdetection,Global Wheat Detection
9813035,0.6717,0,4,/trungthanhnguyen0502/gwd-inference,Global Wheat Detection
9731966,0.721,0,11,/santosh8896/efficientdet-with-pseduo-labeling,Global Wheat Detection
9777712,0.7193,0,1,/wilsonchoo/inference-efficientdet,Global Wheat Detection
9541535,0.5453,2,7,/aviralpamecha/inference-keras-retinanet,Global Wheat Detection
9694654,0.7089,5,17,/maherdeebcv/test-pytorch-faster-r-cnn-with-resnet152-backbone,Global Wheat Detection
9366022,0.1544,3,5,/susant4learning/understandingwheatdataset,Global Wheat Detection
9384292,0.6186,1,0,/ptaneja/detectron2-notebook,Global Wheat Detection
9570975,0.7193,32,271,/shonenkov/inference-efficientdet,Global Wheat Detection
65502,0.90438,0,0,/supeng/data-analysis-for-beginners,Kobe Bryant Shot Selection
61262,0.8395100000000001,14,48,/khozzy/kobe-shots-show-me-your-best-model,Kobe Bryant Shot Selection
59002,0.60868,0,0,/liziyuann/gradient-boosting,Kobe Bryant Shot Selection
56903,0.60854,0,2,/isosnovik/preliminary-exploration,Kobe Bryant Shot Selection
55062,0.6476,0,3,/saurabhg653/kobe-bryant,Kobe Bryant Shot Selection
9256425,0.7751100000000001,0,0,/javival/titanic-javivale,Titanic - Machine Learning from Disaster
14006991,0.7751100000000001,0,0,/qramkrishna/titanic-dl-model,Titanic - Machine Learning from Disaster
14025979,0.79425,0,0,/haoyuwang777/titanic-fcn,Titanic - Machine Learning from Disaster
13997068,0.5526300000000001,4,3,/siva2020s/titanic-final,Titanic - Machine Learning from Disaster
13824870,0.78708,0,2,/antimatter7/titanic-top-14-random-forest,Titanic - Machine Learning from Disaster
13900391,0.78708,0,0,/soyabulislam/titanic,Titanic - Machine Learning from Disaster
9827437,0.76076,0,0,/maerajahmed/decisiontree,Titanic - Machine Learning from Disaster
13868595,0.78468,11,20,/qusid007/titanic,Titanic - Machine Learning from Disaster
13903364,0.67703,0,0,/sho30647/20201230ch2-1,Titanic - Machine Learning from Disaster
13975263,0.7799,0,0,/anamika997/titanic-prediction,Titanic - Machine Learning from Disaster
13539589,0.76315,0,0,/mariacvesterli/ranfor-features-from-data-deep-dive-no-deck,Titanic - Machine Learning from Disaster
13788163,0.75837,0,0,/nilesh0561/titanic-dataset,Titanic - Machine Learning from Disaster
13929398,0.7822899999999999,1,1,/jeantc21/titanic,Titanic - Machine Learning from Disaster
13919027,0.7751100000000001,0,1,/jwlee508/data-analysis-for-kaggle-titanic,Titanic - Machine Learning from Disaster
478135,2.40665,28,160,/rhodiumbeng/classifying-multi-label-comments-0-9741-lb,Toxic Comment Classification Challenge
500316,0.9615,3,28,/kmader/text-classification-with-atrous-convolutions,Toxic Comment Classification Challenge
499263,0.9739,12,30,/sterby/fasttext-like-baseline-with-keras-lb-0-053,Toxic Comment Classification Challenge
11819157,0.96715,0,0,/abhishek2195/toxic-comment-cnn-modeling-final,Toxic Comment Classification Challenge
6942732,0.96684,0,0,/shankarnara/lstm-implementation-1,Toxic Comment Classification Challenge
6569716,0.97459,0,0,/amir78pgd/fork-of-nb-svm-strong-linear-baseline,Toxic Comment Classification Challenge
979935,0.9772,0,0,/gavarnamarn/nb-svm-strong-linear-baseline,Toxic Comment Classification Challenge
740493,0.9687,0,0,/volker48/toxic-comments-feature-engineering-v2,Toxic Comment Classification Challenge
705116,0.8471,0,0,/muthoka/toxic-comment-classification-by-terry-kisomo,Toxic Comment Classification Challenge
687794,0.9803,0,0,/thec03u5/ensamble-and-max-nbsvm-and-tfidflr,Toxic Comment Classification Challenge
678620,0.9772,0,0,/xufei1/nb-svm-strong-linear-baseline,Toxic Comment Classification Challenge
9397259,0.519,0,0,/eugeniematveeva/rnn-baseline-model,Tweet Sentiment Extraction
9519189,0.7120000000000001,3,30,/mdmashurshalehin/tweet-sentiment-insight-eda,Tweet Sentiment Extraction
9242804,0.664,5,19,/ethernext/experimenting-with-sentiment-analysis,Tweet Sentiment Extraction
9418734,0.6629999999999999,0,2,/svadivazhagu/tf-bert-sentiment,Tweet Sentiment Extraction
9288832,0.7040000000000001,0,1,/cairo43227/tweet-sentiment-with-roberta-and-tensorflow-2,Tweet Sentiment Extraction
9403608,0.611,0,0,/hskhawaja/tweet-sentiment-extraction-a-lexicon-approach,Tweet Sentiment Extraction
9213675,0.664,0,4,/pawan2905/twitter-extraction-with-eda-and-model,Tweet Sentiment Extraction
9282927,0.531,4,22,/louise2001/a-beginner-bag-of-words-approach-nltk,Tweet Sentiment Extraction
9070253,0.711,1,10,/sml0820/commented-roberta-training-with-pytorch,Tweet Sentiment Extraction
9086092,0.655,7,8,/jvpasp/tweet-sentiment-extraction,Tweet Sentiment Extraction
9094819,0.472,0,0,/alexskrn/random-forest-clf,Tweet Sentiment Extraction
8998105,0.696,1,5,/rishabhdhiman/roberta-base-with-tensorflow,Tweet Sentiment Extraction
9042551,0.706,0,1,/celestial233/bert-baseline233,Tweet Sentiment Extraction
9020020,0.7040000000000001,5,15,/omegaji/simple-easypeasy-distilbert-qa-d,Tweet Sentiment Extraction
6047922,0.2354,0,1,/plarmuseau/simple-regression,Kannada MNIST
5911651,0.9876,2,6,/zhizuijinmi/kannada-mnist-with-cnn,Kannada MNIST
6014562,0.9788,0,3,/polunwang/a-simple-model-with-10-layer-cnns,Kannada MNIST
5993889,0.9716,2,12,/gabmars/pytorch-lenet5,Kannada MNIST
5948110,0.9892,7,28,/jinbao/kannada-mnist-baseline,Kannada MNIST
5960755,0.983,2,3,/arjunsinghrathod/trying-hands-on-cnn,Kannada MNIST
5954358,0.9758,0,7,/anuragnegi/30-epochs,Kannada MNIST
5926694,0.9862,10,16,/rhodiumbeng/kannada-mnist-convolutional-neural-network,Kannada MNIST
5926403,0.9502,9,11,/parulpandey/kannada-mnist-with-h2o-deeplearning,Kannada MNIST
5935315,0.9736,0,2,/vaidyaprasad84/3-layer-cnn-simple-data-augmentation,Kannada MNIST
5906937,0.9486,0,7,/tunguz/lightgbm-baseline,Kannada MNIST
5871835,0.9818,39,243,/shahules/indian-way-to-learn-cnn,Kannada MNIST
5887722,0.982,2,22,/faizu07/kannada-mnist-with-fastai,Kannada MNIST
5888322,0.9856,1,10,/melissarajaram/fastai-pytorch-with-best-original-mnist-arch,Kannada MNIST
5876561,0.9688,0,4,/melissarajaram/simple-fastai-pytorch-example,Kannada MNIST
5869358,0.9864,0,8,/shaygu/kannada-mnist-simple-cnn,Kannada MNIST
5862328,0.9684,4,14,/pulkitmehtawork1985/simple-model-to-start,Kannada MNIST
5860963,0.977,3,5,/gmonge/pytorch-resnet34-augmentation-starter,Kannada MNIST
12321839,0.1022,0,0,/mkgpgroup/kannada-mnist-accuracy-resent,Kannada MNIST
8774022,0.9584,0,0,/srinathsrinivasan1/kernel64455d776c,Kannada MNIST
6677163,0.9756,0,0,/zeeeeey/pytorch-cnn-kanada,Kannada MNIST
8413753,0.94,2,9,/rohitsingh9990/ion-switching-inference-lb-0-939,University of Liverpool - Ion Switching
8356500,0.162,0,0,/eliasgreen/ions-and-pytorch-nn,University of Liverpool - Ion Switching
8353198,0.925,6,15,/martxelo/fe-and-simple-mlp,University of Liverpool - Ion Switching
8350103,0.935,3,5,/scirpus/complex-gp,University of Liverpool - Ion Switching
8308121,0.901,0,2,/scirpus/for-konrad-gp,University of Liverpool - Ion Switching
8235159,0.937,8,59,/kmat2019/train-test-similarity-analysis,University of Liverpool - Ion Switching
8148340,0.507,2,9,/harshitt21/ion-switching,University of Liverpool - Ion Switching
8178358,0.92,3,23,/super13579/u-net-1d-cnn-with-pytorch,University of Liverpool - Ion Switching
8197584,0.924,0,3,/ragnar123/group-kfold-only-28-features,University of Liverpool - Ion Switching
8189803,0.841,2,22,/cameronlai/getting-started-data-visualization-xgboost,University of Liverpool - Ion Switching
8179069,0.932,2,24,/khoongweihao/ion-switching-uuu-net-ladder-net-s-unet-idea,University of Liverpool - Ion Switching
8194092,0.932,0,5,/teejmahal20/physically-possible-optimized-rounder,University of Liverpool - Ion Switching
8157009,0.932,23,70,/jazivxt/physically-possible,University of Liverpool - Ion Switching
8165229,0.911,2,7,/teejmahal20/regression-with-optimized-rounder,University of Liverpool - Ion Switching
8160792,0.5920000000000001,2,7,/kaushal2896/ion-switching-starter-eda-randomforestregressor,University of Liverpool - Ion Switching
8143870,0.4679999999999999,6,8,/tahirmuslimscientist/ion-switching-best-practice,University of Liverpool - Ion Switching
8152860,0.867,0,2,/danofer/u-net-1d-cnn-with-keras-fork,University of Liverpool - Ion Switching
8131479,0.591,1,10,/konradb/cloned-baseline,University of Liverpool - Ion Switching
8127296,0.14,2,13,/seriousran/simple-regression-w-elasticnet,University of Liverpool - Ion Switching
8253133,0.945,0,0,/patrick0302/postprocessed-models-bland-by-leak,ASHRAE - Great Energy Predictor III
7054864,1.089,0,0,/takbull/ashrae-kfold-lightgbm-without-leak-1-08,ASHRAE - Great Energy Predictor III
6325898,2.436,10,19,/isaienkov/splitting-into-4-lgbm-models-based-on-meter-column,ASHRAE - Great Energy Predictor III
8706386,0.49943,0,5,/alexj21/pytorch-eda-unet-from-scratch-finetuning,Airbus Ship Detection Challenge
8382290,0.5209,0,0,/qiuzy4/u-net-model-with-abstract-layer-submission,Airbus Ship Detection Challenge
1976072,0.6559999999999999,0,4,/danil328/unet34-submission-with-new-data,Airbus Ship Detection Challenge
14097404,0.70263,3,5,/anubhav1302/playground-with-lightgbm,Tabular Playground Series - Jan 2021
14076441,0.71236,1,11,/a763337092/neural-network-starter-with-keras,Tabular Playground Series - Jan 2021
14155963,0.71014,0,0,/gauravgujar/jan-tabular-playground-competition-randomforest,Tabular Playground Series - Jan 2021
14064777,0.7005399999999999,0,2,/vaishnavipatil4848/tabular-playground-series-catboost-shap,Tabular Playground Series - Jan 2021
14063979,0.70267,0,1,/tracyporter/tabular-january-2021,Tabular Playground Series - Jan 2021
14050897,0.7027,6,4,/shyam21/stacked-regression-advanced,Tabular Playground Series - Jan 2021
13985294,0.69687,35,83,/hamditarek/tabular-playground-series-xgboost-lightgbm,Tabular Playground Series - Jan 2021
13965457,0.70443,40,159,/iamleonie/handling-multimodal-distributions-fe-techniques,Tabular Playground Series - Jan 2021
14012527,0.70204,0,1,/anirbansen3027/tps-012021-baseline-pycaret,Tabular Playground Series - Jan 2021
13929150,0.71367,18,120,/inversion/get-started-jan-tabular-playground-competition,Tabular Playground Series - Jan 2021
13966833,0.70824,1,9,/docxian/tabular-playground-1-visual-eda-tabnet,Tabular Playground Series - Jan 2021
13960992,0.7124699999999999,0,8,/slm37102/tabular-using-neural-network-fastai-starter,Tabular Playground Series - Jan 2021
13955599,0.7063,1,4,/jitaiyi/tabular-playground-series-with-xgboost,Tabular Playground Series - Jan 2021
13950395,0.7138100000000001,1,6,/narainp/simple-linear-baseline,Tabular Playground Series - Jan 2021
14590481,0.70704,0,0,/samuraiwarm/tabular-playground-jan-2021-gmm-feat-lightgbm,Tabular Playground Series - Jan 2021
201492,1.04081,0,0,/jtwessel/catdog-jwessel,Dogs vs. Cats Redux: Kernels Edition
12341142,0.15,0,2,/vaillant/rsna-str-pe-submission,RSNA STR Pulmonary Embolism Detection
12478595,0.168,0,4,/orkatz2/fork-of-efficientnet-lstm-pe-3d-dc8720,RSNA STR Pulmonary Embolism Detection
12489619,0.156,1,25,/moewie94/rsna-2020-inference,RSNA STR Pulmonary Embolism Detection
12385172,0.161,0,0,/keremt/12th-place-rsna-pe-inference,RSNA STR Pulmonary Embolism Detection
12506155,0.155,0,6,/darraghdog/rsna512-effnetb5-fold-all-exam-xfrmr-validated,RSNA STR Pulmonary Embolism Detection
12378269,0.233,3,6,/snooptosh/bronze-medal-using-tta-pe,RSNA STR Pulmonary Embolism Detection
12492094,0.221,0,4,/neurallmonk/fast-baseline-with-tta-after-gru-exp1,RSNA STR Pulmonary Embolism Detection
12100746,0.2239999999999999,0,0,/kanbehmw/inference-pe-transformer-model,RSNA STR Pulmonary Embolism Detection
12253849,0.434,5,10,/javagarm/rsna-str-baseline,RSNA STR Pulmonary Embolism Detection
12239073,1.179,4,7,/imnishantg/fastai-model-inference-pipeline,RSNA STR Pulmonary Embolism Detection
12212839,0.512,0,1,/abhimahule/practice-submission,RSNA STR Pulmonary Embolism Detection
12176157,0.518,0,1,/keremt/06-inference-multi-output,RSNA STR Pulmonary Embolism Detection
12036864,0.693,4,15,/kingstying/rsna-ped-check-metric,RSNA STR Pulmonary Embolism Detection
11912489,0.534,0,9,/itsuki9180/using-tpu-pe-detection-inference-phase,RSNA STR Pulmonary Embolism Detection
4919648,0.9399,9,29,/pavelvpster/ieee-fraud-eda-lightgbm-baseline,IEEE-CIS Fraud Detection
5057266,0.9381,0,0,/anuraglal1/ieee-fraud-detection-1,IEEE-CIS Fraud Detection
5066399,0.8576,0,0,/imvignesh/ieee-fraud-attempt-with-tpot-automl-beginner-ds,IEEE-CIS Fraud Detection
5171279,0.9158,9,29,/yoongkang/beginner-s-random-forest-example,IEEE-CIS Fraud Detection
5233257,0.8643,2,2,/zakrea/a-neural-network-approach-for-fraud-detection,IEEE-CIS Fraud Detection
5169456,0.9287,2,12,/jeongyoonlee/kaggler-s-autolgb,IEEE-CIS Fraud Detection
5160211,0.9335,0,1,/patilsumeetv/ieee-fraud-detection-lightgbm-baseline,IEEE-CIS Fraud Detection
5007479,0.9418,14,93,/viswajithkn/fraud-detection,IEEE-CIS Fraud Detection
5087706,0.9455,6,11,/raghaw/ensemble-on-fire,IEEE-CIS Fraud Detection
5042618,0.932,3,20,/nicapotato/tree-split-feature-selection-lgbm-gpu-earlystop,IEEE-CIS Fraud Detection
5024946,0.943,1,16,/stocks/u2-can-be-a-winner,IEEE-CIS Fraud Detection
4969781,0.924,4,13,/chizuchizu/japanese-extraction-of-importance,IEEE-CIS Fraud Detection
4960679,0.9407,0,6,/a18974761777/kernel38a9909bae,IEEE-CIS Fraud Detection
4949133,0.943,16,33,/rajwardhanshinde/stackers-blend-top-4,IEEE-CIS Fraud Detection
4983004,0.9258,1,5,/grroverpr/ieee-catboost-baseline,IEEE-CIS Fraud Detection
4953495,0.9317,1,13,/danofer/xgboost-using-optuna-fastauc-features,IEEE-CIS Fraud Detection
4882346,0.9126,0,17,/danofer/ieee-fraud-new-features-export-0-94-lb,IEEE-CIS Fraud Detection
4913010,0.9384,10,24,/silverstone1903/simple-xgboost-with-feature-interactions,IEEE-CIS Fraud Detection
12068414,0.8652299999999999,3,2,/gizemcemileelik/airbnb-rf-xgboost-label-encoded-resampled-not0-86,Airbnb New User Bookings
9302080,0.8611700000000001,0,1,/mattbast/airbnb-recommendation-engine,Airbnb New User Bookings
5927198,0.8662,0,0,/starngu1/kernel600b7dc6ce,Airbnb New User Bookings
2091371,0.8525299999999999,0,4,/fffjay/airbnb-new-user-bookings,Airbnb New User Bookings
14071384,0.8170000000000001,0,1,/evodswdy/resunet-icip-01112137,HuBMAP - Hacking the Kidney
13816184,0.8440000000000001,5,11,/homiarafarhana/2nd-sub-effunet5-0-845-4983e4,HuBMAP - Hacking the Kidney
13969052,0.8590000000000001,5,5,/divyanshuyadav/inference-hubmap-fpn-single-model-segmentation,HuBMAP - Hacking the Kidney
13630436,0.812,0,1,/shilei2403/baseline-imp1219,HuBMAP - Hacking the Kidney
13706224,0.8140000000000001,1,3,/arunmohan003/hubmap-inference-pytorch,HuBMAP - Hacking the Kidney
13748235,0.799,1,6,/keremt/hubmap-fast-submission,HuBMAP - Hacking the Kidney
13491724,0.847,5,27,/isakev/hubmap-freeze-pretrained-sub-effunet5-valloss,HuBMAP - Hacking the Kidney
13487857,0.8390000000000001,5,10,/finlay/csv-write-for-fast-public-score,HuBMAP - Hacking the Kidney
13389622,0.7709999999999999,0,2,/antonio5444/pytorch-with-training,HuBMAP - Hacking the Kidney
13399114,0.8490000000000001,44,97,/wrrosa/hubmap-tf-with-tpu-efficientunet-512x512-subm,HuBMAP - Hacking the Kidney
13260417,0.835,0,4,/dxchen/public-submission-only-fastest,HuBMAP - Hacking the Kidney
738734,0.795309,0,0,/mihidashr/tensorflow-neural-network-ncaa,Google Cloud & NCAA® ML Competition 2018-Women's
9286751,0.6890000000000001,0,4,/manuelkraus89/steganography-for-beginners,ALASKA2 Image Steganalysis
9259134,0.631,1,4,/danofer/pytorch-transfer-learning-gpu,ALASKA2 Image Steganalysis
9158975,0.782,0,1,/msafi04/alaska-wild-tpu,ALASKA2 Image Steganalysis
9159208,0.5489999999999999,7,56,/naivelamb/alaska2-srnet-baseline-inference,ALASKA2 Image Steganalysis
9159165,0.599,7,6,/yeayates21/alaska2-densenet-keras-starter,ALASKA2 Image Steganalysis
9183459,0.598,0,3,/outrunner/random-seed-1,ALASKA2 Image Steganalysis
13166798,0.0,0,0,/gudlaarunkumar/cassava-leaf-disease-classification,Cassava Leaf Disease Classification
13233696,0.856,0,4,/mohanavel/cassava-leaf-disease-classification-with-resnet34,Cassava Leaf Disease Classification
13231302,0.139,22,47,/vyombhatia/tutorial-a-few-lines-of-code,Cassava Leaf Disease Classification
13276505,0.69,0,0,/thanish/a-newbie-s-resnet50,Cassava Leaf Disease Classification
13212123,0.9,49,165,/mekhdigakhramanian/pytorch-efficientnet-baseline-inference-tta,Cassava Leaf Disease Classification
13153003,0.7559999999999999,3,9,/tpothjuan/efficientnet-xception-k-transferring-tensorflow,Cassava Leaf Disease Classification
13198678,0.857,0,2,/prokaggler/beginner-kit-cassava-leaf-disease-fastai,Cassava Leaf Disease Classification
13265858,0.609,0,0,/ajaykumar7778/tpu-inference-effnets,Cassava Leaf Disease Classification
12989692,0.12,0,0,/aneeshaaschowdhry/fastai-solution-by-a-noob,Cassava Leaf Disease Classification
13000393,0.743,3,9,/dmikar/keras-baseline-ensemble-resnet50-efficientnetb3,Cassava Leaf Disease Classification
13148319,0.877,2,3,/lavanyask/cassava-leaf-disease-inference,Cassava Leaf Disease Classification
13071149,0.898,3,21,/ababino/cutmix-with-fastai-and-efficientnet,Cassava Leaf Disease Classification
13083716,0.5379999999999999,0,0,/simpleaccount/prediction,Cassava Leaf Disease Classification
13000405,0.614,0,8,/domizianostingi/cnn-keras-model,Cassava Leaf Disease Classification
13030363,0.8859999999999999,3,13,/ramkicse/pytorch-efficientnet-baseline,Cassava Leaf Disease Classification
13066608,0.89,8,6,/ravitejam/fastai-after-lesson-2,Cassava Leaf Disease Classification
13339575,0.99153,0,4,/atin10/digit-cnn,Digit Recognizer
13238106,0.97214,1,1,/ibnuthoriqh/digit-recognizer-with-tensorflow-keras,Digit Recognizer
13179035,0.87192,0,0,/illgoshat/simple-logreg-on-mnist,Digit Recognizer
12047031,0.98732,0,0,/aarontanjaya/cnn-digit-recognizer,Digit Recognizer
13111649,0.99371,8,19,/vad13irt/digit-recognizer-cnn-0-994-15,Digit Recognizer
13103724,0.94982,0,1,/aicentral/image-transfer-learning-using-resnet50,Digit Recognizer
13061551,0.9761,0,0,/ektadubey38/mnist-digit-recognition,Digit Recognizer
12667547,0.95828,0,0,/bhaskar321/svm-digit-recognition-for-beginners,Digit Recognizer
10082767,0.7287899999999999,0,0,/visiteur/lab-cats-gr-boosting,Categorical Feature Encoding Challenge II
1687969,0.695,0,1,/ggeo79/tgs-deep-learning-unet,TGS Salt Identification Challenge
1643315,0.813,4,8,/lpachuong/u-net-with-simple-resnet-blocks-v2-new-loss,TGS Salt Identification Challenge
1614199,0.815,2,22,/ashishpatel26/things-that-matter-to-tgs,TGS Salt Identification Challenge
1597181,0.8009999999999999,0,5,/whilefalse/crfapply-0-801lb,TGS Salt Identification Challenge
1561159,0.797,25,65,/aerdem4/u-net-with-simple-resnet-blocks-forked,TGS Salt Identification Challenge
1558997,0.7909999999999999,2,6,/nikhilroxtomar/u-net-with-simple-resnet-blocks,TGS Salt Identification Challenge
1522597,0.386,0,1,/victorhz/cnn-for-tsg,TGS Salt Identification Challenge
1513994,0.472,16,26,/imrandude/tgs-salt-identification-fastai-unet-resnet,TGS Salt Identification Challenge
1496733,0.785,3,17,/zahedi/rle-masks-blend,TGS Salt Identification Challenge
1460436,0.7709999999999999,0,1,/germey/seismic-data-analysis-with-u-net,TGS Salt Identification Challenge
1362586,0.7759999999999999,15,41,/dingli/seismic-data-analysis-with-u-net,TGS Salt Identification Challenge
1411009,0.772,34,119,/alexanderliao/u-net-bn-aug-strat-focal-loss-fixed,TGS Salt Identification Challenge
1400469,0.76,15,42,/dingdiego/u-net-batchnorm-augmentation-stratification,TGS Salt Identification Challenge
1427504,0.753,0,0,/melgor/u-net-batchnorm-augmentation-stratificat-b0026c,TGS Salt Identification Challenge
1341962,0.701,0,15,/nafisur/tgs-salt-keras,TGS Salt Identification Challenge
1336583,0.7140000000000001,63,372,/phoenigs/u-net-dropout-augmentation-stratification,TGS Salt Identification Challenge
3861833,4.49234,0,0,/tmznql1234/3rd-model,TMDB Box Office Prediction
3810337,6.84859,0,2,/nickrood/can-star-power-predict-box-office-revenue,TMDB Box Office Prediction
3828605,1.94915,0,0,/tundraman/extensive-feature-engineering-for-tmdb-xgboost,TMDB Box Office Prediction
3637984,2.60925,0,2,/alexgomes3/tmdb-data-viz,TMDB Box Office Prediction
3637618,13.48088,0,0,/tmznql1234/seoul-coding-academy,TMDB Box Office Prediction
3678671,1.87025,5,9,/sarthakbatra/box-office-revenue-random-forest-tutorial,TMDB Box Office Prediction
3627736,1.96404,5,8,/somang1418/time-to-tune-your-model-shortandsweet,TMDB Box Office Prediction
3576964,1.75665,5,26,/samusram/film-industry-curiosities-box-office-prediction,TMDB Box Office Prediction
3543134,6.15838,0,0,/himanshu16497/kernela874629ddf,TMDB Box Office Prediction
3474594,2.11932,0,5,/wanderdust/lgbm-pytorch-catboost,TMDB Box Office Prediction
3422667,2.30468,6,8,/orion99/movie-box-office-collection-prediction,TMDB Box Office Prediction
2882697,2.62286,0,1,/njpurcell/box-office-knn,TMDB Box Office Prediction
3289245,3.05068,1,2,/ivangord/movie-kernel,TMDB Box Office Prediction
3234508,1.86594,0,0,/mailyousufkhan/feature-engineering-eda-lgb-xgb-cat,TMDB Box Office Prediction
3163662,2.04635,2,4,/metesarang/eda-feature-engineering-regression-models,TMDB Box Office Prediction
3043796,2.67108,1,2,/saptarsi96/tmdb-beginners-level,TMDB Box Office Prediction
3004286,3.79138,0,0,/xsakix/box-office-one,TMDB Box Office Prediction
14122175,0.725,0,0,/tt0721/cassava-pytorch-baseline,Cassava Leaf Disease Classification
14184027,0.851,0,3,/sarangbhatnagar/easy-code-efficientnetb3-submission,Cassava Leaf Disease Classification
14186202,0.89,0,7,/shash152543/cassava-inference-code,Cassava Leaf Disease Classification
14165951,0.8809999999999999,0,0,/narendra/cassava-efficientnet-submision,Cassava Leaf Disease Classification
14103101,0.879,0,0,/julessharova/efficientnetb4-cassava-leaf,Cassava Leaf Disease Classification
14049170,0.877,1,4,/gurharkhalsa/beginer-fastai-progressive-resize,Cassava Leaf Disease Classification
13836304,0.8440000000000001,0,4,/michalpitr/cassava-cnn,Cassava Leaf Disease Classification
14091829,0.879,0,8,/bununtadiresmenmor/submission-starter-keras-efficientnet,Cassava Leaf Disease Classification
14031592,0.867,1,4,/carlwuuu/test-with-trained-model-weights-for-submission,Cassava Leaf Disease Classification
14038024,0.88,0,0,/sergiocalo/model-ensemble,Cassava Leaf Disease Classification
14060830,0.847,0,2,/iraqai/dataefficient-image-transformers-84-inferance,Cassava Leaf Disease Classification
13968923,0.602,0,1,/petermaiervictoria/efficientnet,Cassava Leaf Disease Classification
13673879,0.284,0,6,/imajoc/cnn-base,Cassava Leaf Disease Classification
3933847,0.9403,0,0,/liaobencheng/course-design-final,Histopathologic Cancer Detection
3630399,0.9753,3,2,/leeking/cnn-conv2d-separableconv2d-keras-new-model-1,Histopathologic Cancer Detection
3344153,0.8104,0,1,/abhijit96/histopathologic-cancer-detection,Histopathologic Cancer Detection
3300862,0.9683,0,0,/maxlenormand/vgg19-with-180k-images-public-lb-0-968,Histopathologic Cancer Detection
3397710,0.9781,13,14,/seefun/ensemble-top-3-models-in-public-kernels-0-9781,Histopathologic Cancer Detection
2607633,0.9698,2,0,/toshikazuwatanabe/histopathologic-cancer-detection,Histopathologic Cancer Detection
3173512,0.9636,2,8,/jtmurkz/keras-resnet50-0-96lb,Histopathologic Cancer Detection
3271101,0.9651,2,8,/seefun/a-pytorch-pipeline-from-team-seed-323,Histopathologic Cancer Detection
3220611,0.9661,2,28,/bonhart/pytorch-cnn-from-scratch,Histopathologic Cancer Detection
3135014,0.9386,0,6,/eiffelwong1/basic-cnn-for-cancer-detection-pytorch,Histopathologic Cancer Detection
3084034,0.4974,0,0,/phekima/image-data-gen-test-2,Histopathologic Cancer Detection
2959971,0.9658,3,5,/omniscientist99/metastatic-cancer-in-lymph-nodes-fastai-resnet50,Histopathologic Cancer Detection
2976318,0.9276,1,0,/chachichachi/stripped-down-fast-ai-submission,Histopathologic Cancer Detection
2910225,0.9454,0,0,/phekima/covnet-beginner-lb-0-93,Histopathologic Cancer Detection
2876010,0.9642,4,19,/mentalwanderer/image-classification-workflow-with-fast-ai,Histopathologic Cancer Detection
2802058,0.9552,6,14,/twhitehurst3/cancer-detect-keras-92-acc,Histopathologic Cancer Detection
2741347,0.9596,3,16,/soumya044/histopathologic-cancer-detection,Histopathologic Cancer Detection
2740571,0.9133,2,2,/vinlor/resnet-and-flow-from-dataframe-notebook,Histopathologic Cancer Detection
2601153,0.905,0,1,/tjay4798/histopathologic-cancer-detection,Histopathologic Cancer Detection
2382552,0.9564,8,22,/sdelecourt/cnn-with-keras,Histopathologic Cancer Detection
2297599,0.958,0,13,/ashishpatel26/densenet-169-is-best-as-per-research-article,Histopathologic Cancer Detection
2287525,0.9058,0,11,/ashishpatel26/hc-detection-using-pytorch-resnet-101,Histopathologic Cancer Detection
2874959,0.49381,0,0,/tangyubin/kaggle-bike,Bike Sharing Demand
2509913,0.43904,3,36,/kwonyoung234/for-beginner,Bike Sharing Demand
2392893,1.39047,0,1,/mitulshah97/bike-pricing,Bike Sharing Demand
2200461,0.42678,0,8,/yekang/20181126-bike-sharing-demand,Bike Sharing Demand
2165156,0.40231,1,11,/fredkron/eda-ml-on-bike-sharing,Bike Sharing Demand
1813404,1.37741,0,0,/mikulskibartosz/poz-linear-regression,Bike Sharing Demand
817152,0.4532899999999999,0,6,/delimixx/beginner-second-analytics-eda-gbr,Bike Sharing Demand
687363,0.4417899999999999,0,0,/valeriyparubets/bike-sharing-demand-xgboost-sklearn,Bike Sharing Demand
190034,0.75897,0,0,/shadowfax61/initial,Bike Sharing Demand
10903805,0.4363399999999999,0,0,/preejababu/bike-sharing-demand,Bike Sharing Demand
122886,0.63381,0,1,/aal13n/notebook6a4af7c6da,Outbrain Click Prediction
11840802,0.71331,0,1,/diljotwadia/notebook66faab00c3,Categorical Feature Encoding Challenge
10712399,0.6837,0,0,/rahmanimohsen/feature-encoding-and-classification,Categorical Feature Encoding Challenge
6558211,0.7882,0,0,/adarsh415/categorical-feature-encoding-challenge,Categorical Feature Encoding Challenge
6381610,0.78547,0,0,/ashkash247/categorical-feature-encoding,Categorical Feature Encoding Challenge
8099182,0.80175,0,3,/darwinwin/h2o-automl-30000sec,Categorical Feature Encoding Challenge
7855065,0.8073899999999999,0,2,/pdx250697/logistic-regression-solution,Categorical Feature Encoding Challenge
7441797,0.80314,0,1,/alexandervc/just-lightgbm,Categorical Feature Encoding Challenge
7295929,0.8026399999999999,1,1,/ihorshypunov/kernel1fcd084e65,Categorical Feature Encoding Challenge
6597433,0.6419,0,2,/saibharath12/handling-categorical-variables-encoding-modeling,Categorical Feature Encoding Challenge
6674049,0.8084,15,75,/adaubas/2nd-place-solution-categorical-fe-callenge,Categorical Feature Encoding Challenge
6977042,0.8085,0,9,/dkomyagin/cat-in-the-dat-0-80285-private-lb-solution,Categorical Feature Encoding Challenge
6719253,0.79009,2,0,/srbestha/categorical-feature-encoding,Categorical Feature Encoding Challenge
6851632,0.72205,0,14,/vbmokin/cfec-probability-calibration,Categorical Feature Encoding Challenge
6810145,0.5493600000000001,0,2,/dskagglemt/categorical-feature-encoding-challenge,Categorical Feature Encoding Challenge
6737265,0.76189,0,0,/niranjankumarc/categorical-feature-encoding-modeling,Categorical Feature Encoding Challenge
6675691,0.8074899999999999,1,3,/akgeni/combining-onehot-and-target-encoding-features,Categorical Feature Encoding Challenge
6643254,0.57148,0,0,/imdevskp/what-traditional-encoding-methods-would-give-you,Categorical Feature Encoding Challenge
6559694,0.78622,0,0,/stocks/gm-secret-encoding,Categorical Feature Encoding Challenge
9398965,0.937577,0,1,/sukruaras/fraud-detection-sukru01,IEEE-CIS Fraud Detection
4956802,0.8383,0,1,/tboyle10/01-ieee-cis-fraud-detection-baseline-models,IEEE-CIS Fraud Detection
8212412,0.938105,0,3,/slatawa/basic-data-cleaning-with-lgb-xgboost,IEEE-CIS Fraud Detection
8131011,0.872058,0,2,/darwinwin/h2o-automl-tutorial-ieee-cis-fraud-detection,IEEE-CIS Fraud Detection
5598988,0.9475,0,0,/filemide/ieee-cv-options,IEEE-CIS Fraud Detection
4991658,0.9373,1,1,/acruve15/ieee-fraud-xgboost-with-kfold,IEEE-CIS Fraud Detection
6931536,0.8486469999999999,0,0,/keyurparalkar/ieee-cis-fraud-detection-with-fastai-submissions,IEEE-CIS Fraud Detection
6612421,0.8826809999999999,0,0,/jcmiii/cc-fraud-email-one-hot,IEEE-CIS Fraud Detection
5881045,0.9381,0,0,/rashmiranjan00/fraud-detection-with-xgboost,IEEE-CIS Fraud Detection
5545422,0.9222,0,1,/steboss/keras-nn-autoencoder,IEEE-CIS Fraud Detection
5220756,0.8545,0,0,/prateek91/estimator,IEEE-CIS Fraud Detection
4857441,0.9382,0,0,/abimannan/ieee-fraud-detection,IEEE-CIS Fraud Detection
5864943,0.8851530000000001,0,0,/ruhong/ieee-fraud-detection-keras,IEEE-CIS Fraud Detection
5745276,0.8723,0,0,/ruhong/ieee-fraud-detection-rf,IEEE-CIS Fraud Detection
6133923,0.8739540000000001,1,6,/govind555/ieee-eda-lgbm,IEEE-CIS Fraud Detection
5885025,0.9374,0,1,/darshanpatel11/iee-fraud-detection,IEEE-CIS Fraud Detection
6087563,0.962081,61,372,/cdeotte/xgb-fraud-with-magic-0-9600,IEEE-CIS Fraud Detection
6006176,0.946,1,1,/srishti280992/model-building-for-fraud-detection,IEEE-CIS Fraud Detection
12537521,0.0389399999999999,0,5,/tunguz/cats-vs-dogs-with-eb7-ns-and-rapids-svc,Dogs vs. Cats Redux: Kernels Edition
12225332,0.03743,0,2,/tunguz/cats-vs-dogs-with-eb2-ns,Dogs vs. Cats Redux: Kernels Edition
12225296,0.03516,0,1,/tunguz/cats-vs-dogs-with-eb3-ns,Dogs vs. Cats Redux: Kernels Edition
12194247,0.03102,1,12,/tunguz/cats-vs-dogs-with-eb7-ns,Dogs vs. Cats Redux: Kernels Edition
12173249,0.0367199999999999,0,1,/tunguz/xgb-gpu-cats-vs-dogs-with-eb6,Dogs vs. Cats Redux: Kernels Edition
12171836,0.03578,0,1,/tunguz/cats-vs-dogs-with-eb5-7-pretrained-embeddings,Dogs vs. Cats Redux: Kernels Edition
12170405,0.03758,0,1,/tunguz/cats-vs-dogs-with-eb5-pretrained-embeddings,Dogs vs. Cats Redux: Kernels Edition
12168749,0.04761,0,1,/tunguz/cats-vs-dogs-with-eb1-pretrained-embeddings,Dogs vs. Cats Redux: Kernels Edition
12138587,0.03766,0,2,/tunguz/xgb-gpu-cats-vs-dogs-with-naslarge,Dogs vs. Cats Redux: Kernels Edition
12135086,0.03898,0,1,/tunguz/gpu-xgb-cats-vs-dogs-with-irv2,Dogs vs. Cats Redux: Kernels Edition
12011991,0.05169,0,1,/tunguz/cats-vs-dogs-with-dn169-pretrained-embeddings,Dogs vs. Cats Redux: Kernels Edition
11995987,0.0394399999999999,0,6,/tunguz/cats-vs-dogs-with-irv2-pretrained-embeddings-lr,Dogs vs. Cats Redux: Kernels Edition
11995935,0.06844,1,2,/tunguz/cats-vs-dogs-with-iv3-pretrained-embeddings-lr,Dogs vs. Cats Redux: Kernels Edition
11183655,0.14218,1,4,/lulzseq/tensorflow-cnn-vgg16-classification-loss-0-14,Dogs vs. Cats Redux: Kernels Edition
579973,0.47886,0,0,/pd8073/notebook2915b940ab,Mercari Price Suggestion Challenge
564788,0.45281,0,0,/lucenya/mercari-ridge,Mercari Price Suggestion Challenge
563008,0.6987300000000001,0,0,/liviubrinza/mercari-category-based-mean-price-calculation-test,Mercari Price Suggestion Challenge
556756,0.43443,0,0,/qshiki/fork-of-ridge-category,Mercari Price Suggestion Challenge
510876,0.75029,0,0,/miemiekurisu/test1,Mercari Price Suggestion Challenge
2504759,0.395,5,19,/domcastro/let-s-annoy-abhishek,PetFinder.my Adoption Prediction
2504422,0.331,3,7,/beloruk1/baseline-random-forest,PetFinder.my Adoption Prediction
2502781,0.3389999999999999,2,3,/sumitjha19/humanifyai-petfinder-automl-h2o-package,PetFinder.my Adoption Prediction
2491804,0.319,1,6,/teemingyi/petfinder-eda-lgb-model,PetFinder.my Adoption Prediction
2499226,0.303,1,0,/plarmuseau/nothing-interesting-here,PetFinder.my Adoption Prediction
7121397,0.0,0,0,/ntson1711/petfinder,PetFinder.my Adoption Prediction
3057864,0.303,0,0,/lorevala/combination-lgbm-mlp,PetFinder.my Adoption Prediction
7169511,0.68897,0,3,/dreimd/facial-recognition-model-in-pytorch,Deepfake Detection Challenge
7174216,0.73233,20,101,/zaharch/data-leak-in-metadata,Deepfake Detection Challenge
7125821,0.69314,1,7,/jazivxt/mysteries-of-sight-and-sound,Deepfake Detection Challenge
7017043,0.69314,92,580,/gpreda/deepfake-starter-kit,Deepfake Detection Challenge
7008916,0.69314,67,351,/robikscube/kaggle-deepfake-detection-introduction,Deepfake Detection Challenge
7007261,0.68897,66,274,/timesler/facial-recognition-model-in-pytorch,Deepfake Detection Challenge
7035693,0.6934899999999999,0,1,/tonychenxyz/random-outputs-attempts,Deepfake Detection Challenge
7025570,0.69817,2,5,/unkownhihi/preprocessing-baseline-with-face-detection-mtcnn,Deepfake Detection Challenge
7006268,1.20397,4,12,/artgor/baseline,Deepfake Detection Challenge
8222848,0.69233,0,0,/arseniimustafin/notebook-draft,Deepfake Detection Challenge
8125767,0.43154,0,0,/yatinece/fork-of-fork-of-xception-resnext-ensemble-infer,Deepfake Detection Challenge
12153239,1.158,0,0,/zeynepkurban/ashrae-2,ASHRAE - Great Energy Predictor III
13141518,1.35,0,0,/jeffreyyang1997/cee-498ds-project-11-competition,ASHRAE - Great Energy Predictor III
6419939,1.11,0,0,/nussabav/pretrained-lgbm-by-meter-type-and-site-id,ASHRAE - Great Energy Predictor III
8230856,0.961,0,0,/patrick0302/site-meter-split-sg-no-blend,ASHRAE - Great Energy Predictor III
7099844,0.961,0,0,/yunishi0716/best-weight-searching3,ASHRAE - Great Energy Predictor III
6300802,1.875,0,0,/snthibaud/energy-usage-prediction,ASHRAE - Great Energy Predictor III
6925071,0.946,0,5,/vladimirsydor/add-leak,ASHRAE - Great Energy Predictor III
6920455,1.07,0,1,/vladimirsydor/bland-lgbm-folds,ASHRAE - Great Energy Predictor III
12952820,0.50033,0,0,/hiroshi20180410/otto-7,Otto Group Product Classification Challenge
10792089,0.4521699999999999,0,5,/gengotaka/simple-lightgbm-kfold-on-otto,Otto Group Product Classification Challenge
9679382,0.44113,0,1,/nagomiso/feature-extraction-tfidf,Otto Group Product Classification Challenge
8720989,0.47147,0,0,/wakamezake/otto-stacking-level1,Otto Group Product Classification Challenge
8246468,0.5391600000000001,0,1,/cashfeg/hoxosh-problem-with-pca,Otto Group Product Classification Challenge
2421856,0.5051,0,3,/anycode/otto-using-mlp,Otto Group Product Classification Challenge
1838953,0.56289,0,5,/wakamezake/neural-networks-product-classification-otto,Otto Group Product Classification Challenge
774979,1.49269,0,0,/ankitbiradar/product-classification,Otto Group Product Classification Challenge
509577,0.57519,0,1,/prashant10/fork-of-lr-gbm-rf-ensemble,Otto Group Product Classification Challenge
466325,0.6068100000000001,0,0,/akshayuppal3/notebook73c8ca1639,Otto Group Product Classification Challenge
201726,0.63453,0,0,/xiaoleizhang1/random-forest-starter-with-numerical-features,Two Sigma Connect: Rental Listing Inquiries
1917998,1.443,0,1,/arpitsinha/complete-eda-cleaning-lgb-model,Google Analytics Customer Revenue Prediction
1907308,1.4222,0,2,/scirpus/gp-with-oliviers-script,Google Analytics Customer Revenue Prediction
1761241,1.5372,0,7,/yulinzxc/eda-feature-tool-lightgbm,Google Analytics Customer Revenue Prediction
1869533,1.2964,5,15,/ambitious/leaky-homework-khrylchenko-kirill-msu-mmp,Google Analytics Customer Revenue Prediction
1868187,1.3021,3,13,/maxbourdon/kodryan-mmp-msu-gacrp-my-best,Google Analytics Customer Revenue Prediction
1871265,1.4271,0,2,/nikser/simple-user-level-model-for-homework,Google Analytics Customer Revenue Prediction
1870444,1.4192,0,1,/alekseydumbay/i-have-seen-the-future-modification-for-learning,Google Analytics Customer Revenue Prediction
1706829,1.4453,0,3,/oneraghavan/google-analytics-customer-revenue-eda-baseline,Google Analytics Customer Revenue Prediction
1867567,1.6405,0,1,/eduardok/kernel1b9742a32f,Google Analytics Customer Revenue Prediction
1870118,1.4361,0,2,/itasarom/kernel12fbc709e8,Google Analytics Customer Revenue Prediction
1819167,1.4156,10,65,/ashishpatel26/future-is-here,Google Analytics Customer Revenue Prediction
1800107,1.4324,4,5,/scirpus/feature-fest-with-genetic-programming,Google Analytics Customer Revenue Prediction
1802974,1.7804,0,1,/steveandthedogs/sb-googleanalyticscustomerrevenueprediction-v2,Google Analytics Customer Revenue Prediction
8811249,0.6759999999999999,0,0,/jiteshpabla/sentiment-analysis,Tweet Sentiment Extraction
12715918,0.6602,0,1,/harryvine/spacy-ner-no-neutral,Tweet Sentiment Extraction
13223397,0.6669,0,0,/harryvine/distilbert-ner,Tweet Sentiment Extraction
13109369,0.55132,0,0,/sakshammangal/tweet-sentiment-extraction-saksham,Tweet Sentiment Extraction
12973244,0.71214,0,0,/taritkandpal/taritkandpal-101703583,Tweet Sentiment Extraction
13105638,0.65194,0,0,/yogeshsingla1/notebook773bfa0ee3,Tweet Sentiment Extraction
13023118,0.52412,0,0,/aarushiwadhwa4/tweet-sentiment-rnn,Tweet Sentiment Extraction
13022615,0.52992,0,0,/aarushiwadhwa444/notebook12b7d60b99,Tweet Sentiment Extraction
12813070,0.65458,0,0,/dplutcho/tweet-sent-ext-spacy-ipynb,Tweet Sentiment Extraction
8800512,0.649,0,0,/jparkes/tweet-sentiment-phrase-extraction-via-regression,Tweet Sentiment Extraction
9394428,0.715,0,1,/mtaczynski/tweet-sentiment-extraction,Tweet Sentiment Extraction
12492675,0.69587,0,0,/sais01/tse-bert,Tweet Sentiment Extraction
12236114,0.5674600000000001,0,0,/bharath150/tse-feature-selection,Tweet Sentiment Extraction
9613061,0.7140000000000001,0,0,/adioadebambotoheeb/tse-pytorch,Tweet Sentiment Extraction
10163392,0.708,0,0,/ivince20x4/tweet-sentiment-extraction-project-vince,Tweet Sentiment Extraction
9672983,0.711,0,0,/drack3800/v0-0-5-bertweet,Tweet Sentiment Extraction
9947029,0.7140000000000001,0,1,/ajaykumar7778/tweet-sentiment-roberta-pytorch-2e376b,Tweet Sentiment Extraction
9584714,0.54099,0,0,/parthtank/kernel6161f00a3b,Toxic Comment Classification Challenge
9064060,0.98234,0,0,/qurb48/lgb-gru-lr-lstm-nb-svm-average-ensemble,Toxic Comment Classification Challenge
8899618,0.97687,0,0,/abhishek2195/toxic-comment-classification-project,Toxic Comment Classification Challenge
8160486,0.98448,0,2,/charlesmatthews/toxic-twitter-with-keras-gru-1d-conv,Toxic Comment Classification Challenge
7372171,0.82255,2,3,/athews/toxicity-detection-eda-model,Toxic Comment Classification Challenge
6838907,0.98371,0,2,/jagannathrk/bidirectional-lstm-with-convolution,Toxic Comment Classification Challenge
6811080,0.9468,0,1,/saeedtqp/nbsvm-toxic-cm,Toxic Comment Classification Challenge
6610266,0.98705,1,0,/amir78pgd/fork-of-ensemble-3-blend,Toxic Comment Classification Challenge
6530649,0.96098,0,0,/arifali77/toxic-comment-classification-quora,Toxic Comment Classification Challenge
6430584,0.98329,2,8,/hawkeoni/pytorch-simple-bert,Toxic Comment Classification Challenge
6334284,0.97695,0,0,/amir78pgd/improved-lstm-baseline-glove-dropout-trainta,Toxic Comment Classification Challenge
6377081,0.98692,0,0,/amir78pgd/ensemble-1,Toxic Comment Classification Challenge
6234780,0.98694,0,2,/tunguz/bi-gru-lstm-dual-embedding-with-mish,Toxic Comment Classification Challenge
6131410,0.98043,0,0,/amir78pgd/improved-lstm-baseline-fasttext-dropout,Toxic Comment Classification Challenge
6126810,0.95852,1,5,/ahayek84/fork-of-toxic-comment-classification-gru,Toxic Comment Classification Challenge
5749063,0.97722,0,0,/starngu1/toxic-comment-classification,Toxic Comment Classification Challenge
3871679,0.98702,2,10,/tunguz/bi-gru-lstm-dual-embedding-new-test-cleaned-5,Toxic Comment Classification Challenge
3857127,0.98643,0,1,/tunguz/bi-lstm-gru-dual-embedding-new-test-4,Toxic Comment Classification Challenge
3871646,0.98654,0,1,/tunguz/bi-lstm-gru-dual-embedding-new-test-clean-2,Toxic Comment Classification Challenge
3837912,0.9863,0,1,/tunguz/bi-lstm-gru-dual-embedding-new-test,Toxic Comment Classification Challenge
4916268,0.96557,0,0,/amanbhalla/toxic-comment-challenge-cnn-text,Toxic Comment Classification Challenge
4900265,0.8304600000000001,0,0,/zabir17/bilstm-tf-idf,Toxic Comment Classification Challenge
4403188,-0.924,27,190,/adrianoavelar/eachtype,Predicting Molecular Properties
4436320,-0.313,0,8,/robertburbidge/imputing-molecular-features,Predicting Molecular Properties
4173288,-0.413,6,21,/ggeo79/j-coupling-lightbgm-gpu-dihedral-angle,Predicting Molecular Properties
4210316,0.604,12,39,/buchan/a-neural-network-approach,Predicting Molecular Properties
4143842,-0.442,7,33,/kmat2019/neural-network-modeling-with-multiple-outputs,Predicting Molecular Properties
4141764,1.178,0,3,/lvulliard/linear-regression-approach,Predicting Molecular Properties
4080021,0.289,130,668,/artgor/molecular-properties-eda-and-models,Predicting Molecular Properties
4082324,2.9210000000000003,0,5,/timsonrisa/starter-kernel,Predicting Molecular Properties
4089918,0.437,0,8,/sameerdev7/simple-eda-lightgbm-catboost,Predicting Molecular Properties
4095586,1.237,0,4,/seshadrikolluri/simple-atomic-pair-distance-based-model,Predicting Molecular Properties
14108843,0.9746,2,4,/sajidhussain3/kanadamnist-beginners-model,Kannada MNIST
13735026,0.9714,0,0,/razintailor/kannada-mnist-resnet34,Kannada MNIST
13642485,0.9374,0,2,/gauravduttakiit/kannada-mnist-recognizer-using-catboost,Kannada MNIST
6854872,0.98,0,0,/valeriyarudikova/kernel14c6d795d2,Kannada MNIST
13235428,0.9646,0,0,/yuemengzhang/mobilenet2-2,Kannada MNIST
12469183,0.8864,0,0,/patrickcy/kannadaminst-cnn,Kannada MNIST
12095622,0.952,0,1,/cristianfat/kannada-digit-recogniser,Kannada MNIST
11493291,0.9786,1,6,/mritunjaisingh/kannada-mnist,Kannada MNIST
11320921,0.9414,2,7,/fluffyhamster/kannada-mnist,Kannada MNIST
7559847,0.9828,0,0,/thetrueharvey/kannada-mnist-tensorflow,Kannada MNIST
10825191,0.1152,0,0,/aishwaryshukla/kernel3952add23c,Kannada MNIST
6743562,0.9784,0,2,/datasail/kannada-mnist-baseline,Kannada MNIST
10183862,0.119,0,1,/erickortegadn/proyecto-kannada-mnist,Kannada MNIST
5887794,0.9798,0,2,/leighplt/pytorch-arcmargin,Kannada MNIST
6419853,0.957,0,1,/okeaditya/keras4kannada,Kannada MNIST
6919457,0.9908,0,1,/digantpatel/kernel6b4c433629,Kannada MNIST
9480319,0.9824,0,0,/ashishsinha29/keras-cnn,Kannada MNIST
7014505,0.9794,0,0,/pratikchoudhari/kannada-mnist,Kannada MNIST
7558077,0.9822,1,7,/rainmaker29/kannada-mnist-98,Kannada MNIST
9331322,0.9882,0,0,/opalle/kernel10ecde5976,Kannada MNIST
9146763,0.9884,1,0,/kondratyonok/1-homework,Kannada MNIST
6675817,0.987,0,0,/yuki19940627/neural-nets-cnn,Kannada MNIST
8606830,0.8716,0,0,/nitinmahajan20/nm0326-1,Kannada MNIST
5877512,0.988,0,0,/montimirko/kannada-mnist,Kannada MNIST
10613744,0.7579,0,5,/gyanendradas/ensemble-yolov5,Global Wheat Detection
13726271,0.7242,0,0,/tsehaopeng/faster-rcnn-r50-fpn-attention-0010-dcn-albu-2x-bw,Global Wheat Detection
9551743,0.67,0,2,/sneha5gsm/wheat-detection-yolo,Global Wheat Detection
13877125,0.7294,0,0,/fongrongchang/final-project,Global Wheat Detection
10764697,0.6836,0,0,/weharris/00-fasterrcnn-wheat-inference-v02-eh,Global Wheat Detection
13602421,0.6476,0,0,/chihchiali/notebook774d79731c,Global Wheat Detection
13414143,0.6617,0,0,/shayantaherian/global-wheat-detection-inference,Global Wheat Detection
13555482,0.7193,0,0,/nelson870708/inference-efficientdet,Global Wheat Detection
10102170,0.7294,0,1,/liw5589/wbf-over-tta-single-model-efficientdet,Global Wheat Detection
11038141,0.7053,0,0,/annamel11111/ensemble-d7-d5-model-efficientdet,Global Wheat Detection
11919883,0.6711,0,1,/godeep48/fasterrcnn-inference,Global Wheat Detection
10101386,0.7436,0,12,/x2x21x21x21/3rd-place-solution,Global Wheat Detection
11532204,0.0882,0,2,/kushagrababbar/notebookc4505bdd68,Global Wheat Detection
10506396,0.764,0,0,/farah1608/yolov5-pseudo-labeling,Global Wheat Detection
10483136,0.6973,0,0,/deltaechov/gwd-onecyclefit,Global Wheat Detection
10577897,0.7464,0,0,/ankursingh12/efficientdet640-inference,Global Wheat Detection
11009243,0.759,7,3,/jihangz/wheat-centernet-infer-bifpn-nfolds-tta-v2,Global Wheat Detection
8499749,0.8310200000000001,0,0,/sadiakhalil/covid-19-global-eda-forecast,COVID19 Global Forecasting (Week 1)
8580964,2.6607,0,1,/aadi1141/kernel60ddd2e431,COVID19 Global Forecasting (Week 1)
8533884,2.88464,0,0,/vignesh19497/covid19-global-forecast-version1,COVID19 Global Forecasting (Week 1)
8585832,1.00485,0,0,/puneetbhateja93/covid-forecast-by-stepfunction-v1-4-rf,COVID19 Global Forecasting (Week 1)
8534354,1.45016,1,3,/corochann/fast-no-new-cases-baseline,COVID19 Global Forecasting (Week 1)
8528509,0.52579,1,4,/smasahudu97/randomforest,COVID19 Global Forecasting (Week 1)
8583062,2.26851,0,0,/shaushahi/kernel77f130f918,COVID19 Global Forecasting (Week 1)
8578113,2.78781,0,0,/yemimacarissa/kernel33376fd667,COVID19 Global Forecasting (Week 1)
8509959,1.45016,0,0,/tamreff3290/use-the-last-day-3-11-s-value,COVID19 Global Forecasting (Week 1)
8500677,1.3399299999999998,0,3,/eliasgreen/covid-19-decomposition-regression,COVID19 Global Forecasting (Week 1)
8491009,2.45439,0,0,/djohn8/covid-19-plots-predictions,COVID19 Global Forecasting (Week 1)
8547116,0.74771,0,0,/nitingrover425/kernel2a876aa315,COVID19 Global Forecasting (Week 1)
14531952,0.79186,0,0,/x2020fjx/titanic-x2020fjx,Titanic - Machine Learning from Disaster
14134096,0.75598,0,1,/jayanthappalla/titanic-ml-notebook,Titanic - Machine Learning from Disaster
14641401,0.75837,0,0,/amoghrajesh1999/titanic-ml-dt,Titanic - Machine Learning from Disaster
14641165,0.77272,0,0,/puritynyagweth/titanic,Titanic - Machine Learning from Disaster
14527722,0.7511899999999999,0,0,/ukaszdziechciarczyk/getting-started-with-titanic,Titanic - Machine Learning from Disaster
14576458,0.73923,0,0,/shoaibrkhan/titanic-survival-prediction,Titanic - Machine Learning from Disaster
10506152,0.76555,0,0,/ljr38687/classification-using-python-titanic-dataset,Titanic - Machine Learning from Disaster
14348219,0.74641,0,0,/leeminhwan/titanic,Titanic - Machine Learning from Disaster
12528110,0.7751100000000001,0,0,/vijaybkkrishna/getting-started-with-titanic,Titanic - Machine Learning from Disaster
9525690,0.7751100000000001,0,0,/adityashukzy/getting-started-with-titanic-imthekingoftheworld,Titanic - Machine Learning from Disaster
8584834,0.7196,4,11,/nxpnsv/trds-covid19-gobal-forcasting-week1,COVID19 Global Forecasting (Week 1)
8586120,1.17907,0,0,/sadeka007/covid-19-global-forecasting-week-1,COVID19 Global Forecasting (Week 1)
8571560,0.64985,0,2,/sazinsamin/covid-19-easiest-one-in-laderboard-43,COVID19 Global Forecasting (Week 1)
8568514,0.6625300000000001,2,2,/ivince20x4/covid-19-polynomial,COVID19 Global Forecasting (Week 1)
8545324,0.75517,0,1,/khoongweihao/simple-ensemble-with-decision-trees,COVID19 Global Forecasting (Week 1)
8537868,1.33626,0,1,/yohancheong/covid-19-competition,COVID19 Global Forecasting (Week 1)
8585309,2.08235,0,1,/ignaciomcgallo/covid-19-polynomial-models-and-visualization,COVID19 Global Forecasting (Week 1)
8582957,2.90398,0,1,/rezoanoorrahman/cluster-based-model,COVID19 Global Forecasting (Week 1)
8573078,0.6985399999999999,0,2,/egorokm/manyrfc-covid,COVID19 Global Forecasting (Week 1)
8576257,0.71154,1,0,/wandabwa2004/covid19-week-1,COVID19 Global Forecasting (Week 1)
8544269,0.8363799999999999,7,5,/diamondsnake/covid-19-logistic-curve-fitting-week-1,COVID19 Global Forecasting (Week 1)
8566442,0.74143,0,1,/apoorvm/rf-covid-forcast,COVID19 Global Forecasting (Week 1)
8581651,2.31144,8,37,/shakshisharma/kernelb454ab925d,COVID19 Global Forecasting (Week 1)
8541397,1.1383299999999998,2,5,/gowtamsingulur/kernel60b41bee88,COVID19 Global Forecasting (Week 1)
8521796,2.42973,2,2,/mahmudds/covid19-global-forecasting-week-1,COVID19 Global Forecasting (Week 1)
8150542,0.7751100000000001,0,0,/prajittr/titanic-survival-prediction,Titanic - Machine Learning from Disaster
14319955,0.80861,21,35,/awwalmalhi/titanic-complete-guide-to-top-3,Titanic - Machine Learning from Disaster
14275155,0.75358,0,0,/crilom/titanic-random-forest-cristhian-martinez,Titanic - Machine Learning from Disaster
14653637,0.79186,0,0,/x2020etu/fork-of-titanic-test,Titanic - Machine Learning from Disaster
14643911,0.7440100000000001,0,0,/ninotomo/notebook-titanic-fnn,Titanic - Machine Learning from Disaster
13035547,0.76555,0,0,/shatendrasingh/notebookcf45df58dc,Titanic - Machine Learning from Disaster
13993179,0.66746,0,0,/rodavok/linearsvc-without-sex,Titanic - Machine Learning from Disaster
8027651,0.7751100000000001,0,2,/robbiebeane/titanic-01,Titanic - Machine Learning from Disaster
14235269,0.7751100000000001,0,0,/szpawlowski/titanic-svm-with-python,Titanic - Machine Learning from Disaster
14183326,0.73923,0,0,/anshumakkar/titanic-survival-prediction,Titanic - Machine Learning from Disaster
5287012,-1.895,33,85,/roydatascience/chemistry-of-best-models-1-895,Predicting Molecular Properties
5241922,-1.6769999999999998,10,44,/thomasnelson/a-beginners-guide-to-blending,Predicting Molecular Properties
5016040,-1.286,38,173,/fnands/1-mpnn,Predicting Molecular Properties
4496680,1.278,0,3,/mittalh/predicting-mol-prop,Predicting Molecular Properties
4983787,0.396,0,4,/sagheachraf/feature-engineering-physical-chemical-measurement,Predicting Molecular Properties
4951321,-1.525,12,10,/rajwardhanshinde/another-one,Predicting Molecular Properties
4935232,-0.168,0,3,/carpediemamigo/xgb-skopt-fs-tuning-training-per-molecule-type,Predicting Molecular Properties
4727414,-1.144,3,38,/saurabh7/compact-feature-engineering-lgbm-py-version,Predicting Molecular Properties
4752520,-1.359,9,16,/surajpm/steal-like-an-atom,Predicting Molecular Properties
4076932,0.214,0,3,/clelong95/predicting-molecular-properties-random-forest,Predicting Molecular Properties
4586067,1.503,0,1,/chrisevans/kagglekc-sample-submission,Predicting Molecular Properties
4458969,0.6829999999999999,1,4,/lkuen89/feature-engineering-with-structures-file-and-lgb,Predicting Molecular Properties
4587898,-0.952,6,44,/kingychiu/1-r-3,Predicting Molecular Properties
4412159,-0.5529999999999999,0,3,/khiwila/kernel94fee73d73,Predicting Molecular Properties
4716251,0.98499,0,7,/decoflight/allennlp-example,Toxic Comment Classification Challenge
3895918,0.98267,0,0,/anthonydel/cnn-lstm-fasttext-dropout,Toxic Comment Classification Challenge
4068705,0.97415,0,2,/lxyuan0420/logistic-regression-with-tf-idf-ngram,Toxic Comment Classification Challenge
3907124,0.97905,0,0,/flywithcode/logistic-regression-tfidf,Toxic Comment Classification Challenge
3516258,0.97421,0,0,/hrush777/glove-cudnngru,Toxic Comment Classification Challenge
3080014,0.9559,0,5,/harshel7/bilstm-s-for-toxic-comments,Toxic Comment Classification Challenge
2796536,0.507,1,0,/dromosys/fast-ai-toxic-comment,Toxic Comment Classification Challenge
2359397,0.9736,0,0,/anandvidvat/lstm-gpu,Toxic Comment Classification Challenge
2338723,0.9691,0,1,/classtag/toxic-3-pooled-gru-and-fasttext,Toxic Comment Classification Challenge
1900004,0.9764,0,0,/dhruvt93/going-beyond-jeremy-s-lstm-baseline,Toxic Comment Classification Challenge
2016588,0.9665,0,4,/tunguz/logistic-regression-with-tf-embeddings,Toxic Comment Classification Challenge
492854,1.006,0,0,/hemantnyadav/one-product-random-forest,Corporación Favorita Grocery Sales Forecasting
491446,0.529,0,6,/aharless/ceshine-s-lgb-w-val-data-included-in-training,Corporación Favorita Grocery Sales Forecasting
11739151,0.60492,0,2,/kweonwooj/kc03-day03-dataaug,State Farm Distracted Driver Detection
3443573,0.39658,0,0,/pavanyekabote/driver-activity-detection-py,State Farm Distracted Driver Detection
2084768,3.99373,0,0,/amjadm/kernel3a087585a5,State Farm Distracted Driver Detection
2065491,18.6084,0,0,/amjadm/kernel52180bb0ba,State Farm Distracted Driver Detection
1620520,4.63156,0,0,/ambarish/state-farm-image-analysis-2,State Farm Distracted Driver Detection
1779662,3.3973,4,9,/xavierbourretsicotte/lgbm-error-analysis-negative-downsampling,Google Analytics Customer Revenue Prediction
1703628,1.4439,9,42,/sz8416/lb-1-4439-gacr-prediction-eda-lgb-baseline,Google Analytics Customer Revenue Prediction
1651564,1.7343,0,5,/jacksmengel/gstore-random-forest-walkthrough,Google Analytics Customer Revenue Prediction
1741182,1.7294,0,2,/kartik31/garcb-prdiction-lgb,Google Analytics Customer Revenue Prediction
1725829,1.4486,1,8,/ravikiransm/test1-lgb-catboost,Google Analytics Customer Revenue Prediction
1728939,1.8181,0,1,/steveandthedogs/sb-googleanalyticscustomerrevenueprediction-data,Google Analytics Customer Revenue Prediction
1658269,1.4627,27,84,/youhanlee/stratified-sampling-for-regression-lb-1-4627,Google Analytics Customer Revenue Prediction
1656571,1.4946,8,85,/julian3833/2-quick-study-lgbm-xgb-and-catboost-lb-1-66,Google Analytics Customer Revenue Prediction
1648714,1.5725,8,43,/youhanlee/eda-lgbm-for-starter-lb-1-6878,Google Analytics Customer Revenue Prediction
1644098,1.5207,14,45,/dimitreoliveira/deep-learning-keras-ga-revenue-prediction,Google Analytics Customer Revenue Prediction
1641023,1.4585,20,89,/fabiendaniel/lgbm-starter,Google Analytics Customer Revenue Prediction
1652476,1.4988,0,2,/vanausloos/eda-and-light-gradient-boosting,Google Analytics Customer Revenue Prediction
1642941,1.7722,0,12,/tunguz/yareda-yet-another-revenue-eda,Google Analytics Customer Revenue Prediction
8206048,0.43846,8,36,/nxrprime/ensembler-dfdc,Deepfake Detection Challenge
8138725,0.551,0,0,/bingdong/xception-binary-classifier-inference,Deepfake Detection Challenge
8127756,0.43803,26,72,/khoongweihao/gcloud-ensembling-learning-learning-rates,Deepfake Detection Challenge
7984492,0.76049,0,0,/skylord/starter-kernel-for-inference-on-capsule-networks,Deepfake Detection Challenge
8128770,1.1225,0,0,/kimyoh/deepfake-resnet34-momentum-frame64,Deepfake Detection Challenge
8044856,0.45016,24,45,/khoongweihao/xception-resnext-ensemble-inference,Deepfake Detection Challenge
8028460,0.69123,3,9,/nxhong93/deep-fake-predict,Deepfake Detection Challenge
7985837,0.72051,7,21,/phunghieu/dfdc-multiface-inference,Deepfake Detection Challenge
7930779,0.46441,4,11,/nxrprime/fork-of-frames-per-video-the-ultimate-helper,Deepfake Detection Challenge
7808440,0.46441,18,41,/khoongweihao/frames-per-video-viz,Deepfake Detection Challenge
7763068,0.6884600000000001,0,8,/wuliaokaola/public-test-set-probe,Deepfake Detection Challenge
7470488,0.69314,0,2,/shawon10/face-detection-by-haarcascade-for-deepfake-videos,Deepfake Detection Challenge
7643850,0.71355,3,1,/aknirala/singlevalue,Deepfake Detection Challenge
2955343,0.52552,0,0,/twdickey43/ncaa-19-tabular,Google Cloud & NCAA® ML Competition 2019-Men's
3557487,0.51754,0,0,/scirpus/post-facto-mens,Google Cloud & NCAA® ML Competition 2019-Men's
3278541,0.09761,0,6,/jaylew/ncaa2019-mbb,Google Cloud & NCAA® ML Competition 2019-Men's
3259335,0.5434100000000001,0,0,/jumenta/maher-march-madness-step-by-step-starter-kernel,Google Cloud & NCAA® ML Competition 2019-Men's
2985106,0.5434100000000001,2,6,/duvallwh/ncaa-2k19-starter-kernel-with-graphical-eda,Google Cloud & NCAA® ML Competition 2019-Men's
2502309,0.703,1,8,/dromosys/radek-fast-ai-whale-full-predict,Humpback Whale Identification
2534597,0.8240000000000001,0,23,/axel81/siamese-ensemble-of-ensemble-lb-0-824,Humpback Whale Identification
2470618,0.516,2,23,/suicaokhoailang/martin-piotte-s-siamese-baseline,Humpback Whale Identification
2457623,0.602,3,13,/axel81/denset-121-sgd-with-restarts-lb-0-602,Humpback Whale Identification
2449062,0.7759999999999999,2,12,/axel81/weighted-average-ensemble-v-n-lb-0-776,Humpback Whale Identification
2388592,0.657,0,53,/ateplyuk/resnext50-sz448-lb-0-657,Humpback Whale Identification
2381557,0.726,0,11,/axel81/ensemble-lb-0-728-lb-0-725-lb-0-719,Humpback Whale Identification
2334773,0.655,3,45,/ateplyuk/resnext50-lb-0-655,Humpback Whale Identification
2323190,0.6509999999999999,1,57,/ateplyuk/resnext50-no-0-crop-sz384-0-651,Humpback Whale Identification
2323503,0.628,16,56,/suicaokhoailang/resnet50-bounding-boxes-0-628-lb,Humpback Whale Identification
2293353,0.313,2,7,/willtscott/humpback-whale-identification-with-mobilenet,Humpback Whale Identification
2292287,0.474,10,22,/suicaokhoailang/removing-class-new-whale-is-a-good-idea,Humpback Whale Identification
2287530,0.305,0,2,/viswajithkn/whale-detection,Humpback Whale Identification
2268715,0.276,2,22,/pestipeti/only-new-whale-benchmark,Humpback Whale Identification
2260764,0.402,7,23,/stalkermustang/pytorch-pretraiedmodels-se-resnext101-baseline,Humpback Whale Identification
2262915,0.3879999999999999,1,7,/satian/fork-resnet34-baseline-with-fastai,Humpback Whale Identification
2247889,0.392,12,40,/suicaokhoailang/wip-resnet18-baseline-with-fastai-0-392-lb,Humpback Whale Identification
2248318,0.278,0,3,/ankurshukla03/whale-whale-identification,Humpback Whale Identification
2743418,0.261,0,0,/behcetsenturk/resnet50-keras,Humpback Whale Identification
107773,0.63508,0,0,/su20yu1919/bill-su-and-his-support-group-kernel,Outbrain Click Prediction
6082223,0.951092,4,12,/kurupical/postprocess-19th-place-solution-team,IEEE-CIS Fraud Detection
6039975,0.9543,10,29,/super13579/find-client-by-d1-and-card-leak-use-on-submit,IEEE-CIS Fraud Detection
5686525,0.964,2,21,/whitebird/ieee-internal-blend,IEEE-CIS Fraud Detection
5748066,0.9284,0,0,/ruhong/ieee-fraud-detection-blend,IEEE-CIS Fraud Detection
5748119,0.8882,0,0,/ruhong/ieee-fraud-detection-rf-leafwise,IEEE-CIS Fraud Detection
6038494,0.8903,0,5,/xzdatascience/eda-feature-engineering-prediction,IEEE-CIS Fraud Detection
5968190,0.9005,0,3,/xwxw2929/data-preparation,IEEE-CIS Fraud Detection
5834749,0.5,0,3,/orianao/kernel3b4ba15f54,IEEE-CIS Fraud Detection
5202609,0.9097,0,2,/a45632/ieee-fastai-v4,IEEE-CIS Fraud Detection
5928374,0.9342,1,13,/gpreda/ieee-cis-fraud-detection-eda-model,IEEE-CIS Fraud Detection
5604824,0.952,0,8,/priteshshrivastava/ieee-cis-blend,IEEE-CIS Fraud Detection
5727007,0.9021,0,2,/priteshshrivastava/ieee-pipeline-2-b-model-b-random-forest,IEEE-CIS Fraud Detection
7704883,0.43555,0,0,/dhanyasabari/bike-sharing-demand,Bike Sharing Demand
7923188,0.43076,0,0,/hyeonyeong/time-segmentation-of-bike-sharing-demand-data,Bike Sharing Demand
7365788,0.49962,1,0,/paipitation/kernel96421c0399,Bike Sharing Demand
7673227,0.40568,0,0,/shoishi1106/optuna-lightgbm-tuner,Bike Sharing Demand
4210118,0.40308,0,0,/xinyouren1995/log-count,Bike Sharing Demand
6901265,0.65384,0,1,/pedrokb/projetoam,Bike Sharing Demand
6795038,0.41447,0,0,/gabrieltaranu/bike-sharing-demand-rf-and-gradient-boost,Bike Sharing Demand
6688090,0.43743,0,0,/marcosvafg/iesb-miner-ii-aula-03-random-forest,Bike Sharing Demand
6616254,0.44256,0,1,/pereiramarcia/aula2-random-forest,Bike Sharing Demand
6536533,0.44256,0,2,/fgajlog/iesb-dm-e-ml-2-bike-demand-competition,Bike Sharing Demand
5890541,0.39487,0,0,/ejkim0121/bike-sharing,Bike Sharing Demand
6108853,0.41866,0,0,/onetwojab/bike-sharing-demand-kaggle-practice,Bike Sharing Demand
5943082,0.37235,1,3,/cuijamm/bike-sharing-demand-starter-code-score-0-37235,Bike Sharing Demand
3750726,0.43811,0,0,/sidiclei/aula03-ml-2-aluguel-bike-random-forest,Bike Sharing Demand
3826257,0.43262,0,0,/sidiclei/2019-05-07-iesb-miner-ii-aula-04,Bike Sharing Demand
4904664,0.39499,0,0,/yuping1624/kernel8658d3b2fd,Bike Sharing Demand
4239857,3.08865,0,1,/minarabbit/regression-bike-sharing-demand,Bike Sharing Demand
4159663,0.48157,0,1,/fadiatia/machinelearning,Bike Sharing Demand
3915970,0.39688,0,1,/thdgusdl123/kernel442c80b898,Bike Sharing Demand
3335717,1.3809,0,0,/amelnozieres/eda-and-visualizations-on-bike-rentals,Bike Sharing Demand
3652538,0.41598,0,0,/codefire52/kernel6f14be36b6,Bike Sharing Demand
1841366,0.88356,0,5,/parveshdhawan/bag-of-words-meets-bags-of-popcorn,Bag of Words Meets Bags of Popcorn
1812456,0.8462799999999999,0,2,/zachary3141/kernel426d47eb25,Bag of Words Meets Bags of Popcorn
2201083,0.9631,127,466,/qitvision/a-complete-ml-pipeline-fast-ai,Histopathologic Cancer Detection
2173099,0.9528,29,99,/vbookshelf/cnn-how-to-use-160-000-images-without-crashing,Histopathologic Cancer Detection
2152233,0.8594,1,14,/gxkok21/resnet50-with-pytorch,Histopathologic Cancer Detection
2121342,0.9642,5,54,/suicaokhoailang/wip-densenet121-baseline-with-fastai,Histopathologic Cancer Detection
3296298,0.9611,0,0,/riccocruz/custom-cnn-based-off-vgg-16-with-pytorch,Histopathologic Cancer Detection
2966214,0.9607,0,0,/ritabratamaiti/design-methodology-for-46th-place,Histopathologic Cancer Detection
10810344,0.41165,2,9,/aakashveera/random-forest,Bosch Production Line Performance
14703629,0.98014,5,3,/itsbhups/digit-recognizer,Digit Recognizer
14592751,0.98903,13,11,/henseljahja/simple-tensorflow-cnn-98-8,Digit Recognizer
14483142,0.96839,1,5,/guecoraph/pattern-recognition-beginner,Digit Recognizer
14439479,0.9956,4,4,/archisha26/digit-recognizer-with-augmentation-cnn,Digit Recognizer
14507345,0.99132,0,3,/onejlly/mnist-team-04-0-99132,Digit Recognizer
14556870,0.977,1,2,/sytuannguyen/basic-dense-nn-model,Digit Recognizer
14184820,0.99571,2,2,/merles18/character-recognition-with-fastai,Digit Recognizer
14623108,0.91617,0,1,/chjinny/korean-simple-flow-for-beginners,Digit Recognizer
14560432,0.97135,0,0,/thekatiebr/dsl-meeting-2-2-2021-n2,Digit Recognizer
14453334,0.98989,0,0,/slothfulwave612/pytorch-building-a-digit-recognizer,Digit Recognizer
13630920,0.99285,0,0,/shambhavisoman/digit-recognizer,Digit Recognizer
14296580,0.99371,9,8,/smitshah00/digit-recognition-using-cnn-pytorch,Digit Recognizer
14341666,0.98307,1,1,/parthdedhia/capsule-network-in-tensorflow-digit-recognizer,Digit Recognizer
14584011,0.99567,0,0,/kasevgen/mnist-ensemble-cnn-top-8,Digit Recognizer
2941691,2.21123,0,0,/paulopinheiro/paulo-s-fun-kernel,TMDB Box Office Prediction
2894584,1.97558,22,62,/somang1418/eda-lgb-xgb-modelings-with-a-cute-panda-meme,TMDB Box Office Prediction
2945719,2.26119,2,1,/iamfuture/tmdb-challenge,TMDB Box Office Prediction
2936957,2.44348,0,1,/marcocarnini/feature-engineering-iv,TMDB Box Office Prediction
2899688,2.86177,0,5,/ashirwadsangwan/tmdb-revenue-prediction-for-movies,TMDB Box Office Prediction
2902294,6.1479,0,3,/kannannarayanan/linear-regression-on-box-office-revenue-prediction,TMDB Box Office Prediction
2857293,1.71017,47,188,/kamalchhirang/eda-feature-engineering-lgb-xgb-cat,TMDB Box Office Prediction
2857140,1.92565,2,11,/jazivxt/krush-groove-1985-and-that-s-the-way-it-is,TMDB Box Office Prediction
2862527,2.5825400000000003,0,0,/marcocarnini/improving-the-basic-model,TMDB Box Office Prediction
4323141,1.80913,1,0,/honglou/kerneleb070da1cb,TMDB Box Office Prediction
14540066,0.865,3,0,/sandeepreddie7/cassavaleafdiseasedetection,Cassava Leaf Disease Classification
13712446,0.863,0,0,/darshanramesh/inference-lightning,Cassava Leaf Disease Classification
14309110,0.901,1,24,/salmaneunus/cassava-classification-6,Cassava Leaf Disease Classification
13652220,0.8690000000000001,0,0,/nelsontraversi/colabresnet50,Cassava Leaf Disease Classification
14259667,0.9,0,4,/tiandaye/infer-cspresnext50-512-5folds,Cassava Leaf Disease Classification
14313153,0.899,0,1,/salmaneunus/cassava-classification-7,Cassava Leaf Disease Classification
14260338,0.898,1,11,/shash152543/cassava-inference-code-resnext-efficenetnet,Cassava Leaf Disease Classification
14216398,0.903,31,212,/japandata509/ensemble-resnext50-32x4d-efficientnet-0-903,Cassava Leaf Disease Classification
13677853,0.872,0,0,/mohamedbouabidi/casava-second-run,Cassava Leaf Disease Classification
14302451,0.103,0,1,/dhavalkumar/notebook0a0f16fe57,Cassava Leaf Disease Classification
1349848,0.722,1,8,/nikhilroxtomar/unet-with-layer-concatenation-in-downblock,TGS Salt Identification Challenge
1325422,0.38,7,43,/osciiart/no-mask-prediction,TGS Salt Identification Challenge
1317021,0.231,5,86,/christofhenkel/keras-baseline,TGS Salt Identification Challenge
1317372,0.244,2,13,/ashishpatel26/seasalt-or-not,TGS Salt Identification Challenge
1626752,0.736,0,0,/abimannan/tgs-salt-segment,TGS Salt Identification Challenge
1361479,0.745,0,0,/dingdiego/baseline-0-731-0-158-5-and-more-fold,TGS Salt Identification Challenge
87185,2.38643,0,0,/mikevdickson/brand-and-model-based-benchmarks-fork,TalkingData Mobile User Demographics
11149752,0.75444,0,2,/blaxkdolphin/unet-pytorch,TGS Salt Identification Challenge
2509021,0.833566,2,1,/jmourad100/unet-with-simple-resnet-blocks-1,TGS Salt Identification Challenge
2063940,0.784683,0,0,/gaelblanch/scorepytorch,TGS Salt Identification Challenge
2348304,0.728783,0,0,/bruches/segmentation-pipeline-for-salt,TGS Salt Identification Challenge
1879673,0.7829999999999999,0,0,/amitkvikram/tgs-resnet-final1,TGS Salt Identification Challenge
1380455,0.616,0,0,/peicao/tgs-salt,TGS Salt Identification Challenge
1835889,0.816,0,2,/bigswimatom/trial-of-change-augmentation,TGS Salt Identification Challenge
1756633,0.76,0,0,/jimmy2002916/u-net-resnet34-with-stratifiedkfold,TGS Salt Identification Challenge
1787022,0.421,3,5,/tcapelle/tgs-fastai-vgg-dynamicunet,TGS Salt Identification Challenge
1697073,0.637,0,0,/kumarabhishekone/saltk,TGS Salt Identification Challenge
1791548,0.72,0,0,/zeus75/u-net-using-keras,TGS Salt Identification Challenge
1773599,0.6559999999999999,0,0,/wenjieluo/kernelad4efeb50a,TGS Salt Identification Challenge
1411639,0.794,0,0,/dingdiego/k-fold-merger,TGS Salt Identification Challenge
1349609,0.731,0,0,/dingdiego/baseline-0-731-0-158,TGS Salt Identification Challenge
1520902,0.773,0,0,/dingdiego/baseline-v7,TGS Salt Identification Challenge
1355124,0.8009999999999999,0,0,/dingdiego/kernel497c4f3124,TGS Salt Identification Challenge
1403089,0.769,2,0,/dingdiego/baseline-0-760-0-143-k-fold-0-769,TGS Salt Identification Challenge
13208454,1.96071,0,1,/tatsuyayamamoto/tmdb-xgb,TMDB Box Office Prediction
12025401,2.64113,0,2,/vaishnavkapil/kapil-vaishnav-box-office-prediction,TMDB Box Office Prediction
10216751,3.1719,0,4,/dahouda/tmdb-box-predictions-with-simple-lr-and-keras,TMDB Box Office Prediction
10122380,3.6452,0,1,/shubhamk101/tmdb-box-office-data-analysis,TMDB Box Office Prediction
7029179,3.07648,0,0,/ggarciacastany/the-movie-database,TMDB Box Office Prediction
6224896,3.48308,0,6,/jsvishnuj/movie-revenue-prediction,TMDB Box Office Prediction
5993673,2.0303400000000003,0,0,/daslef/tmdb-eda-2019-sep,TMDB Box Office Prediction
5589516,2.05782,0,4,/aditya100/tmdb-box-office,TMDB Box Office Prediction
3206556,2.67065,0,1,/mohganji/box-office-notebook,TMDB Box Office Prediction
4396816,2.12875,0,0,/krasnovna/exploration-gam,TMDB Box Office Prediction
4217110,2.7785,0,2,/jrichie/random-forest-w-box-office-revenue,TMDB Box Office Prediction
4002475,2.45707,0,0,/leonematias/tmdb-box-office-prediction-keras,TMDB Box Office Prediction
4068918,2.15141,0,0,/nickrood/prediction-with-xgboost,TMDB Box Office Prediction
3329556,1.98758,0,0,/meaoer/kernelc9cf359f7e,TMDB Box Office Prediction
13929071,0.93417,0,0,/sumeetspisal/notebookbb429528ca,Digit Recognizer
13827598,0.9765,1,4,/vinayjaju/tensorflow-2-starter,Digit Recognizer
13805641,0.98864,0,0,/yashaswitasingh/digit-recognizer,Digit Recognizer
13709795,0.99617,28,27,/brendanartley/mnist-keras-cnn-99-6,Digit Recognizer
13754915,0.99075,2,2,/nagsdata/cnn-from-scratch,Digit Recognizer
13706409,0.99378,18,12,/homayoonkhadivi/cnn-for-mnist-digit-datasets,Digit Recognizer
13624418,0.96139,0,2,/muslump/basic-digit-classification-mlp,Digit Recognizer
10198949,0.98667,0,0,/yashsingh25/mnistanalysis,Digit Recognizer
13663724,0.9951,0,0,/yalikesifulei/modified-lenet,Digit Recognizer
13621458,0.97603,1,3,/patrickrussell314/recognizing-digits-with-keras-cnn,Digit Recognizer
13607367,0.91578,0,1,/mariuszgaljan/pca-dimension-reduction,Digit Recognizer
13498210,0.99014,0,0,/howeverforever/digit-recognizer-cnn-using-tf-keras,Digit Recognizer
13487037,0.99117,0,1,/leeminhwan/cnn-on-mnist,Digit Recognizer
13491345,0.95917,0,1,/vldrud/notebook6b7c8add39,Digit Recognizer
13128051,0.99389,0,3,/sayooj98/digit-recognizer,Digit Recognizer
13449808,0.98803,7,3,/nayuts/how-to-use-mxnet-gluon-and-tune-by-optuna,Digit Recognizer
13441242,0.99296,0,3,/vaishnavikhilari/digit-recognizer,Digit Recognizer
13088098,0.99253,0,0,/jaytwo97/novel-mnist-committee-digit-classifier-with-keras,Digit Recognizer
13336346,0.943,0,1,/namankohliml/dogs-vs-cats-noob-version,Digit Recognizer
3020379,0.8590000000000001,0,0,/aminejait/hplc-set,Histopathologic Cancer Detection
8329556,0.7818,0,0,/paulorenatocastro/cancer-detect-proj-01,Histopathologic Cancer Detection
8999662,0.7159,0,0,/sleepybatman42/cancer-detection,Histopathologic Cancer Detection
8597288,0.7839,0,0,/alexanderzv/gpu-notebook,Histopathologic Cancer Detection
7051593,0.9135,0,0,/wish1234/pytorch-cnn,Histopathologic Cancer Detection
2896806,0.9403,0,0,/omkarsabnis/cnn-approaches-for-histopathological-analysis,Histopathologic Cancer Detection
5952751,0.9522,0,1,/diskandar69/histopathologic-cancer-detection-with-fastai,Histopathologic Cancer Detection
2925063,0.9705,0,0,/stone0338/lei-s-model,Histopathologic Cancer Detection
4428681,0.958,4,41,/abhinand05/histopathologic-cancer-detection-using-cnns,Histopathologic Cancer Detection
4118100,0.9657,0,0,/vishal22/hist1,Histopathologic Cancer Detection
9456160,0.644183,0,2,/vedato/cardfraud-fillna-ml,IEEE-CIS Fraud Detection
13836068,0.895609,0,1,/thejravichandran/fraud-detection-v3-cleaning-and-rf,IEEE-CIS Fraud Detection
12952981,0.8584870000000001,0,6,/divyareddyyeruva/tabnet-fraud-detection,IEEE-CIS Fraud Detection
12647687,0.918617,0,0,/shafqaatahmad/ieee-cis-fraud-detection-xgboost,IEEE-CIS Fraud Detection
12341182,0.696449,0,3,/jirakst/ieee-cis-fraud-detection,IEEE-CIS Fraud Detection
5641046,0.9494,0,0,/brodzik/ieee-cis-fraud-detection,IEEE-CIS Fraud Detection
11878954,0.93776,5,10,/jonas0/ieee-fraud-detection-tutorial,IEEE-CIS Fraud Detection
11487328,0.961812,1,7,/gogo827jz/deep-learning-fraud-with-magic,IEEE-CIS Fraud Detection
10929876,0.920036,0,1,/ichijo/use-lightgbm,IEEE-CIS Fraud Detection
10548947,0.938306,0,3,/yurimuniz/predicting-frauds-step-by-step,IEEE-CIS Fraud Detection
9911001,0.8278059999999999,0,10,/chandrimad31/ieee-competition-predicting-fraud-with-lightgbm,IEEE-CIS Fraud Detection
8788063,0.7301300000000001,0,10,/siraznaorem/cats-vs-dogs-baseline-model-1st-dl-practice,Dogs vs. Cats Redux: Kernels Edition
6328470,0.29518,0,0,/vinaydoshi/dogs-vs-cats-alexnet,Dogs vs. Cats Redux: Kernels Edition
6535726,1.31613,0,0,/davidarcila93/dogs-vs-cats,Dogs vs. Cats Redux: Kernels Edition
5617833,0.1081,0,0,/ludi666/nasnetlarge,Dogs vs. Cats Redux: Kernels Edition
4354841,1.10565,0,0,/terminate9298/dog-vs-cat-model,Dogs vs. Cats Redux: Kernels Edition
4815326,0.13102,0,0,/tormoz70/kernel0761cf1a3b,Dogs vs. Cats Redux: Kernels Edition
4699487,0.40358,0,2,/supermode/dogs-and-cats-classification,Dogs vs. Cats Redux: Kernels Edition
4156465,1.1034700000000002,4,6,/aditya100/cats-and-dogs,Dogs vs. Cats Redux: Kernels Edition
6478500,0.7783800000000001,0,4,/kaushal2896/cat-in-the-dat-eda-fe-ensembling-stackig,Categorical Feature Encoding Challenge
6488478,0.78151,0,1,/yutanakamura/45-line-model-of-lightgbm-optuna,Categorical Feature Encoding Challenge
6435493,0.80592,0,1,/teejmahal20/cat-comparing-logistic-regression-and-xgb,Categorical Feature Encoding Challenge
6383545,0.80818,2,10,/sureliu/oh-my-cat,Categorical Feature Encoding Challenge
5888472,0.80406,0,0,/evgeniya1/eda-for-cat-in-dat,Categorical Feature Encoding Challenge
6268274,0.80099,2,8,/q839651612/adversarial-validation-for-data-selection,Categorical Feature Encoding Challenge
6109362,0.8045399999999999,1,1,/mak4alex/categorical-feature-encoding-challenge,Categorical Feature Encoding Challenge
5963154,0.7972,0,2,/jeongyoonlee/embeddingencoder-targetencoder-autolgb,Categorical Feature Encoding Challenge
6126052,0.78927,0,1,/vladlee/categorical-feature-encoding-lgbm,Categorical Feature Encoding Challenge
6062002,0.77451,2,2,/alexkaggle95/labelencoding-xgboost,Categorical Feature Encoding Challenge
6039636,0.80409,2,2,/takaishikawa/catboost-starter-with-k-fold-no-fe,Categorical Feature Encoding Challenge
5944292,0.7904899999999999,2,13,/tolgahancepel/applying-different-encoding-methods-and-eda,Categorical Feature Encoding Challenge
5955952,0.8073899999999999,1,2,/mxiaoyu/logistic-regression-is-strong,Categorical Feature Encoding Challenge
5955287,0.80806,1,2,/errolpereira/blending-with-logisitic,Categorical Feature Encoding Challenge
5872752,0.78447,1,3,/yutanakamura/prml-4-2-3-probabilistic-generative-model,Categorical Feature Encoding Challenge
5809883,0.80506,2,1,/merckel/target-encoding,Categorical Feature Encoding Challenge
5709724,0.80777,0,5,/deepshekhar/categorical-feature-encoding-challenge,Categorical Feature Encoding Challenge
5685187,0.8076300000000001,19,17,/a03102030/compare-logistic-lgbm,Categorical Feature Encoding Challenge
5665223,0.80762,3,23,/hocop1/eda-and-a-neural-network,Categorical Feature Encoding Challenge
5661288,0.6437,1,1,/sudipta10/cat-dat,Categorical Feature Encoding Challenge
5647535,0.5721,1,1,/magreen/my-first-published-kernel,Categorical Feature Encoding Challenge
5484400,0.7803100000000001,8,24,/prazhant/a-detailed-guide-to-different-encoding-schemes,Categorical Feature Encoding Challenge
109598,0.50767,0,2,/mihaelasorostinean/ottogroupchallenge,Otto Group Product Classification Challenge
63927,33.47072,0,0,/cuttlefish/first-notebook-1,Otto Group Product Classification Challenge
24647,0.58555,1,10,/omarelgabry/otto-product-classification-predictions,Otto Group Product Classification Challenge
8279030,0.0,1,3,/ayakhaled2/google-analytics-customer-revenue-prediction,Google Analytics Customer Revenue Prediction
1646489,1.4704,0,0,/hanamaruko/customer-revenue,Google Analytics Customer Revenue Prediction
1850793,1.4848,0,0,/mohit2508/mohit-gstore-prediction,Google Analytics Customer Revenue Prediction
2131489,0.0,1,0,/tchristophern/base-model-v2-with-with-full-features,Google Analytics Customer Revenue Prediction
2001311,1.6980000000000002,0,1,/ramonafli/google-analytics-customer-revenue-prediction,Google Analytics Customer Revenue Prediction
2101800,0.0,11,46,/artgor/fork-of-eda-on-basic-data-and-lgb-in-progress,Google Analytics Customer Revenue Prediction
2073104,0.0,0,4,/qnkhuat/base-model-v2,Google Analytics Customer Revenue Prediction
1926566,1.4961,0,1,/lituokobe/ga-customer-revenue-prediction-data-processing,Google Analytics Customer Revenue Prediction
8134132,0.047,0,1,/grapestone5321/ion-switching-sample-submission,University of Liverpool - Ion Switching
8131107,0.405,1,1,/konradb/liverpool-averaging-median,University of Liverpool - Ion Switching
8155704,0.86014,1,3,/misakrug/kernel397154465c,University of Liverpool - Ion Switching
8919497,0.941,0,0,/akashsuper2000/wavenet-with-1-more-feature,University of Liverpool - Ion Switching
8619498,0.94,0,0,/ashora/gru-keras,University of Liverpool - Ion Switching
6734421,1.06,0,2,/teeyee314/kfold-lightgbm-without-leak-1-062,ASHRAE - Great Energy Predictor III
6953364,1.097,0,5,/tehutahu/2380-models-on-each-building-and-meter,ASHRAE - Great Energy Predictor III
7032982,3.54,0,0,/pierrematthieupair/ashrae-preprocessing-train,ASHRAE - Great Energy Predictor III
7113143,0.979,1,2,/wentzforte/ashrae-test-final,ASHRAE - Great Energy Predictor III
6762836,0.998,0,2,/danmusetoiu/ashrae-blended-noleaks,ASHRAE - Great Energy Predictor III
6978620,1.162,0,1,/jrodrigof/model-rf,ASHRAE - Great Energy Predictor III
6944373,1.14,0,0,/amaity0/ashrae-fifth-try,ASHRAE - Great Energy Predictor III
7008107,1.04,4,11,/hichar/ashrae-divide-and-conquer-fix0,ASHRAE - Great Energy Predictor III
6801043,1.18,0,0,/shukla84manish/energy-consumption,ASHRAE - Great Energy Predictor III
6934405,1.13,8,14,/tunguz/ashrae-histgradientboosting,ASHRAE - Great Energy Predictor III
6903460,1.106,2,26,/starl1ght/ashrae-stacked-regression-lasso-ridge-lgbm,ASHRAE - Great Energy Predictor III
6510542,1.36,0,1,/himanshujoshi13/ashrae-eda-1,ASHRAE - Great Energy Predictor III
6800391,0.98,5,29,/aleksthegreat/public-blend,ASHRAE - Great Energy Predictor III
6807653,1.13,1,3,/liannan2016/ashrae-3-lightgbm,ASHRAE - Great Energy Predictor III
14240885,0.95283,0,1,/krishnarajut/jigsaw-toxic-comment-classification,Toxic Comment Classification Challenge
13812215,0.55215,0,0,/anirbansen3027/jtcc-word2vec,Toxic Comment Classification Challenge
6414698,0.98589,0,0,/amir78pgd/recurrent-capsule-network,Toxic Comment Classification Challenge
13237158,0.90698,0,0,/shishirkumar/fasttext-labelling,Toxic Comment Classification Challenge
13109730,0.97722,0,2,/sandeepsingh3480/svm-toxic-comments-classification-challenge,Toxic Comment Classification Challenge
12821339,0.95252,0,1,/mohamedfarag96/nlp-tf-toxicity-classification,Toxic Comment Classification Challenge
11016205,0.98537,2,7,/kashnitsky/catalyst-distilbert-multilabel-clf-toxic-comments,Toxic Comment Classification Challenge
4735574,0.98321,0,0,/ajithvajrala/dissertation-word-embeding,Toxic Comment Classification Challenge
10203163,0.97766,2,11,/muellerzr/end-to-end-fastai2,Toxic Comment Classification Challenge
9872677,0.96983,0,0,/varshinithatiparthi/kernel64eed79506,Toxic Comment Classification Challenge
10131001,0.7120000000000001,0,0,/aman2000jaiswal/tweet-15-06-20,Tweet Sentiment Extraction
10380351,0.37484,0,4,/mayurjain/tweet-sentiment-using-nmt,Tweet Sentiment Extraction
9283489,0.5329999999999999,0,2,/anshu1man/tweet-sentiment-extraction-tryout,Tweet Sentiment Extraction
10732956,0.72648,0,3,/emily2008/tweet-sentiment-extraction-2-stage-models,Tweet Sentiment Extraction
10062810,0.7198899999999999,1,2,/bacicnikola/final-submit-sessions,Tweet Sentiment Extraction
10610238,0.69819,0,3,/rajnathpatel/tweet-sentiment-extraction-submission,Tweet Sentiment Extraction
9493569,0.7120000000000001,0,0,/ccw530/tensorflow-roberta,Tweet Sentiment Extraction
10522464,0.59448,0,0,/areegwael/pytorch-lightning-data-cleaning,Tweet Sentiment Extraction
10478796,0.7108800000000001,0,0,/dldydtlr93/kernel41ceaac986,Tweet Sentiment Extraction
9957629,0.711,0,0,/josealways123/roberta-base,Tweet Sentiment Extraction
10388435,0.70121,0,1,/sony0125/roberta-3-cnn-layers,Tweet Sentiment Extraction
9191580,0.71,0,2,/rajratnpranesh/roberta-kim-cnn,Tweet Sentiment Extraction
10335223,0.58692,0,1,/salmacmpeg/lstm-with-attention,Tweet Sentiment Extraction
10317476,0.5904,0,1,/salmacmpeg/lstm-and-convolution,Tweet Sentiment Extraction
10121348,0.33374,0,0,/davlanigan/bert-pytorch-qa2,Tweet Sentiment Extraction
10025834,0.7140000000000001,0,2,/weipengfei/tweet-sentiment-extraction-using-roberta-pytorch-v,Tweet Sentiment Extraction
9983179,0.732,0,4,/suicaokhoailang/final-sub-tweet,Tweet Sentiment Extraction
9997950,0.71,0,1,/darshanpatel11/inference-code-cnn,Tweet Sentiment Extraction
10125306,0.406,0,0,/antongolubev5/is-full-best-matching,Tweet Sentiment Extraction
10255983,0.7069,0,2,/rahulsarkar906/tfrobertasentimentanalyser,Tweet Sentiment Extraction
10120405,0.594,0,0,/rajat2341/twitter-sentiment-extraction,Tweet Sentiment Extraction
10233030,0.70982,0,1,/franktimmermans/roberta-datatreat-urlfiltering-tunelossfunc,Tweet Sentiment Extraction
9975862,0.7170000000000001,0,10,/deepakd14/eda-text-extraction-using-roberta,Tweet Sentiment Extraction
10083031,0.7340000000000001,9,39,/aruchomu/no-sampler-ensemble-normal-sub-0-7363,Tweet Sentiment Extraction
9444591,0.713,0,0,/lomen0857/robert,Tweet Sentiment Extraction
10006907,0.713,2,13,/sagar7390/eda-2x-faster-roberta-52nd-rank,Tweet Sentiment Extraction
10061667,0.7240000000000001,3,10,/haythemtellili5/silver-solution,Tweet Sentiment Extraction
10129902,0.725,0,15,/jionie/preprocessing-new-pipeline-pseudo-model-ensemble,Tweet Sentiment Extraction
6757182,0.9834,0,0,/stardust3dd/mnist-kannada,Kannada MNIST
8045382,0.9676,0,1,/jugglingsnakeboarder/kannadamnistwithcnnontpu,Kannada MNIST
7979291,0.9416,2,1,/venkateshprabhug/let-s-teach-kannada-to-the-machines,Kannada MNIST
7923787,0.9634,0,0,/siddheshsathe/kannada-mnist,Kannada MNIST
7996246,0.983,1,1,/dangerx/kernel255b69025c,Kannada MNIST
7923374,0.9634,0,0,/bhaveshpanchal13/kernel7d7ed6ec5c,Kannada MNIST
7868756,0.953,0,0,/tadehalexani/kannada-mnist-using-pca,Kannada MNIST
6704339,0.9896,0,0,/jjbuchanan/pretrained-on-full-data-kannada-mnist,Kannada MNIST
7780610,0.9696,0,0,/yettellasiva/kannada-mnist-cnn,Kannada MNIST
6932505,0.9872,0,0,/olegshamanin/kernel7c12d21063,Kannada MNIST
6194933,0.9908,1,2,/rincewind007/mnist-resnet-bigger-mish-in-shortcut-transforms,Kannada MNIST
7483689,0.9824,2,1,/ajax0564/kernel7318a9713a,Kannada MNIST
7541561,0.977,0,2,/anandsm7/basic-kannada-mnist-tensorflow-2-0-99,Kannada MNIST
6040840,0.987,0,0,/fyuta24/kannada-mnist-cnn-achived-0-988-private-score,Kannada MNIST
7484362,0.898,0,0,/prashanthprao/linear-regression-to-kannada-minst,Kannada MNIST
7403126,0.9818,1,1,/scirpus/begin-with-tensorflow-2-but-use-haar,Kannada MNIST
7352830,0.8906,0,0,/cyzhou99/coincidance-lr-lbp,Kannada MNIST
7390448,0.9688,0,1,/shravanc/kernel7de05e6660,Kannada MNIST
7283795,0.9844,0,1,/nitwmanish/kannada-mnist-simple-cnn-in-keras,Kannada MNIST
7231126,0.978,0,1,/cbarburescu/pytorch-ftw,Kannada MNIST
6300558,0.987,0,0,/kliu1892/kannada-digits-recognition-go-for-better-ranking,Kannada MNIST
7203130,0.9734,1,2,/rahulharlalka/kannada-mnist,Kannada MNIST
6653262,0.9758,0,4,/adarshbiradar/kannada-mnist,Kannada MNIST
6906558,0.9868,0,0,/hanwenzhu/kannada-mnist,Kannada MNIST
7050052,0.9814,0,0,/pikkupr/kannadamnist-cnn-with-hyperparameters-tuning,Kannada MNIST
6118588,0.9792,0,1,/gsdeepakkumar/yet-another-mnist,Kannada MNIST
7077594,0.9862,0,0,/toraveng/kamyno-kannada,Kannada MNIST
7071669,0.0974,0,2,/andrewgao/deep-dive-in-kannadamnist-with-tfkeras,Kannada MNIST
7071735,0.988,0,5,/andrewgao/0-9880-public-accuracy-pytorch,Kannada MNIST
10759806,0.7346,0,1,/ryotak12/wheat-efficientdet-infer-oof-tta-wbf,Global Wheat Detection
10744930,0.7372,0,1,/pythonmaster01/yolov5-pseudo-labeling-oof-evaluation,Global Wheat Detection
10486188,0.6071,0,1,/saimanojakondi/inference-kernel,Global Wheat Detection
11011574,0.7583,0,0,/jihangz/wheat-centernet-pseudo-l-bifpn,Global Wheat Detection
11138537,0.6747,2,2,/trinityger/fasterrcnn-resnet152-inference,Global Wheat Detection
10922738,0.7589,0,8,/marcowu/ap2-0effd-yolov4-pseudo-labeling-oof-evalu,Global Wheat Detection
11015800,0.7214,0,3,/annamel11111/ensemble-rcnn,Global Wheat Detection
10639465,0.7289,0,0,/ashayajbani/efficientdet-pytorch,Global Wheat Detection
10451381,0.7749,0,19,/orkatz2/ensemble-yolov5-pseudo-labeling-d5-d7-resnest,Global Wheat Detection
11026064,0.7556,1,6,/dpyrtfq2372/efficientdet-with-double-psudo-labeling,Global Wheat Detection
11032878,0.7674,1,13,/stonewst98/what-a-pity-only-0-0001-away-from-0-77,Global Wheat Detection
10962398,0.7662,0,8,/ratthachat/wheat-resnest-pseudo-ensemble-framework-for-77pub,Global Wheat Detection
10939498,0.7461,0,4,/truonghoang/yolov3-compliant-0-6859-private-leaderboard,Global Wheat Detection
11040198,0.0124,0,2,/aditya23071991/inference-wheat,Global Wheat Detection
10767322,0.7244,0,1,/jonykarki/fasterrcnn-resnet101-fold-3-tta,Global Wheat Detection
10968753,0.718,0,1,/romanprasolov/global-wheat-detection-efficientdet-d7x,Global Wheat Detection
11035296,0.7488,0,2,/masterroshi/kernel332e439f7a,Global Wheat Detection
10901910,0.7383,0,0,/beelee4085/bayes-opt-wbf-tta-pl-effdet,Global Wheat Detection
9969691,0.7294,0,1,/akashsuper2000/wbf-over-tta-single-model-efficientdet,Global Wheat Detection
11861654,0.0209199999999999,0,3,/batprem/moa-prediction,Mechanisms of Action (MoA) Prediction
11849550,0.02033,0,3,/sudiptog81/moa-prediction-using-ann,Mechanisms of Action (MoA) Prediction
11794218,0.02348,8,8,/benfraser/exploring-unsupervised-features,Mechanisms of Action (MoA) Prediction
11795401,0.0226,0,15,/konradb/anomaly-detection,Mechanisms of Action (MoA) Prediction
11870597,0.02014,3,11,/sudhanshuraheja/moa-cnn,Mechanisms of Action (MoA) Prediction
11805632,0.0224,0,3,/epocxy/moa-rbf-nn-pytorch,Mechanisms of Action (MoA) Prediction
11818001,0.01886,0,0,/takatoyoshikawa/moa-deep-learning,Mechanisms of Action (MoA) Prediction
11766409,0.02002,2,83,/gogo827jz/rapids-svm-on-gpu-6000-models-in-1-hour,Mechanisms of Action (MoA) Prediction
11802794,0.28173,0,1,/matthewmasters/mechanisms-of-action-moa-prediction-45d6ed,Mechanisms of Action (MoA) Prediction
11741798,0.01961,0,3,/krisho007/moa-pytorch-lightning-inference,Mechanisms of Action (MoA) Prediction
11741154,0.019,6,48,/pavelvpster/moa-keras-optuna-rfecv,Mechanisms of Action (MoA) Prediction
11532128,0.02076,0,1,/ris320/deep-nn-training-inference,Mechanisms of Action (MoA) Prediction
11613779,0.01913,4,7,/pathofdata/nn-with-skf-strategy,Mechanisms of Action (MoA) Prediction
11731698,0.01956,4,12,/stanleyjzheng/moa-convolutional-neural-net-on-tabular-data,Mechanisms of Action (MoA) Prediction
11710551,0.02002,3,13,/konradb/model-build-combo,Mechanisms of Action (MoA) Prediction
11732554,0.01985,2,6,/sidharkal/mechanisms-of-action-prediction,Mechanisms of Action (MoA) Prediction
11719297,0.01928,0,5,/dubeyumang/moa-for-beginners,Mechanisms of Action (MoA) Prediction
11693407,0.03016,0,4,/arnabark/moa-basic-xgboost-with-hyperparameter-tuning,Mechanisms of Action (MoA) Prediction
11605710,0.13166,1,6,/zainahmad/moa-multilabel-classification-using-skmultilearn,Mechanisms of Action (MoA) Prediction
11683524,0.02319,0,0,/chriscc/target-stacking,Mechanisms of Action (MoA) Prediction
11612440,0.0199699999999999,7,30,/arpitsolanki14/moa-exploratory-analysis-pca-ensemble-models,Mechanisms of Action (MoA) Prediction
11690658,0.04235,0,5,/sayedathar11/moa-nn-starter-kfold,Mechanisms of Action (MoA) Prediction
11638834,0.01962,0,2,/praveen648/optuna-hyperparameter-neural-network,Mechanisms of Action (MoA) Prediction
11678378,0.01974,0,5,/elvinagammed/moa-eda-feature-eng-custom-loss-tfkeras,Mechanisms of Action (MoA) Prediction
9477433,0.77076,1,1,/ankosk/3i0203-ui1-nlp-akovac,Natural Language Processing with Disaster Tweets
8341073,0.8329700000000001,6,35,/jagdmir/tweet-analysis-ann-bert-cnn-n-gram-cnn,Natural Language Processing with Disaster Tweets
8257302,0.7873100000000001,0,0,/martinhaha/nlp-twitter-disaster,Natural Language Processing with Disaster Tweets
9132740,0.8240799999999999,9,16,/barun2104/transfer-learning-using-tfhub,Natural Language Processing with Disaster Tweets
9077550,0.8311299999999999,0,1,/sachinssingh/real-of-fake-tweets-classification-bert,Natural Language Processing with Disaster Tweets
9044805,0.79957,0,0,/sunnyville01/real-or-not-sklearn-solution,Natural Language Processing with Disaster Tweets
8853238,0.8467600000000001,0,3,/sushanth1995/text-classification-with-bert-xgboost,Natural Language Processing with Disaster Tweets
8869830,0.79098,0,1,/khanheena69/disaster-tweets-nlp,Natural Language Processing with Disaster Tweets
9087560,0.80386,0,2,/gb00000/disaster-sklearn,Natural Language Processing with Disaster Tweets
14452292,0.79904,6,16,/galtvam/getting-top-5-0-79904-without-leakages,Titanic - Machine Learning from Disaster
14495011,0.7751100000000001,9,9,/marto24/titanic-predictions-80,Titanic - Machine Learning from Disaster
14633418,0.7751100000000001,0,4,/sufyansadiq/ml-on-titanic,Titanic - Machine Learning from Disaster
14468025,0.7822899999999999,1,4,/nishantdhingra/titanic-eda-feature-engineering-and-predictions,Titanic - Machine Learning from Disaster
14676409,0.7751100000000001,0,0,/isaras/titanic-survival-classification-with-python,Titanic - Machine Learning from Disaster
14414602,0.8109999999999999,5,10,/vbmokin/fe-feature-importance-advanced-visualization,Titanic - Machine Learning from Disaster
14398634,0.79186,3,5,/justinlyons/getting-started-with-titanic,Titanic - Machine Learning from Disaster
14556854,0.78947,2,3,/devchauhan1/titanic-problem,Titanic - Machine Learning from Disaster
14622091,0.75598,0,2,/shweta0910/begginer-titanic-kaggle,Titanic - Machine Learning from Disaster
14410390,0.7822899999999999,6,2,/joolousada/titanic-predictions,Titanic - Machine Learning from Disaster
7284432,0.79405,0,0,/virajdattt/niks-starter-pack,Natural Language Processing with Disaster Tweets
8791468,0.79803,0,0,/lajari/my-first-step-to-nlp,Natural Language Processing with Disaster Tweets
9030810,0.79803,0,0,/cristache/prepocessing-tf-idf-svm,Natural Language Processing with Disaster Tweets
8292179,0.7024199999999999,0,0,/bcantt/new-method-2,Natural Language Processing with Disaster Tweets
8642298,0.80324,0,2,/bhaskarkishor/nlp-with-fastai,Natural Language Processing with Disaster Tweets
7761794,0.79895,0,0,/denjustden/kernel180bac5149,Natural Language Processing with Disaster Tweets
8788357,0.79497,17,23,/mohitsital/0-80777-simplest-model-naive-bayes,Natural Language Processing with Disaster Tweets
8766497,0.80723,4,4,/benfraser/classical-bag-of-words-with-extensive-cleaning,Natural Language Processing with Disaster Tweets
8732973,0.7992600000000001,18,28,/sahib12/document-embedding-techniques,Natural Language Processing with Disaster Tweets
8294577,0.79957,0,1,/denojitnath/tweet-nlp,Natural Language Processing with Disaster Tweets
8659330,0.79068,2,2,/itsmeprasanna/nlp-tweet-disaster-getting-started-beginner,Natural Language Processing with Disaster Tweets
7755916,0.8152,0,0,/leosuky/disaster-tweets-with-cloud-automl,Natural Language Processing with Disaster Tweets
8591963,0.8329700000000001,17,21,/janvichokshi/real-or-not-nlp-with-bert-spacy-svm,Natural Language Processing with Disaster Tweets
8572584,0.49678,0,0,/gabbygab/it-s-a-disastah,Natural Language Processing with Disaster Tweets
8318844,0.80845,2,6,/uoneway/hello-automl,Natural Language Processing with Disaster Tweets
8536043,0.8317399999999999,0,3,/nhoues1997/bert-model-with-pytorch,Natural Language Processing with Disaster Tweets
14124182,0.73869,3,3,/lingyuxiong/covid19-forecasting,COVID19 Global Forecasting (Week 1)
13465314,0.75878,0,0,/prakashvm/notebook1e9de3f4c5,COVID19 Global Forecasting (Week 1)
11188138,2.46221,0,0,/kxh123/xgboost-model-covid19,COVID19 Global Forecasting (Week 1)
8564118,0.69831,0,0,/ghaiyur/xgboost,COVID19 Global Forecasting (Week 1)
10217016,1.0378,0,1,/kxh123/lightgbm-covid19,COVID19 Global Forecasting (Week 1)
8486693,1.41089,0,0,/grwche/could-word-embeddings-predict-virus-transmission,COVID19 Global Forecasting (Week 1)
8578504,0.70267,0,1,/dott1718/cv19-by-growth-rate-v5-09,COVID19 Global Forecasting (Week 1)
8586424,0.7079,0,3,/gaborfodor/simpleexponentialforecast,COVID19 Global Forecasting (Week 1)
8537746,0.7289800000000001,0,0,/haresrv/covid-19-blitzkreig,COVID19 Global Forecasting (Week 1)
8541827,0.70452,0,0,/folk85/kernel4bb870b25e,COVID19 Global Forecasting (Week 1)
8554824,0.70425,0,0,/liangpang/prediction,COVID19 Global Forecasting (Week 1)
8611263,0.2614,0,0,/rohitmidha23/covid2-starter,COVID19 Global Forecasting (Week 2)
8582435,2.31144,0,1,/ritarana123/kernel5fd8491014,COVID19 Global Forecasting (Week 1)
8549880,0.6761,0,0,/xscripter/stats-233,COVID19 Global Forecasting (Week 1)
8483959,0.72873,0,0,/washingtongold/covid19-forecasting-w-lots-of-external-sources,COVID19 Global Forecasting (Week 1)
8564311,0.7518600000000001,0,2,/davidistrati/arima-forecasting-model-for-covid-19,COVID19 Global Forecasting (Week 1)
8566445,0.71168,2,11,/zusmani/tutorial-covid19-global-forecast,COVID19 Global Forecasting (Week 1)
8496797,0.61716,4,6,/zhiyaoliang/first-week-prediction,COVID19 Global Forecasting (Week 1)
8587095,0.7055,5,8,/pietromarinelli/8th-place-at-day-1-with-lgb-with-few-features,COVID19 Global Forecasting (Week 1)
8578331,1.2279,0,0,/chiraggodaw/covid-19,COVID19 Global Forecasting (Week 1)
11675199,0.02398,1,6,/ibraheemmoosa/from-just-the-targets,Mechanisms of Action (MoA) Prediction
11666020,0.01899,1,5,/hengzheng/gpu-split-neural-network-approach-tf-keras-v2,Mechanisms of Action (MoA) Prediction
11630671,0.04501,0,2,/pathofdata/ranking-with-pairwise-comparison,Mechanisms of Action (MoA) Prediction
11659769,0.02077,1,2,/oostopitre/moa-pytorch-lightning-starter,Mechanisms of Action (MoA) Prediction
11619600,0.01985,0,16,/hetarthchopra/xgboost-catboost-ensemble-baseline-solution,Mechanisms of Action (MoA) Prediction
11588219,0.01875,11,136,/nicohrubec/pytorch-multilabel-neural-network,Mechanisms of Action (MoA) Prediction
11608897,0.0268899999999999,6,45,/konradb/linear-model,Mechanisms of Action (MoA) Prediction
11601473,0.02125,0,10,/kkhandekar/moa-predictions-xgboost-regressor,Mechanisms of Action (MoA) Prediction
11617891,0.0188699999999999,2,21,/yosukeyama/moa-pytorch-starter-weight-decay-top-feats,Mechanisms of Action (MoA) Prediction
11584722,0.0186199999999999,10,67,/underwearfitting/inference-public-only-fast,Mechanisms of Action (MoA) Prediction
11588218,0.0194699999999999,0,3,/harutot/moa-minimal-data-look-in-and-pytorch-baseline,Mechanisms of Action (MoA) Prediction
11583555,0.01896,5,19,/amanmishra4yearbtech/moa-keras-multilabel-neural-network-v-2-0,Mechanisms of Action (MoA) Prediction
11547932,0.01978,6,95,/pavelvpster/moa-lgb-optuna,Mechanisms of Action (MoA) Prediction
11556195,0.69314,4,14,/muthu1698/mechanisms-of-action-moa-prediction-eda,Mechanisms of Action (MoA) Prediction
11553314,0.01885,4,90,/andypenrose/moa-pytorch-nn-starter-with-weight-decay,Mechanisms of Action (MoA) Prediction
11540780,0.0189,16,60,/stanleyjzheng/multilabel-neural-network-improved,Mechanisms of Action (MoA) Prediction
11549411,1.00054,0,3,/singhaditya5842/eda-exploratory-data-analysis,Mechanisms of Action (MoA) Prediction
11574958,0.0427,0,10,/rajathiyer/simple-linear-regression,Mechanisms of Action (MoA) Prediction
11560043,0.0209099999999999,0,4,/tuliofc/extratrees-baseline-multilabel-classification,Mechanisms of Action (MoA) Prediction
11573047,0.1307299999999999,0,2,/kidoen/mechanismofaction-eda-model-pytorch,Mechanisms of Action (MoA) Prediction
10914216,0.6515,0,0,/heorgiibolotov/kernel1a17c2da7e,Global Wheat Detection
10949225,0.6938,0,4,/fengjiedong/begginner-thought,Global Wheat Detection
9906561,0.7244,0,2,/zekun98/inference-efficientdet-abee8b,Global Wheat Detection
10896364,0.6763,0,0,/aakashveera/notebook-rcnn,Global Wheat Detection
10885350,0.7175,0,0,/carryhjr/yolov5-submit,Global Wheat Detection
10849716,0.2408,3,12,/rajivranjansingh/gwd-keras-unet-starter-improved,Global Wheat Detection
10739768,0.6703,0,0,/rickyd/inference-gwd-detectron2,Global Wheat Detection
10846371,0.7244,7,38,/reppic/fcos-fully-convolutional-1-stage-object-detection,Global Wheat Detection
9345710,0.6104,2,3,/pabloberhauser/yolov4-darknet-inference,Global Wheat Detection
10842324,0.7458,0,2,/yearing1017/yolo5,Global Wheat Detection
10641414,0.7568,0,13,/raininbox/cutout-updown-leftright-yolo-v5-pseduo-labeling,Global Wheat Detection
10734064,0.6294,0,3,/lakshya91/wheat-head-detection-inference,Global Wheat Detection
10705696,0.3241,0,0,/muhbasilv/bifpn5-inff,Global Wheat Detection
10603914,0.7409,0,3,/royalxy7/kernel5893d38a,Global Wheat Detection
10649362,0.7085,0,5,/jonykarki/fasterrcnn-resnet101-tta-inference,Global Wheat Detection
10618900,0.7039,2,13,/jonykarki/fasterrcnn-resnet101-inference-as-is,Global Wheat Detection
5390269,-1.404,0,3,/filemide/schnet-starter-kit,Predicting Molecular Properties
6794700,-1.99227,0,0,/hueahgase189/dual-mpnn,Predicting Molecular Properties
6787557,-2.12588,0,0,/hueahgase189/mpnn-skip,Predicting Molecular Properties
4081339,1.266,0,0,/ramanchandra/predicting-molecualr-properties,Predicting Molecular Properties
5431876,-1.07949,1,3,/edeanf/predmolprop-modeling-xgboost,Predicting Molecular Properties
5683529,0.2637199999999999,0,2,/jaehyeongan/molecular-prediction-basic-eda-and-lightgbm,Predicting Molecular Properties
5255582,-1.689,1,9,/priteshshrivastava/molecular-properties-blending-top-kernels,Predicting Molecular Properties
5588655,-2.1168400000000003,2,12,/felipemello/nn-and-lgb-tricks-and-pipeline-for-top-5-lb,Predicting Molecular Properties
5578086,-2.878,4,18,/senkin13/15th-stacking,Predicting Molecular Properties
5268486,-2.87894,1,7,/chechir/champs-ensemble,Predicting Molecular Properties
5563219,-1.871,0,1,/chechir/nncont-seed-26,Predicting Molecular Properties
5556444,-1.1905,0,0,/rpeer333/super-simple-10-nearest-atoms-features,Predicting Molecular Properties
5518229,-0.05621,0,0,/ruhong/champs-scalar-coupling-xgb,Predicting Molecular Properties
5449836,-1.681,2,8,/xwxw2929/best-score-of-each-type-combination,Predicting Molecular Properties
5403124,-1.648,3,46,/abazdyrev/nn-w-o-skew,Predicting Molecular Properties
5384601,-1.672,10,79,/xwxw2929/keras-neural-net-and-distance-features,Predicting Molecular Properties
5379643,-1.6669999999999998,6,32,/filemide/distance-criskiev-hyparam-cont-1-66,Predicting Molecular Properties
5400529,-1.395,0,0,/nikitinale/distance-criskiev-hyparam-cont-1-662,Predicting Molecular Properties
5334485,0.253,1,11,/roydatascience/tuning-molecules-using-bayesian-optimization,Predicting Molecular Properties
5284104,0.8170000000000001,4,12,/laleh83/predicting-molecular-properties-modeling,Predicting Molecular Properties
7055495,0.9832,0,2,/jagdeesh1009/kannada-mnist-check-ebe6f1,Kannada MNIST
7077083,0.9724,0,0,/valerias/kernel608f2a4198,Kannada MNIST
7027371,0.987,2,3,/ccchang801023/se-net-my-top1-baseline-model-with-pytorch,Kannada MNIST
7046498,0.9232,0,0,/r4rajaa/tensorflow2-simple-nn-models,Kannada MNIST
7063214,0.987,2,7,/namanj27/score-0-99-public-score,Kannada MNIST
7007758,0.9846,1,3,/mswieton/2019-12-12-kannada-cnn-with-augmentation,Kannada MNIST
6953366,0.9858,1,4,/annaelshayeb/kannada-digit-recognition,Kannada MNIST
6929803,0.976,0,1,/jagdeesh1009/kannada-mnist-check,Kannada MNIST
6057318,0.9832,0,2,/omerbeden/cnn-classifier,Kannada MNIST
6932864,0.9532,1,3,/tunguz/kannada-mnist-histgradientboostingclassifier,Kannada MNIST
6881893,0.9854,0,0,/partha1189/kannada-fastai-1,Kannada MNIST
6854207,0.8909999999999999,2,2,/scirpus/fork-of-basic-genetic-programming,Kannada MNIST
6847816,0.976,0,1,/aliakseidziadziuk/bsu-dziadziuk-aliaksei,Kannada MNIST
6838284,0.9834,0,1,/gtnikito/kannada-mnist-hw2-bestnet,Kannada MNIST
6653743,0.9026,1,2,/infhyroyage/investigation-about-dig-mnist-csv,Kannada MNIST
6807392,0.8424,2,3,/scirpus/basic-genetic-programming,Kannada MNIST
6767604,0.9882,1,2,/sharif485/the-more-you-teach-me-the-more-i-learn,Kannada MNIST
6761368,0.2178,0,0,/ritzjain/kannada-digit-recognition,Kannada MNIST
6732858,0.9888,0,2,/nandor65/kmnist-trio,Kannada MNIST
6715152,0.9788,0,2,/econccb/kannada-cnn-w-keras,Kannada MNIST
6691721,0.9792,0,2,/chandraroy/mnist-classification-cnn,Kannada MNIST
6645960,0.1008,0,2,/elgendy5576/using-image-data-generator,Kannada MNIST
6514885,0.9888,0,2,/qz701731tby/improvement-of-kannada-mnist-made-simple,Kannada MNIST
6656272,0.9766,0,1,/gtnikito/kannada-mnist-hw1,Kannada MNIST
6504596,0.969,0,0,/zmiecerbyl/zyanon-nash-kandydat,Kannada MNIST
6650104,0.904,0,1,/larandaa/aryzhevich-nn-1,Kannada MNIST
6627260,0.9024,0,0,/antoninavertinskaya/kernel281a8dfa55,Kannada MNIST
6656992,0.9852,0,1,/shuangmingma/kernel66b294b638,Kannada MNIST
9850287,0.7140000000000001,0,3,/ajax0564/with-processing,Tweet Sentiment Extraction
10073549,0.726,0,4,/rohitsingh9990/roberta-prepost-0-726,Tweet Sentiment Extraction
10145904,0.0,0,1,/ziedbaklouti/bow-rnn,Tweet Sentiment Extraction
8788584,0.0,0,0,/akashsuper2000/fun-experiment-for-tweet-sentiment-analysis,Tweet Sentiment Extraction
10162794,0.708,0,1,/ignaciomcgallo/tweet-sentiment-competition,Tweet Sentiment Extraction
10142961,0.7040000000000001,5,11,/abhiex7/tweet-sentiment-extraction-electra,Tweet Sentiment Extraction
9920472,0.636,1,8,/tanyadayanand/tweet-sentiment-extraction-ner,Tweet Sentiment Extraction
9996024,0.637,0,1,/vikasvikkie/roberta-base-cnn-using-pytorch,Tweet Sentiment Extraction
10124902,0.71,0,0,/neomatrix369/tse2020-roberta-pytorch-multi-tpu-10-skfd-2-2,Tweet Sentiment Extraction
10154021,0.442,0,0,/bikkumama/kernel5802e83f3a,Tweet Sentiment Extraction
10073185,0.708,0,5,/samyakkala/tse-analysis-prediction,Tweet Sentiment Extraction
10048707,0.711,0,0,/jeriks/tweet-sentiment-extraction-roberta,Tweet Sentiment Extraction
10017767,0.616,0,0,/trevjones/word-embedding-experiments,Tweet Sentiment Extraction
9944110,0.595,0,0,/josefelixramos/frankoceanfanclub,Tweet Sentiment Extraction
9982872,0.7090000000000001,0,3,/cascadinglight/electra-fastai-span-extraction,Tweet Sentiment Extraction
10028810,0.648,0,1,/nicholasgeorgekiddle/k-fold,Tweet Sentiment Extraction
10028258,0.652,0,1,/nicholasgeorgekiddle/boosting,Tweet Sentiment Extraction
10016910,0.629,0,1,/pranaydate/text-extract,Tweet Sentiment Extraction
10009467,0.7140000000000001,0,2,/senritu/tweet-sentiment-roberta-pytorch,Tweet Sentiment Extraction
9980089,0.642,1,2,/zeyadtshureih/sentimentanalysis,Tweet Sentiment Extraction
9971296,0.713,0,4,/saikalyan9981/roberta-multi-task-learning-ner-span-extraction,Tweet Sentiment Extraction
9972698,0.386,0,1,/anu0012/getting-started-textblob,Tweet Sentiment Extraction
6779365,1.16,0,1,/vladlee/ashrae-energy-simple-baseline-v2,ASHRAE - Great Energy Predictor III
6766820,1.165,0,1,/morituri/lgbm-baseline,ASHRAE - Great Energy Predictor III
6757659,1.1,0,3,/litemort/implicit-merge-operation-by-litemort,ASHRAE - Great Energy Predictor III
6735565,1.0,7,14,/huanglinyan/ashrae-may-make-it-up-to-1-0,ASHRAE - Great Energy Predictor III
6689419,1.01,13,88,/wuliaokaola/ashrae-maybe-this-can-make-public-lb-some-useful,ASHRAE - Great Energy Predictor III
6699531,1.43,0,0,/wittmannf/feature-engineering-load-profile-no-ml,ASHRAE - Great Energy Predictor III
6667612,1.07,11,21,/yamsam/ashrae-highway-kernel-route1,ASHRAE - Great Energy Predictor III
6475734,1.29,0,0,/srbestha/ashare-light-gbm,ASHRAE - Great Energy Predictor III
6335269,1.35,0,1,/simha1214/categorical-neural-network-at-meter-level,ASHRAE - Great Energy Predictor III
6289704,1.44,0,0,/plarmuseau/ashrae-linear-regressions,ASHRAE - Great Energy Predictor III
6538421,1.09,12,41,/grapestone5321/ashrae-stacking-method,ASHRAE - Great Energy Predictor III
6532599,1.09,13,32,/hmendonca/4-ashrae-blended,ASHRAE - Great Energy Predictor III
6480915,1.17,0,15,/muhakabartay/update-on-simple-lightgbm-lb-1-17,ASHRAE - Great Energy Predictor III
6221674,3.22981,1,4,/marcossantanauff/fisheries-monitoring-starter-pack,The Nature Conservancy Fisheries Monitoring
3908425,2.23692,0,0,/pechkin80/kernele7bc2644c4,The Nature Conservancy Fisheries Monitoring
7507437,0.93292,0,3,/pathofdata/dpn50,Deepfake Detection Challenge
7955513,0.47143,4,5,/revanthrex/my-deep-fake-solution,Deepfake Detection Challenge
8684188,0.38443,0,1,/mohammadhatoum/ensemble-xception-v2-efficientnetb1-resnext50-flip,Deepfake Detection Challenge
8119370,0.44827,4,6,/revanthrex/xception-resnext-ensemble-inference-cle-69fb4d,Deepfake Detection Challenge
8522656,0.33198,0,1,/khahuras/blackborder-inference,Deepfake Detection Challenge
8681162,0.32676,2,9,/shonenkov/final-kfold-inference-effb2,Deepfake Detection Challenge
8236131,0.36044,0,0,/greatgamedota/69th-place-solution-mobilenet-inference,Deepfake Detection Challenge
8474447,0.37181,0,0,/keremt/deepfake-single-frame-inference,Deepfake Detection Challenge
8665797,0.98729,0,1,/chandyalex/implementation-of-convolution-lstm-beginners-level,Deepfake Detection Challenge
7019670,0.6936100000000001,0,1,/gtownfoster/deepfake-detection,Deepfake Detection Challenge
8570440,0.70013,0,0,/arseniimustafin/kernel2b9b3ee224,Deepfake Detection Challenge
13467870,0.27645,0,1,/nicapotato/keras-efficientnet,Humpback Whale Identification
2821013,0.574,0,3,/mgiraygokirmak/captain-ahab-in-pursuit-of-md,Humpback Whale Identification
4292350,0.27774,0,0,/zhutaozhuang/pytorch-whale-classifier-c62d3a,Humpback Whale Identification
3784269,0.8553799999999999,0,0,/cmoputer/whale5-0,Humpback Whale Identification
2534481,0.887,38,3,/axel81/siamese-baseline-lb-0-822,Humpback Whale Identification
3025510,0.163,0,0,/isaranja/humpback-siamese-pytorch-resnet18,Humpback Whale Identification
3015831,0.855,17,57,/voglinio/siamese-two-pretrained-weights-0-855,Humpback Whale Identification
3008005,0.292,1,9,/danlester/whales-transfer-learning-from-resnet18-in-pytorch,Humpback Whale Identification
2991609,0.29,0,2,/iishchukov/vgg-19-simplified,Humpback Whale Identification
2971206,0.76,2,9,/frkhit/triplet-loss-from-pretrained-siamese-net-0-76,Humpback Whale Identification
2951626,0.308,0,6,/harshel7/comprehensive-keras-tutorial-for-beginners,Humpback Whale Identification
2875036,0.319,0,1,/kulbir/final-project,Humpback Whale Identification
2794293,0.2769999999999999,1,6,/twhitehurst3/humback-detection-keras-tranfer-learning,Humpback Whale Identification
2795446,0.285,0,0,/ayalamann/transfer-learning-with-bb,Humpback Whale Identification
2180506,0.19255,0,0,/jmourad100/dogsvscats-transfer-learning-with-inceptionv3,Dogs vs. Cats Redux: Kernels Edition
2156812,0.11667,0,0,/jmourad100/fork-dogsvscats-transfer-learning-with-resnet50,Dogs vs. Cats Redux: Kernels Edition
3582370,0.04701,2,3,/renyuanfang/pretained-models,Dogs vs. Cats Redux: Kernels Edition
2871836,0.05642,0,0,/danielnbarbosa/fast-ai-v1-2019-on-dogs-vs-cats,Dogs vs. Cats Redux: Kernels Edition
2593085,0.054,0,5,/overload10/transfer-learning-using-inception-on-full-data,Dogs vs. Cats Redux: Kernels Edition
2391011,0.24328,0,0,/hengulkakaty/cnn-model-with-keras,Dogs vs. Cats Redux: Kernels Edition
2420836,0.13044,0,0,/moriano/transfer-learning-cats-vs-dogs,Dogs vs. Cats Redux: Kernels Edition
2463065,3.4795,0,0,/kriger161/cat-vs-dog,Dogs vs. Cats Redux: Kernels Edition
2049705,0.27448,2,7,/keogh24/dogs-vs-cats-keras-data-augmentation,Dogs vs. Cats Redux: Kernels Edition
1915708,8.784510000000001,0,2,/abhinavjaiswal/cats-vs-dogs-classification,Dogs vs. Cats Redux: Kernels Edition
1763709,0.34815,10,75,/suniliitb96/tutorial-keras-transfer-learning-with-resnet50,Dogs vs. Cats Redux: Kernels Edition
5511901,0.80195,8,42,/artgor/exploring-categorical-encodings,Categorical Feature Encoding Challenge
5510433,0.78789,6,19,/pavelvpster/cat-in-dat-le-ohe-te-fe-lgb-bo,Categorical Feature Encoding Challenge
5482875,0.76703,2,9,/prateek0x/feature-encoding-xgb,Categorical Feature Encoding Challenge
14342817,2.03337,0,3,/jackharding/bike-sharing-eda-random-forest,Bike Sharing Demand
13778213,3.08313,0,1,/letmewin97/ensemble-methods-over-view-0-08-mse,Bike Sharing Demand
13282445,0.40867,0,1,/jeongwonkim10516/bike-sharing-demand,Bike Sharing Demand
13074892,0.43966,0,0,/viveknimsarkar/eda-on-shared-bike-demand-case,Bike Sharing Demand
13029218,0.4379999999999999,0,0,/tosharathshetty/bike-sharing,Bike Sharing Demand
4080034,0.36154,0,0,/suvinlee/bike-sharing-demand-note,Bike Sharing Demand
11713576,0.51372,0,0,/sehgalsakshi/notebookb2ce508d0c,Bike Sharing Demand
10968276,0.52287,0,2,/vishleshgodani/bike-sharing-demand,Bike Sharing Demand
7304408,1.38342,0,4,/seunghyunlee96/bike-sharing-demand,Bike Sharing Demand
10114994,1.3634,0,4,/rithikb24/bike-gang,Bike Sharing Demand
9750453,0.4461699999999999,0,1,/vijays92/bike-sharing-eda-and-randomforest-hyperparameter,Bike Sharing Demand
9229801,0.3554199999999999,0,7,/mohitsital/top-10-bike-sharing-rf-gbm,Bike Sharing Demand
4837612,0.3765199999999999,0,0,/sjun4530/bike-sharing-demand-exercise,Bike Sharing Demand
8791489,0.49251,2,10,/sidagar/using-random-forest-and-xgboost,Bike Sharing Demand
8477639,0.39729,0,1,/shinbg/bike-demand-easy-to-predict-use-randomforest,Bike Sharing Demand
14661887,0.8469200000000001,0,0,/rahulkumarp/sentiment-analysis,Bag of Words Meets Bags of Popcorn
14138797,0.8190000000000001,0,0,/blighpark/imdb-part2-word2vec,Bag of Words Meets Bags of Popcorn
13443692,0.8383200000000001,0,0,/akiekuboishi/forpresentation-part2-3,Bag of Words Meets Bags of Popcorn
12442173,0.8444799999999999,0,1,/jsyphil/part-3-bag-of-words-meets-bags-of-popcorn,Bag of Words Meets Bags of Popcorn
8113127,0.8166399999999999,0,0,/kongnyooong/imdb-review-nlp-tutorial-part-2,Bag of Words Meets Bags of Popcorn
7538844,0.99984,0,2,/gavrilpetrov/tutorial-1-bag-of-words,Bag of Words Meets Bags of Popcorn
6489262,0.892,0,2,/viroviro/sentiment-analysis-tf-idf-logistic-regression,Bag of Words Meets Bags of Popcorn
6556565,0.87608,0,0,/zanin259/bag-of-words,Bag of Words Meets Bags of Popcorn
5526335,0.8833200000000001,0,0,/stasler/filmabend,Bag of Words Meets Bags of Popcorn
5018942,0.78768,0,6,/jiaofenx/imdb-review-word2vec-rnn-tutorial,Bag of Words Meets Bags of Popcorn
4718945,0.8469200000000001,0,0,/apoorvm/bag-of-words-learning,Bag of Words Meets Bags of Popcorn
3952318,0.82924,0,0,/pyy0715/bag-of-words,Bag of Words Meets Bags of Popcorn
3381205,0.8616799999999999,0,0,/dajiaoyang/kernele51f4e5b6e,Bag of Words Meets Bags of Popcorn
2859798,0.93872,1,4,/nichen301/movie-reviews-lstm-with-keras,Bag of Words Meets Bags of Popcorn
2641309,0.97076,6,12,/sameerdev7/93-f-score-bag-of-words-m-bags-of-popcorn-with-rf,Bag of Words Meets Bags of Popcorn
2318366,0.893,0,0,/tharunreddy/tf-idf-0-89,Bag of Words Meets Bags of Popcorn
2033278,0.57324,0,0,/isabeltseng/bag-of-words-meets-bags-of-popcorn,Bag of Words Meets Bags of Popcorn
1912545,0.8443200000000001,0,1,/aamnafea/sklearn-countvectorizer,Bag of Words Meets Bags of Popcorn
14273393,0.858,0,4,/damoonshahhosseini/submission,Cassava Leaf Disease Classification
14049836,0.897,0,3,/wenlia/pytorch-baseline-inference-arcface-amp-aug,Cassava Leaf Disease Classification
14647113,0.826,2,2,/saadbinmanjuradit/cassava-leaf-disease-classification-at-0-8-score,Cassava Leaf Disease Classification
14272089,0.99632,11,20,/alewicka/mnist-digit-recognizer-cnn-in-keras-99-63,Digit Recognizer
14320869,0.9706,3,2,/nilaykhare/mnist-tensorflow-2-0-nilaykhare,Digit Recognizer
14202670,0.96617,0,0,/hongseyoung/use-pca-for-mnist,Digit Recognizer
14189071,0.99285,10,16,/lvalencia/digit-recognizer-keras-data-augmentation,Digit Recognizer
14208528,0.99182,4,11,/legendsplay/simple-cnn-model-acc-99,Digit Recognizer
13144025,0.9905,0,0,/birdychen/mnist-cnn,Digit Recognizer
13176399,0.94014,0,0,/nidhinmohan/handwriting-digit-recognizer,Digit Recognizer
14138898,0.99035,0,2,/jeffreyyang19/pytorch-mnist,Digit Recognizer
14085919,0.99546,0,0,/kasevgen/mnist-cnn-0-9955,Digit Recognizer
14109903,0.95246,2,1,/hpoddar2810/mnist,Digit Recognizer
14051573,0.99578,2,4,/crusher083/digit-recognizer-cnn-in-keras,Digit Recognizer
11978455,0.99414,9,10,/bronyashijiayang/mnist-pytorch-cnn-for-0-experienced,Digit Recognizer
13915125,0.98207,0,1,/craigmthomas/beginner-guide-to-digit-recognition-part-2,Digit Recognizer
13819614,0.9935,0,1,/fomfom/digit-recognizer-torch,Digit Recognizer
41624,0.48721,0,0,/scrivna/sklearn-random-forest,Home Depot Product Search Relevance
9650396,0.5054,0,0,/benben2/torch-xlm,Jigsaw Multilingual Toxic Comment Classification
8983176,0.9383,0,0,/proxyy/jigsaw-toxic,Jigsaw Multilingual Toxic Comment Classification
9500191,0.8234,0,4,/tasnimnishatislam/jigsaw-multilingual-preprocessing,Jigsaw Multilingual Toxic Comment Classification
9400118,0.9231,0,1,/yashobhan/cursed-comments,Jigsaw Multilingual Toxic Comment Classification
9481605,0.7483,0,1,/norahsh/2-lstm-model-for-toxic-classification-comments,Jigsaw Multilingual Toxic Comment Classification
9453780,0.7994,0,1,/rockingromio/bert-jigsaw-classification,Jigsaw Multilingual Toxic Comment Classification
9432741,0.8733,0,2,/mistercopperfield/deep-learning-for-nlp-zero-to-transformers-bert,Jigsaw Multilingual Toxic Comment Classification
9369922,0.9302,0,0,/csabdulelah/kernel33e41b552e,Jigsaw Multilingual Toxic Comment Classification
9281340,0.4999,0,2,/joydeb28/using-bert-please-upvote,Jigsaw Multilingual Toxic Comment Classification
9269254,0.9406,13,17,/kosovanolexandr/toxic-comment-classification-get-started,Jigsaw Multilingual Toxic Comment Classification
9282807,0.8458,0,0,/jitensharma597/jigsaw-tpu-bert-with-huggingface,Jigsaw Multilingual Toxic Comment Classification
9214196,0.9139,0,5,/nikhiljohnk/jigsaw-inference-nikhiljohn,Jigsaw Multilingual Toxic Comment Classification
8915704,0.8583,0,0,/lucca9211/inference-pytorch-multilingual-tpu,Jigsaw Multilingual Toxic Comment Classification
9166542,0.8857,0,0,/vaibhavbhandari2999/tpu-testing,Jigsaw Multilingual Toxic Comment Classification
9089996,0.8194,0,0,/darshaanjay/nlp-with-bert,Jigsaw Multilingual Toxic Comment Classification
8823850,0.9139,1,2,/divyamdj/inference-bert-model,Jigsaw Multilingual Toxic Comment Classification
9058000,0.8577,0,1,/shikhar721/jigsaw-shikhar,Jigsaw Multilingual Toxic Comment Classification
8936052,0.6272,0,0,/baranwalakash/jigsaw,Jigsaw Multilingual Toxic Comment Classification
9006042,0.924,2,21,/raviyadav2398/jigsaw-multilingual-toxic-comments,Jigsaw Multilingual Toxic Comment Classification
8814072,0.9312,0,1,/narenrathore/kernel3e2d75e989,Jigsaw Multilingual Toxic Comment Classification
79245,2.39114,0,0,/dmitryr/xgboost,TalkingData Mobile User Demographics
5064898,89.58133000000002,0,2,/maxgarber/dogs-gan-keras-mg,Generative Dog Images
5153309,104.16124,6,2,/snakayama/lsgan-using-pytorch,Generative Dog Images
5101103,74.02628,1,14,/kuto0633/acgan-by-pytorch,Generative Dog Images
5062325,158.77268,0,0,/conformal/dcgan,Generative Dog Images
4965618,60.11762,27,125,/phoenix9032/gan-dogs-starter-24-jul-custom-layers,Generative Dog Images
4912126,222.8675,0,0,/techytushar/dog-dcgan,Generative Dog Images
4959833,71.30756,13,31,/rsmits/keras-dcgan-with-weight-normalization,Generative Dog Images
4973373,129.33203,0,0,/arkachakraborty/kernel9a2386c710,Generative Dog Images
4875797,113.86804,6,7,/quangbk/stylegan-dog-pytorch,Generative Dog Images
4904937,130.0483,7,13,/rajwardhanshinde/simple-dcgan-pytorch,Generative Dog Images
4825351,114.10059,0,1,/snakayama/beginer-dcgan-lesson-using-pytorch,Generative Dog Images
4778919,173.7997,0,0,/wrosinski/gan-hacks-ralsgan-again,Generative Dog Images
4701059,129.2926,0,0,/tanreinama/dcgan,Generative Dog Images
4631311,83.78492,6,29,/francoisdubois/ralsgan-dogs-improved-parameters,Generative Dog Images
4665741,10.24737,1,29,/jazivxt/imitation-game,Generative Dog Images
4575989,23.93307,0,17,/titericz/imitation-of-imitation-game,Generative Dog Images
4622471,88.41105,3,7,/whizzkid/dcgan-on-cropped-dog-images,Generative Dog Images
4567495,106.11744,69,527,/jesucristo/gan-introduction,Generative Dog Images
4567661,81.60226,25,107,/speedwagon/ralsgan-dogs,Generative Dog Images
3296883,0.76,1,0,/shashanksai/dont-overfit,Don't Overfit! II
3250379,0.845,0,0,/gauravkjain/logistic-regression-with-winsorization,Don't Overfit! II
3175927,0.828,3,5,/anermakov/fe-stability-selection-attempt,Don't Overfit! II
3028338,0.8440000000000001,0,2,/levant/a-step-by-step-quest-to-solution,Don't Overfit! II
2997684,0.805,0,1,/leopoldosprandel/don-t-overfit-ii-choosing-a-model,Don't Overfit! II
2975424,0.825,0,0,/tomehta/logistic-with-grid-search,Don't Overfit! II
2891792,0.787,0,0,/aatria/alemodel,Don't Overfit! II
2878151,0.8270000000000001,0,0,/gaurav146/don-t-overfit,Don't Overfit! II
2937487,0.754,0,0,/pk5097/don-t-overfit,Don't Overfit! II
2891033,0.7829999999999999,0,4,/anand0427/overfit-submission,Don't Overfit! II
2897984,0.845,0,1,/woshichendu/don-t-overfit-baseline,Don't Overfit! II
2891104,0.8490000000000001,6,9,/sovchinnikov/logistic-regression,Don't Overfit! II
2890550,0.621,4,1,/magf46/can-we-predict-if-the-data-is-from-train-or-test,Don't Overfit! II
2884521,0.7879999999999999,2,2,/jasperkoops/logistic-model-with-pipeline,Don't Overfit! II
2873278,0.789,0,4,/jazivxt/over-the-top-hawk-s-journey,Don't Overfit! II
4840440,0.738,2,0,/zhangwx95/add-noise-and-use-rfe,Don't Overfit! II
11387820,0.0,0,0,/haashaatif/google-landmark-recognition,Google Landmark Recognition 2020
11940958,0.6411,0,7,/andy2709/fork-of-recognition-notebook-16367c-511fa4-95d3bf,Google Landmark Recognition 2020
11971577,0.5537,0,7,/yueqiangqin/glr2-final-sub,Google Landmark Recognition 2020
11760855,0.4825,0,2,/syxuming/keras-predict,Google Landmark Recognition 2020
11450432,0.1044,0,1,/chandrimad31/google-landmark-recognition-tf-keras-effnet-b2,Google Landmark Recognition 2020
11803043,0.1046,0,1,/jojoman/landmark-recognition,Google Landmark Recognition 2020
11838069,0.4847,0,1,/akashsuper2000/organizer-s-code-submission-2753f6,Google Landmark Recognition 2020
11732895,0.4856,0,19,/amanacden/baseline-ransac-hyperparameter-tuning-1,Google Landmark Recognition 2020
11751513,0.4849,0,3,/sboy303/google-organizer-s-code-submission,Google Landmark Recognition 2020
11245494,0.0,0,1,/tchristie/landmark-recognition-2020-training,Google Landmark Recognition 2020
11604069,0.0,9,14,/sanjaydsb/efficientnets-in-pytorch-using-tpus-and-gpus,Google Landmark Recognition 2020
11541616,0.4705,54,74,/ragnar123/efficientnetb3-data-pipeline-and-model,Google Landmark Recognition 2020
11056892,0.0,0,7,/dimakyn/landmark-tpu,Google Landmark Recognition 2020
11477775,0.1856,0,10,/chenbaoying/pytorch-efficientnet-submission,Google Landmark Recognition 2020
441768,0.28039,0,1,/briwill/data-prep-and-modeling,Porto Seguro’s Safe Driver Prediction
479831,0.23406,0,3,/joshiankur/xgboost-model,Porto Seguro’s Safe Driver Prediction
415181,0.28458,14,35,/yekenot/2-level-stacker-silver-solution,Porto Seguro’s Safe Driver Prediction
458652,0.24935,0,2,/etujnr/porto-insurance-claims-for-drivers,Porto Seguro’s Safe Driver Prediction
427745,0.2762,0,0,/arthurlpgc/lgbm-ensemble-try-17-on-porto-seguro,Porto Seguro’s Safe Driver Prediction
445454,0.27848,4,8,/scirpus/for-mr-kruegger,Porto Seguro’s Safe Driver Prediction
441891,0.28048,0,2,/anshulprakash/xgboost-upsampling,Porto Seguro’s Safe Driver Prediction
430811,0.28199,7,14,/sudhirnl7/xgboost-with-stratifiedkflod-lb-0-282,Porto Seguro’s Safe Driver Prediction
428087,0.2722199999999999,30,38,/scirpus/regularized-greedy-forest,Porto Seguro’s Safe Driver Prediction
422034,0.2832699999999999,11,35,/aharless/logistic-of-genetic-features,Porto Seguro’s Safe Driver Prediction
422293,0.28307,0,4,/danieleewww/logistic-of-genetic-features,Porto Seguro’s Safe Driver Prediction
2352591,0.99204,0,0,/jimpsull/adjustpredictiondataframe,PLAsTiCC Astronomical Classification
2342283,0.996,1,0,/jimpsull/blendmodels,PLAsTiCC Astronomical Classification
2198076,1.03,0,0,/jimpsull/featuremergingkernelwithaggcustom,PLAsTiCC Astronomical Classification
2120680,1.958,0,0,/jimpsull/train-and-submit,PLAsTiCC Astronomical Classification
2034232,1.431,24,64,/mithrillion/know-your-objective,PLAsTiCC Astronomical Classification
1933615,1.685,5,19,/ashishpatel26/can-this-make-sense-of-the-universe-tuned,PLAsTiCC Astronomical Classification
1778137,0.178848,0,0,/kmader/sound-classifier-baseline,Freesound General-Purpose Audio Tagging Challenge
1341730,0.895,0,10,/nafisur/beginner-s-guide-to-audio-data-90b7f7,Freesound General-Purpose Audio Tagging Challenge
939297,0.821,9,18,/mpotma/learndatascience-presentation-lgbm-lb-0-836,Freesound General-Purpose Audio Tagging Challenge
815025,0.895,58,531,/fizzbuzz/beginner-s-guide-to-audio-data,Freesound General-Purpose Audio Tagging Challenge
807711,0.81,3,15,/thebrownviking20/xgb-using-lda-and-mfcc-opanichev-s-features,Freesound General-Purpose Audio Tagging Challenge
799795,0.209,3,7,/CVxTz/keras-cnn-starter,Freesound General-Purpose Audio Tagging Challenge
109410,0.75178,0,0,/bmatthewtaylor/notebookf3a0328d0c,Forest Cover Type Prediction
97563,0.74937,12,182,/sharmasanthosh/exploratory-study-on-feature-selection,Forest Cover Type Prediction
6039066,0.79123,0,0,/armando9/fork-of-simple-features-0064d3,Home Credit Default Risk
2396547,0.67996,0,0,/tripathisachin/start-here-a-gentle-introduction,Home Credit Default Risk
1545778,0.73397,0,0,/fontoura/start-here-a-gentle-introduction-bd2d53,Home Credit Default Risk
1508484,0.71205,0,0,/andreasgesundberg/rfs-on-dt-nodes-uses-only-first-train-file,Home Credit Default Risk
1311047,0.7440000000000001,0,0,/turbineyang/lightgbm-version-3,Home Credit Default Risk
14571345,1024.673,4,2,/archanabenur/lightgbm-and-catboost-prediction,Jane Street Market Prediction
14629689,7274.2959999999985,4,16,/pyoungkangkim/1dcnn-pytorch-jstreet,Jane Street Market Prediction
14541378,8235.857,15,22,/snippsy/jane-street-densenet-neutralizing-features,Jane Street Market Prediction
14473004,7144.039000000002,3,15,/snippsy/jane-street-densenet,Jane Street Market Prediction
14147815,3480.937,7,12,/taimurislam/jane-street-market-prediction,Jane Street Market Prediction
14405501,117.859,1,10,/gregorycalvez/following-people,Jane Street Market Prediction
14445571,1550.9489999999996,1,7,/munumbutt/w-i-d-e-b-o-i-neural-network-for-the-memes,Jane Street Market Prediction
14553368,4590.532,0,0,/mingsunds/xgb-ming,Jane Street Market Prediction
14479693,217.703,0,3,/mistag/jane-street-gambler,Jane Street Market Prediction
14429269,8311.476,2,3,/somnath796/mlp-for-jane-street,Jane Street Market Prediction
14468444,690.209,0,1,/aman2114/own-jane-street-with-keras-nn,Jane Street Market Prediction
14379513,1871.781,0,1,/jsmithperera/nn-a-la-resnet,Jane Street Market Prediction
12872554,0.01895,0,10,/bayartsogtya/chris-s-kfold-different-inputs-ensemble,Mechanisms of Action (MoA) Prediction
12817591,0.02806,0,2,/ashwathshetty/moa-simple-analysis-and-model,Mechanisms of Action (MoA) Prediction
12856316,0.01891,0,4,/sejalkshirsagar/moa-prediction-keras,Mechanisms of Action (MoA) Prediction
12828804,0.01975,0,1,/srutimallik/moa-v3,Mechanisms of Action (MoA) Prediction
12731008,0.01834,11,24,/datajameson/moa-prediction-pytorch-lb-0-0183,Mechanisms of Action (MoA) Prediction
12599522,0.01992,0,5,/haoweiiil/moa-random-forest-with-pca-and-neural-net,Mechanisms of Action (MoA) Prediction
12745333,0.0341,1,2,/sarthakrastogi/simple-deep-learning-model,Mechanisms of Action (MoA) Prediction
12772447,0.01891,1,12,/takadaat/mechanisms-of-action-moa-invase,Mechanisms of Action (MoA) Prediction
12677892,0.02107,0,1,/bibhabasumohapatra/drug-classification-final-feature-scaled,Mechanisms of Action (MoA) Prediction
12767441,0.02074,0,1,/dliend/fastai-tabular-learner-moa-challenge,Mechanisms of Action (MoA) Prediction
12696424,0.01865,4,38,/felipebihaiek/prediction-with-swap-auto-encoder-features-0-01865,Mechanisms of Action (MoA) Prediction
12737346,0.01866,10,7,/cuikainuaa/20201107fe-fs-dnn-inference-v1,Mechanisms of Action (MoA) Prediction
12669855,0.0186099999999999,54,101,/markpeng/deepinsight-efficientnet-b3-noisystudent,Mechanisms of Action (MoA) Prediction
12722981,0.1307299999999999,3,2,/mamoru1992/all-zero,Mechanisms of Action (MoA) Prediction
12706128,0.0215,0,1,/mineshjethva/smo-v1,Mechanisms of Action (MoA) Prediction
12113634,0.02251,0,1,/vsevolodcherepanov/moa-multiclassification-model,Mechanisms of Action (MoA) Prediction
12662969,0.01999,0,0,/corrrado/pca-logreg,Mechanisms of Action (MoA) Prediction
12670571,0.01878,0,1,/suhailie/moa-drugs,Mechanisms of Action (MoA) Prediction
12667337,0.0189699999999999,0,0,/chttunll/defai-moa,Mechanisms of Action (MoA) Prediction
12599624,0.01839,48,238,/vbmokin/moa-pytorch-rankgauss-pca-nn-upgrade-3d-visual,Mechanisms of Action (MoA) Prediction
2894153,6.686789999999999,0,6,/zmey56/my-learning-kernel,Predicting a Biological Response
5018380,0.62552,11,156,/ateplyuk/pytorch-starter-u-net-resnet,Severstal: Steel Defect Detection
5009961,0.85369,12,67,/seriousran/xception-beseline-model-for-starter-in-keras,Severstal: Steel Defect Detection
5012673,0.85674,0,4,/sanjusci/eda-steel,Severstal: Steel Defect Detection
11309216,0.60936,0,2,/azhar05/donorschoose-project-proposal-by-md-azharuddin,DonorsChoose.org Application Screening
882569,0.80072,0,0,/mvalexander/donorschoose-models-ensemble,DonorsChoose.org Application Screening
1030975,0.5049,0,1,/pulkitgarg33/project-apllication-screening-system,DonorsChoose.org Application Screening
868398,0.66784,0,1,/bricetaton/basic-bag-of-words,DonorsChoose.org Application Screening
800216,0.77636,0,6,/goragus/beginner-s-journey-to-ml-eda-tf-idf-lgbm,DonorsChoose.org Application Screening
768351,0.81817,22,51,/safavieh/ultimate-feature-engineering-xgb-lgb-nn,DonorsChoose.org Application Screening
757293,0.7959,67,469,/fizzbuzz/beginner-s-guide-to-capsule-networks,DonorsChoose.org Application Screening
709332,0.73455,8,165,/artgor/eda-feature-engineering-and-xgb-lgb,DonorsChoose.org Application Screening
712686,0.78583,6,39,/jagangupta/understanding-approval-donorschoose-eda-fe-eli5,DonorsChoose.org Application Screening
695543,0.77683,22,21,/jmbull/xtra-credit-xgb-lgb-tfidf-feature-stacking,DonorsChoose.org Application Screening
702038,0.73973,1,9,/CVxTz/keras-baseline-feature-hashing-price-tfidf,DonorsChoose.org Application Screening
886891,0.70186,0,0,/estasney/learning-from-the-best-v4,DonorsChoose.org Application Screening
12580718,0.02386,0,0,/sasasagagaga1/gb-with-cv,Mechanisms of Action (MoA) Prediction
12646950,0.10447,0,0,/huanghuangzhang/pytorch-cv-0-0145-lb-0-01839,Mechanisms of Action (MoA) Prediction
12677717,0.1307299999999999,0,0,/soyosuke/notebook67cae5c8a0,Mechanisms of Action (MoA) Prediction
12028169,0.01868,0,1,/chriscc/kubi-pytorch-moa-multibrch-ohe,Mechanisms of Action (MoA) Prediction
12516810,0.01918,0,3,/dyakonov/moa-nn-04,Mechanisms of Action (MoA) Prediction
12547351,0.0185,0,1,/georgyk/torch-cv-without-normalisation-pzad,Mechanisms of Action (MoA) Prediction
12561001,0.01931,0,0,/fedorlebed/boss-nn-dropout,Mechanisms of Action (MoA) Prediction
12535826,0.0236,0,0,/fedorlebed/boss-pca,Mechanisms of Action (MoA) Prediction
12562588,0.01961,0,0,/fedorlebed/boss-bayesian,Mechanisms of Action (MoA) Prediction
12580073,0.01933,0,1,/ravasiliev/ravasiliev-model-creation-pzad,Mechanisms of Action (MoA) Prediction
12577794,0.01928,0,0,/nakhodnov17/simplenn,Mechanisms of Action (MoA) Prediction
12493212,0.01844,40,194,/kushal1506/moa-pytorch-feature-engineering-0-01846,Mechanisms of Action (MoA) Prediction
12578363,0.13583,0,0,/bredonos/notebookcae05416cd,Mechanisms of Action (MoA) Prediction
12509616,0.01968,4,6,/riyajm/tensorflow-starter-baseline,Mechanisms of Action (MoA) Prediction
12412479,0.10964,1,8,/subbhashit/moa-analysis-and-prediction-understandable,Mechanisms of Action (MoA) Prediction
12504417,0.0185,0,1,/jkrthief/explore-the-magic-of-mean-top-2-of-the-publiclb,Mechanisms of Action (MoA) Prediction
12467419,0.01859,2,6,/riadalmadani/keras-nn-pca-with-label-smoothing,Mechanisms of Action (MoA) Prediction
12472722,0.01871,0,2,/shakjm/moa-pytorch-nn-starter-addition,Mechanisms of Action (MoA) Prediction
12461599,0.0595,0,1,/amateurdesperado/new-submit,Mechanisms of Action (MoA) Prediction
12384559,0.01875,0,2,/yiqixue/moa-tf-keras-nn,Mechanisms of Action (MoA) Prediction
12271983,0.01875,7,30,/rahulsd91/moa-anomaly-detection,Mechanisms of Action (MoA) Prediction
12443570,0.0186,4,6,/zaevroman/moa-pytorch-improve-cv-0-0186lb-preprocess,Mechanisms of Action (MoA) Prediction
12446418,0.02504,0,0,/sasasagagaga1/nn-baseline,Mechanisms of Action (MoA) Prediction
12441768,0.0209099999999999,0,0,/pavellukianov/solution1-pzad,Mechanisms of Action (MoA) Prediction
239421,0.77636,0,0,/tumbzilla/fork-of-attempt2twosigmax,Two Sigma Connect: Rental Listing Inquiries
233603,0.6340399999999999,0,0,/solaris33/random-forest-starter-with-numerical-features,Two Sigma Connect: Rental Listing Inquiries
11747742,0.5710000000000001,0,0,/returnofsputnik/effnet-b1b2b3-tawara-inceptions-se-finals-v2,Cornell Birdcall Identification
10340961,0.56,0,0,/akashsuper2000/inference-pytorch-birdcall-resnet-baseline,Cornell Birdcall Identification
69542,0.9608,0,2,/barrosm/exploratory-data-analysis,Grupo Bimbo Inventory Demand
69418,0.9608,16,84,/anokas/exploratory-data-analysis,Grupo Bimbo Inventory Demand
1092449,0.6809999999999999,0,2,/xianglong/lstm-app-bu,Home Credit Default Risk
1092318,0.7440000000000001,1,1,/ashukr/lightgbm-hyperparameter-optimisation-10june,Home Credit Default Risk
1065737,0.732,0,2,/xianglong/app-train-feat,Home Credit Default Risk
1052311,0.777,0,17,/blackbee2016/good-fun-with-automation,Home Credit Default Risk
1053478,0.7609999999999999,33,180,/mlisovyi/lightgbm-hyperparameter-optimisation-lb-0-761,Home Credit Default Risk
1014065,0.72,0,3,/gauravtaneja/home-credit-take0,Home Credit Default Risk
1016314,0.755,50,155,/shep312/deep-learning-in-tf-with-upsampling-lb-758,Home Credit Default Risk
1027118,0.721,2,3,/fox10225fox/home-credit-fastai-trial,Home Credit Default Risk
999480,0.755,1,21,/cast42/upsample-minority-class-and-ligthgbm,Home Credit Default Risk
13683125,0.72211,2,1,/nicapotato/tabular-nn-goblins-cat-and-num,"Ghouls, Goblins, and Ghosts... Boo!"
12724835,0.6710699999999999,0,0,/ayusheeagarwal/ghost-ghoul-goblin,"Ghouls, Goblins, and Ghosts... Boo!"
11862600,0.7240000000000001,0,0,/tushardobhal273/notebooke79c982e6c,"Ghouls, Goblins, and Ghosts... Boo!"
11211787,0.7051,0,4,/mikhailg0/monsters-classification-solution,"Ghouls, Goblins, and Ghosts... Boo!"
10331318,0.7240000000000001,0,1,/shounakdesai/ghouls-goblin-and-ghosts,"Ghouls, Goblins, and Ghosts... Boo!"
10876867,0.7051,0,0,/teramera/kernel10ca177c23,"Ghouls, Goblins, and Ghosts... Boo!"
9931128,0.7258899999999999,0,0,/ashishgeorg009/ghostcatcher,"Ghouls, Goblins, and Ghosts... Boo!"
6470356,0.73345,0,2,/santoshapr7/how-to-get-a-perfect-score-of-0-000,"Ghouls, Goblins, and Ghosts... Boo!"
4347168,0.73345,0,2,/drumstasd/ghouls-goblins-and-ghosts-boo,"Ghouls, Goblins, and Ghosts... Boo!"
2209799,0.7296699999999999,0,0,/davengo/project-3,"Ghouls, Goblins, and Ghosts... Boo!"
1560292,0.71077,0,7,/shahules/ghouls-goblins-and-ghosts,"Ghouls, Goblins, and Ghosts... Boo!"
1244072,0.7126600000000001,1,0,/zhoulingyan0228/mysterious-creature-viz-and-classification,"Ghouls, Goblins, and Ghosts... Boo!"
552219,0.73345,10,40,/samratp/machine-learning-with-ghouls-goblins-and-ghosts,"Ghouls, Goblins, and Ghosts... Boo!"
413767,0.28447,33,39,/aharless/stacking-with-log-odds-and-no-intercept,Porto Seguro’s Safe Driver Prediction
393326,0.27821,26,201,/tilii7/hyperparameter-grid-search-with-xgboost,Porto Seguro’s Safe Driver Prediction
393972,0.2792,3,2,/canonwu/notebookbc0d2b7586,Porto Seguro’s Safe Driver Prediction
8099176,1.49564,0,2,/darwinwin/lanl-master-s-features-h2o-automl,LANL Earthquake Prediction
6933806,4.01773,0,1,/wakamezake/lanl-earthquake-prediction-lb-probing,LANL Earthquake Prediction
6743326,1.56799,0,0,/lovedm/basic-feature-benchmark-20191124,LANL Earthquake Prediction
3309897,1.527,0,0,/juliusmannes/kernel792117c7bb,LANL Earthquake Prediction
5159312,1.62945,0,2,/anuraglahon/earthquake,LANL Earthquake Prediction
4384237,1.58967,0,0,/o93333/lanl-earthquake,LANL Earthquake Prediction
3721551,1.442,0,1,/ashish11701521/lanl-earthquake-prediction-by-ashish-singh,LANL Earthquake Prediction
4184367,1.7348599999999998,11,61,/dkaraflos/1-geomean-nn-and-6featlgbm-2-259-private-lb,LANL Earthquake Prediction
3961202,1.474,0,0,/madadinga/model-tuning-catboost,LANL Earthquake Prediction
4145963,1.63749,1,16,/friedchips/simple-cnn-would-have-been-top-25,LANL Earthquake Prediction
3574996,1.844,0,1,/daisukelab/lanl-solution-by-mel-spectrogram-dataset-2,LANL Earthquake Prediction
4017500,1.46091,0,2,/scaomath/lanl-earthquake-lgb-customized-loss-bootstrapping,LANL Earthquake Prediction
4117665,1.4147299999999998,0,4,/ahmedengu/lanl-master-s-features-tpot,LANL Earthquake Prediction
4110892,2.03,5,10,/kugane/reshape-reshape-raw-conv2d-model-eda,LANL Earthquake Prediction
3753067,1.4980000000000002,0,0,/ashking13th/kernel62457219c0,LANL Earthquake Prediction
2717545,2.04724,1,8,/acauveri/network-severity-prediction-automatic-model,Telstra Network Disruptions
4566404,184.25891,10,63,/artgor/dcgan-baseline,Generative Dog Images
5315694,141.29501000000005,0,0,/roshanalexwelikala/kernel58104786d5,Generative Dog Images
5293497,7.0088300000000014,0,0,/saneryee/memorizer-cgan-pytorch-version,Generative Dog Images
5287734,68.81097,0,0,/manyfoldcv/fork-of-fork-of-gan-dogs-hflip,Generative Dog Images
5271627,73.17343000000002,0,0,/returnofsputnik/fork-of-fork-of-acgan-by-pytorch,Generative Dog Images
4815403,228.47716,0,0,/tikerlade/dogs-gan,Generative Dog Images
4674790,7.27623,0,0,/a11rand0m/memorizer-v1,Generative Dog Images
11674492,0.41845,0,0,/vignesh1404/v-rf-model,Costa Rican Household Poverty Level Prediction
12797269,0.4404899999999999,0,1,/willsonbritto/iesb-graduacao-cia028-costa-rica-wilson-b,Costa Rican Household Poverty Level Prediction
12383586,0.43687,0,0,/marcosvafg/iesb-graduacao-cia028-costa-rica,Costa Rican Household Poverty Level Prediction
11593444,0.44109,0,1,/manoeldias/manoel-minerii-random-forest-versaofinal,Costa Rican Household Poverty Level Prediction
11536565,0.37561,0,1,/alancardek/trabalho-final-data-mining-e-machine-learning,Costa Rican Household Poverty Level Prediction
11542634,0.3916199999999999,0,3,/luiguip/costan-rican,Costa Rican Household Poverty Level Prediction
10369038,0.40811,0,1,/kakacosta0810/t2-costa-rica-household-poverty-level-prediction,Costa Rican Household Poverty Level Prediction
10262425,0.38989,0,0,/stonemasons/tml-project,Costa Rican Household Poverty Level Prediction
7935263,0.36756,0,0,/virgilia/iesb-miner-ii-aula-06-random-forest,Costa Rican Household Poverty Level Prediction
7935246,0.36756,2,1,/ivanmarcal/ivan-costa-rican-random-forest,Costa Rican Household Poverty Level Prediction
6365600,0.39289,0,0,/bomba1990/pobresa-de-costa-rica-tensorflow,Costa Rican Household Poverty Level Prediction
5722586,0.31204,0,1,/lhsakurai/pmr3508-2019-55-hhincome,Costa Rican Household Poverty Level Prediction
4654289,0.40102,0,0,/heystar7/kernel0f44f46057,Costa Rican Household Poverty Level Prediction
4605799,0.40353,0,0,/playminjae/kernel1e861cc76a,Costa Rican Household Poverty Level Prediction
4592198,0.36901,0,0,/tdpark/kernel8900e3abe6,Costa Rican Household Poverty Level Prediction
1598129,0.42443,1,13,/ruslanmamedov/poverty-level-prediction-beginer-s-kernel,Costa Rican Household Poverty Level Prediction
4100179,0.31082,0,1,/csy2017/kernel74a1107fef,Costa Rican Household Poverty Level Prediction
3753089,0.36756,0,1,/rakeshm6295/feature-engineering-using-boruta,Costa Rican Household Poverty Level Prediction
2897471,0.42,4,5,/kwonyoung234/clone-of-will-koehrsen-s-translated-in-korean,Costa Rican Household Poverty Level Prediction
2780078,0.4429999999999999,0,0,/tharug/poverty-eda-modeling,Costa Rican Household Poverty Level Prediction
10145485,0.9469,1,6,/demesgal/nb-svm,Jigsaw Multilingual Toxic Comment Classification
9332734,0.9485,0,5,/khoongweihao/ensemble-ii-the-dark-side-of-stacking,Jigsaw Multilingual Toxic Comment Classification
10283460,0.9471,0,3,/xukaibing/single-model-9471-9455-private-lb,Jigsaw Multilingual Toxic Comment Classification
10156288,0.9476,0,3,/hamditarek/fork-of-ensemble,Jigsaw Multilingual Toxic Comment Classification
9822470,0.7825,0,0,/davidbirdy/kernel15114e324c,Jigsaw Multilingual Toxic Comment Classification
10229100,0.9059,0,0,/medrau/train-from-mlm-finetuned-val-per-lang,Jigsaw Multilingual Toxic Comment Classification
8796536,0.9372,2,2,/akashsuper2000/jigsaw-tpu-xlm-roberta,Jigsaw Multilingual Toxic Comment Classification
9994589,0.938,0,0,/arvindsg/parcor-regularised-classification,Jigsaw Multilingual Toxic Comment Classification
10085282,0.857,0,3,/khanradcoder/distilbert-classification,Jigsaw Multilingual Toxic Comment Classification
10061967,0.9414,2,1,/mikhailderevyannykh/train-from-mlm-finetuned-xlm-r-large,Jigsaw Multilingual Toxic Comment Classification
9285087,0.8656,0,2,/nkaps98/jigsaw-multilingual-toxicity-bert-huggingface,Jigsaw Multilingual Toxic Comment Classification
9992772,0.9139,0,0,/mikhailderevyannykh/kernel3c13509779,Jigsaw Multilingual Toxic Comment Classification
9784875,0.9332,13,18,/rishabhdhiman/eda-xlm-roberta-large-on-tpu,Jigsaw Multilingual Toxic Comment Classification
9558495,0.9257,0,0,/zengm71/jigsaw-bert,Jigsaw Multilingual Toxic Comment Classification
9650041,0.6787,0,0,/simrankucheria/fastai-roberta,Jigsaw Multilingual Toxic Comment Classification
9591710,0.8717,0,1,/ych1997ych/gru-v1,Jigsaw Multilingual Toxic Comment Classification
81167,2.3517,0,0,/aagundez/talk-talk,TalkingData Mobile User Demographics
247956,0.3136,3,2,/prokopyev/naive-xgb,Sberbank Russian Housing Market
248544,0.3265099999999999,0,0,/ermochi/fork1,Sberbank Russian Housing Market
245532,0.33199,3,11,/mwaddoups/i-regression-workflow-various-models,Sberbank Russian Housing Market
245837,0.32727,1,2,/lexotrion/xgd-sf,Sberbank Russian Housing Market
245915,0.32503,0,0,/orocklin/test-xgb-notebook,Sberbank Russian Housing Market
1563912,0.068,0,0,/fabiookina/pmr3508-2018-25ff54ec4f-costa-rica,Costa Rican Household Poverty Level Prediction
1563522,0.1939999999999999,0,0,/gunovello/pmr3508-2018-08dbd26023,Costa Rican Household Poverty Level Prediction
1562951,0.1939999999999999,0,0,/stephaniehk/pmr3508-2018-knn,Costa Rican Household Poverty Level Prediction
1561394,0.362,0,0,/fernandokm/pmr3508-knn,Costa Rican Household Poverty Level Prediction
1560181,0.371,0,0,/indiagolf99/pmr3508-knn-normalization-and-variable-reduction,Costa Rican Household Poverty Level Prediction
1559802,0.309,0,0,/miura99/pmr3508-tarefa-1-base-householdincome,Costa Rican Household Poverty Level Prediction
1559493,0.26,0,0,/fermalavasi/pmr3508-2018-0021b1a4a8-2,Costa Rican Household Poverty Level Prediction
1555829,0.313,0,0,/vmbenevides/pmr3508-2018-9ec6d2de6c-householdincome,Costa Rican Household Poverty Level Prediction
1554218,0.213,0,0,/danilopolidoro/pmr3508-costa-rica,Costa Rican Household Poverty Level Prediction
1543579,0.311,0,9,/rupeshwadibhasme/neural-network-keras-playground,Costa Rican Household Poverty Level Prediction
1540257,0.422,1,11,/captcalculator/exploration-and-modeling-first-pass,Costa Rican Household Poverty Level Prediction
1517273,0.292,0,0,/henriqueyda/pmr3508-2018-5f518d7037,Costa Rican Household Poverty Level Prediction
1520066,0.21,0,0,/marcelmk/pmr3508-knn-attempt-heads-only,Costa Rican Household Poverty Level Prediction
1472031,0.421,0,0,/gilesstrong/dnn-ensemble-cat-embeddings-pretrain-class-bal,Costa Rican Household Poverty Level Prediction
1491269,0.407,0,6,/amrish445/costa-rica-explanation,Costa Rican Household Poverty Level Prediction
1487252,0.216,0,1,/victorhz/first-approach,Costa Rican Household Poverty Level Prediction
1442918,0.379,0,0,/sensbod/kaggle-iadb-competition,Costa Rican Household Poverty Level Prediction
1334097,0.424,1,4,/willkoehrsen/dimensionality-reduction-techniques,Costa Rican Household Poverty Level Prediction
1358281,0.445,0,10,/skooch/lgbm-w-random-split-2,Costa Rican Household Poverty Level Prediction
1416480,0.444,10,41,/gaxxxx/exploratory-data-analysis-lightgbm,Costa Rican Household Poverty Level Prediction
1400645,0.387,3,14,/nikitpatel/random-grid-bayes-search-cv-for-xgb,Costa Rican Household Poverty Level Prediction
1401363,0.3389999999999999,3,11,/ashishpatel26/costa-rican-household-poverty-level-prediction,Costa Rican Household Poverty Level Prediction
1353342,0.419,5,27,/ashishpatel26/feature-importance-of-lightgbm,Costa Rican Household Poverty Level Prediction
1384671,0.3829999999999999,0,1,/spmukherjee/poverty-prediction-with-99-recall,Costa Rican Household Poverty Level Prediction
1361064,0.432,8,79,/willkoehrsen/featuretools-for-good,Costa Rican Household Poverty Level Prediction
4791724,166.75741000000005,0,3,/selfishgene/gmm-in-convolutional-autoencoder-space,Generative Dog Images
5236993,101.79676,0,0,/yutateranishi/gan-dog-only,Generative Dog Images
5179960,82.43146999999998,0,0,/khalidnass/spectral-norm-generative-dog-image-standard,Generative Dog Images
5169424,94.01665,0,0,/rektmeister/dcgan,Generative Dog Images
5287121,54.27576,0,0,/kgeorge/gan-training-full,Generative Dog Images
5300753,92.80553,0,0,/vitlinsh/dog-gan-baseline5-faces-center-crop-dropsm-wnorm,Generative Dog Images
5262546,106.14084,0,0,/suryatejaachanta/kernel9a8f95f699,Generative Dog Images
5239607,18.4778,0,1,/joek47/supervised-generative-dog-net,Generative Dog Images
5216736,60.57207,0,1,/tenffe/gan-dogs-starter-24-jul-custom-layers,Generative Dog Images
5254177,30.3334,9,28,/theoviel/conditional-progan-30-public,Generative Dog Images
5207318,38.70296,1,13,/leonshangguan/dcgan-data-cleaning-sub-v1,Generative Dog Images
5087780,38.26191,0,13,/johannl/sn-dcgan-style,Generative Dog Images
4695714,77.94192,0,1,/benru89/dog-dcgan,Generative Dog Images
5283287,47.75757,1,7,/momi64/kernel-standardgan,Generative Dog Images
5162412,68.0665,0,0,/conformal/ralsgan,Generative Dog Images
4633626,47.51796,2,4,/felipefonte99/dcgan-with-comments-lb-47,Generative Dog Images
4752617,41.21885,0,4,/johannl/sn-dcgan,Generative Dog Images
5171899,57.90872,0,1,/jobzhf88/generative-dog-images-dcgan-pytorch-commit,Generative Dog Images
4663639,62.23049,0,0,/kostyaatarik/dcgan1,Generative Dog Images
5261608,63.26111,0,14,/roydatascience/dcgan-with-weight-normalization-final-submission,Generative Dog Images
5001006,14.525529999999998,0,2,/artpro/kernele7c9368470,Generative Dog Images
4836802,81.30126,0,0,/eroswang/edited-dcgan-dogs-images,Generative Dog Images
5049990,67.65646,0,1,/returnofsputnik/gan-dogs-starter-24-jul-custom-layers,Generative Dog Images
4759725,114.09937,0,0,/mandar1010/gan-dogs,Generative Dog Images
5252524,153.66038,0,0,/lognat0704/real-gan-dog-face,Generative Dog Images
5229382,134.38589,0,2,/nassimyagoub/simple-gan-dog-images,Generative Dog Images
11261929,0.1218,28,36,/rsmits/tf-keras-effnet-b2-non-landmark-removal,Google Landmark Recognition 2020
11067594,0.4851,2,40,/mohammedessam97/organizer-s-code-submission,Google Landmark Recognition 2020
11051356,0.0,8,28,/chumajin/eda-for-biginner-updated-to-english-ver,Google Landmark Recognition 2020
10976038,0.0001,7,34,/andypenrose/pytorch-training-inference-efficientnet-b4,Google Landmark Recognition 2020
11999070,0.4855,0,0,/akashsuper2000/organizer-s-code-submission-final,Google Landmark Recognition 2020
8880739,0.51,0,0,/sidagar/don-t-overfit-using-xgboost,Don't Overfit! II
8655418,0.506,1,0,/gustavorauscher/simple-logistic,Don't Overfit! II
6380676,0.61,0,0,/lukemonington/don-t-overfit-ii-gridsearch,Don't Overfit! II
5390785,0.688,0,1,/coffeeman123/overfittus-maximus,Don't Overfit! II
5125533,0.66,0,0,/siddhi17/kernel2850d82806,Don't Overfit! II
4561415,0.843,1,1,/dalip98/dont-overfit,Don't Overfit! II
4252273,0.672,0,1,/sheik0/logistic-regression,Don't Overfit! II
3571169,0.7340000000000001,0,1,/gabrielmv/dont-overfit-ii-neural-network,Don't Overfit! II
3826964,0.89,41,124,/cdeotte/lb-probing-strategies-0-890-2nd-place,Don't Overfit! II
3403340,0.8220000000000001,0,0,/gdmacmillan/ensemble-lasso-cv,Don't Overfit! II
3539554,0.818,2,9,/praxitelisk/don-t-overfit-ii-eda-ml,Don't Overfit! II
3770576,0.848,3,3,/ricardorios/lasso-don-t-overfit,Don't Overfit! II
3725378,0.856,4,9,/vivaroma/overfit-with-deep-learning-cv-vs-lb,Don't Overfit! II
3730551,0.8009999999999999,0,1,/amundov/don-t-overfit-lgbm-xgboost,Don't Overfit! II
3694688,0.737,3,3,/ramiiii/svm-logistic-regression,Don't Overfit! II
3567316,0.861,1,2,/wordroid/get-best-features-and-paramaters-at-the-same-time,Don't Overfit! II
3694103,0.846,0,0,/skotty971/kernel9ec4176339,Don't Overfit! II
3621169,0.735,0,1,/rishikoush/simple-eda-and-model,Don't Overfit! II
3485552,0.825,0,0,/claymoreriful/claymoreriful-don-t-overfit-ii,Don't Overfit! II
3353238,0.742,0,2,/shivamsarawagi/overfitwithrandomforrest,Don't Overfit! II
1759196,2.367,5,34,/johnfarrell/plasticc-2018-metadata-simple-eda,PLAsTiCC Astronomical Classification
2408081,1.58564,0,0,/jimpsull/shallowlgbensembler,PLAsTiCC Astronomical Classification
2148091,1.11,0,0,/jimpsull/smoteappliedeachstepwithoutweighting,PLAsTiCC Astronomical Classification
3645698,1.556,1,4,/sudsyrutledge/quantile-knn-feature-model,LANL Earthquake Prediction
3563079,1.703,1,10,/latimerb/lanl-getting-started-mlp-rf-ensembles,LANL Earthquake Prediction
3602019,1.636,1,6,/pedrormarques/fft-experiment,LANL Earthquake Prediction
3539896,1.595,7,26,/mrganger/lanl-finding-l-estimators-via-pca,LANL Earthquake Prediction
3544189,1.569,16,59,/buchan/transformer-network-with-1d-cnn-feature-extraction,LANL Earthquake Prediction
3379396,1.819,4,20,/ochiwankenobi/conv1d-spectrogram,LANL Earthquake Prediction
3096328,1.616,0,1,/ashay10001/stacking-of-svr-and-catboost,LANL Earthquake Prediction
3181480,1.4480000000000002,6,18,/ranjoranjan/simple-blend,LANL Earthquake Prediction
3134375,1.528,1,12,/jsaguiar/feature-selection,LANL Earthquake Prediction
3102382,1.509,1,9,/harshel7/earthquake-prediction-ensemble-nn,LANL Earthquake Prediction
3079577,1.645,6,12,/devilears/siraj-s-steps-lstm,LANL Earthquake Prediction
3047571,1.602,0,6,/subhamsharma96/earthquake-prediction-eda-featureengineering-svr,LANL Earthquake Prediction
2834562,1.789,0,0,/anuragsahoo/earthquake-prediction-new,LANL Earthquake Prediction
11471755,0.28479,3,3,/vh1981/porto-seguro-lgbm-ensemble-stacking,Porto Seguro’s Safe Driver Prediction
11091667,0.2702,6,4,/rikdifos/lgb-cv-feature-importance-learning-curve,Porto Seguro’s Safe Driver Prediction
8335457,0.27898,0,0,/batofgotham/xg-boost-with-kfold-stratified,Porto Seguro’s Safe Driver Prediction
8130800,0.27547,0,2,/darwinwin/porto-seguro-with-h2o-automl,Porto Seguro’s Safe Driver Prediction
7843164,0.2436199999999999,0,0,/thallapavanreddy/porto-seguro-s-safe-driver-prediction-by-pavan,Porto Seguro’s Safe Driver Prediction
6538690,0.2546199999999999,0,0,/sondregj/safe-driver-prediction,Porto Seguro’s Safe Driver Prediction
5656182,0.27514,0,0,/domchitdomchit/practice-porto,Porto Seguro’s Safe Driver Prediction
4987795,0.28306,0,0,/msmelguizo/lightgbm-demo,Porto Seguro’s Safe Driver Prediction
4531245,0.27232,0,0,/sugawarya/h2o-28800s-portosegro,Porto Seguro’s Safe Driver Prediction
3694664,0.2771099999999999,2,5,/tunguz/porto-seguro-with-h2o-automl,Porto Seguro’s Safe Driver Prediction
2730795,0.2848199999999999,0,0,/lsjsj92/porto-predict-with-unbalanced-data,Porto Seguro’s Safe Driver Prediction
2599131,0.28255,0,1,/kwonyoung234/porto-m-encode-imputer-poly-stack-by-xgboost,Porto Seguro’s Safe Driver Prediction
1063716,0.17727,0,0,/rajats1992/my-code,Porto Seguro’s Safe Driver Prediction
14668756,0.7288,0,0,/nishita17/forest-cover-type-prediction,Forest Cover Type Prediction
14082102,0.73152,0,0,/dhawalsoni/forestcovertype,Forest Cover Type Prediction
13923375,0.6192300000000001,0,0,/archana111/notebook3d7aa62a45,Forest Cover Type Prediction
13676351,0.60124,0,0,/arko007/knn-forest-type-1,Forest Cover Type Prediction
13246053,0.73928,0,0,/semenedel/forest-cover-type,Forest Cover Type Prediction
10519470,0.7290000000000001,0,0,/teramera/kernel43272cea77,Forest Cover Type Prediction
10348843,0.75044,0,5,/vibeeshk/forest-cover-type-prediction,Forest Cover Type Prediction
9437613,0.71016,0,2,/lucasmorato/technique-for-ml-model-selection,Forest Cover Type Prediction
9278535,0.74219,5,3,/andynath/forest-cover-type-analysis-and-predictions,Forest Cover Type Prediction
9051152,0.75631,0,0,/spanda2/forest-cover,Forest Cover Type Prediction
8446989,0.73077,0,0,/veritasium42/forest-cover,Forest Cover Type Prediction
7588384,0.70617,0,0,/dhanyasabari/forest-cover-type-continued,Forest Cover Type Prediction
5793444,0.72416,0,1,/fedomer/keras-example,Forest Cover Type Prediction
2896992,0.79405,0,0,/jatinmittal0001/forest-cover-type-pred-multi-class-classification,Forest Cover Type Prediction
2313240,0.76851,6,89,/kashnitsky/topic-10-practice-with-logit-rf-and-lightgbm,Forest Cover Type Prediction
1715866,0.80388,0,1,/saumyaraj1/forest-cover-type-eda-extra-trees,Forest Cover Type Prediction
467140,0.0,0,0,/newpye/first-attempts-with-python-ml,Forest Cover Type Prediction
1446148,0.795,0,19,/ashishpatel26/kfold-lightgbm,Home Credit Default Risk
1471481,0.775,2,30,/tottenham/10-fold-simple-dnn-with-rank-gauss,Home Credit Default Risk
1462214,0.787,4,22,/scirpus/pure-gp-with-logloss,Home Credit Default Risk
1443616,0.7859999999999999,16,28,/scirpus/pure-gp-with-mean-squared-error,Home Credit Default Risk
1136016,0.7709999999999999,0,0,/dsloet/fork-4-of-home-credit-default-risk-logs,Home Credit Default Risk
1368633,0.7070000000000001,13,20,/adamsfei/knn-on-application-train-pca-for-ensembling,Home Credit Default Risk
1304215,0.789,2,23,/willkoehrsen/clean-manual-feature-engineering,Home Credit Default Risk
1296130,0.772,0,1,/benjamingatti/homecreditrisk-extensive-eda-baseline-0-772,Home Credit Default Risk
1329603,0.754,0,0,/mahsan42292/home-credit-default-risk-competition,Home Credit Default Risk
119169,0.69943,0,0,/ari994/notebook-1,"Ghouls, Goblins, and Ghosts... Boo!"
116317,0.71077,0,1,/casuru/spooky-exploration-and-classification,"Ghouls, Goblins, and Ghosts... Boo!"
115635,0.71833,1,0,/awesomegabe/comparison-between-classifiers,"Ghouls, Goblins, and Ghosts... Boo!"
115148,0.7088800000000001,0,0,/rp7275/novice-ghost-attempt,"Ghouls, Goblins, and Ghosts... Boo!"
14439640,6005.581999999999,0,1,/bmustafa/easy-and-simple-xgboost-good-score-forked,Jane Street Market Prediction
14561292,1908.538,0,0,/huvivian/jane-market-baseline,Jane Street Market Prediction
14402378,5553.749,0,0,/pheman/lightgb-ensemble,Jane Street Market Prediction
13263616,5.915,0,0,/yzgast/jsmp-submission-kernel-keras,Jane Street Market Prediction
14328354,8102.29,6,24,/code1110/janestreet-resnet-starter,Jane Street Market Prediction
14391635,10122.201,8,36,/chentianxing/notebookec7152784a,Jane Street Market Prediction
14215525,3118.55,0,0,/davidmagny/predictive-analytics,Jane Street Market Prediction
14554369,6816.82,0,0,/saatuo/jane-street-with-keras-nn-overfit-add-val,Jane Street Market Prediction
14260417,4424.163,2,10,/hyperbeam/getting-started-with-lgbm-classifiers,Jane Street Market Prediction
14221001,8377.076,0,13,/manavtrivedi/denoised-mlp,Jane Street Market Prediction
14188131,1620.882,2,3,/granja/jane-street-nn-baseline,Jane Street Market Prediction
14132812,7171.199000000001,7,43,/code1110/janestreet-1dcnn-for-feature-extraction-infer,Jane Street Market Prediction
14124135,6041.509,15,36,/glongpan/pytorch-bottleneck-mlp-solution,Jane Street Market Prediction
738963,0.0,0,9,/mauddib/data-science-bowl-tutorial-using-cnn-tensorflow,2018 Data Science Bowl
213985,0.5462,0,0,/czw123/myfirstandtest,Grupo Bimbo Inventory Demand
200426,0.65918,0,0,/sebask/notebook1,Two Sigma Connect: Rental Listing Inquiries
12133351,0.01969,1,6,/pawan2905/moa-eda-and-multilabel-classification,Mechanisms of Action (MoA) Prediction
12189837,0.69314,0,0,/biubiug/notebookee685d99a6,Mechanisms of Action (MoA) Prediction
12128774,0.023,1,1,/shashankpulijala/tensorflow-2-0-version-1,Mechanisms of Action (MoA) Prediction
12013569,0.01995,0,2,/akshatsharma47/moa-dropout-dropcol,Mechanisms of Action (MoA) Prediction
11943728,0.01898,2,15,/bibhash123/moa-simple-transfer-model-using-tensorflow-keras,Mechanisms of Action (MoA) Prediction
12036291,0.01875,1,13,/chriscc/kubi-pytorch-moa-transfer,Mechanisms of Action (MoA) Prediction
12067957,0.01902,2,2,/ragnar123/moa-baseline-easy-permutation-imp,Mechanisms of Action (MoA) Prediction
12068934,0.02002,0,2,/fushigen/baseline-nn,Mechanisms of Action (MoA) Prediction
11956548,0.01941,0,8,/tolgadincer/mlsmote,Mechanisms of Action (MoA) Prediction
11979697,0.02359,0,7,/krisho007/log-loss-dumb-model-lb-score-0-023,Mechanisms of Action (MoA) Prediction
11794997,0.02586,0,0,/lucca9211/inference-of-mechanisms-of-action-moa,Mechanisms of Action (MoA) Prediction
11942562,0.01965,4,46,/gogo827jz/moa-neural-oblivious-decision-ensembles-tf-keras,Mechanisms of Action (MoA) Prediction
11928563,0.01953,3,8,/worldkeeping/mlp-multilabel-baseline-model-pytorch-walkthrough,Mechanisms of Action (MoA) Prediction
11938943,0.03765,0,0,/yanivbl/yaniv2,Mechanisms of Action (MoA) Prediction
11860504,0.01903,0,0,/ducksquadggez/moa-kfold-data-trim-amount2say,Mechanisms of Action (MoA) Prediction
11853829,0.02027,3,19,/benfraser/deep-ann-tuning-and-submission,Mechanisms of Action (MoA) Prediction
815319,0.7606,0,0,/toorkp/dcds2018,DonorsChoose.org Application Screening
3035812,3.73445,0,0,/suprabhat/not-just-ml-who-let-the-dogs-out,Dog Breed Identification
12440849,0.02363,0,0,/fedorlebed/baseline,Mechanisms of Action (MoA) Prediction
12364089,0.01857,28,113,/sinamhd9/mechanisms-of-action-moa-tutorial,Mechanisms of Action (MoA) Prediction
12366060,0.01866,1,6,/aeryss/moa-pca-feature-engineering-keras-neural-net,Mechanisms of Action (MoA) Prediction
12420124,0.0590799999999999,0,0,/tenduck/v1-start,Mechanisms of Action (MoA) Prediction
12454623,0.02656,0,0,/maltsevan/moa-pzad,Mechanisms of Action (MoA) Prediction
12402114,0.02072,0,0,/crafterkolyan/moa-solution-pzad,Mechanisms of Action (MoA) Prediction
12372283,0.0186199999999999,0,3,/eshine123/eda-of-moa-in-drug,Mechanisms of Action (MoA) Prediction
12230792,0.02958,0,2,/aeryss/moa-eda,Mechanisms of Action (MoA) Prediction
12322453,0.01864,22,113,/optimo/tabnetregressor-2-0-train-infer,Mechanisms of Action (MoA) Prediction
12338510,0.01985,0,0,/nur988/moa-pytorch,Mechanisms of Action (MoA) Prediction
12313186,0.01844,5,23,/riadalmadani/averaging-public-kernels-0-01844,Mechanisms of Action (MoA) Prediction
11961518,0.02017,0,3,/jeeperscreepers/svc-model-pca-what-targets-perform-poorly,Mechanisms of Action (MoA) Prediction
12236588,0.01849,26,84,/liuhdme/moa-competition,Mechanisms of Action (MoA) Prediction
12270080,0.1101,2,3,/gauravduttakiit/drug-classification-using-catboost,Mechanisms of Action (MoA) Prediction
12220596,0.01866,0,2,/riadalmadani/the-power-of-nn,Mechanisms of Action (MoA) Prediction
12168013,0.0192,0,2,/benfraser/moa-dnn-cnn-deep-dive,Mechanisms of Action (MoA) Prediction
11860851,0.02081,0,1,/tachyon777/moa-tachyon-v2-sub,Mechanisms of Action (MoA) Prediction
12198203,0.02179,3,13,/ash1706/reducing-moa-autoencoder-pca-t-svd,Mechanisms of Action (MoA) Prediction
12181790,0.01859,2,15,/omniking1999/notebook-v3-0,Mechanisms of Action (MoA) Prediction
12158305,0.01848,28,41,/rahulsd91/moa-starter-inference-blending-pretrained-models,Mechanisms of Action (MoA) Prediction
12158633,0.0287899999999999,0,0,/yeayates21/moa-textbook-sklearn,Mechanisms of Action (MoA) Prediction
12146035,0.01856,5,39,/domizianostingi/top-5-leaderboard,Mechanisms of Action (MoA) Prediction
12168540,0.02074,2,1,/nayuts/moa-histgradientboostingregressor-baseline,Mechanisms of Action (MoA) Prediction
4188311,0.4627199999999999,0,0,/atiafadi/eval-taxi-trip-af,New York City Taxi Trip Duration
3258115,0.43692,0,0,/younesayeb/trynyctaxi,New York City Taxi Trip Duration
10415113,0.1279,0,3,/poteman/local-test,M5 Forecasting - Uncertainty
10363521,0.144,1,33,/yujiariyasu/point-to-uncertainty-for-private,M5 Forecasting - Uncertainty
10341402,0.08407,0,0,/liaowenxiong/m5uncertainity,M5 Forecasting - Uncertainty
10206700,0.11802,9,26,/kamalnaithani/m5uncertainity-score,M5 Forecasting - Uncertainty
10084105,0.1721,4,21,/mpware/quantile-regression-cv3-tf,M5 Forecasting - Uncertainty
8978542,0.16054,0,4,/shaitender/forked-from-point-to-uncertainty,M5 Forecasting - Uncertainty
8889291,0.1792099999999999,0,2,/jagannathrk/from-point-to-uncertainty-prediction,M5 Forecasting - Uncertainty
8726503,0.83224,0,4,/sachina/convert-accuracy-to-uncertainty-poisson,M5 Forecasting - Uncertainty
8642246,0.2483199999999999,1,26,/nxrprime/coefficient-multiplier,M5 Forecasting - Uncertainty
8537453,1.61168,4,9,/robertburbidge/lightgbm-gamma,M5 Forecasting - Uncertainty
10374041,0.12478,0,0,/akashsuper2000/m5uncertainity-score,M5 Forecasting - Uncertainty
8894861,0.1792099999999999,0,0,/akashsuper2000/from-point-to-uncertainty-prediction,M5 Forecasting - Uncertainty
8667949,0.3103,0,0,/akashsuper2000/quantiles-w-custom-loss-func,M5 Forecasting - Uncertainty
180496,0.69875,4,9,/oysteijo/rental-neural-network,Two Sigma Connect: Rental Listing Inquiries
259595,0.55209,0,0,/uditsaini/xgb-starter-in-python-1,Two Sigma Connect: Rental Listing Inquiries
14125024,3973.641,0,1,/luisdavid/lgb-start,Jane Street Market Prediction
13952813,7353.517,0,5,/yuanol/retry-from-8601c5,Jane Street Market Prediction
14024648,4054.821,0,2,/lhagiimn/lightgbm-multiclass-classification,Jane Street Market Prediction
13841282,7495.033,44,117,/lucasmorin/running-algos-fe-for-fast-inference,Jane Street Market Prediction
13925072,3344.738,0,8,/satorushibata/optimized-lightgbm-classifier-cnn-pca-logit,Jane Street Market Prediction
13901901,4604.938,1,4,/aman2114/jsmp-catboost-1-8f92f3,Jane Street Market Prediction
13804411,4808.654,0,1,/anatolymal/jsm-v05-1-encoder-catboost,Jane Street Market Prediction
13793781,262.058,0,1,/aeryss/js-neural-network-rank-gauss,Jane Street Market Prediction
13804465,3459.39,0,1,/contactashish78/js-nn-standard,Jane Street Market Prediction
13811988,1074.958,3,15,/backtracking/lstm-baseline-pytorch,Jane Street Market Prediction
13792691,8443.57,2,11,/lpachuong/fork-of-notebookd9779f8f26,Jane Street Market Prediction
13720461,9331.843,98,304,/aimind/bottleneck-encoder-mlp-keras-tuner-8601c5,Jane Street Market Prediction
13721785,4073.491,5,21,/snippsy/buy-or-sell-regression,Jane Street Market Prediction
122579,0.7429100000000001,0,4,/eponymous/blinky-pinky-inky-and-clyde,"Ghouls, Goblins, and Ghosts... Boo!"
122134,0.7448,0,3,/milian/boooom,"Ghouls, Goblins, and Ghosts... Boo!"
11152595,0.4987899999999999,0,0,/wewefo/kernel3550445f1d,Mercedes-Benz Greener Manufacturing
6044961,0.53769,0,4,/guidosalimbeni/regression-with-convolutional-neural-network-keras,Mercedes-Benz Greener Manufacturing
5159681,0.55704,0,8,/fangkun119/competition-mercedes-benz-greener-manufacturing,Mercedes-Benz Greener Manufacturing
3953740,0.54991,0,1,/arielszabo/how-to-use-bayesian-optimization,Mercedes-Benz Greener Manufacturing
3526040,0.54161,0,1,/gregyb/mercedes-benz-greener-manufacturing-gb,Mercedes-Benz Greener Manufacturing
3190502,0.55215,0,0,/junheo/mercedes-benz-greener-manufacturing-catboost,Mercedes-Benz Greener Manufacturing
1199901,0.42823,0,0,/plarmuseau/finding-robust-data,Mercedes-Benz Greener Manufacturing
509911,0.52675,6,5,/sudhirnl7/linear-regression-benz,Mercedes-Benz Greener Manufacturing
1311350,0.7440000000000001,0,0,/turbineyang/lightgbm-version-4,Home Credit Default Risk
1248081,0.7440000000000001,0,3,/totalrecall/intro-to-credit-default-risk-project,Home Credit Default Risk
1263676,0.743,0,1,/sunnynevarekar/home-credit-default-risk-lightgbm,Home Credit Default Risk
1249981,0.782,43,348,/willkoehrsen/intro-to-model-tuning-grid-and-random-search,Home Credit Default Risk
1258817,0.518,0,1,/depmountaineer/1-nearest-nbor-naive-classifier-for-home-credit,Home Credit Default Risk
1254160,0.78,3,9,/abhinav97dutt/model-stacking,Home Credit Default Risk
1233526,0.77,0,1,/abhinav97dutt/xgb-beginner,Home Credit Default Risk
1189554,0.767,0,8,/nikitpatel/home-credit-xgboost,Home Credit Default Risk
1143658,0.738,0,11,/dromosys/fast-ai-pytorch-train-only,Home Credit Default Risk
1136206,0.748,0,3,/dromosys/fast-ai-pytorch-starter-corr-removed,Home Credit Default Risk
1087344,0.782,0,2,/dfoly1/home-credit-feature-engineering-and-prediction,Home Credit Default Risk
1154375,0.763,3,30,/davidsalazarv95/fast-ai-pytorch-starter-version-two,Home Credit Default Risk
4003241,1.51031,1,4,/itamargr/recurrent,LANL Earthquake Prediction
4026138,1.54,0,0,/jim5uz/lanl-abdumalik-s-kernel-simple-models,LANL Earthquake Prediction
3937644,2.2230000000000003,0,0,/isaranja/lanl-earthquake-statistical-features-with-lstm,LANL Earthquake Prediction
3970507,1.7180000000000002,1,11,/matsumotoshintaro/so-simple-blend-model-new-ver,LANL Earthquake Prediction
3965293,1.92,0,6,/matsumotoshintaro/so-simple-blend-model,LANL Earthquake Prediction
3953137,1.922,0,1,/matsumotoshintaro/so-simple-xgboosting-xgboosting,LANL Earthquake Prediction
3895156,1.546,0,3,/pedrormarques/signal-convolution-v4,LANL Earthquake Prediction
3839672,1.629,0,1,/oguzkoroglu/andrews-features-and-random-forest,LANL Earthquake Prediction
3856283,1.481,2,11,/scirpus/gp-teeny-tiny,LANL Earthquake Prediction
3836787,1.454,3,9,/superluminal098/gplearn-runtime-management-and-regression,LANL Earthquake Prediction
3765706,1.47522,5,16,/scirpus/mfcc-with-gp,LANL Earthquake Prediction
3730621,1.526,2,12,/superluminal098/tsfresh-features-and-regression-blend,LANL Earthquake Prediction
3727784,1.48,1,4,/scirpus/earthquake-wood-for-the-trees,LANL Earthquake Prediction
3665996,1.442,38,200,/artgor/feature-selection-model-interpretation-and-more,LANL Earthquake Prediction
3678653,1.455,26,150,/artgor/even-more-features,LANL Earthquake Prediction
3658344,1.606,0,10,/rsaund/lanl-shap,LANL Earthquake Prediction
3662830,1.737,0,9,/pnussbaum/earthquake-pred-cnn-medical-analogy-v07,LANL Earthquake Prediction
3649406,1.51515,22,111,/abhishek/quite-a-few-features-1-51,LANL Earthquake Prediction
3634794,1.507,4,18,/hsinwenchang/mfcc-randomforestregressor-catboostregressor,LANL Earthquake Prediction
2628456,0.424,0,0,/corvuslee/costa-rican-household-poverty-level-rf-practice,Costa Rican Household Poverty Level Prediction
2590299,0.312,0,0,/varwolf/poor-prediction-knn,Costa Rican Household Poverty Level Prediction
2601354,0.3429999999999999,0,1,/myxiaolu/tese-new,Costa Rican Household Poverty Level Prediction
2329025,0.3279999999999999,0,0,/carlosan1708/my-first-kernel,Costa Rican Household Poverty Level Prediction
2263809,0.422,0,1,/eamonsuen/simple-ensemble,Costa Rican Household Poverty Level Prediction
2202159,0.3779999999999999,1,0,/supermoooonjy/onepick-1,Costa Rican Household Poverty Level Prediction
1996377,0.373,0,0,/taylornelson/costa-tu-rica,Costa Rican Household Poverty Level Prediction
1981763,0.359,0,0,/ivarvb/costa-rican-household-poverty-level-prediction,Costa Rican Household Poverty Level Prediction
1908225,0.4039999999999999,0,0,/rohitgan/costa-rica-household-poverty-prediction,Costa Rican Household Poverty Level Prediction
1632588,0.441,0,7,/nikitsoftweb/lgbm-w-random-split-2,Costa Rican Household Poverty Level Prediction
1675811,0.375,0,0,/wiczer/analysis-v3,Costa Rican Household Poverty Level Prediction
1560607,0.2769999999999999,0,0,/felipegdm/kernel4ec105d9b1,Costa Rican Household Poverty Level Prediction
1506885,0.417,0,0,/stefanie04736/costa-rican-household-poverty-lightgbm,Costa Rican Household Poverty Level Prediction
1625287,0.433,0,0,/yulinzxc/a-newbie-walk-through-with-variety-of-algorithms,Costa Rican Household Poverty Level Prediction
1398019,0.447,0,1,/skooch/ensemble-of-xgboost-lgb-and-randomforest,Costa Rican Household Poverty Level Prediction
1668555,0.425,0,1,/bparesh/costa-rican-poverty-logistic-model-425lb,Costa Rican Household Poverty Level Prediction
1632831,0.363,0,0,/justforgags/stacking-poverty,Costa Rican Household Poverty Level Prediction
1660192,0.297,4,6,/anandi1989/my-first-kernel-stacking,Costa Rican Household Poverty Level Prediction
1647090,0.271,0,0,/godlee/first,Costa Rican Household Poverty Level Prediction
1621280,0.419,0,3,/nephalem98/simple-approach-to-poverty-level-prediction,Costa Rican Household Poverty Level Prediction
1619577,0.318,0,3,/justjun0321/from-cleaning-eda-to-modeling-help-those-indeed,Costa Rican Household Poverty Level Prediction
1565495,0.432,0,4,/reployer/leaderboard-probing-ridge-0-426-0-432,Costa Rican Household Poverty Level Prediction
1564727,0.1939999999999999,0,0,/vhenrique21/pmr3508-2018-f4fcf75dda,Costa Rican Household Poverty Level Prediction
1564573,0.314,0,0,/otaviomserra/pmr3508-costa-rica,Costa Rican Household Poverty Level Prediction
8755999,0.7070000000000001,1,0,/sharif08/kernel534322ad33,Jigsaw Multilingual Toxic Comment Classification
9508032,0.903,0,0,/kayvanshah/roberta-large-2,Jigsaw Multilingual Toxic Comment Classification
11485682,0.846,0,5,/gogo827jz/jigsaw-roberta-large-on-tpu,Jigsaw Multilingual Toxic Comment Classification
11318581,0.945,1,4,/xj609210970/xlm-r-conv1d-single-model-0-9448,Jigsaw Multilingual Toxic Comment Classification
9948348,0.9427,0,4,/swannnn/jigsaw-tpu-xlm-roberta-e3ad07,Jigsaw Multilingual Toxic Comment Classification
11124838,0.8777,0,0,/nigula/kernel68b445bb3e,Jigsaw Multilingual Toxic Comment Classification
11023810,0.8795,0,9,/kalashnimov/distilbert-baseline,Jigsaw Multilingual Toxic Comment Classification
10770980,0.9442,0,1,/mint101/basic-xlm-r-lb-9442-intro,Jigsaw Multilingual Toxic Comment Classification
10278790,0.9482,1,0,/mint101/lb-9482-by-simple-public-result-bf-end-ensemble,Jigsaw Multilingual Toxic Comment Classification
10787820,0.9283,0,0,/zengm71/jigsaw-olid-solid-xlm-roberta,Jigsaw Multilingual Toxic Comment Classification
10476019,0.9327,0,0,/zengm71/jigsaw-bert-zero-shot-offenseval,Jigsaw Multilingual Toxic Comment Classification
10543637,0.9359,0,0,/bond005/siamese-xlm-roberta-for-tpu,Jigsaw Multilingual Toxic Comment Classification
9166087,0.9351,9,68,/tanlikesmath/the-ultimate-pytorch-tpu-tutorial-jigsaw-xlm-r,Jigsaw Multilingual Toxic Comment Classification
245105,0.32623,0,0,/vfdev5/naive-xgb,Sberbank Russian Housing Market
918394,0.3196,0,0,/tamargab/level1-baseline-with-xgb,Sberbank Russian Housing Market
11384903,0.9856,0,0,/windmen/digit-recognizer,Digit Recognizer
11459042,0.97446,0,3,/sachinkadlimatti/digit-recognizer,Digit Recognizer
11437677,0.99342,4,18,/jayfaldu/hand-written-digit-recognition-using-cnn,Digit Recognizer
11439980,0.96982,0,4,/arseniybelkov/digit-recognizer,Digit Recognizer
11232489,0.99435,2,7,/dhruvesh2103/digit-recognizer-tensorflow-cnn,Digit Recognizer
11248388,0.99139,0,1,/simo333/digit-rec-repeatedkfold-99,Digit Recognizer
11423725,0.97846,0,2,/reviveer/98-accuracy-using-simple-keras-model,Digit Recognizer
11400559,0.98975,1,9,/ezeanyi/mnist-digits-classification,Digit Recognizer
11265589,0.99482,7,32,/aditi81k/digit-recognizer,Digit Recognizer
11342302,0.9966,0,6,/tuckerarrants/mnist-tensorflow-overview-cnn-ensemble-996,Digit Recognizer
11333538,0.99664,1,8,/maxmar/digit-recognizer-ensemble,Digit Recognizer
11298742,0.99225,0,5,/lokeshduvvuru/mnist-squeeze-and-excite-block,Digit Recognizer
8895052,0.9242,0,0,/akashsuper2000/jigsaw-tpu-bert-two-stage-training,Jigsaw Multilingual Toxic Comment Classification
7545353,0.05,0,5,/nxrprime/inception-resnet-v2-baseline,Peking University/Baidu - Autonomous Driving
7134235,0.0409999999999999,3,26,/isakev/rb-s-centernet-baseline-pytorch-without-dropout,Peking University/Baidu - Autonomous Driving
6697999,0.039,39,80,/phoenix9032/center-resnet-starter,Peking University/Baidu - Autonomous Driving
6661184,0.032,1,10,/qinhui1999/centernet-baseline-with-map-valid-score,Peking University/Baidu - Autonomous Driving
6391599,0.001,10,29,/theshockwaverider/eda-visualization-baseline,Peking University/Baidu - Autonomous Driving
6346175,0.0,12,101,/seshadrikolluri/vehicle-angle-prediction-understanding-eda,Peking University/Baidu - Autonomous Driving
64903,0.4539,0,0,/omarelgabry/facebook-check-ins-analysis-prediction,Facebook V: Predicting Check Ins
63242,0.0,0,1,/jturkewitz/mad-script-battle-pandas-version,Facebook V: Predicting Check Ins
4338832,0.98225,4,7,/unkownhihi/kernel-lb-score-bug,Plant Pathology 2020 - FGVC7
8889717,0.878,0,1,/urayukitaka/convolutional-neural-network-plant-pathology,Plant Pathology 2020 - FGVC7
8772731,0.504,1,2,/marcossantanauff/fastai-plant-pathology-2020,Plant Pathology 2020 - FGVC7
8731413,0.943,0,1,/drali313/densenet,Plant Pathology 2020 - FGVC7
8683361,0.946,1,4,/sovitrath/pytorch-effcientnet-b3-baseline,Plant Pathology 2020 - FGVC7
8332249,0.561,0,1,/diamondra/plant-panthology-cnn,Plant Pathology 2020 - FGVC7
8714183,0.951,1,0,/lextoumbourou/plant-2020-cutmix-tpu,Plant Pathology 2020 - FGVC7
8472196,0.939,0,5,/jabertuhin/plant-pathology-with-pytorch-lightning,Plant Pathology 2020 - FGVC7
8592702,0.976,4,25,/welkinfeng/plant-pathology-pytorch-efficientnet-b4-gpu,Plant Pathology 2020 - FGVC7
8661497,0.921,0,0,/sujoykg/keras-densenet-with-regularization-fine-tuning,Plant Pathology 2020 - FGVC7
8574753,0.966,0,4,/welkinfeng/plant-pathology-pytorch-tpu-efficientnet-b4,Plant Pathology 2020 - FGVC7
8435119,0.978,17,70,/anyexiezouqu/tpu-tensorflow2-0-978score,Plant Pathology 2020 - FGVC7
8488980,0.962,0,2,/chiraggodaw/plant-pathology,Plant Pathology 2020 - FGVC7
8463768,0.941,3,11,/mamamot/fastai-v2-example,Plant Pathology 2020 - FGVC7
8421098,0.935,4,14,/fkdplc/plant-patology-starter-baseline-using-keras-cnn,Plant Pathology 2020 - FGVC7
8345935,0.97,95,599,/tarunpaparaju/plant-pathology-2020-eda-models,Plant Pathology 2020 - FGVC7
8375339,0.953,1,8,/chtalhaanwar/keras-efficientnet,Plant Pathology 2020 - FGVC7
8389848,0.943,3,5,/ajax0564/plant-pathology,Plant Pathology 2020 - FGVC7
8344601,0.846,2,6,/shawon10/plant-pathology-classification-using-densenet121,Plant Pathology 2020 - FGVC7
14169828,0.9747,0,0,/akataev96/tf-zoo-models-on-tpu-ensemble,Plant Pathology 2020 - FGVC7
12688991,0.96612,0,0,/evgenh76434/fork-of-plant-pathology-keras-efficientnetb4-epoch,Plant Pathology 2020 - FGVC7
8912540,0.957,0,0,/akashsuper2000/tpu-incepresnetv2-enb7,Plant Pathology 2020 - FGVC7
8686527,0.973,0,0,/redwankarimsony/tpu-incepresnetv2-enb7,Plant Pathology 2020 - FGVC7
377599,0.27949,9,29,/infinitewing/k-fold-cv-xgboost-example-0-28,Porto Seguro’s Safe Driver Prediction
8175539,0.2458199999999999,0,0,/batofgotham/decision-tree-custom-ensembel,Porto Seguro’s Safe Driver Prediction
410268,0.15147,0,0,/arthurlpgc/random-forest-try-3-on-porto-seguro,Porto Seguro’s Safe Driver Prediction
14359097,0.7584,0,0,/omarayman/eda-predictive-modelling,Home Credit Default Risk
9972082,0.50061,0,1,/yodaitanaka/home-credit-difficult,Home Credit Default Risk
9544016,0.73939,0,1,/charlievbc/home-credit-default-risk-visualization-analysis,Home Credit Default Risk
9562316,0.73397,0,1,/mahirahmzh/start-here-a-gentle-introduction,Home Credit Default Risk
9272079,0.68876,0,0,/congtru/the-first-baseline,Home Credit Default Risk
9067638,0.79465,31,39,/mathchi/home-credit-risk-with-detailed-feature-engineering,Home Credit Default Risk
26658,0.55394,2,11,/omarelgabry/prudential-insurance-risk-predictions,Prudential Life Insurance Assessment
2924804,0.2512,0,1,/twhitehurst3/fcn-keras-iceberg,Statoil/C-CORE Iceberg Classifier Challenge
580920,0.1978,0,0,/pkolebski/cnn-data-augentation-0-1978,Statoil/C-CORE Iceberg Classifier Challenge
550705,0.1807,0,4,/mpware/features-engineering-lightgbm,Statoil/C-CORE Iceberg Classifier Challenge
553579,0.5686,0,8,/varunkashyapks/basic-keras-model-and-visualization,Statoil/C-CORE Iceberg Classifier Challenge
523889,0.1327,20,77,/submarineering/submarineering-even-better-public-score-until-now,Statoil/C-CORE Iceberg Classifier Challenge
515006,0.1357,0,4,/danieleewww/submarineering-best-public-score-until-now,Statoil/C-CORE Iceberg Classifier Challenge
472408,0.2231,0,0,/tienanh2007/mu-fem-tienanhnguyen,Statoil/C-CORE Iceberg Classifier Challenge
465249,0.1917,1,7,/avarian/tugas-dmkb,Statoil/C-CORE Iceberg Classifier Challenge
442846,0.1793,5,41,/wvadim/keras-tf-lb-0-18,Statoil/C-CORE Iceberg Classifier Challenge
418300,0.3459,0,7,/solomonk/pytorch-modular-and-clean-cnn,Statoil/C-CORE Iceberg Classifier Challenge
417591,0.6827,2,3,/ezietsman/simple-keras-convnet,Statoil/C-CORE Iceberg Classifier Challenge
405330,0.5437,7,113,/brassmonkey381/viewing-leak-and-machine-images,Statoil/C-CORE Iceberg Classifier Challenge
407763,0.6984,1,7,/glebmihaescu/logistic-regression-0-44lb,Statoil/C-CORE Iceberg Classifier Challenge
10462091,733.4,7,26,/yegorbiryukov/pirate-haven,Halite by Two Sigma
3542957,0.98276,0,1,/anuragshas/invasive-species,Invasive Species Monitoring
1592935,0.8286100000000001,0,4,/ambarish/invasive-species-monitoring-analysis,Invasive Species Monitoring
1021000,0.98687,0,6,/lbronchal/keras-pre-trained-vgg16-kaggle-runnable-version,Invasive Species Monitoring
12350058,1.90633,0,0,/nehalbandal/two-sigma-connect-eda-prediction-using-catboost,Two Sigma Connect: Rental Listing Inquiries
3831013,0.55405,1,0,/hugoboum/projet-machine-learning-final,Two Sigma Connect: Rental Listing Inquiries
2418597,0.56606,0,0,/smspillaz/aalto-s-how-to-win-a-kaggle-competition-course,Two Sigma Connect: Rental Listing Inquiries
1922486,0.5928399999999999,0,0,/timsonrisa/two-rental-p-3-xgboost,Two Sigma Connect: Rental Listing Inquiries
1092994,4.354419999999998,0,1,/kuijinxia/xgb-train,Two Sigma Connect: Rental Listing Inquiries
241689,0.55219,0,0,/dm14348083/xgb-starter-in-python,Two Sigma Connect: Rental Listing Inquiries
238749,1.72073,0,1,/reshsekar15/naives-bayes,Two Sigma Connect: Rental Listing Inquiries
238491,0.75265,0,0,/tumbzilla/attempt2twosigmax,Two Sigma Connect: Rental Listing Inquiries
239747,0.55184,0,0,/vignesh2323/fork-of-fork-of-xgboost-trialrun-2-6c4517,Two Sigma Connect: Rental Listing Inquiries
10503294,0.568,15,183,/ttahara/inference-birdsong-baseline-resnest50-fast,Cornell Birdcall Identification
10469839,0.544,2,5,/alansun17904/submission-module-birdcall-classification,Cornell Birdcall Identification
10411966,0.56,0,26,/shaitender/birdcall-identification-with-pytorch,Cornell Birdcall Identification
10378070,0.54,0,3,/radek1/esp-starter-pack-v3,Cornell Birdcall Identification
10249274,0.55,74,387,/hidehisaarai1213/inference-pytorch-birdcall-resnet-baseline,Cornell Birdcall Identification
10224981,0.54,1,23,/radek1/first-model,Cornell Birdcall Identification
10206140,0.54,1,5,/dipta007/birdsong-cnn-pytorch,Cornell Birdcall Identification
10146077,0.54,5,33,/muhakabartay/birdcall-eda-full-basemap-geo-3d-elevation,Cornell Birdcall Identification
10152690,0.54,0,10,/artkulak/birdcall-extensive-eda-simple-rfc-submission,Cornell Birdcall Identification
963250,0.2766,0,1,/skar26/avito-demand-prediction-model-random-forest,Avito Demand Prediction Challenge
951180,0.2422,0,2,/yacropolisy/random-forest-test-for-learning,Avito Demand Prediction Challenge
928889,0.2344,3,6,/inenakhov/simple-catboost-tfidf,Avito Demand Prediction Challenge
913297,0.2408,0,9,/jingqliu/fasttext-conv2d-with-tf-on-title,Avito Demand Prediction Challenge
903667,0.2335,8,71,/artgor/eda-features-engineering-and-lightgbm,Avito Demand Prediction Challenge
903660,0.233,10,23,/iggisv9t/handling-russian-language-inflectional-structure,Avito Demand Prediction Challenge
901603,0.2332,9,66,/classtag/lightgbm-with-mean-encode-feature-0-233,Avito Demand Prediction Challenge
998547,0.2424,0,0,/sujoyde/avito-demand-prediction-exploration,Avito Demand Prediction Challenge
1985529,0.1,0,5,/louisemarieporcher/yolov3-predict-vs-reality,RSNA Pneumonia Detection Challenge
1969622,0.104,0,0,/lextoumbourou/coco-mask-rcnn-with-submission-stage-2,RSNA Pneumonia Detection Challenge
1774298,0.094,9,17,/giuliasavorgnan/0-123-lb-pytorch-unet-run-on-google-cloud,RSNA Pneumonia Detection Challenge
1543781,0.098,0,9,/ashishpatel26/rsna-786,RSNA Pneumonia Detection Challenge
1616392,0.1169999999999999,0,4,/cchadha/cnn-segmentation-connected-components-new-split,RSNA Pneumonia Detection Challenge
1611279,0.0,4,14,/ashishpatel26/beginner-tutorial-nasnet-pneumonia-detection,RSNA Pneumonia Detection Challenge
1535901,0.107,6,32,/ashishpatel26/chexnet-batch-normalization-hyparameter-tuning,RSNA Pneumonia Detection Challenge
1547020,0.104,4,7,/nikhilroxtomar/cnn-unet-segmentation-for-xray-opacity-detection,RSNA Pneumonia Detection Challenge
1526140,0.1159999999999999,44,215,/jonnedtc/cnn-segmentation-connected-components,RSNA Pneumonia Detection Challenge
1543967,0.074,0,3,/victorhz/cnn-segmentation-connected-components,RSNA Pneumonia Detection Challenge
11322761,0.12835,0,2,/mhrizvi/simple-code-top-30-in-competition,House Prices - Advanced Regression Techniques
11395135,0.14049,0,6,/saitej31/house-prediction,House Prices - Advanced Regression Techniques
11387790,0.07372,3,6,/carlmcbrideellis/homemade-gazpacho,House Prices - Advanced Regression Techniques
11379367,0.1221299999999999,0,1,/mohdnasirqureshi/house-sale-price-prediction-easy-python-scrips,House Prices - Advanced Regression Techniques
11382065,0.15074,0,0,/yutohisamatsu/houseprice-simple-model,House Prices - Advanced Regression Techniques
11359973,0.2375699999999999,0,0,/tichakornw/submission-tutorial,House Prices - Advanced Regression Techniques
10650786,0.15953,0,0,/yashnalawade/my-housing,House Prices - Advanced Regression Techniques
11080440,0.12903,0,0,/yutohisamatsu/kaggle-house-prices-with-lightgbm-sutra-practice,House Prices - Advanced Regression Techniques
11319438,0.13062,0,0,/pabloamc/draft-house-sale-price,House Prices - Advanced Regression Techniques
11362362,0.13349,0,0,/thientoantran/notebook1e76882262,House Prices - Advanced Regression Techniques
11308758,0.16549,0,0,/honeysharma0604/housepricepredict,House Prices - Advanced Regression Techniques
11312829,0.13086,0,0,/yutohisamatsu/houseprice-elasticnet-with-crossvalidation,House Prices - Advanced Regression Techniques
11274629,0.1221299999999999,4,19,/ritikasaini/house-price-prediction,House Prices - Advanced Regression Techniques
11300864,0.14026,0,0,/kamiljan/notebook86b1849cdf,House Prices - Advanced Regression Techniques
7231634,0.1361,0,0,/mixtek/house-prices-xgb-reg-refactored,House Prices - Advanced Regression Techniques
5508655,0.8670100000000001,2,20,/xiejialun/keras-u-net-pre-post-processing,Severstal: Steel Defect Detection
5699188,0.88279,0,1,/emattia/kernel56eb1e5810,Severstal: Steel Defect Detection
5613736,0.84416,0,1,/c14103/keras-starter-u-net-with-pretrained,Severstal: Steel Defect Detection
5342306,0.8919799999999999,10,56,/feifanliang/unet-pytorch-inference-kernel,Severstal: Steel Defect Detection
5470525,0.85674,0,7,/rabbitcaptain/keras-unet-elu,Severstal: Steel Defect Detection
5020822,0.89051,27,88,/iafoss/severstal-fast-ai-256x256-crops-sub,Severstal: Steel Defect Detection
5472405,0.8133100000000001,1,2,/mannychm/unet-v2-data-augmentation-no-regulari,Severstal: Steel Defect Detection
5299335,0.72367,0,0,/mannychm/unet-v1-no-data-augmentation-no-regularization,Severstal: Steel Defect Detection
5313498,0.88646,15,68,/orkatz2/keras-efficientnetb2-unet-tta-lb-0-88,Severstal: Steel Defect Detection
5219663,0.88331,36,197,/rishabhiitbhu/unet-pytorch-inference-kernel,Severstal: Steel Defect Detection
5142180,0.85674,0,5,/calintimbus/xception-beseline-model-for-starter-in-keras,Severstal: Steel Defect Detection
13965434,0.88512,0,0,/aymanmaboghonim/dog-breed-vision,Dog Breed Identification
13773639,4.78749,0,1,/horry7/dog-breed,Dog Breed Identification
11592120,4.78512,0,0,/myuferov/notebook30a4f94ab6,Dog Breed Identification
11347731,0.9002100000000001,0,1,/khv1999/dog-breed-mobilenet,Dog Breed Identification
10947468,4.66654,0,0,/julianbenny/dogbreed-keras-cnn,Dog Breed Identification
10634395,0.89928,0,2,/thecamilovisk/dog-breed-prediction-with-tensorflow,Dog Breed Identification
9879072,0.26688,0,4,/kamleshsolanki/dog-breed-classification,Dog Breed Identification
9154533,0.5423899999999999,1,3,/nguyncaoduy/dog-breed-identification-fastai,Dog Breed Identification
8906337,1.54574,0,2,/lz1997/so-many-tricks-v1-0-for-dogs-breed,Dog Breed Identification
8692256,0.28106,0,0,/masfour/90-top-1-accuracy-using-transfer-learning,Dog Breed Identification
3550067,0.69,0,1,/tatianaskv/robot-surfaces-rf,CareerCon 2019 - Help Navigate Robots
3282616,0.73,0,1,/yutewang/yt-multi-headed-convolutional-neural-network,CareerCon 2019 - Help Navigate Robots
3401810,0.6609,0,0,/victororlov/career-con-2019-simple-cnn-rnn-solutuion,CareerCon 2019 - Help Navigate Robots
3562455,0.73,0,0,/guilhermekodama/carrercon-rf-baseline-and-simple-features,CareerCon 2019 - Help Navigate Robots
3563108,0.4398,0,0,/yaswanthkumar/robots,CareerCon 2019 - Help Navigate Robots
3398466,0.44,0,1,/pierretilak/kiss-surface-classifier,CareerCon 2019 - Help Navigate Robots
3564364,0.82,9,42,/prith189/starter-code-for-3rd-place-solution,CareerCon 2019 - Help Navigate Robots
3564072,0.9272,3,12,/ilhamfp31/16-solution-private-0-76,CareerCon 2019 - Help Navigate Robots
3542409,0.57,1,20,/purplejester/a-simple-lstm-based-time-series-classifier,CareerCon 2019 - Help Navigate Robots
3541598,0.7137,2,9,/skondrash/nn-lstm-fft-features,CareerCon 2019 - Help Navigate Robots
3528618,0.36,0,3,/asauve/features-engineering-with-nn,CareerCon 2019 - Help Navigate Robots
3562453,0.48,0,0,/gdaniels/kernel2c55a73c86,CareerCon 2019 - Help Navigate Robots
3280403,0.73,15,28,/hsinwenchang/randomforestclassifier,CareerCon 2019 - Help Navigate Robots
3399455,0.51,0,1,/adarbha/pmm-3,CareerCon 2019 - Help Navigate Robots
3410040,0.06,1,0,/drrdrem/gmmhmm,CareerCon 2019 - Help Navigate Robots
3382657,0.65,0,2,/ashishpatel26/best-model-checking-extratree-classifier,CareerCon 2019 - Help Navigate Robots
3294889,0.55,1,5,/seshadrikolluri/careercon-2019-sample-rnn-approach,CareerCon 2019 - Help Navigate Robots
3371924,0.31,0,3,/jazivxt/random-forest-testing,CareerCon 2019 - Help Navigate Robots
13029124,0.01825,0,5,/manojprabhaakr/inference-model-nn-2-models-1-tabnet-and-1-keras,Mechanisms of Action (MoA) Prediction
13236553,0.01871,0,1,/egm108/moa-mixup-pca-fastai-based,Mechanisms of Action (MoA) Prediction
12775850,0.01908,0,0,/ilosvigil/torch-moa-mlp,Mechanisms of Action (MoA) Prediction
11756133,0.01835,0,0,/alturutin/moa-mlp,Mechanisms of Action (MoA) Prediction
12966068,0.02039,0,0,/marvinsk8/keras-moa,Mechanisms of Action (MoA) Prediction
13176465,0.01858,0,0,/zvukztyshyny/moa-train,Mechanisms of Action (MoA) Prediction
12836062,0.0197,0,1,/wanping7/pytorch-resnet-cnn,Mechanisms of Action (MoA) Prediction
12400684,0.01976,0,0,/marychin/moa-lightgbm-only-private-score-0-01697,Mechanisms of Action (MoA) Prediction
13228153,0.01847,0,0,/skinyx/blend,Mechanisms of Action (MoA) Prediction
11950220,0.01849,0,0,/syedhamzahussain/cv-0140,Mechanisms of Action (MoA) Prediction
12968443,0.0183,0,1,/radadiyamohit/moa-post-processing,Mechanisms of Action (MoA) Prediction
13044042,0.01844,0,0,/junyan01/tabnet-oof-j-inference,Mechanisms of Action (MoA) Prediction
12978617,0.01835,0,0,/junyan01/pytorch-transfer-learningwith-kfoldsdrug-inference,Mechanisms of Action (MoA) Prediction
12316889,0.01882,0,0,/aeryss/moa-feature-engineering-neural-net,Mechanisms of Action (MoA) Prediction
13009101,0.01852,0,0,/aeryss/moa-predictions-overfitting-with-tabnet-cbb40d,Mechanisms of Action (MoA) Prediction
12657037,0.0186199999999999,0,0,/aeryss/moa-tabnet-groupcv,Mechanisms of Action (MoA) Prediction
12722854,0.01822,0,0,/crackle/moa-combined,Mechanisms of Action (MoA) Prediction
12758460,0.02968,0,2,/prokaggler/moa-prediction-keras-v2,Mechanisms of Action (MoA) Prediction
12251945,0.01975,0,1,/yassinealouini/multiple-targets-classification-tabnet,Mechanisms of Action (MoA) Prediction
12083268,0.01859,0,1,/ajax0564/notebook4bf1ffc8f6,Mechanisms of Action (MoA) Prediction
13159344,0.0183,0,0,/tuistan/inference-blending-pretrained-tabnet-and-memory,Mechanisms of Action (MoA) Prediction
13133702,0.01849,0,0,/edchencc/moa-predictions-tabnet-more-fe,Mechanisms of Action (MoA) Prediction
13227907,0.0186199999999999,0,0,/yzyz2015/moa-rep-3-model-training-and-inference-b2d234,Mechanisms of Action (MoA) Prediction
13080265,0.1129599999999999,0,1,/rsesha/moa-lish-with-featurewiz-and-multioutputclassifier,Mechanisms of Action (MoA) Prediction
13065541,0.0264899999999999,0,4,/zahedi/moa2image,Mechanisms of Action (MoA) Prediction
12854255,0.02369,0,2,/heitorbaldo/moa-prediction-pca-dnn-python,Mechanisms of Action (MoA) Prediction
12473183,0.01842,0,2,/vishalvanpariya/main-notebook,Mechanisms of Action (MoA) Prediction
13124747,0.1128,0,0,/ashu11081992/my-first,Mechanisms of Action (MoA) Prediction
13065722,0.02165,0,3,/sg1993/logistic-regression-model,Mechanisms of Action (MoA) Prediction
13071679,0.11625,0,2,/sg1993/ensemble-models,Mechanisms of Action (MoA) Prediction
12152620,0.01838,0,0,/sho123/pytorch-optuna-tuning,Mechanisms of Action (MoA) Prediction
13042987,0.02048,3,2,/giorgosfoukarakis/moa-label-powerset-rakeld,Mechanisms of Action (MoA) Prediction
12948696,0.01825,75,158,/vikazrajpurohit/3-model-training-and-inference,Mechanisms of Action (MoA) Prediction
13008217,0.02658,0,1,/toyox2020/simple-xgboost-model,Mechanisms of Action (MoA) Prediction
12853104,0.01831,0,2,/zekun98/blend-blend-blend,Mechanisms of Action (MoA) Prediction
12996186,0.01936,0,0,/hamzaboulahia/moa-pca-transfert-learning-kerastuner,Mechanisms of Action (MoA) Prediction
12497181,0.01869,0,1,/hasan7/moa-keras-ht,Mechanisms of Action (MoA) Prediction
12940244,0.01884,22,55,/optimo/selfsupervisedtabnet,Mechanisms of Action (MoA) Prediction
12633294,0.69314,0,2,/zeus75/moa-eda-with-autoencoder-and-t-sne,Mechanisms of Action (MoA) Prediction
13041294,0.0209199999999999,0,0,/paulgiesting/three-layer-keras-model-final-additions,Mechanisms of Action (MoA) Prediction
12891376,0.0183599999999999,25,134,/kushal1506/moa-pretrained-non-scored-targets-as-meta-features,Mechanisms of Action (MoA) Prediction
12949786,0.01818,3,3,/lz1997/moa-post-processing-f5bd01,Mechanisms of Action (MoA) Prediction
12887505,0.0182,47,168,/underwearfitting/make-final-submission-the-efficient-way,Mechanisms of Action (MoA) Prediction
12899377,0.01866,1,9,/ash1706/moa-sampling-transfer-learning-non-scored,Mechanisms of Action (MoA) Prediction
12839758,0.02018,2,1,/lavanyask/moa-prediction-neural-network,Mechanisms of Action (MoA) Prediction
12855086,0.0186,0,1,/krisho007/7fold-3seed-inference-tta,Mechanisms of Action (MoA) Prediction
6271827,0.8764799999999999,0,0,/jeongchanwoo/kernel16d21e1458,Severstal: Steel Defect Detection
6370558,0.91854,4,22,/khornlund/fork-of-sever-ensemble-3,Severstal: Steel Defect Detection
6290114,0.91832,1,16,/khornlund/sever-ensemble-classification,Severstal: Steel Defect Detection
6024675,0.8426899999999999,0,0,/vh1981/severstal-steel-defect-detection,Severstal: Steel Defect Detection
6245130,0.8978,6,10,/jiageng/segmentation-cls,Severstal: Steel Defect Detection
6211158,0.8567899999999999,0,0,/nomadix/1cl1sg,Severstal: Steel Defect Detection
6203633,0.0,0,7,/sheriytm/heng-s-model-unet-efficientnet-b5-with-submission,Severstal: Steel Defect Detection
5897449,0.8964799999999999,8,28,/gontcharovd/unet-pytorch-inference-kernel-extended-0-89648,Severstal: Steel Defect Detection
5764137,0.8816299999999999,2,16,/xiejialun/seunet-with-comboloss-swish,Severstal: Steel Defect Detection
5969778,0.85825,1,1,/moctader/kernel29798b524c,Severstal: Steel Defect Detection
5895089,0.85674,0,1,/c14103/keras-starter-u-net-hypercolumn,Severstal: Steel Defect Detection
5643230,0.85272,2,11,/phunghieu/steel-defect-detection-multi-label-u-net,Severstal: Steel Defect Detection
11259652,0.14786,0,0,/aaronpetryio/house-prices-advanced-regression-techniques,House Prices - Advanced Regression Techniques
11245093,0.1648,0,4,/johnmichaelsantos/predictive-analysis-of-the-ames-housing-dataset-r,House Prices - Advanced Regression Techniques
10288569,0.1315599999999999,0,2,/jemmygreen/houseprice-prediction,House Prices - Advanced Regression Techniques
11218479,0.11968,1,5,/c7934597/getting-to-the-top-4-in-house-prices,House Prices - Advanced Regression Techniques
10997748,0.14797,1,1,/emilytries/house-price-prediction,House Prices - Advanced Regression Techniques
11176556,0.14944,2,9,/artemkostrikin/house-prices-advanced-regression-techniques,House Prices - Advanced Regression Techniques
11097845,0.16865,0,0,/miguelcsilva/house-prices-ms,House Prices - Advanced Regression Techniques
11054108,0.12474,0,0,/syuhei23/house-prices-stacking-regression,House Prices - Advanced Regression Techniques
11103345,0.15067,0,0,/sarahjanexionger/house-prices,House Prices - Advanced Regression Techniques
11100152,0.4046,0,2,/ragrawal/pleasanton-ml-group-meeting,House Prices - Advanced Regression Techniques
11082964,0.3730199999999999,0,1,/faresbenayed/regression,House Prices - Advanced Regression Techniques
1529225,0.0,5,16,/kmader/95-percentile-box-guess,RSNA Pneumonia Detection Challenge
1530266,0.0,0,17,/ashishpatel26/lung-opacity-classification-using-densenet-121,RSNA Pneumonia Detection Challenge
1530372,0.0,0,9,/ashishpatel26/resnet50-pneumonia-detection,RSNA Pneumonia Detection Challenge
1534472,0.0,0,0,/returnofsputnik/naive-model,RSNA Pneumonia Detection Challenge
3196733,0.88161,0,0,/malinivy/plantseedling-cnn-keras-beginner,Plant Seedlings Classification
3124876,0.6801,0,0,/masonblier/plant-seedling-simple-cnn,Plant Seedlings Classification
2407948,0.92569,1,1,/stavshem/seedlings,Plant Seedlings Classification
2133148,0.2323599999999999,0,0,/aktaruzzaman/plant-seedling,Plant Seedlings Classification
1575921,0.14861,0,0,/omrialgazi/vgg11-pytorch,Plant Seedlings Classification
1546367,0.39294,0,0,/vipul92/plant-seedling-using-sequential-cnn-keras,Plant Seedlings Classification
1406608,0.07682,3,0,/anebzt/plant-seedling,Plant Seedlings Classification
1260903,0.95843,3,3,/mehradaria/fork-of-plant-seedling-classification-aria,Plant Seedlings Classification
901895,0.97103,0,4,/kuntalcse006/plant-seedlings-classification-using-keras,Plant Seedlings Classification
767422,0.85516,0,1,/dursunkoc/explore-small-cnn,Plant Seedlings Classification
533496,0.8476,4,31,/raoulma/plants-xception-90-06-test-accuracy,Plant Seedlings Classification
448702,0.04534,1,2,/inversion/a-maize-ingly-corny-kernel,Plant Seedlings Classification
6413661,0.8413,0,0,/nouradwai/seedlings,Plant Seedlings Classification
671908,0.78463,0,0,/siddique0/classify-seedlings-remove-bg-keras-conv,Plant Seedlings Classification
11266065,0.568,13,35,/rsinda/ensemble-resnest50-efficient-net,Cornell Birdcall Identification
11264262,0.544,0,4,/roguekk007/resnest-cloud-validation-and-submission,Cornell Birdcall Identification
11103910,0.5670000000000001,13,48,/chanhu/inference-bird-simple-baseline,Cornell Birdcall Identification
11091452,0.562,0,6,/tonychenxyz/inference-birdsong-baseline-resnest50-fast,Cornell Birdcall Identification
10881710,0.5660000000000001,3,21,/jpison/inference-resnest50-fast-with-example-test-audio,Cornell Birdcall Identification
10680471,0.005,0,7,/shikha130vv/getting-started,Cornell Birdcall Identification
10474586,0.544,1,12,/mauriciofigueiredo/intro-to-filtering-process-model-submitting,Cornell Birdcall Identification
12445362,0.39008,0,9,/jinbonnie/subfinal,New York City Taxi Trip Duration
10890014,0.38135,0,0,/ahmedmurad1990/nyc-taxi,New York City Taxi Trip Duration
9781540,0.38135,0,1,/munmun2004/nyc-taxi,New York City Taxi Trip Duration
9009629,0.43373,0,0,/evanricchi/nyc-taxi-trip-duration-final-version,New York City Taxi Trip Duration
8291304,0.46875,0,0,/carladg/final,New York City Taxi Trip Duration
5261804,0.73488,0,0,/junheeshin/crazyj-newyork-taxidur,New York City Taxi Trip Duration
4173588,0.47245,0,0,/millie13/manuela-ghomsi-kernel,New York City Taxi Trip Duration
4173523,0.47822,0,0,/sturquier/taxi-kernel,New York City Taxi Trip Duration
4173533,0.44974,0,0,/lsergent/ls-examtaxi,New York City Taxi Trip Duration
4182477,0.5746899999999999,0,0,/yadechi/kernel-yadechi2,New York City Taxi Trip Duration
4173470,0.44705,0,0,/haddadsteven/kernel-hs,New York City Taxi Trip Duration
4173501,0.4756899999999999,0,0,/fenikkusu/cedric-kernel-new-york-city,New York City Taxi Trip Duration
4188021,0.4723,0,0,/abdenour1992/kernel0edc86be2a,New York City Taxi Trip Duration
4173697,0.46315,0,0,/moadjr/kernel55ade7ff5b,New York City Taxi Trip Duration
4173710,0.4641,0,0,/gpelicant/kernelebf37339c3,New York City Taxi Trip Duration
4187050,0.45063,0,0,/alainsagna/kernel6445d1e7ad,New York City Taxi Trip Duration
3920371,0.40019,0,0,/ikrmhlc/sklearn-xgboost,New York City Taxi Trip Duration
3957679,0.57629,0,0,/nongnoochr/nyctaxitripduration-defaultrandomforest-scaling,New York City Taxi Trip Duration
2555734,0.40325,0,0,/junior1997/gdut-nyc-taxi-data-mining,New York City Taxi Trip Duration
3252529,0.42675,0,0,/baptistedhuicque/nyc-taxi-dhuicque,New York City Taxi Trip Duration
3287352,0.4387899999999999,0,0,/landrymo/nyc-taxi-prediction-momeni,New York City Taxi Trip Duration
2819545,0.4139199999999999,0,3,/eliasaph/nyc-taxi-lyes-bouali,New York City Taxi Trip Duration
2858726,0.4208399999999999,0,0,/floriancpchx/nyc-taxi-trip-duration-fc-hetic,New York City Taxi Trip Duration
2862744,0.426,0,0,/robinmichay/rmichay-nyc-taxi-kaggle,New York City Taxi Trip Duration
2882424,0.4105,0,2,/doneill612/nyc-taxi-trip-duration-v2-don-hetic,New York City Taxi Trip Duration
2862884,0.475,0,0,/hellococo/nyc-trip,New York City Taxi Trip Duration
2858823,0.40647,0,2,/sylvainfranco/nyc-taxi-trip-duration-sfranco,New York City Taxi Trip Duration
7377439,0.6,0,0,/takbull/bert-joint,TensorFlow 2.0 Question Answering
13081007,2517.502,2,21,/yw6916/janestreet-xgb-lgb-stacking-ensemble,Jane Street Market Prediction
13068089,252.01,2,15,/fernandocanteruccio/jane-street-reinforce-agent-exploration,Jane Street Market Prediction
13062530,1021.294,4,18,/drcapa/jane-street-market-prediction-starter-xgb,Jane Street Market Prediction
11384467,0.8825,0,12,/imoore/large-stock-market-visualizations,Jane Street Market Prediction
13064132,4572.319,12,156,/isaienkov/jane-street-market-prediction-fast-understanding,Jane Street Market Prediction
9543386,0.90519,0,1,/layediop/plant-pathology,Plant Pathology 2020 - FGVC7
9340873,0.979,0,0,/apthagowda/plant-pathology-2020-tenserflow-tpu-stratifiedfold,Plant Pathology 2020 - FGVC7
8473090,0.942,0,0,/fkdplc/plant-patology-snapshot-ensemble,Plant Pathology 2020 - FGVC7
9540329,0.98,0,5,/truonghoang/ensemble-top-submit,Plant Pathology 2020 - FGVC7
9551154,0.965,10,8,/muellerzr/resnet152-with-tta-and-fine-tune-fastai2,Plant Pathology 2020 - FGVC7
9465884,0.981,7,11,/gc1023/ensemble-top-kernels,Plant Pathology 2020 - FGVC7
9529071,0.813,0,0,/kasayu/plant-pathology-effnets,Plant Pathology 2020 - FGVC7
9395942,0.962,10,28,/nightwolfbrooks/data-augmentation-and-keras-cnn,Plant Pathology 2020 - FGVC7
9451936,0.963,0,2,/carloalbertobarbano/0-963-in-under-20-minutes-with-pytorchtrainutils,Plant Pathology 2020 - FGVC7
9264007,0.978,9,7,/biruk1230/tpu-ensemble-effnb7-effnb6-inceptresnetv2-etc,Plant Pathology 2020 - FGVC7
9348841,0.828,0,0,/gb00000/plant-transfer-learning,Plant Pathology 2020 - FGVC7
9301239,0.937,2,1,/urayukitaka/comparing-incept-nasnetmobi,Plant Pathology 2020 - FGVC7
9199404,0.971,0,5,/nickteim/plants-tpu,Plant Pathology 2020 - FGVC7
9080141,0.865,1,7,/psaikko/feature-extraction-and-xgboost,Plant Pathology 2020 - FGVC7
8892933,0.868,1,2,/omezario/plantpathology-2020-image-training,Plant Pathology 2020 - FGVC7
8996878,0.979,68,109,/akasharidas/plant-pathology-2020-in-pytorch,Plant Pathology 2020 - FGVC7
9037895,0.964,8,10,/lz1997/so-many-tricks-v1-0-for-plant-pathology,Plant Pathology 2020 - FGVC7
8965510,0.8740000000000001,0,2,/neelambuj717/plant-pathology-fgvc7-pytorch-kernel,Plant Pathology 2020 - FGVC7
8602492,0.95,0,0,/crained/fastai-plant-pathology-recognition,Plant Pathology 2020 - FGVC7
8992402,0.5716,0,0,/mdpaleti/kernel29cadaffd8,Jigsaw Multilingual Toxic Comment Classification
8966854,0.8122,0,0,/kumarsuraj9450/jigsawmulti-submission,Jigsaw Multilingual Toxic Comment Classification
8884205,0.885,2,9,/vbookshelf/bert-as-a-microservice-flask-app,Jigsaw Multilingual Toxic Comment Classification
8886003,0.7935,0,0,/lucca9211/test-exp,Jigsaw Multilingual Toxic Comment Classification
8832798,0.807,0,0,/byannn/gru-lstm-rnn-101,Jigsaw Multilingual Toxic Comment Classification
8757112,0.7655,0,2,/parmarsuraj99/jigsaw-on-tpu,Jigsaw Multilingual Toxic Comment Classification
8695336,0.9383,86,382,/xhlulu/jigsaw-tpu-xlm-roberta,Jigsaw Multilingual Toxic Comment Classification
8737745,0.82,0,0,/eliasgreen/toxic-not-song,Jigsaw Multilingual Toxic Comment Classification
8705461,0.8784,0,1,/davelo12/simple-lstmwithpretrainembed,Jigsaw Multilingual Toxic Comment Classification
8554075,0.9095,59,327,/tarunpaparaju/jigsaw-multilingual-toxicity-eda-models,Jigsaw Multilingual Toxic Comment Classification
8556673,0.6092,0,7,/jonathanbesomi/getting-started-with-tpus-for-beginners,Jigsaw Multilingual Toxic Comment Classification
8601772,0.866,0,3,/davelo12/wordcnnwithoutpretrainembed,Jigsaw Multilingual Toxic Comment Classification
8570434,0.7815,0,5,/dimitreoliveira/jigsaw-classification-distilbert-with-tpu-and-tf,Jigsaw Multilingual Toxic Comment Classification
8559994,0.8201,0,1,/faraksuli/jigsaw-toxic-comment-inference,Jigsaw Multilingual Toxic Comment Classification
12120833,0.9357,0,0,/omkar2795/jigsaw-xlm-roberta2,Jigsaw Multilingual Toxic Comment Classification
10230977,0.9472,0,0,/redwankarimsony/jigsawmultilingualtoxiccomments-ensemble,Jigsaw Multilingual Toxic Comment Classification
9784075,0.9308,0,0,/akashsuper2000/jigsaw-understanding-cross-lingual-models,Jigsaw Multilingual Toxic Comment Classification
9497413,0.8174,0,0,/akhoch/jigsaw-multilingual-getting-started,Jigsaw Multilingual Toxic Comment Classification
11869267,0.99214,0,0,/juniorcl/lenet-5-cnn-architecture-digit-recognizer,Digit Recognizer
11792160,1.0,3,8,/pavan9065/digit-recognizer-v2-0,Digit Recognizer
11762664,0.99664,4,30,/alifrahman/digit-recognizer-for-beginners-0-9966,Digit Recognizer
11817997,0.97914,0,3,/binavgautam/notebook056779dbb3,Digit Recognizer
11737531,0.98364,6,16,/zacharywyman/classifying-digits-quick-beginner-s-tutorial,Digit Recognizer
11629801,0.98803,0,2,/asmaaeltaher/digit-recognizer-dl,Digit Recognizer
11699459,0.98371,0,0,/yuntant/digit-recognizer-pytorch,Digit Recognizer
11631678,0.98332,1,8,/amolikvivian/mnist-keras-cnn,Digit Recognizer
11633506,0.98464,0,5,/armanjr/mnist-digit-recognizer,Digit Recognizer
11666099,0.99092,0,2,/nknshmasaki/notebooked5b194b22,Digit Recognizer
11600662,0.98942,0,5,/parsasam/hand-written-number-recognition-2-mnist-model,Digit Recognizer
5249585,0.99057,0,0,/ghousethanedar/minst-dataset,Digit Recognizer
11541596,0.99482,22,34,/jayfaldu/lenet5-model-for-digit-recognition,Digit Recognizer
11560039,0.67578,0,3,/manishkc06/simple-mlp-classifier-to-recognize-digits,Digit Recognizer
11519835,0.98753,2,6,/gagandeepsohal/digit-cognizer-01,Digit Recognizer
11462953,0.9845,0,6,/aadeshbaral/digit-recognizer,Digit Recognizer
11508385,0.83567,0,0,/tracyporter/recognise-the-digit-decision-tree,Digit Recognizer
5467503,0.63872,0,0,/iurii8000/ultrasound-nerve-segmentation-with-fastai,Ultrasound Nerve Segmentation
11597279,3398.29776,0,1,/akhilkasare/walmart-sales-forecasting-with-99-accuracy,Walmart Recruiting - Store Sales Forecasting
1875783,4762.69658,0,0,/nachogarcia88/walmart-nacho-2,Walmart Recruiting - Store Sales Forecasting
8743093,24847.81095,2,0,/alro10/walmart-ds-challenge,Walmart Recruiting - Store Sales Forecasting
8585252,2837.9407300000007,0,1,/ggludwig/simple-randomforest,Walmart Recruiting - Store Sales Forecasting
6702836,3062.45208,0,5,/balaji03/walmart-store-sale,Walmart Recruiting - Store Sales Forecasting
6213142,2840.83729,0,0,/ejkim0121/walmart-recruiting,Walmart Recruiting - Store Sales Forecasting
3760785,3281.99397,0,8,/abhishekshambhu/walmartsales1,Walmart Recruiting - Store Sales Forecasting
2110762,3269.67473,6,121,/andredornas/tp2-walmart-sales-forecast,Walmart Recruiting - Store Sales Forecasting
13786709,695.659,0,8,/jonykarki/neural-network-pytorch-lightning,Jane Street Market Prediction
13754664,5655.614000000001,1,8,/zhenpingfeng/jane-street-nn-stack,Jane Street Market Prediction
13728166,4723.146,2,7,/jorijnsmit/benchmarking-the-public-leaderboard,Jane Street Market Prediction
13617596,5646.603,39,123,/maunish/jsmp-super-cool-eda-lgbm-baseline,Jane Street Market Prediction
13710977,2672.576,0,1,/michelrmeyer/tarefa-final-janestreet-market-script,Jane Street Market Prediction
13665748,0.1,0,3,/kiritusan/randomsubmit,Jane Street Market Prediction
13611940,2957.845,0,1,/evgkol/attemptpzad,Jane Street Market Prediction
13610672,3312.409,0,2,/pavellukianov/first-solution,Jane Street Market Prediction
13548999,0.0,0,2,/adarcorerlreth/jane-street-data-exploration,Jane Street Market Prediction
13465813,6919.831999999999,16,86,/gkoundry/the-most-important-model-parameter,Jane Street Market Prediction
13489119,631.562,0,3,/onurakkse/random-forest,Jane Street Market Prediction
13442052,7635.64,0,14,/manavtrivedi/gaussiandenoised-deepnn-model,Jane Street Market Prediction
8767900,0.7568600000000001,0,0,/khangtran97/kernel2317725413,Home Credit Default Risk
8700188,0.73795,0,0,/duynm619/home-credit-default-risk,Home Credit Default Risk
1263211,0.743,0,1,/hassanamin/homecreditgbm,Home Credit Default Risk
6956250,0.7383,0,1,/adarsh18213/start-here-a-gentle-introduction,Home Credit Default Risk
6917243,0.73806,0,1,/tomohirofukui/target-variables,Home Credit Default Risk
5966704,0.79102,0,0,/zhuyongsheng/homecredit-auc190927,Home Credit Default Risk
5618367,0.62539,0,0,/mehul8055/home-credit-default-risk,Home Credit Default Risk
5255496,0.74525,0,3,/taozhongxiao/begining-with-lightgbm-in-detail,Home Credit Default Risk
4160759,0.78551,0,2,/davidmillet/bayesian-opt-lgbmclassifier,Home Credit Default Risk
4530737,0.74863,0,0,/maria591/logistic-regression,Home Credit Default Risk
3777090,0.72444,0,0,/zhuicanggaoju/bnu-dataming,Home Credit Default Risk
404935,0.5576,3,36,/mihaskalic/keras-straightforward,Statoil/C-CORE Iceberg Classifier Challenge
901901,0.1863,0,0,/san7911/keras-cnn-statoil-iceberg-lb-0-1995-now-0-1516,Statoil/C-CORE Iceberg Classifier Challenge
6538408,0.50088,0,0,/sachin88/kernel239ebf5e5f,Prudential Life Insurance Assessment
38937,0.66568,0,0,/samrotashoka/genetic-programming-ii-lb-0-662,Prudential Life Insurance Assessment
31556,0.53904,0,0,/abhilashawasthi/prudential-insurance-risk-predictions,Prudential Life Insurance Assessment
30409,0.53613,0,0,/totalrecall/cluster,Prudential Life Insurance Assessment
12755400,387.4,0,6,/jamesmcguigan/rock-paper-scissors-xgboost,"Rock, Paper, Scissors"
227467,0.53592,70,117,/guoday/cv-statistics-better-parameters-and-explaination,Two Sigma Connect: Rental Listing Inquiries
6976265,0.0,0,0,/goodyuyu/kernel188e34e372,iMet Collection 2019 - FGVC6
4111778,0.547,0,2,/tony92151/pytorch-seresnext101-32x4d-kfold-focal-loss-v3-0,iMet Collection 2019 - FGVC6
3879628,0.494,0,1,/poorcow/imet-densenet201,iMet Collection 2019 - FGVC6
3903434,0.397,0,0,/kenakai16/training,iMet Collection 2019 - FGVC6
3732099,0.598,0,17,/mpsampat/fastai-resnet50-imet-v4-2,iMet Collection 2019 - FGVC6
3556400,0.596,11,44,/itslek/fastai-resnet50-imet-v4-2,iMet Collection 2019 - FGVC6
3461141,0.541,15,36,/backaggle/imet-fastai-starter-focal-and-fbeta-loss,iMet Collection 2019 - FGVC6
3435600,0.56,36,93,/mathormad/resnet50-v2-keras-focal-loss-mix-up,iMet Collection 2019 - FGVC6
3440340,0.545,23,64,/mnpinto/imet-fastai-starter,iMet Collection 2019 - FGVC6
3414659,0.367,2,11,/stalkermustang/pytorch-resnet18-pretraied-test,iMet Collection 2019 - FGVC6
3424180,0.4,0,3,/hengzheng/imet-first-try,iMet Collection 2019 - FGVC6
3422492,0.3389999999999999,2,14,/xiuchengwang/cnn-keras-starter-senet-50,iMet Collection 2019 - FGVC6
4125246,0.004,0,0,/maxlenormand/change-model-act-softmax-incresnetv2,iMet Collection 2019 - FGVC6
3607428,0.27,0,0,/sujoykg/xception-keras-imet,iMet Collection 2019 - FGVC6
8130801,0.2301699999999999,0,3,/darwinwin/automl-silver-bullet-or-poison-pill-lb-0-225,Avito Demand Prediction Challenge
3175374,0.23285,1,1,/vikasmalhotra08/eda-and-lightgbm-for-avito,Avito Demand Prediction Challenge
1975151,0.2294,0,0,/nickel/austral-eda,Avito Demand Prediction Challenge
1210028,0.2204,48,61,/dandres/best-public-blend-0-2204,Avito Demand Prediction Challenge
1143463,0.2296,6,12,/demoon/automl-silver-bullet-or-poison-pill-lb-0-225,Avito Demand Prediction Challenge
995380,0.261,0,0,/shibashis/avito-end-to-end,Avito Demand Prediction Challenge
996869,0.2595,0,6,/lux666/avito,Avito Demand Prediction Challenge
1051443,0.2739,0,3,/krithi07/prediction-based-on-basic-mlp,Avito Demand Prediction Challenge
982880,0.2221,15,30,/him4318/lightgbm-with-aggregated-features-v-2-0,Avito Demand Prediction Challenge
1024702,0.2332,0,2,/shreyagaikwad/avito,Avito Demand Prediction Challenge
978542,0.2307,0,2,/nareyko/very-simple-lgbm-0-2307,Avito Demand Prediction Challenge
961767,0.2258,14,20,/sukhyun9673/lgb-nan-image-blurrness-regional-info-date,Avito Demand Prediction Challenge
3354440,0.71,0,7,/axel81/help-humanity-by-helping-robots-fastai,CareerCon 2019 - Help Navigate Robots
3261400,0.7,8,9,/premvardhan/identify-robot-surface-careercon2019,CareerCon 2019 - Help Navigate Robots
3311436,0.51,0,3,/maxl28618/lstm-in-pytorch,CareerCon 2019 - Help Navigate Robots
3290204,0.6,0,1,/maxl28618/first-baseline,CareerCon 2019 - Help Navigate Robots
3288346,0.7167,15,68,/hiralmshah/robot-sensor-eda-fe-and-prediction-improvement,CareerCon 2019 - Help Navigate Robots
3301893,0.73,3,8,/subhamsharma96/careercon-eda-fe-rf,CareerCon 2019 - Help Navigate Robots
3264266,0.63,0,3,/guntherthepenguin/fastai-cnn-approach,CareerCon 2019 - Help Navigate Robots
3254799,0.16,6,9,/nikitpatel/keras-lstm-with-sequences-window-length,CareerCon 2019 - Help Navigate Robots
3274464,0.68,3,17,/nikitagrec/feature-engineering-random-forest,CareerCon 2019 - Help Navigate Robots
3287943,0.63,0,0,/sgorlick/starter-code-for-careercon2019,CareerCon 2019 - Help Navigate Robots
3255308,0.64,39,162,/artgor/where-do-the-robots-drive,CareerCon 2019 - Help Navigate Robots
3261197,0.67,0,7,/ab971631/careercon-lightgbm,CareerCon 2019 - Help Navigate Robots
3262890,0.65,0,7,/sheminy/what-is-the-surface,CareerCon 2019 - Help Navigate Robots
3249500,0.54,1,9,/jazivxt/line-of-sight,CareerCon 2019 - Help Navigate Robots
13728351,0.72727,4,5,/philklein/first-notebook-adaboost-classifier,Titanic - Machine Learning from Disaster
13718990,0.7751100000000001,0,1,/rhushabhgedam/titanic-book,Titanic - Machine Learning from Disaster
13700397,0.7511899999999999,2,6,/akhilesh94/titanic-using-knn-classification,Titanic - Machine Learning from Disaster
13505880,0.80861,98,106,/tanmayunhale/titanic-top-4-hyperparameter-tuning,Titanic - Machine Learning from Disaster
13668673,0.7799,0,0,/mohameddhameem/catboost-model-with-77990-score,Titanic - Machine Learning from Disaster
13086423,0.78947,0,0,/anudeepreddykatta/titanic,Titanic - Machine Learning from Disaster
13113439,0.76315,0,0,/siddharthj92/siddharth-jain-titanic,Titanic - Machine Learning from Disaster
13633060,0.7799,0,0,/shashankshreyaskar/titanic-wreck,Titanic - Machine Learning from Disaster
13737929,0.7822899999999999,0,0,/sarahwiseman/titanic-notebook-with-pipeline,Titanic - Machine Learning from Disaster
13647765,0.6866,9,7,/pankajbhowmik/beginner-titanic-challenge-hp-tuning-pca-rf,Titanic - Machine Learning from Disaster
13587551,0.7822899999999999,6,7,/mdhamani/titanic-pytorch-neural-network-gpu-top-14,Titanic - Machine Learning from Disaster
13565348,0.7751100000000001,0,0,/abhirajdas/getting-started-with-titanic,Titanic - Machine Learning from Disaster
13261128,0.82535,0,0,/mkulio/titanic-modeling-nbayes-dtree-0-8253,Titanic - Machine Learning from Disaster
13144756,0.7703300000000001,0,0,/shiyoh/first-try,Titanic - Machine Learning from Disaster
13562261,0.76555,0,0,/yassinehane/titanic-prediction-challenge,Titanic - Machine Learning from Disaster
13591087,0.7751100000000001,0,0,/vito86/getting-started-with-titanic,Titanic - Machine Learning from Disaster
13482615,0.78947,14,21,/udit1907/titanic-disaster-beginner-python-documentation,Titanic - Machine Learning from Disaster
5863578,0.44081,0,0,/ilyarudyak/vanilla-fastai,Dog Breed Identification
4427190,0.9325,0,2,/christianwallenwein/fastai-baseline-model-dog-breeds,Dog Breed Identification
4090871,4.35002,0,2,/mohabdiab/dog-breeds,Dog Breed Identification
3026474,0.7984,0,4,/chandanpanda2006/not-just-ml-who-let-the-dogs-out,Dog Breed Identification
2826104,13.17541,1,0,/jinudaniel/dog-breed-classification-with-fastai,Dog Breed Identification
13181043,0.78468,0,0,/jebrielabdul/attempt-1-abdul,Titanic - Machine Learning from Disaster
13411275,0.77751,3,6,/priyaark/understanding-the-basics-classification-problem,Titanic - Machine Learning from Disaster
13359181,0.78708,4,9,/alexanderossipov/titanic-simple-voting-based-on-cross-validation,Titanic - Machine Learning from Disaster
13425392,0.76555,0,5,/danielbethell/titanic-survival-prediction-using-ann,Titanic - Machine Learning from Disaster
13408770,0.77751,2,8,/pfc888/titanic-logistic-regression-solution,Titanic - Machine Learning from Disaster
13419079,0.7177,0,0,/tracyporter/titanic-pca,Titanic - Machine Learning from Disaster
13410275,0.7822899999999999,0,1,/aurbcd/titanic-data-viz-science-model-comparison,Titanic - Machine Learning from Disaster
13339042,0.77751,0,8,/azxc9595/tf-lr-without-enumerating-categorical-values,Titanic - Machine Learning from Disaster
13361465,0.76076,0,1,/homayoonkhadivi/new-insight-for-titanic-prediction,Titanic - Machine Learning from Disaster
8783579,0.7751100000000001,0,0,/themightynj/introduction-to-kaggle-competitions-titanic-data,Titanic - Machine Learning from Disaster
13313003,0.77751,0,10,/sinchir0/selfsupervisedtabnet-titanic-comparing-lgbm-nn,Titanic - Machine Learning from Disaster
13055333,0.7822899999999999,0,0,/kasperstouge/fork-of-notebookb3b26659be,Titanic - Machine Learning from Disaster
13290484,0.7799,1,1,/dmcondon/titanic-decision-trees,Titanic - Machine Learning from Disaster
13218661,0.79904,0,0,/artyomkolas/titanic-competition,Titanic - Machine Learning from Disaster
13302861,0.76794,0,0,/ieneames/titanic-competition-analysis-and-prediction,Titanic - Machine Learning from Disaster
13167892,0.7822899999999999,0,2,/dibya56/titanic-machine-learning-from-disaster-notebook,Titanic - Machine Learning from Disaster
13308462,0.76555,0,0,/amirsher/titanic-test-classifiers,Titanic - Machine Learning from Disaster
12831857,0.01892,0,0,/gambrinus/tabnet-moa,Mechanisms of Action (MoA) Prediction
13009296,0.01826,0,1,/riadalmadani/last-one,Mechanisms of Action (MoA) Prediction
13013611,0.01962,0,0,/yzgast/moa-pca-dnn-callbacks-keras,Mechanisms of Action (MoA) Prediction
14253268,0.02339,0,0,/garywei944/pca-lr-ridge-rf-nn-tuning-hyper-parameters,Mechanisms of Action (MoA) Prediction
13089322,0.01843,0,0,/franksheng/notebookbyfranksheng,Mechanisms of Action (MoA) Prediction
12944165,0.01973,0,0,/nadergo/moa-pytorch,Mechanisms of Action (MoA) Prediction
14040418,0.04289,0,0,/sehamahmed/moa-first,Mechanisms of Action (MoA) Prediction
13885376,0.01943,0,0,/simonakolarova/moa-predictions,Mechanisms of Action (MoA) Prediction
12957460,0.01914,0,0,/lavanyask/moa-prediction-nn-kfold,Mechanisms of Action (MoA) Prediction
12860953,0.01799,0,7,/gogo827jz/multi-label-public-best-inference-pl,Mechanisms of Action (MoA) Prediction
13521512,0.0182699999999999,0,9,/ttahara/stacking-2d-cnn-drugcv,Mechanisms of Action (MoA) Prediction
13541086,0.01951,0,0,/neviya19/notebook1-logreg,Mechanisms of Action (MoA) Prediction
13034523,0.01866,0,0,/evgkol/moablending,Mechanisms of Action (MoA) Prediction
11865959,0.02411,1,2,/heitorbaldo/moa-prediction-python,Mechanisms of Action (MoA) Prediction
13145606,0.01823,1,1,/tnmasui/moa-mlp-with-layernorm-inference,Mechanisms of Action (MoA) Prediction
12921886,0.02003,0,0,/aadhika3/notebook1640365c6c,Mechanisms of Action (MoA) Prediction
11631982,0.01884,0,0,/mahmoudvaziri/drug-sigmoid,Mechanisms of Action (MoA) Prediction
13010289,0.01824,0,0,/fushigen/moa-blending,Mechanisms of Action (MoA) Prediction
12102099,0.01954,0,0,/linkrw12/nni-hyperparam-xgboost,Mechanisms of Action (MoA) Prediction
13221914,0.01816,4,36,/kento1993/nn-svm-tabnet-xgb-with-pca-cnn-stacking-without-pp,Mechanisms of Action (MoA) Prediction
13012142,0.01817,9,39,/qiaoshiji/resnet-deep,Mechanisms of Action (MoA) Prediction
13219336,0.01818,3,15,/ttahara/34th-stacking-5-models-by-mlp-1d-cnn-wo,Mechanisms of Action (MoA) Prediction
13163338,0.01825,0,1,/radadiyamohit/3-model-training-and-inference,Mechanisms of Action (MoA) Prediction
2291045,0.45451,0,1,/nikhilpandey360/transfer-learning-using-xception,Dog Breed Identification
2257906,3.42417,0,0,/nikhilpandey360/submission-using-vgg19,Dog Breed Identification
2192665,4.78613,0,1,/wickeds07/dog-breed-classification-assignment-version-1,Dog Breed Identification
1616647,0.35828,0,1,/omniscientist99/dog-breed,Dog Breed Identification
13996247,0.88455,0,0,/b07502089shuwei/severstal-steel-defect-detection,Severstal: Steel Defect Detection
13406023,0.8342700000000001,0,0,/iceteamumu/notebook20ab0a00c0,Severstal: Steel Defect Detection
11487398,0.0,0,3,/ahmedmurad1990/steel-defect-detection-normal-pytorch,Severstal: Steel Defect Detection
6377599,0.91384,0,0,/salilm23/std-catalyst-2fold-heng-effi2-phase-2-tta-2,Severstal: Steel Defect Detection
5199324,0.0,0,0,/mtethan/steel-defect-detection-tl,Severstal: Steel Defect Detection
6012062,0.91052,0,0,/hlerogeron/new-inferance-with-classifier,Severstal: Steel Defect Detection
6926562,0.88776,0,0,/urajkumar/ece285-project-d,Severstal: Steel Defect Detection
6642682,0.7478199999999999,0,0,/poojankhatri/kernelac4a837530,Severstal: Steel Defect Detection
6609866,0.85674,0,2,/ni8hawk/16bit100-098-dl-submission,Severstal: Steel Defect Detection
6473232,0.81455,0,1,/shubhanshi8/dl-case-study,Severstal: Steel Defect Detection
13214501,0.7488,1,2,/abidurkomol/titanic-suvival-prediction,Titanic - Machine Learning from Disaster
13004392,0.77272,0,0,/cmuser/cmuser-notebook,Titanic - Machine Learning from Disaster
13283115,0.75598,0,0,/semenedel/notebook0d1eb8ab9b,Titanic - Machine Learning from Disaster
13062000,0.77272,1,5,/pobedash/titanic-decision-tree-sait-vntu-pobedash,Titanic - Machine Learning from Disaster
12642986,0.7822899999999999,1,3,/zhukovoleksiy/titanic-decision-tree-sait-vntu2,Titanic - Machine Learning from Disaster
13186425,0.73205,0,0,/wjehyeon/dit-20201129,Titanic - Machine Learning from Disaster
13094738,0.77272,0,0,/jithinanievarghese/getting-started-with-titanic,Titanic - Machine Learning from Disaster
12425627,0.77272,0,2,/ibekwekingsley/titanic-kaggle-competition,Titanic - Machine Learning from Disaster
13084204,0.76794,1,4,/georgedwatson/diving-into-the-shipwreck,Titanic - Machine Learning from Disaster
12825546,0.7799,0,0,/qijun5683/getting-started-with-titanic,Titanic - Machine Learning from Disaster
13070360,0.78708,1,9,/blackhurt/my-approach-to-be-in-top-15,Titanic - Machine Learning from Disaster
13011849,0.79665,0,0,/elambrop/titanic-basic-eda-and-modeling,Titanic - Machine Learning from Disaster
12904060,0.75358,0,0,/rvm8343/titanic-ml,Titanic - Machine Learning from Disaster
9949930,0.7511899999999999,0,0,/yusukearai/rev-neural-net,Titanic - Machine Learning from Disaster
9950669,0.7799,0,0,/yusukearai/rev-lenear-leg,Titanic - Machine Learning from Disaster
13576626,0.78468,0,0,/desparzaalba/titanic-with-transformers,Titanic - Machine Learning from Disaster
13431693,0.7751100000000001,0,1,/shameerrao/getting-started-with-titanic,Titanic - Machine Learning from Disaster
13514727,0.78947,1,6,/blackhurt/basic-eda-with-model-comparing-top-14,Titanic - Machine Learning from Disaster
13506177,0.78468,0,0,/codenamekash/titanic-prediction-experiments,Titanic - Machine Learning from Disaster
13497481,0.7511899999999999,6,10,/japandata509/titanic-decisiontreeclassifier,Titanic - Machine Learning from Disaster
13490829,0.7751100000000001,0,0,/minalsawant/survival-predictions-on-titanic,Titanic - Machine Learning from Disaster
9759301,0.7751100000000001,2,2,/arjunchaurasia/kernel27ef258436,Titanic - Machine Learning from Disaster
13478547,0.78708,0,1,/blackhurt/few-steps-to-follow-to-be-in-the-top-20,Titanic - Machine Learning from Disaster
13385017,0.7822899999999999,0,0,/dmcondon/titanic-random-forests,Titanic - Machine Learning from Disaster
9644371,68.9465,0,1,/devendratapdia/web-traffic-forecast,Web Traffic Time Series Forecasting
2913741,23.7142,1,7,/andkul/deep-lstm-to-predict-rainfall,How Much Did It Rain? II
2909366,23.78029,0,0,/ipanchenko/pmdl-submission,How Much Did It Rain? II
4044561,23.75197,0,0,/oldmon/lstm-models,How Much Did It Rain? II
14241237,0.8778299999999999,0,0,/rameela20/transfer-learning-plant-seedlings-classification,Plant Seedlings Classification
13983575,0.91687,1,5,/kinsomaz/plant-seedling-classification-using-keras-0-9726,Plant Seedlings Classification
12155500,0.62468,0,2,/cristianfat/the-ml-botanist,Plant Seedlings Classification
11875654,0.78841,0,0,/kushagrathakral7744/coe14-101703301-kushagra,Plant Seedlings Classification
11725690,0.93576,0,0,/ashutoshiitg/fine-tuning-efficientnetb2,Plant Seedlings Classification
11565765,0.9345,0,1,/ashutoshiitg/efficientnet,Plant Seedlings Classification
8200443,0.6057899999999999,0,0,/kojiiwase/plant-seeding,Plant Seedlings Classification
7526341,0.79345,0,17,/praanj/basic-keras-cnn-with-startified-kfold-evaluation,Plant Seedlings Classification
6243661,0.62468,1,1,/suresrkumar/plant-seedling-using-canny-edge-detection,Plant Seedlings Classification
6238203,0.8639700000000001,0,0,/wfosterhall/seedlings-pretrained-keras-models,Plant Seedlings Classification
4143083,0.9811,1,5,/droid021/fast-ai-v1,Plant Seedlings Classification
218962,0.58321,4,16,/arnaldcat/neural-network-w-feat-engineering-0-583lb,Two Sigma Connect: Rental Listing Inquiries
213624,0.54118,0,0,/tukichen/rental-interest-prediction,Two Sigma Connect: Rental Listing Inquiries
10437504,0.56,0,0,/makarbaderko/birdcall-identification-first-try,Cornell Birdcall Identification
13117299,0.544,0,0,/noelmat/notebook6bf4ed07cf,Cornell Birdcall Identification
11252968,0.544,0,2,/rsinda/introduction-to-sound-event-detection,Cornell Birdcall Identification
11763098,0.624,1,22,/vladimirsydor/4-th-place-solution-inference-and-training-tips,Cornell Birdcall Identification
11759565,0.622,1,18,/vlomme/surfin-bird-2nd-place,Cornell Birdcall Identification
11621511,0.6,1,4,/rguo97/bird-ensemble,Cornell Birdcall Identification
11753329,0.544,0,1,/srinivasdasu/birdcall-recognition,Cornell Birdcall Identification
11443188,0.5710000000000001,0,0,/nvnnghia/bird-submission-fun-test,Cornell Birdcall Identification
11447504,0.56,0,9,/chandrimad31/identifying-birdcalls-audio-processing,Cornell Birdcall Identification
11627819,0.56,0,4,/shams1/inference-pytorch-birdcall-resnet-baseline,Cornell Birdcall Identification
11497224,0.544,0,2,/rakshith1/probe-sampling-rate,Cornell Birdcall Identification
10831146,0.568,0,2,/vineeth1999/inference-birdsong-baseline-resnest50-fast,Cornell Birdcall Identification
11419282,0.563,0,4,/shams1/bird-submission,Cornell Birdcall Identification
11397960,0.544,3,13,/rakshith1/probing-test-set,Cornell Birdcall Identification
2860116,0.43453,1,1,/mojakapow/baseline-nyc-taxi,New York City Taxi Trip Duration
2849076,0.58131,0,1,/landrymo/nyc-trip-duration-landry-momeni,New York City Taxi Trip Duration
2817439,0.4608,4,7,/solozabar/nyc-taxis,New York City Taxi Trip Duration
2819004,0.43848,0,2,/redoo8x/nyc-trip-duration-prediction-redah,New York City Taxi Trip Duration
2818643,0.4681899999999999,0,1,/celineph/new-york-city-taxi-trip-duration,New York City Taxi Trip Duration
2817819,0.49763,0,1,/tayeb31/nyc-taxi-duration,New York City Taxi Trip Duration
2786552,0.43312,0,0,/schuckvincent/nyc-taxi-trip-duration,New York City Taxi Trip Duration
2813342,0.41135,1,0,/yvestrang/nyc-taxi-trip-duration-by-trang-yves,New York City Taxi Trip Duration
2804028,0.45414,0,1,/antoninbln/nyc-taxis-trip-duration,New York City Taxi Trip Duration
2751371,0.38789,2,9,/baoanh/nyc-taxi-trip-duration-by-nguyen-khac-bao-anh,New York City Taxi Trip Duration
2791301,0.43785,1,0,/tazertazer/bocquillon-pierre-randomforest-prediction,New York City Taxi Trip Duration
2817928,0.4497,0,0,/makimoko/kernel046d1899bb,New York City Taxi Trip Duration
1367470,0.4625,0,0,/rchitic17/nyc-cab-xgb,New York City Taxi Trip Duration
1372857,0.48086,0,1,/bertcarremans/xgboost-integrating-pandas-and-sklearn,New York City Taxi Trip Duration
1367844,0.5957600000000001,0,2,/atrisaxena/new-york-city-taxi-playground-with-xgboost,New York City Taxi Trip Duration
1031472,0.57317,0,0,/rdcmdev/2016-nyc-taxi-trip-decision-tree,New York City Taxi Trip Duration
1033901,0.50715,0,0,/rdcmdev/2016-nyc-taxi-trip-classroom-project,New York City Taxi Trip Duration
803320,0.42913,0,2,/mmohitm/mlp-regressor-on-nyc,New York City Taxi Trip Duration
339161,0.57455,1,2,/tiwari85aman/nyc-analysis-prediction,New York City Taxi Trip Duration
10122763,0.73783,0,0,/yerbatry/final-ensemble-clean-0-7,TensorFlow 2.0 Question Answering
7663945,0.13,0,0,/dmytruto/kernel17cddd3c40,TensorFlow 2.0 Question Answering
7683642,0.7391300000000001,2,1,/dinhnguyn/dinh-full,TensorFlow 2.0 Question Answering
7689235,0.67,1,11,/boliu0/7th-place-submission,TensorFlow 2.0 Question Answering
7369300,0.64,0,13,/axel81/inference-use-hugging-face-postprocess,TensorFlow 2.0 Question Answering
6995745,0.6,0,1,/mmmarchetti/tensorflow-2-0-bert,TensorFlow 2.0 Question Answering
7271566,0.61,5,22,/kenkrige/nq-direct-submit,TensorFlow 2.0 Question Answering
7504547,0.57,0,1,/wenbbing/tensorflow-2-0-bert-yes-no-answers,TensorFlow 2.0 Question Answering
7360641,0.6,9,65,/mmmarchetti/bert-joint,TensorFlow 2.0 Question Answering
7246965,0.02,0,2,/ankurgupta1985/alternative-io-read,TensorFlow 2.0 Question Answering
7013270,0.57,4,30,/andrewgao/tensorflow-2-0-bert-yes-no-answers,TensorFlow 2.0 Question Answering
6747646,0.15,0,1,/jagannathrk/tf-2-0-bert-on-nq,TensorFlow 2.0 Question Answering
6859224,0.19,2,0,/skylord/first-long-paragraph,TensorFlow 2.0 Question Answering
6657035,0.48,64,281,/prokaj/bert-joint-baseline-notebook,TensorFlow 2.0 Question Answering
6592576,0.18,1,9,/jazivxt/on-the-professor-and-the-madman,TensorFlow 2.0 Question Answering
6473174,0.21,1,14,/xhlulu/tf2-qa-lstm-inference-kernel,TensorFlow 2.0 Question Answering
6431432,0.24,8,57,/opanichev/text-similarity-baseline,TensorFlow 2.0 Question Answering
1511034,0.7929999999999999,0,0,/luudactam/hc-v500,Home Credit Default Risk
1114299,0.792,0,1,/isaranja/home-credit-nn-ae-lgb,Home Credit Default Risk
1889036,0.74565,0,0,/nisargpatel/home-credit-default-risk-lr-rf-lgbm,Home Credit Default Risk
1410330,0.753,0,1,/brendanhasz/home-credit-group-loan-risk-prediction,Home Credit Default Risk
1196353,0.504,0,0,/henningyan/home-credit-default-risk-eda,Home Credit Default Risk
1523719,0.5,0,0,/alimpolat/alim-copy-eda-basic-fe-and-lgb,Home Credit Default Risk
1414913,0.7659999999999999,0,0,/jayachandra1221/feature-engineering-and-lgbm-classifier,Home Credit Default Risk
1537543,0.8029999999999999,0,10,/nikitsoftweb/different-basic-blends-possible,Home Credit Default Risk
113395,0.7391300000000001,15,49,/daavoo/tensorflow-1vs1,"Ghouls, Goblins, and Ghosts... Boo!"
111991,0.74102,9,18,/yoyocm/let-s-explore-and-classify-monsters,"Ghouls, Goblins, and Ghosts... Boo!"
111893,0.71833,1,4,/juandoso/a-simple-haunted-random-forest,"Ghouls, Goblins, and Ghosts... Boo!"
3457087,0.05822,0,0,/burn874/nomad-clustering-fe,Nomad2018 Predicting Transparent Conductors
596081,0.0551,4,28,/srserves85/boosting-stacking-and-bayes-searching,Nomad2018 Predicting Transparent Conductors
528382,0.0611,1,2,/alyuev/al-py-jupiter,Nomad2018 Predicting Transparent Conductors
519914,0.0617,3,13,/sudhirnl7/simple-ann,Nomad2018 Predicting Transparent Conductors
500763,0.061,1,5,/jeru666/nomadic-xploration-pipeline-2nd-order-features,Nomad2018 Predicting Transparent Conductors
499262,0.0604,0,10,/sudhirnl7/simple-electron-volt-predictor,Nomad2018 Predicting Transparent Conductors
547151,0.0579,1,0,/scirpus/last-gp-for-nomad,Nomad2018 Predicting Transparent Conductors
13366476,8285.274,149,243,/snippsy/bottleneck-encoder-mlp-keras-tuner,Jane Street Market Prediction
13083424,921.245,2,2,/njelicic/tabnet-starter,Jane Street Market Prediction
13292051,8.651,15,53,/gogo827jz/jane-street-deep-reinforcement-learning-approach,Jane Street Market Prediction
13282805,4045.04,0,1,/code1110/janestreet-catboost-has-time,Jane Street Market Prediction
13150310,4349.787,4,19,/rpygamer/xgb-classifier-validation-utility-scoring-func,Jane Street Market Prediction
13193288,3298.264,0,6,/miklgr500/optuna-xgbclassifier-parameters-optimize,Jane Street Market Prediction
13175617,1680.701,16,7,/tchaye59/jmarket-keras-starter-submit,Jane Street Market Prediction
13133233,0.0,2,10,/markmipt/jane-street-how-to-deal-with-timeout-error,Jane Street Market Prediction
2816981,1.5490000000000002,0,3,/mmailloux22/i-m-quaking-in-my-boots,LANL Earthquake Prediction
2751278,1.523,1,6,/johnjarmitage/forest-regression-using-lightgbm,LANL Earthquake Prediction
2715840,1.692,3,21,/johnnyd113/baseline-with-explanations-how-to-get-started,LANL Earthquake Prediction
2706350,1.611,3,14,/flaport/linear-regression-on-180-features,LANL Earthquake Prediction
2623873,1.581,16,125,/zikazika/useful-new-features-and-a-optimised-model,LANL Earthquake Prediction
2651253,1.881,0,3,/jpiyush3008/basic-feature-benchmark-503e36,LANL Earthquake Prediction
2619433,1.456,85,546,/gpreda/lanl-earthquake-eda-and-prediction,LANL Earthquake Prediction
2623662,1.758,2,17,/nikitagribov/seismic-signal-eda-analysis-function,LANL Earthquake Prediction
2620253,2.963,0,4,/matthewarthur/earthquake-fastai-analytics-vidhya-base,LANL Earthquake Prediction
2619312,1.931,0,1,/matthewarthur/earthquake-preds,LANL Earthquake Prediction
2607951,1.638,1,12,/byfone/basic-feature-feat-catboost,LANL Earthquake Prediction
2609967,1.561,0,6,/manyregression/lanl-earthquake-prediction-fastai-random-forest,LANL Earthquake Prediction
9221571,0.963,0,0,/ibraheemmoosa/plant-pathology-2020-fgvc7-fastai-r18,Plant Pathology 2020 - FGVC7
13570619,0.84059,0,1,/duyanhphilippepham/image-processing-fourier-transform-densenet,Plant Pathology 2020 - FGVC7
13151127,0.97683,0,0,/hamonk/plant-pathology-fastai-seresnext50,Plant Pathology 2020 - FGVC7
13142033,0.973,0,1,/anku5hk/simple-submission,Plant Pathology 2020 - FGVC7
12933907,0.9812,0,1,/normall777/my-ensemble,Plant Pathology 2020 - FGVC7
8645003,0.972,0,0,/tanlikesmath/plant-pathology-fastai2-se-resnext50,Plant Pathology 2020 - FGVC7
12067751,0.93825,2,1,/sajidali22/fastai-resnet34,Plant Pathology 2020 - FGVC7
10734657,0.96931,0,3,/naimur978/plant-pathology-eda-stratified-cv-tta,Plant Pathology 2020 - FGVC7
9562569,0.981,0,1,/akashsuper2000/ensemble-top-kernels-2b9951,Plant Pathology 2020 - FGVC7
13051670,0.866,2,8,/sinamhd9/keras-available-models-part-2-inference,Cassava Leaf Disease Classification
13039151,0.901,18,46,/manojprabhaakr/leaf-classification-resnext-50-32-4d,Cassava Leaf Disease Classification
13054359,0.614,0,2,/elvinagammed/custom-cnn-vs-densenet,Cassava Leaf Disease Classification
13011840,0.9,7,61,/itsuki9180/efficientnet-and-cutmixup-with-tpu-predict-phase,Cassava Leaf Disease Classification
12991733,0.9,26,265,/khyeh0719/pytorch-efficientnet-baseline-inference-tta,Cassava Leaf Disease Classification
13052885,0.614,0,0,/balakrishcodes/inf-pytorch-efficient-net-classifie,Cassava Leaf Disease Classification
12985935,0.8909999999999999,26,189,/tanlikesmath/cassava-classification-eda-fastai-starter,Cassava Leaf Disease Classification
12997503,0.7559999999999999,0,0,/vanvalkenberg/cassava-leaf-disease-classification-v16,Cassava Leaf Disease Classification
13019702,0.878,2,8,/muellerzr/submission-notebook,Cassava Leaf Disease Classification
12998967,0.845,11,50,/muellerzr/cassava-fastai-starter,Cassava Leaf Disease Classification
12998229,0.861,3,7,/vfomenko/keras-baseline,Cassava Leaf Disease Classification
12994659,0.861,2,14,/pestipeti/cassava-pytorch-starter-inference,Cassava Leaf Disease Classification
1368360,0.419,0,1,/minhduc191/lgb-up-rank-by-feature-pruning,Costa Rican Household Poverty Level Prediction
1359115,0.416,1,5,/anktplwl91/predicting-using-xgboost-catboost-and-lightgbm,Costa Rican Household Poverty Level Prediction
1351200,0.434,6,18,/ischurov/more-feature-eng-lgb-5-fold-early-stopping,Costa Rican Household Poverty Level Prediction
1336247,0.413,0,2,/skooch/ensemble-take-8,Costa Rican Household Poverty Level Prediction
1330294,0.401,8,13,/nikitpatel/best-for-beginner-featureengineering-best-model,Costa Rican Household Poverty Level Prediction
1326603,0.431,4,12,/nityeshaga/detailed-analysis-with-a-focus-on-preprocessing,Costa Rican Household Poverty Level Prediction
1332849,0.405,0,11,/ashishpatel26/feature-engineering-model-tuning,Costa Rican Household Poverty Level Prediction
1332812,0.362,3,3,/sagarrawat/naiveclassifier,Costa Rican Household Poverty Level Prediction
1327482,0.392,0,1,/peaceful/feature-engineering-selection-with-xgb-models,Costa Rican Household Poverty Level Prediction
1329696,0.414,0,2,/minhduc191/lgb-feature-engineering-explained,Costa Rican Household Poverty Level Prediction
1323408,0.433,7,32,/mlisovyi/feature-engineering-lighgbm-with-f1-macro,Costa Rican Household Poverty Level Prediction
1328263,0.3339999999999999,0,1,/ruthwikmasina/poverty-level-prediction-using-xgb-svm,Costa Rican Household Poverty Level Prediction
1320422,0.413,2,3,/azharsindhi/beginning-lightgbm,Costa Rican Household Poverty Level Prediction
1322061,0.342,0,2,/minhduc191/eda-and-lgb,Costa Rican Household Poverty Level Prediction
1320070,0.423,4,9,/c0conuts/xgb-k-folds-fastai,Costa Rican Household Poverty Level Prediction
1315299,0.3929999999999999,2,21,/tunguz/eda-model,Costa Rican Household Poverty Level Prediction
1317114,0.376,0,9,/rupeshwadibhasme/boost-out-of-poverty-xgb-starter,Costa Rican Household Poverty Level Prediction
1316507,0.3879999999999999,0,8,/artgor/extensive-poverty-eda-feature-engineering-and-lgb,Costa Rican Household Poverty Level Prediction
4670869,0.38581,0,0,/jjungeunzzu/eda-model,Costa Rican Household Poverty Level Prediction
4151385,0.37284,0,0,/sidiclei/iesb-miner-ii-aula-08-parte-2,Costa Rican Household Poverty Level Prediction
1353671,0.35,0,0,/nocturnaltribe/costa-rica-project,Costa Rican Household Poverty Level Prediction
11400829,0.0,0,5,/ayushkumar0801/a-simple-bert-model,Gendered Pronoun Resolution
3837117,0.0,0,1,/omarqasim/end2end-gap,Gendered Pronoun Resolution
3640289,0.0,0,2,/jazivxt/fork-of-ml-model-example-with-train-test,Gendered Pronoun Resolution
3308267,0.49528,0,0,/isikkuntay/bert-feature,Gendered Pronoun Resolution
3527760,0.60259,6,26,/pheell/look-ma-no-embeddings,Gendered Pronoun Resolution
3445445,0.55777,0,5,/isikkuntay/generalizing-names,Gendered Pronoun Resolution
3443063,0.52822,0,4,/ceshine/pytorch-bert-simplified-score-layer,Gendered Pronoun Resolution
3424825,0.52933,1,3,/ceshine/pytorch-using-deterministic-bert-features,Gendered Pronoun Resolution
3155308,0.5305,57,155,/mateiionita/taming-the-bert-a-baseline,Gendered Pronoun Resolution
3124921,0.6574,6,3,/mamamot/catching-up-with-bert-stacking-public-solutions,Gendered Pronoun Resolution
3065122,0.6912,1,20,/mamamot/fastai-awd-lstm-solution-0-71-lb,Gendered Pronoun Resolution
2843645,0.8295799999999999,1,4,/fschilder/simple-wiki-title-baseline,Gendered Pronoun Resolution
2891961,2.0908900000000004,0,1,/ruchibahl18/some-dumb-formula,Gendered Pronoun Resolution
2844661,0.94494,0,25,/jazivxt/pro-noun-pro-gram,Gendered Pronoun Resolution
3663832,0.0,0,0,/victorhz/submission-densenet,Gendered Pronoun Resolution
11087818,3012.05271,4,14,/chandrimad31/claims-severity-analysis-of-models-in-depth,Allstate Claims Severity
7667564,1126.74417,0,1,/tushvjti/eda-allstate,Allstate Claims Severity
6679235,1135.46501,0,0,/venkateshprabhug/severity-of-insurance-claim,Allstate Claims Severity
6218169,1155.10595,0,0,/christianrorholtmoe/all-state-claim-severity-neural-net,Allstate Claims Severity
5942025,1113.12994,2,5,/cuijamm/allstate-claims-severity-score-1113-12994,Allstate Claims Severity
1133470,1142.4106800000004,0,2,/raviprakash438/allstate-claims-severity,Allstate Claims Severity
11884149,6.68053,1,16,/panks03/optimizing-neural-network-with-kerastuner,House Prices - Advanced Regression Techniques
11892884,0.13217,0,6,/jayesh134/house-price-prediction-data-visualization,House Prices - Advanced Regression Techniques
11795544,0.14318,1,9,/marcelopesse/house-prices-machine-learning-with-sklearn,House Prices - Advanced Regression Techniques
11889009,0.14318,0,0,/ramchapa/house-prices-predection-ml-with-sklearn,House Prices - Advanced Regression Techniques
11726382,0.12299,3,12,/jonas0/feature-selection-tutorial,House Prices - Advanced Regression Techniques
11791355,0.41152,10,10,/dipankarsrirag/linear-regression-with-pca-housing-rates,House Prices - Advanced Regression Techniques
10972184,0.1195799999999999,0,0,/jaishanker/predicting-house-prices,House Prices - Advanced Regression Techniques
11536270,0.12203,3,16,/mabalogun/using-xgboost-and-lightgbm-to-predict-house-price,House Prices - Advanced Regression Techniques
11712393,0.00044,1,3,/mehmetbabur/house-price-project,House Prices - Advanced Regression Techniques
11695810,0.12593,1,8,/paulrohan2020/eda-and-simple-linear-regression-for-house-price,House Prices - Advanced Regression Techniques
10850728,0.14658,25,17,/amandeepsingh12/2-house-price-prediction,House Prices - Advanced Regression Techniques
11709851,0.12596,1,6,/jamesragivedominique/notebook14097335ab,House Prices - Advanced Regression Techniques
2192222,0.6954,1,8,/kiyo22/eda-script-67-analyst-in-japanese,Two Sigma: Using News to Predict Stock Movements
2120624,0.4438899999999999,0,2,/donkeys/rolling-with-the-features,Two Sigma: Using News to Predict Stock Movements
2091790,0.63551,0,36,/zikazika/lightgbm-2sigma,Two Sigma: Using News to Predict Stock Movements
2025921,0.6954,50,231,/qqgeogor/eda-script-67,Two Sigma: Using News to Predict Stock Movements
2042728,0.57244,0,0,/adekunlebak/herdey-kay,Two Sigma: Using News to Predict Stock Movements
2035889,0.5746899999999999,0,2,/maxrodkin/eda-feature-engineering-and-everything-in-russian,Two Sigma: Using News to Predict Stock Movements
2012657,0.63521,0,1,/akihirosanada/1st-attack-lightgbm-testing,Two Sigma: Using News to Predict Stock Movements
1984323,68.5683,7,43,/nyakamura/lb-4-modified-reciprocal-of-r,Two Sigma: Using News to Predict Stock Movements
1979345,0.63449,1,4,/gaussmake1994/xgboost-scikit-learn-validation-pipeline,Two Sigma: Using News to Predict Stock Movements
1996057,3.04325,0,0,/zhangyang/reciprocal-of-previous-10-day-benchmark,Two Sigma: Using News to Predict Stock Movements
1953512,0.5501,2,2,/returnofsputnik/your-validation-strategy-is-misleading,Two Sigma: Using News to Predict Stock Movements
1766365,0.57809,1,0,/sivaadi92/experiments,Two Sigma: Using News to Predict Stock Movements
1951315,0.0678,0,0,/bazylidebowski/baseline-and-explore,Two Sigma: Using News to Predict Stock Movements
1913838,0.65704,1,48,/guowenrui/market-nn-if-you-like-you-can-use-it-and-upvote,Two Sigma: Using News to Predict Stock Movements
1904683,0.65441,7,24,/codlife/baseline-with-xgb,Two Sigma: Using News to Predict Stock Movements
1913346,0.08358,2,6,/wrosinski/lgbm-shifted-aggregates-example,Two Sigma: Using News to Predict Stock Movements
6704283,0.536,107,625,/artgor/quick-and-dirty-regression,2019 Data Science Bowl
6726010,0.523,0,1,/kotanishimura/quick-and-dirty-regression,2019 Data Science Bowl
11541980,-7.2642,1,12,/furcifer/q-regression-with-ct-tabular-features-pytorch,OSIC Pulmonary Fibrosis Progression
11467421,-24.7981,0,3,/konumaru/effcientnet-table-quantileregression,OSIC Pulmonary Fibrosis Progression
11252509,-6.9270000000000005,22,76,/hfutybx/osic-feature-extract-from-ct,OSIC Pulmonary Fibrosis Progression
11424029,-6.9477,0,9,/aniketmaurya/linear-decay-keras,OSIC Pulmonary Fibrosis Progression
11419675,-6.925,1,7,/tenten88/osic-no-ct-nn-with-custom-loss,OSIC Pulmonary Fibrosis Progression
11418586,-6.9357,0,9,/vineeth1999/multiple-quantile-regression-with-2-lstm,OSIC Pulmonary Fibrosis Progression
11392456,-6.9023,22,92,/carlossouza/bayesian-experiments,OSIC Pulmonary Fibrosis Progression
11322640,-6.9622,0,4,/atrisaxena/quantile-regression-pytorch-tabular-data-only,OSIC Pulmonary Fibrosis Progression
11344744,-7.1057,29,98,/maunish/osic-super-cool-eda-and-pytorch-baseline,OSIC Pulmonary Fibrosis Progression
11339039,-13.1045,1,2,/zefirchik/fibroze-sub,OSIC Pulmonary Fibrosis Progression
11306382,-8.2678,3,6,/akhileshdkapse/quick-starter-giude-eda-ml-genetic-algorithm,OSIC Pulmonary Fibrosis Progression
11306000,-7.2564,3,11,/shiqbal/efficientnets-quantile-regression-inference,OSIC Pulmonary Fibrosis Progression
11278302,-10.8608,1,9,/srikanthpotukuchi/neural-network-model-submission-score,OSIC Pulmonary Fibrosis Progression
11216545,-6.9197,0,0,/sunxiaen/816osic-tabular-data-only-sxe,OSIC Pulmonary Fibrosis Progression
9451363,0.0013599999999999,0,0,/ouwyukha/cpp-turi-fr,Coupon Purchase Prediction
12275479,10874641.0,1,2,/zhaoxtxtxt/first-try,INGV - Volcanic Eruption Prediction
12254138,6121191.0,32,167,/isaienkov/ingv-volcanic-eruption-prediction-eda-modeling,INGV - Volcanic Eruption Prediction
53845,0.5353600000000001,0,0,/pecoraro/decision-tree,BNP Paribas Cardif Claims Management
53229,0.51741,0,1,/potterxu/first-python,BNP Paribas Cardif Claims Management
51796,0.47015,0,1,/towhid1/checking2,BNP Paribas Cardif Claims Management
46780,0.45395,0,1,/ranpan/whatisthis,BNP Paribas Cardif Claims Management
46143,0.4580899999999999,0,0,/bakiev/boosting,BNP Paribas Cardif Claims Management
9302533,0.0930899999999999,0,0,/ouwyukha/imba-surprise-svd,Instacart Market Basket Analysis
9339961,0.31085,0,0,/ouwyukha/imba-turicreate-itemsim-pearson-pol,Instacart Market Basket Analysis
9339825,0.3064199999999999,0,0,/ouwyukha/imba-turicreate-fr-sgd,Instacart Market Basket Analysis
9274681,0.06216,0,0,/drainvers/instacart-sandbox-als,Instacart Market Basket Analysis
9297673,0.30705,0,0,/ouwyukha/imba-turicreate-rfr-ials,Instacart Market Basket Analysis
5806502,0.37193,0,4,/errolpereira/xgboost-with-feature-engineering,Instacart Market Basket Analysis
5790424,0.37344,0,0,/errolpereira/fork-of-light-gradient-boosting,Instacart Market Basket Analysis
4074165,0.37277,0,1,/whatifanalysisdim/instacart-ml-3-notebook,Instacart Market Basket Analysis
925055,0.0654618,0,0,/vishwassr/zillow-keras-cnn,Zillow Prize: Zillow’s Home Value Prediction (Zestimate)
383905,0.064895,0,0,/tvscitechtalk/zillow-test-1,Zillow Prize: Zillow’s Home Value Prediction (Zestimate)
6616779,0.0138699999999999,0,0,/funaki/random-forest-classifier-of-funaki-optuna,NFL Big Data Bowl
6637331,0.01243,2,7,/scirpus/location-eda-8eb410-556827,NFL Big Data Bowl
6626048,0.0155,0,4,/s13658695/xgboost-earlystopping-no-cv,NFL Big Data Bowl
6523728,0.01579,0,3,/funaki/random-forest-classifier-of-funaki-nfl-3-tuning,NFL Big Data Bowl
6537552,0.01408,0,0,/yukomiya/komiya-nfl-lgbm,NFL Big Data Bowl
6492203,0.0138699999999999,1,16,/vbmokin/lgbm-multiple-classifier-with-max-depth-5,NFL Big Data Bowl
6214790,0.0153599999999999,1,3,/donghoonoh/2018195017-qrm,NFL Big Data Bowl
6480968,0.0127599999999999,0,4,/abhikjha/cox-proportional-hazard-model,NFL Big Data Bowl
6213396,0.01402,0,4,/thehemen/nn-feature-engineering-with-genetic-programming,NFL Big Data Bowl
6455901,0.01464,0,1,/code1110/let-s-ask-catboost-about-cat-s,NFL Big Data Bowl
6407081,0.0136699999999999,0,30,/rooshroosh/continue-tune-hybrid-gp-and-nn,NFL Big Data Bowl
6399087,0.0136699999999999,0,18,/datahobbit/hybrid-gp-and-nn-slightly-tuned,NFL Big Data Bowl
6426392,0.02434,0,0,/funaki/funaki-nfl-3,NFL Big Data Bowl
6380746,0.01436,4,13,/kyonaganuma/hierarchical-bayesian-modeling-using-pystan,NFL Big Data Bowl
6412387,0.01931,0,0,/funaki/funaki-nfl-2,NFL Big Data Bowl
6378188,0.01911,0,0,/jmaslek/nflbowl-nearestdefender-myfirstnn,NFL Big Data Bowl
6324522,0.0138699999999999,0,23,/truenikita/neural-networks-multiple-output-stadium-clean,NFL Big Data Bowl
1345525,1.31,0,5,/gpreda/mix-leak-and-love,Santander Value Prediction Challenge
1334106,1.47,0,0,/ashirahama/my-first-lightgbm-svp,Santander Value Prediction Challenge
1311612,0.77,64,281,/tezdhar/breaking-lb-fresh-start,Santander Value Prediction Challenge
1313118,0.77,0,10,/ashishpatel26/is-it-solusion,Santander Value Prediction Challenge
1176830,1.6531900000000002,0,2,/plasticgrammer/santander-value-prediction,Santander Value Prediction Challenge
1283138,1.41,0,5,/meaninglesslives/vecstack-ensemble-skopt-46feat,Santander Value Prediction Challenge
1269527,1.4,11,75,/bminixhofer/a-different-validation-technique,Santander Value Prediction Challenge
1206478,1.71,0,0,/vasilis73/satander-analysis,Santander Value Prediction Challenge
1224382,1.55,4,22,/ianchute/geometric-mean-of-each-row-lb-1-55,Santander Value Prediction Challenge
1208704,1.48,0,13,/abhilashawasthi/fs-lasso-hyperparamtuning-hyperopt,Santander Value Prediction Challenge
1195752,1.39,75,230,/alexpengxiao/preprocessing-model-averaging-by-xgb-lgb-1-39,Santander Value Prediction Challenge
1208792,1.43,9,31,/ashishpatel26/preprocessing-lightgbm-xgboost,Santander Value Prediction Challenge
1206045,1.56,6,11,/nanomathias/linear-regression-with-elastic-net,Santander Value Prediction Challenge
1207954,1.75,2,7,/saivarunk/lgb-baseline-using-encoded-features-auto-encoder,Santander Value Prediction Challenge
1202181,1.48,0,1,/muonneutrino/eda-and-lightgbm-starter,Santander Value Prediction Challenge
2209673,0.907,4,36,/huyenvyvy/fork-of-combining-cnn-and-rnn,"Quick, Draw! Doodle Recognition Challenge"
1892808,0.794,0,2,/morrisb/raw-stroke-sequences-in-1d-cnn,"Quick, Draw! Doodle Recognition Challenge"
1973740,0.6509999999999999,0,0,/olgabelitskaya/quick-draw-doodle-recognition-opencv2,"Quick, Draw! Doodle Recognition Challenge"
1885285,0.782,2,22,/titericz/black-white-cnn-lb-0-782,"Quick, Draw! Doodle Recognition Challenge"
1811880,0.847,17,32,/amneves/quick-draw-keras-cnn-model,"Quick, Draw! Doodle Recognition Challenge"
1777210,0.626,7,54,/kmader/quickdraw-with-wavenet-classifier,"Quick, Draw! Doodle Recognition Challenge"
1751526,0.002,15,89,/gaborfodor/how-to-draw-an-owl-lb-0-002,"Quick, Draw! Doodle Recognition Challenge"
1739712,0.0,3,56,/mihaskalic/when-in-doubt-convnets,"Quick, Draw! Doodle Recognition Challenge"
3962917,0.69745,0,0,/ddw02141/bilstm-torch,"Quick, Draw! Doodle Recognition Challenge"
2215759,0.91,0,0,/gaelblanch/fold-two-doodler6,"Quick, Draw! Doodle Recognition Challenge"
223764,0.35372,0,0,/xushengyao/123123123123123123123123113,Quora Question Pairs
223147,0.37998,0,0,/artimous/data-analysis-xgboost-starter-0-32460-lb,Quora Question Pairs
219804,0.4107399999999999,0,9,/heraldxchaos/adventures-in-scikitlearn-and-nltk,Quora Question Pairs
219263,0.5541,0,0,/premshah/mean-of-train-as-submission,Quora Question Pairs
219222,0.47155,0,0,/heraldxchaos/adventures-in-nltk,Quora Question Pairs
233526,0.35633,3,21,/davidthaler/pandas-model-no-ml-lb-0-356,Quora Question Pairs
232698,0.35372,0,0,/hermetist/notebookxxaa,Quora Question Pairs
231000,0.5541,0,0,/wertu234/exploring-quora-data-set,Quora Question Pairs
8494445,0.68805,0,0,/dmitrijstrizna/lightgbm-malware-prediction,Microsoft Malware Prediction
7917588,0.58362,0,3,/happycloud/bq-ml-ms-maleware,Microsoft Malware Prediction
3200277,0.643,0,0,/vonkorff/jv-ms-malware-predict,Microsoft Malware Prediction
3240482,0.6955899999999999,4,38,/cdeotte/embeddings-network-malware-0-697-0-773,Microsoft Malware Prediction
3252387,0.70424,10,17,/ilu000/private-leaderboard-0-775,Microsoft Malware Prediction
3257344,0.691,1,1,/sheriytm/missed-gold-medal-private-lb-0-736,Microsoft Malware Prediction
3212879,0.6920000000000001,4,17,/roydatascience/malware-detection-stacking-2-0,Microsoft Malware Prediction
3097478,0.67715,10,22,/praxitelisk/microsoft-malware-detection-eda-xgboost,Microsoft Malware Prediction
3134534,0.69,6,31,/bgeier/word2vec-keras-ltsm-capsule-try,Microsoft Malware Prediction
2950092,0.6809999999999999,87,169,/cdeotte/time-split-validation-malware-0-68,Microsoft Malware Prediction
2948539,0.573,0,0,/tonyder/helloworld1,Microsoft Malware Prediction
2904740,0.653,2,8,/sarmat/fast-encoding-baseline-edited,Microsoft Malware Prediction
2659070,0.647,29,163,/cdeotte/time-series-eda-malware-0-64,Microsoft Malware Prediction
2677644,0.6859999999999999,14,71,/sjb1988/lgb-python-basic-features-only,Microsoft Malware Prediction
30403,0.95295,0,1,/theiya/fease-rf,Homesite Quote Conversion
28071,0.96144,0,1,/theiya/improve-lr,Homesite Quote Conversion
7279289,0.9545,5,12,/pukkinming/grapheme-fast-ai-starter-with-resnet18-inference,Bengali.AI Handwritten Grapheme Classification
7209869,0.9258,9,21,/kaushal2896/bengali-graphemes-multi-output-resnet-50,Bengali.AI Handwritten Grapheme Classification
7200896,0.9483,7,20,/machinelp/pytorch-resnet50-inference-0-9412-lb,Bengali.AI Handwritten Grapheme Classification
7173354,0.8540000000000001,6,19,/nandhuelan/densernet-pytorch-bengali-v2,Bengali.AI Handwritten Grapheme Classification
7133106,0.7654,11,70,/pestipeti/simple-pytorch-inference,Bengali.AI Handwritten Grapheme Classification
7133854,0.0614,0,13,/vladislavleketush/fast-parquet-loading-example,Bengali.AI Handwritten Grapheme Classification
7121704,0.0619,2,10,/reasat/evaluating-model-performance-on-the-test-set,Bengali.AI Handwritten Grapheme Classification
7119806,0.0614,2,10,/tunguz/bengali-sample-submission-starter,Bengali.AI Handwritten Grapheme Classification
8275110,0.9506,0,0,/jd81197/kernel2ecf7416bf,Bengali.AI Handwritten Grapheme Classification
8187067,0.957,0,0,/fakegeek1981105/resnet34-xh,Bengali.AI Handwritten Grapheme Classification
1361543,0.13842,0,0,/insotc/rossmann-prediction,Rossmann Store Sales
475857,0.12797,0,5,/orlando23/h2o-py-random-forest,Rossmann Store Sales
245675,0.23349,0,0,/mfseyf/omarovtemirkhanhw3,Rossmann Store Sales
244742,0.15283,0,5,/rogachev/rogachevalexander-technosphere,Rossmann Store Sales
244837,0.1461299999999999,0,1,/iluzin/hw3-luzin,Rossmann Store Sales
243940,0.14455,0,10,/temkahap/rossman-mini-rfr-final,Rossmann Store Sales
244547,0.18868,0,0,/ikrylov/kiss-linear-model-ikrylov-sphere-rmspe-2,Rossmann Store Sales
245807,0.61503,0,0,/petertrr/notebook1ec3fbbc79,Rossmann Store Sales
242073,0.12308,0,0,/zaitcevav/home-work3,Rossmann Store Sales
9703787,0.1616,0,1,/nizamuddin/trends-neuroimaging,TReNDS Neuroimaging
10438181,0.1568,5,12,/robertenglert/pni-features-and-models,TReNDS Neuroimaging
9549044,0.17858,0,3,/tunguz/trends-with-sklearn-nusvr,TReNDS Neuroimaging
9382712,0.15895,4,6,/yushg123/auto-score-based-blending,TReNDS Neuroimaging
10148916,0.1715,0,4,/kaerunantoka/pred-3dresnet-pytorch,TReNDS Neuroimaging
10179355,0.1698,0,2,/iamprateek/neuroimaging-trends,TReNDS Neuroimaging
10350770,0.2401,0,2,/akashsuper2000/trends-neuroimaging-2d-slice-resnet-pytorch,TReNDS Neuroimaging
10166345,0.1621,10,46,/phoenix9032/seutao-3d-resnet34-model-image-tabular-monai,TReNDS Neuroimaging
9205106,0.159,0,1,/akashsuper2000/svm-on-trends-neuroimaging,TReNDS Neuroimaging
9953638,0.1593,14,58,/andypenrose/baggingregressor-rapids-ensemble,TReNDS Neuroimaging
9255470,0.1602,0,2,/amrabed/trends-neuro-imaging-multioutput-bayesianridge,TReNDS Neuroimaging
9727845,0.171,1,53,/phoenix9032/trends-google-tabnet-baseline,TReNDS Neuroimaging
9512137,0.163,0,6,/tunguz/trends-with-histgradientboostingregressor,TReNDS Neuroimaging
9472681,0.168,0,16,/tunguz/rapids-knn-on-trends-neuroimaging,TReNDS Neuroimaging
1178855,1.47,8,30,/nafisur/bird-eye-view-of-different-machine-learning-model,Santander Value Prediction Challenge
1181216,1.64,1,5,/nicapotato/parameter-search-visualization-dr-extratree,Santander Value Prediction Challenge
1175423,1.53094,23,221,/samratp/lightgbm-xgboost-catboost,Santander Value Prediction Challenge
1176329,1.47,36,134,/tunguz/yaeda-yet-another-eda,Santander Value Prediction Challenge
1175879,1.47,1,9,/youhanlee/simple-strategy-to-reduce-features-using-r-value,Santander Value Prediction Challenge
1183269,1.4,6,8,/yusukesaito0141/loop-feature-selections-lightgbm,Santander Value Prediction Challenge
1178900,1.77,1,16,/mortido/keras-simple-model,Santander Value Prediction Challenge
1180732,1.49,0,8,/ashishsinhaiitr/eda-simple-xgb-rbf-lgbm-model,Santander Value Prediction Challenge
1177336,1.53,6,5,/debabratakaggle/pca-lgbm-base-model,Santander Value Prediction Challenge
1462471,1.87,0,0,/marcelin/santander-prediction,Santander Value Prediction Challenge
1186032,2.15,0,0,/curtarelli/just-another-boring-day,Santander Value Prediction Challenge
6263719,0.01418,5,13,/ben519/empirical-cdfs-benchmark,NFL Big Data Bowl
6271102,0.01865,0,2,/takaishikawa/very-newbie-notebook,NFL Big Data Bowl
6173271,0.01423,15,42,/shahules/how-about-some-nn-keras-starter,NFL Big Data Bowl
6193495,0.0169199999999999,2,17,/hamditarek/neural-networks-feature-engineering-for-the-win,NFL Big Data Bowl
6175261,0.01434,17,115,/ryches/model-free-benchmark,NFL Big Data Bowl
6164308,0.01314,83,507,/bgmello/neural-networks-feature-engineering-for-the-win,NFL Big Data Bowl
6185891,0.01429,4,32,/lovedm/fork-of-neural-networks-feature-luck-computer,NFL Big Data Bowl
6181868,0.01571,0,19,/rdizzl3/nfl-big-data-bowl-classification-perspective,NFL Big Data Bowl
6182361,0.01569,3,1,/scirpus/gradbm11,NFL Big Data Bowl
6157597,0.01576,1,22,/code1110/eda-optimizing-lightgbm-hyperparameters,NFL Big Data Bowl
6158596,0.02061,0,19,/ryches/nfl-big-data-bowl-team-feature-simple-model,NFL Big Data Bowl
13741942,0.013736,0,0,/srlee6/fork-of-neural-networks-different-2-comm,NFL Big Data Bowl
6752004,0.0124599999999999,0,0,/masasuke/nfl0708,NFL Big Data Bowl
6666028,0.0149,0,0,/oliveia/nfl-big-data-bowl-with-random-forests,NFL Big Data Bowl
14311680,0.83726,3,4,/taherhaggui/great-explorations-and-bert-implementation,Natural Language Processing with Disaster Tweets
14367546,0.7793399999999999,5,3,/nilaykhare/nlp-diastertweets-using-tf,Natural Language Processing with Disaster Tweets
13718825,0.7814800000000001,0,1,/aadimator/disaster-prediction-pytorch,Natural Language Processing with Disaster Tweets
14407462,0.83144,0,0,/thegodchurch/bertmodel2,Natural Language Processing with Disaster Tweets
14338625,0.7830199999999999,1,7,/jamesmcguigan/nlp-laser-embeddings-keras,Natural Language Processing with Disaster Tweets
14342093,0.8004899999999999,8,8,/pashupatigupta/data-cleaning-glove-bidirectional-lstm,Natural Language Processing with Disaster Tweets
8543886,0.81765,0,0,/uoneway/ka-kr-in-hapjeong-v19,Natural Language Processing with Disaster Tweets
14363485,0.76677,0,0,/rat360/notebookaa90badeec,Natural Language Processing with Disaster Tweets
14202472,0.79711,4,5,/anith88/true-disasters,Natural Language Processing with Disaster Tweets
14120279,0.76647,4,9,/pawanbhandarkar/knn-vs-approximate-knn-what-s-the-difference,Natural Language Processing with Disaster Tweets
14068986,1.0,3,8,/zinebkhanjari/disaster-tweets-multiple-vectorizers-and-models,Natural Language Processing with Disaster Tweets
13951834,0.75145,0,0,/fleek12/disaster-tweet-classification-using-lstm-model,Natural Language Processing with Disaster Tweets
13987154,0.77382,0,0,/sohaelshafey/disaster-tweets-classification,Natural Language Processing with Disaster Tweets
13985799,0.81765,0,7,/ayushggarg/nlp-with-disaster-tweets-eda-bert,Natural Language Processing with Disaster Tweets
4502645,0.1727,2,6,/safavieh/playing-around-with-meta-data,SIIM-ACR Pneumothorax Segmentation
12569237,0.80099,1,4,/funxexcel/santander-xgb-pipeline-auc-0-80,Santander Customer Satisfaction
12492364,0.8350799999999999,0,3,/pedrocouto39/feature-selection-bayesian-optimisation,Santander Customer Satisfaction
10986053,0.8367399999999999,0,6,/srinivasdasu/santander-customer-satisfaction,Santander Customer Satisfaction
8282966,0.55294,0,0,/ayakhaled2/santander-customer-satisfaction,Santander Customer Satisfaction
3324379,0.83525,0,0,/naushads/customer-happiness-prediction,Santander Customer Satisfaction
5896622,0.81906,1,5,/ashuprasadgermany/ml-group-assignment,Santander Customer Satisfaction
4396002,0.8381799999999999,0,0,/zygimantaskazlauskas/bclassproject-santandercustomersatisfaction,Santander Customer Satisfaction
981521,0.8230860000000001,1,0,/seifeleslam/udacity-capstone-santander-customer-satisfaction,Santander Customer Satisfaction
939680,0.836888,0,0,/kcbighuge/santander-customer-satisfaction-with-xgboost,Santander Customer Satisfaction
12051937,-8.1277,0,0,/katsuyanomura/notebook7a7091313c,OSIC Pulmonary Fibrosis Progression
11967789,-19.0938,0,2,/veronicaloy/dual-input-nn-image-csv,OSIC Pulmonary Fibrosis Progression
11943119,-19.0938,0,3,/kanesoban/osic-pulmonary-fibrosys-rnn-3dcnn,OSIC Pulmonary Fibrosis Progression
11886046,-6.8296,4,15,/gilfernandes/lightgbm-statsmodels-keras-ensemble,OSIC Pulmonary Fibrosis Progression
10747801,-6.8367,9,24,/yohannwattiez/simple-mlp-training,OSIC Pulmonary Fibrosis Progression
11795773,-8.3621,0,6,/alincijov/osic-cnn-pytorch,OSIC Pulmonary Fibrosis Progression
11746714,-24.7981,2,16,/currypurin/osic-lb-probing-number-of-patients-in-test-data,OSIC Pulmonary Fibrosis Progression
11760419,-19.0938,0,0,/viveksahukar/first-model-test,OSIC Pulmonary Fibrosis Progression
11722592,-6.9703,0,0,/kvsnoufal/submissiontemplate,OSIC Pulmonary Fibrosis Progression
11629540,-7.2434,2,6,/binman159/osic-lung,OSIC Pulmonary Fibrosis Progression
11591119,-7.2544,0,0,/sawans/osic-pytorch-baseline,OSIC Pulmonary Fibrosis Progression
11556734,-6.811,8,76,/reighns/higher-lb-score-by-tuning-mloss-around-6-811,OSIC Pulmonary Fibrosis Progression
11560764,-7.5264,2,11,/adinishad/osic-pulmonary-fibrosis,OSIC Pulmonary Fibrosis Progression
11407011,-6.8788,0,11,/yohannwattiez/load-trained-model-and-predict,OSIC Pulmonary Fibrosis Progression
13701384,0.725,4,4,/danofer/ranzcr-chexnet-x-ray-transfer-learning-extractor,RANZCR CLiP - Catheter and Line Position Challenge
13536669,0.945,10,13,/bjoernholzhauer/inference-for-trained-fastai-efficientnet-b4,RANZCR CLiP - Catheter and Line Position Challenge
13557724,0.955,18,44,/ipythonx/tf-keras-ranzcr-multi-attention-efficientnet,RANZCR CLiP - Catheter and Line Position Challenge
13517902,0.755,9,26,/titericz/baseline-transfer-learning-randomforest-gpu,RANZCR CLiP - Catheter and Line Position Challenge
13541999,0.742,0,0,/venkat555/ranzcr-clip-tpu-densenet-with-kfold-inference,RANZCR CLiP - Catheter and Line Position Challenge
13535513,0.898,0,1,/harveenchadha/efficientnetb3-tf2-keras-baseline-inference,RANZCR CLiP - Catheter and Line Position Challenge
6386990,0.163,0,7,/neibyr/target-encoding-public-kernel,2019 Data Science Bowl
2457039,0.57091,0,1,/regonn/eda-script-67-optuna-params,Two Sigma: Using News to Predict Stock Movements
1780868,0.52918,0,8,/ytokmakov/tell-her-about-bayes-and-350000-return,Two Sigma: Using News to Predict Stock Movements
2249523,0.6553,0,1,/shikha130vv/news-feature-engineering,Two Sigma: Using News to Predict Stock Movements
2375672,0.5926,0,0,/katerinaptv/2sedas,Two Sigma: Using News to Predict Stock Movements
2137157,0.63465,0,2,/jmyoon/using-news-to-predict-stock-movements,Two Sigma: Using News to Predict Stock Movements
2177431,0.38727,0,2,/tonmoybhattacharya/news-stockvalue-impact-ver-1,Two Sigma: Using News to Predict Stock Movements
2355265,0.64875,0,3,/urvishp80/first-serious-attempt-to-predict-stocks-direction,Two Sigma: Using News to Predict Stock Movements
1950067,0.44192,0,0,/coetzee2/keras-with-scalers,Two Sigma: Using News to Predict Stock Movements
2244724,0.5926,0,0,/daobuliao/eda-script-67-df740c,Two Sigma: Using News to Predict Stock Movements
2248005,0.13417,0,0,/aradlbeck/a-simple-ensamble-model,Two Sigma: Using News to Predict Stock Movements
5958681,0.28,0,1,/srsteinkamp/using-hu-sino-histograms-for-classification,RSNA Intracranial Hemorrhage Detection
5931484,0.497,29,71,/mobassir/keras-efficientnetb4-for-intracranial-hemorrhage,RSNA Intracranial Hemorrhage Detection
5865599,0.139,30,95,/xhlulu/rsna-intracranial-simple-densenet-in-keras,RSNA Intracranial Hemorrhage Detection
5864172,0.146,1,42,/jesucristo/rsna-introduction-eda-models,RSNA Intracranial Hemorrhage Detection
11722048,0.15116,0,1,/hirototakaoka/notebook12ecbccde6,House Prices - Advanced Regression Techniques
11487952,0.13421,0,0,/teelytran/house-price-prediction-advanced-regression,House Prices - Advanced Regression Techniques
11634545,0.12999,0,3,/pallavisinha12/houseprice-prediction,House Prices - Advanced Regression Techniques
11603457,0.12286,0,2,/sohelranaccselab/house-prices-advanced-regression-with-ml-lb-0-12,House Prices - Advanced Regression Techniques
11590924,0.13624,0,4,/zerryberry/top-10-in-house-price-prediction,House Prices - Advanced Regression Techniques
11564088,0.1886,0,7,/ekzemplaro/house-prices-sep06,House Prices - Advanced Regression Techniques
11549017,0.12841,0,8,/yutohisamatsu/houseprice-lightgbm,House Prices - Advanced Regression Techniques
11594895,4.382619999999998,0,0,/shayantaherian/notebooke8e10a9d9d,House Prices - Advanced Regression Techniques
3164983,0.11999,0,1,/mastmustu/stacked-regression-housing-price-predictions,House Prices - Advanced Regression Techniques
11166203,0.45102,0,0,/martialwang/kernel3b62cff63e,House Prices - Advanced Regression Techniques
11524192,0.1203,0,4,/keilorgilbert/stacking-ensembling-top-8,House Prices - Advanced Regression Techniques
11518059,0.1477099999999999,0,5,/carlmcbrideellis/automatic-tuning-of-xgboost-with-xgbtune,House Prices - Advanced Regression Techniques
11491090,0.15869,0,9,/ianchute/generalized-additive-model-gammagam,House Prices - Advanced Regression Techniques
10635422,0.79292,0,1,/ahmedmurad1990/house-price-ml,House Prices - Advanced Regression Techniques
11418007,0.1195099999999999,0,9,/lazrus/advance-regression-model-stacking-data-leakage,House Prices - Advanced Regression Techniques
11132971,0.13157,4,3,/gwenbayarbaatar/house-prices-analysis-for-beginners-linear-reg,House Prices - Advanced Regression Techniques
11417648,0.14841,0,5,/yutohisamatsu/fork-of-houseprice-simple-model,House Prices - Advanced Regression Techniques
128441,1132.40633,0,1,/aakash2121995/new-ensemble,Allstate Claims Severity
3946453,0.85472,0,0,/spectrox0/red-neuronal-keras,Santander Customer Transaction Prediction
3505199,0.901,0,0,/taohoang/santander-magic-lgb-0-901-a2f5f9,Santander Customer Transaction Prediction
3441477,0.9,0,0,/mayankkestwal10/santander-catboost,Santander Customer Transaction Prediction
3330687,0.899,0,0,/isaranja/santander-lgb-ae,Santander Customer Transaction Prediction
55649,0.8342790000000001,3,1,/gilbar/xgboost-learning-curve,Santander Customer Satisfaction
4486392,0.93856,0,0,/saileshmohanty/b-l-25-75,Jigsaw Unintended Bias in Toxicity Classification
3914635,0.85226,0,0,/darestrepov/model1-naive-bayes-with-logreg,Jigsaw Unintended Bias in Toxicity Classification
3660037,0.53503,0,0,/matsumotoshintaro/kernel0cc2a9145d,Jigsaw Unintended Bias in Toxicity Classification
13994978,5471778.0,0,0,/maostack/rank-gauss-hyperopt-tuning-lightgbm,INGV - Volcanic Eruption Prediction
13965385,5183355.0,0,5,/ricorauschkolb/xgboost-stft-extended-features-score-5101188,INGV - Volcanic Eruption Prediction
13760373,5269852.0,2,1,/florian12/linear-regression-trees-and-neural-networks,INGV - Volcanic Eruption Prediction
13432587,5715154.0,2,2,/damoonshahhosseini/volcano,INGV - Volcanic Eruption Prediction
12284005,6691281.0,0,1,/robertlangdonvinci/volcanic-eruption-catboost,INGV - Volcanic Eruption Prediction
12599504,7433078.0,1,6,/dschettler8845/basic-squeezenet-architecture-w-tfrecords,INGV - Volcanic Eruption Prediction
12601147,5443874.0,2,4,/eladwar/volcanoes,INGV - Volcanic Eruption Prediction
12472182,5948628.0,1,12,/davidedwards1/volcano-prediction-xgb-v1,INGV - Volcanic Eruption Prediction
12372959,6945618.0,0,3,/josemori/ingv-eda-basemodel,INGV - Volcanic Eruption Prediction
12325748,7251311.0,11,73,/jesperdramsch/introduction-to-volcanology-seismograms-and-lgbm,INGV - Volcanic Eruption Prediction
12320912,6443380.0,0,7,/carpediemamigo/ingv-xgb-skopt-publ-6462355,INGV - Volcanic Eruption Prediction
12293866,7297955.0,0,6,/carlmcbrideellis/the-volcano-and-the-regularized-greedy-forest,INGV - Volcanic Eruption Prediction
13104084,0.8409399999999999,0,5,/massinissaguendoul/nlp-disaster-tweet,Natural Language Processing with Disaster Tweets
12981393,0.83052,0,0,/nattachanningnoi/kaggle-project-nlp-disaster-tweet-final,Natural Language Processing with Disaster Tweets
12874290,0.79589,1,0,/peeravichsinta/naive-bayes-text-classification-for-beginners,Natural Language Processing with Disaster Tweets
13013050,0.7802600000000001,0,0,/sasimanimma/test-of-beginner,Natural Language Processing with Disaster Tweets
12895997,0.80294,0,0,/tushardobhal273/nlp-tushar,Natural Language Processing with Disaster Tweets
13037893,0.80447,0,0,/paweeyaphumwanphen/notebookd735deb06d,Natural Language Processing with Disaster Tweets
13013100,0.80355,0,1,/paweeyaphumwanphen/notebooke094735eb4,Natural Language Processing with Disaster Tweets
10517929,0.8452299999999999,0,1,/leblanc03/quick-look-disaster-tweets-simpletransformers,Natural Language Processing with Disaster Tweets
12854901,0.82439,0,0,/jamescorbin/nlp-disaster-tweets-with-bert-and-other-tf-models,Natural Language Processing with Disaster Tweets
12787926,0.7916,0,1,/hankarmostafa/predicting-disaster-tweets-with-naive-bayes,Natural Language Processing with Disaster Tweets
12739028,0.7955800000000001,0,0,/aryansakhala/nlparyan,Natural Language Processing with Disaster Tweets
12565063,0.79497,2,9,/nikhiljohnk/powerful-glove-embeddings-bilstm-network,Natural Language Processing with Disaster Tweets
12513336,0.80937,0,0,/denisahinski/ensemble-rf-svm-logreg-bernoullinaivebayes,Natural Language Processing with Disaster Tweets
6419927,0.01304,0,0,/sa0987/nfl-big-data-bowl-saman,NFL Big Data Bowl
9643829,0.0125329999999999,0,1,/s903124/fork-of-pytorch-transformer-public-14th-private,NFL Big Data Bowl
7765387,0.012625,0,0,/s903124/pytorch-fixed-random-synthesizer,NFL Big Data Bowl
6755191,0.011911,0,6,/jccampos/nfl-2020-winner-solution-the-zoo,NFL Big Data Bowl
6778727,0.01127,1,9,/wimwim/nfl-transformer-final-run,NFL Big Data Bowl
6793079,0.0123,0,0,/samtaylor54321/nfl-big-data-bowl-2019,NFL Big Data Bowl
6539862,0.01235,0,6,/shishu1421/nfl-bowl-with-location,NFL Big Data Bowl
6773271,0.01236,0,0,/lazyer/nfl-pipeline-v4,NFL Big Data Bowl
6439066,0.01182,0,2,/robertehshi/nfl-big-data-bowl-physics-model,NFL Big Data Bowl
6686659,0.0133699999999999,0,1,/chichengzhang/gary-nfl-big-data-bowl,NFL Big Data Bowl
6746945,0.0147599999999999,0,1,/jagannathrk/nfl-big-data-bowl-model-using-lightgbm,NFL Big Data Bowl
6472728,0.013,0,0,/mehrler/dataminingproject,NFL Big Data Bowl
6784355,0.01162,15,151,/cpmpml/graph-transfomer,NFL Big Data Bowl
6669370,0.01324,0,0,/dmintry/stack-rf-cb,NFL Big Data Bowl
6779783,0.01434,0,1,/matheuspush/rushin-to-the-top,NFL Big Data Bowl
6783122,0.01903,0,0,/pashadude/catboost,NFL Big Data Bowl
6559151,0.01395,0,0,/jonbown/simple-keras-nn,NFL Big Data Bowl
6733107,0.01365,0,1,/enzoamp/location-eda-with-combined-rusher-features,NFL Big Data Bowl
6679380,0.0136199999999999,12,63,/dandrocec/location-eda-with-rusher-features,NFL Big Data Bowl
6640962,0.01363,21,77,/gogo827jz/blending-nn-and-lgbm-rf,NFL Big Data Bowl
6634270,0.01316,0,20,/anandavati/ngboost-for-nfl,NFL Big Data Bowl
13377289,1.54162,2,9,/paulrohan2020/tutorial-kernel-2-lightgbm-xgboost-and-catboost,Rossmann Store Sales
11962876,11.97407,0,3,/dskagglemt/santander-value-prediction-challenge-v2,Santander Value Prediction Challenge
5913908,1.46309,0,1,/zhouhong0/lgbbest,Santander Value Prediction Challenge
5869682,1.49903,0,0,/daphnetree/xbg-model,Santander Value Prediction Challenge
4222267,1.71961,0,0,/rolandas1369/kernel98b1f9aba6,Santander Value Prediction Challenge
1480310,0.56,1,5,/tienen/love-is-the-answer-ac1bcd,Santander Value Prediction Challenge
2889645,2.01306,0,0,/nazirashaikh/santander-value-prediction,Santander Value Prediction Challenge
2216850,1.5287,0,0,/louis030195/legit-model-stacking,Santander Value Prediction Challenge
1265317,1.73,0,0,/swarnabha/santander-1-2-1,Santander Value Prediction Challenge
1489321,0.47938,2,24,/joeytaj/leak-fe-ml-from-scratch-baseline,Santander Value Prediction Challenge
1445120,1.31,0,2,/darrellulm/feature-extraction-regression-test-run,Santander Value Prediction Challenge
1265158,1.82,0,0,/rishgupta34/practice,Santander Value Prediction Challenge
212684,0.3546,161,1350,/anokas/data-analysis-xgboost-starter-0-35460-lb,Quora Question Pairs
212844,0.35477,30,178,/sudalairajkumar/simple-leaky-exploration-notebook-quora,Quora Question Pairs
9142400,0.95318,0,0,/ttagu99/prediction-single-model-2epoch,"Quick, Draw! Doodle Recognition Challenge"
6605130,0.8186899999999999,1,3,/dowonna/tado-kynet,"Quick, Draw! Doodle Recognition Challenge"
6488361,0.63978,0,1,/spurdy/quickdraw-baseline-lstm-reading-and-submission,"Quick, Draw! Doodle Recognition Challenge"
6443153,0.6710699999999999,0,2,/yunjinpark/image-based-cnn,"Quick, Draw! Doodle Recognition Challenge"
3879807,0.8867799999999999,0,1,/ironkim/quick-draw-doodle-recognition-challenge,"Quick, Draw! Doodle Recognition Challenge"
6477458,0.00557,0,1,/bohyunlee/programmers-ds,"Quick, Draw! Doodle Recognition Challenge"
6442478,0.00502,0,2,/peacecheejecake/kernel546550ad6e,"Quick, Draw! Doodle Recognition Challenge"
3959098,0.87675,1,0,/jeongchanwoo/merge-model,"Quick, Draw! Doodle Recognition Challenge"
3951418,0.89513,0,0,/foonia/pgmrs,"Quick, Draw! Doodle Recognition Challenge"
3940896,0.8943,0,1,/mnmjh1215/mobilenet-v1-with-per-epoch-dataset,"Quick, Draw! Doodle Recognition Challenge"
3948799,0.8994200000000001,0,0,/cocopambag/kernelf18cf1fccd,"Quick, Draw! Doodle Recognition Challenge"
3947435,0.66714,0,0,/yjkwon/kernel6dde19a5ef,"Quick, Draw! Doodle Recognition Challenge"
3951151,0.5463600000000001,0,0,/ddw02141/kernelb28f75c4bb,"Quick, Draw! Doodle Recognition Challenge"
3906641,0.7751100000000001,0,0,/rookiebox/quick-draw-cnn,"Quick, Draw! Doodle Recognition Challenge"
1937202,0.923,2,21,/echomil/mobilenet-126x126x3-100k-per-class,"Quick, Draw! Doodle Recognition Challenge"
2241490,0.825,0,0,/suyash93/quick-draw-using-estimator-and-data-pipelines,"Quick, Draw! Doodle Recognition Challenge"
2618211,0.679,18,76,/tunguz/eda-with-python-datatable,Microsoft Malware Prediction
2561339,0.688,4,42,/jazivxt/fun-with-ms-dataset,Microsoft Malware Prediction
2495435,0.6890000000000001,0,17,/hung96ad/new-blend,Microsoft Malware Prediction
2506434,0.6,2,1,/mattemyo/tips-for-large-datasets,Microsoft Malware Prediction
2493201,0.688,0,27,/ashishpatel26/two-style-of-blending-and-double-blend,Microsoft Malware Prediction
2475260,0.6920000000000001,11,36,/roydatascience/light-gbm-on-stratified-k-folds-malwares,Microsoft Malware Prediction
2403020,0.5379999999999999,0,2,/sooperdooper/microsoft,Microsoft Malware Prediction
2411223,0.6709999999999999,10,59,/adityaecdrid/simple-feature-engineering-xd,Microsoft Malware Prediction
2395771,0.638,0,10,/delayedkarma/simple-catboost-starter,Microsoft Malware Prediction
2372386,0.6779999999999999,47,251,/fabiendaniel/detecting-malwares-with-lgbm,Microsoft Malware Prediction
2376478,0.6729999999999999,11,30,/delayedkarma/the-story-so-far-lb-0-673,Microsoft Malware Prediction
754587,0.39444,0,0,/nguyenchinhbk/cnn-sentence-pair,Quora Question Pairs
12744912,0.18222,0,1,/yindachen/rossmann,Rossmann Store Sales
9904611,0.14393,0,2,/chinugupta/predicting-rosmann-stores-salesprice,Rossmann Store Sales
8790678,0.21181,0,1,/mooventhchiyan/decision-tree-regressor,Rossmann Store Sales
7465372,0.13609,0,3,/moon2002/store-sales-prediction,Rossmann Store Sales
6511456,0.2557,0,0,/gurramkondasaicharan/rossmann-store-sales,Rossmann Store Sales
7004483,0.21364,0,1,/mnoman/densenet-noman-rohit-vinita,Rossmann Store Sales
6511403,0.31154,0,2,/sm12120552/rossmann-store-sales,Rossmann Store Sales
6511415,0.51502,0,9,/juhi1994/rossmann-store-sales-case-study,Rossmann Store Sales
6511379,0.5049100000000001,0,2,/abhisarnarkhede/rossman-store-sales,Rossmann Store Sales
4029935,0.12302,0,0,/terminate9298/rossmann-store-sale-prediction,Rossmann Store Sales
2568883,0.09805,0,4,/omgrodas/rossmann-deep-learning-with-fast-ai-v1,Rossmann Store Sales
2610459,0.16145,0,0,/omgrodas/rossmann-deep-learning-with-fast-ai-v1-simple,Rossmann Store Sales
9113413,0.185,6,70,/tarunpaparaju/trends-neuroimaging-2d-slice-resnet-pytorch,TReNDS Neuroimaging
9146974,0.424,0,2,/digvijayyadav/nn-using-keras-with-corr,TReNDS Neuroimaging
9145593,0.195,0,1,/grapestone5321/trends-neuroimaging-sample-submission,TReNDS Neuroimaging
9086191,0.162,3,59,/yasufuminakama/trends-lgb-baseline,TReNDS Neuroimaging
9085572,0.16,1,17,/akurmukov/trends-ridge-0-160,TReNDS Neuroimaging
9087335,0.185,0,1,/jafarib/start-from-here-trends-eda-fe-submissions,TReNDS Neuroimaging
5686553,0.9006,13,41,/mobassir/unet-with-se-resnext50-32x4d-encoder-for-stage-2,SIIM-ACR Pneumothorax Segmentation
5483126,0.7886,0,2,/harendrap/fastai-unet-resnet34,SIIM-ACR Pneumothorax Segmentation
4883862,0.833,1,25,/meaninglesslives/pneumothorax-classifier,SIIM-ACR Pneumothorax Segmentation
4798230,0.8357,37,158,/meaninglesslives/unet-with-efficientnet-encoder-in-keras,SIIM-ACR Pneumothorax Segmentation
4814188,0.8343,0,9,/vaishvik25/ensemble-v2,SIIM-ACR Pneumothorax Segmentation
4735840,0.7959,0,2,/aussie84/pneumotorax-prediction,SIIM-ACR Pneumothorax Segmentation
4667692,0.8312,65,220,/iafoss/hypercolumns-pneumothorax-fastai-0-831-lb,SIIM-ACR Pneumothorax Segmentation
4584108,0.7886,50,181,/ekhtiar/finding-pneumo-part-1-eda-and-unet,SIIM-ACR Pneumothorax Segmentation
4534233,0.8087,11,105,/abhishek/inference-for-mask-rcnn,SIIM-ACR Pneumothorax Segmentation
4551155,0.7904,12,12,/hmendonca/inference-for-mask-rcnn-filtered,SIIM-ACR Pneumothorax Segmentation
4503312,0.1676,9,31,/karanjakhar/starter-siim,SIIM-ACR Pneumothorax Segmentation
13991134,0.8063100000000001,0,1,/craigmthomas/tfidf-with-support-vector-machines,Natural Language Processing with Disaster Tweets
13972377,0.74164,1,4,/jamesmcguigan/nlp-logistic-regression,Natural Language Processing with Disaster Tweets
13834289,0.79282,4,16,/fatmakursun/disaster-tweets-nlp-for-beginners,Natural Language Processing with Disaster Tweets
13785461,0.79589,1,2,/craigmthomas/logistic-regression-lightgbm-fe,Natural Language Processing with Disaster Tweets
7860717,0.79252,0,0,/salmanbaqri/word2vec-logistic-regression,Natural Language Processing with Disaster Tweets
13524307,0.5976,0,0,/yutoricstar/nlp-disaster-tweet-xgb,Natural Language Processing with Disaster Tweets
13518520,0.8167300000000001,0,3,/paulrohan2020/disaster-nlp-keras-bert-using-tfhub,Natural Language Processing with Disaster Tweets
13081318,0.80447,0,1,/ggsehun/real-or-not-nlp-with-disaster-tweets-dsi206,Natural Language Processing with Disaster Tweets
13475937,0.75452,0,3,/xyf1222/tweets-disaster-prediction-using-rnns,Natural Language Processing with Disaster Tweets
13429939,0.80018,0,0,/suryaprakashpathak/nlp-twitter-data,Natural Language Processing with Disaster Tweets
13479931,0.76647,0,1,/saurav044/notebook7cf934b6fb,Natural Language Processing with Disaster Tweets
13357597,0.8004899999999999,2,9,/salmanhiro/glove-baseline-lstm,Natural Language Processing with Disaster Tweets
13324323,0.79497,0,0,/mohdkashifakhtar/notebook20cc87a764,Natural Language Processing with Disaster Tweets
13194607,0.7863899999999999,1,2,/nagakalyan2784/disaster-tweets-analysis,Natural Language Processing with Disaster Tweets
13168703,0.77198,1,3,/ashwinrachha1/nlp-disaster-tweets,Natural Language Processing with Disaster Tweets
13033538,0.79282,1,1,/phitchayut/real-or-not-nlp-with-disaster-tweets,Natural Language Processing with Disaster Tweets
12990220,0.7992600000000001,0,2,/prakharprasad/nlp-disaster-tweets-simple-approach-to-80-score,Natural Language Processing with Disaster Tweets
13000974,0.80079,0,6,/arochamorsin/nlp-with-disaster-tweet,Natural Language Processing with Disaster Tweets
11234475,0.06417,0,0,/deepakk92/notebook211fdc91df,Zillow Prize: Zillow’s Home Value Prediction (Zestimate)
8988878,0.0663,0,0,/krparekh24/zillow-home-price-prediction,Zillow Prize: Zillow’s Home Value Prediction (Zestimate)
6804380,0.0644,0,0,/wakamezake/simple-lgbm-for-zillow-prize,Zillow Prize: Zillow’s Home Value Prediction (Zestimate)
4062023,0.0663,0,4,/thiagoandrade/kernel-zillow,Zillow Prize: Zillow’s Home Value Prediction (Zestimate)
3314004,0.06427,0,0,/mailyousufkhan/zillow,Zillow Prize: Zillow’s Home Value Prediction (Zestimate)
400117,0.064337,1,7,/aharless/my-solution-2nd-part-viii-second-ensemble,Zillow Prize: Zillow’s Home Value Prediction (Zestimate)
378061,0.0653411999999999,0,0,/vidhyainkaggle/fill-missing-data,Zillow Prize: Zillow’s Home Value Prediction (Zestimate)
379695,0.0643644,0,4,/fmfkfd/xgboost-lightgbm-nn-ols,Zillow Prize: Zillow’s Home Value Prediction (Zestimate)
374042,0.0643647,1,1,/youhouhou/xgboost-lightgbm-ols-nn,Zillow Prize: Zillow’s Home Value Prediction (Zestimate)
14570708,0.946,2,14,/jy2tong/efficientnet-b2-soft-attention,RANZCR CLiP - Catheter and Line Position Challenge
14476794,0.959,1,4,/ammarali32/efficientnetb5-inference-single-model,RANZCR CLiP - Catheter and Line Position Challenge
14298658,0.947,3,2,/aravindpadman/efficientnet-inference,RANZCR CLiP - Catheter and Line Position Challenge
14203766,0.908,2,17,/prvnkmr/ranzcr-tf-baseline-lb-0-908,RANZCR CLiP - Catheter and Line Position Challenge
13922104,0.513,0,0,/eutechsystemltd/ranzcr-eutech,RANZCR CLiP - Catheter and Line Position Challenge
14015577,0.913,0,4,/tolgahancepel/catheter-position-eda-and-prediction-on-gpu,RANZCR CLiP - Catheter and Line Position Challenge
13918451,0.918,1,0,/wuhanxin01/first-vsersion,RANZCR CLiP - Catheter and Line Position Challenge
13942867,0.944,13,9,/tpothjuan/efficientnetb7-tfrecords,RANZCR CLiP - Catheter and Line Position Challenge
13898599,0.959,12,78,/yasufuminakama/ranzcr-resnet200d-3-stage-training-sub,RANZCR CLiP - Catheter and Line Position Challenge
13871913,0.959,25,44,/khoongweihao/x-ray-needle-augmentation-et-al,RANZCR CLiP - Catheter and Line Position Challenge
13828403,0.958,5,52,/abhishek/ranzcr-tez-inference-efficientnet5-512-tta,RANZCR CLiP - Catheter and Line Position Challenge
13791050,0.8320000000000001,0,1,/jackstapleton/rccl-1x144-f-inference,RANZCR CLiP - Catheter and Line Position Challenge
12107224,-6.8836,0,0,/mikkojkvaak/inference-notebook-qr-final,OSIC Pulmonary Fibrosis Progression
11986227,-6.858,0,3,/dktalaicha/pulmonary-fibrosis-eda-model,OSIC Pulmonary Fibrosis Progression
11295047,-7.0009,0,0,/tskelton12/osic-pf-randomforest,OSIC Pulmonary Fibrosis Progression
10898030,-6.8253,0,0,/wburchenal/basic-pulmonary-fibrosis-analysis-of-tabular-data,OSIC Pulmonary Fibrosis Progression
12482093,-7.1294,0,2,/mikhailkennerley/ensemble,OSIC Pulmonary Fibrosis Progression
10831774,-6.8186,0,2,/satnam007/osic-m-fine-tune,OSIC Pulmonary Fibrosis Progression
11838514,-13.1045,0,0,/goelpravin/concatconv2-5andtabular,OSIC Pulmonary Fibrosis Progression
12062246,-8.1277,0,1,/arslan23/osic-pulmonary-fibrosis-grey-cells,OSIC Pulmonary Fibrosis Progression
11827156,-6.881,4,6,/abhishekgbhat/quantreg-linear-decay-efficientnet-b1-su,OSIC Pulmonary Fibrosis Progression
11470987,-6.9107,0,0,/neithermannormachine/serious-model-tabular-data,OSIC Pulmonary Fibrosis Progression
11215266,-6.9060000000000015,0,0,/brodzik/osic-pytorch-quantile-regression,OSIC Pulmonary Fibrosis Progression
12072179,-6.9073,0,6,/jagadish13/osic-baseline-elasticnet-eda-testing,OSIC Pulmonary Fibrosis Progression
12025842,-6.8504,0,2,/manojprabhaakr/quantile-regression-bayesian-ridge-ridge,OSIC Pulmonary Fibrosis Progression
12170972,-6.8945,0,0,/takuyatone/qr-confidence-tuning-tabular-data-only,OSIC Pulmonary Fibrosis Progression
11635226,-6.9575,0,0,/melvin97n/base-7,OSIC Pulmonary Fibrosis Progression
10929012,-6.915,0,0,/archimedus/osic-competition,OSIC Pulmonary Fibrosis Progression
11964088,-6.8073,0,2,/alifrahman/higher-lb-score-by-tuning-mloss-upgrade-1696e2,OSIC Pulmonary Fibrosis Progression
12091189,-7.405,0,1,/gilfernandes/pytorch-resnet-slope-prediction-2,OSIC Pulmonary Fibrosis Progression
11814144,-6.8479,0,0,/greatgamedota/osic-inference2,OSIC Pulmonary Fibrosis Progression
12008967,-6.8514,0,0,/hfutybx/residual-train-pytorch-osic-multiple-quantile,OSIC Pulmonary Fibrosis Progression
12017412,-11.7558,3,0,/lsllee/notebook0f1f2942a7,OSIC Pulmonary Fibrosis Progression
3642262,0.89259,0,2,/quincyqiang/01-tfidf-lr-0-89259,Jigsaw Unintended Bias in Toxicity Classification
3630539,0.89586,14,30,/elcaiseri/toxicity-bias-logistic-regression-tfidfvectorizer,Jigsaw Unintended Bias in Toxicity Classification
3612557,0.88651,2,14,/francoisdubois/build-a-word-embedding-with-glove-matrix,Jigsaw Unintended Bias in Toxicity Classification
3622611,0.89555,0,0,/rohankumar0002/jigsaw-nb,Jigsaw Unintended Bias in Toxicity Classification
3570245,0.91703,7,23,/uysimty/simple-toxicity-classification,Jigsaw Unintended Bias in Toxicity Classification
3539218,0.93524,37,226,/kunwar31/simple-lstm-with-identity-parameters-fastai,Jigsaw Unintended Bias in Toxicity Classification
3524962,0.92308,5,21,/dnik007/jigsaw-challenge-data-pre-processing-and-keras,Jigsaw Unintended Bias in Toxicity Classification
3518006,0.89691,13,92,/taindow/bert-a-fine-tuning-example,Jigsaw Unintended Bias in Toxicity Classification
3476485,0.9288,0,7,/powercode/bilstm-gru-caps,Jigsaw Unintended Bias in Toxicity Classification
3534141,0.8951399999999999,1,1,/rohankumar0002/using-gensim-and-keras-lstm,Jigsaw Unintended Bias in Toxicity Classification
3502601,0.91455,2,6,/elias8888/bert-baseline-score-0-91,Jigsaw Unintended Bias in Toxicity Classification
3485750,0.92837,3,7,/hung96ad/gru-lstm,Jigsaw Unintended Bias in Toxicity Classification
3473051,0.92537,2,10,/stanislavblinov/sweet-bi-lstm-attentionwithcontext,Jigsaw Unintended Bias in Toxicity Classification
3463173,0.90587,0,4,/beloruk1/comparing-embeddings,Jigsaw Unintended Bias in Toxicity Classification
3450916,0.8790399999999999,18,37,/christofhenkel/ulmfit-fast-ai-starter,Jigsaw Unintended Bias in Toxicity Classification
3454849,0.92262,4,32,/sergeykalutsky/pytorch-starter,Jigsaw Unintended Bias in Toxicity Classification
3456137,0.92334,0,6,/sandeepkumar121995/keras-bi-gru-lstm-attention-fasttext,Jigsaw Unintended Bias in Toxicity Classification
3485062,0.83629,0,0,/jkfirst/jigsaw-submission,Jigsaw Unintended Bias in Toxicity Classification
3438651,0.92501,9,73,/adityaecdrid/regex-primer-annoying-artgor-xd,Jigsaw Unintended Bias in Toxicity Classification
9980523,0.0,0,0,/erikhou/pytorch-bert-inference,Jigsaw Unintended Bias in Toxicity Classification
2410785,2.68766,3,22,/gracewan/plot-model,Two Sigma: Using News to Predict Stock Movements
2139309,3.05926,0,0,/xianglong/the-original-4th-solution,Two Sigma: Using News to Predict Stock Movements
2502377,0.70305,0,0,/sijinwu/fork-of-the-best-repair,Two Sigma: Using News to Predict Stock Movements
2585994,2.06615,0,1,/returnofsputnik/fork-of-fork-of-lstm-only-for-real-b2fffc,Two Sigma: Using News to Predict Stock Movements
2364779,0.64608,0,1,/chrisiew/twosigmakernel,Two Sigma: Using News to Predict Stock Movements
2553882,1.37025,0,0,/limin1996/combine-1,Two Sigma: Using News to Predict Stock Movements
2390440,0.64169,0,0,/harikiran/2-sigma-final,Two Sigma: Using News to Predict Stock Movements
2570653,0.62097,0,0,/hongwingl/sigmatation-fault,Two Sigma: Using News to Predict Stock Movements
2571905,3.02772,0,2,/andycscsmaple/sma-only-v2,Two Sigma: Using News to Predict Stock Movements
3058516,0.778,0,0,/nagulapatianusha369/santander-work-in-progress,Santander Customer Transaction Prediction
3051893,0.5,0,0,/woshichendu/santander,Santander Customer Transaction Prediction
3044933,0.892,0,0,/souravbose/a-start-in-python,Santander Customer Transaction Prediction
3066389,0.873,1,0,/nagulapatianusha369/santander-upsampling-minmaxscaler-bo-lgbm,Santander Customer Transaction Prediction
3001341,0.858,4,3,/kalyankkr/simple-nn,Santander Customer Transaction Prediction
3011027,0.86,6,32,/vishesh17/keras-nn-with-scaling-and-regularization,Santander Customer Transaction Prediction
3009990,0.8590000000000001,6,9,/ldm314/dnn-keras-santander,Santander Customer Transaction Prediction
3005826,0.8759999999999999,4,6,/rajaswa/simple-catboost-classifier,Santander Customer Transaction Prediction
2996882,0.6859999999999999,3,10,/deepakdodi/simple-neural-network-model-with-adam-optimizer,Santander Customer Transaction Prediction
2980394,0.899,6,19,/sungdoo/sctp-working-lgb,Santander Customer Transaction Prediction
2944832,0.9,186,1135,/gpreda/santander-eda-and-prediction,Santander Customer Transaction Prediction
2982269,0.8959999999999999,0,0,/ortempo/3-lightgbm,Santander Customer Transaction Prediction
2955680,0.898,3,21,/plasticgrammer/santander-customer-transaction-playground,Santander Customer Transaction Prediction
2941931,0.899,22,164,/chocozzz/santander-lightgbm-baseline-lb-0-899,Santander Customer Transaction Prediction
2942382,0.898,45,102,/fayzur/lightgbm-customer-transaction-prediction,Santander Customer Transaction Prediction
2959439,0.853,1,1,/alepacheco/basic-nn-approach,Santander Customer Transaction Prediction
2945225,0.893,7,13,/ashishpatel26/lightgbm-gbdt-dart,Santander Customer Transaction Prediction
2943346,0.8959999999999999,0,5,/unerue/simple-eda-and-building-a-model,Santander Customer Transaction Prediction
6126900,0.103,0,0,/arachauhan/resnet50-rsna,RSNA Intracranial Hemorrhage Detection
6468190,0.602,0,0,/fanconic/ensembling-of-models,RSNA Intracranial Hemorrhage Detection
6474289,0.251,0,0,/fanconic/windowing-inceptionv3-keras,RSNA Intracranial Hemorrhage Detection
6292664,0.095,1,2,/srinesh/baseline-resnext50,RSNA Intracranial Hemorrhage Detection
6082396,0.149,0,4,/srinesh/baseline-vggnet,RSNA Intracranial Hemorrhage Detection
5866452,0.36888,2,9,/ksooklall/simple-eda-inception-v3,RSNA Intracranial Hemorrhage Detection
3099535,0.0041178,0,1,/skw216014/practice-recommend,Santander Product Recommendation
1688184,0.0229235,0,0,/hanene1/random-forest-with-demographic-644587,Santander Product Recommendation
1423030,0.0135231999999999,0,3,/hachemsfar/decision-tree-with-demographic,Santander Product Recommendation
1398544,0.0167124,0,2,/hachemsfar/decision-tree,Santander Product Recommendation
1396848,0.0183743,0,1,/hachemsfar/randomforestclassifier,Santander Product Recommendation
956794,0.0169094,0,1,/hachemsfar/collaborativefiltering,Santander Product Recommendation
1922870,0.0,0,1,/sreenathds/sreenath-carvana,Carvana Image Masking Challenge
1981789,0.382,21,29,/rejpalcz/gapnet-pl-lb-0-385,Human Protein Atlas Image Classification
1943099,0.391,7,24,/iluxave/inceptionv3-with-sliding-window-image-breakdown,Human Protein Atlas Image Classification
1856626,0.246,1,12,/ashishpatel26/two-branch-inceptionv2lb-0-3,Human Protein Atlas Image Classification
1838733,0.175,2,18,/ashishpatel26/transfer-learning-with-inception-v3,Human Protein Atlas Image Classification
1818368,0.054,0,3,/victorhz/fork-of-cnn-with-keras,Human Protein Atlas Image Classification
1796449,0.05,4,19,/kmader/rgb-transfer-learning-with-inceptionv3-for-protein,Human Protein Atlas Image Classification
2370606,0.58749,0,0,/ajithvallabai/market-data-nn-baseline-test-1,Two Sigma: Using News to Predict Stock Movements
1997607,0.65787,0,0,/nixonjames/tuning-hyper-params-in-lgbm-achieve-0-66-i-5a9d40,Two Sigma: Using News to Predict Stock Movements
1896622,0.62903,0,0,/etherqua/market-data-nn-baseline,Two Sigma: Using News to Predict Stock Movements
12838922,0.9476,0,4,/askarliu/data-augmentations-densenet201,Flower Classification with TPUs
9293371,0.98005,0,0,/redwankarimsony/flowerflowerwhoareyou-onlysubmissions-ensembling,Flower Classification with TPUs
9037274,0.94063,0,0,/sohamsave44/flower-classifier-using-densenet,Flower Classification with TPUs
11636966,0.95042,0,1,/sohelranaccselab/flower-classification-with-tpus-cnn,Flower Classification with TPUs
10622180,0.92462,0,0,/srivastava09/flower-classification-using-tpu,Flower Classification with TPUs
9276235,0.96464,0,0,/kritikagarg/flowernotebook895,Flower Classification with TPUs
10211293,0.92702,0,3,/yaoyi970403/flower-classification-project,Flower Classification with TPUs
9317568,0.97619,0,0,/akashsuper2000/tpu-multi-tier-training-with-external-datasets,Flower Classification with TPUs
8032659,0.63889,0,0,/akashsuper2000/getting-started-with-100-flowers-on-tpu,Flower Classification with TPUs
9960981,0.88838,0,0,/xyfuuuuuu/flowers-tpu-concise-efficientnet-b7,Flower Classification with TPUs
8292891,0.96793,0,0,/zenerdiode818/flower-8-3-20,Flower Classification with TPUs
9062168,0.95347,0,0,/abhinavnreddy/tpu-flowerclassify,Flower Classification with TPUs
9473045,0.95404,0,5,/superficiallybot/efficient-net-b6,Flower Classification with TPUs
8784174,0.96628,0,0,/akashsuper2000/enetb7-model,Flower Classification with TPUs
9336779,0.96324,0,0,/qinhui1999/tpu-enet-b7-incepention-b6-new,Flower Classification with TPUs
9342362,0.96886,0,0,/qinhui1999/104-flowers-blending-0507,Flower Classification with TPUs
7925262,0.92988,1,0,/catadanna/getting-started-with-100-flowers-on-tpu,Flower Classification with TPUs
9378544,0.95685,0,4,/stevenevan99/ensemble-augmented-on-tpu,Flower Classification with TPUs
8733634,0.96333,6,30,/romanweilguny/tpu-flowers-first-love,Flower Classification with TPUs
9395437,0.95912,0,0,/aemulcahy/kernel74d3009a39,Flower Classification with TPUs
9352878,0.97824,3,13,/serosh/enetb7-512px-no-ensembling,Flower Classification with TPUs
9160548,0.9767,0,0,/zungmann/densenet-effnet-with-100-flowers-on-tpu,Flower Classification with TPUs
9276586,0.92777,0,0,/azazdeaz/tl-with-inception-v3-trained-on-inaturalist,Flower Classification with TPUs
14082020,0.06002,0,0,/dhawalsoni/dgk-knn,Don't Get Kicked!
13620658,0.0567299999999999,0,0,/varunsimhareddy/varun-don-t-get-kicked,Don't Get Kicked!
10954689,0.08791,1,11,/funxexcel/starter-code-don-t-get-kicked-rf-model,Don't Get Kicked!
10537807,0.06344,2,3,/julianbenny/don-t-get-kicked-knn,Don't Get Kicked!
9509800,0.0567299999999999,0,0,/dhruvgupta2801/don-t-get-kicked,Don't Get Kicked!
10230620,0.0567299999999999,0,0,/ramensingh/don-t-get-kicked,Don't Get Kicked!
517675,0.4403899999999999,0,0,/anunay17/logisticregression-0-44039,Spooky Author Identification
424829,0.58027,0,0,/ayanmaity/fork-of-spooky-1,Spooky Author Identification
7035956,0.519,0,1,/jozefc/voting-models,2019 Data Science Bowl
1561270,1.16319,4,25,/john850512/predict-future-sales-lstm,Predict Future Sales
1407882,1.0518299999999998,1,27,/sanket30/predicting-sales-using-lightgbm,Predict Future Sales
1268899,1.04551,0,3,/tkaleczyc/1-c-sale,Predict Future Sales
1097671,1.21744,0,11,/ashishpatel26/predict-future-sales,Predict Future Sales
1068001,1.06578,0,4,/nicapotato/cutting-edge-kiss-method,Predict Future Sales
1049649,1.04857,0,0,/plarmuseau/weekly,Predict Future Sales
1013356,1.04162,0,4,/plarmuseau/forecast-log,Predict Future Sales
909869,1.01976,6,20,/kcbighuge/predicting-sales-with-a-nested-lstm,Predict Future Sales
757327,1.29387,8,0,/holmesjiang/consider-some-feathers-by-one-hot,Predict Future Sales
687577,1.04683,3,20,/mrbeer/basic-time-series-preprocessing-and-filtering,Predict Future Sales
13384579,1.09516,0,0,/amitalexander/predicting-future-sales-with-neural-networks,Predict Future Sales
12133948,0.8923700000000001,0,0,/taidopurason/stacked-model,Predict Future Sales
11843256,9.10946,0,0,/fathialasali/predict-future-sales-comp,Predict Future Sales
11080089,0.90684,0,0,/swatisk2702/predict-shop-sales-xgboost-2,Predict Future Sales
10781195,-6.9023,4,15,/jameschapman19/dropout-as-bayesian-estimation,OSIC Pulmonary Fibrosis Progression
10769834,-6.879,1,7,/korosensie/osic-pulmonary-fibrosis-progression,OSIC Pulmonary Fibrosis Progression
10712207,-6.9755,1,11,/mavillan/lightgbm-quantile-regression,OSIC Pulmonary Fibrosis Progression
10813499,-6.9605,0,0,/rschanv5/baseline,OSIC Pulmonary Fibrosis Progression
10629273,-6.858,0,2,/souro12/osic-k-fold,OSIC Pulmonary Fibrosis Progression
10655739,-6.9431,9,23,/jameschapman19/pytorch-tabular-direct-prediction,OSIC Pulmonary Fibrosis Progression
10640281,-6.86,6,10,/dipampaul17/osic-420,OSIC Pulmonary Fibrosis Progression
10627728,-6.867999999999999,1,7,/satnam007/osic-003,OSIC Pulmonary Fibrosis Progression
10577409,-6.8718,9,41,/ulrich07/osic-keras-starter-with-tabular-data-comp-metrics,OSIC Pulmonary Fibrosis Progression
10592058,-7.03,0,10,/satnam007/osic-0002,OSIC Pulmonary Fibrosis Progression
10593066,-6.959,0,4,/kushagrawadhwa/model-1-1,OSIC Pulmonary Fibrosis Progression
10544522,-7.468,13,143,/titericz/tabular-simple-eda-linear-model,OSIC Pulmonary Fibrosis Progression
10556565,-6.959,9,31,/manojprabhaakr/basic-eda-dicom-images-and-lightgbm,OSIC Pulmonary Fibrosis Progression
474192,0.81112,0,0,/toorkp/churn-wsdm,WSDM - KKBox's Churn Prediction Challenge
11400002,1.0,2,3,/ozcan15/nlp-disaster-tweets-with-electra-base,Natural Language Processing with Disaster Tweets
11365515,0.79711,0,5,/choubane/disaster-tweets-very-simple-lstm-model,Natural Language Processing with Disaster Tweets
11414037,0.78087,0,0,/rsrinivasaraghavan/notebook8fbff622d4,Natural Language Processing with Disaster Tweets
11309718,0.8387899999999999,0,0,/doncovkonstantin/bayes-chooses-the-best-transformer-architecture,Natural Language Processing with Disaster Tweets
11324059,0.57033,0,2,/aakritsinghal/nlp-disaster-relief,Natural Language Processing with Disaster Tweets
7371124,0.82745,4,11,/rohitr4307/bert-real-or-not-nlp-with-diaster-tweets,Natural Language Processing with Disaster Tweets
11292835,0.8057,0,5,/ollywelch/nlp-with-disaster-tweets-glove-keras-lstm,Natural Language Processing with Disaster Tweets
11271397,0.0,0,1,/malahai/lightgbm-tf-idf-baseline,Natural Language Processing with Disaster Tweets
11250553,0.7983399999999999,1,5,/akshitrai/nlp-disaster-tweets,Natural Language Processing with Disaster Tweets
11223376,0.8014,2,19,/josutk/ensemble-stack-eda,Natural Language Processing with Disaster Tweets
11210584,0.7106899999999999,2,8,/sachinkadlimatti/nlp-real-or-not-disaster-prediction,Natural Language Processing with Disaster Tweets
10456252,0.79282,0,3,/kshtijroy/real-or-fake,Natural Language Processing with Disaster Tweets
10997615,0.81182,2,7,/atrisaxena/disaster-tweets-eda-tfidf-glove-bert,Natural Language Processing with Disaster Tweets
11000995,0.77198,2,20,/ssaketh97/prediction-with-pytorch-bilstm-gpu-glove,Natural Language Processing with Disaster Tweets
11092846,0.80294,1,3,/wongguoxuan/eda-tf-idf-voting-clfr-disaster-tweets,Natural Language Processing with Disaster Tweets
11538886,0.02038,3,23,/swarajshinde/mechanisms-of-action-moa-eda-lightgbm-baseline,Mechanisms of Action (MoA) Prediction
11553792,0.02081,3,15,/ajaykumar7778/fastai-tabular,Mechanisms of Action (MoA) Prediction
11555937,0.01995,2,9,/amanmishra4yearbtech/moa-keras-nn-with-swa,Mechanisms of Action (MoA) Prediction
11535683,0.02063,1,20,/senkin13/moa-lightgbm-starter-with-nonscored-meta-feature,Mechanisms of Action (MoA) Prediction
11521312,0.01903,13,107,/artgor/lish-moa-baseline-approach,Mechanisms of Action (MoA) Prediction
11545283,0.02149,0,1,/shashaalam/moa-drugs-prediction-with-catboost,Mechanisms of Action (MoA) Prediction
11540313,0.01955,1,15,/mudittiwari255/pytorch-lightning-baseline,Mechanisms of Action (MoA) Prediction
11531711,0.02149,1,20,/namanj27/catboost-moa-eda-starter,Mechanisms of Action (MoA) Prediction
11540243,0.02367,0,7,/adityak80/moa-eda-and-baseline-submission,Mechanisms of Action (MoA) Prediction
11534688,0.02035,0,8,/kuberiitb/mechanisms-of-action-prediction-various-models,Mechanisms of Action (MoA) Prediction
11531889,0.02024,0,4,/hongym7/base-model-for-beginner,Mechanisms of Action (MoA) Prediction
11521491,0.036,0,3,/aman2000jaiswal/explore-to-train,Mechanisms of Action (MoA) Prediction
11553798,0.03162,0,0,/thehumblefool11/notebook6f607a1000,Mechanisms of Action (MoA) Prediction
13569510,0.01893,0,0,/alexmorerog/k-folds,Mechanisms of Action (MoA) Prediction
13227846,0.01832,0,0,/yxohrxn/votingclassifier-predict-without-postprocessing,Mechanisms of Action (MoA) Prediction
13178066,0.01819,0,0,/shimaw28/blend-blend-blend,Mechanisms of Action (MoA) Prediction
13169514,0.0183599999999999,0,0,/huanghuangzhang/moa-pretrained-non-scored-targets-as-meta-features,Mechanisms of Action (MoA) Prediction
13101312,0.13094,0,0,/hasan7/moa-kera-wnorm-adamw-nonscored-targets,Mechanisms of Action (MoA) Prediction
13048201,0.06949,0,0,/ergisstepanov/baseline6,Mechanisms of Action (MoA) Prediction
6463949,0.01545,0,0,/funaki/fork-of-fork-of-funaki-nfl-3,NFL Big Data Bowl
7447346,0.9663,0,1,/tangchengshun/bengali-seresnext-prediction-with-pytorch1-0,Bengali.AI Handwritten Grapheme Classification
7896705,0.9211,3,16,/mobassir/se-resnext50-pytorch-inference,Bengali.AI Handwritten Grapheme Classification
7718030,0.9552,1,3,/shawon10/transfer-learning-bengali-graphemes-classification,Bengali.AI Handwritten Grapheme Classification
7782033,0.9518,1,2,/nerobugger/bengali-ai-eda-base-cnn,Bengali.AI Handwritten Grapheme Classification
7821230,0.9607,0,1,/amaity0/bengali-grapheme-third-inference,Bengali.AI Handwritten Grapheme Classification
7707401,0.8895,0,5,/taherhaggui/great-starter,Bengali.AI Handwritten Grapheme Classification
7607124,0.9438,0,0,/gaur128/bengali-graphemems-model-inference,Bengali.AI Handwritten Grapheme Classification
7594918,0.0614,0,2,/kaerunantoka/pred-bengali-resnet18-exp5,Bengali.AI Handwritten Grapheme Classification
7404020,0.9183,0,9,/bitthal/starterkernel-pytorch-resnet34,Bengali.AI Handwritten Grapheme Classification
2280111,0.69,0,0,/seynog/fork-attention-with-changed-weights,Quora Insincere Questions Classification
2356871,0.649,0,0,/strifonov/combined-embeddings,Quora Insincere Questions Classification
2385859,0.6920000000000001,0,1,/katerinaptv/srnnwith4clr,Quora Insincere Questions Classification
2403372,0.66,0,0,/xsakix/all-embeddings-f1,Quora Insincere Questions Classification
2403891,0.644,0,0,/xsakix/bilstm-att-base-classifier-word2vec,Quora Insincere Questions Classification
2379738,0.09,0,0,/slashtea/toxic-questions-classification,Quora Insincere Questions Classification
2346088,0.6940000000000001,6,43,/hengzheng/attention-capsule-why-not-both-lb-0-694,Quora Insincere Questions Classification
2358239,0.629,0,1,/xsakix/lstm-classifier-word2vec,Quora Insincere Questions Classification
2355319,0.518,0,2,/nagarajanngl/quora-classification-by-using-ml,Quora Insincere Questions Classification
2347764,0.547,0,0,/alexandruuu/glove-lr-cv,Quora Insincere Questions Classification
2121057,0.4379999999999999,1,1,/alexandruuu/spacy-preprocessing,Quora Insincere Questions Classification
2097410,0.693,54,309,/gmhost/gru-capsule,Quora Insincere Questions Classification
2324703,0.682,0,35,/christofhenkel/gru-crf,Quora Insincere Questions Classification
2352370,0.628,0,0,/xsakix/base-classifier-word2vec,Quora Insincere Questions Classification
2293672,0.622,0,0,/enigmadecoder/basic-model,Quora Insincere Questions Classification
2316886,0.3379999999999999,0,0,/alexfilippov/second,Quora Insincere Questions Classification
2336562,0.637,0,0,/xsakix/embeddings-lstm-simple,Quora Insincere Questions Classification
2212758,0.122,0,1,/nikhilroxtomar/tensorflow-bigru,Quora Insincere Questions Classification
2248142,0.691,3,15,/rasvob/let-s-try-clr-v3,Quora Insincere Questions Classification
2289026,0.657,3,1,/xsakix/all-embeddings-9,Quora Insincere Questions Classification
2268002,0.639,2,10,/brucedai003/no-cnn-no-rnn-pure-transformer-encoder,Quora Insincere Questions Classification
2277418,0.623,0,7,/richarde/cnn-n-gram,Quora Insincere Questions Classification
2263841,0.65,0,1,/xsakix/all-embeddings-3,Quora Insincere Questions Classification
2163582,0.682,0,0,/arretvice/dcq-with-attention-layer-stack-of-models,Quora Insincere Questions Classification
2282209,0.5920000000000001,0,0,/xsakix/all-embeddings-6,Quora Insincere Questions Classification
2231474,0.483,0,3,/tboyle10/baseline-models-with-downsampling,Quora Insincere Questions Classification
246721,0.52836,0,4,/ksayantani/initial-analysis,Quora Question Pairs
247867,0.37378,0,0,/plarmuseau/playing-with-anokas-xgboost-starter-0-35460-lb,Quora Question Pairs
996990,0.4328,32,189,/mindcool/unrolling-of-helices-outliers-removal,TrackML Particle Tracking Challenge
12851258,0.86759,0,5,/tunguz/instant-gratification-with-rapids,Instant Gratification
4386706,0.97464,0,0,/lovedm/fliy-label-and-gmm-v0-1-8-97480,Instant Gratification
6404727,0.80878,0,0,/rutviklathiya/logistic-regression-0-800,Instant Gratification
3997658,0.96948,0,1,/naushads/instant-gratification-2019,Instant Gratification
4685939,0.97522,0,0,/a70070947/instant-gratification-script,Instant Gratification
4420778,0.97475,0,6,/cpmpml/fork-of-cpmp-017-2-stage-mean-precisision-76aa21,Instant Gratification
4526270,0.97016,0,0,/bootiu/stacking-qda-nusvc-gmm,Instant Gratification
4407517,0.97446,12,32,/infinite/v2-all-gmm,Instant Gratification
4396481,0.97473,4,24,/waylongo/5th-solution,Instant Gratification
4415664,0.97486,3,18,/rinnqd/kmeans-4-clusters-per-label-gmm-33th-lb-9756,Instant Gratification
4315239,0.97483,1,9,/merkylove/10th-public-8th-private-solution,Instant Gratification
4406447,0.97425,2,4,/meistermorxrc/0-97598-in-private-score,Instant Gratification
4376622,0.97474,1,7,/jionie/shrunkcovariance-clusters-1-3-3-3-priviate-0-97576,Instant Gratification
4426478,0.97462,0,2,/taksants/few-cluster-per-class-0-975,Instant Gratification
4418009,0.97468,2,5,/graf10a/gmm-2-clusters-per-class,Instant Gratification
4397102,0.97022,1,3,/felipefonte99/ensemble,Instant Gratification
4339579,0.97134,0,0,/suuuuuu/flip-y-pl-qda-with-ls-feat,Instant Gratification
4409179,0.6899,0,0,/arunkumishra/simple-lr-split-on-magic-2,Instant Gratification
4297119,0.97,0,3,/jinbao/instant-gratification-jinbao,Instant Gratification
4428670,0.50835,0,0,/prysie/kerneldf3b6ac69f,Instant Gratification
4318921,0.96999,3,10,/taigokuriyama/instant-gratification-several-models-ensembling,Instant Gratification
4359013,0.9677,0,1,/avenkidur/easy-notebook,Instant Gratification
4326636,0.9698,0,12,/tenffe/try-to-go-up,Instant Gratification
2259315,0.61,0,0,/xsakix/all-embeddings-2,Quora Insincere Questions Classification
2235201,0.682,0,11,/rohandx1996/bad-text-out-of-my-class-rest-pls-pay-attention,Quora Insincere Questions Classification
2211908,0.6920000000000001,19,44,/suicaokhoailang/magic-numbers-is-all-you-need-0-692-lb,Quora Insincere Questions Classification
2216042,0.623,0,14,/jaguar00/xgboost-baseline,Quora Insincere Questions Classification
2202240,0.2019999999999999,0,0,/xsakix/fourth-try,Quora Insincere Questions Classification
2198206,0.647,0,19,/sh73ch/pytorch-torchtext-ignite-spacy,Quora Insincere Questions Classification
2183764,0.6920000000000001,72,412,/shujian/single-rnn-with-4-folds-clr,Quora Insincere Questions Classification
2209978,0.518,0,0,/guillermolissa/quoranaivebayes,Quora Insincere Questions Classification
2190027,0.561,0,1,/plarmuseau/tf-idf-approach-on-insincere-questions,Quora Insincere Questions Classification
2177169,0.6890000000000001,3,31,/nikhilroxtomar/gru-with-kfold-lb-0-689,Quora Insincere Questions Classification
2162061,0.7,44,273,/artgor/text-modelling-in-pytorch,Quora Insincere Questions Classification
2158424,0.685,5,30,/shujian/single-rnn-with-5-folds,Quora Insincere Questions Classification
2159352,0.669,4,56,/ziliwang/baseline-pytorch-bilstm,Quora Insincere Questions Classification
2139331,0.696,13,111,/ashishpatel26/nlp-text-analytics-solution-quora,Quora Insincere Questions Classification
2140126,0.6729999999999999,5,16,/manrunning/attention-is-all-you-need-with-embeddings,Quora Insincere Questions Classification
2140008,0.63,5,7,/joydeb28/lstm-please-upvote,Quora Insincere Questions Classification
2165303,0.6729999999999999,1,0,/dineshramasamy/hashing-trick-applied-to-embeddings,Quora Insincere Questions Classification
2098531,0.52,7,17,/fareise/multi-head-self-attention-for-text-classification,Quora Insincere Questions Classification
2098401,0.6829999999999999,7,60,/nikhilroxtomar/embeddings-cnn-lstm-models-lb-0-683,Quora Insincere Questions Classification
24315,0.14882,7,78,/omarelgabry/a-journey-through-rossmann-stores,Rossmann Store Sales
24208,0.11042,0,0,/smilesun/xgboost-feature-importance,Rossmann Store Sales
8355937,0.9363,0,0,/meysonua/simple-squeezenet,Bengali.AI Handwritten Grapheme Classification
8411504,0.9461,0,0,/cloudy4next/bengali-grapheme,Bengali.AI Handwritten Grapheme Classification
8308868,0.9605,0,1,/havingfun/bengali-ai-inference-from-abhishek-thakur-s-yt,Bengali.AI Handwritten Grapheme Classification
8267622,0.9708,5,40,/seesee/3-submit,Bengali.AI Handwritten Grapheme Classification
8264112,0.9488,0,4,/adarshsng/eda-d-cnn,Bengali.AI Handwritten Grapheme Classification
8254570,0.7212,2,11,/ipythonx/keras-stratified-training-ensemble,Bengali.AI Handwritten Grapheme Classification
8235771,0.9703,0,3,/nxrprime/keras-efficientnet-b3-with-image-preprocessing,Bengali.AI Handwritten Grapheme Classification
8090260,0.9152,0,0,/prishat/bengali-handwriting-classification-1,Bengali.AI Handwritten Grapheme Classification
7863645,0.9696,0,3,/vineeth1999/a-good-one,Bengali.AI Handwritten Grapheme Classification
8121714,0.5003,0,2,/davidlicause/bengali-ai-starter-kernel-with-resnet,Bengali.AI Handwritten Grapheme Classification
8103255,0.9624,9,11,/bmabir17/bengali-ai-inference,Bengali.AI Handwritten Grapheme Classification
8123934,0.9696,0,1,/marcelosanchezortega/version1-0-9696,Bengali.AI Handwritten Grapheme Classification
7899799,0.9658,0,0,/julienbeaulieu/bengaliai-model-inference-02-jb,Bengali.AI Handwritten Grapheme Classification
7966032,0.9506,1,6,/lkatran/base-model-bengali-graphemes-keras,Bengali.AI Handwritten Grapheme Classification
8058933,0.9529,0,0,/toruhasegawa/ocr-introduction-with-muti-output-cnn,Bengali.AI Handwritten Grapheme Classification
7921105,0.0631,0,0,/abebe9849/test-for,Bengali.AI Handwritten Grapheme Classification
7930006,0.9296,0,6,/salazarslytherin/grapheme-001,Bengali.AI Handwritten Grapheme Classification
14503479,0.84646,0,0,/sumeetsawant/stumble-upon-challenge-auc-private-lb-0-85,StumbleUpon Evergreen Classification Challenge
12968283,0.53874,0,0,/alexandrafedorova98/moa-fedorova,Mechanisms of Action (MoA) Prediction
12811618,0.01948,0,0,/alexandervc/moa36-2-logreg-blend-v2,Mechanisms of Action (MoA) Prediction
12280905,0.0185,0,0,/akashsuper2000/moa-ensemble,Mechanisms of Action (MoA) Prediction
11742389,0.0214199999999999,0,0,/avivlevi815/baseline-multi-catboost,Mechanisms of Action (MoA) Prediction
10967380,0.83726,0,0,/joswin/pytorch-models-try,Natural Language Processing with Disaster Tweets
10340126,0.80355,5,8,/machinemonk/80-nlp-disastrous-tweet-detection-using-glove,Natural Language Processing with Disaster Tweets
10981838,0.80815,0,8,/aditya08/twittersentiment-nbsvm-optuna-hyperparam-tuning,Natural Language Processing with Disaster Tweets
10938026,0.79742,0,1,/dibyawantrivedi/first-nlp-project,Natural Language Processing with Disaster Tweets
10905205,0.6733,0,0,/varshinithatiparthi/nlp-team-project,Natural Language Processing with Disaster Tweets
10879039,0.84278,0,15,/vbmokin/real-or-not-supershort-nlp-classification-nb,Natural Language Processing with Disaster Tweets
10637925,0.8210200000000001,0,3,/fstcap/twitter-transorfromer,Natural Language Processing with Disaster Tweets
10857169,0.8268399999999999,0,2,/avigupta2612/sentiment-analysis-using-bert,Natural Language Processing with Disaster Tweets
10758120,0.80386,0,4,/ambityga/lets-go-traditional-booooooo-bert,Natural Language Processing with Disaster Tweets
9307480,0.78945,0,2,/benyaengineering/svm-for-nlp1,Natural Language Processing with Disaster Tweets
10784248,0.8240799999999999,3,7,/tadiuz/real-or-not-universal-sentence-encoder,Natural Language Processing with Disaster Tweets
10436776,0.80171,0,8,/shruticode/disaster-classification,Natural Language Processing with Disaster Tweets
8296806,0.8026300000000001,2,2,/sabyasachi10/nlp-for-disaster-tweets,Natural Language Processing with Disaster Tweets
10740424,0.8004899999999999,0,3,/thalesbruno/nlp-getting-started-tutorial-top-38,Natural Language Processing with Disaster Tweets
11163166,-6.9348,2,4,/sovitrath/simple-linear-neural-network-using-pytorch,OSIC Pulmonary Fibrosis Progression
11229532,-7.7022,2,7,/jonykarki/pytorch-qr,OSIC Pulmonary Fibrosis Progression
11158261,-6.9503,0,4,/sovitrath/simple-linear-neural-network-regression,OSIC Pulmonary Fibrosis Progression
11093385,-7.3794,0,4,/donkeys/keras-mlp-with-extended-training-data,OSIC Pulmonary Fibrosis Progression
10986572,-6.8545,2,13,/an0utlier/baseline-model-using-nn-without-ct-scan-data,OSIC Pulmonary Fibrosis Progression
11089591,-6.8837,0,4,/rayanaay/osic-variable-s-adding-kmeans-dbscan,OSIC Pulmonary Fibrosis Progression
10966976,-7.2757,0,3,/jameschapman19/lgbm-quantile-nested-cv,OSIC Pulmonary Fibrosis Progression
10864139,-6.8672,2,4,/prem134/osic-pulmonary-fibrosis-progression-prem,OSIC Pulmonary Fibrosis Progression
10940351,-6.9205,0,8,/jameschapman19/pytorch-tabular-qr,OSIC Pulmonary Fibrosis Progression
10961532,-7.0751,0,0,/zhengyuandonshen/kernelfacb0742df,OSIC Pulmonary Fibrosis Progression
4526094,0.94349,0,0,/ostamand/submission-2-bert-gpt2,Jigsaw Unintended Bias in Toxicity Classification
4327831,0.68535,0,0,/sarveshsinghsvn/kernel451498e34a,Jigsaw Unintended Bias in Toxicity Classification
4176382,0.92561,0,0,/hzk123/total-preprocess,Jigsaw Unintended Bias in Toxicity Classification
4432150,0.93469,0,0,/weilinwuweilinwu/bidirectional-lstm-with-weighted-samples,Jigsaw Unintended Bias in Toxicity Classification
3471634,0.58655,0,2,/anubhav1302/j-toxicity-code,Jigsaw Unintended Bias in Toxicity Classification
7161791,0.0,0,0,/singhaditya5842/stacked-model,Jigsaw Unintended Bias in Toxicity Classification
6741493,0.0,0,0,/karan100194/kernel7c52d4d57a,Jigsaw Unintended Bias in Toxicity Classification
6850926,0.0,0,0,/johnlau/jigsaw-convulotional-lstm,Jigsaw Unintended Bias in Toxicity Classification
3878525,0.9236,0,0,/thinhn/data-sci-embedding-bidirectional-lstm,Jigsaw Unintended Bias in Toxicity Classification
4052902,0.91808,0,1,/soulmachine/jigsaw-pytorch-bert,Jigsaw Unintended Bias in Toxicity Classification
4360108,0.93428,0,0,/saileshmohanty/bert-trial,Jigsaw Unintended Bias in Toxicity Classification
5979479,0.0,0,1,/souravdas4/jigsaw-unintended-bias-in-toxicity-classification,Jigsaw Unintended Bias in Toxicity Classification
4092455,0.93834,0,0,/tanreinama/fastai-file-stream-training,Jigsaw Unintended Bias in Toxicity Classification
5798046,0.0,0,0,/charan977/kernel7b611e177b,Jigsaw Unintended Bias in Toxicity Classification
5617375,0.0,0,0,/ilyamironov/jigsaw-toxicity-classification-lstm-v4,Jigsaw Unintended Bias in Toxicity Classification
3753969,0.90146,0,0,/samarthsarin/lstm-with-text-cleaning,Jigsaw Unintended Bias in Toxicity Classification
4890138,0.0,0,3,/gauripradhan/toxicity-detection-using-bert-embeddings,Jigsaw Unintended Bias in Toxicity Classification
5006981,0.0,0,0,/gsailalitha/kerneld019288abf,Jigsaw Unintended Bias in Toxicity Classification
4881289,0.0,0,0,/kenil020/sentiment-analysis-for-toxicity-in-a-statement,Jigsaw Unintended Bias in Toxicity Classification
4915706,0.0,0,1,/pochineni/base-model-lstm-model-without-pretrained-embedding,Jigsaw Unintended Bias in Toxicity Classification
4323702,0.9517,0,0,/abhinav2308/courseera-ensemble-final,Predict Future Sales
4258573,1.09895,0,0,/mmcblk0p1/kernelc61c95f29c,Predict Future Sales
3593585,1.3152,0,0,/jatinmittal0001/predict-future-sales-part-1,Predict Future Sales
3749156,2.24481,0,1,/alechelyar/predict-future-sales-competition-entry,Predict Future Sales
3851705,1.22791,0,1,/alpharadia/lb-probing-public-private-split-and-simple-season,Predict Future Sales
2437798,1.21791,0,0,/amelnozieres/predict-future-sales-by-shop,Predict Future Sales
3313720,0.92415,23,111,/sarthakbatra/predicting-sales-tutorial,Predict Future Sales
3147712,1.47394,1,1,/dharm0us/replace-item-id-with-leave-one-out-feature,Predict Future Sales
2591563,1.16462,0,1,/earthshaker/coursera,Predict Future Sales
1661235,1.15959,0,8,/karanjakhar/predict-future-sale,Predict Future Sales
2100973,1.0199,27,117,/karanjakhar/simple-and-easy-aprroach-using-lstm,Predict Future Sales
1780958,1.23646,0,1,/bapanes/time-series-basics-expl-trad-ts-bapanes,Predict Future Sales
6870723,0.531,0,2,/vh1981/lgb-bayesian-blending,2019 Data Science Bowl
7158524,0.496,0,2,/keremt/fastai-model-part2-upgraded,2019 Data Science Bowl
6806388,0.524,6,55,/satsaras/data-bowl-let-s-regress-now,2019 Data Science Bowl
6875959,0.523,0,0,/adambang/model-and-predict-to-2019-data-science-bowl,2019 Data Science Bowl
468109,0.8762,0,0,/prakashpvss/simple-word-count-classifier,Spooky Author Identification
466439,0.57592,1,0,/anu0012/basic-approach-with-countvectorizer,Spooky Author Identification
443255,0.4201,0,3,/kamalkishor1991/basic-python-cnn-with-embeddings-42,Spooky Author Identification
439378,0.3741,4,13,/anu0012/basic-approach-with-neural-network,Spooky Author Identification
427173,1.14023,0,1,/mahendrasinghmeena/fork-of-using-embedding-layer-87a441,Spooky Author Identification
414383,0.46738,0,3,/ayanmaity/spooky,Spooky Author Identification
420580,0.35325,8,16,/sandpiturtle/eda-fe-nb-xgb,Spooky Author Identification
414324,0.37169,0,20,/rhodiumbeng/identifying-authors-who-wrote-that,Spooky Author Identification
408432,0.32693,75,255,/sudalairajkumar/simple-feature-engg-notebook-spooky-author,Spooky Author Identification
408890,0.38386,6,15,/defeater/baseline-horror-what-is-word2vec,Spooky Author Identification
408684,0.60959,4,11,/kanav0183/spooky-halloween-eda-lb-0-6,Spooky Author Identification
5414986,0.617,10,41,/saneryee/understanding-clouds-keras-unet,Understanding Clouds from Satellite Images
5435522,0.578,20,68,/frlemarchand/maskrcnn-for-cloud-classification-keras,Understanding Clouds from Satellite Images
5355006,0.645,270,685,/artgor/segmentation-in-pytorch-using-convenient-tools,Understanding Clouds from Satellite Images
1852192,0.6352300000000001,6,17,/returnofsputnik/remove-2007-and-2008-data,Two Sigma: Using News to Predict Stock Movements
1770535,0.5882,0,5,/richardgg93/two-sigma-news-first-try,Two Sigma: Using News to Predict Stock Movements
1792524,0.42445,0,2,/iliyas1155/a-simple-model-using-the-market-and-news-dd0b33,Two Sigma: Using News to Predict Stock Movements
1776916,-0.0437,0,3,/nyakamura/japanese-translated-getting-started-kernel,Two Sigma: Using News to Predict Stock Movements
1754586,0.57023,3,7,/poznyakovskiy/naive-prediction,Two Sigma: Using News to Predict Stock Movements
1750396,0.60359,15,61,/jannesklaas/lb-0-63-xgboost-baseline,Two Sigma: Using News to Predict Stock Movements
1750744,0.4985699999999999,0,20,/ashishpatel26/two-sigma-lightgbm-vs-xgboost-vs-extratree,Two Sigma: Using News to Predict Stock Movements
1734464,0.64927,24,156,/ashishpatel26/bird-eye-view-of-two-sigma-nn-approach,Two Sigma: Using News to Predict Stock Movements
1730088,0.0,30,133,/pestipeti/simple-eda-two-sigma,Two Sigma: Using News to Predict Stock Movements
2436659,0.6199100000000001,0,0,/cannsoygenc/200-estimator-size-0-25-6,Two Sigma: Using News to Predict Stock Movements
109470,1191.51947,0,0,/julienpantz/regression,Allstate Claims Severity
130334,0.0268418,0,0,/s55082/test-files,Santander Product Recommendation
6818796,78997.98,22,78,/isaienkov/genetic-algorithm-basics,Santa's Workshop Tour 2019
2776893,0.39592,0,1,/omoekan09/hpa-model-256,Human Protein Atlas Image Classification
2486398,0.416,1,1,/antitak/own-env-notebook,Human Protein Atlas Image Classification
2546401,0.06299,0,1,/gdmacmillan/hpa-notes-from-loss-development-and-eda,Human Protein Atlas Image Classification
2446940,0.3229999999999999,7,23,/hortonhearsafoo/fastai-v1-starter-pack-kernel-edition-lb-0-323,Human Protein Atlas Image Classification
2346263,0.035,0,1,/gaojunsu/adm-of-ee258-project2-seresnet50,Human Protein Atlas Image Classification
2352877,0.272,0,0,/anastasb/tensorflow-model-different-losses,Human Protein Atlas Image Classification
2256936,0.108,0,0,/smehta12/modeling-protein-identification,Human Protein Atlas Image Classification
2301677,0.255,0,2,/nikhilpandey360/beginner-eda-and-transfer-learning,Human Protein Atlas Image Classification
2067573,0.416,6,23,/wordroid/inceptionresnetv2-resize256-f1loss-lb0-419,Human Protein Atlas Image Classification
9074112,0.70785,0,0,/lucca9211/imageclassificationtpu,Flower Classification with TPUs
9172426,0.952,12,21,/chekoduadarsh/starters-guide-custom-cnn-standard-models-tpu,Flower Classification with TPUs
8924409,0.8760100000000001,0,0,/srashtigoyal/flowers-with-tpu,Flower Classification with TPUs
9082471,0.94153,0,1,/wangdaxia/cv-ar-homework,Flower Classification with TPUs
9109690,0.86905,2,1,/pratyakshajha/getting-started-on-100-flowers-with-fastai,Flower Classification with TPUs
8779246,0.97947,4,8,/tusharkendre/tpu-flowers,Flower Classification with TPUs
9113818,0.92765,0,0,/q597482326/flowers-classification,Flower Classification with TPUs
8681949,0.95654,0,0,/vasukalariya/flower2,Flower Classification with TPUs
8973010,0.8377700000000001,0,0,/xufangbin/flower,Flower Classification with TPUs
8898415,0.92843,0,0,/mamansour/kernel67acc5c039,Flower Classification with TPUs
8821906,0.94141,0,2,/calebeverett/efficientnetb6-with-transformation,Flower Classification with TPUs
8692198,0.67555,2,5,/shaunthesheep/flower-classification-with-tpus,Flower Classification with TPUs
8328724,0.96551,0,5,/jagannathrk/flower-classification-enet-b7-densenet,Flower Classification with TPUs
8435405,0.9579,0,0,/ysanojpn/flowers-on-tpu-efficient-inception-ens,Flower Classification with TPUs
7996020,0.88636,0,1,/gsdeepakkumar/working-with-tpus-learner-s-kernel,Flower Classification with TPUs
8186865,0.91115,0,2,/darwinwin/tpu-flowerclasses,Flower Classification with TPUs
8055037,0.90636,2,10,/kishor1210/tpu-flowerclasses,Flower Classification with TPUs
8029387,0.63094,0,1,/fireballbyedimyrnmom/tpus-and-flowers-v3,Flower Classification with TPUs
7994336,0.90651,0,0,/hddchelsea/100-flowers-on-tpu-enet-b7-lr,Flower Classification with TPUs
7938777,0.95728,3,8,/akihironomura/tpu-efficientnetb7-inceptionresnetv2,Flower Classification with TPUs
11107968,0.96432,0,0,/jumpingmandt/digital-recognition,Digit Recognizer
6342445,0.98585,0,0,/pandamia92/digit-recognizer,Digit Recognizer
11008433,0.97271,0,2,/simo333/mnist-base-model1,Digit Recognizer
11054002,0.99557,0,0,/mia111111/kernel404,Digit Recognizer
10965883,0.97792,2,5,/simo333/digit-recog,Digit Recognizer
10881722,0.9955,0,4,/darkcore/prediction-with-cnn,Digit Recognizer
11016032,0.98003,1,5,/hhs1516/mnist-digit,Digit Recognizer
11000118,0.98892,0,1,/rameez471/mnist-with-lenet-5,Digit Recognizer
10953593,0.99196,2,11,/ligtfeather/introduction-to-fastai,Digit Recognizer
10770776,0.99628,0,2,/lgreig/using-vgg16-for-mnist-in-keras-gpu,Digit Recognizer
10922821,0.10003,1,4,/hiromaru/fork-of-data-sciencee,Digit Recognizer
10854841,0.97453,1,1,/johntgz/mnist-cnn-2,Digit Recognizer
10682420,0.98239,0,0,/group02/kerneld7e123a424,Digit Recognizer
10876363,0.99121,0,0,/jaiminp/digit-recognition-mnist,Digit Recognizer
9743376,0.6920000000000001,9,86,/titericz/simple-baseline,SIIM-ISIC Melanoma Classification
9758586,0.897,0,14,/soham1024/melanoma-efficientnetb6-inference,SIIM-ISIC Melanoma Classification
9748155,0.8621,1,13,/anubhav1302/melanoma-efficientnet,SIIM-ISIC Melanoma Classification
11045641,0.9561,0,0,/akashsuper2000/eda-modelling-of-the-external-data-inc-ensemble,SIIM-ISIC Melanoma Classification
10492502,0.924,0,0,/akashsuper2000/efficientnet-x-384,SIIM-ISIC Melanoma Classification
7232441,0.457,0,1,/rokiso/dsb-kernel,2019 Data Science Bowl
6776846,0.535,0,8,/tarobxl/quick-and-dirty-regression-semi,2019 Data Science Bowl
3557430,0.40714,1,1,/scirpus/post-facto,Google Cloud & NCAA® ML Competition 2019-Women's
3327522,19.73677,0,17,/akash14/google-march-madness,Google Cloud & NCAA® ML Competition 2019-Women's
3125588,0.12347,0,16,/akash14/google-cloud-ncaa-ml-competition-2019-women-s,Google Cloud & NCAA® ML Competition 2019-Women's
2972059,0.44638,0,58,/ateplyuk/lgbm-str-w,Google Cloud & NCAA® ML Competition 2019-Women's
3332780,4.1668,0,0,/wangab/courtside-seat-2019w-competitiveness,Google Cloud & NCAA® ML Competition 2019-Women's
9350685,0.3064199999999999,0,0,/ouwyukha/imba-turicreate-itemsim-jaccard,Instacart Market Basket Analysis
9339702,0.30643,0,0,/ouwyukha/imba-turicreate-fr-sgd-pol,Instacart Market Basket Analysis
9339569,0.0930899999999999,0,0,/ouwyukha/imba-surprise-slopeone,Instacart Market Basket Analysis
9339488,0.0930899999999999,0,0,/ouwyukha/imba-surprise-baseline-als,Instacart Market Basket Analysis
3954483,0.3345599999999999,0,0,/charalambos/instacart-ml-2-notebook-try-5,Instacart Market Basket Analysis
10543827,-8.127,0,7,/user123454321/sample-submission,OSIC Pulmonary Fibrosis Progression
12073919,-6.8072,0,0,/akashsuper2000/higher-lb-score-by-tuning-mloss-upgrade-1696e2,OSIC Pulmonary Fibrosis Progression
11939100,-6.8073,0,0,/ruofanhe/copy-of-upgrade-1696e2-no-modify,OSIC Pulmonary Fibrosis Progression
11343010,-6.8195,0,0,/milan400/pulmonary-inference,OSIC Pulmonary Fibrosis Progression
3999002,0.92892,2,3,/sameerdev7/90-accuracy-using-double-lstm,Jigsaw Unintended Bias in Toxicity Classification
3946950,0.93298,0,0,/cabarajasb2019/second,Jigsaw Unintended Bias in Toxicity Classification
3965048,0.92155,0,0,/luisk1504/kernel8b0f797879-2,Jigsaw Unintended Bias in Toxicity Classification
3936150,0.86555,7,7,/arcisad/keras-bidirectional-lstm-self-attention,Jigsaw Unintended Bias in Toxicity Classification
3956734,0.93524,0,0,/fortizs/simple-lstm-with-identity-parameters-fastai,Jigsaw Unintended Bias in Toxicity Classification
3946530,0.93467,1,0,/gaguerrerora/miia-p3,Jigsaw Unintended Bias in Toxicity Classification
3909954,0.93537,0,6,/duykhanh99/lstm-fast-ai-tuning,Jigsaw Unintended Bias in Toxicity Classification
3877729,0.80596,0,1,/tomkpace/simple-nn-starter-kernel,Jigsaw Unintended Bias in Toxicity Classification
3836247,0.92197,1,6,/protan/lstm-cnn-torchtext-with-ignite,Jigsaw Unintended Bias in Toxicity Classification
3828035,0.64908,0,1,/vnbhat/nlp-jigsaw,Jigsaw Unintended Bias in Toxicity Classification
3826105,0.91394,0,2,/lachonman/fastai-lm-on-extended-dataset-cv-regression,Jigsaw Unintended Bias in Toxicity Classification
3668376,0.9181,0,1,/fafiab/kernelb5f3536e06,Jigsaw Unintended Bias in Toxicity Classification
3795681,0.92346,4,11,/artgor/pytorch-text-processing-and-other-things,Jigsaw Unintended Bias in Toxicity Classification
3812760,0.89586,0,1,/iavinas/toxicity-bias-tfidfvectorizer,Jigsaw Unintended Bias in Toxicity Classification
3709727,0.9337,1,14,/duykhanh99/nlp-basic,Jigsaw Unintended Bias in Toxicity Classification
3508823,0.9101,21,25,/nikhilsharma00/clean-data-keras-embbedings-cudnn-predict,Jigsaw Unintended Bias in Toxicity Classification
3438091,0.93217,7,34,/leighplt/pytorch-torchtext-glove,Jigsaw Unintended Bias in Toxicity Classification
3751592,0.91524,4,10,/adrianoavelar/lstm-eda-solution-for-jigsaw-toxicity,Jigsaw Unintended Bias in Toxicity Classification
3588494,0.5018600000000001,0,0,/mvpvalues/jigsaw-unintended-bias-kernel,Jigsaw Unintended Bias in Toxicity Classification
3687471,0.93375,0,4,/skumar2007ctae/text-pre-processing-applied-simple-lstm,Jigsaw Unintended Bias in Toxicity Classification
3654211,0.935,33,220,/bminixhofer/speed-up-your-rnn-with-sequence-bucketing,Jigsaw Unintended Bias in Toxicity Classification
11447202,1.0,5,22,/mitramir5/simple-bert-with-video,Natural Language Processing with Disaster Tweets
11929792,0.7539,0,0,/hajimetch/nlp-transformer,Natural Language Processing with Disaster Tweets
11852549,0.82929,1,10,/mastmustu/simple-bert-classification-model-using-torch,Natural Language Processing with Disaster Tweets
11792393,1.0,0,5,/pavan9065/nlp-disaster-tweets-with-electra-base,Natural Language Processing with Disaster Tweets
11680667,0.8004899999999999,0,0,/averywu/disaster-200912,Natural Language Processing with Disaster Tweets
11689381,0.6923,0,7,/jaipaldeora/simple-detection-of-real-disasters,Natural Language Processing with Disaster Tweets
11387901,0.73766,5,6,/saitej31/nlp-getting-started,Natural Language Processing with Disaster Tweets
11575483,0.5976,0,5,/avloss/naive-umap-nearest-neighbour,Natural Language Processing with Disaster Tweets
11542808,0.78976,2,10,/ujjwalsharma26/nlp-ml-tfidf-svc-and-dl-glove-bilstm-approach,Natural Language Processing with Disaster Tweets
11449263,0.80753,0,1,/koheishima/nlp-word2vector-and-lgbm,Natural Language Processing with Disaster Tweets
11373963,0.82837,0,1,/ollywelch/nlp-with-bert-and-tpu,Natural Language Processing with Disaster Tweets
11559912,0.7821,0,0,/naniwazu/basic-eda-cleaning-and-glove,Natural Language Processing with Disaster Tweets
11485075,0.7912899999999999,1,8,/prakashjiban/nlp-with-feature-engineering,Natural Language Processing with Disaster Tweets
232223,0.48905,0,0,/mttzju/quora-eda-model-selection-roc-pr-plots,Quora Question Pairs
228602,0.48905,0,0,/woters/quora-eda-model-selection-roc-pr-plots,Quora Question Pairs
3960678,0.59334,1,30,/baomengjiao/something-interesting-about-magic,Instant Gratification
4017078,0.72749,0,0,/avenkidur/code-golf-cognitive-illusion-ruler-check,Instant Gratification
3950871,0.73763,12,73,/abhishek/neural-network-with-embedding-layer,Instant Gratification
3971552,0.95511,2,0,/autuanliuyc/baseline-for-instant-gratification,Instant Gratification
3941606,0.70796,3,10,/bigironsphere/starter-lightgbm-with-some-fine-tuning,Instant Gratification
3941917,0.81091,0,29,/dimitreoliveira/instant-gratification-deep-learning,Instant Gratification
3952542,0.80878,0,3,/shivamsarawagi/instantgratification,Instant Gratification
3936774,0.78428,2,12,/robikscube/eda-and-baseline-lgb-for-instant-gratification,Instant Gratification
3946717,0.69308,0,3,/rakibilly/fastai-starter-instant-gratification,Instant Gratification
3940973,0.79737,2,8,/atikur/instant-gratification-keras-starter,Instant Gratification
3936393,0.57321,0,9,/naivelamb/xgb-starter,Instant Gratification
3940067,0.5533100000000001,0,0,/ryomaekura/easy-data-visualization-lightgbm-stater-code,Instant Gratification
4415711,0.97451,0,0,/yoshiyukiohiwa/honmei-c3-rseed-max-nsplit11,Instant Gratification
4392432,0.97479,0,0,/tks0123456789/gl-gm,Instant Gratification
4311308,0.8841600000000001,0,0,/ma7555/svm-radial-basis-function,Instant Gratification
3950021,0.5071100000000001,0,0,/lsinev/ig-5fold-catboost-gpu-with-lr-meta,Instant Gratification
13773586,0.2690099999999999,1,1,/dpipi17/pytorch-and-bert-demetre-pipia,Quora Question Pairs
10148429,0.4229199999999999,0,0,/ddliky/malstm-with-word2vec,Quora Question Pairs
6272905,0.3565,0,4,/qqgeogor/starter-keras-siamese,Quora Question Pairs
4410033,0.16898,4,16,/benjaminkz/quora-question-pairs-xgboost,Quora Question Pairs
4303313,12.75887,0,1,/mashavasilenko/quora-questions-pairs-hw3,Quora Question Pairs
2205306,0.37928,0,0,/dineshramasamy/gru-similarity-network,Quora Question Pairs
1124965,0.35405,0,2,/wx854811200/data-analysis-xgboost-starter-0-35460-lb,Quora Question Pairs
4335154,0.96579,0,4,/code1110/which-classifier-to-use-in-the-stacking,Instant Gratification
4327192,0.71745,0,2,/bejeweled/plsregression-instant-gratification,Instant Gratification
4311508,0.96975,0,1,/nikhileshp12/gratification-through-discrimination,Instant Gratification
4274634,0.76258,0,46,/ateplyuk/starter,Instant Gratification
4297568,0.81113,0,3,/tourist55/fastai-tabular,Instant Gratification
4196190,0.50002,4,1,/delai50/instant-gratification-0-50002,Instant Gratification
4221700,0.96965,12,135,/christofhenkel/graphicallasso-gaussianmixture,Instant Gratification
4138707,0.9659,0,7,/gstvolvr/instant-gratification-focus-on-the-stragglers,Instant Gratification
4214379,0.9628,0,10,/pikamonique/my-first-gratification,Instant Gratification
4152077,0.96948,97,481,/cdeotte/pseudo-labeling-qda-0-969,Instant Gratification
4170847,0.9697,17,58,/yizhitao/flip-y-lb-0-9697,Instant Gratification
4184885,0.96971,4,11,/tobikaggle/flip-y-lb-0-9697,Instant Gratification
4139076,0.96623,7,10,/bootiu/ensemble-votingclassifier,Instant Gratification
4091614,0.96774,3,12,/onslaught/pca-qda-nusvc-knn-0-96774,Instant Gratification
4178871,0.97369,1,1,/mks2192/kernel35fff90608,Instant Gratification
4086748,0.9661,12,78,/graf10a/single-qda-lb-0-96610-time-1-min,Instant Gratification
4034501,0.95985,14,36,/hyeonho/pca-nusvc-0-95985,Instant Gratification
4025589,0.9585,5,29,/jazivxt/bojan-chris-cv-2,Instant Gratification
4021835,0.93379,2,7,/hogehogewhale/nusvc-by-bayesianoptimal,Instant Gratification
4017428,0.95814,4,8,/indranilbhattacharya/bojan-chris-cv,Instant Gratification
3989198,0.52142,1,2,/iavinas/instant-gratification,Instant Gratification
7333766,0.9722,0,0,/ibraheemmoosa/bangla-handwritten-grapheme-inference-ensemble,Jigsaw Unintended Bias in Toxicity Classification
13749711,0.9573,0,0,/jamesccc/bengali-multihead,Jigsaw Unintended Bias in Toxicity Classification
8379542,0.9849,0,0,/lightnezzofbeing/5-fold-average,Jigsaw Unintended Bias in Toxicity Classification
10914334,0.9613,0,1,/abisheksrivastav/bengali-ai,Bengali.AI Handwritten Grapheme Classification
7528397,0.9493,0,0,/thetrueharvey/handwritten-grapheme-classification,Bengali.AI Handwritten Grapheme Classification
8396285,0.9566,0,0,/haoche/nodrop-16-256-resnet-18,Bengali.AI Handwritten Grapheme Classification
9826717,0.4873,0,0,/mashiat/googlenet-resnet,Bengali.AI Handwritten Grapheme Classification
8302209,0.9703,0,3,/salazarslytherin/keras-efficientnet-b3-training-inference,Bengali.AI Handwritten Grapheme Classification
8083371,0.9167,0,0,/mrnastik/cnn-finally-implemented,Bengali.AI Handwritten Grapheme Classification
9024735,0.9737,0,0,/shanshanpeng/10th-another-try,Bengali.AI Handwritten Grapheme Classification
7198004,0.9685,0,1,/vincentyong97/efficientnet-b7-inference,Bengali.AI Handwritten Grapheme Classification
8993530,0.9861,0,0,/mohammadzunaed/efficientnet-b5-inference-kernel-pytorch,Bengali.AI Handwritten Grapheme Classification
8694569,0.962,0,0,/leonisviridis/bengali-submission-3-0,Bengali.AI Handwritten Grapheme Classification
7795369,0.0614,0,0,/bmk007/bengali-handwritten-grapheme,Bengali.AI Handwritten Grapheme Classification
8181584,0.966,0,0,/srjony/densenet161-training-inference-no-aug-0-9334,Bengali.AI Handwritten Grapheme Classification
8522903,0.5444,0,15,/linshokaku/cyclegan-submission,Bengali.AI Handwritten Grapheme Classification
8471070,0.9941,26,54,/bamps53/private0-9704-tpu-keras-metric-learning,Bengali.AI Handwritten Grapheme Classification
8206998,0.9596,0,0,/egm108/top15-kernel,Bengali.AI Handwritten Grapheme Classification
8336062,0.9536,0,0,/shawon10/efficientnetb3-inference,Bengali.AI Handwritten Grapheme Classification
8352982,0.9777,0,6,/sachingarg1998/3-multi-model-inference-tpu,Bengali.AI Handwritten Grapheme Classification
8428513,0.9735,1,7,/anmspro/ghostnet-densenet121-inference-first-layer,Bengali.AI Handwritten Grapheme Classification
7125601,0.0614,1,4,/anmspro/first-trial-submission,Bengali.AI Handwritten Grapheme Classification
8410228,0.9515,1,4,/shivyshiv/inference-of-efficientnet-gridmask,Bengali.AI Handwritten Grapheme Classification
99183,0.02077,12,9,/apapiu/neural-network-through-keras,Leaf Classification
10705289,0.77873,0,1,/deepakat002/lstm-and-gru-models,Natural Language Processing with Disaster Tweets
12416933,0.78394,0,2,/mayur7garg/nlp-for-disaster-tweets,Natural Language Processing with Disaster Tweets
7305823,0.79681,0,0,/meenavyas/realornot-nlpwithdisastertweets,Natural Language Processing with Disaster Tweets
12276627,0.6110899999999999,1,6,/jayantawasthi/disaster-tweet-with-ml-and-keras,Natural Language Processing with Disaster Tweets
12182973,0.82286,1,3,/infof4221wang/disasternlp-bert-model-using-keras,Natural Language Processing with Disaster Tweets
12165368,0.80876,0,1,/barthelemy/bert-nlp-augmentation-fastai-classification,Natural Language Processing with Disaster Tweets
12201236,0.79405,0,3,/naim99/text-classification-grid-svm,Natural Language Processing with Disaster Tweets
12108082,0.8152,0,1,/maleroy/keras-bert-with-text-and-location-preprocessing,Natural Language Processing with Disaster Tweets
12065083,0.79374,2,11,/leodaniel/real-or-not-nlp-basic-solutions,Natural Language Processing with Disaster Tweets
12143242,0.78915,0,0,/rushitav/disaster-tweets,Natural Language Processing with Disaster Tweets
12112681,0.79466,2,5,/samawel97/disaster-tweets-logistic-regression-vs-svm,Natural Language Processing with Disaster Tweets
12092518,0.8004899999999999,0,0,/samawel97/predicting-disaster-tweets-logistic-regression,Natural Language Processing with Disaster Tweets
12002390,0.75329,0,1,/saquibhashmi/counter-disaster-tweet-with-ai-from-head-to-toe,Natural Language Processing with Disaster Tweets
12047061,0.7919,0,1,/maciejgronczynski/nlp-1-2,Natural Language Processing with Disaster Tweets
12022178,0.59117,0,0,/samaamansour/third-submission,Natural Language Processing with Disaster Tweets
12002375,0.7444,0,2,/chitramdasgupta/disaster-or-not,Natural Language Processing with Disaster Tweets
11957418,0.64787,0,7,/thomaskonstantin/disaster-tweets-analysis-and-prediction,Natural Language Processing with Disaster Tweets
12023430,0.80416,0,0,/rehamshouman/notebook3393b7b3b5,Natural Language Processing with Disaster Tweets
49685,0.834829,0,0,/abhishek333/santander,Santander Customer Satisfaction
49591,0.8368540000000001,0,0,/aesnau/code1,Santander Customer Satisfaction
48013,0.740298,0,0,/robertehshi/adabooster,Santander Customer Satisfaction
47750,0.5868760000000001,0,0,/robertehshi/principalvectormachine,Santander Customer Satisfaction
46671,0.8387690000000001,0,0,/alphalep/model-and-feature-selection-then-xgboos,Santander Customer Satisfaction
44790,0.834226,0,0,/rishikksh20/basic-analysis-1,Santander Customer Satisfaction
10982355,0.51789,0,0,/astronautl/kernel5e7e679136,Sentiment Analysis on Movie Reviews
8345890,0.5757800000000001,0,1,/ashmitsit/movie-review,Sentiment Analysis on Movie Reviews
7497960,0.6412,0,0,/wernerechezuria/sentiment-analysis-movie-reviews,Sentiment Analysis on Movie Reviews
5860807,0.6978,0,3,/ymcdull/bert-experiment,Sentiment Analysis on Movie Reviews
4350041,0.53623,0,0,/madhavan93/sentiment-analysis-rbf-based-svm,Sentiment Analysis on Movie Reviews
1860696,0.51789,1,11,/ynouri/rotten-tomatoes-sentiment-analysis,Sentiment Analysis on Movie Reviews
4357425,0.94221,2,7,/prithvi1029/unprocessed-comments-worked-well,Jigsaw Unintended Bias in Toxicity Classification
4839565,0.0,0,0,/takumiito/commented-bert-a-fine-tuning-example,Jigsaw Unintended Bias in Toxicity Classification
4637347,0.0,0,1,/jaehyeongan/jigsaw-toxicity-basic-embedding-lstm,Jigsaw Unintended Bias in Toxicity Classification
4502115,0.0,3,56,/sakami/single-lstm-3rd-place,Jigsaw Unintended Bias in Toxicity Classification
4525206,0.8739299999999999,0,4,/osciiart/character-level-cnn,Jigsaw Unintended Bias in Toxicity Classification
4350410,0.9312,0,5,/tenffe/fastai-bert-inference-with-9-epoch,Jigsaw Unintended Bias in Toxicity Classification
4317730,0.91714,0,4,/jt0321/fine-tuning-bert-with-mxnet-gluon,Jigsaw Unintended Bias in Toxicity Classification
4253959,0.93091,1,7,/aussie84/basic-lstm-explanatory-walkthrough,Jigsaw Unintended Bias in Toxicity Classification
4283852,0.93802,0,1,/takumiito/bert-lstm-rank-blender-commented,Jigsaw Unintended Bias in Toxicity Classification
4290378,0.92499,0,1,/shubham505/keras-lstm-5-fold,Jigsaw Unintended Bias in Toxicity Classification
4279100,0.91914,0,0,/k123321141/bert-baseline,Jigsaw Unintended Bias in Toxicity Classification
4085485,0.93875,16,101,/timon88/bert-lstm-simple-blender-0-93844-lb,Jigsaw Unintended Bias in Toxicity Classification
4012253,0.90159,0,0,/kishore6157/jigsaw-toxicity-classification-lstm-v4,Jigsaw Unintended Bias in Toxicity Classification
14177670,1798775.8714,6,8,/khanhdnguyen/restaurantrevenueprediction,Restaurant Revenue Prediction
13503938,1908003.7978,0,0,/akm132000/restaurant-review-prediction,Restaurant Revenue Prediction
12087342,1749604.22911,1,0,/massyue/ds-stdy-restaurant-uezato,Restaurant Revenue Prediction
12343159,1683846.79538,6,10,/allenkong/restaurant-revenue-prediction,Restaurant Revenue Prediction
11682568,1907762.50662,0,0,/garryarielcussoy/predict-restaurant-revenue-using-regression,Restaurant Revenue Prediction
11097956,1856289.96954,0,0,/israakhalil/restaurant-revenue-prediction-small,Restaurant Revenue Prediction
10908891,2265650.39268,1,12,/abdalazez/restaurant-revenue-predictive,Restaurant Revenue Prediction
10702707,2297586.35414,0,1,/fatimaafifi/restaurant-revenue-prediction-rf,Restaurant Revenue Prediction
10424951,1752629.9450299996,0,7,/vibeeshk/restaurant-revenue-prediction,Restaurant Revenue Prediction
7964633,1826793.54611,1,3,/taruto1215/stacking-tutorial-xgb-lgbm-catboost-mlp-svr-knn,Restaurant Revenue Prediction
6535572,2065741.28344,0,2,/harshitt21/restaurant-revenue-prediction,Restaurant Revenue Prediction
5729566,2332489.89607,1,2,/abeerabuzayed/restaurant-revenue-prediction-v2,Restaurant Revenue Prediction
5466844,1972932.78106,0,0,/adibakm/restaurant-revenue-prediction,Restaurant Revenue Prediction
2637869,1942633.75467,0,1,/myxiaolu/restaurant-new,Restaurant Revenue Prediction
2081043,1704818.98772,0,2,/jquesadar/1st-place-restaurant-revenue-competition,Restaurant Revenue Prediction
11410370,2034798.07617,0,0,/nakagawam/20200828-restaurant-ver01,Restaurant Revenue Prediction
11247216,0.99557,0,1,/mfrancis23/intro-to-efficientnet-0-99646-accuracy,Digit Recognizer
11282263,1.0,1,1,/ghaiyur/mnist-vcv2,Digit Recognizer
11145042,0.978,0,0,/barshalomlaniado/digits-recognition,Digit Recognizer
10972425,0.99142,0,1,/kushagrakinjawadekar/handwrittendigit-recognition,Digit Recognizer
8738762,1.0,1,18,/brendan45774/digit-identifier-solution,Digit Recognizer
11204806,0.9891,0,0,/nbe1233/digit-recognizer-cnn,Digit Recognizer
11179877,0.97478,0,4,/mikhailg0/digit-recognizer-solution-sklearn,Digit Recognizer
11168270,0.99157,11,15,/datawarriors/digit-recognizer-detailed-step-wise,Digit Recognizer
4401419,0.99285,0,0,/peraktong/20190619-basic-example,Digit Recognizer
11125973,0.96882,0,9,/rahulsingh731/mnist-autoencoder-and-classification-easy,Digit Recognizer
10941180,0.99178,1,5,/aritrag/mnist-demo,Digit Recognizer
11108214,0.97975,2,3,/aakashawesome/cnn-model-digit,Digit Recognizer
13782706,0.43714,0,0,/jhotor/spooky-classification-albert,Spooky Author Identification
13600335,0.3764,0,1,/nicapotato/spooky-simple-bert,Spooky Author Identification
10553837,1.01884,0,2,/hu18838965591/kernel-spooky,Spooky Author Identification
3969515,0.38483,0,1,/rhodiumbeng/generate-word2vec-word-embeddings,Spooky Author Identification
3332653,0.85015,0,0,/amosroei/word-count-ratio-and-difference-by-author,Spooky Author Identification
1368075,0.5448,0,0,/zhoulingyan0228/spooky-author-id-simple-models-on-word-count,Spooky Author Identification
1193134,0.58027,0,4,/pritambar/multiclass-classification-naive-bayes,Spooky Author Identification
988484,0.44317,1,1,/limitpointinf0/sentiment-and-naive-bayes,Spooky Author Identification
666217,0.51904,0,2,/antmarakis/word-count-and-naive-bayes,Spooky Author Identification
433447,0.39938,0,1,/jeru666/my-spooky-notebook,Spooky Author Identification
479184,1.4355,0,8,/suyue715/tfidfvectorizer,Spooky Author Identification
6654012,0.6544,0,1,/qkrwlsdn96/kernel4a2d06485a,Understanding Clouds from Satellite Images
6640837,0.59236,0,2,/kaivan29/maskrcnn-for-cloud-classification-keras,Understanding Clouds from Satellite Images
6830010,0.65545,0,1,/jagannathrk/cloud-classifier-for-post-processing,Understanding Clouds from Satellite Images
6636264,0.6649,2,8,/nemethpeti/ensemble,Understanding Clouds from Satellite Images
6338895,0.6594,0,1,/brijesh41/cloud-notebook-dl,Understanding Clouds from Satellite Images
6446539,0.628,39,60,/cdeotte/train-with-crops-lb-0-63,Understanding Clouds from Satellite Images
6145495,0.655,3,11,/datachampion/keras-efficientnetb4,Understanding Clouds from Satellite Images
6076977,0.5489999999999999,6,39,/artgor/classification-in-catalyst-with-utility-scripts,Understanding Clouds from Satellite Images
5858107,0.242,0,3,/anubhav1302/simple-clouds-masking-unet,Understanding Clouds from Satellite Images
5581710,0.649,20,79,/ryches/turbo-charging-andrew-s-pytorch,Understanding Clouds from Satellite Images
8007122,0.95744,0,0,/adeelajmal/flower-classification-using-tpu,Flower Classification with TPUs
7983606,0.81785,0,4,/phunghieu/flowers-with-tpu-vgg19-focalloss,Flower Classification with TPUs
7983588,0.8669100000000001,0,3,/phunghieu/flowers-with-tpu-mobilenet-focalloss,Flower Classification with TPUs
7977149,0.94116,0,6,/phunghieu/flowers-with-tpu-densenet201-focalloss,Flower Classification with TPUs
7962293,0.96043,3,23,/ragnar123/4-kfold-densenet201,Flower Classification with TPUs
7959038,0.94885,2,12,/phunghieu/flowers-with-tpu-efficientnetb7-focalloss,Flower Classification with TPUs
7934619,0.96338,11,46,/msheriey/flowers-on-tpu-ensemble-lr-schedule,Flower Classification with TPUs
7931131,0.87111,0,1,/sharansmenon/flower-classification-tensorflow-tpu,Flower Classification with TPUs
7919724,0.5625399999999999,1,7,/duketemon/getting-started-with-100-flowers-on-tpu,Flower Classification with TPUs
7735571,0.25443,67,356,/mgornergoogle/getting-started-with-100-flowers-on-tpu,Flower Classification with TPUs
8225632,0.96431,0,0,/qinhui1999/kernel4-aug,Flower Classification with TPUs
8053934,0.7296600000000001,0,0,/guitaricet/super-minimalistic-starter,Flower Classification with TPUs
7597656,68888.04,4,15,/wrrosa/optimal-solution-s,Santa's Workshop Tour 2019
7559734,69463.76,1,7,/wrrosa/mip-preference-semi-accounting-cost,Santa's Workshop Tour 2019
7231408,69092.34,3,5,/lucamassaron/mixup-of-public-kernels-to-reach-69098-14,Santa's Workshop Tour 2019
7420756,71232.4,0,1,/dmintry/heuristic-ensemble-c,Santa's Workshop Tour 2019
7249357,69761.84,8,85,/golubev/optimization-preference-cost-mincostflow,Santa's Workshop Tour 2019
6905779,275062.49,2,3,/toshikazuwatanabe/santa-q-learning,Santa's Workshop Tour 2019
7087660,69983.82,25,80,/chudak/another-pytorch-implementation,Santa's Workshop Tour 2019
7021414,70754.2,1,13,/drcapa/santas-tour-bazaar-optimization,Santa's Workshop Tour 2019
6942227,72100.02,7,27,/kathakaliseth/santa-s-assistant-learning-lp-from-others,Santa's Workshop Tour 2019
6915535,72057.06,43,127,/vipito/santa-ip,Santa's Workshop Tour 2019
6891809,9318062.06,7,18,/marlesson/genetic-algorithm-with-deap,Santa's Workshop Tour 2019
6842098,117334.58,4,22,/pavelvod/pytorch-starter-solution,Santa's Workshop Tour 2019
6829336,77246.77,8,52,/nickel/santa-s-2019-fast-pythonic-cost-23-s,Santa's Workshop Tour 2019
6825814,84433.08,17,36,/pulkitmehtawork1985/fast-jonker-volgenant-algorithm,Santa's Workshop Tour 2019
6820238,78350.24,5,38,/ilu000/greedy-dual-and-tripple-shuffle-with-fast-scoring,Santa's Workshop Tour 2019
6810758,127758.94,3,42,/sekrier/fast-scoring-using-c-42-usec,Santa's Workshop Tour 2019
6797003,419234.51,5,21,/pulkitmehtawork1985/fast-optimization-on-greedy-initialization,Santa's Workshop Tour 2019
6795859,10552936.54,3,5,/kaushal2896/random-assignment-benchmark,Santa's Workshop Tour 2019
109617,1208.3061,5,5,/alazark/allstateclaims,Allstate Claims Severity
13139806,0.14689,0,0,/parkerhyde/cgl-pytorch,Conway's Reverse Game of Life 2020
13184958,0.05416,3,11,/ebouteillon/top-10-with-first-genetic-algorithm-on-gpu,Conway's Reverse Game of Life 2020
11781603,0.00639,1,2,/ebouteillon/conway-commit,Conway's Reverse Game of Life 2020
13157828,0.1816,0,2,/lluis94/setting-treshold-for-major-accuracy,Conway's Reverse Game of Life 2020
12768963,0.14277,0,0,/motivic/cnn-model-redux,Conway's Reverse Game of Life 2020
13339287,103502.0,0,1,/x2alexteam/drones-and-warehouses,Hash Code Archive - Drone Delivery
13360404,114269.0,3,11,/royceda/drone-linear-prog-and-routing-heuristic-90-done,Hash Code Archive - Drone Delivery
11973669,113851.0,3,11,/spacelx/2020-hc-dd-sample-submission-evaluation,Hash Code Archive - Drone Delivery
11889745,81762.0,9,15,/yegorbiryukov/hash-code-archive-swarm-of-benders-delivery,Hash Code Archive - Drone Delivery
11620746,93985.0,18,103,/jpmiller/application-of-google-or-tools,Hash Code Archive - Drone Delivery
11634287,999.0,2,10,/seraphwedd18/naive-approach-to-tsp-type-solution,Hash Code Archive - Drone Delivery
13741497,0.226,0,1,/c7934597/both-zones-2class-object-detection-strict-filter,NFL 1st and Future - Impact Detection
14018577,0.4387,3,29,/ks2019/yolo-with-player-assignment-pp,NFL 1st and Future - Impact Detection
13233874,0.1329,0,9,/ahmedewida/nfl-object-detection-inference,NFL 1st and Future - Impact Detection
11451825,44.2631,1,5,/doanquanvietnamca/submit-cyclegan,I’m Something of a Painter Myself
14539880,73.0476,0,0,/peaceduck/monet-cyclegan-tutorial,I’m Something of a Painter Myself
14662060,0.307,13,27,/samusram/hpa-rgb-model-rgby-cell-level-classification,Human Protein Atlas - Single Cell Classification
14533016,0.0,6,6,/akhileshdkapse/hpa-cell-classification-efficientnets-tpu-infer,Human Protein Atlas - Single Cell Classification
14618388,0.84216,4,11,/kirillklyukvin/playground-series-february-21,Tabular Playground Series - Feb 2021
14693263,0.8477299999999999,15,9,/ryanzhang/pytorch-dae-starter-code,Tabular Playground Series - Feb 2021
14686246,0.86779,0,0,/tracyporter/feb-21-linear-regression,Tabular Playground Series - Feb 2021
14672177,0.8422,6,19,/tunguz/tps-feb-2021-with-lgbmregressor,Tabular Playground Series - Feb 2021
14605605,0.84221,7,26,/ttahara/tps-feb-2021-3gbdts-ensemble-baseline,Tabular Playground Series - Feb 2021
14604774,0.8504200000000001,2,8,/hamditarek/tp-series-feb-21-catboost-classifier-gpu,Tabular Playground Series - Feb 2021
14625134,0.8436100000000001,10,14,/rajgandhi/tabular-playground-box-cox-transform-k-fold-cv,Tabular Playground Series - Feb 2021
14603036,0.84503,0,2,/drcapa/playground-series-feb-2021-xgb-tutorial,Tabular Playground Series - Feb 2021
14603097,0.84405,0,0,/nayuts/starter-lgbm-with-lightgbm-tuner,Tabular Playground Series - Feb 2021
14616428,0.8608299999999999,0,0,/jmsayson/get-started-feb-tabular-playground-competition,Tabular Playground Series - Feb 2021
13307657,38.77648,0,0,/unfriendlyai/cyclegan-with-dg-pretraining,I’m Something of a Painter Myself
11550292,38.38419,0,4,/animesh2099/monet-gan,I’m Something of a Painter Myself
12454038,46.58969,1,17,/bootiu/cyclegan-pytorch-lightning,I’m Something of a Painter Myself
12394323,79.23843000000002,2,6,/matkneky/monet-cyclegan-trials,I’m Something of a Painter Myself
11453571,40.40002,2,46,/doanquanvietnamca/the-beauty-of-cyclegan,I’m Something of a Painter Myself
10667469,0.79537,0,5,/janninga/petals-to-the-metal,Petals to the Metal - Flower Classification on TPU
10609970,0.8341799999999999,0,5,/burhangarari/petals-to-the-metal-using-xception-model,Petals to the Metal - Flower Classification on TPU
10546601,0.8175899999999999,2,14,/salmaneunus/petals-to-the-metals-competition-inceptionv3,Petals to the Metal - Flower Classification on TPU
10503327,0.8722700000000001,0,2,/trideepdas42291/exercise-create-your-first-submission,Petals to the Metal - Flower Classification on TPU
10378659,0.97931,0,23,/akashsuper2000/flower-classification-with-tpus,Petals to the Metal - Flower Classification on TPU
10283659,0.88634,0,4,/jayitabhattacharyya/efficientnetb7-petals,Petals to the Metal - Flower Classification on TPU
10218760,0.96285,0,19,/atamazian/flower-classification-ensemble-effnet-densenet,Petals to the Metal - Flower Classification on TPU
14620725,0.0437,0,0,/wiwiteria/create-your-first-submission,Petals to the Metal - Flower Classification on TPU
14153282,0.97181,0,0,/akataev96/start-with-ensemble-v2,Petals to the Metal - Flower Classification on TPU
14041455,0.95575,0,0,/andrysazonov/start-with-pre-train,Petals to the Metal - Flower Classification on TPU
13875502,0.91789,0,0,/varlou23/start-with-ensemble-v2-fde60e,Petals to the Metal - Flower Classification on TPU
13462264,0.9499,0,0,/adebelyi/start-with-ensemble,Petals to the Metal - Flower Classification on TPU
13292818,0.94277,0,0,/grudindmitry/start-with-pre-train,Petals to the Metal - Flower Classification on TPU
13198271,0.94925,0,0,/yuriromamov/more-data-with-densenet201,Petals to the Metal - Flower Classification on TPU
12970311,0.92138,0,0,/varlou23/start-with-pre-train-image-size-experiment,Petals to the Metal - Flower Classification on TPU
12689718,0.94679,0,0,/omkarmb/color-classification,Petals to the Metal - Flower Classification on TPU
12534471,0.06566,0,0,/smirnyaginandr/start-w-o-pre-train,Petals to the Metal - Flower Classification on TPU
12354523,0.4629,0,0,/safonenkomax/start-w-o-pre-train,Petals to the Metal - Flower Classification on TPU
11503575,0.8393,0,0,/prabhusantoshpanda/exercise-create-your-first-submission,Petals to the Metal - Flower Classification on TPU
10986971,0.24619,0,0,/aycancal/exercise-create-your-first-submission,Petals to the Metal - Flower Classification on TPU
11043234,0.7973,0,8,/kkhandekar/contradiction-detection-xlm-roberta,"Contradictory, My Dear Watson"
11016766,0.92916,0,8,/doanquanvietnamca/ensemble-submission-csv,"Contradictory, My Dear Watson"
11007759,0.60923,0,2,/kkhandekar/contradiction-detection-multilingual-bert,"Contradictory, My Dear Watson"
10997559,0.81443,0,11,/rli596/contradictory-my-dear-watson-data-aug,"Contradictory, My Dear Watson"
10996771,0.7076,0,5,/doanquanvietnamca/training-xlm-roberta,"Contradictory, My Dear Watson"
10978416,0.7872899999999999,0,5,/shirishsharma/xlm-roberta-and-eda-my-dear-watson,"Contradictory, My Dear Watson"
10900952,0.63349,23,180,/anasofiauzsoy/tutorial-notebook,"Contradictory, My Dear Watson"
10962695,0.7742,11,51,/xhlulu/contradictory-watson-concise-keras-xlm-r-on-tpu,"Contradictory, My Dear Watson"
10973244,0.8102,0,14,/parmarsuraj99/transforming-contradictory-sentences,"Contradictory, My Dear Watson"
10973321,0.6177,1,5,/ajax0564/bert-base-multilingual-cased,"Contradictory, My Dear Watson"
12261133,0.8644799999999999,0,0,/adebelyi/start-w-o-pre-train,Petals to the Metal - Flower Classification on TPU
12486043,0.93152,0,0,/sneky369/start-with-pre-train,Petals to the Metal - Flower Classification on TPU
12393699,0.93497,0,0,/sergeyakulich/notebook1cf87d6468,Petals to the Metal - Flower Classification on TPU
12239850,0.85854,0,0,/rajashreedahal/rpetals-to-the-metal,Petals to the Metal - Flower Classification on TPU
12042833,0.94746,0,2,/lkatran/start-with-pre-train,Petals to the Metal - Flower Classification on TPU
11896796,0.93614,0,4,/santhoshgoku/nasnet-inceptionresnet-battleground,Petals to the Metal - Flower Classification on TPU
11728938,0.86469,0,4,/artemnechitaylo/flowers-with-xception-trained-on-tpu,Petals to the Metal - Flower Classification on TPU
11756240,0.96425,0,3,/sohelranaccselab/petals-to-the-metal-flower-classification-final,Petals to the Metal - Flower Classification on TPU
11521017,0.94114,6,34,/servietsky/pretrained-cnn-epic-fight,Petals to the Metal - Flower Classification on TPU
11593914,0.9466,6,6,/ozdemirh/deep-learning-with-tpu-tfrecord-and-efficientnet,Petals to the Metal - Flower Classification on TPU
11465475,0.82806,1,2,/aratrikabera93/densenet-flower-classification-tpus,Petals to the Metal - Flower Classification on TPU
11448113,0.78998,0,5,/elvinagammed/tpu-flower-classification-resnet,Petals to the Metal - Flower Classification on TPU
11414583,0.95092,5,8,/artemkostrikin/using-tpu-to-classify-colors,Petals to the Metal - Flower Classification on TPU
11225265,0.97354,0,6,/atrisaxena/efficientnet-tpu-augmentation-flower-prediction,Petals to the Metal - Flower Classification on TPU
11281017,0.9536,0,3,/sudhanshuraheja/petals-tpu-tta-external-data-0-95360-lb,Petals to the Metal - Flower Classification on TPU
11193229,0.44425,2,5,/sanjitschouhan/flower-classification-on-tpu,Petals to the Metal - Flower Classification on TPU
11172062,0.92555,0,3,/fuyixing/flower-classification-on-tpu-with-keras-tuner,Petals to the Metal - Flower Classification on TPU
11105520,0.65559,0,2,/aman2000jaiswal/exercise-create-your-first-submission,Petals to the Metal - Flower Classification on TPU
11081037,0.2477,1,5,/rubix9821/exercise-create-your-first-submission,Petals to the Metal - Flower Classification on TPU
11010976,0.96395,2,8,/hughzzw/flower-efficientnetb7-densenet201-0-96,Petals to the Metal - Flower Classification on TPU
10972984,0.90736,0,1,/srutimallik/flower-classification-with-tpu,Petals to the Metal - Flower Classification on TPU
10652053,0.25459,0,3,/mekhdigakhramanian/exercise-create-your-first-submission,Petals to the Metal - Flower Classification on TPU
10325435,0.87661,0,2,/kmtk49/exercise-create-your-first-submission,Petals to the Metal - Flower Classification on TPU
10514418,0.95143,0,1,/swatisk2702/flower-classification-on-tpus-with-efficient-net,Petals to the Metal - Flower Classification on TPU
10860160,0.00391,0,0,/anuragreddy333/exercise-create-your-first-submission,Petals to the Metal - Flower Classification on TPU
10757160,0.94719,0,8,/vpkprasanna/efficientnetb7-xception-model,Petals to the Metal - Flower Classification on TPU
10683657,0.9491,0,3,/mattbast/image-classification-with-tpus,Petals to the Metal - Flower Classification on TPU
14481602,0.7599600000000001,2,6,/ceshine/mt5-base-mnli-zero-shot,"Contradictory, My Dear Watson"
14625096,0.76073,0,0,/yoohyuck/multilingual-roberta,"Contradictory, My Dear Watson"
13666218,0.76458,0,0,/jbagdon/predict-with-tf-xlm-roberta-large,"Contradictory, My Dear Watson"
13799737,0.47025,0,0,/eladwar/text-catboost,"Contradictory, My Dear Watson"
13511536,0.87584,0,1,/rajnathpatel/multilingual-nli,"Contradictory, My Dear Watson"
12875757,0.65158,0,5,/naiara/nlp-contradictory-mr-watson,"Contradictory, My Dear Watson"
11775677,0.81289,1,5,/arhouati/bert-based-model-for-contradiction-detection,"Contradictory, My Dear Watson"
11470784,0.80076,6,2,/yeayates21/fork-watson-keras-roberta-ssl-tweet-lb-0-80,"Contradictory, My Dear Watson"
11685223,0.81,0,3,/debashissanyal/nli-with-tf2-2-and-3-1,"Contradictory, My Dear Watson"
11394622,0.66179,1,5,/sonyrajan/contradictory-on-xlm,"Contradictory, My Dear Watson"
11543499,0.9767,0,13,/alturutin/watson-xlm-r-nli-inference,"Contradictory, My Dear Watson"
11476696,0.4177,0,10,/thomaskonstantin/my-dear-watson-analysis-and-prediction,"Contradictory, My Dear Watson"
11308837,0.37305,0,1,/johndoe23456/contradictory-my-dear-watson,"Contradictory, My Dear Watson"
11335386,0.75534,0,2,/barteksadlej123/external-data-with-baseline-model,"Contradictory, My Dear Watson"
11288522,0.6485,0,1,/aiswaryaramachandran/elementary-my-dear-watson,"Contradictory, My Dear Watson"
11144692,0.83214,4,14,/forwet/data-upsampling-data-visualization-robert-model,"Contradictory, My Dear Watson"
11248074,0.94359,2,22,/qinhui1999/more-nli-datasets-xmlr-large,"Contradictory, My Dear Watson"
11162722,0.8067300000000001,19,71,/rohanrao/tpu-sherlocked-one-stop-for-with-tf,"Contradictory, My Dear Watson"
11104798,0.78652,1,2,/swatisk2702/natural-language-inferencing,"Contradictory, My Dear Watson"
11103479,0.64677,11,42,/samansiadati/watson-nli-with-tensorflow-and-transformers,"Contradictory, My Dear Watson"
11062246,0.7701600000000001,0,0,/msafi04/watson-and-contradiction-keras-tpu,"Contradictory, My Dear Watson"
11107371,0.64562,0,4,/avaniudupa/contradictory-my-dear-watson,"Contradictory, My Dear Watson"
14332861,0.9258,0,4,/imabhilash/flowerclassificationusingtpus,Petals to the Metal - Flower Classification on TPU
14424280,0.7905399999999999,0,1,/vinayharyan/flower-classification-mobilenet,Petals to the Metal - Flower Classification on TPU
14420047,0.87949,0,0,/datbuidinh/flowerclassificationusingtpus,Petals to the Metal - Flower Classification on TPU
14160233,0.92708,0,1,/juliastl/flower-classification-with-densenet201,Petals to the Metal - Flower Classification on TPU
14185452,0.8726200000000001,6,6,/weidongxu/transfer-learning-efficientnet-b0-flowers,Petals to the Metal - Flower Classification on TPU
13965344,0.90634,0,1,/xuanzhihuang/flower-classification-with-densenet-201-revised,Petals to the Metal - Flower Classification on TPU
13987600,0.30737,0,4,/bhushanaditya/flower-classification-tpu,Petals to the Metal - Flower Classification on TPU
13990810,0.90286,0,0,/drs251/xception-with-data-augmentation,Petals to the Metal - Flower Classification on TPU
13618247,0.94433,0,0,/dagongren/flower-classification-effnet-densenet,Petals to the Metal - Flower Classification on TPU
13858092,0.97794,24,60,/georgezoto/computer-vision-petals-to-the-metal,Petals to the Metal - Flower Classification on TPU
12712310,0.97956,0,2,/dmitrynokhrin/densenet201-aug-additional-data,Petals to the Metal - Flower Classification on TPU
12742576,0.98218,3,3,/dmitrynokhrin/start-with-ensemble-v2,Petals to the Metal - Flower Classification on TPU
11122273,0.8661700000000001,0,0,/dahouda/flower-classification-with-tpus-keras,Petals to the Metal - Flower Classification on TPU
13540576,0.95014,3,2,/kennyvan/flower-classification-on-tpu,Petals to the Metal - Flower Classification on TPU
13081600,0.93,0,0,/sneky369/start-with-ensemble-v2-117b7e,Petals to the Metal - Flower Classification on TPU
13377373,0.95605,1,2,/matveevayulia/start-with-ensemble-v2,Petals to the Metal - Flower Classification on TPU
13357670,0.93717,0,0,/julessharova/start-with-ensemble-sharova,Petals to the Metal - Flower Classification on TPU
12997685,0.8991100000000001,0,0,/julessharova/augmentation-start-with-pre-train-mobilenet,Petals to the Metal - Flower Classification on TPU
12787179,0.6391,0,3,/jagdmir/flower-classification-tpus,Petals to the Metal - Flower Classification on TPU
12962877,0.40004,0,0,/stanislavkorda/start-w-o-pre-train,Petals to the Metal - Flower Classification on TPU
12787469,0.92272,0,0,/nurkasimov/start-with-pre-train-79887a,Petals to the Metal - Flower Classification on TPU
12720567,0.92841,0,3,/ashishsingh226/flower-classification-simple-model-for-beginners,Petals to the Metal - Flower Classification on TPU
12687093,0.95886,0,0,/lkatran/start-with-ensemble,Petals to the Metal - Flower Classification on TPU
12590806,0.91588,0,0,/nachiket273/pytorch-tpu-vision-transformer,Petals to the Metal - Flower Classification on TPU
12460661,0.90728,2,5,/volkandl/tpu-cicek-siniflandirmasi-aciklamali-turkce,Petals to the Metal - Flower Classification on TPU
12564732,0.9012,0,0,/julessharova/start-with-pre-train-mobilenet,Petals to the Metal - Flower Classification on TPU
12553169,0.8381299999999999,0,0,/denismetelev/start-with-pre-train-c4af96,Petals to the Metal - Flower Classification on TPU
12512593,0.91781,0,0,/nurkasimov/start-with-pre-train,Petals to the Metal - Flower Classification on TPU
8326415,0.99728,0,1,/ovsienkobohdan/digit-recognizer,Digit Recognizer
8486687,0.55255,0,0,/vivekpandain/basic-lstm,Natural Language Processing with Disaster Tweets
5289235,3.21696,0,0,/masfour/facial-keypoints-detection-with-rmse-3-2,Facial Keypoints Detection
4140298,0.1160099999999999,0,5,/mike201905/house-saleprice-prediction-practice,House Prices - Advanced Regression Techniques
2944068,0.79425,0,5,/nikkisharma536/titanic-with-deep-learning-fastai-beginner,Titanic - Machine Learning from Disaster
7034662,0.99571,0,6,/marketneutral/mnist-99-57-human-in-the-loop-fastai-pytorch,Digit Recognizer
6065217,0.98828,3,7,/jsvishnuj/image-classification-using-cnn-for-beginners,Digit Recognizer
3975332,0.99542,11,8,/rhodiumbeng/digit-recognizer-convolutional-neural-network,Digit Recognizer
6526349,0.78468,0,2,/shom10/deep-learning-to-solve-titanic,Titanic - Machine Learning from Disaster
9704543,0.96757,0,2,/arifintahu/pytorch-handwritten-digit-prediction,Digit Recognizer
7207018,0.81979,2,17,/athews/prediction-automl-tf-lstm-lightgbm,Natural Language Processing with Disaster Tweets
6603210,0.13393,0,5,/poltigo/house-prices-prediction-with-fastai,House Prices - Advanced Regression Techniques
3558402,0.98,0,2,/whizzkid/cnn-pytorch-beginners-code,Digit Recognizer
2612562,0.96157,0,2,/raipiyush558/neural-net-for-mnist,Digit Recognizer
3048669,0.99571,1,2,/julfes/ensemble-classifier-out-of-seven-cnns-99-6,Digit Recognizer
6614346,0.991,0,0,/boulderluderbase/digit-recognition,Digit Recognizer
3050804,0.99457,0,2,/mychen76/hand-writing-digit-recognition-in-deep-learning,Digit Recognizer
1899555,0.98942,0,2,/tarunpaparaju/mnist-digit-recognition-with-cnns,Digit Recognizer
578227,0.6411399999999999,1,5,/mattsu/titanic-with-deeplearning-simple-tensorflow,Titanic - Machine Learning from Disaster
1898014,0.79904,0,1,/tarunpaparaju/titanic-survival-prediction-deep-nn,Titanic - Machine Learning from Disaster
1079802,0.99671,1,6,/parth05rohilla/mnist-digit-recognizer-using-keras-top-7,Digit Recognizer
3358480,0.7799,4,3,/abhishekdobhal/neural-network-from-scratch-no-libraries,Titanic - Machine Learning from Disaster
1831706,0.95385,0,4,/soumikrakshit/digit-recognition-using-lenet-5,Digit Recognizer
4081536,0.99414,1,1,/utkarshtiwari/simple-cnn-implementation-using-keras,Digit Recognizer
4073628,0.97542,0,1,/utkarshtiwari/digit-recognizer-using-mlp-for-beginners,Digit Recognizer
1577905,0.892,1,12,/samsonqian/softmax-regression-with-tensorflow-mnist,Digit Recognizer
1029364,0.99471,0,6,/mshokry/mnist-keras-cnn-getting-started,Digit Recognizer
1278424,0.99571,4,23,/charel/learn-by-example-neural-networks-hello-world,Digit Recognizer
11170139,0.78708,18,39,/sshikamaru/titanic-keras-neural-network,Titanic - Machine Learning from Disaster
797209,0.99585,4,9,/sinakhorami/digit-recognition-using-deep-learning,Digit Recognizer
4899984,0.99571,1,7,/drcapa/digit-recognizer-cnn,Digit Recognizer
10029170,0.9941,23,59,/blurredmachine/mnist-classification-eda-pca-cnn-99-7-score,Digit Recognizer
5560938,0.80382,2,5,/kjkr73/titanic-dataset-eda-visualization-ml-dl,Titanic - Machine Learning from Disaster
7384080,0.83634,14,52,/basu369victor/learning-bert-for-the-first-time,Natural Language Processing with Disaster Tweets
7971078,0.8345,6,4,/shubhamai/predicting-disaster-tweet,Natural Language Processing with Disaster Tweets
8460609,0.997,2,7,/gauthampughazh/digit-recognizer-using-cnn-top-9,Digit Recognizer
8436687,0.98514,0,0,/vivekpandain/99-5-cnn,Digit Recognizer
9128493,0.98996,0,0,/mohitmandlecha/fork-of-mnist-kernel,Digit Recognizer
9024077,0.78468,1,10,/patrikdurdevic/the-titanic-tensorflow-deep-learning-crisp-dm,Titanic - Machine Learning from Disaster
1425655,0.99371,5,8,/gpreda/simple-introduction-to-cnn-for-mnist-99-37,Digit Recognizer
5074309,0.78947,8,11,/saifulislamplabon/titanic-simple-neural-network-with-eda,Titanic - Machine Learning from Disaster
4812884,0.11742,3,9,/vaishvik25/top-1-lgb-xbg-stacking,House Prices - Advanced Regression Techniques
1343371,0.98642,0,1,/leekltw1/use-tensorboard-s-graph-lenet-to-understand-cnn,Digit Recognizer
2712650,0.999,0,1,/starkking07/digit-recognition-conv2d,Digit Recognizer
2188530,0.99328,0,1,/pierrenicolaspiquin/mnist-from-data-visualization-to-submission,Digit Recognizer
2695477,0.98571,0,0,/nikkisharma536/digit-recognisation,Digit Recognizer
5542640,0.53493,0,1,/okeaditya/regeression-keras-feedforwardnetwork,House Prices - Advanced Regression Techniques
5385148,0.98385,0,0,/saurograndi/tf-cnn-tensorflow-convolutional-nn,Digit Recognizer
5811807,0.99428,2,1,/sanwal092/tensorflow-and-cnn-99-accuracy,Digit Recognizer
547689,0.98985,0,0,/kaustubholpadkar/digit-recognizer-cnn1-2,Digit Recognizer
929171,0.79425,0,1,/hnike25/titanic-deep-learning-keras-79-acc,Titanic - Machine Learning from Disaster
1265141,0.99114,0,2,/khotveer1/digit-recognition-solution,Digit Recognizer
2619265,0.99657,3,6,/babbler/low-parameter-inception-model-for-mnist,Digit Recognizer
3363178,0.975,0,4,/bcosta12/handwritten-digit-recognition-mxnet-mlp,Digit Recognizer
3335908,0.98614,1,2,/jagadeeshkotra/mnist-with-pytorch-the-easy-way-with-cnn-s,Digit Recognizer
3371230,0.98685,0,5,/bcosta12/handwritten-digit-recognition-mxnet-cnn,Digit Recognizer
6498036,0.80382,0,3,/shunichiuehara/titanic-keras-mixup,Titanic - Machine Learning from Disaster
497227,0.7703300000000001,9,19,/stefanbergstein/keras-deep-learning-on-titanic-data,Titanic - Machine Learning from Disaster
9177988,0.98071,0,1,/dinasinclair/intro-to-cnns-and-keras-tuner-using-mnist,Digit Recognizer
8497709,0.99314,0,3,/fkdplc/snapshot-ensemble-tutorial-with-keras,Digit Recognizer
7839398,0.81642,24,95,/mariapushkareva/nlp-disaster-tweets-with-glove-and-lstm,Natural Language Processing with Disaster Tweets
3266938,0.99928,41,109,/elcaiseri/mnist-simple-cnn-keras-accuracy-0-99-top-1,Digit Recognizer
4385369,0.99457,4,7,/okeaditya/begginers-guide-keras-cnn,Digit Recognizer
1697814,0.7703300000000001,8,50,/bulentsiyah/keras-deep-learning-to-solve-titanic,Titanic - Machine Learning from Disaster
1349231,0.997,98,477,/cdeotte/how-to-choose-cnn-architecture-mnist,Digit Recognizer
515058,0.9274,3,5,/del=c74b6d10d2982d5b/toxic-comment-classification-analysis,Toxic Comment Classification Challenge
13842033,0.80232,0,0,/craigmthomas/disaster-tweets-nlp-for-beginners-with-catboost,Natural Language Processing with Disaster Tweets
11756822,0.75358,0,0,/tahabuyar/titanic-data-exploration-v2,Titanic - Machine Learning from Disaster
10862355,0.78468,2,13,/samrat96/titanic-machine-learning,Titanic - Machine Learning from Disaster
8051965,0.75758,1,1,/vignesh1694/twitter-fault-sentiment-analysis,Natural Language Processing with Disaster Tweets
7794554,0.8014,0,1,/sunnynevarekar/disaster-tweets-starter-notebook,Natural Language Processing with Disaster Tweets
7524849,0.13965,4,7,/vpfahad/housing-price-regression-for-absolute-beginners,House Prices - Advanced Regression Techniques
7322275,0.82194,6,18,/frednavruzov/starter-graph-based-eda-and-baseline-v1,Natural Language Processing with Disaster Tweets
6959732,0.7822899999999999,0,3,/mrcwalton/titanic-simple-detailed-walkthrough-decision-tree,Titanic - Machine Learning from Disaster
6952790,0.1205799999999999,12,28,/avelinocaio/house-prices-complete-guide,House Prices - Advanced Regression Techniques
6748622,0.76076,1,7,/dixhom/bayesian-optimization-with-optuna-stacking,Titanic - Machine Learning from Disaster
6546690,0.1771299999999999,4,10,/swinalmeshram/house-price-predictions-datacleaning-ml,House Prices - Advanced Regression Techniques
6490094,0.82296,3,6,/guesejustin/advanced-fe-gridcv-feature-elimination,Titanic - Machine Learning from Disaster
5989749,0.7368399999999999,5,10,/kajolg/titanic-machine-learning,Titanic - Machine Learning from Disaster
5936197,0.11824,0,1,/chmaxx/slim-data-cleaning-modelling-weighted-ensemble,House Prices - Advanced Regression Techniques
5770842,0.14664,2,5,/ashishbarvaliya/house-price-modeling-xgb-with-gridsearch,House Prices - Advanced Regression Techniques
3580103,0.13105,0,4,/haianh88/detailed-feature-processing-and-gbregressor,House Prices - Advanced Regression Techniques
3365084,0.80861,17,17,/snocco/80-a-tour-on-the-titanic-testing-many-functions,Titanic - Machine Learning from Disaster
3145574,0.1341,0,5,/karanjakhar/start-here,House Prices - Advanced Regression Techniques
2889372,0.22351,4,1,/vaghulb1992/house-price-prediction-walkthrough,House Prices - Advanced Regression Techniques
2684355,0.78947,0,1,/andriyantohalim/titanic-kernel-v1,Titanic - Machine Learning from Disaster
2614604,0.78468,2,10,/lpdataninja/data-science-glossary-20-grid-search,Titanic - Machine Learning from Disaster
1987765,0.81818,0,0,/szelidvihar/titanic-beginner-kernel-for-experimenting,Titanic - Machine Learning from Disaster
1981782,0.79425,0,2,/gsarti/titanic-a-standard-data-analysis-workflow,Titanic - Machine Learning from Disaster
1032961,0.13646,0,1,/raviprakash438/predict-house-pricing,House Prices - Advanced Regression Techniques
893910,0.80382,0,20,/chenyingjie/titanic-survival-s-prediction-with-voting,Titanic - Machine Learning from Disaster
889688,0.78947,2,13,/daniel83fr/titanic-how-to-start-a-beginners-path,Titanic - Machine Learning from Disaster
877561,0.1336099999999999,0,1,/niklasdonges/predict-housing-prices-with-elasticnet,House Prices - Advanced Regression Techniques
604207,0.78947,54,101,/vin1234/best-titanic-survival-prediction-for-beginners,Titanic - Machine Learning from Disaster
8005170,0.0,7,30,/warkingleo2000/eda-with-sparse-matrix,Google Cloud & NCAA® ML Competition 2020-NCAAM
9203876,0.12462,8,10,/konovalex/house-prices-eda-nn-boosts-lin-models,House Prices - Advanced Regression Techniques
7200693,0.78823,11,10,/nulldata/nlp-on-disaster-tweets-eda-and-baseline-models,Natural Language Processing with Disaster Tweets
8121881,0.54935,1,3,/jollibobert/ncaam2020-logistic-regression-baseline-cv-0-55,Google Cloud & NCAA® ML Competition 2020-NCAAM
8596735,0.40142,4,2,/vjshinde04/simple-data-transformation-forecasting,COVID19 Global Forecasting (Week 2)
9113944,0.7511899999999999,2,2,/rohandawar/titanic-eda-ml-using-logistic-regression,Titanic - Machine Learning from Disaster
4093646,0.96271,14,20,/akhileshrai/intro-cnn-pytorch-pca-tnse-isomap,Digit Recognizer
10579474,0.7822899999999999,0,2,/rudsam/getting-started-with-titanic,Titanic - Machine Learning from Disaster
9494798,0.7822899999999999,8,19,/pradyut23/titanic-survivors,Titanic - Machine Learning from Disaster
9511174,0.75598,0,0,/pandaalbert/titanic-notebook,Titanic - Machine Learning from Disaster
10168298,0.1222,0,0,/doncovkonstantin/house-prices-prediction-using-metalearner,House Prices - Advanced Regression Techniques
2559264,0.11946,0,5,/nephalem98/a-beginner-approach-to-advanced-regression-models,House Prices - Advanced Regression Techniques
2509935,0.82775,7,9,/fffjay/titanic-survivor-prediction,Titanic - Machine Learning from Disaster
10031210,0.13524,2,2,/zidanejain/house-prices-eda-linear-regression-gboost,House Prices - Advanced Regression Techniques
11336716,0.76315,0,2,/ashutoshp19/titanic-disaster-analysis-novice-s-attempt,Titanic - Machine Learning from Disaster
715277,0.78468,1,5,/vikasbz/titanic-survivors-using-python,Titanic - Machine Learning from Disaster
959053,0.14467,0,1,/tukrre/eda-data-imputation-with-randomforest,House Prices - Advanced Regression Techniques
5946951,0.75598,0,1,/asilkanv/titanic-eda-ml,Titanic - Machine Learning from Disaster
10641352,0.77751,0,4,/sivaprksh/titanic-eda-feature-engineering-logistic-reg,Titanic - Machine Learning from Disaster
10555527,0.8157800000000001,4,20,/gordotron85/titanic-random-forest-classification-top-4,Titanic - Machine Learning from Disaster
7395265,0.11949,6,42,/risabbiswas19/ensembling-for-regression-top-6-on-lb,House Prices - Advanced Regression Techniques
11162984,0.77751,0,12,/gauravduttakiit/predict-the-survival-using-xgbclassifier,Titanic - Machine Learning from Disaster
11162277,0.7751100000000001,0,6,/gauravduttakiit/predict-the-survival-using-random-forest,Titanic - Machine Learning from Disaster
11568144,0.77751,10,9,/dcshah/titanic-survival-prediction-and-eda,Titanic - Machine Learning from Disaster
11471818,0.79186,0,2,/shaunthesheep/titanic-disaster-who-survived,Titanic - Machine Learning from Disaster
10661381,0.7799,3,7,/saitej31/titanic-survival,Titanic - Machine Learning from Disaster
10785592,0.78947,0,0,/harshvardhananand/titanic,Titanic - Machine Learning from Disaster
12512656,0.79186,0,2,/hamzabjitro/titanic-top-8-eda-xgb-ensemble,Titanic - Machine Learning from Disaster
11341330,0.0,1,13,/sude00/titanic-eda-tutorial,Titanic - Machine Learning from Disaster
11011071,0.75358,13,51,/themlphdstudent/titanic-survive-prediction-tutorial-for-beginners,Titanic - Machine Learning from Disaster
12004261,0.78947,2,7,/toyox2020/titanic-eda-hyperopt-cv-ensembling-modeling,Titanic - Machine Learning from Disaster
10598995,0.79904,29,35,/tanmay111999/top-9-0-79904-using-knn,Titanic - Machine Learning from Disaster
12808705,0.77272,0,15,/vbmokin/ai-ml-ds-training-l1t-titanic-decision-tree,Titanic - Machine Learning from Disaster
8604602,1.5197200000000002,2,5,/girish01/covid-19-eda-and-deep-analysis-using-visualization,COVID19 Global Forecasting (Week 2)
5782188,0.16059,6,8,/shaunthesheep/house-prediction-advance-regression-technique,House Prices - Advanced Regression Techniques
10740282,0.12119,3,12,/ankur123xyz/eda-outlier-handling-stacking-top-11,House Prices - Advanced Regression Techniques
4252362,0.80382,8,12,/modojj/eda-handling-missing-values-using-regression,Titanic - Machine Learning from Disaster
11252650,0.77751,0,6,/abhishekdubey8322/titanic-solved-from-basic-eda-till-ml-algorithms,Titanic - Machine Learning from Disaster
5179731,0.78468,1,0,/averageaf/titanic-beginner-logistic-regression,Titanic - Machine Learning from Disaster
2300413,0.78947,1,4,/cagkanbay/titanic-ml,Titanic - Machine Learning from Disaster
10330948,0.80861,4,8,/vadimsokolov/titanic-ensemble-models,Titanic - Machine Learning from Disaster
10726230,0.12401,8,10,/gordotron85/stacking-regressors-top-5,House Prices - Advanced Regression Techniques
11687349,0.76076,0,1,/neil001/titanic-disaster-machinelearning,Titanic - Machine Learning from Disaster
12153848,0.76794,1,3,/yukunaka1/titanic-exploratory-data-analysis,Titanic - Machine Learning from Disaster
9479891,0.78947,1,2,/jeffymerin/titanic-predictions,Titanic - Machine Learning from Disaster
7995295,0.0,24,32,/corochann/2020-ncaa-eda-all-files-explained,Google Cloud & NCAA® ML Competition 2020-NCAAM
3267753,0.6668,15,92,/vanshjatana/surface-recognition,CareerCon 2019 - Help Navigate Robots
3559312,0.95214,0,2,/rania92/digit-recognizer-using-non-linear-svm,Digit Recognizer
375204,0.76555,0,0,/fernandoramacciotti/ocean-data-science,Titanic - Machine Learning from Disaster
7290396,0.78947,0,1,/mclods/my-titanic-model-svm-rbf,Titanic - Machine Learning from Disaster
459782,0.7799,0,0,/anu0012/survival-analysis-basic-approach,Titanic - Machine Learning from Disaster
1302385,9.44034,1,8,/srisudheera/house,House Prices - Advanced Regression Techniques
592143,0.12115,4,18,/samratp/guide-to-house-prices-regression-eda-and-model,House Prices - Advanced Regression Techniques
13013084,0.7822899999999999,0,0,/vikaskm/exploratory-data-analysis-and-feature-engineering,Titanic - Machine Learning from Disaster
2162535,0.1179,4,21,/ashirwadsangwan/house-prices-prediction-ensemble-technique,House Prices - Advanced Regression Techniques
3113315,0.79904,6,50,/jirakst/titanic-auc-92,Titanic - Machine Learning from Disaster
11748875,0.78708,4,9,/yashudua/titanic-eda-feature-engineering-prediction,Titanic - Machine Learning from Disaster
2442085,0.12807,1,4,/datahobbit/exploratory-data-analysis-lightgbm-house-prices,House Prices - Advanced Regression Techniques
9930359,0.76315,11,52,/vinayshaw/for-beginner-titanic-model-eda-80-accuracy,Titanic - Machine Learning from Disaster
5977210,0.7799,0,1,/pierpaolo28/titanic-ensemble-learning,Titanic - Machine Learning from Disaster
14507523,0.99103,0,0,/qiuhanji/digit-recogniter,Digit Recognizer
11612424,0.63636,2,1,/soraka/titanic-svm-0-82-accuracy-score-beginner,Titanic - Machine Learning from Disaster
11206011,0.728,0,1,/lennyom/iwildcam-2020-using-fastai-resnet50-mixup-and-tta,iWildCam 2020 - FGVC7
11141550,0.77272,0,0,/rishavbhurtel/random-forest-with-hyperparameter-tuning,Titanic - Machine Learning from Disaster
10844943,0.9961,3,14,/kalashnimov/keras-cnn-with-99-6-acc,Digit Recognizer
10755309,0.8825,7,26,/ianmoone0617/tabular-and-cnn-auc-optimisation,SIIM-ISIC Melanoma Classification
10594850,0.99403,0,1,/anhtu96/digit-recognizer-with-cnn,Digit Recognizer
10558223,0.97389,0,0,/uuiodo/mnist-resnet,Digit Recognizer
10550356,0.98157,0,2,/thirunayandinesh/mnist-classifcation-convnets,Digit Recognizer
10475153,0.99475,3,8,/akashsdas/digit-recognizer,Digit Recognizer
10318430,0.94617,6,11,/muhammeddalkran/review-on-cnn-with-digit-recognizer-0-99325,Digit Recognizer
10117257,0.921,3,18,/zhuangliu1939/train-inference-gpu-baseline,ALASKA2 Image Steganalysis
9949900,0.92328,0,0,/ashimdahal/neural-net-from-scratch-for-classifier,Digit Recognizer
9877165,0.99914,0,7,/akirahirohashi/v16-of-digit-rcognizer,Digit Recognizer
9687814,2.94804,0,0,/pranshigarg/face-key-detection-points,Facial Keypoints Detection
9636507,0.77272,8,15,/winnietan666/titanic-eda-visualization-ml,Titanic - Machine Learning from Disaster
9589130,0.99014,0,2,/yamaimo/digit-recognizer-transfer-learning-using-resnet50,Digit Recognizer
9556788,0.99728,1,4,/captaintyping/mnist-challenging-with-ml-conquering-with-dl,Digit Recognizer
9468425,0.99585,3,6,/jagadeesh23/digit-recognizer-cnn-99-585,Digit Recognizer
9039809,0.99239,0,0,/saumyaborwankar/final-digit,Digit Recognizer
8885855,0.99457,12,10,/roblexnana/mnist-digit-recognition-with-lenet-on-keras,Digit Recognizer
8826653,0.09103,0,0,/paulorzp/covid-19-global-forecast-ensemble-2m,COVID19 Global Forecasting (Week 3)
8678201,0.06476,0,0,/samlester/covid-19-eda-lstm,COVID19 Global Forecasting (Week 2)
11143482,0.74162,16,30,/gauravduttakiit/titanic-survivors-logistic-regression-model,Titanic - Machine Learning from Disaster
11101836,0.7822899999999999,1,3,/ravels1991/titanic-with-pytorch-and-skorch-score-0-78,Titanic - Machine Learning from Disaster
10890283,0.97053,2,5,/amgdhussein/digit-recognizer-compete,Digit Recognizer
10831177,0.97925,2,3,/socathie/mnist-w-fg-unet,Digit Recognizer
10762457,0.99589,2,11,/gordotron85/introduction-to-cnn-with-mnist,Digit Recognizer
10757100,0.9056,1,28,/ianmoone0617/resnest-50-5folds-optimised-auc-mixup,SIIM-ISIC Melanoma Classification
10719118,0.41596,0,0,/mksaad/mnist-digit-recognizer-cnn,Digit Recognizer
10286233,0.99178,2,3,/snehalkumar05/digit-classifier-1,Digit Recognizer
9914401,0.93092,1,7,/pansofluck/custom-residual-network-keras-digits-recognition,Digit Recognizer
9703655,0.989,1,6,/medyasun/digit-recognizer-cnn-with-keras,Digit Recognizer
9701832,0.79904,1,2,/akioonodera/titanic-with-chainer,Titanic - Machine Learning from Disaster
9661353,0.7751100000000001,5,9,/priyanath/titanic-disaster,Titanic - Machine Learning from Disaster
9566226,0.9956,0,2,/anmolkumar/mnist-digit-classification-99-5-accuracy,Digit Recognizer
9524533,0.79711,0,0,/jjbuchanan/disaster-tweets-linear-bag-of-words-models,Natural Language Processing with Disaster Tweets
9303840,0.99742,0,0,/ndhpro/mnist-cnn-ensemble,Digit Recognizer
9241530,0.99571,8,9,/chekoduadarsh/convolutional-xgboost,Digit Recognizer
9235788,0.986,0,1,/mohammadariyan/using-convolutional-nn-for-mnist-dataset,Digit Recognizer
9113007,0.7120000000000001,48,212,/raenish/tweet-sentiment-insight-eda,Tweet Sentiment Extraction
9056795,0.99003,0,1,/sachinssingh/digit-recognition-cnn-keras,Digit Recognizer
9004984,0.97214,0,0,/sunnyville01/digit-recognizer-deep-neural-networks,Digit Recognizer
8946188,0.99382,2,3,/tmkggl/tensorflow-cnn-hand-digit-recognizer,Digit Recognizer
8803161,0.25443,0,1,/syzymon/covid-19-tabnet-fast-ai-v2,COVID19 Global Forecasting (Week 3)
8801587,0.22218,0,0,/shamsvahid2/covid-week3-xgboost,COVID19 Global Forecasting (Week 3)
8655915,0.99457,0,4,/nickteim/mnist-fastai-v3,Digit Recognizer
8608230,0.98714,0,4,/ravirajsinh45/digit-recognizer-with-keras,Digit Recognizer
8530185,0.99214,0,0,/ankschoubey/20200326-pytorch-mnist,Digit Recognizer
8526265,0.98842,0,1,/zichengsaber/zicheng-cnn-digital,Digit Recognizer
11138769,0.75837,0,1,/sofuwaoo/titanic-survival-prediction,Titanic - Machine Learning from Disaster
10868981,0.77272,8,31,/darkknight98/titanic-dataset-zero-to-hero-within-top-3,Titanic - Machine Learning from Disaster
10384893,0.14942,0,6,/mrhippo/house-price-prediction-and-analysis-with-dsh,House Prices - Advanced Regression Techniques
10161774,0.78947,40,41,/sanchitvj/titanic,Titanic - Machine Learning from Disaster
10120597,0.77272,6,5,/praneshmukhopadhyay/a-step-by-step-approach-to-the-max-voting,Titanic - Machine Learning from Disaster
9761238,0.78468,0,4,/muhammetalikula/titanic-ml-project,Titanic - Machine Learning from Disaster
9594427,0.76076,6,12,/danoozy44/titanic-random-forest,Titanic - Machine Learning from Disaster
9287401,0.80382,19,24,/leonhackl96/titanic-first-machine-learning-project,Titanic - Machine Learning from Disaster
9282987,0.99071,1,1,/tavanesh4/mnist-dataset-with-deep-learning-cnn,Digit Recognizer
9239095,0.7751100000000001,0,1,/ishannfs/getting-started-with-titanic,Titanic - Machine Learning from Disaster
8659445,0.82296,2,7,/umarsajjad/top-3-titanic-survival-determination,Titanic - Machine Learning from Disaster
8241549,0.42677,0,4,/jtrotman/blend-ncaam-with-2020-vision,Google Cloud & NCAA® ML Competition 2020-NCAAM
7249481,0.5899399999999999,2,5,/basu369victor/nlp-with-disaster-tweets-analysis-and-prediction,Natural Language Processing with Disaster Tweets
6956162,0.99442,0,3,/samrat77/digit-recognition-using-cnn-with-keras-0-9942,Digit Recognizer
6127049,0.99028,2,9,/kajolg/digit-recognizer,Digit Recognizer
5782803,0.11922,3,12,/codesail/boston-explore-regression,House Prices - Advanced Regression Techniques
5455381,0.81339,2,2,/savanthegreat/code-notebook,Titanic - Machine Learning from Disaster
5293701,0.0,1,2,/loyalmandal/titanic-analysis,Titanic - Machine Learning from Disaster
5144422,0.14022,0,2,/scottyiu/random-forest-house-prices,House Prices - Advanced Regression Techniques
4907541,0.1202,0,7,/ysjf13/house-price-prediction-stacking-feature-eng,House Prices - Advanced Regression Techniques
4810829,0.74641,0,1,/akashs2021/data-visualization-and-logistic-regression,Titanic - Machine Learning from Disaster
4382969,0.7511899999999999,2,10,/marami21/a-complete-begginer-guide-to-titanic,Titanic - Machine Learning from Disaster
3995435,0.996,0,1,/umemiyaumeume/cnn-in-keras-japanese-comment-include,Digit Recognizer
3804937,0.7799,0,2,/chavesfm/eda-logistic-classification-and-parameter-tuning,Titanic - Machine Learning from Disaster
3798226,0.18283,0,0,/devinni/taking-a-shot-at-feature-selection,House Prices - Advanced Regression Techniques
11514849,0.7822899999999999,0,2,/balcosandreea/machine-learning-with-titanic-beginner,Titanic - Machine Learning from Disaster
11088958,0.7703300000000001,0,7,/senaduman/predict-survival-on-titanic-disaster,Titanic - Machine Learning from Disaster
11066913,0.8062199999999999,0,1,/urmisen1202/titanic-simple-ml-ensembling-top-6,Titanic - Machine Learning from Disaster
5640208,0.1145099999999999,1,13,/eiosifov/top-8-without-feature-engineering,House Prices - Advanced Regression Techniques
5335740,0.11871,45,86,/vikassingh1996/extensive-data-preprocessing-and-modeling,House Prices - Advanced Regression Techniques
5241138,0.7368399999999999,0,1,/inspectordata/titanic-logistic-regression-model,Titanic - Machine Learning from Disaster
4961493,9.4542,1,10,/moonisafairy/feature-engineering,House Prices - Advanced Regression Techniques
4906242,0.12019,5,14,/ssadaf/house-prices-prediction-top-27,House Prices - Advanced Regression Techniques
4337324,0.7799,0,1,/okeaditya/titanic-ml-analysis,Titanic - Machine Learning from Disaster
3536319,0.12067,0,5,/akumaldo/housing-price-prediction-stackingregressor-lgbm,House Prices - Advanced Regression Techniques
2773958,0.81339,6,35,/ar2017/titanic-end-to-end-ml-workflow-top-7,Titanic - Machine Learning from Disaster
2392983,0.2004,6,14,/alhankeser/beginner-eda-and-data-cleaning,House Prices - Advanced Regression Techniques
2295536,0.78947,0,0,/nikkisharma536/titanic-prediction,Titanic - Machine Learning from Disaster
1461189,0.1451599999999999,0,1,/rbpatt2019/machine-learning-with-pipelines,House Prices - Advanced Regression Techniques
1261674,0.1202299999999999,0,2,/smehta12/house-values-prediction-top-20,House Prices - Advanced Regression Techniques
1259505,0.7799,0,23,/pragyanbo/ensemble-learning-methods-using-titanic-dataset,Titanic - Machine Learning from Disaster
1248437,0.78947,3,16,/fuzzywizard/in-depth-survival-analysis-85-for-beginners,Titanic - Machine Learning from Disaster
1129041,0.78947,4,12,/jetnew/titanic-my-first-kaggle-dataset,Titanic - Machine Learning from Disaster
843089,0.16733,0,0,/krishnamsheth31/house-prices-advanced-regression-techniques,House Prices - Advanced Regression Techniques
11317713,0.79425,0,0,/novelin/top-10-titanic-survived-all-features,Titanic - Machine Learning from Disaster
11041807,0.7751100000000001,3,26,/sshikamaru/titanic-extensive-eda-and-6-models,Titanic - Machine Learning from Disaster
10870311,0.40613,0,5,/socathie/baseline-get-dummies-and-random-forest,House Prices - Advanced Regression Techniques
8701862,0.18703,0,4,/resheto/covid-19-prediction-of-deltas-using-extended-data,COVID19 Global Forecasting (Week 2)
8660923,0.46798,1,13,/darshanjain29/working-solution-progressing-on-leaderboard,COVID19 Global Forecasting (Week 2)
7858359,0.84462,5,29,/wrrosa/keras-bert-using-tfhub-modified-train-data,Natural Language Processing with Disaster Tweets
6600591,0.76555,2,11,/swinalmeshram/titanic-short-simple-accurate,Titanic - Machine Learning from Disaster
5207024,0.14307,1,7,/himaoka/house-simple-random-forest-regression,House Prices - Advanced Regression Techniques
5171544,0.11554,30,54,/redaabdou/house-prices-solution-data-cleaning-ml,House Prices - Advanced Regression Techniques
4286398,0.80861,0,10,/franjmartin21/titanic-pipelines-k-fold-validation-hp-tuning,Titanic - Machine Learning from Disaster
4211056,0.78947,3,2,/ishmeet/elim5-randomforest-on-titanic-dataset,Titanic - Machine Learning from Disaster
3168495,0.15991,0,2,/omarsayed7/kernel3e9f36db97,House Prices - Advanced Regression Techniques
2534485,0.78947,4,8,/ankur1401/titanic-survival,Titanic - Machine Learning from Disaster
2508663,0.81818,7,11,/himaoka/titanic-ensemble-rfc-gbc-svm,Titanic - Machine Learning from Disaster
2362653,0.74641,0,4,/ayoubchebbi/sample-submits-in-kaggle,Titanic - Machine Learning from Disaster
1995435,0.78947,58,129,/frtgnn/a-simple-guide-to-titanic-survival-classifier,Titanic - Machine Learning from Disaster
1436112,0.12828,1,5,/asemokby/eda-gridsearch-elasticnet-lasso-model,House Prices - Advanced Regression Techniques
1409802,0.12805,1,19,/drscarlat/house-prices-all-done-via-pipeline,House Prices - Advanced Regression Techniques
1395403,0.14783,35,67,/samsonqian/predicting-house-prices-with-regression,House Prices - Advanced Regression Techniques
766847,0.76555,0,1,/zahoorahmad/fork-of-titanic-step-wise-data-cleaning-and-predic,Titanic - Machine Learning from Disaster
394817,0.79425,8,34,/mayurjain/surviving-titanic-tragedy-using-python,Titanic - Machine Learning from Disaster
12411541,0.7751100000000001,1,3,/suteekshnmahajan/titanicsurvivalrate,Titanic - Machine Learning from Disaster
11126798,0.79904,6,19,/arnabs007/titanic-get-inside-top-10-the-key-is-eda,Titanic - Machine Learning from Disaster
10828575,0.79665,4,9,/harshsdw/interactive-training-of-random-forest-top-8,Titanic - Machine Learning from Disaster
9877320,0.7799,63,131,/blurredmachine/titanic-survival-a-complete-guide-for-beginners,Titanic - Machine Learning from Disaster
9740016,0.47151,21,50,/siddheshpujari/eda-and-prediction-of-house-price,House Prices - Advanced Regression Techniques
9623574,0.1309799999999999,3,36,/shubhamksingh/top-3-stacking-blending-in-depth-eda,House Prices - Advanced Regression Techniques
9501907,0.78708,11,11,/ashkhagan/exploring-titanic-tragedy-eda-submission,Titanic - Machine Learning from Disaster
8356226,0.7751100000000001,1,11,/rahulgupta21/survival-prediction,Titanic - Machine Learning from Disaster
8341189,0.37,24,64,/nayuts/iwildcam-2020-overviewing-for-start,iWildCam 2020 - FGVC7
8136431,0.82316,5,8,/gauthampughazh/disaster-or-not-plotly-use-tfidf-h2o-ai-automl,Natural Language Processing with Disaster Tweets
7610605,0.17204,0,1,/hpchreseson/house-prices-regression,House Prices - Advanced Regression Techniques
6723741,0.7799,0,0,/hverified/titanic-disaster-analysis-and-prediction,Titanic - Machine Learning from Disaster
5835781,0.7511899999999999,41,98,/marcovasquez/machine-learning-on-board-titanic-17-algothim,Titanic - Machine Learning from Disaster
5106186,0.7799,0,5,/pcborty/titanic-classifying-survival-with-eda,Titanic - Machine Learning from Disaster
4456876,0.78468,0,6,/thisisjisu/introduction-to-decision-trees-korean-ver,Titanic - Machine Learning from Disaster
3974929,0.78947,5,9,/bhaveshsk/getting-started-with-titanic-dataset,Titanic - Machine Learning from Disaster
3971606,0.99557,10,50,/shaygu/fast-cnn-for-beginners-0-9955,Digit Recognizer
3775898,0.123,2,8,/bonhart/pytorch-eda-and-resnet,iWildCam 2019 - FGVC6
14633802,0.76794,1,0,/petermichel/rip-titanic,Titanic - Machine Learning from Disaster
11353301,0.78947,0,2,/javivaleiras/titanic-novice-notebook,Titanic - Machine Learning from Disaster
8496284,0.78468,17,15,/dssant85/titanic-survival-prediction,Titanic - Machine Learning from Disaster
8013825,0.77751,0,3,/rahulgupta28/getting-started-with-titanic-dataset,Titanic - Machine Learning from Disaster
5217179,0.99642,0,2,/fernandoeac/simple-keras-conv2d-no-outside-data,Digit Recognizer
12850197,0.8349200000000001,25,42,/sreevishnudamodaran/ultimate-eda-fe-neural-network-model-top-2,Titanic - Machine Learning from Disaster
11482088,0.82775,21,33,/mviola/titanic-wcg-knns-ensemble-0-82775-top-1,Titanic - Machine Learning from Disaster
11432380,0.76794,3,14,/andressantossanz/titanic-competition-dv-ml,Titanic - Machine Learning from Disaster
10854282,0.78947,5,8,/rangarajanm/titanic-survivors,Titanic - Machine Learning from Disaster
9725565,1.0,44,130,/soham1024/titanic-data-science-eda-with-meme-solution,Titanic - Machine Learning from Disaster
8785208,0.0785299999999999,0,11,/mrmorj/covid-19-adv-eda-lstm,COVID19 Global Forecasting (Week 3)
8325212,0.79425,0,1,/saiharsha111/kernel6715c5b13d,Titanic - Machine Learning from Disaster
8263293,0.7793399999999999,0,0,/aykarekar/firstnotebook-nlp-twitter-analysis,Natural Language Processing with Disaster Tweets
8112853,0.77566,0,7,/sagaramu/nlp-using-nltk-voted-classifier,Natural Language Processing with Disaster Tweets
7976123,0.79425,0,0,/robertotorre23/titanic-my-first-ml-top-12,Titanic - Machine Learning from Disaster
7469494,0.7751100000000001,9,13,/jiaming21/titanic-data-science-solutions-beginner-level,Titanic - Machine Learning from Disaster
7366854,0.99485,0,1,/gundamb2/my-attempt-at-mnist,Digit Recognizer
6789424,0.99942,0,0,/alexkct/mnist-fast-ai-alexkct,Digit Recognizer
5213367,0.80382,2,6,/sspeedy99/titanic-data-analysis-ensemble-learning-and-eda,Titanic - Machine Learning from Disaster
5043030,0.78468,31,33,/praanj/titanic-decision-tree-complete-evaluation,Titanic - Machine Learning from Disaster
4929498,0.7703300000000001,0,4,/thrillanalysis/titanic-different-working-classifier,Titanic - Machine Learning from Disaster
10245646,0.15242,0,3,/ahffan/house-price-prediction-eda-simple-model,House Prices - Advanced Regression Techniques
3256505,0.12188,5,10,/sabanasimbutt/an-easy-approach-to-regularized-linear-regression,House Prices - Advanced Regression Techniques
10486854,0.7488,12,23,/rawanreda/titanic-dataset-full-tutorial,Titanic - Machine Learning from Disaster
1470418,0.1206,13,18,/monthepp/house-prices-advanced-regression-techniques,House Prices - Advanced Regression Techniques
5890064,0.78468,12,58,/vbmokin/three-lines-of-code-for-titanic-top-20,Titanic - Machine Learning from Disaster
8086560,0.78792,0,0,/obiaf88/nlp-tweets-classification,Natural Language Processing with Disaster Tweets
1433272,0.1713,0,0,/saychelsea11/predicting-housing-prices-using-gbr,House Prices - Advanced Regression Techniques
5514744,0.11763,6,7,/dakimoto/house-prices-my-first-submission-eda-to-stacking,House Prices - Advanced Regression Techniques
11166309,0.79665,2,12,/duttasd28/a-glance-at-feature-engineering,Titanic - Machine Learning from Disaster
12704490,0.73444,5,17,/danielbethell/titanic-survival-prediction-improved,Titanic - Machine Learning from Disaster
7959083,0.80382,0,1,/alice815/top-9-with-simple-model,Titanic - Machine Learning from Disaster
2064952,0.79425,17,81,/rp1611/step-by-step-tutorial-for-beginners,Titanic - Machine Learning from Disaster
10345958,0.80382,3,9,/winter9182/titanic-dataset-feature-engineering,Titanic - Machine Learning from Disaster
10013375,0.12132,0,2,/nehakumar31/house-price-prediction-5,House Prices - Advanced Regression Techniques
1988032,0.1222,0,10,/bashkeel/eda-to-ensemble-model-lasso-ridge-xgboost,House Prices - Advanced Regression Techniques
8053294,0.04872,11,72,/vbmokin/mm-ncaam-no-leaks-lgb-xgb-logreg,Google Cloud & NCAA® ML Competition 2020-NCAAM
7576514,0.78945,0,6,/mayurjain/binary-classification-using-nlp,Natural Language Processing with Disaster Tweets
8769803,0.05992,0,2,/czyum26/covid-19-forecasting-using-elasticnet-ii,COVID19 Global Forecasting (Week 3)
12392871,0.7799,0,0,/jonasumlauft/gaussian-process-classification-kernel-benchmark,Titanic - Machine Learning from Disaster
751853,0.98528,11,20,/harunshimanto/digit-recognizetion-with-python,Digit Recognizer
10757188,0.7822899999999999,0,0,/jenshansen/getting-started-with-titanic,Titanic - Machine Learning from Disaster
10257425,0.80861,7,10,/gabrielshiu/beginner-titanic-feature-engineering-gscv-voting,Titanic - Machine Learning from Disaster
2403150,0.80382,0,1,/abuzuverov/classification-based-on-simple-rule,Titanic - Machine Learning from Disaster
2275803,0.99,0,0,/deepiblis/mnist-with-feature-engineering,Digit Recognizer
6786557,0.81339,3,11,/avelinocaio/top-5-voting-classifier-in-python,Titanic - Machine Learning from Disaster
2817510,0.78947,0,0,/tcerda/titanic-competition-from-eda-to-model-selection,Titanic - Machine Learning from Disaster
3048663,0.79904,2,5,/sasiking/titanic-prediction,Titanic - Machine Learning from Disaster
1235681,0.13906,0,1,/infinitus11/house-prices,House Prices - Advanced Regression Techniques
1316433,0.79904,0,0,/apurvayan/titanic-exploration,Titanic - Machine Learning from Disaster
3322141,0.76076,0,4,/mralakija/titanic-who-survived-who-did-not,Titanic - Machine Learning from Disaster
11057802,0.622,0,5,/kkhandekar/model-compare-fine-tuning,Titanic - Machine Learning from Disaster
6515315,0.78468,3,11,/drcapa/titanic-eda-feature-enginneering-decisiontree,Titanic - Machine Learning from Disaster
10374790,0.8325299999999999,12,18,/bhatiashivam/ahoy-top-3-the-only-notebook-you-need,Titanic - Machine Learning from Disaster
6065447,0.76555,2,7,/rikdifos/titanic-fe-xgboost,Titanic - Machine Learning from Disaster
721688,0.79425,0,1,/bobby33/beginner-tutorial-cruising-on-the-titanic-dataset,Titanic - Machine Learning from Disaster
870007,0.10982,42,156,/agehsbarg/top-10-0-10943-stacking-mice-and-brutal-force,House Prices - Advanced Regression Techniques
1034460,0.81818,20,63,/josh24990/simple-end-to-end-ml-workflow-top-5-score,Titanic - Machine Learning from Disaster
1808700,0.7799,16,21,/chirag19/titanic-survival-prediction-beginner,Titanic - Machine Learning from Disaster
9251987,0.82775,72,179,/datafan07/titanic-eda-and-several-modelling-approaches,Titanic - Machine Learning from Disaster
1373360,0.80382,32,84,/serkanpeldek/object-oriented-titanics,Titanic - Machine Learning from Disaster
2725427,0.8373200000000001,190,1016,/gunesevitan/titanic-advanced-feature-engineering-tutorial,Titanic - Machine Learning from Disaster
13051106,0.602,7,49,/vishalsiram50/classification-with-resnet50,Cassava Leaf Disease Classification
11081591,0.33102,1,8,/ianmoone0617/google-quest-q-a-inference-fastai,Google QUEST Q&A Labeling
10809543,0.76794,6,7,/iamsvp/titanic-with-ensemble-and-or-pipeline-making,Titanic - Machine Learning from Disaster
10768563,2.27832,0,4,/vinodhb95/charlie-version2,Facial Keypoints Detection
10425390,0.99653,0,3,/doncovkonstantin/cnn-with-lr-cosine-annealing-and-model-checkpoints,Digit Recognizer
10295372,0.9736,3,6,/yashnalawade/digit-recognition,Digit Recognizer
10163033,0.988,0,3,/willnn/simple-cnn-explanation,Digit Recognizer
10062640,0.8490000000000001,1,3,/unknownfhhgj/tta-starter,SIIM-ISIC Melanoma Classification
9939984,0.98614,2,3,/falconcode/digit-recognizer-lenet5,Digit Recognizer
9579036,0.98942,2,2,/mahmoudima/cnn-he-normal-elu,Digit Recognizer
9149413,0.99442,0,0,/noelmat/mnist-99-5-acc-with-less-than-8000-parameters,Digit Recognizer
8663175,1.42913,0,1,/roger10015/roger10015-rnn-covid19,COVID19 Global Forecasting (Week 2)
8627317,0.6958300000000001,0,0,/fmobrj1975/first-experiments,COVID19 Global Forecasting (Week 2)
8546980,0.8332799999999999,0,0,/duanx075/nlp-with-disaster-tweets-learning-bert,Natural Language Processing with Disaster Tweets
11970023,0.77751,0,1,/mikhailshcherbakov/titanic-solution,Titanic - Machine Learning from Disaster
11548643,0.98803,3,20,/hitesh1724/titanic-1-fastai-beginner-tutorial,Titanic - Machine Learning from Disaster
537009,0.19866,0,0,/hac123/my-model-05f1d8,House Prices - Advanced Regression Techniques
7933268,0.11614,3,1,/parlin987p/digit-recognizer-fastai-vision,Digit Recognizer
3494478,0.15157,0,0,/arpitjain007/first-competition,House Prices - Advanced Regression Techniques
8427392,0.79957,0,0,/ajisamudra/nlp-count-tf-idf-hashing-vectorizer,Natural Language Processing with Disaster Tweets
8428136,0.99228,0,0,/mghanava/mnist-handwritten-digit-classification-thru-cnn,Digit Recognizer
8282314,0.76708,0,0,/arpytanshu/grouped-keyword-bert-model,Natural Language Processing with Disaster Tweets
7750946,0.83604,0,4,/lhideki/bert-with-kfold,Natural Language Processing with Disaster Tweets
8134868,0.99228,0,0,/denojitnath/mnist-fastai,Digit Recognizer
4162873,0.97342,0,0,/tonylek/introduction-to-cnn-keras-0-997-top-6,Digit Recognizer
4005064,0.78468,0,0,/carlosdl09/proyecto1-titanic-d-lacoste-decastro-arama,Titanic - Machine Learning from Disaster
4408047,0.99171,0,0,/calder10/digit-recognizer-using-cnn,Digit Recognizer
5052043,0.7703300000000001,1,2,/thededlier/titanic-fastai,Titanic - Machine Learning from Disaster
4697513,0.98385,0,0,/jiweiliu/mnist-fastai-cnn,Digit Recognizer
5380372,0.994,0,3,/muerbingsha/mnist-vgg13,Digit Recognizer
4143339,0.996,0,0,/sunixliu/digit-recognition-cnn-keras,Digit Recognizer
4849562,0.98885,0,2,/yuchengluo/lenet,Digit Recognizer
4810437,0.7751100000000001,0,0,/rolfrokseth/titanic-ml-comp,Titanic - Machine Learning from Disaster
4845691,0.97485,0,0,/harshith246/rd-keras-mnist,Digit Recognizer
4826028,0.7703300000000001,0,1,/khoinet/titanic-data-notebook,Titanic - Machine Learning from Disaster
4564916,0.99514,0,0,/ayotui/model-selection-in-digit-recogniser,Digit Recognizer
4607785,0.76555,2,10,/apresswala52/titanic-predict-outcomes-for-beginners-0-7942,Titanic - Machine Learning from Disaster
5512742,0.99771,1,6,/timokerremans/cnn-for-minst-dataset,Digit Recognizer
7898957,0.83144,0,1,/zichengliu0226/nlp-getting-started-bert,Natural Language Processing with Disaster Tweets
6487634,0.99657,2,9,/jakelj/mnist-digits-novice-to-master,Digit Recognizer
4891026,0.17655,0,0,/ankschoubey/fastai-tabular,House Prices - Advanced Regression Techniques
6762101,0.99628,0,5,/billynguyen/tf2-beginner-with-original-mnist,Digit Recognizer
3948321,0.989,0,1,/descrierx/recognize-digit,Digit Recognizer
3139296,0.98314,0,1,/yuhaya9/very-simple-example,Digit Recognizer
5209300,0.98971,1,20,/lightforever/mnist-catalyst,Digit Recognizer
5633967,0.98042,0,0,/magokecol/mnist-fc,Digit Recognizer
7440107,0.9754,0,5,/ianmoone0617/kannada-mnist-cnn,Kannada MNIST
3312174,0.1218,0,1,/janmarcelkezmann/regression-tests-2,House Prices - Advanced Regression Techniques
7916583,0.8268399999999999,0,2,/nicapotato/disaster-simple-lstm,Natural Language Processing with Disaster Tweets
7823861,0.46,1,2,/zzyucoding/fork-of-roberta-large-featue-lstm-inference-9a3d44,Google QUEST Q&A Labeling
4060699,0.99157,0,0,/mohamed1993/kernelb12c992fc6,Digit Recognizer
7808761,0.98642,0,0,/sglee487/tf2-1-w-o-data-augmentation,Digit Recognizer
5949541,0.7511899999999999,0,1,/rituparno/titanic-survival-prediction-ml,Titanic - Machine Learning from Disaster
6920668,0.12982,2,7,/binaicrai/hppred,House Prices - Advanced Regression Techniques
6488543,0.79425,0,3,/sanshengshi/introduction-to-ensembling-stacking-in-python,Titanic - Machine Learning from Disaster
4636846,0.99385,0,0,/sunyuanxi/digit-recognizer-keras-solution,Digit Recognizer
4858519,0.76076,0,0,/grapestone5321/build-xgboost,Titanic - Machine Learning from Disaster
2617477,0.99671,1,5,/kaimingk/a-beginner-s-guide-cnn-with-keras,Digit Recognizer
4441086,0.98685,0,0,/arkajyotimukherjee/mnist-using-cnn-v2,Digit Recognizer
2667420,0.99157,0,1,/micky123/digit-recognition,Digit Recognizer
4677638,0.81842,0,3,/jiweiliu/mnist-pytorch,Digit Recognizer
7033760,0.99657,0,4,/koshiu/pytorch-aug-with-albumentation-model-ensemble,Digit Recognizer
3821285,0.99628,0,0,/khangtran97/digit-recognizer,Digit Recognizer
7140753,0.76076,1,1,/abhaysingh19/how-to-survive-the-titanic,Titanic - Machine Learning from Disaster
7405218,0.99542,0,1,/jagannathrk/digit-recognizer,Digit Recognizer
7303150,0.81795,0,0,/ryomakawata/twitter3,Natural Language Processing with Disaster Tweets
7753580,0.99614,1,3,/yclaudel/mnist-data-augmentation,Digit Recognizer
8275059,0.83481,0,2,/jagannathrk/nlp-with-disaster-tweets-transformers,Natural Language Processing with Disaster Tweets
8443456,0.13998,0,1,/royisland/price-this-house,House Prices - Advanced Regression Techniques
4449954,0.99314,0,0,/weiwei142/keras-cnn,Digit Recognizer
4538514,0.76555,0,0,/bayuaji1997/titanic-death-prediction-using-gradient-boosting,Titanic - Machine Learning from Disaster
4474691,0.984,0,0,/whizzkid/training-best-cnn-model-pytorch,Digit Recognizer
6677869,0.989,0,0,/shengzi/resnet50-in-pure-tf-1-x-api-for-mnist,Digit Recognizer
7407207,0.99385,0,1,/gpdsec/digit-recognizer,Digit Recognizer
4013269,0.12692,0,3,/rfairon/kaggle-house-price-predict-challenge,House Prices - Advanced Regression Techniques
4518406,0.98628,0,0,/tanmayvijay27/digit-recognizer-using-conv,Digit Recognizer
4065500,0.98928,0,1,/harishreddy18/lenet-greyscale-image-digit-classification,Digit Recognizer
967865,0.99657,1,8,/rhtsingh/cnn-with-keras,Digit Recognizer
4105123,0.76076,0,0,/weiwei142/titanic-simplemlp,Titanic - Machine Learning from Disaster
4328604,0.99128,2,1,/pikkupr/mnist-convnn-ensemble,Digit Recognizer
4276046,0.98971,0,1,/yoshito/mnist-cnn,Digit Recognizer
4818968,0.98214,0,0,/jica98/digit-recognizer-using-tensorflow,Digit Recognizer
3445258,0.92505,5,30,/aisaactirona/cudnnlstm-cudnngru,Jigsaw Unintended Bias in Toxicity Classification
5157700,59.87451,0,0,/noctisplus/gan-dogs,empty
2158849,0.972,0,0,/ntchdy/mnist-basic-classifier-using-keras,Digit Recognizer
4048733,0.99414,0,0,/abstruse020/mnist-1,Digit Recognizer
7258897,0.78468,3,16,/joshuajhchoi/titanic-v3,Titanic - Machine Learning from Disaster
3287070,0.99528,0,0,/lalit527/digit-recognizer,Digit Recognizer
3336849,0.97785,0,0,/babameme/kernel967b61b3e5,Digit Recognizer
4960302,0.9429,1,5,/wangshengquan/sqwang,IEEE-CIS Fraud Detection
3921402,0.98685,0,0,/vikasmanohar/mnist-vikas,Digit Recognizer
3172076,0.74162,2,10,/sauravkantkumar/titanic-survivors-prediction,Titanic - Machine Learning from Disaster
5546921,0.99142,0,0,/petitbonney/digit-recognizer-with-keras,Digit Recognizer
5548432,0.94485,0,1,/tustunkok/vann-mnist-tbd,Digit Recognizer
7836435,0.9605,0,0,/yourjiebro/submit,Categorical Feature Encoding Challenge
6293082,0.992,0,2,/iamsiddhant/digit-recogniser-using-python-99-2,Digit Recognizer
3821292,0.99657,0,1,/nguyenvlm/digit-recognizer-cnn,Digit Recognizer
4503469,0.18868,0,1,/eabdul/house-pricing,House Prices - Advanced Regression Techniques
5412769,0.79904,0,0,/tovvelie/titanik-find-parameters-for-rfc,Titanic - Machine Learning from Disaster
3070649,0.99442,6,18,/heye0507/fastai-1-0-with-customized-itemlist,Digit Recognizer
5698264,0.7979999999999999,0,2,/account22/starter-kernel-for-0-79,APTOS 2019 Blindness Detection
12918821,0.7799,8,18,/padmanabhabanerjee/a-noob-s-first-approach-to-the-titanic-dataset,Titanic - Machine Learning from Disaster
11558395,0.0192199999999999,2,15,/ianmoone0617/moa-5fold-model,Mechanisms of Action (MoA) Prediction
11248937,0.77751,0,0,/rezadarmawan/titanic-survival-prediction-with-keras-nn,Titanic - Machine Learning from Disaster
10949171,0.7751100000000001,13,16,/ugocarotti/titanic-multiple-classifiers-solution-78,Titanic - Machine Learning from Disaster
8872016,0.99614,1,0,/saravananoppila/mnist-lenet-5-cnn-train-acc-99-86-test-acc-99-62,Digit Recognizer
8383819,0.937,3,12,/ianmoone0617/plant-pathology-fastai-simple-custom-metrics,Plant Pathology 2020 - FGVC7
8135775,0.79742,3,6,/sawarn69/edas-basic-models-and-lstms,Natural Language Processing with Disaster Tweets
7764469,0.99685,2,2,/seraphwedd18/keras-cnn-with-multi-tower-convolution,Digit Recognizer
6596891,0.97592,0,1,/sarthak3398/cnn-mnnist,Digit Recognizer
6521313,0.99,0,0,/doms2a/simple-pytorch-cnn-solution,Digit Recognizer
5803400,0.97257,0,4,/mittalh/mnist,Digit Recognizer
5786490,0.99614,0,2,/bustam/cnn-in-keras,Digit Recognizer
5402466,0.98614,1,3,/roshangupta13/cnn-for-beginner,Digit Recognizer
4083826,0.98771,0,0,/siddheshsathe/digit-recognition-conv2d,Digit Recognizer
3005089,0.97985,4,3,/shyambajaj/digit-recognizer-using-deep-learning,Digit Recognizer
2761277,0.98485,0,1,/relativity2000/cnn-on-mnist-using-keras,Digit Recognizer
2539513,0.99485,0,0,/harines/digit-recognizer,Digit Recognizer
1424277,0.98928,3,46,/uysimty/get-start-image-classification,Digit Recognizer
1261720,0.98485,0,12,/ashishpatel26/mxnet-for-mnist,Digit Recognizer
1252102,0.97728,2,1,/abhijit96/mnist-kernel-2,Digit Recognizer
1094114,0.96714,0,0,/lenka98/mnist-number-recognition,Digit Recognizer
727837,0.98771,1,1,/mauddib/digit-recogniser-tutorial-using-a-cnn-tensorflow,Digit Recognizer
9456372,0.78947,3,10,/rbud613/taitanic-predictor-using-pipeline-processing,Titanic - Machine Learning from Disaster
8095919,0.78947,0,8,/matheuscoradini/titanic-feature-engineering-logistic-regression,Titanic - Machine Learning from Disaster
5639062,0.79425,0,5,/purist1024/titanic-experimental-analysis-of-feature-effects,Titanic - Machine Learning from Disaster
3045541,0.868,0,12,/moradnejad/humpback-ensemble-lb-0-868,Humpback Whale Identification
4883168,0.1282599999999999,0,1,/roshangupta13/eda-feature-engineering-regression-model,House Prices - Advanced Regression Techniques
3152716,0.119,3,6,/paragraph/rmlse-0-119-17-house-prices-regression,House Prices - Advanced Regression Techniques
9829851,9.45927,0,6,/veb101/project-2-p2-model-building,House Prices - Advanced Regression Techniques
1266101,0.78947,2,1,/danielmartinezb/titanic-prediction-using-ensemble-learning,Titanic - Machine Learning from Disaster
1375064,0.94457,0,2,/reppic/stroke-path-approximation-rnn-keras,Digit Recognizer
1640336,0.13323,3,8,/srishti280992/starter-code-eda-feature-engg-basic-model-v2,House Prices - Advanced Regression Techniques
10972783,0.13584,0,0,/lobodemonte/predicting-housing-prices-w-gradientboost,House Prices - Advanced Regression Techniques
7697684,0.79803,17,30,/dremovd/micro-challenge-vectorizers,Natural Language Processing with Disaster Tweets
9687592,0.79425,22,45,/vishalvanpariya/titanic-top-6,Titanic - Machine Learning from Disaster
1472711,0.8325299999999999,5,23,/csw4192/titanic-randomforest,Titanic - Machine Learning from Disaster
12615158,0.77272,0,0,/abdelrahmanmuhammad/data-science-titanic,Titanic - Machine Learning from Disaster
8755132,0.0432699999999999,0,0,/yatinece/update-xgb,COVID19 Global Forecasting (Week 3)
8119418,0.80861,0,0,/mruczykij/women-children-first-titanic-ml-from-disaster,Titanic - Machine Learning from Disaster
394495,0.0,0,2,/zuckgo/exploring-survival-on-the-titanic-python-version,Titanic - Machine Learning from Disaster
1757379,0.11635,56,76,/josh24990/simple-stacking-approach-top-12-score,House Prices - Advanced Regression Techniques
3282978,0.79425,5,15,/sarthakbatra/titanic-eda-and-logistic-regression,Titanic - Machine Learning from Disaster
902619,0.82775,3,18,/shaochuanwang/titanic-ml-tutorial-on-small-dataset-0-82296,Titanic - Machine Learning from Disaster
9063813,0.12005,0,3,/djousto/house-price-imputation-and-feature-engineering,House Prices - Advanced Regression Techniques
9436646,0.11968,0,3,/davidrivasphd/stacked-regressions-house-prices,House Prices - Advanced Regression Techniques
1068583,0.1171799999999999,1,3,/chintanshah24/house-price-grid-search-meta-learning,House Prices - Advanced Regression Techniques
1088503,0.7751100000000001,0,0,/rafibarash/beginner-attempt-at-titanic-competition,Titanic - Machine Learning from Disaster
716549,0.14431,0,1,/dralmostright/gradient-boost-regression-model-tuning-submit,House Prices - Advanced Regression Techniques
8612326,0.81339,5,19,/petersu198/simple-logistic-with-titanic-81-3-test-score,Titanic - Machine Learning from Disaster
11019551,0.8157800000000001,3,9,/ligtfeather/knn-with-gridsearch-titanic-top-3,Titanic - Machine Learning from Disaster
5825430,0.72248,0,2,/asuenotabizi/kernel18920a974a,Titanic - Machine Learning from Disaster
8396610,0.97171,0,0,/prudhvi9999/digit-recognizer,Digit Recognizer
7324632,0.995,1,1,/ssaketh97/number-prediction,Digit Recognizer
7827339,0.83757,0,3,/fedjkeee/keras-bert-tf-hub-svm,Natural Language Processing with Disaster Tweets
3811526,0.995,0,1,/uvxy1234/mnist-lenet-implementation,Digit Recognizer
3782114,0.81339,2,11,/abdelrahmangamil/rose-gonna-survive,Titanic - Machine Learning from Disaster
1725590,0.91185,1,3,/supratimhaldar/digit-recognizer-mnist-onevsall-approach,Digit Recognizer
8117354,0.83726,0,1,/jugglingsnakeboarder/realornotnlp-bert-use-svm,Natural Language Processing with Disaster Tweets
3889813,0.72248,0,4,/ashank19/machine-learning-on-titanic-dataset,Titanic - Machine Learning from Disaster
7721251,0.97314,0,0,/christiangawron/mnist-sklearn,Digit Recognizer
4115508,1.48366,0,11,/iloveyyp/xgboost-based-model,"Ghouls, Goblins, and Ghosts... Boo!"
4066026,0.75598,1,1,/hosokawa1309/0529-hoshikawa-special,Titanic - Machine Learning from Disaster
4285477,0.99685,1,1,/zoomelectrico/proyecto-2-emergente,Digit Recognizer
4315566,0.99557,0,1,/vivianatepedino/vvcp2,Digit Recognizer
3550806,0.98957,0,0,/annametz/cnn-first-steps,Digit Recognizer
1562612,0.99642,0,0,/gainknowledge/mnist-with-cnn-and-keras,Digit Recognizer
3326054,0.81818,1,7,/smivvla/titanic-kernel,Titanic - Machine Learning from Disaster
6066161,0.99271,0,3,/hatorix/mnist-4-099271,Digit Recognizer
5157192,0.95728,0,0,/mani23493/cnn-pytorch-mnist-classification,Digit Recognizer
5159556,0.97585,0,0,/gnaneeswar/e19011-pytorch-mnist-gnanee,Digit Recognizer
4981951,0.7703300000000001,0,1,/saurabhghosh/titanic-with-nn-v1,Titanic - Machine Learning from Disaster
3574915,0.937,0,1,/dobriigoblin/nad-lab-2,Digit Recognizer
3593820,2.05558,4,11,/nitron/facial-keypoints-fastai-image-regression,Facial Keypoints Detection
3786214,0.78947,0,3,/daftby/exercicio-nb-01,Titanic - Machine Learning from Disaster
3931427,0.7511899999999999,0,1,/siltrim/kernel58345f6c62,Titanic - Machine Learning from Disaster
4555629,0.11644,0,1,/mil00se/house-prices-tests,House Prices - Advanced Regression Techniques
4204812,0.98285,0,1,/arshadgeek/digit,Digit Recognizer
5859729,0.97942,1,2,/layediop/digit-recognizer,Digit Recognizer
7960918,0.99357,0,0,/abbasidaniyal/digit-recogniser-cnn,Digit Recognizer
4538037,0.99,4,6,/sovitrath/mnist-cnn,Digit Recognizer
5271542,0.98642,0,2,/coffeeman123/mnist-cnn,Digit Recognizer
3924162,0.99028,0,10,/sunilsj99/digit-recognition-using-cnn-keras,Digit Recognizer
5257634,0.98328,0,0,/adwaitbhope/mnist-digit-recognizer-using-cnn,Digit Recognizer
5760941,0.98442,0,1,/alexanderdbooth/digit-recognizer2-from-deep-learning-course,Digit Recognizer
6805765,0.99542,1,3,/noeasywayout/keras-cnn-study,Digit Recognizer
4714751,0.12,0,2,/aditya100/iwildcam-2019,iWildCam 2019 - FGVC6
3480957,0.95928,0,5,/loovmj/mnist-challenge2-ver-3,Digit Recognizer
5024599,0.97557,0,1,/ankush87/simple-neural-model-for-mnist-with-accurac-98,Digit Recognizer
5148989,0.75598,10,22,/maf345/titanic-data-set-a-beginner-friendly-solution,Titanic - Machine Learning from Disaster
10597702,0.78468,0,3,/islammohaisen/titanic-competition-dtc,Titanic - Machine Learning from Disaster
11273623,0.75598,0,1,/g9jiggy/titanic-solution-an-approach-from-a-beginner-s,Titanic - Machine Learning from Disaster
11196065,0.7822899999999999,0,5,/akramnarejo/titanic-survival-predictions-with-easy-approach,Titanic - Machine Learning from Disaster
9888019,0.8325299999999999,8,14,/kumarselvakumaran/journey-to-top-2-clean-pipelines-and-ensemble,Titanic - Machine Learning from Disaster
2659738,0.74641,18,26,/littleraj30/detailed-ensemble-v-s-other-model-on-titanic,Titanic - Machine Learning from Disaster
2915558,0.76076,5,4,/aaronmitchellharris/my-first-ds-project-predict-survival-on-titanic,Titanic - Machine Learning from Disaster
1909759,0.99428,0,0,/felper/cnn-with-keras,Digit Recognizer
1521972,0.13304,0,2,/hardenju/house-price-predicting,House Prices - Advanced Regression Techniques
2000052,0.97314,1,3,/soumya044/mnist-digit-recognizer-using-kernel-svm,Digit Recognizer
4940763,0.99314,0,1,/blueye/mnist-with-fastai,Digit Recognizer
1083209,0.78947,0,0,/dominikstraessle/titanic,Titanic - Machine Learning from Disaster
1401515,0.7703300000000001,0,1,/lider123/titanic,Titanic - Machine Learning from Disaster
5885014,0.97228,0,1,/tim4ous/cnn-mnist-recognition-with-keras,Digit Recognizer
919206,0.7511899999999999,0,0,/gobes1980/titanic-basic-classification-wip,Titanic - Machine Learning from Disaster
2673467,0.7751100000000001,0,3,/ludovicoristori/titanic-for-dummies-eda-logistic-regression,Titanic - Machine Learning from Disaster
800022,0.996,2,1,/naveenc131/cnn-accuracy-0-99,Digit Recognizer
943976,0.81818,41,81,/davidcoxon/titanic-practice-by-davidcoxon,Titanic - Machine Learning from Disaster
3924253,0.118,1,7,/akumaldo/resnet-from-scratch-keras,iWildCam 2019 - FGVC6
1632646,0.40191,1,0,/tarunpaparaju/titanic-survival-prediction,Titanic - Machine Learning from Disaster
8498864,0.79711,21,30,/podsyp/nlp-with-simple-eda-kfold-svd-tf-idf,Natural Language Processing with Disaster Tweets
5214044,0.79425,1,4,/jonasg/standard-ml,Titanic - Machine Learning from Disaster
1636227,0.99628,2,5,/dimitreoliveira/simple-keras-and-deep-learning-digit-recognizer,Digit Recognizer
10079787,0.77751,0,7,/moamenelsayed/titanic-survival-prediction-with-7-classifiers,Titanic - Machine Learning from Disaster
6678936,0.8325299999999999,26,95,/vbmokin/autoselection-from-20-classifier-models-l-curves,Titanic - Machine Learning from Disaster
7617779,0.71345,4,7,/slatawa/simple-implementation-of-word2vec,Natural Language Processing with Disaster Tweets
965330,0.80861,0,8,/dlarionov/titanic-catboost,Titanic - Machine Learning from Disaster
12658013,0.76076,0,0,/parthpatel13/project-titanic-dataset,Titanic - Machine Learning from Disaster
737908,0.7799,0,16,/azriobr/titanic-dataset-my-first-basic-approach,Titanic - Machine Learning from Disaster
9772985,0.13338,29,44,/medyasun/house-price-all-regressor-algorithms,House Prices - Advanced Regression Techniques
207597,0.991,44,67,/jcodogno/neural-network-using-sgd-98-9,Digit Recognizer
11400975,0.78468,28,95,/ruchi798/break-the-ice,Titanic - Machine Learning from Disaster
7241037,1.0,177,913,/gunesevitan/nlp-with-disaster-tweets-eda-cleaning-and-bert,Natural Language Processing with Disaster Tweets
4609836,0.96714,0,1,/ricardtrinchet/digit-recognizer,Digit Recognizer
5515762,0.79425,4,4,/mtszkw/titanic-eda-random-forest-classification,Titanic - Machine Learning from Disaster
9732975,0.7751100000000001,0,3,/yushg123/using-automl-h2o-tpot,Titanic - Machine Learning from Disaster
5942752,0.97242,0,1,/manas0991/basic-multi-layer-perceptron-with-keras,Digit Recognizer
3702556,0.1159999999999999,4,14,/rblcoder/cnn-in-tf-coursera-course-iwildcam-2019-mobilenet,iWildCam 2019 - FGVC6
2158474,0.99171,0,1,/lider123/mnist,Digit Recognizer
3833944,0.96214,0,0,/jasperkoops/digits-clf-random-forest-gridsearch-pipeline,Digit Recognizer
5082686,0.99571,0,3,/ppicheta/mnist-0-995-in-a-few-lines-of-code,Digit Recognizer
2103529,0.74641,0,0,/sidm2009/titanic-competition,Titanic - Machine Learning from Disaster
2302192,0.7799,1,4,/delayedkarma/basic-eda-feature-engineering-and-modeling,Titanic - Machine Learning from Disaster
7805801,0.75598,0,0,/karthisena/ml-with-titanic-v1-0,Titanic - Machine Learning from Disaster
2213168,0.78947,0,3,/khushbushah/titanic-survival-prediction,Titanic - Machine Learning from Disaster
10691229,0.7511899999999999,2,11,/makhloufsabir/titanic-survived-classification,Titanic - Machine Learning from Disaster
9366037,0.97614,9,11,/demidova/handwritten-digit-recognition-with-pytorch-fnn,Digit Recognizer
1003820,0.78468,0,0,/dcampeao/how-to-use-generative-models-for-classification,Titanic - Machine Learning from Disaster
998653,0.80382,1,16,/davidcoxon/deeply-titanic,Titanic - Machine Learning from Disaster
11414290,0.7751100000000001,2,12,/sevgisarac/machine-learning-with-titanic,Titanic - Machine Learning from Disaster
11548504,0.75598,0,0,/samarth1771/titanic-dataset-exploration-with-svc,Titanic - Machine Learning from Disaster
9649480,0.78947,0,10,/danoozy44/titanic-xgboost,Titanic - Machine Learning from Disaster
7593434,0.7851600000000001,2,10,/iamsiddhant/real-or-not,Natural Language Processing with Disaster Tweets
11670832,0.77272,1,2,/ayushikaushik/using-sklearn-pipeline,Titanic - Machine Learning from Disaster
9474156,0.67224,4,5,/hs1214lee/a-simple-tutorial-for-beginners-1-3,Titanic - Machine Learning from Disaster
3700581,0.76076,2,3,/cmahesh/titanic-ml-kernel,Titanic - Machine Learning from Disaster
5999501,0.76555,6,22,/parulpandey/deep-dive-into-logistic-regression-for-beginners,Titanic - Machine Learning from Disaster
777941,0.7799,0,0,/myselfuma/learning-data-titanic-prediction-in-python,Titanic - Machine Learning from Disaster
3609544,0.74162,2,0,/iepsenn/random-forest-vs-decision-tree-vs-mlp-vs-xgboost,Titanic - Machine Learning from Disaster
12251326,0.75598,5,10,/anantgupt/titanic-bon-voyage-top-2,Titanic - Machine Learning from Disaster
7975555,0.78271,0,0,/catadanna/simple-nlp-classification,Natural Language Processing with Disaster Tweets
373189,0.79425,10,31,/jatturat/finding-important-factors-to-survive-titanic,Titanic - Machine Learning from Disaster
4211063,0.97671,9,84,/abhinand05/mnist-introduction-to-computervision-with-pytorch,Digit Recognizer
2075583,0.99642,0,28,/mgiraygokirmak/keras-cnn-multi-model-ensemble-with-voting,Digit Recognizer
8760686,0.51095,3,3,/ekzemplaro/covid19-global-forecasting,COVID19 Global Forecasting (Week 3)
8642384,0.35953,3,5,/yohancheong/predicting-covid-19-infections-with-lstm,COVID19 Global Forecasting (Week 2)
8779099,0.03317,0,0,/mathseer/eda-lstm-week3,COVID19 Global Forecasting (Week 3)
8728798,0.1592299999999999,0,0,/paralleltree/covid19-simple-logistic-fitting-week3,COVID19 Global Forecasting (Week 3)
8187720,0.7992600000000001,0,0,/kaiteli14/kaite-li-1001645704-practice,Natural Language Processing with Disaster Tweets
4085077,0.1178099999999999,0,0,/bornhansie/house-prices-regression-team-11,House Prices - Advanced Regression Techniques
4017353,0.97385,0,0,/kingofmidas/kernela5cc5811c4,Digit Recognizer
8327722,0.72632,0,1,/jagannathrk/facebook-google-nlp-sota,Natural Language Processing with Disaster Tweets
4060422,0.7799,0,1,/sergejsn/titanic-competition-score-0-7799,Titanic - Machine Learning from Disaster
3804811,0.13969,0,0,/ruhong/house-prices-advanced-regression-techniques-gbm,House Prices - Advanced Regression Techniques
3816564,0.1704,0,1,/ruhong/house-prices-advanced-regression-techniques-gbmpca,House Prices - Advanced Regression Techniques
8921813,0.9896,4,9,/mattbast/image-classification-tensorflow-cnn,Digit Recognizer
8810090,0.08207,0,4,/golubev/using-xgboost,COVID19 Global Forecasting (Week 3)
12363428,0.7751100000000001,0,5,/suhasinikonimeti/notebooka86b987f16,Titanic - Machine Learning from Disaster
8177897,0.83573,0,3,/dmitri9149/transformer-simple-baseline-model-s,Natural Language Processing with Disaster Tweets
8552698,0.7368399999999999,0,0,/meghanwatson/titanic-2,Titanic - Machine Learning from Disaster
8944108,0.03816,0,0,/moradnejad/covid-w4-sub-v1,COVID19 Global Forecasting (Week 4)
12611570,0.7751100000000001,0,0,/ahmadagungzs/getting-started-with-titanic,Titanic - Machine Learning from Disaster
10741068,0.12235,13,27,/rahulmakwana/house-price-prediction-top-14-xgboost,House Prices - Advanced Regression Techniques
9009040,0.7751100000000001,0,0,/sumedhjsh/titanic,Titanic - Machine Learning from Disaster
4172245,0.76555,2,1,/mohira/as-01-decision-tree,Titanic - Machine Learning from Disaster
8303838,0.76076,0,0,/ads071/kernel47ad1d845e,Titanic - Machine Learning from Disaster
9164332,0.74641,0,0,/hongsungmin/kernel4a1ccbb96c,Titanic - Machine Learning from Disaster
9109626,4.65396,0,0,/nobletp/house-prices-advanced-regression,House Prices - Advanced Regression Techniques
7368364,0.7885300000000001,0,0,/rajeshcv/stacking-cv-classifier-nlp,Natural Language Processing with Disaster Tweets
9264189,0.13419,4,5,/rj81309050/house-prices-prediction,House Prices - Advanced Regression Techniques
11959861,0.7751100000000001,0,0,/manis92/notebooke76315e122,Titanic - Machine Learning from Disaster
8537526,0.76076,0,0,/jarinosuke/upura-kaggle-tutorial-01-first-submission,Titanic - Machine Learning from Disaster
8679649,3.44291,0,0,/rohanrao7/covid-19-pandemic-forecasting-using-xgboost,COVID19 Global Forecasting (Week 2)
8697339,0.51786,0,0,/skyhuang/curve-fitting,COVID19 Global Forecasting (Week 2)
8687334,0.06357,0,0,/caodai/c200331,COVID19 Global Forecasting (Week 2)
8671133,0.4338,0,0,/cpthappy/fork-of-test-exploration,COVID19 Global Forecasting (Week 2)
8682800,0.18666,0,0,/fmobrj1975/model-inference-2day,COVID19 Global Forecasting (Week 2)
8707200,0.78878,0,0,/hemilshah/arima-final,COVID19 Global Forecasting (Week 2)
9233874,0.7751100000000001,0,1,/sumit21/titanic-problem,Titanic - Machine Learning from Disaster
8730982,2.62365,3,13,/ritarana123/kernel6bb9d38623,COVID19 Global Forecasting (Week 3)
7596816,0.5693699999999999,0,0,/huanqin/titanic-project,Titanic - Machine Learning from Disaster
8048992,0.98957,0,0,/benfraser/mnist-digit-recognition-three-different-methods,Digit Recognizer
8629463,0.99085,0,1,/mateusz8005/mnist-cnn-keras,Digit Recognizer
7562277,0.7511899999999999,0,1,/mylee2009/titanic-naive-bayes,Titanic - Machine Learning from Disaster
7957348,0.78947,0,0,/clover1qq5/8th-titanic-heominsuk,Titanic - Machine Learning from Disaster
8496082,1.0207700000000002,40,110,/anjum48/seir-hcd-model,COVID19 Global Forecasting (Week 3)
8794000,0.04989,0,0,/robertmarsland/covid-19-prediction-with-erf,COVID19 Global Forecasting (Week 3)
8825784,0.0626,0,0,/petersorensen360/base360,COVID19 Global Forecasting (Week 3)
8824227,0.05534,0,3,/gaborfodor/covid-19-w3-a-few-charts-and-submission,COVID19 Global Forecasting (Week 3)
8140706,0.79425,0,0,/apoorvm/titanic-feature-engineering-with-catboost-encoding,Titanic - Machine Learning from Disaster
8147699,0.7751100000000001,0,0,/danrusei/exploring-tensorflow-keras-dnn,Titanic - Machine Learning from Disaster
3949369,0.11604,1,9,/pankeshpatel/housing-price-prediction,House Prices - Advanced Regression Techniques
8194532,0.7751100000000001,0,0,/jackrobsongateshead/getting-started-with-titantic,Titanic - Machine Learning from Disaster
4053330,0.7703300000000001,0,2,/shotaku/titanic-prediction-using-neural-nework,Titanic - Machine Learning from Disaster
4033797,0.98828,0,1,/vanshikachauhan/kernel01cec58915,Digit Recognizer
3969196,0.1484599999999999,0,2,/sasankav/xgb-adaboost-housing-prices,House Prices - Advanced Regression Techniques
8355010,0.7799,0,0,/yclaudel/titanic-prediction-with-a-nn,Titanic - Machine Learning from Disaster
9256684,0.6411399999999999,1,1,/uedaryo/titanic,Titanic - Machine Learning from Disaster
8226699,0.7916,1,3,/sishihara/catboost-with-text-feature,Natural Language Processing with Disaster Tweets
8229138,0.7257100000000001,0,0,/sishihara/catboost-with-no-text-feature,Natural Language Processing with Disaster Tweets
11507820,0.7751100000000001,0,0,/vitorlavor/getting-started-with-titanic,Titanic - Machine Learning from Disaster
9075094,0.76555,0,0,/egorchernov/logisticregression,Titanic - Machine Learning from Disaster
9074751,0.79425,0,0,/egorchernov/randomforestclassifier,Titanic - Machine Learning from Disaster
8420236,0.76555,0,0,/masayakimura/lrortree-program,Titanic - Machine Learning from Disaster
7717053,0.7751100000000001,2,2,/vitorefazevedo/titanic-challenge,Titanic - Machine Learning from Disaster
12400178,0.7751100000000001,3,9,/lalisfeed/titanic-novice,Titanic - Machine Learning from Disaster
7533920,0.97271,0,1,/zekaiz1/simple-mnist-numpy-from-scratch,Digit Recognizer
8900071,3.81747,0,0,/ramandeepjagdev/ramandeep-jagdev-ee257,COVID19 Global Forecasting (Week 3)
8703797,0.76555,0,0,/gotutiyan/titanic-minimal-tutorial-xgboost,Titanic - Machine Learning from Disaster
897687,0.99842,1,5,/willianw/digit-recognizing-with-keras-augmentation,Digit Recognizer
3808829,0.81818,0,3,/thachhoang2410/titanic-second-try,Titanic - Machine Learning from Disaster
12519479,0.76555,0,0,/shravanhonaganahalli/cs-100-data-science-661789,Titanic - Machine Learning from Disaster
12521033,0.7822899999999999,0,0,/magdalenaboufal/cs-100-data-science-e7fee8,Titanic - Machine Learning from Disaster
8619374,0.14192,0,1,/dannellyz/basic-feature-selection-and-model-building,House Prices - Advanced Regression Techniques
7899544,0.7751100000000001,0,0,/harshitgodha/titanic-basic-solution-with-logistic-regression,Titanic - Machine Learning from Disaster
7899506,0.7751100000000001,0,0,/rajeshpuri/titanic-basic-solution-with-logistic-regression,Titanic - Machine Learning from Disaster
7899548,0.7751100000000001,0,1,/pradeepkv81/titanic-basic-solution-with-logistic-regression,Titanic - Machine Learning from Disaster
7899619,0.7751100000000001,0,0,/srinugoruputi/titanic-basic-solution-with-logistic-regression,Titanic - Machine Learning from Disaster
4380690,0.14706,0,1,/markonkaggle/five-choice-ridge-regression,House Prices - Advanced Regression Techniques
7141187,0.11917,0,1,/tarunkumar323/advance-regression-house-data,House Prices - Advanced Regression Techniques
11791262,0.67464,0,0,/snjssk/practice-titanic-machine-learning-from-disaster,Titanic - Machine Learning from Disaster
8864825,0.1016,0,0,/sanjeevkallepalli/kernel133aaf9430,COVID19 Global Forecasting (Week 3)
11946521,0.7751100000000001,0,0,/kanishkmittal/kanishk-maya-titanic-submission,Titanic - Machine Learning from Disaster
8677326,0.7751100000000001,0,0,/rakeshravi1992/kernel1bef26c5ea,Titanic - Machine Learning from Disaster
11771248,0.7751100000000001,0,0,/fabiogomezsilva/titanic-prediction-v1,Titanic - Machine Learning from Disaster
5616828,0.76555,0,0,/tonyj93/starter-pack-tutorial-with-detailed-notes,Titanic - Machine Learning from Disaster
8708118,1.59988,1,2,/mahmudds/covid19-global-forecasting-week-2,COVID19 Global Forecasting (Week 2)
8841274,0.7751100000000001,0,0,/imrozmohiuddin/titanic-basic-solution-with-logistic-regression,Titanic - Machine Learning from Disaster
6089931,0.98857,2,2,/yushg123/base-pipeline-for-mnist-98-8-accuracy,Digit Recognizer
8630977,0.7863600000000001,0,0,/lnmkey68/kernel17bb293b3f,COVID19 Global Forecasting (Week 2)
6952055,0.89871,0,1,/masama/chainer-mnistclassifier-base,Digit Recognizer
8653990,0.73041,2,4,/casras/covid19-week3-exponential-curve-fit,COVID19 Global Forecasting (Week 3)
8808306,0.03293,1,1,/skorsun/covid19-week3-exponential,COVID19 Global Forecasting (Week 3)
8726820,0.22644,0,4,/skeller/week-3-arima-with-influenza-baselines,COVID19 Global Forecasting (Week 3)
4094568,0.11804,1,1,/duwiii/kernelcebd8309ed,House Prices - Advanced Regression Techniques
8203511,0.83052,0,0,/tbhavnani/simple-feature-engineering-tfhub-and-ml,Natural Language Processing with Disaster Tweets
4031765,0.1251,1,1,/jackgisby/ames-housing-xgboost-lasso,House Prices - Advanced Regression Techniques
4031590,0.97214,1,5,/priteshshrivastava/mnist-digit-recognizer-using-pytorch-fastai,Digit Recognizer
8717337,0.15894,0,0,/shajin/house-price-analysis,House Prices - Advanced Regression Techniques
8045032,0.39544,0,3,/sakana/simple-feature-enginering-with-lightgbm,Google Cloud & NCAA® ML Competition 2020-NCAAW
8110441,0.7955800000000001,0,1,/arifastics07/disaster-tweets-prediction-with-svm,Natural Language Processing with Disaster Tweets
4037597,0.11919,0,0,/kumakichiwel/house-price-1,House Prices - Advanced Regression Techniques
8693458,3.25798,0,0,/jacklbruck/kernel559e7ebe1e,COVID19 Global Forecasting (Week 2)
3806803,0.7511899999999999,0,0,/fjdksl123859/iot-hw,Titanic - Machine Learning from Disaster
12362302,0.78468,0,0,/noahbaxley/fork-of-cs-100-data-science,Titanic - Machine Learning from Disaster
12424800,0.78468,0,0,/ryanmanthy/the-working-one,Titanic - Machine Learning from Disaster
8517172,0.7751100000000001,0,0,/veensk/titanic-first-ml-program,Titanic - Machine Learning from Disaster
3970581,0.1197599999999999,0,0,/jamesrowland/eda-and-lasso-forest-gbm-comparison-and-stacking,House Prices - Advanced Regression Techniques
7780225,0.997,10,7,/biswarupray/15-cnn-models-99,Digit Recognizer
8811609,0.7751100000000001,0,0,/justinflanagan/learning-kaggle-with-titanic-competition,Titanic - Machine Learning from Disaster
6637182,0.80861,2,10,/mgerdas/1st-kaggle-classification-project,Titanic - Machine Learning from Disaster
9131139,0.76076,0,0,/eternalgenin/titanic-data-prediction-using-logistic-regression,Titanic - Machine Learning from Disaster
8503016,0.1188099999999999,0,0,/streed07/house-prices,House Prices - Advanced Regression Techniques
7899542,0.7751100000000001,0,0,/ravindrar/titanic-basic-solution-with-logistic-regression,Titanic - Machine Learning from Disaster
7899529,0.7751100000000001,0,0,/rahuljain8987/titanic-basic-solution-with-logistic-regression,Titanic - Machine Learning from Disaster
8620454,2.95298,0,0,/johndoeoeod/covid-19-lstm-xgbr-svr,COVID19 Global Forecasting (Week 2)
8708408,0.993,0,2,/blueturtle/mnist-cnn-train-inference,Digit Recognizer
7481879,0.79425,0,0,/hithesh111/kernel13c856e03f,Titanic - Machine Learning from Disaster
4485311,0.76555,0,0,/neonninja/gender-golf,Titanic - Machine Learning from Disaster
9168391,0.7511899999999999,1,1,/kolukulurivijaya/kernel12c552d617,Titanic - Machine Learning from Disaster
8692693,0.12936,0,0,/lzhu88/covid-week-2,COVID19 Global Forecasting (Week 2)
11707526,0.77272,0,0,/mohak29/13th-sept-titanic,Titanic - Machine Learning from Disaster
8711279,0.0825,0,4,/tunguz/covid-19-a-few-charts-and-a-simple-baseline,COVID19 Global Forecasting (Week 2)
8709784,3.67885,0,0,/stepstosuccess/ummmmm,COVID19 Global Forecasting (Week 2)
8619509,0.92402,0,1,/jlgleason/covid19-global-forecasts-using-probabilistic-rnns,COVID19 Global Forecasting (Week 2)
8802526,0.51974,0,0,/shubham108/kernel2ba610ca51,COVID19 Global Forecasting (Week 3)
5623110,0.7511899999999999,0,0,/krishthw/titanic-survival-prediction,Titanic - Machine Learning from Disaster
7517995,0.79803,0,2,/xwalker/crossvall,Natural Language Processing with Disaster Tweets
8803205,0.0323899999999999,0,0,/caodai/sub-0407-2,COVID19 Global Forecasting (Week 3)
7919258,0.0,0,2,/sklasfeld/word-embeddings-and-pytorch-tutorial-sk-v1,Natural Language Processing with Disaster Tweets
7970082,0.0,0,0,/bhavesh09/titanic-starter,Titanic - Machine Learning from Disaster
7926863,0.7900699999999999,0,0,/priteshjain/eda-cleaning-and-lstm-model-with-no-pre-training,Natural Language Processing with Disaster Tweets
3233000,0.99628,4,10,/modojj/mnist-data-augmentation-and-ensembling,Digit Recognizer
4950920,0.993,0,7,/valentynsichkar/cnn-for-mnist-competition,Digit Recognizer
3081290,0.993,2,1,/grossmend/mnist-simple-deep-learning-cnn-99-3-by-keras,Digit Recognizer
3323005,0.98885,2,2,/abhigupta4981/capsule-net-for-mnist-using-pytorch,Digit Recognizer
12005427,0.7488,0,1,/haroldship/getting-started-with-titanic-using-pytorch,Titanic - Machine Learning from Disaster
3439316,0.14214,0,2,/grossmend/house-prices-deep-learning-by-keras,House Prices - Advanced Regression Techniques
10901055,0.9521,0,1,/anarthal/mnist-digit-recognition-plain-network-in-keras,Digit Recognizer
2022476,0.92785,0,9,/soumya044/intro-to-ann-with-digit-recognizer-challenge,Digit Recognizer
1759840,0.79904,39,130,/nhlr21/complete-titanic-tutorial-with-ml-nn-ensembling,Titanic - Machine Learning from Disaster
8094004,0.8378700000000001,2,8,/dmitri9149/multilingual-encoder-support-vector-machine,Natural Language Processing with Disaster Tweets
8198045,0.76076,0,0,/sairam6087/chpt-3-hands-on-ml2-exercise-solutions-3,Titanic - Machine Learning from Disaster
7677885,0.7799,0,1,/arjasepp/titanic-competition-from-beginner-to-beginner,Titanic - Machine Learning from Disaster
8179034,0.3763,4,8,/kmatsuyama/to-avoid-overfitting-ensemble-with-trueskill,Google Cloud & NCAA® ML Competition 2020-NCAAM
7705538,0.7799,18,7,/biswarupray/advanced-titanic,Titanic - Machine Learning from Disaster
7718019,0.7799,0,4,/sishihara/python-kaggle-start-book-ch02-05,Titanic - Machine Learning from Disaster
3102219,0.01586,2,4,/moradnejad/ncaa-womens-made-datasets-public,Google Cloud & NCAA® ML Competition 2019-Women's
4020214,0.73205,0,1,/gauransh/titanic-challange-kernal,Titanic - Machine Learning from Disaster
4936739,0.94114,0,4,/pramodini18/handwritten-digit-recognition-using-svm,Digit Recognizer
5815519,0.11857,7,30,/chmaxx/sklearn-pipeline-playground-for-12-classifiers,House Prices - Advanced Regression Techniques
3615643,0.7751100000000001,1,4,/micheldas01/decisiontree-vs-random-forest-classification,Titanic - Machine Learning from Disaster
6720079,0.12327,0,0,/rakhi1/house-price-prediction,House Prices - Advanced Regression Techniques
9900396,0.81339,4,27,/vincentdion/reach-0-79-as-a-beginner,Titanic - Machine Learning from Disaster
709184,0.25648,0,0,/sandeepcr01/iowa-house-price-ml,House Prices - Advanced Regression Techniques
3102615,0.76076,2,0,/sohaibanwaar1203/titanic-machine-learning,Titanic - Machine Learning from Disaster
10484968,0.7440100000000001,2,5,/souravsamrat/titanic-survived,Titanic - Machine Learning from Disaster
1565060,0.13905,0,5,/alexzhuzk/linear-xgb-regression-novice,House Prices - Advanced Regression Techniques
1568035,0.13,3,9,/moghazy/feature-engineering-with-ensemble-learning,House Prices - Advanced Regression Techniques
6297206,0.73205,1,3,/cketant/titanic-survival-prediction-newbie-edition,Titanic - Machine Learning from Disaster
2067866,0.7799,0,0,/pirsqrd/xgb-classifier-to-predict-the-desaster,Titanic - Machine Learning from Disaster
944148,0.1148,1,6,/liuhdsgoal/just-nn-use-gluon-top-5,House Prices - Advanced Regression Techniques
2951432,0.97314,0,0,/chunyuan0221/cnn-with-tf-keras,Digit Recognizer
941625,0.7751100000000001,0,1,/sishihara/standard-approach-for-kaggle,Titanic - Machine Learning from Disaster
3234389,0.12117,0,1,/joshjanjua/housing-prices-comp,House Prices - Advanced Regression Techniques
1166834,0.99128,0,1,/abhinav08/pytorch-deep-neural-network,Digit Recognizer
1312397,0.98957,0,0,/corvuslee/first-dl-notebook-for-mnist,Digit Recognizer
3334999,0.95671,2,3,/jagadeeshkotra/mnist-with-pytorch-the-easy-way,Digit Recognizer
528653,0.1534,0,0,/drwilliamssteven/housing-data-random-forest,House Prices - Advanced Regression Techniques
672295,0.78468,0,0,/masa39023/keras-beginner-s-code,Titanic - Machine Learning from Disaster
4185244,0.7751100000000001,1,5,/rahu7292/ann-from-scratch-with-85-accuracy,Titanic - Machine Learning from Disaster
744942,0.19128,0,2,/swordfaith/my-first-machine-learning-model,House Prices - Advanced Regression Techniques
2012156,0.1171599999999999,0,1,/acaciopassos/house-price-predictions,House Prices - Advanced Regression Techniques
1744678,0.79425,0,0,/itsskg/titanic-problem-for-begineers,Titanic - Machine Learning from Disaster
12808234,0.7703300000000001,0,1,/amineandam/machine-learning-algorithms-for-titanic,Titanic - Machine Learning from Disaster
6498830,0.2222,1,4,/suhasrao/house-pricing-randomforest,House Prices - Advanced Regression Techniques
7383159,0.12159,6,6,/znamensky/xgboost-for-beginners,House Prices - Advanced Regression Techniques
9223134,0.17234,0,5,/carlmcbrideellis/hyperparameter-grid-search-simple-example,House Prices - Advanced Regression Techniques
8761738,9.45378,0,3,/chawaritphumchan/house-prices-advanced-regression-techniques,House Prices - Advanced Regression Techniques
8720946,0.13582,0,0,/bharatsinghkushwah/handling-missing-values-categorical-data,House Prices - Advanced Regression Techniques
9239361,0.97471,7,7,/samprati97/digits-recognizer-using-keras-0-974-accuracy,Digit Recognizer
10818207,0.96621,1,5,/derinformatiker/simple-nn-from-scratch-without-nn-frameworks,Digit Recognizer
8887237,0.1180799999999999,11,19,/shubhammahajan3110/simple-approach-to-predict-house-price,House Prices - Advanced Regression Techniques
9551186,0.7511899999999999,0,0,/adi28online/titanic-survival-prediction-using-different-models,Titanic - Machine Learning from Disaster
7334754,0.79619,0,5,/catris25/simple-classification-svm,Natural Language Processing with Disaster Tweets
7577377,0.79282,0,5,/iavinas/nlp-spacy-basics,Natural Language Processing with Disaster Tweets
3335356,0.16449,0,4,/loovmj/predict-the-price-of-house,House Prices - Advanced Regression Techniques
1637620,0.7799,0,0,/iwanttobelieve/titanic-who-survived-randomforest-and-svm,Titanic - Machine Learning from Disaster
2079856,0.74162,0,1,/ritwikbiswas/titanic-passenger-survival-prediction,Titanic - Machine Learning from Disaster
2064257,0.7511899999999999,0,2,/aroramanish/andrew-ng-concepts,Titanic - Machine Learning from Disaster
1432681,0.74162,4,6,/thebhushanmhatre/my-firsttry-on-kaggle,Titanic - Machine Learning from Disaster
2858410,0.99614,0,1,/vikramseth/kernele2b4d269f3,Digit Recognizer
692928,0.7511899999999999,0,1,/christianbh/titanic-analysis-and-survival-prediction,Titanic - Machine Learning from Disaster
585608,0.80382,0,0,/nancymahajan/titanic-example,Titanic - Machine Learning from Disaster
3709207,0.75598,1,5,/srishilesh/titanic-solution,Titanic - Machine Learning from Disaster
1673784,0.125,15,7,/mikelkn/comparing-microsofts-light-gbm-and-xgboost-models,House Prices - Advanced Regression Techniques
1753085,0.76555,0,0,/alfonsomiranda/titanic-starter-python-kernel,Titanic - Machine Learning from Disaster
481342,0.80861,0,0,/snorreeikeland/simple-analysis-for-titanic,Titanic - Machine Learning from Disaster
437791,0.12144,4,5,/l3r4nd/house-prices-prediction-plotly-eda,House Prices - Advanced Regression Techniques
2050425,0.19277,0,0,/nvinayshetty/hello-world-to-data-science-and-ml,House Prices - Advanced Regression Techniques
594887,0.99471,4,5,/codeastar/fast-and-easy-cnn-for-starters-in-keras-0-99471,Digit Recognizer
9815155,0.78468,14,33,/duttasd28/ahoy-get-on-the-titanic-80,Titanic - Machine Learning from Disaster
11231592,0.7751100000000001,0,5,/firefortysix/getting-started-with-titanic,Titanic - Machine Learning from Disaster
3951220,0.991,0,3,/shreeviknesh/digit-recognition-using-cnn-keras-score-99-1,Digit Recognizer
6950793,0.80382,0,0,/jcardenzana/titanic-scikit-learn-random-forest,Titanic - Machine Learning from Disaster
10512499,0.7822899999999999,0,1,/helenikeda/titanic-disaster-random-forest,Titanic - Machine Learning from Disaster
7277054,144413.82,0,1,/drcapa/santa-tour-revenge-bazaar,Santa 2019: Revenge of the Accountants
8174151,0.79711,0,2,/muthu1698/real-or-not-nlp,Natural Language Processing with Disaster Tweets
9824549,0.97342,1,13,/sidagar/digit-recognizer-using-simple-neural-network,Digit Recognizer
10242261,0.99403,0,4,/cyberl0rd/handwritten-digit-recognization,Digit Recognizer
9450944,0.77751,6,9,/vaidicjain/titanic-easy-deeplearning-acc-78,Titanic - Machine Learning from Disaster
5222878,0.12477,0,1,/mromanelli9/house-prices-complete-solution,House Prices - Advanced Regression Techniques
849207,0.17374,0,0,/faameem/kaggle-learn-machine-learning,House Prices - Advanced Regression Techniques
1473775,0.7751100000000001,0,0,/deepanshkhurana/titanic-solution-attempt-2,Titanic - Machine Learning from Disaster
1436436,1.16083,0,0,/paulbrabban/sheffieldml-august-2018-getting-started-kernel,House Prices - Advanced Regression Techniques
10686836,0.76076,5,14,/hocop1/3-approaches-to-hyperparameter-search-bayesian,Titanic - Machine Learning from Disaster
11608925,0.75837,4,17,/jackharding/eda-logistic-regression,Titanic - Machine Learning from Disaster
467142,0.81339,264,1208,/masumrumi/a-statistical-analysis-ml-workflow-of-titanic,Titanic - Machine Learning from Disaster
167936,0.99028,188,932,/poonaml/deep-neural-network-keras-way,Digit Recognizer
145170,0.7751100000000001,70,394,/poonaml/titanic-survival-prediction-end-to-end-ml-pipeline,Titanic - Machine Learning from Disaster
3213502,0.10649,95,435,/jesucristo/1-house-prices-solution-top-1,House Prices - Advanced Regression Techniques
3427294,0.6650699999999999,4,530,/sishihara/upura-kaggle-tutorial-01-first-submission,Titanic - Machine Learning from Disaster
872539,0.14857,64,269,/dejavu23/house-prices-eda-to-ml-beginner,House Prices - Advanced Regression Techniques
7274427,1.0,45,200,/szelee/a-real-disaster-leaked-label,Natural Language Processing with Disaster Tweets
7275319,0.8430799999999999,59,271,/xhlulu/disaster-nlp-keras-bert-using-tfhub,Natural Language Processing with Disaster Tweets
556065,0.8325299999999999,101,209,/konstantinmasich/titanic-0-82-0-83,Titanic - Machine Learning from Disaster
6251933,0.11569,95,322,/amiiiney/price-prediction-regularization-gbms,House Prices - Advanced Regression Techniques
191323,0.78468,43,215,/dmilla/introduction-to-decision-trees-titanic-dataset,Titanic - Machine Learning from Disaster
7694986,0.79711,42,210,/parulpandey/getting-started-with-nlp-a-general-intro,Natural Language Processing with Disaster Tweets
23162,0.96657,21,95,/kobakhit/digit-recognizer-in-python-using-cnn,Digit Recognizer
5371331,0.962,39,99,/christianwallenwein/beginners-guide-to-mnist-with-fast-ai,Digit Recognizer
1287359,0.1309,51,80,/serkanpeldek/ev-fiyatlar-n-n-tahmini,House Prices - Advanced Regression Techniques
10916397,-7.0303,5,56,/laksh171998/eda-analysis,OSIC Pulmonary Fibrosis Progression
9746734,0.422,140,597,/nxrprime/siim-d3-eda-augmentations-and-resnext,SIIM-ISIC Melanoma Classification
93017,0.1219,2,25,/zoupet/xgboost-ridge-lasso,House Prices - Advanced Regression Techniques
462016,0.14878,1,38,/adachowicz/house-prices-random-forest-regression-analysis,House Prices - Advanced Regression Techniques
9905568,0.8972,38,95,/ipythonx/tf-keras-melanoma-classification-starter-tabnet,SIIM-ISIC Melanoma Classification
10546686,-8.127,3,65,/muhakabartay/osic-pulmonary-fibrosis-eda-dicom-full,OSIC Pulmonary Fibrosis Progression
10545908,1.08216,0,24,/amarkumar2/m5-forecasting-accuracy-easy-eda-prediction,Natural Language Processing with Disaster Tweets
10545703,0.73939,0,23,/amarkumar2/home-credit-default-risk-easy-solution,Home Credit Default Risk
56356,0.0,3,13,/letfly/preliminary-exploration,Titanic - Machine Learning from Disaster
10885178,0.76794,3,13,/joaocampista/aplicando-data-science-no-caso-titanic,Titanic - Machine Learning from Disaster
9138608,0.85,28,70,/rohitsingh9990/panda-inference-ensemble-trying-various-models,Prostate cANcer graDe Assessment (PANDA) Challenge
7201233,115863.49,3,12,/vipito/fork-of-santa-ip,Santa 2019: Revenge of the Accountants
8855532,0.7751100000000001,5,8,/jesudasdsouza/titanic-starter-kernel-using-tf-keras-0-78,Titanic - Machine Learning from Disaster
8688103,0.7751100000000001,6,17,/khotijahs1/titanic-machine-learning-from-disaster-rfcla,Titanic - Machine Learning from Disaster
8959491,0.48488,2,28,/anyexiezouqu/magic-ensemble-top-kernels,M5 Forecasting - Accuracy
10052059,0.73205,2,6,/ristola/titanic,Titanic - Machine Learning from Disaster
9758577,0.75837,3,54,/vishalsiram50/svm-xgboost-random-forest-and-ann,Titanic - Machine Learning from Disaster
9870532,0.99471,4,12,/namanj27/beginner-code-top-2-solution,Digit Recognizer
9752001,0.8674,65,295,/allunia/don-t-turn-into-a-smoothie-after-the-shake-up,SIIM-ISIC Melanoma Classification
10493342,0.8586299999999999,21,18,/piyushagni5/top-2-solution-eda-hyperparameter-xgb-catboost,Summer Analytics 2020 Capstone Project
9824785,0.684,9,15,/digvijayyadav/analyzing-melanoma-through-interactive-plots,SIIM-ISIC Melanoma Classification
9809739,0.13157,3,4,/mustafabozkurt/house-price-xgb-tuned,House Prices - Advanced Regression Techniques
9816218,0.76794,10,25,/megr25/titanic-keras-5-machine-learning-model,Titanic - Machine Learning from Disaster
10546394,0.12272,0,9,/salmaneunus/house-prices-advanced-improved-score,House Prices - Advanced Regression Techniques
5023135,60212.61818,0,4,/keromon/eda-modelling,I-RICH ML COMPETITION
10900383,0.1224,6,11,/tbsoaresvalkms/xgbregressor-house-prices,House Prices - Advanced Regression Techniques
8977245,0.11026,1,11,/shaitender/ensembles,House Prices - Advanced Regression Techniques
8662355,0.1353099999999999,4,30,/amarkumar2/house-price-predection-using-regression,House Prices - Advanced Regression Techniques
10876356,0.78057,0,3,/zzaibis/nlp-for-disaster-tweets,Natural Language Processing with Disaster Tweets
8895840,0.974,7,10,/arnimen5/plant-keras-resnet-tta,Plant Pathology 2020 - FGVC7
9293880,0.7799,0,5,/mahirahmzh/getting-started-with-titanic,Titanic - Machine Learning from Disaster
8833979,0.0355,11,10,/ranjithks/ran-covid-19-week4-ma,COVID19 Global Forecasting (Week 4)
9007067,0.7751100000000001,0,3,/kudoszhang/get-started-with-titanic,Titanic - Machine Learning from Disaster
9011532,0.99207,0,7,/patrikdurdevic/digit-recognizer-mnist-tensorflow-cnn,Digit Recognizer
8687602,0.98157,2,16,/khotijahs1/digit-recognizer-support-vector-machine,Digit Recognizer
9093398,0.34759,1,3,/hatunina/adversarial-validation,YKC-cup-1st
10541400,0.75837,1,4,/mohgsam/titanic-comp,Titanic - Machine Learning from Disaster
10545899,0.78468,5,7,/debiprasadmishra/titanic-data-survival-predictions,Titanic - Machine Learning from Disaster
8705213,2.00996,0,2,/fraserew/covid-19-lstm-cases-fatalities-prediction,COVID19 Global Forecasting (Week 2)
10532977,0.7703300000000001,4,10,/divyosmi2009/titanic,Titanic - Machine Learning from Disaster
10502087,0.75598,0,2,/hirototakaoka/fork-of-kernel6b32aabe55,Titanic - Machine Learning from Disaster
10553184,0.76076,1,2,/fathialasali/titanic-comp,Titanic - Machine Learning from Disaster
8822016,0.38721,1,2,/osciiart/covid-19-lightgbm-week-2,COVID19 Global Forecasting (Week 3)
9863967,0.65108,0,8,/michalbrezk/natural-language-processing-with-bi-gru-top-10,Sentiment Analysis on Movie Reviews
10538889,0.2511,1,4,/muhammadbasilv/yolov4-inference2,Global Wheat Detection
10556992,0.1246599999999999,0,31,/mainakchaudhuri/house-price-prediction-a-detailed-eda,House Prices - Advanced Regression Techniques
4937600,78157.58012,0,2,/zharfan104/kerneld9e68a431e,I-RICH ML COMPETITION
8757927,0.03515,3,1,/datahadi/covid19-global-forecasting-week-3-c-split,COVID19 Global Forecasting (Week 3)
8810978,0.78468,2,4,/urayukitaka/starting-titanic-prediction-of-survivor,Titanic - Machine Learning from Disaster
8802037,0.76663,0,19,/khotijahs1/influencers-in-social-networks,Influencers in Social Networks
9419515,0.76076,1,4,/omartronco/kerneldbc2827456-o,Titanic - Machine Learning from Disaster
10598862,0.76555,1,5,/mohammedkuheil/titanic-xgboosting,Titanic - Machine Learning from Disaster
10589149,0.13979,0,2,/kaito1412/house-prices-after,House Prices - Advanced Regression Techniques
10593963,0.7511899999999999,0,2,/maimahdi/titanicrf,Titanic - Machine Learning from Disaster
8688376,1.17894,4,22,/mdmahmudferdous/covid-19-global-forecasting-2,COVID19 Global Forecasting (Week 2)
9831508,0.1386599999999999,1,2,/saptarshineogi/housing-price-problem,House Prices - Advanced Regression Techniques
9260127,0.7962899999999999,3,3,/blackitten13/texts-classification-baseline2,Text classification
10608825,0.76555,0,3,/tanmayshukla/titanic-model-buildup,Titanic - Machine Learning from Disaster
9122235,0.84688,0,11,/jokerdd/titanic-dongguating-final,Titanic - Machine Learning from Disaster
9323728,0.76076,4,2,/adyajaiswal/titanic-data-analysis,Titanic - Machine Learning from Disaster
9166983,0.7751100000000001,11,27,/mathchi/titanic-machine-learning-from-disaster-3-models,Titanic - Machine Learning from Disaster
9078049,0.13638,2,8,/nikhilsharma4/eda-and-ann-for-advance-regression-problem,House Prices - Advanced Regression Techniques
9089321,0.0,5,19,/jafarib/fork-of-for-best-score-use-this-notebook-up-vote,House Prices - Advanced Regression Techniques
8769874,0.56387,1,1,/boskaiolo/covid-19-predictions-week3-tf-censored-timeseries,COVID19 Global Forecasting (Week 3)
10604656,0.46168,0,2,/fikrikhair/logistic-no-holiday-count,[Open] Shopee Code League - Logistics
10073555,4.2401,0,1,/abhimanyusethi/facial-keypoint-detection-cnn,Facial Keypoints Detection
10614240,0.14405,0,1,/kaggluserjp/kernel17e5acfa4e,House Prices - Advanced Regression Techniques
9644084,0.7703300000000001,5,11,/an0utlier/machine-learning-for-beginners-titanic-sink,Titanic - Machine Learning from Disaster
9374034,0.6504,1,10,/sumitjha19/yolov3-inference-v2-full-code,Global Wheat Detection
10005102,0.7140000000000001,0,3,/charan24/roberta-pytorch-1,Tweet Sentiment Extraction
9217405,0.98714,2,2,/gb00000/keras-cnn-digit,Digit Recognizer
9991448,0.7751100000000001,0,1,/atiehkhaleghi/titanic,Titanic - Machine Learning from Disaster
9374977,0.7751100000000001,1,3,/minliyu/titanic-my1-featureengineeringon-age-title,Titanic - Machine Learning from Disaster
9235765,0.78468,1,1,/gb00000/titanic-median-age-from-title,Titanic - Machine Learning from Disaster
8861736,0.6674100000000001,0,2,/mskazantsev/bdsm-team-kernel,Car loan default
8834702,0.13504,1,9,/dinasinclair/xgboost-tuning-tutorial-housing-prices,House Prices - Advanced Regression Techniques
9397725,0.74162,1,2,/maximilianblacher/titanic-first-shot-with-python,Titanic - Machine Learning from Disaster
8687592,3.71769,0,2,/haricharanbk/corona-fatality-and-confirmed-cases-prediction,COVID19 Global Forecasting (Week 2)
9171758,0.13316,0,3,/nguyncaoduy/house-pricing-prediction-fastai-tabular-learner,House Prices - Advanced Regression Techniques
10028710,0.67224,0,2,/arnav8/titanic-challenge-using-knn,Titanic - Machine Learning from Disaster
8825780,0.03516,0,1,/ben975/xgboost-based-model,COVID19 Global Forecasting (Week 3)
8706874,0.2349699999999999,0,2,/davidistrati/arima-week-2,COVID19 Global Forecasting (Week 2)
8701356,1.35395,0,1,/czyum26/covid-19-forecasting-using-elasticnet,COVID19 Global Forecasting (Week 2)
8964493,0.76555,0,2,/sebinduke/sebin-s-titanic-prediction,Titanic - Machine Learning from Disaster
10894240,0.67224,0,1,/rentakano/titanic-test,Titanic - Machine Learning from Disaster
10891437,0.70485,0,2,/minamids/last-s2-lgbmmodel-0726-tuning-merge,Homework for Students
8905594,0.0,0,1,/samarthvaru/kernel28aa8dad47,Titanic - Machine Learning from Disaster
8917244,0.925,2,4,/ashutosh619sudo/ion-channel-with-conv1d-and-bidirectional-gru,University of Liverpool - Ion Switching
10918520,0.76794,0,4,/romanbezaev/firsttryever,Titanic - Machine Learning from Disaster
8915633,0.7914100000000001,13,7,/akshitsharma206/nlp-disaster-competition-lstm-bert-xgboost,Natural Language Processing with Disaster Tweets
9773179,0.67464,4,5,/vaibhavibandi/titanic-analysis,Titanic - Machine Learning from Disaster
8622669,0.1777099999999999,4,11,/shakewingo/covid-19-eda-lstm,COVID19 Global Forecasting (Week 2)
9420601,0.82296,0,4,/peterbre/ui1-tita,Titanic - Machine Learning from Disaster
9746662,0.78947,3,6,/vedato/titanic-ml,Titanic - Machine Learning from Disaster
8662763,0.80861,0,1,/taoguan/titanic-use-keras,Titanic - Machine Learning from Disaster
8628909,0.6976399999999999,1,16,/anshuls235/covid19-eda-predictions,COVID19 Global Forecasting (Week 2)
8558202,0.7751100000000001,1,1,/munjalmukul/mukul-titanic-survival-prediction,Titanic - Machine Learning from Disaster
9327150,0.7751100000000001,0,2,/ebinbaby/titanic-survival-prediction-learning-problem-2,Titanic - Machine Learning from Disaster
9033381,0.81339,2,8,/medmaatar/titanic-simple-ml-dl-with-sklearn-and-pytorch,Titanic - Machine Learning from Disaster
10543398,0.78947,1,6,/abdalazez/titanic-prediction,Titanic - Machine Learning from Disaster
9347922,0.7703300000000001,0,1,/alamakbar/first-kaggle-project,Titanic - Machine Learning from Disaster
10541177,0.1368599999999999,0,3,/ahmedalghaliz/house-prices-advance-competetion-keras,House Prices - Advanced Regression Techniques
10537096,0.78708,1,2,/aseelalshorafa/titanic-modeling,Titanic - Machine Learning from Disaster
4271761,0.0,0,1,/tongxue35/surved,Titanic - Machine Learning from Disaster
9924232,0.12245,0,4,/munmun2004/house-prices-for-begginers,House Prices - Advanced Regression Techniques
9881190,0.7751100000000001,1,7,/sureshmecad/titanic-predict-survival,Titanic - Machine Learning from Disaster
9300681,0.8220000000000001,0,1,/ajax0564/tpu-bert-multi-lang-modify-crossentropy,Jigsaw Multilingual Toxic Comment Classification
10553517,11.97497,0,1,/engnadersarsour/house-price-compitition,House Prices - Advanced Regression Techniques
8401116,0.0,0,1,/zhuangliu1939/nlp-text-analytics-quora-insincere-questions,COVID19 Global Forecasting (Week 4)
204889,0.0,0,1,/zengping/an-interactive-data-science-tutorial-test,Titanic - Machine Learning from Disaster
82839,0.0,0,1,/seantacular/boarding-a-sinking-ship,Titanic - Machine Learning from Disaster
10513115,0.7822899999999999,2,3,/doaajaber/titanic,Titanic - Machine Learning from Disaster
9770354,0.8109999999999999,0,0,/hq1314/titanic-with-random-forest,Titanic - Machine Learning from Disaster
9174426,0.79425,3,2,/eternalgenin/titanic-data-prediction-using-random-forest,Titanic - Machine Learning from Disaster
9761058,0.7751100000000001,0,0,/shubh2005/kernel7b8de64349,Titanic - Machine Learning from Disaster
9467874,0.76555,0,0,/emekaihedilionye/final-titanic-solutions,Titanic - Machine Learning from Disaster
9718347,0.7751100000000001,0,0,/nachiketnema/kernel211bacea4d,Titanic - Machine Learning from Disaster
9727460,0.7751100000000001,0,0,/abhimanyups/titanium-forest,Titanic - Machine Learning from Disaster
9724330,0.76076,0,0,/himarosa/fork-of-kernel374f05fa37,Titanic - Machine Learning from Disaster
8927461,0.8605,0,0,/yeayates21/jigsaw-lstm-w-custom-embedding-tf-keras2-0,Jigsaw Multilingual Toxic Comment Classification
9713844,0.62679,0,0,/karenkang/titanic-tutorial-pytorch,Titanic - Machine Learning from Disaster
10050683,0.7703300000000001,0,0,/matsuyamatamotsu/kernel667068ba40,Titanic - Machine Learning from Disaster
9287383,0.7511899999999999,0,0,/rj81309050/titanic-survival-prediction,Titanic - Machine Learning from Disaster
9730497,0.79904,0,0,/vamsikrishnabommidi/getting-started-with-titanic,Titanic - Machine Learning from Disaster
8801098,0.95691,0,0,/marsel171/covid19-week3,COVID19 Global Forecasting (Week 3)
9048917,0.14171,0,0,/thanhhungnguyen/house-price-ver-2,House Prices - Advanced Regression Techniques
8807169,0.0,0,2,/abdumaa/similarity-search-solution-template,Similarity Search Project
8804356,0.7751100000000001,0,0,/shahzinakhan/titanic-comp1,Titanic - Machine Learning from Disaster
10551789,0.13321,0,1,/c612035044/kernel91ff6cf6de,House Prices - Advanced Regression Techniques
9478336,0.7751100000000001,0,0,/ankykaushik/titanic-survival-predictions-with-ann,Titanic - Machine Learning from Disaster
9478969,0.19935,0,0,/anuj007a/kernel1a07e0e55b,House Prices - Advanced Regression Techniques
10551221,0.77272,0,0,/mahmoudalhallaq/titanic-mahmod-com,Titanic - Machine Learning from Disaster
9047634,0.13965,0,1,/ayisharincy/house-price,House Prices - Advanced Regression Techniques
8797132,1.22433,0,0,/sudhamshsuraj/kernel709b40232c,COVID19 Global Forecasting (Week 3)
9496126,0.153,0,1,/nikhilroxtomar/simple-gru-with-pre-trained-embeddings,Tweet Sentiment Extraction
10546478,0.7751100000000001,1,3,/fatimaafifi/titanic,Titanic - Machine Learning from Disaster
9368598,0.34757,0,0,/jwmyers82/msds422-assignment5,Digit Recognizer
9606142,0.7799,0,0,/mrhash/getting-started-with-titanic,Titanic - Machine Learning from Disaster
9379738,0.78947,0,0,/eternalgenin/titanic-data-prediction-improvements-using-xgb,Titanic - Machine Learning from Disaster
9614150,0.7751100000000001,0,0,/thenishant/titanic,Titanic - Machine Learning from Disaster
10033229,0.98214,0,0,/maziprimareza/digit-recognition-with-cnn,Digit Recognizer
8988266,0.8373200000000001,1,8,/girijeshcse/titanic-advanced-feature-engineering-tutorial,Titanic - Machine Learning from Disaster
8989016,0.30317,0,2,/hotton/m5-uncertainty-simply-by-weekday-distribution,M5 Forecasting - Uncertainty
9009347,0.14246,0,0,/rajneeshkumar0509/house-price-prediction-rajneesh,House Prices - Advanced Regression Techniques
9672152,0.76555,0,3,/lazyd3v/logistic-regression-approach,Titanic - Machine Learning from Disaster
9649166,0.1425,0,0,/devendratapdia/house-prices-prediction,House Prices - Advanced Regression Techniques
8873698,0.7703300000000001,0,0,/ad8mir/kernele6e54c25be,Titanic - Machine Learning from Disaster
9623254,0.78468,0,0,/adnanasif/who-survives,Titanic - Machine Learning from Disaster
9310240,1781.9015600000002,0,0,/chua23/t1-fire-extinguishers,ASN10e Final Submission - Detect COML Faces
8965295,0.79425,3,4,/justinflanagan/titanic-using-keras-sequential-ai-for-beginners,Titanic - Machine Learning from Disaster
9311936,740.06532,0,0,/sagars729/t5-not-lazy-just-training,ASN10e Final Submission - Detect COML Faces
8912378,0.0,0,1,/viktorpopov/kerneltitanic,Titanic - Machine Learning from Disaster
9301232,0.80382,0,0,/carlesorf/titanic-deep-in-the-sea-compare-models-and-keras,Titanic - Machine Learning from Disaster
8921843,4.2394,0,1,/ee257sp20darshan/darshan-s-r-ee257-ml-sp20,COVID19 Global Forecasting (Week 4)
9305018,0.7751100000000001,0,0,/muzammiliftikhar/my-version-of-titanic-ml,Titanic - Machine Learning from Disaster
9034965,0.0,1,10,/nitindantu/100-accurate,House Prices - Advanced Regression Techniques
10587829,0.13979,0,0,/hirotakakawachi/kernel68be16a86f,House Prices - Advanced Regression Techniques
10586804,0.13084,0,0,/ttkkssmm/kernel700c0cfe2a,House Prices - Advanced Regression Techniques
8994939,0.14437,0,0,/nayonika/house-pricing-regression-problem,House Prices - Advanced Regression Techniques
9001546,0.2433,0,2,/giginghn/covid19-analysis-seir-model,COVID19 Global Forecasting (Week 4)
10078396,0.99085,0,0,/arideno/mnist-99-99,Digit Recognizer
9238599,0.7751100000000001,0,0,/jayanthm/kernel77564df111,Titanic - Machine Learning from Disaster
9843543,0.7751100000000001,0,0,/satishgunjal/prediction-using-titanic-data,Titanic - Machine Learning from Disaster
9835317,0.7751100000000001,0,0,/roberttolan/getting-started-with-titanic,Titanic - Machine Learning from Disaster
9857491,0.11968,0,2,/kasayu/stacked-regressions-top-4-on-leaderboard,House Prices - Advanced Regression Techniques
9820403,0.5,0,0,/anirbansen3027/melanoma-classification-eda-beginner,SIIM-ISIC Melanoma Classification
9821600,0.17854,5,16,/erikcabeza/feature-engineering-and-xgbregressor,House Prices - Advanced Regression Techniques
9967004,0.75598,0,0,/motohiro/kernel726d228659,Titanic - Machine Learning from Disaster
8945211,0.23181,3,6,/carlmcbrideellis/very-simple-neural-network-regression,House Prices - Advanced Regression Techniques
8944698,0.03537,0,0,/moradnejad/covid-w4-sub-ensemble,COVID19 Global Forecasting (Week 4)
8784359,2.1621,0,0,/devalindey/covid19-global-forecasting-week-3-with-lightgbm,COVID19 Global Forecasting (Week 3)
8791119,0.1122799999999999,0,1,/dkozlov/p9gzscywhnf2syrb,COVID19 Global Forecasting (Week 3)
9947633,0.74641,0,0,/dhyeylalseta/first-time,Titanic - Machine Learning from Disaster
9217671,0.7703300000000001,0,0,/aleksandrdremov/titanicml,Titanic - Machine Learning from Disaster
9985092,0.96085,0,0,/etudiant233/neural-network-without-conv-layers-pytorch,Digit Recognizer
9987637,0.71214,12,30,/chandrimad31/tweet-sentiment-extraction-using-roberta-5-fold,Tweet Sentiment Extraction
9999514,0.99214,0,0,/karthikpuranik/digit-recognizer-mnist,Digit Recognizer
211369,0.0,0,0,/songwhu/an-interactive-data-science-tutorial-92d4ff,Titanic - Machine Learning from Disaster
216399,0.0,0,0,/xpavlic4/an-interactive-data-science-tutorial,Titanic - Machine Learning from Disaster
209855,0.0,0,0,/bgoldfeder/an-interactive-data-science-tutorial,Titanic - Machine Learning from Disaster
220344,0.0,0,0,/zubairahmed/an-interactive-data-science-tutorial,Titanic - Machine Learning from Disaster
239726,0.0,0,0,/firice/an-interactive-data-science-tutorial,Titanic - Machine Learning from Disaster
227529,0.0,0,0,/ipaqpaq/an-interactive-data-science-tutorial-4481f8,Titanic - Machine Learning from Disaster
221428,0.0,0,0,/manual/an-interactive-data-science-tutorial,Titanic - Machine Learning from Disaster
241485,0.0,0,0,/gauss102/titanic-test,Titanic - Machine Learning from Disaster
170091,0.0,0,0,/yl1991/an-interactive-data-science-tutorial,Titanic - Machine Learning from Disaster
159527,0.0,0,0,/tgandhi/notebook252e72c6e8,Titanic - Machine Learning from Disaster
183780,0.0,0,0,/tlimousin/an-interactive-data-science-tutorial,Titanic - Machine Learning from Disaster
239934,0.0,0,0,/medusacascade/an-interactive-data-science-tutorial,Titanic - Machine Learning from Disaster
1090373,0.0,0,0,/codacus/titanic-surviver-prediction,Titanic - Machine Learning from Disaster
891075,0.0,0,0,/tingli2010/first-try-on-titanic,Titanic - Machine Learning from Disaster
1184871,0.0,0,0,/shayanb2004/titanic-codes,Titanic - Machine Learning from Disaster
265457,0.0,0,0,/asking28/an-interactive-data-science-tutorial-fc30aa,Titanic - Machine Learning from Disaster
255780,0.0,0,0,/nambkpfiev/an-interactive-data-science-tutorial,Titanic - Machine Learning from Disaster
242140,0.0,0,0,/alexhanbing2016/an-interactive-data-science-tutorial,Titanic - Machine Learning from Disaster
574764,0.0,0,0,/aditi2009/titanic-data-science-solution,Titanic - Machine Learning from Disaster
9894431,0.60287,0,0,/mollywiener/titanic-3,Titanic - Machine Learning from Disaster
5736534,0.0,0,0,/umairnsr87/titanic-kernel,Titanic - Machine Learning from Disaster
10486539,0.75598,0,0,/tsudashota/kernel30b561515d,Titanic - Machine Learning from Disaster
10490034,0.75598,0,0,/sk2062/titanic-machine-learning-from-disaster-beginner,Titanic - Machine Learning from Disaster
9348036,0.86162,0,0,/ianmckenzie/test-submission,COVID19 Global Forecasting (Week 2)
9338229,0.75598,0,0,/benjonasherbertz/kernel322f933524,Titanic - Machine Learning from Disaster
9349764,1.10115,0,0,/adil10743/kernel-covid,COVID19 Global Forecasting (Week 2)
8702904,0.06331,0,3,/nkiith/covid19,COVID19 Global Forecasting (Week 2)
9327797,1957.45185,0,0,/nicholaskarch/t3-kjsn,ASN10e Final Submission - Detect COML Faces
9329713,0.17242,0,0,/orazkarl/endterm-karl,House Prices - Advanced Regression Techniques
9159601,0.42962,0,0,/nakagawam/kdl20200428,House Prices - Advanced Regression Techniques
8854696,0.81339,0,0,/igorlaryushin/kernel5f0edbf535,Titanic - Machine Learning from Disaster
9424661,0.7751100000000001,0,0,/moamennasser/first-submission,Titanic - Machine Learning from Disaster
9417707,0.9,0,0,/tkmacs/kernel6bfbc01b20,Brain Cancer Classification
9432958,0.1201,0,0,/adamkmec/ui1-kmec,House Prices - Advanced Regression Techniques
9434906,0.82296,0,0,/martinslanicay/ui-1-martinslanicay,Titanic - Machine Learning from Disaster
9401023,0.7822899999999999,0,0,/kamleshsolanki/titanic-machine-learning,Titanic - Machine Learning from Disaster
9398977,0.8,0,0,/junki0514/braincancerclassification,Brain Cancer Classification
9408483,0.6,0,0,/ohbatomoaki/brain-cancer-classification-ohba,Brain Cancer Classification
9408731,0.7799,2,4,/shyam21/getting-started-with-titanic-datasets,Titanic - Machine Learning from Disaster
8686025,0.54698,0,0,/wenruipsu/submits,COVID19 Global Forecasting (Week 2)
8700624,0.96142,0,0,/michalinho/michal-dyczko-digit-recognizer,Digit Recognizer
8697494,1.46757,0,0,/danevans/covid-19-global-week-2-logistic-curve-fitting,COVID19 Global Forecasting (Week 2)
9944029,0.14568,0,0,/mohameddjebloun/entry-to-house-pricing-competition,House Prices - Advanced Regression Techniques
8679319,1.84559,0,0,/pritha21/covid-19-week-2-forecasting,COVID19 Global Forecasting (Week 2)
8827384,0.03483,0,0,/zfzfzf/kernel64fe06483a,COVID19 Global Forecasting (Week 3)
8826791,0.4593899999999999,0,0,/tigeriv/offset-wdi,COVID19 Global Forecasting (Week 3)
8826131,1.29191,0,0,/rserban/kernel6b71994589,COVID19 Global Forecasting (Week 3)
8823854,0.0442199999999999,0,0,/harshbelani/profit-and-exp,COVID19 Global Forecasting (Week 3)
8824598,1.3208799999999998,0,2,/stecasasso/cv19-w3-bt-sub1,COVID19 Global Forecasting (Week 3)
8825245,2.48604,0,0,/surajitcba2021/week3-covid-1,COVID19 Global Forecasting (Week 3)
8820034,0.0569799999999999,0,0,/ivince20x4/covid-19-week3-prediction3,COVID19 Global Forecasting (Week 3)
8822646,2.23101,0,0,/lishiyu/kernel79a00ad724,COVID19 Global Forecasting (Week 3)
8816812,0.0308,0,0,/nkiith/covid19-week3-modified-model,COVID19 Global Forecasting (Week 3)
8819507,0.48498,0,0,/amitstei/gbdt-baseline-covid-19,COVID19 Global Forecasting (Week 3)
9090572,0.7751100000000001,3,4,/aamirsaifi/titanic-solution-random-forest,Titanic - Machine Learning from Disaster
9059334,0.7751100000000001,0,0,/rohitsharma0206/kernel1d55f2d467,Titanic - Machine Learning from Disaster
9068336,0.7751100000000001,0,2,/adeyoyintemidayo/titanic-predictions,Titanic - Machine Learning from Disaster
8772733,0.15184,0,0,/danmusetoiu/xgb-catboost-kiss,COVID19 Global Forecasting (Week 3)
9101854,0.7799,1,1,/gaurangmehra/titanic-1,Titanic - Machine Learning from Disaster
8621576,0.76555,0,0,/diogovilaviosa/titanic,Titanic - Machine Learning from Disaster
8029951,0.979,0,1,/thunder2901/mnist-digit,Digit Recognizer
9122032,0.12789,0,0,/urayukitaka/onehot-and-ensemble-prediction,House Prices - Advanced Regression Techniques
9121810,0.79186,1,3,/kazuhito00/pycaret1-0-blend-models-fold-n-titanic-sample,Titanic - Machine Learning from Disaster
10878767,0.1439099999999999,0,0,/kaggluserjp/kernel4136fd523d-case2,House Prices - Advanced Regression Techniques
9132153,0.99085,4,2,/daenys2000/mnist-digit-recognition,Digit Recognizer
10880298,0.19411,0,0,/kaggluserjp/kernel4136fd523d-case1,House Prices - Advanced Regression Techniques
10893877,0.71432,0,0,/tennisjatmgezwebnejp/kernel35d63dc33d,Digit Recognizer
10892115,0.69963,0,1,/teppeihosoi/kernel19a7857d1a,Homework for Students
10888622,0.12983,0,0,/lordjoedin/house-price-regression-model,House Prices - Advanced Regression Techniques
3828859,0.9993,0,0,/ayang98/cacti-yeet,Aerial Cactus Identification
3821266,0.997,0,1,/alainminda/kernelf9581109fe,Aerial Cactus Identification
3780517,0.9928,0,0,/amanooo/step-ttaining-of-vgg16,Aerial Cactus Identification
3685705,0.999,0,6,/paulsantonastaso/classifying-cacti-with-pytorch-cnn,Aerial Cactus Identification
3709172,0.992,0,0,/anndd3/ariel-cactus,Aerial Cactus Identification
3601634,1.0,2,10,/kurianbenoy/solving-aerial-cactus-challenge-using-fast-ai,Aerial Cactus Identification
3652105,0.995,0,2,/parmarsuraj99/cnn-pytorch,Aerial Cactus Identification
3373429,1.0,0,2,/nathanh12/fastai-1-0lb-300s,Aerial Cactus Identification
3527682,0.9999,9,16,/gabrielmv/aerial-cactus-identification-keras,Aerial Cactus Identification
3578171,0.9981,36,236,/shahules/getting-started-with-cnn-and-vgg16,Aerial Cactus Identification
3553846,1.0,1,5,/navneeth/feedforward-cnn-fastai,Aerial Cactus Identification
3552560,0.8438,0,0,/prateek28/kernelf43ed5ff90,Aerial Cactus Identification
3503862,0.9542,0,0,/zhuhai/resnet-imagegenerator,Aerial Cactus Identification
3501002,0.9982,0,1,/shotaho/kernel4265908402,Aerial Cactus Identification
3437580,0.9968,2,6,/vladminzatu/cactus-detection-with-tensorflow-2-0,Aerial Cactus Identification
3456626,0.9999,0,0,/asura93/simple-fastai-exercise,Aerial Cactus Identification
3370001,1.0,1,11,/interneuron/fast-fastai-with-condensenet,Aerial Cactus Identification
3264640,0.9999,2,22,/mariammohamed/simple-cnn,Aerial Cactus Identification
3330460,0.5044,0,1,/yizhezx/aerial-cactus,Aerial Cactus Identification
3330515,0.9996,5,6,/alperkoc/data-augmentation-vgg16-cnn,Aerial Cactus Identification
3281556,0.8311,5,17,/rohandx1996/pca-mlp-vs-pca-cnn-focal-loss-resnet50-vs-vgg16,Aerial Cactus Identification
3275412,0.9992,1,7,/muhammedfathi/aerial-cactus-with-keras,Aerial Cactus Identification
3239101,0.9403,0,2,/jiajunc/aerial-cactus-identification-pytorch,Aerial Cactus Identification
3250549,0.9999,0,13,/ivanwang2016/baseline,Aerial Cactus Identification
3224012,0.9976,0,0,/abhigupta4981/just-a-random-fast-ai-notebook-flying-by,Aerial Cactus Identification
3189424,0.9625,8,78,/ateplyuk/keras-transfer-vgg16,Aerial Cactus Identification
3192498,0.997,2,12,/harshel7/cactus-identification-ensemble-transfer-learning,Aerial Cactus Identification
10416851,0.9929,0,0,/robertorajunior/cnn-vgg16,Aerial Cactus Identification
7231007,0.9828,0,0,/lagmoellertim/aerial-cactus-identification-using-fastai,Aerial Cactus Identification
4054001,3.27133,0,0,/mukul09/new-york-taxi-fare-prediction,New York City Taxi Fare Prediction
1658004,4.04343,0,0,/peicao/fork-of-nyc-taxi-fare-prediction-2-0,New York City Taxi Fare Prediction
2892134,6.04501,0,0,/sprakash08/heat-maps-and-linear-model-exploration,New York City Taxi Fare Prediction
2595741,3.62702,0,0,/kealanman/ny-taxi-fares,New York City Taxi Fare Prediction
2519218,3.93192,0,0,/pkkondamuri/nyc-taxi-fare-kpk,New York City Taxi Fare Prediction
2296229,3.49427,0,0,/tuanflash/newyorkcitytaxifareprediction-kaggle-course,New York City Taxi Fare Prediction
2155145,3.74883,0,0,/caomanhdat/taxi-fare,New York City Taxi Fare Prediction
1946395,3.72606,0,0,/bapanes/taxifare-gridsearch-bapanes,New York City Taxi Fare Prediction
1992335,3.22629,0,1,/humamfauzi/date-expansion-and-incremental-learning,New York City Taxi Fare Prediction
1979596,3.67612,0,1,/anthokalel/new-york-city-taxi-fare-prediction-polytech,New York City Taxi Fare Prediction
1782585,4.019430000000002,0,0,/sunnets/taxi-fare-prediction-try-3,New York City Taxi Fare Prediction
1719821,3.42642,0,0,/smanimaran0309/new-york-city-taxi-fare-prediction-final,New York City Taxi Fare Prediction
1538623,3.20388,0,6,/monthepp/new-york-city-taxi-fare-prediction,New York City Taxi Fare Prediction
1581572,5.69152,0,0,/martinsantome/ny-taxi,New York City Taxi Fare Prediction
1692698,3.65391,0,0,/cmwhknz/assigmnent-3-2,New York City Taxi Fare Prediction
1682416,3.47035,0,1,/ffedericoni/nyc-taxi-fare-with-tf-boostedtrees-regressor,New York City Taxi Fare Prediction
4187554,0.64682,0,0,/partrita/satisfied-santander,Santander Customer Transaction Prediction
4139224,0.8974799999999999,0,0,/mickybanks/banks-customer-trans-pred,Santander Customer Transaction Prediction
3975385,0.85715,0,1,/danielisanchez/red-neuronal-keras-441d76,Santander Customer Transaction Prediction
3941197,0.83726,0,1,/athiats/red-neuronal-keras,Santander Customer Transaction Prediction
3978341,0.8551200000000001,0,1,/cristianjacob1/red-neuronal-keras,Santander Customer Transaction Prediction
3135782,0.898,0,0,/poppins/lgbm-extended-features-optimized,Santander Customer Transaction Prediction
4001013,0.84965,0,0,/christianzarur/red-neuronal-keras,Santander Customer Transaction Prediction
3887935,0.85015,0,0,/luisgraterol/red-neuronal-keras,Santander Customer Transaction Prediction
3966556,0.85105,0,0,/marcoasp/red-neuronal-keras,Santander Customer Transaction Prediction
3932311,0.857,0,0,/marivit/red-neuronal-keras,Santander Customer Transaction Prediction
3601814,0.60774,0,0,/smohubal/santander-customer-transaction-prediction,Santander Customer Transaction Prediction
3090847,0.899,0,1,/gsdeepakkumar/predict-the-transactions,Santander Customer Transaction Prediction
3639143,0.6304,0,0,/rishikoush/customer-transaction-prediction,Santander Customer Transaction Prediction
3402465,0.901,0,0,/returnofsputnik/lgbm-count-differences-2,Santander Customer Transaction Prediction
3564527,0.9154,25,63,/titericz/single-model-using-only-train-counts-information,Santander Customer Transaction Prediction
3552140,0.92289,24,74,/qitian51212/simple-magic-var-0-922,Santander Customer Transaction Prediction
3528311,0.91924,0,36,/zfturbo/magic-feature-generator,Santander Customer Transaction Prediction
2944565,0.912,11,22,/timon88/santander-catboost-finalscript-public-lb-0-919,Santander Customer Transaction Prediction
3144101,0.782,0,7,/akhileshrai003/logistic-regression-with-new-features-feather,Santander Customer Transaction Prediction
14309250,0.13326,13,21,/josephchan524/housepricesregressor-using-lightgbm,House Prices - Advanced Regression Techniques
13807097,0.14232,45,81,/chanakyavivekkapoor/house-price-prediction,House Prices - Advanced Regression Techniques
14405512,0.1458799999999999,13,20,/milankalkenings/deep-dive-feature-importance-house-prices,House Prices - Advanced Regression Techniques
14433369,0.14565,5,18,/ankitverma2010/house-prices-prediction-beginner-to-advanced,House Prices - Advanced Regression Techniques
14427125,0.13554,2,8,/solegalli/feature-engineering-and-model-stacking,House Prices - Advanced Regression Techniques
13785351,0.37192,0,1,/priyankagaidhani/house-price-prediction-using-pytorch-embedding,House Prices - Advanced Regression Techniques
13616287,0.20121,1,3,/drakedyban/housing-prices-advanced-regression-techniques,House Prices - Advanced Regression Techniques
14553872,0.13372,0,0,/yosghdv/houseprice-prediction,House Prices - Advanced Regression Techniques
11756358,0.1699,0,0,/pakkanmeric/house-prices-elasticnet,House Prices - Advanced Regression Techniques
12256452,0.13903,0,0,/jonahtabbal/housing,House Prices - Advanced Regression Techniques
14310337,0.11457,3,4,/firebee/eda-feature-engineering,House Prices - Advanced Regression Techniques
14314152,0.1739099999999999,0,0,/jeandersonbc/house-prices-bare-minimum-solution,House Prices - Advanced Regression Techniques
14652976,0.15349,0,0,/tracyporter/house-prices-reduce-columns-bagging,House Prices - Advanced Regression Techniques
14291241,0.14164,35,23,/riteshpatil8998/top-49-house-pricing-using-ridge-and-lasso,House Prices - Advanced Regression Techniques
14235027,0.13347,48,40,/andreshg/house-price-eda-and-prediction,House Prices - Advanced Regression Techniques
12997139,0.7490000000000001,0,5,/dmikar/baseline-for-riiid-lightgbm,Riiid Answer Correctness Prediction
13138372,0.748,1,10,/chistyakov/vowpal-wabbit-with-low-memory-usage,Riiid Answer Correctness Prediction
12406142,0.664,0,1,/saijasthi/notebook-for-submissions-of-models,Riiid Answer Correctness Prediction
12957302,0.722,0,8,/samratthapa/inference-using-pandas-vs-inference-using-numpy,Riiid Answer Correctness Prediction
12933489,0.747,0,2,/sarthakrastogi/neural-net-for-riiid-1,Riiid Answer Correctness Prediction
12851880,0.648,0,1,/saijasthi/karas-nn-epoch-30-data-4-lr-0001,Riiid Answer Correctness Prediction
12829215,0.758,5,7,/houssemayed/lightgbm-for-riiid,Riiid Answer Correctness Prediction
12659758,0.7559999999999999,1,8,/chenmingml/dnn-based-on-lgbm-iii-data-preparation,Riiid Answer Correctness Prediction
12740569,0.76,61,351,/its7171/lgbm-with-loop-feature-engineering,Riiid Answer Correctness Prediction
12648223,0.7490000000000001,9,11,/jcesquiveld/lightgbm-baseline-with-basic-features,Riiid Answer Correctness Prediction
12728158,0.758,22,126,/shoheiazuma/riiid-lgbm-starter,Riiid Answer Correctness Prediction
12677583,0.762,18,82,/calebeverett/riiid-submit,Riiid Answer Correctness Prediction
12687471,0.7559999999999999,0,12,/sergei416/hyperparameter-tuning-lgb-ii,Riiid Answer Correctness Prediction
12498736,0.758,8,17,/amaity0/riiid-catboost-attempt,Riiid Answer Correctness Prediction
12564092,0.753,2,43,/pratikskarnik/riiid-keras-transformer-starter,Riiid Answer Correctness Prediction
12530305,0.7559999999999999,11,71,/takamotoki/lgbm-iii-part3-adding-lecture-features,Riiid Answer Correctness Prediction
12541945,0.7509999999999999,0,6,/pratikskarnik/riid-keras,Riiid Answer Correctness Prediction
10630570,0.8681,0,1,/mushaya/melanoma-class-3,SIIM-ISIC Melanoma Classification
10822472,0.6817,4,4,/amneves/melanoma-with-xgboost-cv,SIIM-ISIC Melanoma Classification
10831513,0.6902,9,24,/namanj27/xgboost-basic-preprocessing-tabular-data,SIIM-ISIC Melanoma Classification
10250312,0.7653,10,64,/tanulsingh077/exploring-limits-of-meta-features-tabnet-lb-0-77,SIIM-ISIC Melanoma Classification
9906644,0.831,1,2,/vaidicjain/siim-isic-melanoma-classification,SIIM-ISIC Melanoma Classification
10767984,0.9429,0,3,/digvijayyadav/getting-started-with-tfrecords,SIIM-ISIC Melanoma Classification
10682457,0.8565,0,28,/jagdmir/siim-melanoma-classification-modelling,SIIM-ISIC Melanoma Classification
10779784,0.8912,0,8,/ashutosh619sudo/learn-medical-image-classification-with-pytorch,SIIM-ISIC Melanoma Classification
10573709,0.8321,0,16,/amneves/tensorflow-efficientnetbx-transfer-learning,SIIM-ISIC Melanoma Classification
10322011,0.7143,0,3,/adnaiksachin25/melanoma-v1-svm,SIIM-ISIC Melanoma Classification
10709724,0.8295,17,136,/cdeotte/rapids-cuml-knn-find-duplicates,SIIM-ISIC Melanoma Classification
10707321,0.9526,9,66,/solomonk/minmax-ensemble-0-9526-lb,SIIM-ISIC Melanoma Classification
10581874,0.9504,9,27,/solomonk/classification-np-log2-ensemble-0-9504-lb,SIIM-ISIC Melanoma Classification
10565630,0.923,0,0,/sangeethamanoharan/melanoma-classification,SIIM-ISIC Melanoma Classification
10656043,0.8692,2,6,/noelmat/siim-starter-quick-start-mixup,SIIM-ISIC Melanoma Classification
14539422,0.151,1,6,/bryanb/vinbigdata-chest-x-ray-ensembling-approach,Predict Future Sales
14094659,0.087,0,1,/mariazorkaltseva/vinbigdata-eda-faster-rcnn-icevision-inference,Predict Future Sales
8834822,1.1023,0,1,/urayukitaka/to-try-prediction-future-sale,Predict Future Sales
8869719,0.95146,0,0,/vitrioil/simple-eda-stack,Predict Future Sales
8541449,0.8942200000000001,2,26,/tristanleclercq/predict-future-sales-full-solution-xgboost,Predict Future Sales
8469720,1.0373,0,6,/kojiiwase/predict-future-sales-xgboost,Predict Future Sales
8387661,29.61621,0,1,/nickteim/pipeline-simpel-model,Predict Future Sales
7933064,1.17692,0,0,/theolq/predict-future-sales-recurrent-neural-network,Predict Future Sales
8113266,0.90684,0,2,/wangqiyuan/predict-future-sales-xgboost,Predict Future Sales
7848018,0.93349,0,1,/dimonrtm/random-forest-regressor,Predict Future Sales
7844414,2.51946,0,0,/saha8631/kernel350f27fe21,Predict Future Sales
7854015,0.99025,0,0,/alafan/lightgbm-without-tuning,Predict Future Sales
7756694,1.01014,1,1,/victoriashishlenina/lgbmregressor-and-data-with-lags,Predict Future Sales
7041735,1.0784,0,2,/saga21/start-with-kaggle-comps-future-sales-v0,Predict Future Sales
7658441,0.93571,0,0,/gauravkjain/modeling-and-training,Predict Future Sales
7643242,0.8962,1,13,/noeasywayout/eda-fe-lgbm-model,Predict Future Sales
7508442,2.1836,0,0,/rajibdas977/rajib,Predict Future Sales
7475840,1.24259,1,4,/wernerechezuria/predict-future-sales,Predict Future Sales
7441408,1.21904,0,0,/noamwunch/predict-the-future-yahalom-noam,Predict Future Sales
6972386,0.99164,1,0,/dan3dewey/remember-2-sales-in-november,Predict Future Sales
87939,0.8868370000000001,0,0,/bckatarzyna/redhatboost,Predicting Red Hat Business Value
7291933,0.7686430000000001,1,0,/mika30/efficientnets-for-diabetic-retinopathy-detection,APTOS 2019 Blindness Detection
4613938,0.701,0,1,/xooca1/aptos-2,APTOS 2019 Blindness Detection
5290472,0.743,0,1,/anubhav1302/aptos,APTOS 2019 Blindness Detection
5274453,0.797,0,0,/krammerg/submission-11-old-new-data-l-strat-augment,APTOS 2019 Blindness Detection
6648933,0.763649,0,0,/nnick14/aptos-submissions-cv,APTOS 2019 Blindness Detection
5675267,0.539,0,7,/mgiraygokirmak/mobilenetv2,APTOS 2019 Blindness Detection
4567890,0.7290000000000001,0,1,/user164919/aptos-cnn-solution,APTOS 2019 Blindness Detection
6118102,0.61897,0,0,/zhutianyucs/kernel3364a6675f,APTOS 2019 Blindness Detection
6193269,0.0,0,0,/pawankpathak/kernel2b3a78121a,APTOS 2019 Blindness Detection
5635382,0.715,0,1,/rajnishe/aptos-resnxt101,APTOS 2019 Blindness Detection
5296602,0.486,0,0,/abhishek24021996/aptos-blindness-cb-6-1,APTOS 2019 Blindness Detection
4739973,0.755,1,2,/samlep/aptos-2019-blindness-detection-diabetic-retinopa,APTOS 2019 Blindness Detection
1894628,0.0281,0,0,/tanmaydisoriya/pubgpred-2-0,PUBG Finish Placement Prediction (Kernels Only)
2944216,0.05937,0,1,/damienpark/prediction-using-deep-learning,PUBG Finish Placement Prediction (Kernels Only)
2761193,0.02086,1,3,/iamarjunchandra/pubg-feature-engineering-lightgbm-explained,PUBG Finish Placement Prediction (Kernels Only)
2090150,0.0203,0,0,/pavelvpster/pubg-lightgbm,PUBG Finish Placement Prediction (Kernels Only)
2587409,0.0197,2,5,/rohitagarwal/12th-place-solution,PUBG Finish Placement Prediction (Kernels Only)
2230555,0.0199,0,0,/pavelvpster/pubg-pytorch,PUBG Finish Placement Prediction (Kernels Only)
2708708,0.02001,5,3,/plasticgrammer/pubg-finish-placement-prediction-keras,PUBG Finish Placement Prediction (Kernels Only)
2686454,0.0367,0,1,/tianyixu/pubg-finish-placement-prediction-pairwise-ranking,PUBG Finish Placement Prediction (Kernels Only)
2655362,0.0596,0,0,/kousakumiyake/2019-01-17,PUBG Finish Placement Prediction (Kernels Only)
2470772,0.0248,0,3,/swaggyh/multilayer-perceptron,PUBG Finish Placement Prediction (Kernels Only)
2462046,0.0255,0,0,/swaggyh/lightgbm,PUBG Finish Placement Prediction (Kernels Only)
2568669,0.1241,0,0,/vigneshtcd/vignesh-notebook,PUBG Finish Placement Prediction (Kernels Only)
2510416,0.0787,0,2,/mihiraman/pubg-win-prediction,PUBG Finish Placement Prediction (Kernels Only)
2431328,0.061,0,1,/vaseline555/1d-cnn-for-numerical-features,PUBG Finish Placement Prediction (Kernels Only)
2423067,0.0206,2,3,/zhanelya8/zh0108,PUBG Finish Placement Prediction (Kernels Only)
2410222,0.0408,0,1,/aaronhma/pubg-finish-placement-prediction-competition,PUBG Finish Placement Prediction (Kernels Only)
2392813,0.0589,0,1,/lavanyatalluru96/ee258-final-code,PUBG Finish Placement Prediction (Kernels Only)
2272002,0.064,0,0,/arunsankar/learning-ml-algorithms-with-pubg-data,PUBG Finish Placement Prediction (Kernels Only)
2076099,0.0583,0,0,/alucard1177/pubg-finish-prediction,PUBG Finish Placement Prediction (Kernels Only)
2411249,0.0649,0,0,/michelle39140/kernelc8e17d5134,PUBG Finish Placement Prediction (Kernels Only)
14270941,0.828,0,2,/felipebihaiek/fork-of-baseline-implementation-resnet34,Rainforest Connection Species Audio Detection
14499660,0.877,34,75,/aikhmelnytskyy/resnet-wavenet-my-best-single-model-ensemble,Rainforest Connection Species Audio Detection
14618968,0.878,1,13,/aikhmelnytskyy/bagging-rainforest,Rainforest Connection Species Audio Detection
13969694,0.825,3,7,/duythanhng/rfcx-adas-optimizer-pytorch,Rainforest Connection Species Audio Detection
14130241,0.8490000000000001,20,51,/aikhmelnytskyy/resnet-tpu-on-colab-and-kaggle,Rainforest Connection Species Audio Detection
14107799,0.7759999999999999,32,75,/gopidurgaprasad/rfcx-sed-model-stater,Rainforest Connection Species Audio Detection
13696337,0.779,1,9,/mekhdigakhramanian/rfcx-train-inference-seresnet34-tpu,Rainforest Connection Species Audio Detection
13281577,0.268,0,2,/maxzub/notebooke2ad9be359,Rainforest Connection Species Audio Detection
13444221,0.772,0,2,/satorushibata/rfcx-residual-network-with-tpu-customized,Rainforest Connection Species Audio Detection
13361207,0.7929999999999999,24,128,/yosshi999/rfcx-train-resnet50-with-tpu,Rainforest Connection Species Audio Detection
13318532,0.7390000000000001,0,11,/aikhmelnytskyy/rfcx-baseline-for-beginners-with-stratifiedkfold,Rainforest Connection Species Audio Detection
10243044,0.8296,0,0,/biowowow/bert-disaster-tweets,Natural Language Processing with Disaster Tweets
10262679,0.79895,0,0,/zeeniye/nlp-tweet-disaster,Natural Language Processing with Disaster Tweets
10182876,0.78455,0,10,/erikcabeza/nlp-using-spacy-to-categorize-tweets,Natural Language Processing with Disaster Tweets
10194303,0.8280700000000001,0,0,/antongolubev5/absa-bert-pair-model-nli,Natural Language Processing with Disaster Tweets
10172026,0.77995,0,0,/hoangpham51/attention-pytorch,Natural Language Processing with Disaster Tweets
8011601,0.74642,0,1,/rinisett/disaster-tweets-using-word2vec,Natural Language Processing with Disaster Tweets
9889669,0.8133600000000001,3,7,/aishwaryapalit/nlp-with-eda-cleaning-bert,Natural Language Processing with Disaster Tweets
8284262,0.8053899999999999,0,0,/iamsdt/disaster-nlp-keras-bert-using-tfhub,Natural Language Processing with Disaster Tweets
9874991,0.81703,0,1,/gb00000/disaster-bert,Natural Language Processing with Disaster Tweets
9802584,0.79098,0,0,/valadares/predictions-tweets-real-disasters-or-not-real,Natural Language Processing with Disaster Tweets
9751071,0.78945,0,0,/vaishnavibv/tweet-classification,Natural Language Processing with Disaster Tweets
7589174,0.79865,0,1,/prabanch/nlp-eda-bag-of-words-tf-idf-glove-rnn,Natural Language Processing with Disaster Tweets
9659103,0.79528,0,0,/jjbuchanan/disaster-tweets-naive-bayes-models,Natural Language Processing with Disaster Tweets
9591627,0.80845,2,3,/danielbilitewski/word2vec-and-logistic-regression,Natural Language Processing with Disaster Tweets
9543040,0.8452299999999999,11,18,/dhruv1234/huggingface-tfbertmodel,Natural Language Processing with Disaster Tweets
9402540,0.4747399999999999,14,80,/ramitjaal/m5-groupkfold-0-47474,M5 Forecasting - Accuracy
9333895,0.64127,0,9,/syoheihoh/simple-lightgbm-model,M5 Forecasting - Accuracy
9212822,1.1474,2,3,/nishantpatyal/m5-forcasting-basic-fbprophet,M5 Forecasting - Accuracy
9056442,0.47506,4,61,/ejunichi/m5-three-shades-of-dark-darker-magic,M5 Forecasting - Accuracy
9143668,0.4781399999999999,3,2,/jafarib/m5-forecasting-accuracy,M5 Forecasting - Accuracy
8994955,0.62153,3,4,/qcw171717/time-series-transformation,M5 Forecasting - Accuracy
9354321,0.85,0,3,/dldmw579/reg-panda-cv-0-870,Prostate cANcer graDe Assessment (PANDA) Challenge
10376153,0.12,6,16,/ajenningsfrankston/extending-simple-baseline-keras-vgg16,Prostate cANcer graDe Assessment (PANDA) Challenge
10015451,0.8,0,2,/huynhdoo/panda-keras-model-inference,Prostate cANcer graDe Assessment (PANDA) Challenge
9409200,0.79,0,2,/akashsuper2000/winitninference,Prostate cANcer graDe Assessment (PANDA) Challenge
10122955,0.54,0,0,/yjiaowla/panda-tiles-tf-keras-cohen-kappa-loss-baseline,Prostate cANcer graDe Assessment (PANDA) Challenge
10006165,0.69,0,0,/duccongduong/25epoch-resnext50-tts-inference,Prostate cANcer graDe Assessment (PANDA) Challenge
9998739,-0.01,0,0,/congdd/kernel9786827f1f,Prostate cANcer graDe Assessment (PANDA) Challenge
9708802,0.61,0,1,/madfalcon/panda-keras-resnet50-submit,Prostate cANcer graDe Assessment (PANDA) Challenge
171273,1.26581,0,0,/octavianh/10-classifier-showdown-in-scikit-learn,Leaf Classification
2464753,0.6609999999999999,0,0,/strifonov/stacked-cnn-predictions,Quora Insincere Questions Classification
2552560,0.659,0,0,/xsakix/cnn-base-classifier-fold-meta-2,Quora Insincere Questions Classification
2526345,0.5710000000000001,0,0,/kerch007/quora,Quora Insincere Questions Classification
2549381,0.677,0,0,/xsakix/cnn-base-classifier-fold-unfreeze-meta-v4,Quora Insincere Questions Classification
2545106,0.688,0,0,/xsakix/cnn-base-classifier-fold-unfreeze-meta-v2,Quora Insincere Questions Classification
2511605,0.691,0,6,/mlwhiz/initializing-pytorch-layers-weight-with-kaiming,Quora Insincere Questions Classification
2530252,0.613,0,0,/xsakix/base-classifier-fold-unfreeze,Quora Insincere Questions Classification
2508442,0.006,0,0,/lalitapatel/scikitlearn-lr-on-histographic-features,Quora Insincere Questions Classification
2517549,0.669,0,0,/xsakix/bilstm-att-base-classifier-residua-word2vec,Quora Insincere Questions Classification
2478895,0.3289999999999999,0,1,/adritab/sub1-no-deep-learning-vanilla-tfidf-logreg-svm,Quora Insincere Questions Classification
2450178,0.55,0,1,/ksayantani/quora-insincere-qa,Quora Insincere Questions Classification
2433309,0.611,0,1,/jatina/quora-insincerity-prediction,Quora Insincere Questions Classification
2204206,0.618,0,2,/blackboards/singlemodel-tfidf-lr-or-lgb,Quora Insincere Questions Classification
2465221,0.635,0,0,/xsakix/word2vec-filter-bilstm-att,Quora Insincere Questions Classification
2418262,0.624,0,1,/shouvikdos/balancing-train-data,Quora Insincere Questions Classification
2412318,0.628,0,0,/xsakix/bilstm-att-base-classifier-word2vec-f1,Quora Insincere Questions Classification
2413687,0.633,0,0,/xsakix/cnn-word2vec-experiment,Quora Insincere Questions Classification
2449610,0.542,0,0,/xsakix/filter-bilstm-base-spacy,Quora Insincere Questions Classification
2073235,0.486,0,1,/dhirus/quora-classification-challenge,Quora Insincere Questions Classification
2418612,0.603,0,1,/xsakix/baseline,Quora Insincere Questions Classification
2287701,0.6609999999999999,3,27,/mlwhiz/learning-text-classification-textcnn,Quora Insincere Questions Classification
2395669,0.4529999999999999,0,0,/alexandruuu/tfidf-tree-model,Quora Insincere Questions Classification
2099542,0.605,0,7,/riblu123/quora-insincerity-prediction,Quora Insincere Questions Classification
2050736,0.633,10,72,/mschumacher/adding-an-auxiliary-task,Quora Insincere Questions Classification
8885531,0.08095,0,0,/janosk21/dsanet-v2,COVID19 Global Forecasting (Week 4)
8869188,0.10076,0,0,/sonu26072001/covid4,COVID19 Global Forecasting (Week 4)
8852576,0.72825,0,0,/aniket165/covid-global-forecast-sir-model-ml-regressions,COVID19 Global Forecasting (Week 4)
8835904,0.03447,0,1,/ludovicoristori/covid-fc-wk4,COVID19 Global Forecasting (Week 4)
8835344,0.03632,1,11,/atharvachute/simple-polynomial-regression-model,COVID19 Global Forecasting (Week 4)
8828636,0.21828,0,0,/deeshantk/kernel67902d0a4f,COVID19 Global Forecasting (Week 4)
8833162,0.0364,3,12,/eswarchandt/covid-19-forecasting-xgboost-week-4,COVID19 Global Forecasting (Week 4)
8848701,1.36946,0,0,/ashora/lightgbm-final-covid-forecasting-kernel,COVID19 Global Forecasting (Week 4)
8834880,0.06186,0,4,/czyum26/covid-19-forecasting-using-elasticnet-iii,COVID19 Global Forecasting (Week 4)
8828398,0.49837,3,4,/akioonodera/covid19-week4-using-regression-analysis,COVID19 Global Forecasting (Week 4)
8811932,0.02981,0,4,/haplophyrne/ensemble,COVID19 Global Forecasting (Week 4)
8865306,0.61088,0,0,/javadiva/kernel6adc53efb6,COVID19 Global Forecasting (Week 4)
8856422,0.17437,0,0,/prashant268/covid-19-week4-xgboost,COVID19 Global Forecasting (Week 4)
8699068,3.03242,0,0,/mathseer/lgb-mad-w4,COVID19 Global Forecasting (Week 4)
8625481,0.06104,4,10,/kaniya/covid-global-forecast-sir-xgboost,COVID19 Global Forecasting (Week 4)
8717412,0.40744,1,13,/diamondsnake/covid-19-logistic-curve-fitting-and-correlation,COVID19 Global Forecasting (Week 4)
8594697,0.76222,69,276,/frlemarchand/covid-19-forecasting-with-an-rnn,COVID19 Global Forecasting (Week 4)
8554094,0.32686,14,66,/super13579/covid-19-global-forecast-seir-visualize,COVID19 Global Forecasting (Week 4)
8474295,0.72825,240,738,/saga21/covid-global-forecast-sir-model-ml-regressions,COVID19 Global Forecasting (Week 4)
9206171,0.50565,0,0,/kirderf/ensemble-short-long-term-indeweight-lock-oshaya-pp,COVID19 Global Forecasting (Week 4)
9032088,0.01135,0,0,/aniket165/polynomial-fit-xgb,COVID19 Global Forecasting (Week 4)
8950474,0.03992,0,0,/kirichenko17roman/old-dsanet-approach,COVID19 Global Forecasting (Week 4)
8950046,0.7402,0,0,/pietromarinelli/chinapp-4-6-2-8-cummax-regression-vs-poisson-vs,COVID19 Global Forecasting (Week 4)
8949323,0.03639,0,0,/priyankakondaparthii/covid-global-forecast,COVID19 Global Forecasting (Week 4)
8946363,0.09951,0,0,/yaroshevskiy/covid-19-parallel-procesing-poly-fit-etr-w4,COVID19 Global Forecasting (Week 4)
8937549,0.03281,0,0,/aerdem4/covid-19-w4-submission,COVID19 Global Forecasting (Week 4)
8933190,0.1635,0,0,/pdevine/covid-19-global-forecasting-4-top-6-notebook,COVID19 Global Forecasting (Week 4)
8917828,3.5313,0,0,/xiaojucui/kernel6a24262cba,COVID19 Global Forecasting (Week 4)
6365622,0.044,31,95,/meaninglesslives/lyft3d-inference-prediction-visualization,Lyft 3D Object Detection for Autonomous Vehicles
6214059,0.042,5,38,/asimandia/lyft3d-inference-kernel,Lyft 3D Object Detection for Autonomous Vehicles
5870015,0.034,12,100,/meaninglesslives/lyft3d-inference-kernel,Lyft 3D Object Detection for Autonomous Vehicles
10970023,0.7800600000000001,0,0,/wcaine/flask,What's Cooking? (Kernels Only)
9300992,0.75975,0,1,/aoutomromanov/kernel1786d85d74,What's Cooking? (Kernels Only)
7437889,0.74346,0,0,/vilceanumihnea97/kernel1fd023b510,What's Cooking? (Kernels Only)
6324460,0.69348,0,1,/jdbricker/naive-bayes-is-cooking,What's Cooking? (Kernels Only)
4952258,0.6439,0,0,/drbalan/kernelb6bc87c03a,What's Cooking? (Kernels Only)
4196970,0.12017,0,2,/bilal75210/ir-project,What's Cooking? (Kernels Only)
3950252,0.8280299999999999,0,0,/longyg/svc-classification,What's Cooking? (Kernels Only)
3565222,0.7712100000000001,0,1,/diwang137/kernelcc21421019,What's Cooking? (Kernels Only)
3508656,0.8228,0,0,/alok268/what-s-your-cuisine,What's Cooking? (Kernels Only)
3150195,0.78228,4,6,/anjanatiha/cuisine-prediction,What's Cooking? (Kernels Only)
3074333,0.19267,0,0,/madoliver/kerneldf4919d6bc,What's Cooking? (Kernels Only)
1758566,0.76508,0,1,/pathofdata/whats-cooking-simple-embedding-model,What's Cooking? (Kernels Only)
2753152,0.79625,0,0,/claasen/seminar-notebook-minimized,What's Cooking? (Kernels Only)
1499542,0.70977,0,0,/ericxu10101/ideas,What's Cooking? (Kernels Only)
2544678,0.7791600000000001,0,0,/akashsinha3008/cuisine-classification,What's Cooking? (Kernels Only)
2224503,0.78308,0,7,/sshadylov/text-classification-ova-passive-aggressive,What's Cooking? (Kernels Only)
2135426,0.77463,0,0,/anovax/1811-submission,What's Cooking? (Kernels Only)
1958523,0.7820699999999999,0,0,/baburam1985/whats-cooking,What's Cooking? (Kernels Only)
1669140,0.82491,0,13,/ashishpatel26/think-differently-what-s-cooking,What's Cooking? (Kernels Only)
1907225,0.7874,0,0,/hugosev/cooking-with-tfidf,What's Cooking? (Kernels Only)
1614155,0.82411,0,2,/kskarakostas/ml-combo,What's Cooking? (Kernels Only)
1675825,0.7734300000000001,0,0,/naveenc131/cooking-with-cnn,What's Cooking? (Kernels Only)
1698013,0.80128,2,8,/welgum/tf-idf-with-simple-nn-keras,What's Cooking? (Kernels Only)
1668804,0.7834800000000001,0,2,/mohanrao/take-1-whats-cooking,What's Cooking? (Kernels Only)
1676903,0.12067,1,0,/anoojnair/simple-classifier,What's Cooking? (Kernels Only)
64977,0.87721,0,0,/francalvo/ailurofilicos,Shelter Animal Outcomes
59989,0.7149399999999999,7,9,/zoupet/clean-feat-fit-and-submit,Shelter Animal Outcomes
2528595,0.69,0,2,/hengzheng/extra-data-with-pos-tag-v3,Quora Insincere Questions Classification
2690638,0.6709999999999999,0,0,/xsakix/torch-lstm-att,Quora Insincere Questions Classification
2655672,0.634,0,1,/amitabhac/simple-lstm-attention-with-glove,Quora Insincere Questions Classification
2649438,0.619,0,0,/abdul0807/ensemble-simple-ml-models-for-beginners,Quora Insincere Questions Classification
2561086,0.484,0,7,/kobynim/quora-questions-revealed-zbn-kn-nf-1-2019-v7,Quora Insincere Questions Classification
2435782,0.653,0,0,/gn53720366/test-quora,Quora Insincere Questions Classification
2634425,0.672,0,0,/xsakix/torch-lstm-glove-para-2,Quora Insincere Questions Classification
2613588,0.396,2,1,/jialinzhang/lightgbm-quora-question,Quora Insincere Questions Classification
2545502,0.581,0,0,/tboyle10/stacking-cnn-google-news-vectors,Quora Insincere Questions Classification
2547935,0.675,0,0,/red8012/gru-final,Quora Insincere Questions Classification
2478871,0.6759999999999999,0,0,/gn53720366/different-embeddings,Quora Insincere Questions Classification
2431443,0.695,0,10,/yesheng1984/pytorch-weightdroplstm,Quora Insincere Questions Classification
2599717,0.62,0,0,/yshubham/complex-model,Quora Insincere Questions Classification
2603275,0.6779999999999999,0,0,/xsakix/torch-lstm,Quora Insincere Questions Classification
2608236,0.6890000000000001,0,0,/zubrabubra/pytorch-starter,Quora Insincere Questions Classification
2597070,0.601,0,0,/xsakix/torch-chaos-clr,Quora Insincere Questions Classification
2592464,0.512,0,0,/xsakix/torch-clr-unfreeze,Quora Insincere Questions Classification
2587578,0.5760000000000001,0,0,/xsakix/torch-with-clr,Quora Insincere Questions Classification
2547731,0.626,0,2,/lemonwaffle/quora-pytorch-torchtext,Quora Insincere Questions Classification
2542904,0.607,0,6,/lpdataninja/data-science-glossary-8-naive-bayes,Quora Insincere Questions Classification
149823,0.13067,0,0,/clevereve/notebook3c7c777d4d,Leaf Classification
2761006,3.687,7,35,/saurabh502/why-no-simple-blend-plb-3-687,Elo Merchant Category Recommendation
2404877,3.703,0,0,/ronniemiller/bgu-dl-assignmnt2-competition,Elo Merchant Category Recommendation
2497426,3.886,0,0,/anirbank/elo-cards-predictive-modeling-scratchpad,Elo Merchant Category Recommendation
2398049,3.692,48,239,/waitingli/combining-your-model-with-a-model-without-outlier,Elo Merchant Category Recommendation
8975787,0.53354,2,18,/mayer79/m5-forecast-dept-by-dept-and-step-by-step,M5 Forecasting - Accuracy
8586574,0.78045,2,13,/qcw171717/top-down-distribution-method,M5 Forecasting - Accuracy
8630273,18.27987000000001,0,0,/akihirokkkkk/seq2seq,M5 Forecasting - Accuracy
8535851,0.72945,0,2,/robertburbidge/ensemble-starter-stochastic,M5 Forecasting - Accuracy
9460905,0.75,8,23,/vgarshin/panda-keras-timedistributed,Prostate cANcer graDe Assessment (PANDA) Challenge
9306440,0.77,20,88,/yasufuminakama/panda-se-resnext50-regression-baseline,Prostate cANcer graDe Assessment (PANDA) Challenge
9332423,0.06,0,2,/madfalcon/inceptionv3-keras-dummy-training-v1,Prostate cANcer graDe Assessment (PANDA) Challenge
9187367,0.16,0,2,/ioanang/vgg16-keras-dummy-training-without-masks,Prostate cANcer graDe Assessment (PANDA) Challenge
9156235,0.69,3,58,/ateplyuk/panda-starter-infer,Prostate cANcer graDe Assessment (PANDA) Challenge
9169689,0.65,0,4,/greatgamedota/panda-5fold-inference,Prostate cANcer graDe Assessment (PANDA) Challenge
9108063,0.7,1,24,/vladvdv/pytorch-inference-multiple-models-and-folds,Prostate cANcer graDe Assessment (PANDA) Challenge
9114962,0.64,2,11,/prateekagnihotri/panda-resnext-inference-tta-blending,Prostate cANcer graDe Assessment (PANDA) Challenge
4628203,0.58033,0,0,/jake126/vsb-power-lstm-understanding,VSB Power Line Fault Detection
10688434,0.8387899999999999,1,4,/pablomarino/from-shallow-learning-to-2020-sota-gpt-2-roberta,Natural Language Processing with Disaster Tweets
10381854,0.82071,0,0,/makhloufsabir/nlp-disaster-tweets-bertforsequenceclassif,Natural Language Processing with Disaster Tweets
10585866,0.80202,0,0,/tatsu88/kernel25eeb93e15,Natural Language Processing with Disaster Tweets
10628367,0.76432,18,30,/vishalvanpariya/nlp-for-beginners,Natural Language Processing with Disaster Tweets
10491856,0.52436,0,5,/imakaruamikurah/real-or-not-gru-lstm-glove-text-cleaning,Natural Language Processing with Disaster Tweets
10587768,0.84278,6,26,/vbmokin/nlp-with-dt-simple-transformers-research,Natural Language Processing with Disaster Tweets
10591691,0.78179,0,0,/g4team/kernel6452476a77,Natural Language Processing with Disaster Tweets
7850338,0.7955800000000001,0,0,/anki112279/identifying-disaster-by-tweets,Natural Language Processing with Disaster Tweets
10544021,0.8351200000000001,0,6,/doanquanvietnamca/transformer-to-get-1-lb,Natural Language Processing with Disaster Tweets
10571336,0.79711,0,0,/masagokoji/kernel3edcd96c1b,Natural Language Processing with Disaster Tweets
10452503,0.8053899999999999,0,1,/jmisra08/tensorflow-hub,Natural Language Processing with Disaster Tweets
10329500,0.8194899999999999,0,5,/sawans/disaster-tweets-using-bert,Natural Language Processing with Disaster Tweets
10334439,0.7771899999999999,0,2,/zaber666/getting-started-with-pytorch-roberta,Natural Language Processing with Disaster Tweets
10252326,0.7900699999999999,8,18,/rahulvv/bidirectional-lstm-glove200d,Natural Language Processing with Disaster Tweets
1315733,0.494,0,0,/gobrando/restaurant-forecasting-ml-means-walk-through,Recruit Restaurant Visitor Forecasting
5202320,0.5660000000000001,0,3,/infoabhitech/detect-diabetic-retinopathy-keras-load-resnet,APTOS 2019 Blindness Detection
5691518,0.8190000000000001,8,43,/mikelkl/43th-place-top2-solution-stacking-inference,APTOS 2019 Blindness Detection
5731894,0.802,0,9,/dimitreoliveira/175th-place-5-fold-efficientnetb5,APTOS 2019 Blindness Detection
5714329,0.831,0,10,/lextoumbourou/2-x-b3-densenet201-blend-0-926-on-private-lb,APTOS 2019 Blindness Detection
4862587,0.743,0,3,/fanconic/efficientnetb3-train-keras,APTOS 2019 Blindness Detection
5534787,0.787,0,1,/fanconic/efficientnetb3-regression-single-inference,APTOS 2019 Blindness Detection
5586744,0.114,1,10,/serengil/blindness-prediction-automl,APTOS 2019 Blindness Detection
4818345,0.733,0,5,/xcz12138/eff-atteintion-task-train,APTOS 2019 Blindness Detection
5558130,0.176,0,0,/darshan5206/aptos-blindeness-detection-cnn,APTOS 2019 Blindness Detection
4692559,0.63,0,0,/rektmeister/aptos-2019-submission,APTOS 2019 Blindness Detection
5603887,0.03,0,0,/skslater/kernel2c9b3e693a,APTOS 2019 Blindness Detection
4892416,0.0,0,1,/tanlikesmath/private-lb-probing-adversarial-validation,APTOS 2019 Blindness Detection
5490314,0.75,1,15,/kunstmord/resnet101-no-pre-processing,APTOS 2019 Blindness Detection
5415935,0.742,1,2,/harendrap/fastai-resnet,APTOS 2019 Blindness Detection
5354213,0.777,1,11,/virajbagal/aptos-clahe-efficiennetb5,APTOS 2019 Blindness Detection
5279246,0.3779999999999999,0,0,/kimsunghun00/detect-diabetic-retinopathy,APTOS 2019 Blindness Detection
5370447,0.703,0,0,/karlo11/aptos-2019-efficientnet-b3-keras,APTOS 2019 Blindness Detection
5373696,0.711,4,17,/ren4yu/aptos-2019-probing-private-test-image-sizes,APTOS 2019 Blindness Detection
7498178,0.0551899999999999,0,1,/andy172008/linearregression,PUBG Finish Placement Prediction (Kernels Only)
7495574,0.0579299999999999,2,2,/jiebcoder/my-random-forest,PUBG Finish Placement Prediction (Kernels Only)
7504570,0.07122,0,0,/bsy1997/kernele978ac15a0,PUBG Finish Placement Prediction (Kernels Only)
7508939,0.04451,0,0,/bilizhazha/pubg-lin2,PUBG Finish Placement Prediction (Kernels Only)
7509870,0.05767,0,0,/gjcrimson/kernel7099c3b06e,PUBG Finish Placement Prediction (Kernels Only)
7340372,0.12862,0,1,/wqbzdgqsmmz/hwx-s-group,PUBG Finish Placement Prediction (Kernels Only)
6992032,0.0204199999999999,0,1,/bzhang0625/kernel73ecb80046,PUBG Finish Placement Prediction (Kernels Only)
5725010,0.0568399999999999,0,0,/urbanside/pubg-linghtgbm-to-heroz,PUBG Finish Placement Prediction (Kernels Only)
5014817,0.05844,0,0,/timetoshow/9-2-stacking-method,PUBG Finish Placement Prediction (Kernels Only)
2285397,0.2675,0,1,/thiagoandrade/xgboost-dt-rfr-e-svr,PUBG Finish Placement Prediction (Kernels Only)
4440805,0.25323,0,0,/bencenagy/first-attempt-at-understanding-regression,PUBG Finish Placement Prediction (Kernels Only)
4193945,0.09774,0,0,/songjaewon/kernel2494e1596c,PUBG Finish Placement Prediction (Kernels Only)
4099851,0.4733699999999999,0,0,/siddheshsathe/pubg-kernel,PUBG Finish Placement Prediction (Kernels Only)
3869939,0.06681,0,0,/louiechiu/trying,PUBG Finish Placement Prediction (Kernels Only)
2361268,0.0599,0,1,/andreiv4/catboostregressor,PUBG Finish Placement Prediction (Kernels Only)
3347975,0.55891,0,0,/yousefzook/dnn-solution,PUBG Finish Placement Prediction (Kernels Only)
2615723,0.0574,0,0,/tanyeejet/pubg-finish-placement-prediction-xgb,PUBG Finish Placement Prediction (Kernels Only)
3509896,0.06064,0,0,/bledingedge/pubg-koustav,PUBG Finish Placement Prediction (Kernels Only)
3089752,0.02661,0,4,/toldo171/a-beginner-guide-to-top-35-lasso-rf-lgbm,PUBG Finish Placement Prediction (Kernels Only)
3423722,3915.91871,0,0,/fajb420/kernel8a4c712887,PUBG Finish Placement Prediction (Kernels Only)
1817443,0.0758,0,1,/liaobowen/pubg-finish-predict,PUBG Finish Placement Prediction (Kernels Only)
9257045,0.8601700000000001,1,1,/jagadeesh06/give-me-some-credit-eda-imputation-rf-xgb,Give Me Some Credit
9143929,0.86008,1,4,/sainikhilesh/give-me-some-credit-classification,Give Me Some Credit
5728319,1.78798,0,0,/mohamedalhawi/finshed,Predict Future Sales
7039922,0.8966799999999999,3,37,/karell/xgb-baseline-advanced-feature-engineering,Predict Future Sales
6930035,1.53085,0,2,/kaushal2896/future-sales-prediction-simple-lighgbm,Predict Future Sales
6741376,1.30743,0,0,/lenora1231/predict-future-sales,Predict Future Sales
6573590,1.23646,0,0,/hyobeen/kaggle-predict-future-sales,Predict Future Sales
6483031,1.02018,0,1,/sarthak017/kernel2d93402f3a,Predict Future Sales
6223919,0.90586,0,1,/litemort/single-model-lb-0-9058-by-litemort,Predict Future Sales
5550921,1.41198,0,1,/khiwila/kernel3a49714230,Predict Future Sales
5507268,0.95026,0,0,/flora1888/predict-baseline,Predict Future Sales
4750401,0.98977,0,0,/senasista/predict-future-sales,Predict Future Sales
12123404,0.741,0,2,/subash03/riiid-simple-average-no-modeling-eda,Riiid Answer Correctness Prediction
12410393,0.754,4,19,/takamotoki/lgbm-iii-part2,Riiid Answer Correctness Prediction
12401803,0.7390000000000001,0,23,/gogo827jz/riiid-neural-oblivious-decision-ensembles,Riiid Answer Correctness Prediction
12425938,0.742,0,0,/saijasthi/needs-work,Riiid Answer Correctness Prediction
12234242,0.737,0,19,/domizianostingi/second-model,Riiid Answer Correctness Prediction
12378214,0.682,0,0,/sunilmahala/riid-fe-modeling,Riiid Answer Correctness Prediction
12277945,0.705,4,13,/doctorkael/riiid-pandas-baseline-online-vs-static-learning,Riiid Answer Correctness Prediction
12154147,0.731,0,20,/domizianostingi/first-model,Riiid Answer Correctness Prediction
12196434,0.726,13,36,/shahules/pytorch-entity-embedding,Riiid Answer Correctness Prediction
12230567,0.7509999999999999,0,2,/aman2114/lgbm-fe-val1,Riiid Answer Correctness Prediction
12175641,0.706,0,6,/sanabdriss/eda-on-questions-explanations-heuristic-model,Riiid Answer Correctness Prediction
12160507,0.706,0,0,/anushakasi/riiid-eda-starter,Riiid Answer Correctness Prediction
12185124,0.6409999999999999,4,3,/shivamjohri/riiid-ensemble-modeling-starter-notebook,Riiid Answer Correctness Prediction
12204007,0.74,1,13,/code1110/riiid-gbdt-pipeline-baseline,Riiid Answer Correctness Prediction
12161544,0.6970000000000001,9,57,/maunish/riiid-super-cool-eda-and-pytorch-baseline,Riiid Answer Correctness Prediction
10649790,0.6990000000000001,2,10,/priteshshrivastava/melanoma-simple-baseline-69-9-auroc,SIIM-ISIC Melanoma Classification
10591177,0.893,3,12,/aninda/melanoma-using-fastai2,SIIM-ISIC Melanoma Classification
10621195,0.945,0,31,/mahmudds/siim-isic-melanoma-classification,SIIM-ISIC Melanoma Classification
10487725,0.8490000000000001,20,71,/mobassir/in-depth-melanoma-with-modeling,SIIM-ISIC Melanoma Classification
10470590,0.8859999999999999,1,11,/shikha130vv/data-pipeline,SIIM-ISIC Melanoma Classification
10507602,0.924,9,13,/aryanpandey1109/melanoma-tpu-attempt,SIIM-ISIC Melanoma Classification
10075897,0.853,0,4,/aryanpandey1109/melanoma-combining-text-input-with-efficientnetb7,SIIM-ISIC Melanoma Classification
10501558,0.909,3,11,/abiolatti/efficientnetb5-with-balanced-dataset-tf,SIIM-ISIC Melanoma Classification
10373873,0.929,27,91,/hmendonca/melanoma-neat-pytorch-lightning-native-amp,SIIM-ISIC Melanoma Classification
10491383,0.794,0,2,/tunguz/melanoma-with-h2o-automl-3,SIIM-ISIC Melanoma Classification
10450574,0.67,2,9,/prashantarorat/catboost-classification-using-meta-data,SIIM-ISIC Melanoma Classification
7764575,0.90041,3,13,/mrushan3/santander-customer-transaction-prediction-eda,Santander Customer Transaction Prediction
7577093,0.89131,4,8,/hassanamin/customer-transaction-prediction-using-xgboost,Santander Customer Transaction Prediction
6960664,0.49998,0,0,/morenovanton/kernel5eb5469a90,Santander Customer Transaction Prediction
6764309,0.85915,0,0,/kavinder31phogat/kernel7f763c5c60,Santander Customer Transaction Prediction
3334446,0.893,0,0,/rahullalu/santander-ctp-feature-clustering,Santander Customer Transaction Prediction
6225036,0.7923600000000001,0,0,/venkateshprabhug/customer-transactions-santander,Santander Customer Transaction Prediction
4147021,0.5827100000000001,0,0,/flubber/santander-customer-transaction-prediction,Santander Customer Transaction Prediction
3066099,0.89,0,7,/mgiraygokirmak/xgboost-gpu-w-feature-attention,Santander Customer Transaction Prediction
5168092,0.8891899999999999,0,0,/a1pacaz/santander-test,Santander Customer Transaction Prediction
4449821,0.8970799999999999,0,0,/vanquan/santander-customer-transaction-prediction-lgb,Santander Customer Transaction Prediction
3388826,0.608,0,0,/anki54/satander-customer-transaction-prediction,Santander Customer Transaction Prediction
4460112,0.86603,0,1,/stmohd/santander-ml,Santander Customer Transaction Prediction
5367241,0.9941,0,0,/yukinao/aerial-cactus-identification-keras-efficientnet-b3,Aerial Cactus Identification
4635576,0.9999,0,0,/droid021/cactus,Aerial Cactus Identification
4303839,0.5,0,0,/saintjinhog/kernel8cb19eab65,Aerial Cactus Identification
3628406,0.9968,0,0,/cikbok/first-try-cnn,Aerial Cactus Identification
3190088,0.996,0,0,/yanggu/cactus-identification-fastai-v1-0-46-baseline,Aerial Cactus Identification
1369065,3.47309,0,3,/returnofsputnik/xgboost-ing-taxi-fares-with-rotated-lat-long,New York City Taxi Fare Prediction
1615479,3.10856,0,2,/jiridobes/nyc-taxi-fare-prediction-lgbm-visualization,New York City Taxi Fare Prediction
1613515,3.63888,1,3,/dude431/fare-pred-by-xgboost-and-lot-of-amazing-thing,New York City Taxi Fare Prediction
1559761,3.48115,0,0,/minxuzhang/kernel85b6675d3e,New York City Taxi Fare Prediction
1529614,3.6804,2,5,/ffedericoni/taxifare-using-tf-data-to-process-full-dataset,New York City Taxi Fare Prediction
1511047,3.17349,17,52,/dimitreoliveira/taxi-fare-prediction-with-keras-deep-learning,New York City Taxi Fare Prediction
1472133,3.24913,0,0,/michaelwiner/taxixgb,New York City Taxi Fare Prediction
10451718,3.21451,0,4,/vibeeshk/nyc-taxi-fare-prediction2,New York City Taxi Fare Prediction
1384464,3.84905,0,0,/agrawalpuneet/nyc-taxi-fare-data-exploration-d6c785,New York City Taxi Fare Prediction
7053152,3.23862,0,0,/impurestpath/kernel7bb4949b56,New York City Taxi Fare Prediction
6121568,6.48013,0,4,/jsvishnuj/eda-feature-generation-regression-analysis,New York City Taxi Fare Prediction
5374096,4.10469,0,3,/pierpaolo28/new-york-city-taxi-fare-prediction,New York City Taxi Fare Prediction
4330074,7.50824,0,1,/ozgurb/ny-taxi-eda-v1,New York City Taxi Fare Prediction
4561494,3.22079,0,1,/ozgurb/ny-taxi-v3,New York City Taxi Fare Prediction
14197530,0.14244,6,5,/franciscodiasneto/house-prices,House Prices - Advanced Regression Techniques
14225748,0.34363,0,0,/nanimack/house-price-prediction,House Prices - Advanced Regression Techniques
14192357,0.1451599999999999,3,6,/sominathavhad21/house-prices-advanced-regression-techniques,House Prices - Advanced Regression Techniques
14194265,0.11457,3,5,/mustafacicek/comprehensive-eda-feature-engineering-top-4,House Prices - Advanced Regression Techniques
14159298,0.12224,4,7,/narayanareddych/advanced-house-price-prediction,House Prices - Advanced Regression Techniques
14125921,0.22039,1,1,/subhamsagarpaira/linear-regression-house-prices,House Prices - Advanced Regression Techniques
14165044,0.14018,0,3,/nilaykhare/notebook8b804b03a9,House Prices - Advanced Regression Techniques
14038243,0.12618,1,11,/solegalli/predict-house-price-with-feature-engine,House Prices - Advanced Regression Techniques
13965081,0.14611,5,12,/kritidoneria/automl-beginner-housing-prices-using-pycaret,House Prices - Advanced Regression Techniques
13802583,0.14828,1,3,/liyilang/house-prices,House Prices - Advanced Regression Techniques
13930819,0.13244,0,1,/eschibli/simple-lightgbm,House Prices - Advanced Regression Techniques
13551864,0.23589,0,2,/nitiruengcharoen/house-price-advance-regression,House Prices - Advanced Regression Techniques
13875933,0.12929,1,2,/argishti/lightgbm-house-prices-rmsle-0-127-score-0-98,House Prices - Advanced Regression Techniques
13928478,0.11968,0,0,/ajinkyaspatil/notebook24c855275e,House Prices - Advanced Regression Techniques
13882250,0.12527,0,0,/batprem/hpp-after-eda-and-stack-regression,House Prices - Advanced Regression Techniques
13910280,0.091,9,33,/pestipeti/vinbigdata-fasterrcnn-pytorch-inference,Predict Future Sales
13907911,0.052,16,57,/mrutyunjaybiswal/vbd-chest-x-ray-abnormalities-detection-eda,Predict Future Sales
13914101,0.052,0,4,/leighplt/starter-faster-r-cnn,Predict Future Sales
9976039,0.787,15,79,/awsaf49/xgboost-tabular-data-ml-cv-85-lb-787,SIIM-ISIC Melanoma Classification
9869179,0.8540000000000001,0,7,/vinaybhupalam/tensorflow-undersampling-10-fold-gpu,SIIM-ISIC Melanoma Classification
9789855,0.759,2,4,/raddar/simple-baseline-revamped,SIIM-ISIC Melanoma Classification
9837165,0.8896,42,104,/anshuls235/siim-isic-melanoma-analysis-eda-prediction,SIIM-ISIC Melanoma Classification
9826042,0.925,131,416,/nroman/melanoma-pytorch-starter-efficientnet,SIIM-ISIC Melanoma Classification
9839034,0.914,18,93,/cdeotte/image-and-tabular-data-0-915,SIIM-ISIC Melanoma Classification
9748592,0.7240000000000001,8,8,/shawon10/melanoma-classification-eda-and-densenet121,SIIM-ISIC Melanoma Classification
9826263,0.772,0,4,/soumya9977/1st-featuredcom-submission-baseline-keras-vgg16,SIIM-ISIC Melanoma Classification
9787722,0.878,2,8,/bitthal/baseline-pytorch-efficientnet,SIIM-ISIC Melanoma Classification
9855874,0.721,0,5,/tunguz/rapids-svm-regressor,SIIM-ISIC Melanoma Classification
9801822,0.894,18,37,/zzy990106/pytorch-5-fold-efficientnet-baseline,SIIM-ISIC Melanoma Classification
9818458,0.753,0,2,/akashram/dimensional-reduction-techniques-baseline-rf,SIIM-ISIC Melanoma Classification
9795269,0.895,3,23,/redwankarimsony/melanoma-eda-efficentnets-densenet-ensemble,SIIM-ISIC Melanoma Classification
9744592,0.879,8,38,/arroqc/siim-isic-pytorch-lightning-starter-seresnext50,SIIM-ISIC Melanoma Classification
9757831,0.91,20,97,/ajaykumar7778/melanoma-tpu-efficientnet-b5-dense-head,SIIM-ISIC Melanoma Classification
9766902,0.794,8,33,/tunguz/melanoma-classification-eda-and-modeling,SIIM-ISIC Melanoma Classification
12919374,0.625,0,0,/niaibrahim/basic-karas-nn-made-epoch-small-for-save-07-86403a,Riiid Answer Correctness Prediction
12710581,0.7509999999999999,1,13,/isaienkov/riiid-answer-correctness-prediction-keras-nn-2,Riiid Answer Correctness Prediction
12602982,0.748,0,0,/saijasthi/lr-0-0001,Riiid Answer Correctness Prediction
9081126,1.2496,2,24,/koheimuramatsu/prophet-lightgbm-eda-feature-engineering-tuning,Predict Future Sales
9469076,0.97193,0,2,/rrrrrikimaru/simple-e-to-e-eda-to-ensemble,Predict Future Sales
9429235,1.14168,0,0,/avvinci/time-series-forecasting-beginners,Predict Future Sales
8998050,0.8955200000000001,6,14,/tymurprorochenko/in-depth-eda-cb-rf-knn-ensamble,Predict Future Sales
4698392,0.367,0,1,/harshavardhanbabu/keras-densenet-aptos,APTOS 2019 Blindness Detection
5450318,0.754,0,0,/ibraheemmoosa/aptos-pytorch-inference,APTOS 2019 Blindness Detection
4733496,0.662,0,0,/ibraheemmoosa/aptos-densenet201-progressive-resizing,APTOS 2019 Blindness Detection
13459198,0.8257700000000001,0,0,/stormdiv/nctu-cs-t0828-final-aptos-2019-0856152,APTOS 2019 Blindness Detection
10304021,0.671717,0,0,/shidqie/cv-8-roc-auc,APTOS 2019 Blindness Detection
10454801,-0.062048,0,0,/gautamgottipati/kernel6a35809a2f,APTOS 2019 Blindness Detection
5630061,0.529,0,0,/marinan67/kernel-optos0109,APTOS 2019 Blindness Detection
4765437,0.747,0,0,/subhajitbarh/inference-model,APTOS 2019 Blindness Detection
5617830,0.624,0,0,/azupero/efficientnet-b6-inference-0901-1,APTOS 2019 Blindness Detection
5033117,0.7,0,1,/ashwindasr/fork-of-aptos-2,APTOS 2019 Blindness Detection
12983540,0.231,0,5,/salmaneunus/rainforest-species-detection-from-audio-1,Rainforest Connection Species Audio Detection
12942730,0.597,17,88,/hamditarek/rainforest-connection-analysis-using-librosa,Rainforest Connection Species Audio Detection
12963721,0.406,5,22,/jackvial/pytorch-lightning-starter,Rainforest Connection Species Audio Detection
124011,0.00567,14,21,/tobikaggle/nn-through-keras-copied-mod-shuffle,Leaf Classification
122454,0.00936,6,12,/zenstat/nn-through-keras-learn-through-trial-and-error,Leaf Classification
121118,0.13102,0,0,/agoncharenko1992/simple-network-with-lasagne,Leaf Classification
119854,1.22946,0,0,/toddgaron/leaf-classification-attempt-0,Leaf Classification
10967776,0.89449,0,0,/kyoshioka47/late-famrepro-fam-reproaru-ensemble-0725,Prostate cANcer graDe Assessment (PANDA) Challenge
11531829,0.89995,0,1,/rahulmunet1206/panda-inference-w-36-tiles-256,Prostate cANcer graDe Assessment (PANDA) Challenge
11496647,0.77983,0,1,/spears27/panda-fastai-inference,Prostate cANcer graDe Assessment (PANDA) Challenge
10805485,0.91793,0,3,/iafoss/panda-init-class-128-voting,Prostate cANcer graDe Assessment (PANDA) Challenge
9223971,0.7642899999999999,0,1,/abebe9849/panda-16x128x128-tiles,Prostate cANcer graDe Assessment (PANDA) Challenge
10839570,0.89487,0,2,/coreacasa/12th-place-solution-inference-notebook,Prostate cANcer graDe Assessment (PANDA) Challenge
11307577,0.02438,0,0,/jotaporras/edwin-submission-1-copy,Prostate cANcer graDe Assessment (PANDA) Challenge
10814733,0.90343,0,1,/shentao/fork-of-panda-inference-final-ave,Prostate cANcer graDe Assessment (PANDA) Challenge
10970827,0.01676,0,1,/hernoo/submission-panda,Prostate cANcer graDe Assessment (PANDA) Challenge
10791829,0.8059999999999999,0,2,/malyshevvalery/tilewise-pipeline,Prostate cANcer graDe Assessment (PANDA) Challenge
10134293,0.884,0,1,/bluffmaster111/final,Prostate cANcer graDe Assessment (PANDA) Challenge
10772610,0.919,0,1,/jakobw/panda-submit-clas-twodl,Prostate cANcer graDe Assessment (PANDA) Challenge
10530410,0.882,0,3,/tejask98/blend-efficientnet-b0-and-b1,Prostate cANcer graDe Assessment (PANDA) Challenge
10548506,0.63,4,23,/snide713/basic-pipeline-for-pytorchers,Prostate cANcer graDe Assessment (PANDA) Challenge
10155475,0.87,0,0,/akashsuper2000/panda-inference-w-36-tiles-256,Prostate cANcer graDe Assessment (PANDA) Challenge
9782593,2.46163,0,0,/autowave/m5-accuracy-model-01-baseline-seq2seq,M5 Forecasting - Accuracy
9433543,0.4747399999999999,0,0,/akashsuper2000/m5-groupkfold,M5 Forecasting - Accuracy
8785368,0.51423,0,0,/nklinzhiming/m5-forecast-v2-python,M5 Forecasting - Accuracy
8472382,0.64442,0,0,/akashsuper2000/m5-baseline,M5 Forecasting - Accuracy
3311739,0.53899,0,1,/derekhsu2000/preprocessed-word-count,Quora Insincere Questions Classification
3297182,0.5274399999999999,0,0,/bhavyalabishetty/quora-text-classification,Quora Insincere Questions Classification
3227347,0.64149,0,0,/statseon/gensim,Quora Insincere Questions Classification
3213529,0.54727,0,0,/statseon/tfidf-epoch-2,Quora Insincere Questions Classification
3187282,0.43154,0,0,/noexittv/minimalistic-pipeline,Quora Insincere Questions Classification
2619347,0.402,0,0,/alexfilippov/quora-lda-model,Quora Insincere Questions Classification
3092371,0.65261,0,0,/statseon/quora-gru-seon,Quora Insincere Questions Classification
3045628,0.7054199999999999,3,46,/kfujikawa/4th-place,Quora Insincere Questions Classification
2560687,0.696,0,1,/bgeier/ltsm-capsule,Quora Insincere Questions Classification
2952821,0.71035,0,27,/dromosys/notebook-3rd-place,Quora Insincere Questions Classification
2809059,0.6990000000000001,0,1,/yeshila/final-kernel-1,Quora Insincere Questions Classification
2831082,0.69976,0,18,/kentaronakanishi/18th-place-solution,Quora Insincere Questions Classification
2955373,0.6963699999999999,0,3,/r1cky7/101st-place-solution,Quora Insincere Questions Classification
2955104,0.70401,0,2,/shohbek/kerneleabdc083f0,Quora Insincere Questions Classification
1336318,0.75532,0,0,/aeter42/mix-those-ingredients-nn-sum-of-embeddings,What's Cooking? (Kernels Only)
1262973,0.7810699999999999,0,0,/vijaykris/prediction-using-svm-modified-regularisation,What's Cooking? (Kernels Only)
8950075,0.03639,0,0,/ben975/xgboost-v2,COVID19 Global Forecasting (Week 4)
8947692,1.24508,0,0,/mattjezza/covid-19-prediction-using-a-lasso-model,COVID19 Global Forecasting (Week 4)
8851931,0.1208299999999999,0,0,/egissys/gb-model-v1,COVID19 Global Forecasting (Week 4)
8940261,0.0343399999999999,0,0,/nonstochastic147/covid-19-week-4,COVID19 Global Forecasting (Week 4)
8902350,0.19621,0,0,/viiids/fb-prophet-week-4,COVID19 Global Forecasting (Week 4)
8907792,2.17036,0,0,/gamishilbhaakilan/covid19-global-forecasting-week-4,COVID19 Global Forecasting (Week 4)
8860037,0.06675,0,0,/regismagnus/covid19-week4,COVID19 Global Forecasting (Week 4)
8878117,1.6385299999999998,0,0,/vrushabhlengade/kernel83b3e391d6,COVID19 Global Forecasting (Week 4)
8936866,0.56253,0,0,/rushisavulge/justdoit,COVID19 Global Forecasting (Week 4)
8901853,0.0614399999999999,0,0,/mchatters/covid-xgboost-v1,COVID19 Global Forecasting (Week 4)
8949011,0.19714,0,0,/jaehooncha/kernel85b2e344a4,COVID19 Global Forecasting (Week 4)
8923187,0.48526,0,1,/cthierfelder/kernel755e4cfa58,COVID19 Global Forecasting (Week 4)
8977940,0.0298899999999999,0,1,/ashrithsher/kernel3200f1835e,COVID19 Global Forecasting (Week 4)
8845481,0.03639,0,4,/muhammad4hmed/go-carona-go,COVID19 Global Forecasting (Week 4)
8975680,0.30062,0,0,/eluzquadros/covid-19-global-forecast-seir-visualize,COVID19 Global Forecasting (Week 4)
8851021,2.1064,0,0,/gunavardhanjakkidi/secondapp,COVID19 Global Forecasting (Week 4)
8897726,0.6863100000000001,0,1,/georgem20/rnn-modeling,COVID19 Global Forecasting (Week 4)
8835463,0.6059,0,0,/rajatk9962/kernel6a8f2f586b,COVID19 Global Forecasting (Week 4)
8916106,1.11936,0,0,/malkodee/covid-19-eda-time-series-forecast-week-4,COVID19 Global Forecasting (Week 4)
8908392,0.1113099999999999,10,12,/kamalnaithani/india-world-live-update,COVID19 Global Forecasting (Week 4)
8897039,2.92885,0,0,/nayonika/covid-19-forecast-regression,COVID19 Global Forecasting (Week 4)
8888422,0.12732,0,0,/samarendra109/kernel205938776d,COVID19 Global Forecasting (Week 4)
8950345,0.04881,0,0,/sayan341/kernel568ca2fb2d,COVID19 Global Forecasting (Week 4)
8910010,0.43922,0,3,/lisphilar/combination-of-fitting-and-math-model-week-4,COVID19 Global Forecasting (Week 4)
8879704,0.03639,0,0,/blackmantis/kernel65f8192b51,COVID19 Global Forecasting (Week 4)
8915529,0.06105,0,0,/akshitsharma206/trying-to-get-to-top-with-xgboost-covid-19-week-4,COVID19 Global Forecasting (Week 4)
8926172,0.14454,0,0,/uvinetz/week-4-submission-poisson,COVID19 Global Forecasting (Week 4)
8937403,0.09343,0,0,/resheto/covid-19-week-4-with-weather-and-response-data,COVID19 Global Forecasting (Week 4)
8841740,0.21043,0,0,/osciiart/covid-19-lightgbm-week-4-no-leak,COVID19 Global Forecasting (Week 4)
8901885,0.04997,0,1,/ranjithks/ran-covid-19-week4-polyreg,COVID19 Global Forecasting (Week 4)
8863483,0.05631,0,0,/lalasadheekollu/trmf-with-clusters,COVID19 Global Forecasting (Week 4)
8905651,0.03848,0,0,/mikeskim/kernel52e97e345e,COVID19 Global Forecasting (Week 4)
8929415,0.03899,0,0,/andrekos/kernel569e358f43,COVID19 Global Forecasting (Week 4)
8883677,0.06604,2,11,/ashutosh619sudo/forecasting-covid-19,COVID19 Global Forecasting (Week 4)
8933286,0.0578399999999999,0,0,/xscripter/kernel631bae5a27,COVID19 Global Forecasting (Week 4)
8948019,0.03953,0,0,/dimaquick/kernel4410ba4140,COVID19 Global Forecasting (Week 4)
8849629,1.49336,0,0,/viiids/xg-boost,COVID19 Global Forecasting (Week 4)
8949486,4.2394,0,0,/simonpetertandi/kernel14d11c644d,COVID19 Global Forecasting (Week 4)
8939520,0.03639,0,1,/pearlprime/forecasting-with-xgbregressor,COVID19 Global Forecasting (Week 4)
8918528,0.03639,0,1,/mandeep2/kernel125ae70533,COVID19 Global Forecasting (Week 4)
8888654,0.23645,0,0,/prashant268/covid-19-forecasting-with-sarima-model,COVID19 Global Forecasting (Week 4)
8876885,0.05945,0,4,/amitstei/lgbm-baseline-short-elegant,COVID19 Global Forecasting (Week 4)
8877734,0.17898,0,2,/hossein2015/covid-19-week-4-xgboost-hossein,COVID19 Global Forecasting (Week 4)
8875181,0.23429,1,2,/gabrielmilan/custom-regressors-ensembling,COVID19 Global Forecasting (Week 4)
8832575,0.51034,0,1,/bitsnpieces/covid19-forecast-wk4-poly-gaussian-fit,COVID19 Global Forecasting (Week 4)
8922890,1.65199,0,0,/ankt24/covid-eda-seir-model-week-4-data,COVID19 Global Forecasting (Week 4)
8869297,0.7316699999999999,0,0,/wenyuc/kernel578f4ff14c,COVID19 Global Forecasting (Week 4)
8859132,0.06409,0,7,/abhishekkumkar/xgboost-algo,COVID19 Global Forecasting (Week 4)
8861103,0.23213,0,2,/gabrielmilan/gompertz-regressor-ensemble,COVID19 Global Forecasting (Week 4)
8861534,0.99818,0,1,/boskaiolo/time-series-predictions-are-based-on-14-days-back,COVID19 Global Forecasting (Week 4)
8846894,3.78798,0,0,/divyacnambiar/covid-19-using-random-forest,COVID19 Global Forecasting (Week 4)
1584945,0.79927,13,73,/gloriahristova/a-walkthrough-eda-vizualizations-unigram-model,What's Cooking? (Kernels Only)
1547731,0.82119,0,1,/zeus75/cooking-eda-with-svc,What's Cooking? (Kernels Only)
1578457,0.80289,0,3,/jnjnqy/cooking-nn-0-80,What's Cooking? (Kernels Only)
1554708,0.79304,0,0,/bebaek/ingredients-nn-cuisine,What's Cooking? (Kernels Only)
1542857,0.7882100000000001,0,0,/pallaviallada/tfidf-svm-linear,What's Cooking? (Kernels Only)
1527813,0.7581399999999999,5,6,/gcmartinelli/sklearn-randomforestclassifier-1st-submission,What's Cooking? (Kernels Only)
1265214,0.8043,0,0,/nonreviad/what-s-cooking,What's Cooking? (Kernels Only)
1463719,0.78861,0,0,/timothycwillard/tuning-a-logistic-regression,What's Cooking? (Kernels Only)
1465389,0.7819699999999999,0,4,/ksayantani/cuisine-analysis,What's Cooking? (Kernels Only)
1426691,0.7511,0,0,/janeyb/what-s-cooking-kernel,What's Cooking? (Kernels Only)
1407777,0.57662,4,5,/pavlin/jaccard-similarity,What's Cooking? (Kernels Only)
1382024,0.79304,0,0,/aalchemist/tfidr-pca-neural,What's Cooking? (Kernels Only)
1388624,0.2529099999999999,0,1,/suyashgulati/knowing-700-values-of-test-data-without-learning,What's Cooking? (Kernels Only)
1383497,0.7872,0,1,/djsaunde/logistic-regression-cv-on-tf-idf-data,What's Cooking? (Kernels Only)
1374031,0.73722,0,7,/kstathou/word-embeddings-logistic-regression,What's Cooking? (Kernels Only)
1303098,0.77473,0,1,/aalchemist/pca-logistic-regression,What's Cooking? (Kernels Only)
1327595,0.78751,1,3,/omrialgazi/cooking-with-pytorch,What's Cooking? (Kernels Only)
1301383,0.78258,0,6,/belousych/tfidf-lg-mlp-feature-importance,What's Cooking? (Kernels Only)
1302244,0.82109,0,3,/bathiamkwrdsbt/what-scookingsvm,What's Cooking? (Kernels Only)
1286605,0.78509,0,2,/ch124uec/logisticregression,What's Cooking? (Kernels Only)
1275471,0.52363,0,0,/michelcarroll/simple-tf-idf-scoring,What's Cooking? (Kernels Only)
1256803,0.78761,0,0,/pangtong/what-s-cooking-tf-idf-onevsrest-linersvc,What's Cooking? (Kernels Only)
1237091,0.78117,0,1,/alfonsogarcia/first-kaggle,What's Cooking? (Kernels Only)
1222395,0.10901,5,15,/ashishpatel26/scrumptious-cooking-foods,What's Cooking? (Kernels Only)
1194277,0.79867,4,28,/codename007/cooking-cooking-cooking,What's Cooking? (Kernels Only)
4922846,0.91946,0,0,/summeraa/feature-engineering-xgboost-ef83d6,Predict Future Sales
4567135,0.91022,0,0,/christianmarechal/futur-sales-xgboost-tuning,Predict Future Sales
2295215,0.91946,0,0,/jledru/feature-engineering-xgboost,Predict Future Sales
2780353,0.67313,0,0,/stefanobromuri/smoted-but-it-did-not-work-very-well,Quora Insincere Questions Classification
2951428,0.69619,0,4,/jihangz/20th-solution-4-folds-2-models-mixed-loss,Quora Insincere Questions Classification
2838955,0.6829999999999999,0,4,/dicksonchin93/kfold-tfidf-trial,Quora Insincere Questions Classification
2831613,0.698,0,3,/luudactam/final-sub,Quora Insincere Questions Classification
2830135,0.6990000000000001,0,1,/jihangz/lt-conc-g-f-lg-mean-g-p-light,Quora Insincere Questions Classification
2819743,0.7040000000000001,9,10,/peining/fork-of-third-place-model-2th,Quora Insincere Questions Classification
2479188,0.607,2,2,/mnpinto/quora-fastai-v1-0-baseline,Quora Insincere Questions Classification
2186079,0.625,0,1,/amirkeren/bidirectional-lstm-with-dropout,Quora Insincere Questions Classification
2435291,0.53819,0,0,/prabanch/quora-insincere-questions-classification,Quora Insincere Questions Classification
2673258,0.638,0,13,/mabrek/simple-fasttext-pretrained,Quora Insincere Questions Classification
2604116,0.65,0,0,/kleikev/quora-project,Quora Insincere Questions Classification
2814620,0.0,0,0,/stathisbranikas/first,Quora Insincere Questions Classification
2805061,0.649,0,0,/alexandruuu/preprocessing-bilstm,Quora Insincere Questions Classification
2763861,0.7,7,35,/sfzero/fork-of-gamma-bianli-feature-1-1-i-16,Quora Insincere Questions Classification
2784235,0.6890000000000001,2,5,/hamishdickson/single-rnn-with-4-folds-pca,Quora Insincere Questions Classification
2736357,0.6940000000000001,4,28,/artgor/text-modelling-in-pytorch-v2,Quora Insincere Questions Classification
2736408,0.669,2,32,/jannen/model-error-analysis,Quora Insincere Questions Classification
2674843,0.685,0,0,/strifonov/avg-simple-rnn-with-attention-and-preprocessing,Quora Insincere Questions Classification
2664548,0.649,0,2,/kamal2611/quora-insincere,Quora Insincere Questions Classification
2398235,0.6729999999999999,0,0,/jayachandra1221/quora,Quora Insincere Questions Classification
2698141,0.569,0,2,/kristinehala/benchmark-predictions,Quora Insincere Questions Classification
2690279,0.514,0,5,/walidbachri/final-predictions,Quora Insincere Questions Classification
9995648,0.76178,0,0,/dblabs/fork-of-kernelce75741753,Shelter Animal Outcomes
7069996,0.95968,0,1,/rmabjish/austin-animal-center-wrangling-predicting,Shelter Animal Outcomes
763548,0.83626,0,0,/yy1252450987/animal-ml,Shelter Animal Outcomes
533703,0.084583,0,4,/kmader/cnn-with-gap-for-camera-detection,IEEE's Signal Processing Society - Camera Model Identification
71721,0.75937,0,0,/xameleoh/untitled,Avito Duplicate Ads Detection
8488722,0.67402,0,19,/chrisrichardmiles/numpy-pandas-challenge-current-leader-0-67402,M5 Forecasting - Accuracy
8436261,0.61257,31,249,/girmdshinsei/for-japanese-beginner-with-wrmsse-in-lgbm,M5 Forecasting - Accuracy
8307137,1.08216,0,0,/ms2019/m5-competition-eda-models,M5 Forecasting - Accuracy
8285722,0.6208,28,175,/mayer79/m5-forecast-keras-with-categorical-embeddings-v2,M5 Forecasting - Accuracy
8226022,0.63384,56,373,/ragnar123/very-fst-model,M5 Forecasting - Accuracy
8271936,1.07965,2,7,/latimerb/m5-persistence-moving-average-forecasts,M5 Forecasting - Accuracy
8233866,2.07228,0,4,/graymant/baseline-lstm-example,M5 Forecasting - Accuracy
8228608,1.2133200000000002,18,71,/li325040229/eda-and-an-encoder-decoder-lstm-with-9-features,M5 Forecasting - Accuracy
8242278,1.06756,4,26,/siavrez/simple-eda-with-croston-method,M5 Forecasting - Accuracy
8226879,1.1596799999999998,12,38,/beezus666/end-to-end-data-wrangling-simple-random-forest,M5 Forecasting - Accuracy
8239031,0.7117899999999999,0,6,/zmnako/lgbm-update-0-85632,M5 Forecasting - Accuracy
8232736,1.06439,0,1,/sidharthkumar/ema-model-new-baseline,M5 Forecasting - Accuracy
8226966,1.90131,0,3,/kuroko1t/simple-lstm,M5 Forecasting - Accuracy
8225995,2.48932,1,3,/victorhz/a-ramdom-submission,M5 Forecasting - Accuracy
12078735,0.74256,0,0,/rikdifos/gpu-lightgbm-timeseriessplit-cv,M5 Forecasting - Accuracy
141445,0.0243199999999999,0,0,/ymcdull/nn-through-keras-copied-mod-forked,Leaf Classification
138551,0.01526,0,5,/alexionby/copy-mode,Leaf Classification
137743,0.009,32,108,/abhmul/keras-convnet-lb-0-0052-w-visualization,Leaf Classification
131074,0.03296,0,0,/gauravjoshi1986/leaf-classification-through-keras,Leaf Classification
130101,0.04526,0,0,/richarlee/leaf-classification-richarlee,Leaf Classification
3062126,3.688,0,0,/rajeshcv/final-models,Elo Merchant Category Recommendation
2893486,3.685,0,1,/mks2192/fork-of-train-test-features-old-new-combin-150-fea,Elo Merchant Category Recommendation
2734599,3.71,0,0,/luischreiter/elo-project-final-version,Elo Merchant Category Recommendation
2724919,3.876,0,3,/tarunaryyan/xtreme-gradient-boosting-hypertuning-26,Elo Merchant Category Recommendation
2919596,3.887,0,12,/cywwayne/starter-code-fe-for-time-series-model-lstm,Elo Merchant Category Recommendation
3775777,0.66993,1,9,/ratthachat/workshop-lstm,VSB Power Line Fault Detection
2840142,0.588,0,0,/silence2/lstm-feature-engineering,VSB Power Line Fault Detection
2923422,0.7170000000000001,6,11,/bigswimatom/5-fold-lstm-attention-stateful-metrics-with-exp,VSB Power Line Fault Detection
3252853,0.6659999999999999,3,4,/stanislavblinov/bidirectional-hell,VSB Power Line Fault Detection
3216667,0.312,0,1,/ludovicoristori/trying-to-join-the-ann-party,VSB Power Line Fault Detection
3148589,0.68192,5,47,/roydatascience/eda-iso-pca-lle-stratified-lstm-attention,VSB Power Line Fault Detection
2791739,0.502,0,0,/padmakartj/cudnn,VSB Power Line Fault Detection
2982409,0.6990000000000001,19,111,/tarunpaparaju/vsb-competition-attention-bilstm-with-features,VSB Power Line Fault Detection
2945144,0.594,0,2,/piyush28/cnn-tensorflow-estimator-dataset-api,VSB Power Line Fault Detection
2797016,0.628,0,6,/jackvial/lstm-concat-phases-fp-correction-attempt,VSB Power Line Fault Detection
2637601,0.5529999999999999,13,77,/braquino/vsb-power-lstm-attention,VSB Power Line Fault Detection
2619498,0.4589999999999999,4,14,/jackvial/cnn-lstm-with-test-set-multiprocessing,VSB Power Line Fault Detection
2509568,0.652,12,55,/ashishpatel26/redesign-stacked-lstm-advance-parameter-tuning,VSB Power Line Fault Detection
2459049,0.1669999999999999,0,8,/delayedkarma/lightgbm-signal-fe-cv-0-863,VSB Power Line Fault Detection
508128,0.628,0,0,/anvesh525/iteration3-my-first-kaggle-project-anvesh,Recruit Restaurant Visitor Forecasting
6790235,0.51146,0,1,/wakamezake/single-lightgbmtuner,Recruit Restaurant Visitor Forecasting
5076236,0.49309,0,7,/danofer/swap-noise-denoising-autoencoder,Recruit Restaurant Visitor Forecasting
1644837,0.8859999999999999,2,0,/jima0720/recruit-eda,Recruit Restaurant Visitor Forecasting
544254,0.544,2,2,/kravdiy/prophet-forecasting,Recruit Restaurant Visitor Forecasting
567131,0.485,0,0,/aharless/nitin-s-surprise-me-with-early-reservations-only,Recruit Restaurant Visitor Forecasting
527736,0.792,4,2,/sherring/recruit-restaurant-visitor-forecasting-first-try,Recruit Restaurant Visitor Forecasting
510995,0.541,0,1,/arunrajagopalan/recruit-restaurant,Recruit Restaurant Visitor Forecasting
498512,0.855,0,2,/sunhwan/a-quick-and-dirty-submission,Recruit Restaurant Visitor Forecasting
481636,0.507,7,22,/jmbull/no-xgb-starter-here-s-one-lb-507,Recruit Restaurant Visitor Forecasting
13506391,0.0205699999999999,0,0,/nms2016145/gbr-lightgbm-test,PUBG Finish Placement Prediction (Kernels Only)
12636656,0.2277,0,1,/tripathiji/final-pubg-prediction,PUBG Finish Placement Prediction (Kernels Only)
12370010,0.06795,0,0,/leeyongsung/deeplearning-to-practice,PUBG Finish Placement Prediction (Kernels Only)
12139128,0.49561,0,0,/eeshwarib/pubg-analytics,PUBG Finish Placement Prediction (Kernels Only)
12076993,0.05683,0,3,/nitinver19/rajasthan-gujarat-cluster,PUBG Finish Placement Prediction (Kernels Only)
11211210,0.05797,0,1,/rahulpawade/xgbregressor,PUBG Finish Placement Prediction (Kernels Only)
10911466,0.06942,0,3,/sachinramachandran/pubg-sachin,PUBG Finish Placement Prediction (Kernels Only)
10543470,0.12899,0,0,/kimsouce/submit,PUBG Finish Placement Prediction (Kernels Only)
10296897,0.11417,0,4,/jeffluczak/pubg-kernel,PUBG Finish Placement Prediction (Kernels Only)
9725300,0.05792,0,0,/kannankumar/pubg-finish-placement-prediction-fast-ai-ml,PUBG Finish Placement Prediction (Kernels Only)
9508915,0.02029,0,0,/adamyang/ensemble-model-by-team-3aml,PUBG Finish Placement Prediction (Kernels Only)
9094851,0.10805,0,0,/rohitsharma0206/kernel1e25a5521c,PUBG Finish Placement Prediction (Kernels Only)
7455863,0.0205699999999999,0,0,/nmsf1916001/kernel1cefe28e23,PUBG Finish Placement Prediction (Kernels Only)
8011214,0.4733699999999999,0,0,/coolsyn/pubg-nn,PUBG Finish Placement Prediction (Kernels Only)
7539404,0.06648,0,0,/nijianing/nmsx1916049,PUBG Finish Placement Prediction (Kernels Only)
7519885,0.05968,0,0,/betatu/nmsf1916056,PUBG Finish Placement Prediction (Kernels Only)
91963,0.953434,0,0,/akash222/single-unified-table-0-95-sklearn-added-date,Predicting Red Hat Business Value
10371785,0.9938,0,4,/vitalygryaznov/predict-sales,Predict Future Sales
10351452,45.56848,0,3,/muhammedalidilekci/160202093-b-y-k-veri-final,Predict Future Sales
10299247,1.20356,3,6,/brkyzdmr/future-sales-prediction-with-cnn-lstm,Predict Future Sales
10326727,1.94554,0,8,/serkanyava/ko-b-y-k-veri-final-160202063,Predict Future Sales
10036459,1.2062700000000002,0,7,/danoozy44/time-series-future-sales-wacky-approach,Predict Future Sales
9770519,0.925,0,1,/namansingh2803/melanoma-tpu-effnetb3-b7,SIIM-ISIC Melanoma Classification
10348974,0.922,0,1,/fadzlinrafi/l2-regularization-to-improve-accuracy,SIIM-ISIC Melanoma Classification
10100865,0.9387,21,26,/orionpax00/melanoma-detection-single-model-gpu-tpu-yapl,SIIM-ISIC Melanoma Classification
10186457,0.9216,10,49,/sayakdasgupta/siim-isic-melanoma-efficientnet-on-pytorch-tpus,SIIM-ISIC Melanoma Classification
10286942,0.926,0,2,/fadzlinrafi/testing-different-efficientnets,SIIM-ISIC Melanoma Classification
10135905,0.895,12,8,/niteshx2/melanoma-beginner-tpu-efficientnet,SIIM-ISIC Melanoma Classification
10239682,0.911,4,9,/fadzlinrafi/different-image-sizes,SIIM-ISIC Melanoma Classification
10168942,0.738,0,0,/allenljw/skin-cancer-image-classification-2,SIIM-ISIC Melanoma Classification
9993441,0.8,3,11,/niteshx2/melanoma-starter-simplest-explanation-and-model,SIIM-ISIC Melanoma Classification
10103170,0.915,1,7,/redwankarimsony/siim-isic-melanoma-1-model-4-datasets-starter,SIIM-ISIC Melanoma Classification
10107571,0.784,0,8,/tunguz/melanoma-with-h2o-automl-2,SIIM-ISIC Melanoma Classification
9800810,0.929,2,10,/louise2001/ensemble,SIIM-ISIC Melanoma Classification
9994674,0.928,19,41,/shonenkov/inference-melanoma-crazy-fast,SIIM-ISIC Melanoma Classification
12149374,0.708,0,11,/mrutyunjaybiswal/riiid-neural-nets-starter-with-keras-tuner,Riiid Answer Correctness Prediction
12178806,0.705,0,2,/bocchi/baseline-mean-of-first-trials,Riiid Answer Correctness Prediction
12163025,0.6679999999999999,6,12,/shivamjohri/riiid-catboost-starter-notebook,Riiid Answer Correctness Prediction
12134364,0.747,4,56,/ulrich07/riiid-keras-starter,Riiid Answer Correctness Prediction
12134464,0.735,5,29,/kneroma/riid-user-and-content-mean-predictor,Riiid Answer Correctness Prediction
12145961,0.726,1,7,/tkm2261/riiid-correctness-with-question-feats,Riiid Answer Correctness Prediction
12115269,0.633,3,22,/sishihara/riiid-answered-correctly-benchmark,Riiid Answer Correctness Prediction
12117074,0.688,1,9,/dwchen/riiid-simpleeda-and-prediction-1m-data,Riiid Answer Correctness Prediction
13642966,0.743,0,0,/bestwishes007/riiid-lgbm-catboost-baseline-weights-optimization,Riiid Answer Correctness Prediction
13459313,0.772,0,0,/zekun98/riiid-lgbm-bagging2,Riiid Answer Correctness Prediction
13419174,0.7440000000000001,0,0,/zekun98/tabnet-with-loop-feature-engineering-explained,Riiid Answer Correctness Prediction
13334365,0.718,0,0,/zekun98/fork-of-riid-modeling-cv,Riiid Answer Correctness Prediction
13273214,0.7490000000000001,0,0,/niaibrahim/fork-of-cnn-reduced-learning-rate,Riiid Answer Correctness Prediction
13154520,0.75,0,0,/niaibrahim/cnn-dropout-10-dense-64-conv-128,Riiid Answer Correctness Prediction
13787446,5.6493400000000005,0,0,/rainbowhyena/houseprices-new,House Prices - Advanced Regression Techniques
5308672,0.33046,0,2,/projeet/house-prices,House Prices - Advanced Regression Techniques
12976810,0.1258,0,2,/qaisar95/advance-feature-engineering-top,House Prices - Advanced Regression Techniques
13521817,0.1518,0,0,/startover205/fastai-2-house-pricing,House Prices - Advanced Regression Techniques
13675558,0.12292,0,1,/shukashimura/notebook-for-presentation-12-20,House Prices - Advanced Regression Techniques
13647836,0.1325,0,1,/jackttai/house-prices-using-lgbm,House Prices - Advanced Regression Techniques
13559312,0.14722,1,5,/santoshadhikari/predicting-house-prices-with-machine-learning,House Prices - Advanced Regression Techniques
13475960,0.14407,0,0,/jamescorbin/automated-feature-preprocessing-and-run-1k-models,House Prices - Advanced Regression Techniques
13423507,0.1702,0,2,/sangminyoon/house-price-prediction-with-catboostregressor-ba,House Prices - Advanced Regression Techniques
13400005,0.14677,0,2,/smartmizz/house,House Prices - Advanced Regression Techniques
13122743,0.13377,1,8,/prosenjit123/house-price-prediction,House Prices - Advanced Regression Techniques
12211774,0.14125,3,7,/nishimitakeru/in-japanese-house-prices-with-lasso-regression,House Prices - Advanced Regression Techniques
13537425,0.88538,0,1,/alandata666/customer-prediction-model-xgboost-and-optimize,Santander Customer Transaction Prediction
13059969,0.89856,0,1,/massyl/santander-customer-transaction-prediction,Santander Customer Transaction Prediction
11158726,0.63958,0,4,/juanandressimonetti/c2-santander-customer-transaction-prediction,Santander Customer Transaction Prediction
10843604,0.70767,1,2,/samlakhmani/santander-please-looking-for-advice-beginner,Santander Customer Transaction Prediction
9880413,0.80614,0,0,/olucasferreira/customer-transaction-pca-gaussiannb,Santander Customer Transaction Prediction
9456320,0.87377,0,1,/teemusoininen/santander-simple-kernel,Santander Customer Transaction Prediction
8375019,0.85001,0,0,/nurseitabdiraimov/abdn-mid,Santander Customer Transaction Prediction
7757800,0.377,0,1,/zzuczy/kernel49aa114431,Google QUEST Q&A Labeling
7620734,0.377,0,2,/zzuczy/kernel6b256be09d,Google QUEST Q&A Labeling
7718106,0.185,0,4,/jskimkorea/google-quest-practice,Google QUEST Q&A Labeling
7705845,0.367,1,2,/imoisharma/distilbert-use-features-oof,Google QUEST Q&A Labeling
7639477,0.364,38,106,/kabure/qa-eda-and-nlp-modelling-insights-vis-bert,Google QUEST Q&A Labeling
7646216,0.0289999999999999,1,5,/paulram/google-quest-q-a-labeling-with-keras,Google QUEST Q&A Labeling
7420589,0.073,11,40,/erelin6613/google-competition,Google QUEST Q&A Labeling
7319737,0.3829999999999999,1,2,/vinaydoshi/tf-minimalist-bert-with-last-4-hidden-states,Google QUEST Q&A Labeling
7479352,-0.003,0,0,/stitch/roberta-submission-check,Google QUEST Q&A Labeling
6803050,0.39,12,70,/lhohoz/bert-base-pytorch-inference,Google QUEST Q&A Labeling
7216915,-0.006,6,17,/stitch/albert-in-keras-tf2-using-huggingface-explained,Google QUEST Q&A Labeling
7360889,0.3429999999999999,3,6,/melissarajaram/distilbert-fastai-huggingface-transformers,Google QUEST Q&A Labeling
7300419,0.356,2,7,/arvissu/simple-bert-pytorch-ver,Google QUEST Q&A Labeling
7040767,0.389,35,127,/khoongweihao/bert-base-tf2-0-minimalistic-iii,Google QUEST Q&A Labeling
7038407,0.308,4,2,/meghakapoor/pytorch-bert-base-minimalistic,Google QUEST Q&A Labeling
7037494,0.384,16,18,/khoongweihao/bert-base-tf2-0-minimalistic-ii,Google QUEST Q&A Labeling
6955006,-0.003,0,2,/hs999518/google-quest-and-label-text,Google QUEST Q&A Labeling
6924133,0.325,6,14,/nandhuelan/google-quest-for-nlp-v7,Google QUEST Q&A Labeling
6881093,0.361,2,34,/akensert/quest-use-classifier-with-tf-gradienttape,Google QUEST Q&A Labeling
6897395,0.371,14,42,/ldm314/quest-encoding-ensemble,Google QUEST Q&A Labeling
4336099,1.0,0,10,/kirankunapuli/fastai-efficientnet-b3,Aerial Cactus Identification
4894536,0.9373,0,0,/ytzmlll/cactus,Aerial Cactus Identification
5609324,0.984,0,0,/jarrous/aerial-cactus-identification,Aerial Cactus Identification
5240940,0.964,0,2,/nvinayvarma189/cactus-identification,Aerial Cactus Identification
4849021,0.9185,0,1,/arqtty/make-it-cactusy,Aerial Cactus Identification
4797815,0.9661,1,0,/etozhepivka/kernel6b06ead420,Aerial Cactus Identification
3472548,0.9995,0,1,/mohammedsunasra/cactus-pytorch-no-cv,Aerial Cactus Identification
4798037,0.8496,2,2,/aninda/cactus-fastai,Aerial Cactus Identification
4395602,0.9997,0,0,/ankschoubey/fastai-resnet50,Aerial Cactus Identification
4712426,0.998,2,3,/shwetagoyal4/identification-of-cactus-keras-vgg16,Aerial Cactus Identification
4710757,0.9806,0,0,/bharat2905/simple-keras-cnn,Aerial Cactus Identification
4047575,0.9986,0,0,/tataganesh/cactus-classification-vanilla-tensorflow,Aerial Cactus Identification
4721488,0.9838,0,1,/ahkhalwai55/fork-of-simple-fastai-exercise-resnet-152,Aerial Cactus Identification
4721884,0.9999,0,1,/ahkhalwai55/simple-fastai-exercise-vgg19-bn,Aerial Cactus Identification
4666545,0.9953,0,0,/kimlabiee/kernel650d1a413d,Aerial Cactus Identification
4623359,0.9738,3,3,/okeaditya/tutorial-on-transfer-learning-cnn-with-keras,Aerial Cactus Identification
4607861,0.9998,0,0,/visali/cactus-classification-using-fastai,Aerial Cactus Identification
4432037,0.9923,0,3,/ianthaml/aerial-cactus-classification-using-pytorch,Aerial Cactus Identification
4030050,0.9243,0,0,/supratikoley/aerial-cactus-identification,Aerial Cactus Identification
4031541,0.9996,0,0,/larawusl123/cactus-practice-predictions,Aerial Cactus Identification
4573305,0.9999,0,0,/kalikichandu/cactus-identification-fastai,Aerial Cactus Identification
4599898,0.9991,0,0,/maxwellshannonlevin/aerial-cactus-identification-with-keras,Aerial Cactus Identification
4575831,0.9995,4,4,/valleyzw/easy-keras-cnn,Aerial Cactus Identification
4375031,1.0,0,1,/silvioakempf/boateazul-dama-de-vermelho,Aerial Cactus Identification
4445933,0.9961,0,0,/marcelonepo/cnn-augment-vgg16,Aerial Cactus Identification
4567731,0.5546,0,0,/jaquelineduarte/kernel-mobilenetv2-mandacaru-2f,Aerial Cactus Identification
4425898,0.9984,2,3,/adkarhe/cactus-identification,Aerial Cactus Identification
1729913,3.10921,0,0,/amitkvikram/newyorkfare-final,New York City Taxi Fare Prediction
3253816,0.9,2,0,/youlei0106/kernel5c5f0b715c,Santander Customer Transaction Prediction
3439537,0.9,0,3,/vanetreg/santander-eda-and-prediction-copy,Santander Customer Transaction Prediction
3428124,0.887,2,11,/hjd810/the-weighted-prediction-cross-validation-approach,Santander Customer Transaction Prediction
3398084,0.901,11,65,/niteshx2/beginner-explained-lgb-2-leaves-augment,Santander Customer Transaction Prediction
3423704,0.9,0,1,/mauryaravi/snatander-customer-transaction-prediction,Santander Customer Transaction Prediction
3422125,0.8240000000000001,3,7,/vikram687/eda-santander-customer-transaction,Santander Customer Transaction Prediction
3450841,0.899,0,0,/coolaks/lgbm-kfold-feat-eng,Santander Customer Transaction Prediction
3406785,0.5,1,7,/nivedas/santander-ann,Santander Customer Transaction Prediction
3404410,0.889,1,2,/kaggle2007/santander-predictions-with-xgboost,Santander Customer Transaction Prediction
3399992,0.875,2,3,/miljan/overfitting-nn,Santander Customer Transaction Prediction
3392429,0.897,4,7,/ricksun/xgboost-stratifiedkfold-for-beginner,Santander Customer Transaction Prediction
3006456,0.852,1,1,/sinhagaurav02/santander-deep-learning-implementation,Santander Customer Transaction Prediction
3364081,0.894,4,37,/jiazhuang/demonstrate-naive-bayes,Santander Customer Transaction Prediction
3350111,0.8990299999999999,105,357,/cdeotte/modified-naive-bayes-santander-0-899,Santander Customer Transaction Prediction
3343550,0.901,1,21,/roydatascience/blender-of-0-901-solutions,Santander Customer Transaction Prediction
3358443,0.897,2,21,/sandeep8530/keras-89-7-few-lessons-learnt,Santander Customer Transaction Prediction
3254459,0.314,0,0,/adarsh415/santander-customer-transaction-tensorflow,Santander Customer Transaction Prediction
3371603,0.884,0,0,/mayank1261998/santander-avengers-ensemble,Santander Customer Transaction Prediction
3369494,0.86,0,1,/alexpetit12/getting-started-with-logistic-regression,Santander Customer Transaction Prediction
3347215,0.8690000000000001,18,65,/wuliaokaola/santander-lb-0-869-no-need-to-train,Santander Customer Transaction Prediction
3385962,0.655,0,0,/prasad22s/kernelffd24dfd31,Santander Customer Transaction Prediction
3188674,0.7879999999999999,0,1,/prayanshratan/santander-customer,Santander Customer Transaction Prediction
3337272,0.901,10,29,/darbin/principal-component-analysis-approach,Santander Customer Transaction Prediction
3344541,0.899,2,9,/jiazhuang/z-value-as-features-to-train-nn-lgb-nb-models,Santander Customer Transaction Prediction
12220609,0.17404,0,1,/anyesharay/data-science-301-kaggle-competition-a-ray,House Prices - Advanced Regression Techniques
6513635,0.11728,0,1,/rookie0417/mark2-house-price-predict,House Prices - Advanced Regression Techniques
9656373,0.14777,0,1,/pratheepknadar/housing-sale-prediction-with-feature-eng-sklearn,House Prices - Advanced Regression Techniques
12192709,0.17017,2,1,/hackonion/house-price,House Prices - Advanced Regression Techniques
12230583,0.2479199999999999,0,0,/seungah/housing-competition,House Prices - Advanced Regression Techniques
12224900,0.26707,0,0,/svashi/housing-dataset,House Prices - Advanced Regression Techniques
12127411,0.13269,0,0,/juanpauloreguyal/house-prices-advanced-regression-techniques,House Prices - Advanced Regression Techniques
12066759,0.1699,1,6,/pakkanmeric/house-prices-linear-regression-model,House Prices - Advanced Regression Techniques
11480202,0.15492,1,6,/vadimsokolov/regression-models-for-house-prices,House Prices - Advanced Regression Techniques
12007354,0.14213,0,1,/janamachutova/house-prices-category-split,House Prices - Advanced Regression Techniques
12003908,0.14079,4,4,/bununtadiresmenmor/handling-missing-values-with-house-prices-dataset,House Prices - Advanced Regression Techniques
11984508,0.22194,0,2,/carlmcbrideellis/simple-two-variable-model,House Prices - Advanced Regression Techniques
11951766,0.26923,6,8,/nik3092/house-prices-advanced-eda-analysis-prediction,House Prices - Advanced Regression Techniques
6239097,0.13485,0,0,/alafan/house-prices-alafan-advanced,House Prices - Advanced Regression Techniques
809724,0.5243,0,1,/shahir/simple-random-forest-classifier-model,TalkingData AdTracking Fraud Detection Challenge
803892,0.9381,9,5,/antmarakis/random-forest-base-validation,TalkingData AdTracking Fraud Detection Challenge
719859,0.9321,0,0,/bobbykim31/talkingdata,TalkingData AdTracking Fraud Detection Challenge
710860,0.5448,1,3,/jiegzhan/adtracking-fraud-detection,TalkingData AdTracking Fraud Detection Challenge
829104,0.9585,0,0,/am1to2/balanced-data-xgboost,TalkingData AdTracking Fraud Detection Challenge
718121,0.5004,0,0,/gagandeep16/ad-tracking-fraud-detection-machine-learning,TalkingData AdTracking Fraud Detection Challenge
28665,12.80664,2,0,/dimaiyou/naive,San Francisco Crime Classification
24661,2.5994900000000003,0,0,/omarelgabry/san-francisco-crimes,San Francisco Crime Classification
13958906,0.785,0,0,/kaerunantoka/riiid-transformer-v11-inference,Riiid Answer Correctness Prediction
13335181,0.746,0,0,/zekun98/featureriid-modeling-cv,Riiid Answer Correctness Prediction
13960099,0.565,1,0,/tfukuda675/japanese-riiid-simple-lgbm-tag-one-hot-ver,Riiid Answer Correctness Prediction
13869807,0.764,10,5,/narendra/riiiid-saint-sakt-baselinesubmission,Riiid Answer Correctness Prediction
13668781,0.7829999999999999,0,117,/julianguo/fork-of-riiid-lgbm-bagging2-1-471152,Riiid Answer Correctness Prediction
13795415,0.78,17,58,/zyy2016/0-780-unoptimized-lgbm-interesting-features,Riiid Answer Correctness Prediction
13793921,0.616,6,17,/zephyrwang666/riiid-saint-tf-transformer-inference,Riiid Answer Correctness Prediction
13865660,0.75,0,0,/shigeoaoki/study-riiid,Riiid Answer Correctness Prediction
13781388,0.7809999999999999,3,16,/satorushibata/optimized-lightgbm-with-optuna-adding-sakt-model,Riiid Answer Correctness Prediction
12629751,0.636,0,0,/shubhamnagaria96/notebook97e3a75264,Riiid Answer Correctness Prediction
13751679,0.774,2,5,/rohithansdah/riiid-model-lgbm-optimized,Riiid Answer Correctness Prediction
13720285,0.5,0,1,/rodolphelampe/competition-api-detailed-introduction,Riiid Answer Correctness Prediction
12754623,0.595,0,0,/niaibrahim/basic-karas-nn-made-epoch-small-for-save-074eb7,Riiid Answer Correctness Prediction
13205819,0.75,0,0,/niaibrahim/conv2d,Riiid Answer Correctness Prediction
10345742,0.928,0,0,/iamprateek/melanoma-tpu-efficientnet,SIIM-ISIC Melanoma Classification
10104840,0.8590000000000001,0,0,/chiraggodaw/melanoma-gpu-training-using-pipeline,SIIM-ISIC Melanoma Classification
11260792,0.9613,6,20,/truonghoang/first-silver-medal,SIIM-ISIC Melanoma Classification
10470140,0.925,0,1,/darshanpatel11/incredible-tpus-finetune-effnetb0-b6-at-once,SIIM-ISIC Melanoma Classification
11271290,0.9559,3,8,/helgith/stacking-with-catboost-0-9432-private-lb,SIIM-ISIC Melanoma Classification
11247792,0.9519,0,5,/samklein/stacking-predictions-and-image-encodings,SIIM-ISIC Melanoma Classification
11242970,0.9658,1,16,/ilosvigil/simple-ensemble-public-private-lb-0-9658-0-9369,SIIM-ISIC Melanoma Classification
11253602,0.9397,1,5,/foodaholic/single-model-effnetb6-with-metadata-2019-2020,SIIM-ISIC Melanoma Classification
11247627,0.9512,0,7,/mpsampat/final-simple-oof-ensembling-methods-6-models,SIIM-ISIC Melanoma Classification
11228975,0.9287,3,27,/tuckerarrants/melanoma-5fold-efficientnet-augmentation-s,SIIM-ISIC Melanoma Classification
11159255,0.9501,0,2,/ilosvigil/stratified-kfold-with-tfrecords-gridmask,SIIM-ISIC Melanoma Classification
10100365,0.933,0,2,/redwankarimsony/siim-isic-melanoma-ensemble-tabular,SIIM-ISIC Melanoma Classification
11236175,0.7517,0,2,/nhm1440/image-metadata-with-keras-imagedatagenerator,SIIM-ISIC Melanoma Classification
11232528,0.792,1,7,/gennarorodrigues/vgg16-xgboost,SIIM-ISIC Melanoma Classification
11046031,0.9441,0,0,/ladanova/siim-isic-melanoma-classification,SIIM-ISIC Melanoma Classification
10965392,0.73,0,0,/kaerunantoka/effn-b4-pseudo-labeling-coarse-dropout-gpu,SIIM-ISIC Melanoma Classification
11197700,0.8701,0,2,/krisho007/melanoma-with-pylightning-128-b0,SIIM-ISIC Melanoma Classification
11002648,0.9507,0,0,/itsuki9180/kernel204223933e,SIIM-ISIC Melanoma Classification
10479376,0.8565,0,0,/a5bhowmik/melavgg,SIIM-ISIC Melanoma Classification
11108067,0.58,0,2,/rajnishe/rc-siim-xgboost-tabdata,SIIM-ISIC Melanoma Classification
11104085,0.925,0,0,/nelsontseng/train-cv,SIIM-ISIC Melanoma Classification
11023947,0.9346,0,1,/quangnhatbui/cancer-2-131dd9,SIIM-ISIC Melanoma Classification
11194141,0.8635,0,0,/krisho007/melanoma-with-pylightning-384-b0,SIIM-ISIC Melanoma Classification
11151868,0.9634,0,8,/azaemon/blending-outputs,SIIM-ISIC Melanoma Classification
11545134,242.044,0,19,/kneroma/simple-nn-on-lyft-tabular-data-with-custom-loss,Lyft Motion Prediction for Autonomous Vehicles
11530504,1078.692,0,4,/gyfastas/ensembling-into-one-prediction,Lyft Motion Prediction for Autonomous Vehicles
11440666,200.054,3,53,/kneroma/lgbm-on-lyft-tabular-data-inference,Lyft Motion Prediction for Autonomous Vehicles
11357567,134.091,32,164,/pestipeti/pytorch-baseline-inference,Lyft Motion Prediction for Autonomous Vehicles
11380078,1999.604,1,17,/rhtsingh/gpu-training-lyft-torch-baseline,Lyft Motion Prediction for Autonomous Vehicles
11363971,7350.5430000000015,1,23,/shaitender/lyft-eda-visualization-starter,Lyft Motion Prediction for Autonomous Vehicles
12759791,23.58,0,0,/alannerfranklin/lyft-complete-train-and-prediction-pipeline,Lyft Motion Prediction for Autonomous Vehicles
12460371,23.622,0,0,/sietseschrder/submit-6e52a3,Lyft Motion Prediction for Autonomous Vehicles
5118901,0.3034,1,16,/jiaofenx/expedia-hotel-recommendations,Expedia Hotel Recommendations
5393972,0.488,0,0,/geofflee01/ok-kernel,APTOS 2019 Blindness Detection
5407699,0.677,0,0,/chrisfs/new-data-fast-ai-starter-with-resnet-152,APTOS 2019 Blindness Detection
5086773,0.665,1,7,/adityakumar01/aptos-resnet34-with-progressive-resizing-fastai,APTOS 2019 Blindness Detection
5336205,0.5770000000000001,0,0,/dannymac180/aptos-blindness-2,APTOS 2019 Blindness Detection
5263365,0.7140000000000001,3,8,/harsh1999/blindness-detection,APTOS 2019 Blindness Detection
5285319,0.7090000000000001,2,7,/aaronbcj/aptos-v4,APTOS 2019 Blindness Detection
5259397,0.569,0,0,/deeplearn1/aptos-resnet101-pytorch,APTOS 2019 Blindness Detection
4994273,0.7809999999999999,0,1,/evandeng/aptos-vote,APTOS 2019 Blindness Detection
5250437,0.745,0,5,/nanditab35/diabetic-retinopathy-inception-v3,APTOS 2019 Blindness Detection
5250080,0.647,0,1,/phantomakame/mobilenet-and-nasnet,APTOS 2019 Blindness Detection
5223161,0.0,0,0,/jiangjixiang/public-and-private-test-data-have-the-same-number,APTOS 2019 Blindness Detection
4920144,0.35,0,1,/manimaranp/resnet101-progressive-resizing-trial,APTOS 2019 Blindness Detection
4710580,0.522,0,0,/dipam7/basic-train,APTOS 2019 Blindness Detection
4646121,0.5589999999999999,0,3,/gowrishankarin/resnet50-vs-inceptionv3-vs-xception-vs-nasnet,APTOS 2019 Blindness Detection
4965682,0.015,0,7,/rblcoder/aptos-u-net,APTOS 2019 Blindness Detection
4962554,0.69,1,5,/hitoidas/inference-kernel-pytorch,APTOS 2019 Blindness Detection
4994508,0.732,0,2,/yohalf/all-about-efficiency,APTOS 2019 Blindness Detection
2013901,0.0603,1,6,/sidharthbolar/pubg-chicken-dinner,PUBG Finish Placement Prediction (Kernels Only)
2036018,0.1309,0,0,/nakyamurya/testjjj,PUBG Finish Placement Prediction (Kernels Only)
2024785,0.0797,0,1,/zijiewu/simple-nn,PUBG Finish Placement Prediction (Kernels Only)
2020696,0.0534,1,2,/akihirosanada/prediction-with-lightgbm,PUBG Finish Placement Prediction (Kernels Only)
1928452,0.0423,0,2,/hyperc/pubg-eda-w-interactive-graphs-regression,PUBG Finish Placement Prediction (Kernels Only)
1855564,0.0555,0,2,/overload10/pubg-predicting-chicken-dinner,PUBG Finish Placement Prediction (Kernels Only)
1996588,0.0399,0,3,/billiummoto/baseline-lightgbm,PUBG Finish Placement Prediction (Kernels Only)
1978076,0.0601,0,1,/sachinjchorge/pubg-light-gbm,PUBG Finish Placement Prediction (Kernels Only)
1932790,0.0565,0,0,/andreymarkov/pubg-lgbm-v2,PUBG Finish Placement Prediction (Kernels Only)
1923421,0.2816,0,0,/lawngaiyan/eda-pubg-yluo-part-1,PUBG Finish Placement Prediction (Kernels Only)
1948751,0.0252,1,2,/lizhenyun/test20181025-v2,PUBG Finish Placement Prediction (Kernels Only)
1929986,0.1141,0,0,/mathfour/pubg-lr-on-5-features,PUBG Finish Placement Prediction (Kernels Only)
1916350,0.0253,11,19,/modmari/how-to-score-0-0255-0-0245-top-10-score,PUBG Finish Placement Prediction (Kernels Only)
1932067,0.1931,1,0,/maxrodkin/kernel7cb1517cc6,PUBG Finish Placement Prediction (Kernels Only)
1830737,0.0759,0,0,/justforgags/pubg-predictions,PUBG Finish Placement Prediction (Kernels Only)
1868462,0.0624,0,0,/ravi2512/pubg-model-catboost,PUBG Finish Placement Prediction (Kernels Only)
1887173,0.1302,0,1,/brianheredia/pubg-eda,PUBG Finish Placement Prediction (Kernels Only)
1867754,0.0818,0,0,/lhideki/pubg-finish-placement-prediction,PUBG Finish Placement Prediction (Kernels Only)
1876774,0.0622,0,0,/navuhodonosor/my-xgboost-training,PUBG Finish Placement Prediction (Kernels Only)
1867830,0.0666,0,0,/dhavaltaunk/randomforest-and-pubg,PUBG Finish Placement Prediction (Kernels Only)
1862718,0.0881,1,0,/beckhz/pubg-compitition,PUBG Finish Placement Prediction (Kernels Only)
1857317,0.1007,0,1,/rakeshea/pubg-data,PUBG Finish Placement Prediction (Kernels Only)
182031,0.02004,0,0,/udaysa/separate-network-for-margin-shape-texture,Leaf Classification
177796,0.11241,0,3,/ernie55ernie/keras-model-usage-on-leaf-classification,Leaf Classification
158320,1.24582,0,0,/nolanlee/leaf-classifier,Leaf Classification
153156,0.0155,0,0,/xiaowang111/keras-convnet-lb-0-0052-w-visualization,Leaf Classification
11050495,0.65328,0,0,/nihalhaboush/leaf-classification-adv-reg-gsearchcv-xgb,Leaf Classification
5094245,0.12758,0,0,/zhudongxiao/cnn-for-classifier,Leaf Classification
10420673,0.47902,0,4,/mahmudds/m5-forecasting-accuracy,M5 Forecasting - Accuracy
10366860,0.54265,0,1,/mrgrigorii/m5-forecasting-accuracy-linear-models,M5 Forecasting - Accuracy
9536952,1.12137,0,0,/leonzz/one-hot-encoding,M5 Forecasting - Accuracy
10334716,0.0,0,35,/kneroma/m5-fpnu0-50-updated,M5 Forecasting - Accuracy
10228585,0.73915,7,10,/prakash711/m5-forecasting-with-tensorflow-bilstm,M5 Forecasting - Accuracy
10017825,0.54597,0,1,/fbergh/ensemble-evaluation-submission,M5 Forecasting - Accuracy
9877269,0.66087,0,1,/mikhailbulygin/mlip-team-wall-e-deep-cnn,M5 Forecasting - Accuracy
9817086,0.68892,0,2,/tchaye59/m5-acc-boosting,M5 Forecasting - Accuracy
9110307,1.45536,0,0,/gijshendriksen/m5-forecasting-rnn,M5 Forecasting - Accuracy
9995722,1.15569,0,2,/joydeb28/keras-cudnnlstm-baseline,M5 Forecasting - Accuracy
9758124,0.55579,0,0,/fbergh/rausnaus-lightgbm-per-category,M5 Forecasting - Accuracy
11885197,0.41117,15,59,/arunprathap/transformer-encoder-implementation,OpenVaccine: COVID-19 mRNA Vaccine Degradation Prediction
11867581,0.27716,3,34,/yawata/lightgbm-with-network-features,OpenVaccine: COVID-19 mRNA Vaccine Degradation Prediction
11710947,0.30977,0,1,/iljaavadiev/vaccine-orig,OpenVaccine: COVID-19 mRNA Vaccine Degradation Prediction
11824489,0.30444,0,26,/code1110/openvaccine-is-bpp-s-the-most-important,OpenVaccine: COVID-19 mRNA Vaccine Degradation Prediction
11719785,0.47821,0,4,/satorushibata/covid-19-with-h2oautoml-baseline,OpenVaccine: COVID-19 mRNA Vaccine Degradation Prediction
11821588,0.33595,1,8,/namanj27/tfdistilbert-mrna-tpus-tf,OpenVaccine: COVID-19 mRNA Vaccine Degradation Prediction
11775190,0.25883,13,83,/gandagorn/gru-lstm-mix-with-custom-loss,OpenVaccine: COVID-19 mRNA Vaccine Degradation Prediction
11763238,0.26278,0,4,/mukesh2630/mvan-covid-mrna-vaccine-analysis-notebook-268,OpenVaccine: COVID-19 mRNA Vaccine Degradation Prediction
11758726,0.27286,0,48,/masashisode/pytorch-implementation-of-mcrmseloss,OpenVaccine: COVID-19 mRNA Vaccine Degradation Prediction
11784098,0.26139,2,7,/mahmudds/vaccine-degradation-prediction,OpenVaccine: COVID-19 mRNA Vaccine Degradation Prediction
11762828,0.3342699999999999,3,21,/timetraveller98/bert-for-mrna,OpenVaccine: COVID-19 mRNA Vaccine Degradation Prediction
11737433,0.28339,1,44,/hiroshun/pytorch-implementation-gru-lstm,OpenVaccine: COVID-19 mRNA Vaccine Degradation Prediction
11697211,0.28538,0,18,/eladwar/openvaccine-bert-model,OpenVaccine: COVID-19 mRNA Vaccine Degradation Prediction
11755153,0.42826,0,2,/eladwar/npys-model,OpenVaccine: COVID-19 mRNA Vaccine Degradation Prediction
11695930,0.25478,44,271,/tuckerarrants/openvaccine-gru-lstm,OpenVaccine: COVID-19 mRNA Vaccine Degradation Prediction
11682242,0.27024,33,291,/xhlulu/openvaccine-simple-gru-model,OpenVaccine: COVID-19 mRNA Vaccine Degradation Prediction
11679277,0.4782399999999999,0,7,/tuliofc/histgradientboosting-baseline,OpenVaccine: COVID-19 mRNA Vaccine Degradation Prediction
11686750,0.64728,2,8,/danofer/covid-rna-lstm-self-attention-keras-model,OpenVaccine: COVID-19 mRNA Vaccine Degradation Prediction
11250690,0.89484,0,0,/ctrasd123/panda-singlenet-submit,Prostate cANcer graDe Assessment (PANDA) Challenge
10374975,0.73,0,0,/duccongduong/15epoch-efficientnetb3-tts-inference,Prostate cANcer graDe Assessment (PANDA) Challenge
12940064,0.60278,1,1,/dkhos17/nlp-hw3,Quora Insincere Questions Classification
13020875,0.66996,1,0,/bignottirom/insincereclassification,Quora Insincere Questions Classification
11873684,0.53822,0,3,/maverickmonk94/qqic-ml-notebook,Quora Insincere Questions Classification
10840328,0.58505,1,14,/vchauhanusf/95-8-test-accuracy-quora-insincere-questions,Quora Insincere Questions Classification
10704056,0.51661,0,2,/skathirmani/bfl-ta-july-session1-part2,Quora Insincere Questions Classification
10394602,0.54279,0,2,/maksimbahdanchyk/nlp-trial,Quora Insincere Questions Classification
9518191,0.6623,0,0,/benjamingeyer/kernel5cd708ea59,Quora Insincere Questions Classification
9352386,0.48596,1,4,/xiu0714/ensemble-nb-lr-svm,Quora Insincere Questions Classification
2217448,0.6709999999999999,0,0,/amiryousefi1/third-model,Quora Insincere Questions Classification
5427442,0.59233,0,0,/vinaydoshi/baseline-model,Quora Insincere Questions Classification
12172511,0.06395,0,0,/anujsoni/notebook2db2bc75e9,COVID19 Global Forecasting (Week 4)
10751567,0.81605,0,0,/luyoucong/xgboost-covid19-week4,COVID19 Global Forecasting (Week 4)
9840859,0.0,0,0,/zhangdaotong/covid11,COVID19 Global Forecasting (Week 4)
9584847,0.55333,0,1,/nichasakulyuenyong/fe-panel-regression,COVID19 Global Forecasting (Week 4)
9242297,1.33343,0,0,/akhilsai11/kernel3061d7be2f,COVID19 Global Forecasting (Week 4)
9188648,0.29935,3,2,/vishumudgal/covid19-visuals-doubling-rate,COVID19 Global Forecasting (Week 4)
9119382,1.58575,0,0,/pragyarathore/data-visualization-and-fe,COVID19 Global Forecasting (Week 4)
2042438,0.5870000000000001,0,11,/christofhenkel/jeremys-nbsvm-baseline,Quora Insincere Questions Classification
2038535,0.076,2,14,/mihaskalic/have-you-tried-shallow-before-going-deep,Quora Insincere Questions Classification
2042581,0.615,0,1,/nikhilroxtomar/simple-starter-code,Quora Insincere Questions Classification
6778558,0.61222,0,0,/aaneloy/quora-sincere-final,Quora Insincere Questions Classification
5143549,0.65184,0,0,/adarshshift/quora-bilstm,Quora Insincere Questions Classification
3972995,0.53465,0,0,/soniyogesh/quora-insincere-mail,Quora Insincere Questions Classification
3674804,0.68816,0,0,/bharath25/quora-qt,Quora Insincere Questions Classification
3029785,0.7004600000000001,0,0,/oysiyl/107-place-solution-using-public-kernel,Quora Insincere Questions Classification
2820687,0.698,0,0,/qhd0081/fork-of-gamma-bianli-feature-1-1-i-16,Quora Insincere Questions Classification
2688864,0.7,0,0,/johnkyon/fork-from-bilstm-attention-kfold-0115-81a8d9,Quora Insincere Questions Classification
2545066,0.672,0,0,/xsakix/cnn-base-classifier-fold-meta,Quora Insincere Questions Classification
2456798,0.605,0,0,/xsakix/filter-bilstm-base-ntlk,Quora Insincere Questions Classification
2396553,0.531,0,0,/alexandruuu/glove-xgboost,Quora Insincere Questions Classification
2071937,0.537,0,0,/snehanshu17/quora-problem-naive-bayes,Quora Insincere Questions Classification
5820211,0.00631,5,15,/anlthms/resnet18-baseline-pytorch-two-path-network,Recursion Cellular Image Classification
5726908,0.005,0,6,/ymicky/pytorch-lightning-resnet34-baseline,Recursion Cellular Image Classification
4975600,0.201,9,40,/xhlulu/recursion-2-headed-efficientnet-2-stage-training,Recursion Cellular Image Classification
4868241,0.119,11,62,/yhn112/resnet18-baseline-pytorch-ignite,Recursion Cellular Image Classification
4561608,0.036,9,7,/alexanderkhar/transfer-learning-keras-starter-by-alex-khar,Recursion Cellular Image Classification
4564912,0.086,17,82,/leighplt/densenet121-pytorch,Recursion Cellular Image Classification
2095915,0.619,17,72,/shujian/transformer-initial-attempt,Quora Insincere Questions Classification
2111971,0.354,0,2,/donkeys/lda-feature-test,Quora Insincere Questions Classification
2067546,0.6579999999999999,1,5,/ghostiphate/capsule-net,Quora Insincere Questions Classification
2069316,0.657,0,0,/jinudaniel/lstm-word-embeddings-and-cnn,Quora Insincere Questions Classification
2064717,0.6729999999999999,8,137,/shujian/blend-of-lstm-and-cnn-with-4-embeddings-1200d,Quora Insincere Questions Classification
2049855,0.612,0,9,/rajatranjan/quora-simple-text-classification,Quora Insincere Questions Classification
2064329,0.67,17,92,/christofhenkel/inceptioncnn-with-flip,Quora Insincere Questions Classification
2058445,0.67,0,1,/abhibisht89/using-bi-directional-lstm-v1,Quora Insincere Questions Classification
2074649,0.653,1,3,/dkmerona/dpcnn-try,Quora Insincere Questions Classification
2073519,0.615,0,1,/shravankoninti/simple-code-lstm-using-keras,Quora Insincere Questions Classification
2066093,0.62,0,4,/salonsai/simple-text-eda-and-lgbm-baseline-lb-0-62,Quora Insincere Questions Classification
2060210,0.624,0,8,/nikhilroxtomar/lstm-cnn-1d-with-lstm-attention,Quora Insincere Questions Classification
2038977,0.642,24,286,/mihaskalic/lstm-is-all-you-need-well-maybe-embeddings-also,Quora Insincere Questions Classification
2043119,0.6709999999999999,20,107,/yekenot/2dcnn-textclassifier,Quora Insincere Questions Classification
2038869,0.597,5,60,/christofhenkel/keras-starter,Quora Insincere Questions Classification
2041655,0.607,5,18,/stardust0/naive-bayes-and-logistic-regression-baseline,Quora Insincere Questions Classification
2042926,0.6559999999999999,4,16,/danofer/embeddings-baseline-65-lb,Quora Insincere Questions Classification
2045085,0.58,1,10,/youhanlee/cnn-1d-also-can-read-sentences,Quora Insincere Questions Classification
3497759,0.118,0,1,/hung96ad/restnet,Freesound Audio Tagging 2019
3494728,0.128,2,12,/stanislavblinov/keras-cnn-starter-0-128,Freesound Audio Tagging 2019
3491281,0.109,1,8,/jazivxt/bumblebee-s-radio,Freesound Audio Tagging 2019
11664959,0.43217,4,126,/artgor/openvaccine-eda-feature-engineering-and-modelling,OpenVaccine: COVID-19 mRNA Vaccine Degradation Prediction
11683986,0.28559,0,8,/kaushal2896/openvaccine-ensemble,OpenVaccine: COVID-19 mRNA Vaccine Degradation Prediction
11692537,0.2898,0,13,/ajaykumar7778/gru-model,OpenVaccine: COVID-19 mRNA Vaccine Degradation Prediction
11688434,0.32676,0,9,/ajaykumar7778/fastai-tabular-covid,OpenVaccine: COVID-19 mRNA Vaccine Degradation Prediction
11674527,0.34345,0,8,/doanquanvietnamca/resnet18-mini-baseline-inference,OpenVaccine: COVID-19 mRNA Vaccine Degradation Prediction
11664516,0.4751,2,22,/kaushal2896/openvaccine-xgboost-baseline,OpenVaccine: COVID-19 mRNA Vaccine Degradation Prediction
11666534,0.49499,1,12,/carlmcbrideellis/baseline-mean-values-gaussian-noise-0-495,OpenVaccine: COVID-19 mRNA Vaccine Degradation Prediction
12073905,0.25905,0,0,/akashsuper2000/open-vaccine-pytorch-ii,OpenVaccine: COVID-19 mRNA Vaccine Degradation Prediction
11667844,0.30103,13,189,/isaienkov/openvaccine-eda-feature-engineering-modeling,OpenVaccine: COVID-19 mRNA Vaccine Degradation Prediction
9832458,1.15544,0,4,/nkoprowicz/time-series-at-the-store-level-divide-by-item,M5 Forecasting - Accuracy
9864553,0.62097,2,6,/urayukitaka/m5-forecast-lgbm-model,M5 Forecasting - Accuracy
9824200,0.65782,7,14,/shin0000/m5-lightgbm-baseline,M5 Forecasting - Accuracy
9829419,0.45863,3,5,/santosh8896/m5-dark-witch-time-by-store,M5 Forecasting - Accuracy
9756353,0.58826,0,5,/ryohgy/simple-lgbm,M5 Forecasting - Accuracy
9504182,0.68053,0,2,/allenljw/158755-m5-forecasting,M5 Forecasting - Accuracy
9373643,0.62746,0,0,/azupero/m5-baseline,M5 Forecasting - Accuracy
9469785,0.63884,0,3,/robertburbidge/state-space-model-and-lstm,M5 Forecasting - Accuracy
4903653,0.009,0,3,/joshi98kishan/blindness-detection-fastai,APTOS 2019 Blindness Detection
4991098,0.633,0,0,/ssoni0924/kernel3fede292d2,APTOS 2019 Blindness Detection
4837452,0.696,4,33,/abhinand05/blindness-detection-complete-pytorch-training,APTOS 2019 Blindness Detection
4896201,0.421,2,34,/kmader/attention-inceptionv3-for-blindness,APTOS 2019 Blindness Detection
4852363,0.716,19,67,/dimitreoliveira/diabetic-retinopathy-shap-model-explainability,APTOS 2019 Blindness Detection
4888273,0.6890000000000001,0,1,/vikasmalhotra08/aptos19-resnet50-old-and-new-data-inference,APTOS 2019 Blindness Detection
4778001,0.777,38,160,/chanhu/eye-inference-num-class-1-ver3,APTOS 2019 Blindness Detection
4808049,0.672,0,0,/bohraboxer/image-classification,APTOS 2019 Blindness Detection
4815311,0.528,0,0,/hackstock/aptos-baseline-submission,APTOS 2019 Blindness Detection
4814940,0.022,0,0,/inukaiko/kernel33e2064065,APTOS 2019 Blindness Detection
4654359,0.0,2,7,/naumov1889/keras-cnn,APTOS 2019 Blindness Detection
4752912,0.746,13,35,/jtbontinck/cnn-xgb-end-to-end-0-11,APTOS 2019 Blindness Detection
4670197,0.05,4,7,/spaceman04/diabetic-retinopathy,APTOS 2019 Blindness Detection
4692976,0.715,34,98,/bharatsingh213/keras-resnet-tta,APTOS 2019 Blindness Detection
4697074,0.693,2,7,/sidhanthholalkere/keras-vgg19-starter,APTOS 2019 Blindness Detection
4677483,0.693,28,70,/hmendonca/aptos19-regressor-fastai-oversampling-tta,APTOS 2019 Blindness Detection
2336004,0.0579,0,1,/shyamspr/random-forest-approach,PUBG Finish Placement Prediction (Kernels Only)
2353073,0.06366,0,0,/armanhak/pubg-gamers-place-prediction,PUBG Finish Placement Prediction (Kernels Only)
2332829,0.0592,0,0,/nikkisharma536/pubg-winner,PUBG Finish Placement Prediction (Kernels Only)
2084747,0.0725,0,3,/nikhilbhaskar16/pubg-player-rankings,PUBG Finish Placement Prediction (Kernels Only)
2295891,0.0589,0,0,/yanyiming137/regression,PUBG Finish Placement Prediction (Kernels Only)
2278039,0.05281,5,8,/nhlr21/lgbm-simple-fast-solution,PUBG Finish Placement Prediction (Kernels Only)
2092190,0.0566,0,0,/yururoi/pubg-winplaceperc-predict,PUBG Finish Placement Prediction (Kernels Only)
2258574,0.1823,0,0,/hyerimpak/eveeve2,PUBG Finish Placement Prediction (Kernels Only)
2078014,0.0589,0,0,/yanyiming137/feature-analysis-and-linear-regression,PUBG Finish Placement Prediction (Kernels Only)
2193572,0.0646,0,0,/beaubellamy/pubg-feature-engineering-matchtype-gb,PUBG Finish Placement Prediction (Kernels Only)
2215876,0.0638,0,0,/vanin66/kernel11d512c5a2,PUBG Finish Placement Prediction (Kernels Only)
2187843,0.0914,0,1,/naveenkumawat/pubgwithkushal-linearregression-neuralnetwork,PUBG Finish Placement Prediction (Kernels Only)
2174015,0.0698,0,1,/chicagojosh/pubg-placement-using-xgboost,PUBG Finish Placement Prediction (Kernels Only)
2123889,0.0569,0,0,/stupidbot/pubg-winplaceperc-predictor,PUBG Finish Placement Prediction (Kernels Only)
2157701,0.0571,0,2,/teemingyi/pubg-lightgbm-model-by-maxplace,PUBG Finish Placement Prediction (Kernels Only)
2153435,0.0959,3,4,/sdip28/pubg-exploratory-data-analysis-prediction,PUBG Finish Placement Prediction (Kernels Only)
2085804,0.183,0,3,/joocheol/q20181124,PUBG Finish Placement Prediction (Kernels Only)
2059080,0.0247,0,3,/powercode/sklearn-mlp,PUBG Finish Placement Prediction (Kernels Only)
2091337,0.0453,0,0,/justinmatters/pubg-predictions-1-random-forest,PUBG Finish Placement Prediction (Kernels Only)
2083257,0.0679,4,6,/dineshmk594/pubg-eda-vif-gradient-boosting,PUBG Finish Placement Prediction (Kernels Only)
2034004,0.0579,1,11,/nitinaggarwal008/simple-random-forest-approach-0-06,PUBG Finish Placement Prediction (Kernels Only)
1914151,0.0564,0,1,/shashanksagarjha/pugb-xgboost,PUBG Finish Placement Prediction (Kernels Only)
13899760,0.91103,4,4,/benamarareda/predit-sales,Predict Future Sales
14141710,1.4069200000000002,0,0,/ibrahimaltay/171307067,Predict Future Sales
13921460,2.37252,0,1,/shengy90/predict-future-sales-date-feature-engineering,Predict Future Sales
13623795,0.9009,1,0,/obougacha/coursera-challenge,Predict Future Sales
13696435,0.94809,0,0,/sergeybakhterev/notebook82d3b7afa8,Predict Future Sales
13337154,0.9324,0,0,/speeddemon/004-xgb,Predict Future Sales
3279187,0.8559899999999999,0,0,/muthuraja/red-hat-muthuraja,Predicting Red Hat Business Value
136206,0.89243,0,0,/sharmin/minus1v1,Predicting Red Hat Business Value
12602937,0.7509999999999999,0,0,/saijasthi/fork-of-baseline,Riiid Answer Correctness Prediction
13735391,0.705,0,0,/fjodorfomin/riid-random-forest,Riiid Answer Correctness Prediction
13590231,0.772,6,39,/scaomath/riiid-sakt-train-with-a-warm-up-scheduler,Riiid Answer Correctness Prediction
13560290,0.762,25,52,/mlanddl/riiid-catboost-baseline,Riiid Answer Correctness Prediction
13498413,0.774,41,108,/manikanthr5/riiid-sakt-model-training-public,Riiid Answer Correctness Prediction
13466013,0.5,0,3,/ccliu7/riiid-model,Riiid Answer Correctness Prediction
13458937,0.774,17,75,/manikanthr5/riiid-ensemble-lgbm-sakt-inference,Riiid Answer Correctness Prediction
13449402,0.772,23,113,/zephyrwang666/riiid-lgbm-bagging2,Riiid Answer Correctness Prediction
13440951,0.76,0,9,/satorushibata/lightgbm-with-the-inference-empirical-analysis,Riiid Answer Correctness Prediction
13358959,0.748,0,2,/zekun98/baseline-for-riiid-lightgbm,Riiid Answer Correctness Prediction
13298952,0.6890000000000001,0,0,/dennischhun/project-ii-1,Riiid Answer Correctness Prediction
13266275,0.7040000000000001,8,19,/andleebhayath/lgb-catboost-random-forest-and-xgboost,Riiid Answer Correctness Prediction
13279668,0.758,1,17,/sahilmaheshwari/making-ensemble-of-catboost-and-lgbm,Riiid Answer Correctness Prediction
13297155,0.705,0,3,/poesia/riiid-submissions,Riiid Answer Correctness Prediction
12237408,0.662,0,1,/diamondsnake/riiid-answer-correctness-prediction,Riiid Answer Correctness Prediction
12839793,0.753,1,10,/ammarnassanalhajali/riiid-answer-correctness-prediction-lgbm,Riiid Answer Correctness Prediction
12571705,0.7120000000000001,0,2,/yumiyashi131/model-focusing-on-parts-and-difficulties,Riiid Answer Correctness Prediction
13138258,0.7440000000000001,0,1,/daihaoxue/test-version,Riiid Answer Correctness Prediction
13146328,0.765,30,144,/wangsg/a-self-attentive-model-for-knowledge-tracing,Riiid Answer Correctness Prediction
11147176,0.8867,0,5,/krisho007/melanoma-with-pytorch-lightning-single-model,SIIM-ISIC Melanoma Classification
11110277,0.9458,15,39,/hiramcho/melanoma-efficientnetb6-with-attention-mechanism,SIIM-ISIC Melanoma Classification
10893635,0.8604,0,1,/jpremkaggle/mobilenet-weighted-27423,SIIM-ISIC Melanoma Classification
11079492,0.8617,2,13,/zainahmad/melanoma-classification-transfer-learning-with-tf,SIIM-ISIC Melanoma Classification
11090569,0.9031,0,3,/jaronthompson/pytorch-efficient-net-with-2019-2020-isic-data,SIIM-ISIC Melanoma Classification
10754210,0.9122,0,0,/nvnvashisth/pytorch-efficientnet-b0-gpu,SIIM-ISIC Melanoma Classification
11025610,0.9581,16,122,/khoongweihao/post-processing-technique-c-f-1st-place-jigsaw,SIIM-ISIC Melanoma Classification
11036237,0.7867,0,1,/bootiu/melanoma-pytorch-lightning-starter,SIIM-ISIC Melanoma Classification
11014261,0.9103,2,17,/wrrosa/gcs-bucket-addresses-utility-package,SIIM-ISIC Melanoma Classification
10388277,0.921,0,2,/tikoboss/effb7-rn152v2-inceptionv3,SIIM-ISIC Melanoma Classification
11012404,0.6985,1,9,/abhijeetbhilare/siim-isic-melanoma-xgboost,SIIM-ISIC Melanoma Classification
10921645,0.8096,1,2,/praveenthenraj/melanomaclassification-inceptionv3-gpu,SIIM-ISIC Melanoma Classification
10914123,0.4773,6,9,/timothyalexjohn/melanoma-classification-challenge,SIIM-ISIC Melanoma Classification
10942614,0.9486,11,29,/bryanb/let-the-magic-begin,SIIM-ISIC Melanoma Classification
10922958,0.8831,1,20,/ipythonx/tresnet-hp-gpu-dedicated-net-grad-accumulation-tta,SIIM-ISIC Melanoma Classification
10955390,0.5938,0,0,/lucca9211/kernel31e535c36e,SIIM-ISIC Melanoma Classification
10881387,0.927,4,10,/urvishp80/images-and-metadata-in-single-efnet-b6,SIIM-ISIC Melanoma Classification
10837120,0.9161,57,144,/cdeotte/tfrecord-experiments-upsample-and-coarse-dropout,SIIM-ISIC Melanoma Classification
136060,27.0492,7,36,/nitinvijay23/predict-the-crime-category-knn-logistic,San Francisco Crime Classification
2606287,1514960.71,8,35,/khahuras/super-fast-cumsum-trick-8th-place-demo-solution,Traveling Santa 2018 - Prime Paths
2600851,1515564.4,4,36,/golubev/reversing-and-shifting,Traveling Santa 2018 - Prime Paths
2605109,1515555.94,0,5,/therealroman/blending-public-kernels,Traveling Santa 2018 - Prime Paths
2556116,1515559.71,0,2,/zfturbo/not-a-5-and-5-halves-opt-0efc12,Traveling Santa 2018 - Prime Paths
2535372,1515651.4,0,1,/zfturbo/local-optimization-using-google-or-tool,Traveling Santa 2018 - Prime Paths
2526943,1515581.38,1,39,/jsaguiar/pseudo-k-opt,Traveling Santa 2018 - Prime Paths
2464947,1516094.59,25,113,/kostyaatarik/not-a-3-and-3-halves-opt,Traveling Santa 2018 - Prime Paths
2348168,1516697.75,0,18,/katsuomi/santa,Traveling Santa 2018 - Prime Paths
2307163,1516728.97,1,32,/jazivxt/winter-avalanche-2,Traveling Santa 2018 - Prime Paths
2199714,1533500.57,4,19,/jasonduncanwilson/exploring-nearest-neighbor-assignment-optimization,Traveling Santa 2018 - Prime Paths
2166785,1762074.05,1,9,/nonreviad/reverse-best-first-search-and-simulated-annealing,Traveling Santa 2018 - Prime Paths
2280205,1533155.03,3,7,/amanpradhan123/kernel5b6bfdf263,Traveling Santa 2018 - Prime Paths
2218123,1518296.59,15,98,/blacksix/concorde-for-5-min,Traveling Santa 2018 - Prime Paths
2216242,1524495.86,6,13,/kostyaatarik/concorde-solver-with-scaling-and-visualizations,Traveling Santa 2018 - Prime Paths
2191274,1812550.5,5,20,/haightdj/helping-santa-eda-simple-solutions,Traveling Santa 2018 - Prime Paths
2165706,1532809.25,13,70,/thexyzt/xyzt-s-visualizations-and-various-tsp-solvers,Traveling Santa 2018 - Prime Paths
3496431,0.923,2,7,/chizhu2018/augment-catv2,Santander Customer Transaction Prediction
3551633,0.92128,0,9,/sagol79/catboost-magic-starter,Santander Customer Transaction Prediction
3351866,0.7759999999999999,0,0,/jaredturner/sgd-classifier-no-probs,Santander Customer Transaction Prediction
3408576,0.9,0,0,/wlakinsson/santander-testing-pipeline,Santander Customer Transaction Prediction
3552509,0.92452,27,108,/titericz/giba-single-model-public-0-9245-private-0-9234,Santander Customer Transaction Prediction
3514586,0.893,4,57,/fuhang/weighted-kernel-naive-bayes-with-lasso-elimination,Santander Customer Transaction Prediction
3376647,0.898,1,2,/darbin/lgb-on-pca-features,Santander Customer Transaction Prediction
3511847,0.899,0,0,/lzsraja/kernal-fe-gp-lgbm-keras,Santander Customer Transaction Prediction
3522465,0.9,0,0,/leonyang/blending-all,Santander Customer Transaction Prediction
3496100,0.887,0,0,/erseler/simplest-way-to-get-lb-score-0-887-gaussiannb,Santander Customer Transaction Prediction
3524334,0.512,0,1,/manishks/kernelee24d12c85,Santander Customer Transaction Prediction
3334365,0.606,0,1,/matthew886/deep-learning,Santander Customer Transaction Prediction
3244678,0.603,0,0,/samueltommzy/ctprediction-model,Santander Customer Transaction Prediction
3308611,0.545,0,0,/nandishsr/santander-customer-transaction-prediction,Santander Customer Transaction Prediction
3468762,0.899,1,2,/dude431/is-there-any-magic-for-cat,Santander Customer Transaction Prediction
3440473,0.684,0,1,/mkowoods/rfc-baseline,Santander Customer Transaction Prediction
3428434,0.901,1,40,/josh24990/santander-customer-transaction-prediction,Santander Customer Transaction Prediction
3402001,0.846,2,3,/karangautam/oversampling-the-input,Santander Customer Transaction Prediction
12399300,0.12758,18,45,/ekrembayar/house-price-prediction-lgbm-lb-27,House Prices - Advanced Regression Techniques
12396164,0.00044,2,14,/kishkun/house-pricing-analysis-model,House Prices - Advanced Regression Techniques
12333322,0.12395,0,0,/mikhailmaksakov/house-pricing-2,House Prices - Advanced Regression Techniques
12341894,0.17098,0,1,/amarloni/housing-price-revision,House Prices - Advanced Regression Techniques
12022659,0.13065,0,8,/csvyas/the-25-club-4-simple-steps,House Prices - Advanced Regression Techniques
12292978,0.13086,22,20,/toyox2020/house-prices-simple-and-easy-3-steps-to-submit,House Prices - Advanced Regression Techniques
12248010,0.12822,0,4,/khangtran97/cs675-mid-term-project,House Prices - Advanced Regression Techniques
12267175,0.41502,0,1,/ayesha0616/hw-4-for-inst414,House Prices - Advanced Regression Techniques
12114613,0.14057,0,3,/abhayraghuwanshi/household-pricing,House Prices - Advanced Regression Techniques
12251648,0.13526,1,1,/yangzi33/housepriceprediction,House Prices - Advanced Regression Techniques
12232300,0.1285599999999999,1,0,/iet461/baseline,House Prices - Advanced Regression Techniques
12223383,0.21251,0,3,/andersonmatias/house-prices-competition,House Prices - Advanced Regression Techniques
12225938,0.2215599999999999,0,1,/naberhausj/ds-301-kaggle-competition,House Prices - Advanced Regression Techniques
4447135,0.9036,0,0,/pablocarrera/aerialcactusidentification,Aerial Cactus Identification
4429080,0.9501,0,0,/gstvolvr/aerial-cactus-identification-exploring-pytorch,Aerial Cactus Identification
4405683,0.8425,0,0,/yuriao/basic-cnn-classifier,Aerial Cactus Identification
4013407,0.9999,0,1,/tenffe/baseline-cactus-detection,Aerial Cactus Identification
4327776,0.9986,11,67,/abhinand05/in-depth-guide-to-convolutional-neural-networks,Aerial Cactus Identification
4335296,0.9943,0,0,/vishal03092000/aerial-cactus-identi-vgg16,Aerial Cactus Identification
4291439,0.9453,1,46,/ateplyuk/starter-pytorch,Aerial Cactus Identification
4295715,0.9959,0,2,/prabanch/aerial-cactus-identification-using-cnn,Aerial Cactus Identification
4241566,0.9996,1,6,/rhodiumbeng/aerial-cactus-identification-using-cnn,Aerial Cactus Identification
4279836,0.9985,0,0,/zanderpease/rc-cactus,Aerial Cactus Identification
4290402,1.0,0,9,/rhtsingh/fast-ai-cactus-identification-100,Aerial Cactus Identification
4146830,0.9808,7,16,/aleksandradeis/arial-cactus-identification-with-pytorch-and-vgg16,Aerial Cactus Identification
4159410,0.9999,0,2,/samshenvi/cactus-detection-using-fastai-resnet34,Aerial Cactus Identification
4177677,0.8547,0,2,/siddheshsathe/aerial-cactus-identification-using-vgg16,Aerial Cactus Identification
4169800,0.9999,2,2,/sandeepkumar121995/fastai-densenet161-roc-1-00,Aerial Cactus Identification
3646674,0.9986,0,0,/aakaashjois/has-cactus-with-fastai,Aerial Cactus Identification
4110329,0.9923,9,74,/ateplyuk/keras-starter-efficientnet,Aerial Cactus Identification
4156059,0.9997,0,0,/vijayabhaskar96/aerial-cactus-with-transfer-learning,Aerial Cactus Identification
4141242,0.9982,0,0,/umemiyaumeume/simple-cnn,Aerial Cactus Identification
3438952,0.9996,0,1,/muhamamdasim/cactusnet-in-tensorflow-with-augmentor,Aerial Cactus Identification
4094573,0.9611,1,1,/owiz21/kernelf6416c42b7,Aerial Cactus Identification
3987704,0.9516,0,0,/koushikcon/using-pytorch,Aerial Cactus Identification
3881509,0.9996,0,1,/lukzmu/aerial-cactus-classification,Aerial Cactus Identification
3999682,0.9996,0,1,/pdx250697/corrected-cnn,Aerial Cactus Identification
3986787,0.9965,0,1,/pdx250697/my-cnn-submisison,Aerial Cactus Identification
3973066,0.9933,0,2,/arcticmarmot/using-vgg16,Aerial Cactus Identification
3967219,0.9997,0,0,/gabrielfior/simple-fast-ai-v3-kernel,Aerial Cactus Identification
3885966,0.9946,3,5,/samarthsarin/simple-cnn-in-keras-from-scratch,Aerial Cactus Identification
3905669,1.0,0,2,/ganweifa123/simple-fastai-exercise-6500ee,Aerial Cactus Identification
3868073,0.9926,0,0,/ayush9398/aerial-cactus,Aerial Cactus Identification
6820936,0.00555,4,26,/carlolepelaars/understanding-the-metric-spearman-s-rho,Google QUEST Q&A Labeling
6801289,0.316,4,9,/abhikjha/bert-with-fastai,Google QUEST Q&A Labeling
6820347,0.367,27,340,/abhishek/distilbert-use-features-oof,Google QUEST Q&A Labeling
6783075,0.304,0,2,/viswajithkn/understand-q-a,Google QUEST Q&A Labeling
6768864,0.357,24,149,/abazdyrev/use-features-oof,Google QUEST Q&A Labeling
6781479,0.315,1,1,/aldrickpaul/simple-ltsm-model-keras-and-embedding-enrichment,Google QUEST Q&A Labeling
6754095,0.3289999999999999,14,73,/ldm314/universal-sentence-encoder-keras-nn,Google QUEST Q&A Labeling
6746650,0.33,2,32,/ryches/distillbert-tfidf-keras-dense-nn,Google QUEST Q&A Labeling
6740049,0.305,6,25,/labdmitriy/baseline-linear,Google QUEST Q&A Labeling
6732618,0.312,5,42,/pavelvpster/google-q-a-labeling-tf-idf-pytorch,Google QUEST Q&A Labeling
6734253,0.303,0,19,/corochann/tfidf-swem-approach-number-chars,Google QUEST Q&A Labeling
6731026,0.305,4,17,/hukuda222/tfidf-swem-approach,Google QUEST Q&A Labeling
6730939,0.29,0,11,/sediment/a-gentle-introduction-eda-tfidf-word2vec,Google QUEST Q&A Labeling
6731927,0.225,2,8,/kaushal2896/google-quest-q-a-labeling-eda-fe-modeling,Google QUEST Q&A Labeling
6730516,0.253,0,4,/enzoamp/nb-svm-strong-linear-baseline-w-category-dummies,Google QUEST Q&A Labeling
7882419,0.317,0,0,/nayuts/quest-bert-large-tf2-0,Google QUEST Q&A Labeling
4825369,0.20119,11,22,/nhlr21/tf-hub-bounding-boxes-coordinates-corrected,Open Images 2019 - Object Detection
430130,0.6259399999999999,0,1,/mahendrasinghmeena/notebookce982a6579,WSDM - KKBox's Music Recommendation Challenge
8614643,0.9926,0,0,/robbiebeane/cactus-identification-v01,Aerial Cactus Identification
4572918,0.9734,0,0,/anshu1man/aerial-cactus,Aerial Cactus Identification
11608091,0.9935,0,0,/ljh415/notebook543c6b8ad9,Aerial Cactus Identification
10045007,0.9993,0,0,/sumeetsawant/cactus-recognition-using-fastai,Aerial Cactus Identification
10161047,0.9393,0,0,/youngchuchu/200617-kaggle-classification-cactus-alexnet-yk,Aerial Cactus Identification
8790777,0.9925,0,0,/spanda2/cactus-identification-share,Aerial Cactus Identification
9232871,0.9966,1,1,/aisatriano/cactuses-for-fastai-resnet34-and-resnet50,Aerial Cactus Identification
8848389,0.9861,0,0,/hanhdao123/cactus-identification,Aerial Cactus Identification
4721251,0.9999,0,0,/akhileshk1/fastai-vision,Aerial Cactus Identification
8413985,0.988,0,0,/williamdahan/kernel7e8e86686d,Aerial Cactus Identification
7978715,0.9878,0,2,/viewside/fork-of-kernel69349e981c,Aerial Cactus Identification
3964497,0.9999,0,0,/ramkripal/cactus-classification-with-fastai,Aerial Cactus Identification
7627112,0.9982,0,0,/xaviersulkowski/aerial-cactus-identification-basics-with-keras,Aerial Cactus Identification
6932813,0.9963,0,2,/japanese910/aerial-cactus-identification,Aerial Cactus Identification
6831388,0.9981,0,0,/marcosbritobda/bda-2020,Aerial Cactus Identification
6855340,0.5,1,0,/renatovaladares/kernel516e9c8962,Aerial Cactus Identification
3365391,0.9999,0,1,/mnk812/aerial-keras-baseline,Aerial Cactus Identification
6470752,0.996,0,1,/stopmosk/cactus-pytorch-baseline,Aerial Cactus Identification
6233445,0.9999,0,0,/mgtrinadh/basic-fastai-model,Aerial Cactus Identification
6129739,0.9999,0,1,/poltigo/aerial-cactus-with-fastai-resnet50-model,Aerial Cactus Identification
4143001,0.9886,0,0,/hungtp/kernel-1,Aerial Cactus Identification
7350285,0.4629999999999999,0,0,/a763337092/bert-blending-v1,Google QUEST Q&A Labeling
7878477,0.4639999999999999,0,1,/serigne/bert-qa-deepanxiety-roberta-xlnet-tonghui-backup,Google QUEST Q&A Labeling
10896526,0.37182,0,1,/varunsaproo/google-q-a-using-lstm,Google QUEST Q&A Labeling
7963885,0.35772,0,0,/mathayus1729/kernel7de774bc1a,Google QUEST Q&A Labeling
9243995,0.38028,0,0,/rajprakhar/google-quest-q-a-labelling,Google QUEST Q&A Labeling
7786251,0.348,0,0,/manyregression/fastai-ulmfit-google-quest-sp,Google QUEST Q&A Labeling
7753553,0.38,0,0,/buntyshah/google-quest-q-a-labeling-hugging-face,Google QUEST Q&A Labeling
8120168,0.46893,0,3,/kashnitsky/google-quest-q-a-submit-from-a-csv-file,Google QUEST Q&A Labeling
7994218,0.2754,0,0,/adityaadarsh99/kernel4bf45b5b1a,Google QUEST Q&A Labeling
7899052,0.451,0,5,/phoenix9032/threshold-base-4toru,Google QUEST Q&A Labeling
7866125,0.439,0,7,/leonshangguan/roberta-single-models-with-optimization,Google QUEST Q&A Labeling
7357782,0.385,0,1,/tangchengshun/bert-tf2-0-eda,Google QUEST Q&A Labeling
7912583,0.391,0,1,/tangchengshun/bert-base-pretrained-models2-0,Google QUEST Q&A Labeling
7920976,0.44128,0,3,/rashmibanthia/googlequest-inference,Google QUEST Q&A Labeling
7867192,0.38231,0,1,/zzuczy/bert-base-ensemble-pytorch,Google QUEST Q&A Labeling
7686088,0.349,0,0,/daigohirooka/tfbert-and-use-feature,Google QUEST Q&A Labeling
7613444,0.4,0,0,/vinaydoshi/tfbert-ensemble-preprocess-v1,Google QUEST Q&A Labeling
7018718,0.439,0,1,/alexeykarnachev/kernel1864bcfc13,Google QUEST Q&A Labeling
7421449,0.231,0,0,/buntyshah/google-quest-q-a-labeling-bert-regression,Google QUEST Q&A Labeling
7837902,0.2269999999999999,0,0,/volody/bert-demo-v2,Google QUEST Q&A Labeling
7505237,0.327,0,2,/tridungduong16/preprocessing-feature-engineering-finetune-bert,Google QUEST Q&A Labeling
12612721,0.9552,1,2,/dcpatton/td-fraud-detector-nn,TalkingData AdTracking Fraud Detection Challenge
7599892,0.88899,0,0,/atashnezhad/xgboost-hyperparameters-and-ai,TalkingData AdTracking Fraud Detection Challenge
6207294,0.8959999999999999,0,5,/atashnezhad/fraud-detection,TalkingData AdTracking Fraud Detection Challenge
1936927,0.8658872999999999,0,0,/boomberung/click-explorational-lightgbm,TalkingData AdTracking Fraud Detection Challenge
1187671,0.8741598,1,1,/kylingu/talkingdata-adtracking-learning,TalkingData AdTracking Fraud Detection Challenge
894810,0.9549,0,0,/mohamedeltair/tair95,TalkingData AdTracking Fraud Detection Challenge
728751,0.9574,0,1,/aishanhang/xgboost-3-13,TalkingData AdTracking Fraud Detection Challenge
842792,0.9778,11,36,/aharless/simple-linear-stacking-with-ranks-lb-0-9760,TalkingData AdTracking Fraud Detection Challenge
824376,0.9551,0,0,/tomekkorbak/lightgbm-experiments,TalkingData AdTracking Fraud Detection Challenge
13196157,0.13235,5,11,/kamakshisoni/comprehensive-eda-and-prediction,House Prices - Advanced Regression Techniques
13124179,0.13066,8,18,/naninm/houses-price-prediction-top-3,House Prices - Advanced Regression Techniques
13018851,0.12916,0,0,/yansayfullin/house-prices-lightgbm-and-xgboost,House Prices - Advanced Regression Techniques
12879134,0.14125,0,0,/eclipser33/dendrite-net-for-regression,House Prices - Advanced Regression Techniques
12954982,0.20959,0,0,/donaldcth/linerregression-ver-1-3,House Prices - Advanced Regression Techniques
12932854,0.15894,0,4,/namankohliml/randomforest-for-noobs,House Prices - Advanced Regression Techniques
12747276,0.14232,0,0,/batprem/house-price-model,House Prices - Advanced Regression Techniques
3219508,0.12507,0,0,/gawarek/linear-and-logistic-regression,House Prices - Advanced Regression Techniques
3338094,0.773,0,2,/frommer/dnn-santa,Santander Customer Transaction Prediction
3307848,0.898,0,3,/drdivya/santandar-baseline-model-light-gbm,Santander Customer Transaction Prediction
3329271,0.706,0,1,/interneuron/simply-sorting,Santander Customer Transaction Prediction
3326329,0.888,1,1,/jurajm/simple-and-quick-solution-with-naive-bayes,Santander Customer Transaction Prediction
3299499,0.899,8,18,/zxspectrum/catboost-gpu,Santander Customer Transaction Prediction
3275737,0.9,3,7,/bejeweled/stats-features-lgbm,Santander Customer Transaction Prediction
3282832,0.9,0,7,/mailyousufkhan/santander,Santander Customer Transaction Prediction
3275882,0.871,0,1,/sandeeppat/fastai-tabular-learner-baseline,Santander Customer Transaction Prediction
3143835,0.7879999999999999,12,25,/mikelkn/right-wrong-ways-to-oversample-for-beginners-0-788,Santander Customer Transaction Prediction
3303559,0.888,0,1,/selakavon/kernel476e3326b3,Santander Customer Transaction Prediction
3092056,0.9,6,9,/bharatsingh213/santander-customer-transaction-prediction,Santander Customer Transaction Prediction
3115058,0.8909999999999999,0,1,/vishalbajaj2000/santander-lgbm-baseline,Santander Customer Transaction Prediction
3063678,0.899,0,0,/apj1995/create-an-ensemble-model,Santander Customer Transaction Prediction
3166819,0.899,0,0,/drsergio/save-intermediate-results-for-stacking,Santander Customer Transaction Prediction
3196943,0.899,0,0,/jinudaniel/lightgbm-xgboost-and-catboost,Santander Customer Transaction Prediction
3175079,0.89102,14,43,/krithi07/logistic-to-lightgbm-for-beginners,Santander Customer Transaction Prediction
3220090,0.9,3,15,/lavanyadml/santander-ls,Santander Customer Transaction Prediction
3147612,0.899,0,1,/tomehta/adding-features-and-evaluation,Santander Customer Transaction Prediction
3214744,0.526,3,4,/sreekarchidurala/santander,Santander Customer Transaction Prediction
3202585,0.897,2,11,/qinhui1999/denoising-autoencoder-sample-v1,Santander Customer Transaction Prediction
3206626,0.887,0,0,/akihirosanada/201903102100test-various-models,Santander Customer Transaction Prediction
10159973,0.8334,0,0,/hyunkic/melanoma-cnn-approach-imgen,SIIM-ISIC Melanoma Classification
9972435,0.897,0,0,/krsnewwave/melanoma-modeling,SIIM-ISIC Melanoma Classification
12550935,0.8592,0,1,/marialundinbrenna/siim-melanoma-classification-modelling,SIIM-ISIC Melanoma Classification
12078830,0.5,2,2,/sd4321/latest-fastai-v2-training-inference-predictions,SIIM-ISIC Melanoma Classification
10455488,0.7979999999999999,0,0,/rohitagarwal08/kernel261e9f461f,SIIM-ISIC Melanoma Classification
10474374,0.94,0,0,/iamprateek/melanoma-tpu-efficientnet-256,SIIM-ISIC Melanoma Classification
13752832,0.74,0,0,/filipinogambino/riiid-lightgbm,Riiid Answer Correctness Prediction
13902271,0.775,0,0,/ydl1y17/riiid-model-lgbm,Riiid Answer Correctness Prediction
13737716,0.502,0,1,/edgarandres/first-approach,Riiid Answer Correctness Prediction
14130019,0.7929999999999999,2,4,/markwijkhuizen/riiid-training-and-prediction-using-a-state-final,Riiid Answer Correctness Prediction
13220361,0.799,4,47,/tomooinubushi/62nd-solution-lightgbm-single-model-lb-0-801,Riiid Answer Correctness Prediction
13742604,0.758,0,0,/qiujiahui/riiid,Riiid Answer Correctness Prediction
13192211,0.6920000000000001,1,0,/jarupula/riiid-answer-prediction-beginner-base-model,Riiid Answer Correctness Prediction
13988285,0.794,5,13,/tkyiws/single-lgb-model-with-about-23-features,Riiid Answer Correctness Prediction
13949696,0.799,3,12,/m10515009/saint-is-all-you-need-inference-private-0-801,Riiid Answer Correctness Prediction
14102696,0.792,0,4,/nicohrubec/single-fold-lgb-794-inference,Riiid Answer Correctness Prediction
13937275,0.7859999999999999,8,5,/shinomoriaoshi/riiid-saint,Riiid Answer Correctness Prediction
14016911,0.807,1,3,/iivvaann22001188/transformer-encoder-only,Riiid Answer Correctness Prediction
13490306,182.32,0,1,/kristiinakeps/batch-pred,Lyft Motion Prediction for Autonomous Vehicles
13480553,246.251,0,0,/kristiinakeps/batch-test,Lyft Motion Prediction for Autonomous Vehicles
12908759,13.353,1,10,/doanquanvietnamca/22st-solution-kkiller,Lyft Motion Prediction for Autonomous Vehicles
12001416,25.742,0,1,/akashsuper2000/lyft-prediction-with-multi-mode-confidence,Lyft Motion Prediction for Autonomous Vehicles
12204091,6267.024,1,0,/shanyaanand/conv-attn-lstm,Lyft Motion Prediction for Autonomous Vehicles
11369887,7360.228,0,0,/gamebo11/lyft-basic-visualizations,Lyft Motion Prediction for Autonomous Vehicles
11798635,7925.81,0,3,/minhtam/lyft-eda-pytorch-baseline-multi-mode-train-eval,Lyft Motion Prediction for Autonomous Vehicles
11501084,82.24600000000002,16,30,/lucabergamini/lyft-baseline-09-02,Lyft Motion Prediction for Autonomous Vehicles
11600245,1172.262,8,32,/mclikmb4/lyft-pytorch-efficientnet-b0-tpu,Lyft Motion Prediction for Autonomous Vehicles
11574693,46.18899999999999,2,18,/mekhdigakhramanian/lyft-eda-ensemble-top-lb,Lyft Motion Prediction for Autonomous Vehicles
11366593,1.0435,3,6,/diksha659/predict-future-sales,Predict Future Sales
10943635,2.08376,1,3,/iekaterina/lstm-and-just-one-feature-tensorflow,Predict Future Sales
11154919,0.94363,1,5,/jswxhd/preicting-sales-with-xgboost,Predict Future Sales
11063801,1.88059,0,4,/razanabudagen/future-sales-prediction-using-xgbregressor,Predict Future Sales
11053752,1.94655,0,0,/kamiljan/kernel375f4c9b20,Predict Future Sales
9443477,1.72914,0,3,/ramadianri/predict-future-sales,Predict Future Sales
10533990,0.8930899999999999,2,9,/naimur978/who-ll-sell-the-most,Predict Future Sales
10507969,0.94667,2,5,/juandag97/xgboost-jd,Predict Future Sales
1849304,0.0699,0,1,/kanji10494/simple-mlp-regressor-using-pytorch,PUBG Finish Placement Prediction (Kernels Only)
1830417,0.0353,6,6,/roelvisser/chicken-dinner-in-the-random-forest,PUBG Finish Placement Prediction (Kernels Only)
1844931,0.2356,0,0,/ashutoshdutt/newbie-pubgplacement,PUBG Finish Placement Prediction (Kernels Only)
1837074,0.3781,3,5,/ananthreddy/simple-model-using-keras,PUBG Finish Placement Prediction (Kernels Only)
1820380,0.0559999999999999,14,18,/srcecde/simple-eda-xgboost,PUBG Finish Placement Prediction (Kernels Only)
1832062,0.1008,0,1,/parmarsuraj99/pubg-pubg-finish-placement-prediction-test,PUBG Finish Placement Prediction (Kernels Only)
1827090,0.0626,1,2,/even311379/pubg-keras-mlp,PUBG Finish Placement Prediction (Kernels Only)
1828205,0.063,0,0,/robert76/lgbm-chicken-dinner,PUBG Finish Placement Prediction (Kernels Only)
1813761,0.048,12,31,/shahules/feature-engineering-and-model-stacking,PUBG Finish Placement Prediction (Kernels Only)
1799815,0.0496,0,12,/sidjhanji/pubg-kill-them-all,PUBG Finish Placement Prediction (Kernels Only)
1798611,0.0404,1,16,/sorashido/pubg-simple-lightgbm,PUBG Finish Placement Prediction (Kernels Only)
1808020,0.0493,0,5,/chiranjeevbit/pubg-finish-place-prediction-and-eda,PUBG Finish Placement Prediction (Kernels Only)
1799677,0.0492,2,11,/zhy450324080/comprehensive-eda-baseline-model,PUBG Finish Placement Prediction (Kernels Only)
1801997,0.1007,0,2,/xiaoazuzong/ridge,PUBG Finish Placement Prediction (Kernels Only)
1797883,0.0615,0,2,/fvendrameto/catboost-by-a-newbie,PUBG Finish Placement Prediction (Kernels Only)
14185453,0.0665,0,0,/nmsf2016027/nmsf2016027,PUBG Finish Placement Prediction (Kernels Only)
8150735,0.05819,0,0,/koreadataboy/pubg-model,PUBG Finish Placement Prediction (Kernels Only)
7506452,0.0249099999999999,0,0,/qingyuanwu/deep-neural-network,PUBG Finish Placement Prediction (Kernels Only)
3627832,0.05965,0,0,/adityadesai0199/pubg-prediction-1,PUBG Finish Placement Prediction (Kernels Only)
2585336,0.0597,0,0,/boksman/rf-naive,PUBG Finish Placement Prediction (Kernels Only)
2424942,0.0562,0,0,/jaehyeokjang/simple-lightgbm-regression,PUBG Finish Placement Prediction (Kernels Only)
5786891,0.01614,0,0,/vassalo/whale-data-augmentation,Quora Question Pairs
5774659,0.0016899999999999,0,0,/nelsongomesneto/whale-categorization,Humpback Whale Identification Challenge
776012,0.4215899999999999,9,45,/anezka/cnn-with-keras-for-humpback-whale-id,Humpback Whale Identification Challenge
67421,0.3034,0,0,/chenchaodev/expedia-160531-tony,Expedia Hotel Recommendations
63460,0.07947,0,0,/gautamsihag/withallattempt2-1,Expedia Hotel Recommendations
9372300,0.69767,0,4,/buin6319/m5-modeling,M5 Forecasting - Accuracy
13145446,0.55542,0,4,/nemuritarinai/m5-accuracy,M5 Forecasting - Accuracy
9296836,0.6756800000000001,0,2,/qkrwlsdn96/baseline-lstm-with-keras-0-8,M5 Forecasting - Accuracy
10373826,0.75541,0,0,/slashie/m5-forecast-all,M5 Forecasting - Accuracy
10727046,0.51327,0,0,/aakashveera/m5-accuracy-final,M5 Forecasting - Accuracy
10354426,0.74302,0,3,/rikdifos/timeseriessplit-cv-poisson,M5 Forecasting - Accuracy
9872539,0.58432,1,4,/mpware/prophet-top-down,M5 Forecasting - Accuracy
10307210,0.58057,0,2,/mrgrigorii/m5-forecasting-accuracy-v2,M5 Forecasting - Accuracy
12441268,0.25098,0,9,/kingychiu/mrna-base-degradation-keras-cnn-gcn-rnn,OpenVaccine: COVID-19 mRNA Vaccine Degradation Prediction
12214487,0.24283,0,32,/nyanpn/6th-place-cnn-gcn,OpenVaccine: COVID-19 mRNA Vaccine Degradation Prediction
12144958,0.25003,0,7,/yakuben/openvaccine-89th-place-single-model,OpenVaccine: COVID-19 mRNA Vaccine Degradation Prediction
12129291,0.2482599999999999,0,2,/manchunhui/openvaccine-gru-lstm-hyptuned,OpenVaccine: COVID-19 mRNA Vaccine Degradation Prediction
11999170,0.24818,0,1,/akashsuper2000/autoencoder-pretrain-gnn-attn-cnn,OpenVaccine: COVID-19 mRNA Vaccine Degradation Prediction
12028549,0.39488,0,0,/philboaz/open-vacine-3009,OpenVaccine: COVID-19 mRNA Vaccine Degradation Prediction
12014963,0.2492199999999999,0,0,/gdonchyts/gru-lstm-with-features-aug-error,OpenVaccine: COVID-19 mRNA Vaccine Degradation Prediction
11872373,0.2466199999999999,4,0,/snakayama/se-wave-block-cbr-cudnngru-cudnnlstm-epoch60,OpenVaccine: COVID-19 mRNA Vaccine Degradation Prediction
12051212,0.43126,0,1,/josecarmona/openvaccine-simple-eda,OpenVaccine: COVID-19 mRNA Vaccine Degradation Prediction
11980471,0.26428,8,67,/reppic/neighborhood-attention-architecture,OpenVaccine: COVID-19 mRNA Vaccine Degradation Prediction
11973017,0.27074,0,10,/jagdmir/covid-19-mrna-vaccine-gru,OpenVaccine: COVID-19 mRNA Vaccine Degradation Prediction
11981923,0.27436,4,45,/yosukeyama/lightgbm-augment-bpps-optuna-tuning,OpenVaccine: COVID-19 mRNA Vaccine Degradation Prediction
12013192,0.2615099999999999,0,0,/michaelpearce2/openvaccine-rnn-models,OpenVaccine: COVID-19 mRNA Vaccine Degradation Prediction
11991521,0.27591,0,0,/josecarmona/openvaccine-v-0-3-10-bigrulstm-approach,OpenVaccine: COVID-19 mRNA Vaccine Degradation Prediction
11869940,0.25185,1,23,/kank2130/covax-gru-lstm,OpenVaccine: COVID-19 mRNA Vaccine Degradation Prediction
10752278,0.85429,0,0,/asiaahmedabushawish/leaf-classification,Leaf Classification
10909005,0.8096899999999999,0,2,/islammohaisen/leaf-classification-rfc,Leaf Classification
10902632,0.94922,0,2,/ahmedalghaliz/leaf-classification,Leaf Classification
10787832,0.85429,0,2,/fatimaafifi/leaf-classification,Leaf Classification
10701245,0.65328,0,0,/nahedalsharif/leaf-classification-ml-classic-classification-xgb,Leaf Classification
10679030,0.65266,0,0,/doaajaber/leaf-classification,Leaf Classification
4988607,0.98204,0,1,/venkatarathnam/leaf-classification,Leaf Classification
2192961,0.92036,0,0,/dhiru29/leaf-classification,Leaf Classification
1244380,0.13061,1,0,/zhoulingyan0228/lda-nn-on-leaf-classification,Leaf Classification
805328,0.77423,0,1,/peeyushsahu/classification-of-leaf,Leaf Classification
581682,0.01909,0,1,/mark13juna/keras-model,Leaf Classification
204156,4.60832,0,1,/ypargaien/keras-convnet-lb-0-0052-w-visualization,Leaf Classification
7175503,0.70004,0,0,/jagjitshinde/kernel448ff7655d,Quora Insincere Questions Classification
2620933,0.696,0,2,/jmourad100/nlp-text-analytics-quora-insincere-questions,Quora Insincere Questions Classification
6793617,0.65574,0,0,/gershond5/proyecto-3-emergente-bugallo-douek,Quora Insincere Questions Classification
6684483,0.62166,0,0,/mariocatapano/proyecto-3-quora-rnn,Quora Insincere Questions Classification
6783539,0.65145,0,1,/ivanpbf/proyecto-3-montenegro-loscher,Quora Insincere Questions Classification
6697614,0.67548,0,3,/gaokun227/a-fast-solution-with-lstm-in-25min-using-gpu,Quora Insincere Questions Classification
6628175,0.68034,0,0,/aminnasim/amin-nasim-saravi,Quora Insincere Questions Classification
6488457,0.69926,0,1,/baharsp/pytorch-starter,Quora Insincere Questions Classification
6310573,0.6441600000000001,0,0,/raqeeb/quora-wordembedding,Quora Insincere Questions Classification
5858349,0.69892,0,0,/therabiulawal/insincere-approach-2,Quora Insincere Questions Classification
2327809,0.67,0,0,/smitpatel/single-arch-no-threshold-v1,Quora Insincere Questions Classification
2470973,0.604,0,1,/agtadarsh/tfidf-linearsvc-metafeatures,Quora Insincere Questions Classification
2719263,0.654,0,0,/agtadarsh/kernel3a4631cfcd,Quora Insincere Questions Classification
2655390,0.662,0,0,/rishabhjain2764/word-embeddings-with-glove,Quora Insincere Questions Classification
2672548,0.703,0,0,/cchyun/qiqc-pytorch-2,Quora Insincere Questions Classification
2403237,0.67709,0,0,/tgregory98/quora-notebook-glove-embedding-lstm,Quora Insincere Questions Classification
2829468,0.69763,0,1,/theoviel/quora-final-submission,Quora Insincere Questions Classification
9123572,0.01135,0,0,/pragyarathore/model-3-xgb-regression,COVID19 Global Forecasting (Week 4)
8980923,0.01135,0,0,/vipultawde/regression-no-external-dataset,COVID19 Global Forecasting (Week 4)
8921864,0.08045,0,4,/wuhong60909/nonlinear-regression-gompertz-model-2020-4-22,COVID19 Global Forecasting (Week 4)
8859456,0.04431,0,1,/marwaguemira/model-xgbr,COVID19 Global Forecasting (Week 4)
9083705,0.33582,0,0,/yougeshr/kernel58bbd19f1f,COVID19 Global Forecasting (Week 4)
8840864,0.10192,0,1,/marwa01/dsanet-additional-data,COVID19 Global Forecasting (Week 4)
8994422,0.01135,0,3,/harshilgohel/covid-19-forecasting,COVID19 Global Forecasting (Week 4)
8885812,6e-05,0,11,/muhakabartay/covid-19-forecasting-xgboost-week-4-30dace,COVID19 Global Forecasting (Week 4)
8899460,0.70401,1,0,/garg1072/covid-week-4,COVID19 Global Forecasting (Week 4)
8934855,4.201169999999999,0,8,/anshuls235/covid19-predictions-week4,COVID19 Global Forecasting (Week 4)
8866643,0.34601,0,0,/taohoang/fbprophet,COVID19 Global Forecasting (Week 4)
8950679,0.50565,0,2,/sasrdw/gbt5e,COVID19 Global Forecasting (Week 4)
8918903,0.0610799999999999,0,3,/huyquoctrinh/huy-late,COVID19 Global Forecasting (Week 4)
8931549,0.0375,0,1,/belgrader/covid-19-week-4,COVID19 Global Forecasting (Week 4)
8861344,0.68078,0,0,/seuvitor/covid-19-week-4,COVID19 Global Forecasting (Week 4)
8937435,0.2604,3,0,/akimiyano/v1-covid19-global-forecasting-week-4,COVID19 Global Forecasting (Week 4)
8880217,0.78199,0,0,/mohres/covid-19-week-4,COVID19 Global Forecasting (Week 4)
8937240,0.76261,0,0,/jawahm/tutorial-run,COVID19 Global Forecasting (Week 4)
8921800,0.6110399999999999,0,1,/syedrizvi786/fork-of-week-4,COVID19 Global Forecasting (Week 4)
8927136,0.6270899999999999,0,1,/esotericazzo/xgbregressor-model,COVID19 Global Forecasting (Week 4)
8951238,0.50565,0,13,/sasrdw/gbt5f,COVID19 Global Forecasting (Week 4)
8850711,0.1638099999999999,0,0,/tcwilliams/covid-forecasting-week-4-w-xgboost,COVID19 Global Forecasting (Week 4)
8947045,0.03472,0,0,/rohanpotru/iterative,COVID19 Global Forecasting (Week 4)
8833835,0.13207,0,0,/lamothy/covid19-week4,COVID19 Global Forecasting (Week 4)
8833092,0.23795,0,0,/vivek2589/covid-19-prediction-may-week-2,COVID19 Global Forecasting (Week 4)
8926005,0.06384,0,1,/digimagi/sarimax-model-for-week-4,COVID19 Global Forecasting (Week 4)
8878722,0.04981,0,0,/vipultawde/covid-polynomial-regression,COVID19 Global Forecasting (Week 4)
8913805,0.03853,0,0,/rohitmidha23/covid4-stats,COVID19 Global Forecasting (Week 4)
8947225,0.03929,0,0,/zhengli0817/lroger-covid19-global-forecasting-week-4-kaz,COVID19 Global Forecasting (Week 4)
8950550,0.11427,0,1,/anatidae/kernel5907f32e13,COVID19 Global Forecasting (Week 4)
8915038,0.26869,0,0,/ronakbadhe/logistic-xgboost,COVID19 Global Forecasting (Week 4)
8894256,0.33503,0,0,/andrewshevelev/kernel561e84a523week4forsber,COVID19 Global Forecasting (Week 4)
8947932,3.2859199999999995,0,0,/paulorzp/covid-19-forecasting-week-4-ensemble,COVID19 Global Forecasting (Week 4)
8933086,1.40952,0,0,/mmfaus/covid-arima-peak-finder,COVID19 Global Forecasting (Week 4)
8856576,0.05202,0,1,/seshurajup/covid19-week-4,COVID19 Global Forecasting (Week 4)
8907831,0.03563,0,0,/georbuz/dsanet-approach-70385d,COVID19 Global Forecasting (Week 4)
8839244,2.74436,0,0,/mehdi16/week-4-tree-regressor,COVID19 Global Forecasting (Week 4)
8910707,0.07858,0,1,/mubarak2000/baseline,COVID19 Global Forecasting (Week 4)
8834713,0.0918799999999999,1,2,/ahmedewida/covid-19-week4-lstm-ridge,COVID19 Global Forecasting (Week 4)
8947915,0.11245,0,2,/christofhenkel/cv19w4-full-pdd-oscii-david-vopa-sub2,COVID19 Global Forecasting (Week 4)
8939972,0.0381699999999999,0,2,/gaborfodor/covid-19-w4-finally-some-ml,COVID19 Global Forecasting (Week 4)
8870049,0.03436,0,2,/syzymon/covid19-fast-ai-tabnet,COVID19 Global Forecasting (Week 4)
8837299,0.71281,0,1,/mathurinache/mathurin-week4-prevision1,COVID19 Global Forecasting (Week 4)
8877386,0.03333,0,0,/ludovicoristori/covid-fc-wk4-version-2-polynomial-fit-xgb,COVID19 Global Forecasting (Week 4)
8948613,0.05193,0,0,/yatinece/ensemble-trmf-and-r,COVID19 Global Forecasting (Week 4)
8828632,0.03572,0,1,/yustasalex/covid19-gf-week4-sarimax,COVID19 Global Forecasting (Week 4)
8861086,0.0343399999999999,3,10,/benbla/covid-19-linear-regression-and-arima,COVID19 Global Forecasting (Week 4)
8948118,0.04365,0,0,/skorsun/covid19-week4-exponential,COVID19 Global Forecasting (Week 4)
8944305,3.2859199999999995,1,0,/rohanrao/covid-19-w4-lgb-mad,COVID19 Global Forecasting (Week 4)
8832114,4.59195,0,1,/cmanning/covid-svd-ets-wk4,COVID19 Global Forecasting (Week 4)
8946896,3.2859199999999995,0,0,/andrekos/covid-19-forecasting-week-4,COVID19 Global Forecasting (Week 4)
8897052,0.04478,0,1,/abiolasalau/simple-covid19-week-4-prediction-with-xgbregressor,COVID19 Global Forecasting (Week 4)
8879524,1.40121,0,0,/hsuanchunlin/lstm-practice,COVID19 Global Forecasting (Week 4)
8843678,0.0464199999999999,0,0,/alexitkes/covid-week-4-linear-countries,COVID19 Global Forecasting (Week 4)
8946110,0.0343399999999999,0,1,/dmitri9149/kernel17c4d0094e-sarima,COVID19 Global Forecasting (Week 4)
8945088,0.17887,0,0,/ericfreeman/osciiart-with-some-new-features,COVID19 Global Forecasting (Week 4)
4757550,0.67348,1,1,/diabliyo12/proyecto-3,Quora Insincere Questions Classification
2625250,0.6920000000000001,0,0,/vectors/quoraversion-1,Quora Insincere Questions Classification
4682813,0.6674899999999999,0,1,/arturojose19/proyecto-3-quora,Quora Insincere Questions Classification
2102779,0.631,0,0,/darshanadiga/quora-lstm-v1,Quora Insincere Questions Classification
3297090,0.5274399999999999,0,0,/phanireddy04/phani-quora,Quora Insincere Questions Classification
4229926,0.62834,0,1,/quasikris/question-classification-quora,Quora Insincere Questions Classification
3973007,0.5334,0,0,/cavishek/quora-insincere-questions-classification,Quora Insincere Questions Classification
3972996,0.53465,0,0,/abhishekavula/kernelfa11833d59,Quora Insincere Questions Classification
3972987,0.53465,0,0,/ashish981/quora-textmining,Quora Insincere Questions Classification
2556559,0.6940000000000001,0,0,/pavelholubik/capsulenet,Quora Insincere Questions Classification
3584290,0.54986,3,6,/shrutimechlearn/nlp-bag-of-words-full-concept-with-quora,Quora Insincere Questions Classification
2706928,0.6679999999999999,0,0,/naraque/bilstm-with-timedistributed,Quora Insincere Questions Classification
3495978,0.6614399999999999,0,1,/wei213/cnn-with-googlenews,Quora Insincere Questions Classification
3552617,0.4649,0,0,/aadilsrivastava01/data-exploration-base-line-model-qiqc,Quora Insincere Questions Classification
2807179,0.6940000000000001,0,0,/matsuik/maxlen70-ensemble-w-o-lstm-para-elu-optuna,Quora Insincere Questions Classification
3506042,0.5628,0,0,/mathorius/simple-insincere-text-classification,Quora Insincere Questions Classification
3110625,0.61326,0,0,/zeinabbanoo/textproc-keras,Quora Insincere Questions Classification
3444542,0.5152899999999999,1,0,/skathirmani/jigsaw-march-2019-2,Quora Insincere Questions Classification
2391050,0.624,0,0,/ordosnb/ben-s-quora-insincere-questions-classification,Quora Insincere Questions Classification
3356850,0.70029,1,1,/bruno16/fork-from-bilstm-attention-kfold-0115-81a8d-a9d9b1,Quora Insincere Questions Classification
3658757,0.596,0,0,/liamsch/simple-2d-cnn-classifier-with-pytorch,Freesound Audio Tagging 2019
4223043,0.667,0,1,/ashishsinhaiitr/2d-dense-cnn,Freesound Audio Tagging 2019
4202011,0.3229999999999999,1,2,/joelstan/lstm-with-custom-spectrograms,Freesound Audio Tagging 2019
4187857,0.046,0,0,/egorsp/best-earthquake-features-and-simple-lgbm,Freesound Audio Tagging 2019
4174022,0.654,2,13,/vinayaks/2d-cnn-pytorch,Freesound Audio Tagging 2019
4021701,0.636,3,40,/tanlikesmath/2d-cnn-high-score,Freesound Audio Tagging 2019
3990131,0.6729999999999999,19,30,/sailorwei/fat2019-2d-cnn-with-mixup-lb-0-673,Freesound Audio Tagging 2019
3879274,0.074,1,16,/baomengjiao/xgb-using-mfcc,Freesound Audio Tagging 2019
3592780,0.575,7,26,/toldo171/fat-2019-beginner-guide-with-starter-code,Freesound Audio Tagging 2019
3778103,0.632,30,82,/ratthachat/fat19-mixup-keras-on-preprocesseddata-lb632,Freesound Audio Tagging 2019
3770529,0.037,0,0,/christoffer/na-ve-label-based-baseline-curated-only,Freesound Audio Tagging 2019
3699366,0.367,0,2,/steer70/kernel-convolution1d-naive,Freesound Audio Tagging 2019
3593246,0.30905,0,9,/talmanr/cnn-with-pytorch-using-mel-features,Freesound Audio Tagging 2019
3609209,0.203,4,23,/titericz/lightgbm-simple-solution-lb-0-203,Freesound Audio Tagging 2019
3574401,0.467,8,29,/daisukelab/cnn-2d-basic-2-preprocessed-dataset-noisy,Freesound Audio Tagging 2019
3574862,0.356,0,2,/kbhartiya83/sound-recogniser-bidirectionallstm-attention,Freesound Audio Tagging 2019
3490452,0.174,0,14,/ashirahama/simple-keras-cnn-with-mfcc,Freesound Audio Tagging 2019
62571,0.22175,0,0,/bagriaditya/withall,Expedia Hotel Recommendations
54995,0.18872,0,1,/jwegas/randomforest-test-20160418,Expedia Hotel Recommendations
2304079,0.027,0,0,/chittu2794/chaitanya-s-notebook-kernel,PUBG Finish Placement Prediction (Kernels Only)
2270767,0.0587,0,0,/benalbert/jaded-bayes-pubg-placement-predictions,PUBG Finish Placement Prediction (Kernels Only)
2214849,0.0476,0,0,/mattburt07/pubg-data-analysis-using-random-forest-approach,PUBG Finish Placement Prediction (Kernels Only)
2090727,0.0579999999999999,1,0,/kvkr1997/nn-by-ghost-x,PUBG Finish Placement Prediction (Kernels Only)
1955030,0.0596,0,0,/dangvinh/first-trial-with-gradient-boosting-for-raw-data,PUBG Finish Placement Prediction (Kernels Only)
1870687,0.0758,0,0,/ravi2512/pubg-nn-second,PUBG Finish Placement Prediction (Kernels Only)
1850825,0.0786,0,0,/mapharazzo/fork-of-kernelf1a1176982,PUBG Finish Placement Prediction (Kernels Only)
4700139,0.633,6,6,/akash2sharma/aptos-keras-starter-densenet,APTOS 2019 Blindness Detection
4693166,0.713,0,4,/waseeqshiekh/intro-aptos-diabetic-retinopathy-eda-starter,APTOS 2019 Blindness Detection
4697415,0.001,0,0,/snakayama/vgg16-keras,APTOS 2019 Blindness Detection
4668973,0.665,4,11,/akash2sharma/aptos-keras-starter-resnet,APTOS 2019 Blindness Detection
4622764,0.618,4,24,/tanlikesmath/training-on-previous-dataset-for-aptos,APTOS 2019 Blindness Detection
4571749,0.439,0,0,/ibansalankit/aptos-blindness-detection,APTOS 2019 Blindness Detection
4576737,0.597,0,1,/mayankkestwal10/aptos-diabetic-retinopathy,APTOS 2019 Blindness Detection
4628712,0.7090000000000001,13,32,/yonghan/aptos-keras-baseline-tta,APTOS 2019 Blindness Detection
4607433,0.7490000000000001,10,18,/tanlikesmath/aptos-cv-inference,APTOS 2019 Blindness Detection
4565557,0.013,0,0,/venkateshprabhug/diabetic-retinopathy-classifier-starter,APTOS 2019 Blindness Detection
4586691,0.695,20,67,/dimitreoliveira/aptos-blindness-detection-eda-and-keras-resnet50,APTOS 2019 Blindness Detection
4570903,0.203,2,1,/dipeshpoudel/blindness-detection-using-cnn,APTOS 2019 Blindness Detection
4576559,0.67,0,0,/khayashima/pytorch-eda-and-augmentation-v1,APTOS 2019 Blindness Detection
4556402,0.6859999999999999,27,164,/mathormad/aptos-resnet50-baseline,APTOS 2019 Blindness Detection
4558626,0.192,4,72,/ateplyuk/aptos-keras-starter,APTOS 2019 Blindness Detection
4558128,0.018,14,46,/ashwan1/understanding-kappa-using-dummy-classifier,APTOS 2019 Blindness Detection
4567989,-0.005,0,12,/kageyama/keras-blindness-detection-simple-cnn,APTOS 2019 Blindness Detection
4574346,0.042,0,1,/benoitcharmettant/basic-custom-cnn,APTOS 2019 Blindness Detection
4556608,0.0,4,24,/puremath86/visualization-starter,APTOS 2019 Blindness Detection
4560234,0.019,3,14,/iluvmahheart/simple-beginner-blindness-detector,APTOS 2019 Blindness Detection
11292720,0.98164,0,0,/jessicakoehnke/final-submission,Predict Future Sales
11720654,0.90976,0,0,/fguinier/amd-predict-future-sale-eda-and-data-preparation,Predict Future Sales
12277186,0.90684,0,1,/abhishekanand101/copied-notebook-predict-future-sales,Predict Future Sales
12221149,1.00399,5,14,/orhankaramancode/eda-with-plotly-and-reframing-the-time-series,Predict Future Sales
12128912,0.89338,0,0,/taidopurason/lightgbm-model,Predict Future Sales
11815296,0.92847,1,3,/namylase/future-sales-xgb-first-sale-features,Predict Future Sales
9996855,0.97008,0,2,/indermohanbains/future-sales-prediction,Predict Future Sales
11665505,0.95047,0,4,/marcelotsj/fe-for-time-series-and-lgbm,Predict Future Sales
11658081,3.10522,0,1,/hanantabak/future-sale-prediction-using-xgbregressor,Predict Future Sales
11437010,0.86459,23,44,/uladzimirkapeika/feature-engineering-lightgbm-top-1,Predict Future Sales
13325115,9.28258,0,0,/abhayraghuwanshi/crime-master-gogo,San Francisco Crime Classification
12200035,2.47648,0,0,/streetlamb/ensemble-with-xgboost-and-random-forest,San Francisco Crime Classification
6080549,2.29943,0,0,/seahur/crime-detection,San Francisco Crime Classification
6626888,10.38844,0,0,/dimple2801/kernel716f5771ac,San Francisco Crime Classification
5185632,2.67064,0,0,/sjun4530/crime-classification-basic,San Francisco Crime Classification
8074226,2.35941,0,1,/doublepoi/lightgbm-test-4,San Francisco Crime Classification
6338229,2.3586400000000003,0,0,/michelmelo/san-francisco-crime,San Francisco Crime Classification
6145423,2.61346,0,1,/rgoodman/sf-crime,San Francisco Crime Classification
4313280,2.24911,0,1,/shah01/mysol,San Francisco Crime Classification
3070507,2.53257,0,6,/hamzael1/sf-crimes-prediction-walkthrough-with-xgboost,San Francisco Crime Classification
1980829,2.71003,0,3,/luisfredgs/classifica-o-de-crimes-em-san-fran-projeto-am,San Francisco Crime Classification
1525349,3.66386,0,0,/impratiksingh/sf-crime-classification-beginner-tutorial,San Francisco Crime Classification
922922,2.46104,0,2,/kullapat/random-forest-model-prediction,San Francisco Crime Classification
12968683,0.27588,0,0,/donaldcth/naive-bayes-ver-1-5,House Prices - Advanced Regression Techniques
12852063,0.1241299999999999,0,0,/virtual888/house-prices-prediction-with-lightgbm-lb-21,House Prices - Advanced Regression Techniques
12838131,0.12284,1,0,/virtual888/house-prices-prediction-with-xgboost-lb-15,House Prices - Advanced Regression Techniques
11144533,0.11853,0,0,/sarthak1799/houseprice17,House Prices - Advanced Regression Techniques
12811814,0.14242,0,0,/virtual888/lr-with-elasticnet-regularization,House Prices - Advanced Regression Techniques
12749418,0.13179,0,3,/alistairdouglas/house-pricing-prediction-ensembling,House Prices - Advanced Regression Techniques
12754553,1.5961,0,0,/dwz9406/house-prices-v2-lr-std,House Prices - Advanced Regression Techniques
12717884,0.12053,0,0,/owentann/advance-house-pricing-regression,House Prices - Advanced Regression Techniques
12645165,0.45633,0,1,/ravindravenkat/house-price-regression,House Prices - Advanced Regression Techniques
12505023,0.1226299999999999,1,3,/juneyao666/house-price-prediction-machine-learning,House Prices - Advanced Regression Techniques
3121013,0.899,0,0,/silvia09/sctp-test,Santander Customer Transaction Prediction
3130604,0.899,10,86,/super13579/pytorch-nn-cyclelr-k-fold-0-897-lightgbm-0-899,Santander Customer Transaction Prediction
3074239,0.885,0,0,/dineshzero/simple-lgb-model,Santander Customer Transaction Prediction
3152959,0.523,0,0,/luancaius/santander-transactions-v1,Santander Customer Transaction Prediction
3139134,0.8590000000000001,0,0,/ytochii/dnn-linear-batchnormalization-using-chainer,Santander Customer Transaction Prediction
3052581,0.9,4,58,/yuzusan/santander-draft-v3-eda-lgb-nn,Santander Customer Transaction Prediction
3102744,0.899,25,89,/frtgnn/santander-eda-prediction-augmentation,Santander Customer Transaction Prediction
3111007,0.894,1,2,/sandan0o0/lgb-regression,Santander Customer Transaction Prediction
3087296,0.8809999999999999,2,30,/qqgeogor/keras-nn-mixup,Santander Customer Transaction Prediction
3109875,0.868,0,0,/jacksonisaac/initial-eda-fastai-tabular-default-baseline,Santander Customer Transaction Prediction
3096547,0.8959999999999999,4,4,/mmreza/santander-lgb-with-kfold,Santander Customer Transaction Prediction
3087888,0.8909999999999999,2,6,/sarmat/lgbm-stacking-example,Santander Customer Transaction Prediction
3076395,0.7390000000000001,0,1,/amishgadigi/blind-xgboost-prediction,Santander Customer Transaction Prediction
3065739,0.851,2,2,/zakraicik/fast-ai-start-no-resample,Santander Customer Transaction Prediction
2163156,1533440.01,27,183,/wcukierski/concorde-solver,Traveling Santa 2018 - Prime Paths
2162079,1811964.66,13,81,/theoviel/greedy-reindeer-starter-code,Traveling Santa 2018 - Prime Paths
2170794,1533479.87,0,12,/ashishpatel26/travelling-santa-2018-using-tsp-solver-forked,Traveling Santa 2018 - Prime Paths
2154965,241118305.92,3,35,/afgonczol/an-easter-egg-this-christmas,Traveling Santa 2018 - Prime Paths
2154661,2135914.68,1,6,/nadare/zigzag-initialization-random-swap,Traveling Santa 2018 - Prime Paths
2418188,1515567.72,0,0,/zfturbo/close-ends-chunks-optimization-aka-2-opt,Traveling Santa 2018 - Prime Paths
11220007,0.99382,0,0,/vinesmsuic/digit-recognizer-cnn-data-augmentation,Digit Recognizer
12201447,0.99357,1,6,/bhaskar47/scoring-almost-100-on-mnist,Digit Recognizer
12294222,0.99564,5,7,/anantgupt/mnist-digit-recognition-99567-score,Digit Recognizer
12286315,0.9825,0,1,/dataislife8/easiest-cnn-digit-recognizer-0-998,Digit Recognizer
12145914,0.6984600000000001,0,0,/chumajin/digitrecognizer-practice,Digit Recognizer
12181194,0.73582,0,2,/gauravduttakiit/digit-recognizer-using-adaboostclassifier,Digit Recognizer
12110917,0.99478,0,2,/ssaisuryateja/digit-recognizer,Digit Recognizer
12107650,0.98957,1,6,/sachinsharma1123/mnist-for-freshers,Digit Recognizer
13792470,0.14,0,3,/binodsuman/cassava-leaf-disease-step-by-step-solution,Cassava Leaf Disease Classification
13660838,0.602,0,0,/ednelcha/notebook4f7f962c21,Cassava Leaf Disease Classification
13719141,0.139,0,0,/sandeepganesh049/submission-basic,Cassava Leaf Disease Classification
13619163,0.875,0,0,/matheusgelsdorf1/inference-notebook,Cassava Leaf Disease Classification
13512194,0.8,0,0,/karlisesoares/cassava,Cassava Leaf Disease Classification
13628296,0.875,7,21,/tuckerarrants/cassava-rapids-knn,Cassava Leaf Disease Classification
13665231,0.7609999999999999,0,0,/upmer11/submit,Cassava Leaf Disease Classification
13620114,0.888,0,2,/noelmat/tta-submission-kernel-resnet18-0-866-public,Cassava Leaf Disease Classification
13484450,0.884,0,0,/nikitasuharev/cassava-predict,Cassava Leaf Disease Classification
13556030,0.898,0,1,/ggyuki/pytorch-efficientnet-baseline-inference-tta,Cassava Leaf Disease Classification
13524177,0.86,6,6,/deepakat002/efficientnetb3-inference-0-860,Cassava Leaf Disease Classification
13077369,0.602,0,0,/kamleshshimpi78/easy-cassava-leaf-disease-detection-in-keras,Cassava Leaf Disease Classification
11150265,0.52735,0,16,/jagdmir/feature-selection-label-encoding-techniques,Categorical Feature Encoding Challenge II
10919821,0.7841,1,15,/jagdmir/categorical-feature-encoding-challenge-ii-model,Categorical Feature Encoding Challenge II
10308362,0.78437,0,1,/abisheksrivastav/categorical-feature-encoding-challenge-ii,Categorical Feature Encoding Challenge II
9111539,0.74222,0,0,/anarthal/classification-using-categorical-variables,Categorical Feature Encoding Challenge II
8183648,0.7492800000000001,0,0,/alynmi18/kernel131abd511f,Categorical Feature Encoding Challenge II
9230943,0.7858,0,0,/superant/oh-my-sklearn,Categorical Feature Encoding Challenge II
8314211,0.78568,0,1,/itsbitan/categorical-features-encoding,Categorical Feature Encoding Challenge II
8655845,0.78605,4,8,/robinmunier/categorical-feature-encoding-challenge-ii,Categorical Feature Encoding Challenge II
8598339,0.7803399999999999,4,8,/schorsi/fastai-tabular-for-cat-in-the-dat,Categorical Feature Encoding Challenge II
8578375,0.77885,2,4,/hahmed747/stacking-model-fastai2-nn-xgb-rf-lr-knn,Categorical Feature Encoding Challenge II
8545829,0.78284,5,9,/aitikgupta/my-first-kaggle-notebook,Categorical Feature Encoding Challenge II
8387218,0.7531899999999999,0,0,/apocryphon/categoricl-encoding-challenge-quick-look,Categorical Feature Encoding Challenge II
8524130,0.7805300000000001,3,14,/podsyp/catdata-eda-meanenc-logreg-l1-l2-enet,Categorical Feature Encoding Challenge II
8483707,0.78278,4,3,/pchlq82/simple-entity-embeddings-with-pytorch,Categorical Feature Encoding Challenge II
8437195,0.49973,0,0,/rahulvks/pca-smote-xgbclassifier,Categorical Feature Encoding Challenge II
8185336,0.5211100000000001,1,3,/gokalpumur/simple-pipeline-demo,Categorical Feature Encoding Challenge II
7836738,0.76662,0,12,/guidant/workflow-guide-pca-logistic-wide-and-deep-nn,Categorical Feature Encoding Challenge II
8088160,0.50365,0,2,/nickteim/simpel-model-for-cat,Categorical Feature Encoding Challenge II
8010054,0.7853899999999999,0,1,/horohoro/cat-feat-encoding2-try-xlearn,Categorical Feature Encoding Challenge II
7971750,0.71186,5,4,/vahidsa/simple-way-logisticregression-labelencoder,Categorical Feature Encoding Challenge II
7951757,0.78657,6,20,/pavelvpster/cat-in-dat-2-stack,Categorical Feature Encoding Challenge II
7914552,0.73134,1,0,/tadehalexani/cat-in-the-dat-ii-using-lr,Categorical Feature Encoding Challenge II
3921236,0.7490000000000001,0,2,/atrisaxena/facenet-baseline-in-keras,Northeastern SMILE Lab - Recognizing Faces in the Wild
4924207,0.903,1,1,/ranjitkumar1/recognizing-face-with-one-shot-learning,Northeastern SMILE Lab - Recognizing Faces in the Wild
5247106,0.893,0,7,/mattemilio/smile-best-who-smile-last,Northeastern SMILE Lab - Recognizing Faces in the Wild
4109379,0.8340000000000001,0,0,/heet2201/kinship-relationship-using-vggface,Northeastern SMILE Lab - Recognizing Faces in the Wild
4800449,0.899,3,15,/vaishvik25/blend-of-smiles,Northeastern SMILE Lab - Recognizing Faces in the Wild
4310979,0.875,6,10,/shivamsarawagi/wildimagedetection-0-875,Northeastern SMILE Lab - Recognizing Faces in the Wild
4377687,0.862,0,2,/castep/vggface,Northeastern SMILE Lab - Recognizing Faces in the Wild
4259973,0.6,0,0,/gokulanss/kernelsimplesiamese,Northeastern SMILE Lab - Recognizing Faces in the Wild
4247694,0.49,0,1,/gokulanss/kernelgoku,Northeastern SMILE Lab - Recognizing Faces in the Wild
4060666,0.787,5,13,/adityajn105/getting-started-with-vggface-0-787-lb,Northeastern SMILE Lab - Recognizing Faces in the Wild
3980611,0.5870000000000001,2,3,/hulkbulk/simple-siamese-neural-network-0-587lb,Northeastern SMILE Lab - Recognizing Faces in the Wild
3900890,0.529,1,7,/simonjonespu/facial-landmark-detection-and-triplet-loss,Northeastern SMILE Lab - Recognizing Faces in the Wild
3908098,0.7490000000000001,7,94,/suicaokhoailang/facenet-baseline-in-keras-0-749-lb,Northeastern SMILE Lab - Recognizing Faces in the Wild
563869,0.6631600000000001,0,0,/mightmay/mercari,Mercari Price Suggestion Challenge
636511,0.4627,0,0,/jug971990/xgboost-123,Mercari Price Suggestion Challenge
634420,0.46645,0,0,/virtonos/mercari,Mercari Price Suggestion Challenge
630494,0.8263799999999999,0,2,/michaelabehsera/mercari-first-test-try,Mercari Price Suggestion Challenge
644892,0.61698,0,0,/abiernacka/fork-of-biernacka-price-statistics-high-speed-na,Mercari Price Suggestion Challenge
606291,0.44572,0,1,/bkkaggle/mercari-rnn-ridge-99-train-0-15-to-1,Mercari Price Suggestion Challenge
587854,0.6934100000000001,0,0,/harpreetvishnoi/mercari-nn-keras,Mercari Price Suggestion Challenge
522172,0.73907,4,3,/anu0012/using-different-regression-techniques,Mercari Price Suggestion Challenge
578962,0.47835,7,10,/baghern/bayesian-optimization-of-ridge-model-0-478,Mercari Price Suggestion Challenge
568254,0.96158,0,3,/safinar/mercari-suggestion,Mercari Price Suggestion Challenge
575941,0.7881600000000001,0,0,/shwetasahu/fork-of-fork-of-price-prediction,Mercari Price Suggestion Challenge
570483,0.56191,0,2,/danielmarahrens/mercadi-baseline,Mercari Price Suggestion Challenge
562865,0.4439399999999999,8,21,/kenwat/single-lgbm-lb-0-44399-run-time-2687sec,Mercari Price Suggestion Challenge
553398,0.70373,0,1,/patrickhyland/pat-s-notebook,Mercari Price Suggestion Challenge
544228,0.5666,0,13,/mbkinaci/ann-with-keras-161-input-features,Mercari Price Suggestion Challenge
520937,1.5007700000000002,0,2,/karan1276/zombi,Mercari Price Suggestion Challenge
529816,0.4240899999999999,9,40,/nvhbk16k53/associated-model-rnn-ridge,Mercari Price Suggestion Challenge
521403,0.45106,1,5,/richinmind/kaggel-competition-tryout,Mercari Price Suggestion Challenge
514332,0.45618,0,1,/danofer/mercari-copycat,Mercari Price Suggestion Challenge
486696,0.68103,0,5,/rimunoz/explore-data,Mercari Price Suggestion Challenge
505019,0.55508,0,3,/luanho/random-forest-regressor-0-5-lb,Mercari Price Suggestion Challenge
489728,0.7127899999999999,0,0,/fangwc1226/pandas-dummy,Mercari Price Suggestion Challenge
486313,0.81401,0,2,/tao416/lgb-mercari,Mercari Price Suggestion Challenge
485075,0.4435199999999999,0,0,/jairomateo/mercari-copycat,Mercari Price Suggestion Challenge
483524,0.4723199999999999,0,0,/alifanov/lb-0-47232-validation-score-compare,Mercari Price Suggestion Challenge
31880,0.85359,0,0,/axplusb/baseline-uniform-predictions-0-85359,Airbnb New User Bookings
31420,0.67908,0,0,/niranjansonachalam/airbnb,Airbnb New User Bookings
27393,0.0,0,1,/retroam/airbnb,Airbnb New User Bookings
5880835,0.9381,0,2,/jaehyeongan/ieee-fraud-detection-lightgbm-0-938,IEEE-CIS Fraud Detection
5879132,0.9397,0,1,/denkimagic/remake-eda-and-models,IEEE-CIS Fraud Detection
5841049,0.945,0,2,/rafay12/is-it-really-fraud,IEEE-CIS Fraud Detection
5809658,0.9372,1,6,/sarmat/lgbm-permutation-importance,IEEE-CIS Fraud Detection
5808962,0.6932,1,3,/alexandrerays/simple-logistic-regression-baseline,IEEE-CIS Fraud Detection
5506934,0.7075,0,3,/barindersingh/fraud-detection,IEEE-CIS Fraud Detection
5612248,0.9077,6,27,/tharug/ieee-fraud-detection,IEEE-CIS Fraud Detection
5733395,0.5236,0,2,/plarmuseau/what-if-we-focus-on-the-user,IEEE-CIS Fraud Detection
5737448,0.9371,0,1,/sabiipoks/fraud-detection-benchmark-model,IEEE-CIS Fraud Detection
5710528,0.9525,1,17,/nolank/internal-blend-0-9525,IEEE-CIS Fraud Detection
5685240,0.9364,2,18,/kimchiwoong/simple-eda-ensemble-for-xgboost-and-lgbm,IEEE-CIS Fraud Detection
5388159,0.9344,0,0,/jacksmengel/fraud-detection,IEEE-CIS Fraud Detection
5582867,0.9518,29,197,/kyakovlev/ieee-internal-blend,IEEE-CIS Fraud Detection
12295627,0.6673899999999999,0,1,/nehalbandal/predicting-an-employee-s-access-needs,Amazon.com - Employee Access Challenge
10307360,0.6873600000000001,0,5,/vibeeshk/amazon-employees-access-prediction,Amazon.com - Employee Access Challenge
6220615,0.88702,3,15,/lucamassaron/deep-learning-for-tabular-data,Amazon.com - Employee Access Challenge
3389306,0.87977,5,35,/dmitrylarko/kaggledays-sf-1-amazon-baseline,Amazon.com - Employee Access Challenge
3527001,0.92266,3,28,/dmitrylarko/kaggledays-sf-4-amazon-end-to-end,Amazon.com - Employee Access Challenge
3111063,0.337,0,0,/edwardjross/neural-network-tabuler-vision-fastai-poc,PetFinder.my Adoption Prediction
3220256,0.391,0,4,/numarkevich/the-most-beautiful-kernel,PetFinder.my Adoption Prediction
3297809,0.344,5,26,/adityaecdrid/simple-xlearn,PetFinder.my Adoption Prediction
3256632,0.385,0,0,/juliusmannes/just-mlip-things-final-kernel,PetFinder.my Adoption Prediction
3292496,0.297,0,0,/kiaragitoto/simple-random-forest-try,PetFinder.my Adoption Prediction
3199843,0.1689999999999999,0,3,/alatortrix/practice-with-predictions,PetFinder.my Adoption Prediction
3164689,0.4529999999999999,21,139,/ranjoranjan/single-xgboost-model,PetFinder.my Adoption Prediction
3013851,0.433,1,6,/zhoujcc/3-21-lgb,PetFinder.my Adoption Prediction
3116401,0.3929999999999999,3,41,/cgundlach/keras-text-cnn-densenet121-image-features,PetFinder.my Adoption Prediction
3130611,0.319,0,0,/boulahiat/petfinder-prediction,PetFinder.my Adoption Prediction
3108458,0.439,15,20,/zhangyang/why-is-this-allowed-to-submit,PetFinder.my Adoption Prediction
3074669,0.349,1,11,/ksaaskil/pets-definitive-catboost-tuning,PetFinder.my Adoption Prediction
3075568,0.415,5,23,/hengzheng/lgb-bayesian-parameters-finding,PetFinder.my Adoption Prediction
2900843,0.45591,0,1,/guilhermekodama/eda-petfinder-competition-catboost-baseline,PetFinder.my Adoption Prediction
2937395,0.368,6,67,/alexandralorenzo/models-combination,PetFinder.my Adoption Prediction
2951476,0.258,1,1,/matthewarthur/fastai-tabular-dataloader-api-tutorial,PetFinder.my Adoption Prediction
2834293,0.41,0,12,/ashishpatel26/model-tuning-with-lgbm,PetFinder.my Adoption Prediction
2719149,0.287,0,0,/abhineethmishra/using-a-nn,PetFinder.my Adoption Prediction
2876680,0.335,0,0,/weegee/analysis-of-adopt-my-pet-data,PetFinder.my Adoption Prediction
2827011,0.417,6,32,/risntforpirates/petfinder-simple-lgbm,PetFinder.my Adoption Prediction
14387180,0.71944,10,20,/terrifictitan12/tabular-playground-submission,Tabular Playground Series - Jan 2021
14388835,0.69787,10,15,/hirazawahiroshi/visualization-tuning-ridge-lasso-xgboost,Tabular Playground Series - Jan 2021
14059185,0.70414,9,10,/brukselka/xgb-regressor,Tabular Playground Series - Jan 2021
14548094,0.727,3,9,/guecoraph/regression-project-on-tabular-data,Tabular Playground Series - Jan 2021
14593865,0.6957899999999999,3,6,/davidedwards1/jan21-tabular-playground-4-lb-final-blend,Tabular Playground Series - Jan 2021
14454005,0.6997800000000001,1,4,/tunguz/tps-jan-2021-with-histgradientboostingregressor,Tabular Playground Series - Jan 2021
14201098,0.69728,0,3,/maunish/tps-simple-ensemble,Tabular Playground Series - Jan 2021
14593174,0.69809,5,2,/davidedwards1/jan21-tabplayground-nn-final-fewer-features,Tabular Playground Series - Jan 2021
14401709,0.6984899999999999,0,2,/kingabzpro/mljar-autoeda-automl-lb-0-69849,Tabular Playground Series - Jan 2021
14464696,0.69703,0,1,/maostack/english-pseudo-labeling,Tabular Playground Series - Jan 2021
14498987,0.70001,0,1,/imsparsh/tabular-playground-series,Tabular Playground Series - Jan 2021
14152625,0.71355,0,0,/loycelorenzo/tabular-playground-series-jan-2021,Tabular Playground Series - Jan 2021
14411361,0.72783,0,0,/ctlockhart3/tabularplayground-january,Tabular Playground Series - Jan 2021
14163313,0.69729,0,0,/shemskurtoglu/tabular-00,Tabular Playground Series - Jan 2021
12605167,0.93295,0,5,/divyansh22/lgbm-classifier-ion-switch-5-groupkfold-strategy,University of Liverpool - Ion Switching
12532939,0.93378,2,5,/divyansh22/catboost-regressor-10-groupkfold-ion-switch,University of Liverpool - Ion Switching
9364094,0.926,0,0,/shanu1988/new-deep-nlp,University of Liverpool - Ion Switching
9781300,0.94404,0,14,/titericz/gpu-rf-xgb,University of Liverpool - Ion Switching
9764938,0.92647,5,3,/sheriytm/private-0-97266-a-better-but-useless-solution,University of Liverpool - Ion Switching
9618133,0.93196,4,30,/frtgnn/xgb-validation-using-rapids,University of Liverpool - Ion Switching
9553793,0.926,0,0,/josecarmona/test-of-transformer-over-data-without-drift,University of Liverpool - Ion Switching
9704717,0.9462,0,19,/vicensgaitan/2-wavenet-swa,University of Liverpool - Ion Switching
8955280,0.933,0,11,/frtgnn/simple-lgbm-5-fold-validation,University of Liverpool - Ion Switching
8955555,0.93442,3,9,/divyansh22/lgbm-regressor-ion-switch-10-groupkfold-strategy,University of Liverpool - Ion Switching
9309328,0.94,0,6,/vbmokin/wavenet-with-shifted-rfc-proba-cbr-fe-upgrade,University of Liverpool - Ion Switching
9734006,0.93441,0,0,/neomatrix369/16-features-model-from-54-features,University of Liverpool - Ion Switching
9601003,0.938,2,7,/amoiza1/simple-catboost-high-accuracy-0-938,University of Liverpool - Ion Switching
9324763,0.939,0,1,/shinogi/lgbm-optuna-wavenet-with-shifted-rfc-proba-and-cb,University of Liverpool - Ion Switching
9437059,0.935,2,12,/vzaguskin/gaussiannb-plus-markov-chain-rnn-like,University of Liverpool - Ion Switching
9341849,0.939,1,7,/ashoksrinivas/ion-switching-with-lgbm-0-939,University of Liverpool - Ion Switching
9239429,0.941,1,18,/khoongweihao/busu-net-with-tensorflow-keras,University of Liverpool - Ion Switching
3615745,0.4152399999999999,0,0,/rijuldhir/img-seg-new,Airbus Ship Detection Challenge
13366362,0.7338,0,0,/roohisharma/what-s-cooking-logistic-regression,What's Cooking?
12485223,0.7729199999999999,0,0,/ayusheeagarwal/what-s-cooking,What's Cooking?
10718177,0.7729199999999999,0,1,/teramera/kernel3c404ec4df,What's Cooking?
2512341,0.7581399999999999,0,1,/prithiviraj/cooking-dl-notebook,What's Cooking?
1885970,0.7945399999999999,0,0,/dimanpro/my-svm-test,What's Cooking?
487406,0.6864399999999999,0,3,/owlz84/classifying-cuisine-with-keras-and-glove,What's Cooking?
3451406,0.79324,0,0,/wowachicka/what-s-cooking,What's Cooking?
1940533,0.9738,0,0,/xinruchen/my-nlp-exercises,Toxic Comment Classification Challenge
1398399,0.6999,0,0,/itamarmushkin/basic-nlp-attempt-thanks-to-pydata-tlv,Toxic Comment Classification Challenge
1284341,0.903,2,1,/kmader/text-classification-with-qrnn,Toxic Comment Classification Challenge
544823,0.7440000000000001,0,0,/aquibjkhan/notebook43051938ae,Toxic Comment Classification Challenge
630248,0.9789,0,5,/gimunu/tf-idf-cv-tuned-logistic-regression,Toxic Comment Classification Challenge
743184,0.9615,13,14,/janhavip/destroyers-of-the-toxic,Toxic Comment Classification Challenge
553863,0.9822,0,3,/rohitag13/bidirectional-gru-cnn,Toxic Comment Classification Challenge
737284,0.983,0,20,/fizzbuzz/capsule-net-with-gru-on-preprocessed-data,Toxic Comment Classification Challenge
676146,0.9809,0,9,/mmpossi/logitcomment-preprocessing,Toxic Comment Classification Challenge
726596,0.9814,2,4,/shihabshahriar/pooledgru,Toxic Comment Classification Challenge
713883,0.9804,1,27,/chernyshov/logistic-regression-with-preprocessing,Toxic Comment Classification Challenge
705739,0.9841,46,125,/eashish/bidirectional-gru-with-convolution,Toxic Comment Classification Challenge
702691,0.9841,25,86,/konohayui/bi-gru-cnn-poolings,Toxic Comment Classification Challenge
698440,0.982,0,3,/qfzgs1994/pooled-gru-glove,Toxic Comment Classification Challenge
682866,0.8451,0,0,/ybaojia/simple-keywords-to-predict-toxic,Toxic Comment Classification Challenge
8617592,0.687,12,71,/yutanakamura/dear-pytorch-lovers-bert-transformers-lightning,Tweet Sentiment Extraction
8643617,0.6559999999999999,1,2,/phanisrikanth/pytorch-bert-w-sentiment-inference-lb-0-616,Tweet Sentiment Extraction
8574083,0.659,13,32,/rohitsingh9990/ner-inference-using-spacy-lb-0-628,Tweet Sentiment Extraction
8601629,0.299,1,2,/parmarsuraj99/bert-qna,Tweet Sentiment Extraction
8610476,0.4639999999999999,1,1,/samarthsarin/vader-sentiment-analysis-using-nltk,Tweet Sentiment Extraction
8559843,0.62,11,26,/shawon10/tweet-sentiment-eda-pre-processing-extractor,Tweet Sentiment Extraction
8563575,0.589,3,12,/khoongweihao/feature-engineering-lightgbm-model-starter-kit,Tweet Sentiment Extraction
8552280,0.594,4,10,/dimitreoliveira/tweet-sentiment-extraction-eda-and-baseline,Tweet Sentiment Extraction
13189975,0.63026,0,0,/hunnygupta7/assignment2-sol,Tweet Sentiment Extraction
10993205,0.609,0,0,/a529261027/bert-base-uncased-using-pytorch,Tweet Sentiment Extraction
10043211,0.594,0,0,/samanthaestrada/kernel6ac819e02f,Tweet Sentiment Extraction
9891351,0.711,0,0,/akashsuper2000/tse2020-roberta-cnn-outlier-analysis,Tweet Sentiment Extraction
8856754,0.594,1,0,/aayush29071996/start-from-here-complete-eda-baseline-sub,Tweet Sentiment Extraction
14210573,0.7751100000000001,0,0,/sohaelshafey/titanic-disaster-classification,Titanic - Machine Learning from Disaster
14245867,0.7799,0,3,/webermeng/titantic,Titanic - Machine Learning from Disaster
14284162,0.7751100000000001,0,0,/ndiaga/getting-started-with-titanic,Titanic - Machine Learning from Disaster
14271675,0.78708,0,1,/hananxx/titanic,Titanic - Machine Learning from Disaster
14301061,0.7799,0,0,/yyuan94/titanic-with-boosted-algorithm,Titanic - Machine Learning from Disaster
14255995,0.7799,6,5,/junghoonkim36/automl-tutorial-titanic-with-ray-tune,Titanic - Machine Learning from Disaster
14259485,0.75837,0,2,/mayurnawal/titanic-analysis-and-classification,Titanic - Machine Learning from Disaster
14316076,0.77751,0,1,/rat360/notebook8f88723d64,Titanic - Machine Learning from Disaster
14242857,0.78468,12,6,/yejining99/titanic-decision-tree-random-forest-logistic,Titanic - Machine Learning from Disaster
14245466,0.75598,0,0,/sanikad/titanic-eda-logistic-regression,Titanic - Machine Learning from Disaster
14260712,0.7751100000000001,0,0,/jasonng7/titanic-tutorial,Titanic - Machine Learning from Disaster
14220048,0.76076,0,0,/barretp/logistic-regression,Titanic - Machine Learning from Disaster
14206631,0.79665,3,15,/arindamg/titanic-xgboost-lightgbm,Titanic - Machine Learning from Disaster
14215580,0.75598,6,6,/youssefalsoufi/tuning-hyper-parameters-titanic-survived-label,Titanic - Machine Learning from Disaster
13772589,0.76315,0,0,/yutoricstar/titanic-practice,Titanic - Machine Learning from Disaster
14198732,0.78947,0,0,/blagoyh/deep-learning-on-small-datasets-78-9,Titanic - Machine Learning from Disaster
14109563,0.80143,0,15,/diulhio/titanic-trilha-de-ia-venturus,Titanic - Machine Learning from Disaster
11435032,0.70095,0,0,/anith88/titanic-randomforest,Titanic - Machine Learning from Disaster
13942261,0.77272,0,1,/ryokiti/titanic-ml-in-japanese,Titanic - Machine Learning from Disaster
8299841,1.0,9,20,/alvaroma/bert-abstraction,Abstraction and Reasoning Challenge
8225287,1.0,7,26,/hocop1/ca-cnn-with-adaptive-computation-time,Abstraction and Reasoning Challenge
8195638,1.0,0,2,/osciiart/arc-eda1,Abstraction and Reasoning Challenge
8104644,1.0,4,5,/nagiss/anything-is-allowed,Abstraction and Reasoning Challenge
8044047,1.0,4,28,/paulorzp/15-tasks-crop-resize-repeat,Abstraction and Reasoning Challenge
8045673,1.0,4,29,/danielbecker/arcifier,Abstraction and Reasoning Challenge
8039189,1.0,0,0,/grapestone5321/abstraction-and-reasoning-sample-submission,Abstraction and Reasoning Challenge
8001271,1.0,0,3,/safavieh/filler-starter-notebook,Abstraction and Reasoning Challenge
7818349,1.0,9,159,/inversion/abstraction-and-reasoning-starter-notebook,Abstraction and Reasoning Challenge
7959130,1.0,3,19,/pdx250697/abstraction-and-reasoning-starter-notebook,Abstraction and Reasoning Challenge
9729673,0.98,0,0,/akashsuper2000/fork-of-decision-tree-smart-data-augmentation,Abstraction and Reasoning Challenge
9107409,0.98,0,0,/akashsuper2000/ensemble-from-public-kernels,Abstraction and Reasoning Challenge
13445026,0.7377,0,0,/stormdiv/yolov5-pseudo-labeling,Global Wheat Detection
11034997,0.7573,0,0,/m1nglei/fork-of-fork-of-effdet-d5-inference-pseudo-len0,Global Wheat Detection
10993377,0.7702,0,0,/abdulbaseermohammed/yolov5-pseudo-labeling-oof-evaluation-abd2,Global Wheat Detection
10945066,0.71,0,0,/ufownl/global-wheat-detection-inference-608x608,Global Wheat Detection
10436807,0.6739,0,0,/hyeonggwon/pytorch-starter-fasterrcnn-inference-d28c5d,Global Wheat Detection
9592106,0.6688,1,2,/yangxiao945/mysubmit-kernel,Global Wheat Detection
9504008,0.7143,41,166,/nvnnghia/awesome-augmentation,Global Wheat Detection
9455523,0.5412,0,2,/mistag/object-detection-with-detecto-pytorch,Global Wheat Detection
9462460,0.6735,30,252,/shonenkov/wbf-approach-for-ensemble,Global Wheat Detection
9390850,0.6449,44,71,/ipythonx/keras-global-wheat-detection-with-mask-rcnn,Global Wheat Detection
9350547,0.6962,1,26,/gc1023/fork-of-fasterrcnn-pseudo-labeling,Global Wheat Detection
9310757,0.0039,1,3,/armin25/gwd-starter-gpu-v2-infer,Global Wheat Detection
13481558,0.68904,0,0,/arturszczotarski/kobe-bryant-shots,Kobe Bryant Shot Selection
10524387,18.81025,0,0,/adityaajay05/kobe-bryant-shot-selection,Kobe Bryant Shot Selection
4406497,0.78223,0,0,/jinxingw/kernel2a1f82dd2b,Kobe Bryant Shot Selection
1211380,0.62179,0,0,/myxue4869/kobe-grid-search-hyperparameter-tuning,Kobe Bryant Shot Selection
744052,0.7572800000000001,0,0,/sungminlee/simple-deep-learning-using-keras,Kobe Bryant Shot Selection
8481637,0.70662,0,0,/santitorresj/covid-19,COVID19 Global Forecasting (Week 1)
8482344,0.71849,0,6,/parask11/kernel1bdd665155,COVID19 Global Forecasting (Week 1)
8523457,1.36634,0,0,/akashsuper2000/sigmoid-per-country,COVID19 Global Forecasting (Week 1)
14242321,0.7703300000000001,0,0,/learningkeep/notebookf137cd47b2,Titanic - Machine Learning from Disaster
13563857,0.79904,2,2,/dheemanthbhat/titanic-mortality,Titanic - Machine Learning from Disaster
14160111,0.78468,6,7,/atultyagi2000/titanic-survival-prediction-using-logisticregressi,Titanic - Machine Learning from Disaster
14054037,0.77751,1,2,/klausbaer/exploring-the-titanic-dataset,Titanic - Machine Learning from Disaster
14154036,0.7751100000000001,0,0,/sophievincoff/getting-started-with-titanic,Titanic - Machine Learning from Disaster
14117377,0.74641,0,1,/shivashankar369/titanic,Titanic - Machine Learning from Disaster
14123238,0.78468,1,1,/phani3344/titanic,Titanic - Machine Learning from Disaster
14129375,0.78468,0,0,/hekkta/titanic-competition-mlp-and-random-forest,Titanic - Machine Learning from Disaster
14139420,0.7751100000000001,0,0,/kaggleganten/getting-started-with-titanic,Titanic - Machine Learning from Disaster
14119682,0.76555,0,3,/dkumar12/titanic-using-svc,Titanic - Machine Learning from Disaster
14110105,0.79425,1,8,/dkumar12/titanic-using-only-logistic-regression,Titanic - Machine Learning from Disaster
14096495,0.78947,18,16,/awwalmalhi/comprehensive-beginners-guide-to-top-6,Titanic - Machine Learning from Disaster
14170173,0.77272,0,1,/nilaykhare/notebook4667356020,Titanic - Machine Learning from Disaster
13978929,0.76555,0,0,/lllllwnnn/notebookb88395f3c6,Titanic - Machine Learning from Disaster
14093610,0.77751,3,7,/gurharkhalsa/beginner-random-forest-fastai-neural-net-top-14,Titanic - Machine Learning from Disaster
14149603,0.77272,0,0,/blackhurt/notebook3829555adb,Titanic - Machine Learning from Disaster
14032493,0.76555,28,41,/girayhakan/titanic-my-first-competition,Titanic - Machine Learning from Disaster
14142829,0.7799,0,2,/areezy/data-mining-project,Titanic - Machine Learning from Disaster
14087374,0.7751100000000001,0,0,/shaimaa1234/predict-survival-on-the-titanic-random-forest,Titanic - Machine Learning from Disaster
14012293,0.76315,16,40,/rafaelpleite/titanic-analysis-and-predict,Titanic - Machine Learning from Disaster
14061490,0.77751,0,0,/dagmbari/notebook2fe0151c93,Titanic - Machine Learning from Disaster
8958393,0.176,0,0,/plarmuseau/fork-of-kmeans-tfidf-clusters-for-prediction,Tweet Sentiment Extraction
8999688,0.664,0,1,/akshitsharma206/tweet-sentiment-extraction-starter-code-eda,Tweet Sentiment Extraction
8955751,0.6970000000000001,8,21,/rishabhdhiman/sentiment-analysis-distilbert-squad-q-a-tech,Tweet Sentiment Extraction
8878562,0.6409999999999999,0,1,/nrutyad/j013-j021-j040,Tweet Sentiment Extraction
8875117,0.649,0,0,/aryanshukla/kernel6325064838,Tweet Sentiment Extraction
8853327,0.445,0,0,/sitask/tweet,Tweet Sentiment Extraction
8879721,0.701,2,6,/riddhimehta1409/final-code,Tweet Sentiment Extraction
8877287,0.44,0,0,/hrithikashukla/nlp-m3-j043-j049-j050,Tweet Sentiment Extraction
8875271,0.5770000000000001,0,0,/sidhsatam/j022-j042-j060,Tweet Sentiment Extraction
8864464,0.649,0,0,/jenil1237/tweet-sentiment-extraction-j061-j057,Tweet Sentiment Extraction
8868309,0.685,0,0,/jonykarki/kernel333bf3821a,Tweet Sentiment Extraction
8877951,0.696,0,0,/harshg16/nlp-m3-j016-j017-final,Tweet Sentiment Extraction
8766704,0.7120000000000001,109,388,/abhishek/roberta-inference-5-folds,Tweet Sentiment Extraction
8758239,0.2689999999999999,1,5,/rhtsingh/twitter-sentiments-text-summarization-approach,Tweet Sentiment Extraction
8749970,0.0,2,10,/abhishek/bert-inference-5-folds,Tweet Sentiment Extraction
8704340,0.696,1,15,/sofieneressifi/twitter-sentiment-extraction-eda-and-modeling,Tweet Sentiment Extraction
8732296,0.6970000000000001,0,0,/viswajithkn/tweet-sentiment-analysis,Tweet Sentiment Extraction
8618917,0.6509999999999999,28,82,/nkoprowicz/a-simple-solution-using-only-word-counts,Tweet Sentiment Extraction
8667781,0.649,3,9,/zeeshanarif53/a-method-using-sentimentintensityanalyzer,Tweet Sentiment Extraction
688126,0.9828,0,3,/thec03u5/avg-linear-lstm-and-max-linear-lstm,Toxic Comment Classification Challenge
687501,0.9772,0,1,/thec03u5/nbsvm,Toxic Comment Classification Challenge
677540,0.9732,6,38,/sbongo/tackling-toxic-problem-with-char-gram-cnn-lstm,Toxic Comment Classification Challenge
671597,0.9584,0,2,/mattwills8/scikit-learn-nltk-no-bullsh-t,Toxic Comment Classification Challenge
605470,0.9776,0,1,/ranjanabhay/toxic-comment-classification-challenge,Toxic Comment Classification Challenge
584023,0.9772,0,1,/shreemoyeesarkar/notebook1,Toxic Comment Classification Challenge
559591,0.8797,11,36,/danielokeeffe/simple-text-classification-with-apache-spark,Toxic Comment Classification Challenge
516604,0.9543,9,73,/vsmolyakov/keras-cnn-with-fasttext-embeddings,Toxic Comment Classification Challenge
513960,0.9817,11,30,/demesgal/lstm-with-bn-nb-svm-lr-on-conv-ai-lb-0-041,Toxic Comment Classification Challenge
509364,0.9811,16,203,/jhoward/minimal-lstm-nb-svm-baseline-ensemble,Toxic Comment Classification Challenge
510822,0.9748,3,1,/ipg2014xxx/simple-toxic-comment-classifier,Toxic Comment Classification Challenge
4077747,0.7390000000000001,3,19,/tunguz/atomic-distances-with-h2o-automl,Predicting Molecular Properties
1888956,0.6990000000000001,1,19,/iafoss/remove-overlap,Airbus Ship Detection Challenge
1577587,0.889,0,12,/ashishpatel26/unet34-submission,Airbus Ship Detection Challenge
1485731,0.853,11,58,/hmendonca/classification-and-segmentation-fp,Airbus Ship Detection Challenge
1395053,0.847,34,131,/hmendonca/u-net-model-with-submission,Airbus Ship Detection Challenge
1382294,0.503,13,51,/kmader/from-trained-u-net-to-submission-part-2,Airbus Ship Detection Challenge
6752187,0.54492,0,0,/adldotori/basic-image-segmentation,Airbus Ship Detection Challenge
2809329,0.34,2,2,/kawaiy/feature-engineering-and-model-ensemble,PetFinder.my Adoption Prediction
2800334,0.319,3,7,/kawaiy/my-first-kernel-with-xgboost,PetFinder.my Adoption Prediction
2740457,0.175,0,5,/waydegg/baseline-tabular-learner-with-fastai-v1,PetFinder.my Adoption Prediction
2711182,0.176,0,0,/thomasroy/test-petfinder,PetFinder.my Adoption Prediction
2696405,0.316,0,0,/roarke/draft-first-look,PetFinder.my Adoption Prediction
2726438,0.319,0,0,/ashirahama/exploration-of-data-step-by-step,PetFinder.my Adoption Prediction
2680518,0.387,0,1,/econdata/petfinderprediction,PetFinder.my Adoption Prediction
2682947,0.317,0,0,/g6j3cl4/kernele1062873b4,PetFinder.my Adoption Prediction
2587958,0.3379999999999999,0,1,/reginashay/petfinder-xgb,PetFinder.my Adoption Prediction
2590465,0.319,1,9,/lpdataninja/data-science-glossary-12-eda-feature-engineering,PetFinder.my Adoption Prediction
2509403,0.33,0,5,/evaart/nlp-and-easy-ml,PetFinder.my Adoption Prediction
2570519,0.37,8,22,/dan3dewey/baseline-random-forest-with-get-class-bounds,PetFinder.my Adoption Prediction
2546510,0.344,1,9,/teemingyi/lgb-model-with-image-statistics,PetFinder.my Adoption Prediction
2517524,0.412,3,7,/mesutmutlu/exploratory-analysis-and-lgbm,PetFinder.my Adoption Prediction
2502039,0.09,0,0,/jankoch/simple-baseline-score-per-animal-type,PetFinder.my Adoption Prediction
2493315,0.337,0,5,/joydeb28/pet-finder-with-lgb,PetFinder.my Adoption Prediction
2494873,0.376,0,7,/nicapotato/text-and-structured-data-lgbm,PetFinder.my Adoption Prediction
2512502,0.315,2,6,/ranfea/pet-adoption-speed-prediction,PetFinder.my Adoption Prediction
140123,0.63713,0,1,/duncan1213/notebook0cc759b5be,Outbrain Click Prediction
11960870,0.45,1,9,/keremt/03-baseline-inference,RSNA STR Pulmonary Embolism Detection
11860183,0.4539999999999999,7,63,/kozodoi/lightgbm-on-meta-features,RSNA STR Pulmonary Embolism Detection
11685583,0.434,9,31,/jagdmir/extract-meta-data-from-dicom-files,RSNA STR Pulmonary Embolism Detection
11747563,0.434,11,64,/paulorzp/mean-baseline,RSNA STR Pulmonary Embolism Detection
11721387,0.503,2,9,/rezwan249/keras-model-creation-and-pe-detection,RSNA STR Pulmonary Embolism Detection
12498312,0.173,0,0,/yeeseng/rsna-pe-submission-final-pydicom,RSNA STR Pulmonary Embolism Detection
164050,17.269779999999994,0,3,/ayaseeli/cnn-cat-vs-dog,Dogs vs. Cats Redux: Kernels Edition
5614342,0.9121,0,3,/venkig/ieee-step-by-step-implementation,IEEE-CIS Fraud Detection
5376087,0.9454,5,50,/pipboyguy/catboost-and-eda,IEEE-CIS Fraud Detection
5531653,0.8690000000000001,0,1,/jazmad/developing-an-understanding-of-the-data,IEEE-CIS Fraud Detection
5427199,0.8982,0,0,/l16phill/final-submission,IEEE-CIS Fraud Detection
5469108,0.8829,8,24,/rodgomrod/very-simple-nn-in-pytorch-with-sparsetensor,IEEE-CIS Fraud Detection
5467574,0.5,2,3,/plsjlq/ieee-cis-fraud-detection-test-a-no-fraud,IEEE-CIS Fraud Detection
5463797,0.8565,1,2,/yuewangmoophy/fraud-detection-random-forest,IEEE-CIS Fraud Detection
5413740,0.9214,6,42,/rajeshcv/features-based-on-nulls-and-lgbm-model,IEEE-CIS Fraud Detection
5461598,0.6998,0,0,/steboss/flavour-of-autoencoder-0-0,IEEE-CIS Fraud Detection
5372495,0.9367,8,19,/yasagure/contrive-usage-of-v-features-in-official-xgboost,IEEE-CIS Fraud Detection
5287572,0.9457,15,63,/yw6916/lgb-xgb-ensemble-stacking-based-on-fea-eng,IEEE-CIS Fraud Detection
5317504,0.9468,39,256,/kyakovlev/ieee-gb-2-make-amount-useful-again,IEEE-CIS Fraud Detection
5266846,0.9433,22,121,/iasnobmatsu/xgb-model-with-feature-engineering,IEEE-CIS Fraud Detection
465893,0.4570399999999999,0,7,/yliu9999/average-of-lightgbm-ridge-0-45704lb,Mercari Price Suggestion Challenge
469402,0.79102,0,0,/kedington/linear-regression,Mercari Price Suggestion Challenge
455731,0.47091,5,36,/tunguz/a-simple-nn-solution-with-keras-0-48611-p-12a776,Mercari Price Suggestion Challenge
450372,0.82114,0,0,/agenright/just-a-quick-test-run,Mercari Price Suggestion Challenge
454090,0.59904,0,0,/abhishekjha13/prod-code,Mercari Price Suggestion Challenge
449944,0.52764,0,19,/grroverpr/random-forest-with-tuning-lb-0-52764,Mercari Price Suggestion Challenge
448007,0.53254,23,47,/shikhar1/base-random-forest-lb-532,Mercari Price Suggestion Challenge
450894,0.7548600000000001,0,2,/dsbilalmahmood/baseline-median-price,Mercari Price Suggestion Challenge
450681,0.59386,0,1,/yifeiren/simple-model,Mercari Price Suggestion Challenge
446797,0.82478,6,12,/tunguz/sample,Mercari Price Suggestion Challenge
6456723,0.6036,2,0,/yharuna/kernel5acf235e18,Mercari Price Suggestion Challenge
844771,0.82478,0,0,/saitoxu/mean-price,Mercari Price Suggestion Challenge
705617,0.41073,0,0,/shanth84/rnn-wordbatch-price-recommendation-text-mining,Mercari Price Suggestion Challenge
649088,0.4859699999999999,0,0,/pmarciano/simple-neural-network-solution,Mercari Price Suggestion Challenge
593850,0.71317,0,0,/nareshsrikakulapu/mer-xgboost-log,Mercari Price Suggestion Challenge
2719857,0.45055,0,0,/hajekim/for-beginner,Bike Sharing Demand
883641,0.5534899999999999,0,0,/jayspeidell/visualizing-and-modeling-dc-bikeshare-ridership,Bike Sharing Demand
662511,0.71628,0,0,/hyunkyung12/kernele62fbf11fd,Bike Sharing Demand
67301,0.46356,0,0,/ymcdull/frank-bike-test,Bike Sharing Demand
13050274,0.003,0,3,/rikozhushko/hubmap-memory-efficient-submission-using-disk,HuBMAP - Hacking the Kidney
13005989,0.517,6,22,/marcosnovaes/hubmap-memory-efficient-submission-using-disk,HuBMAP - Hacking the Kidney
13461299,0.8270000000000001,0,0,/shilei2403/pytorch-fcn-resnet50-in-20-minute,HuBMAP - Hacking the Kidney
10777281,0.6890000000000001,0,0,/jaidevchittoria/submit,ALASKA2 Image Steganalysis
10799424,0.929,0,6,/tunguz/best-b2-inference,ALASKA2 Image Steganalysis
10621999,0.931,0,3,/authman/inference1,ALASKA2 Image Steganalysis
10594292,0.921,2,7,/krishnaharish/train-inference-gpu-baseline,ALASKA2 Image Steganalysis
10295144,0.753,0,0,/snakayama/keras-efficientnetb0-noisy-student,ALASKA2 Image Steganalysis
9224445,0.779,0,2,/akashsuper2000/alaska2-efficientnet-on-tpus,ALASKA2 Image Steganalysis
9457575,0.8759999999999999,1,0,/akashsuper2000/alaska2-cnn-multiclass-classifier,ALASKA2 Image Steganalysis
9361035,0.769,2,4,/urayukitaka/alaska2-try-to-predict-by-efficientnet,ALASKA2 Image Steganalysis
128409,3.43888,0,0,/xieyufish/my-2nd-try-and-learn-from-dune-dweller,TalkingData Mobile User Demographics
128186,2.27771,0,0,/xieyufish/my-first-try-and-learn-from-dune-dweller,TalkingData Mobile User Demographics
11335922,0.991,0,1,/maciejgronczynski/digit-recognizer-my-take,Digit Recognizer
12012446,0.98778,0,0,/alexinicab/keras-largenet,Digit Recognizer
11984527,0.991,0,0,/maleroy/digit-recognizer-cnn-with-data-aug,Digit Recognizer
11858028,0.99292,1,9,/harkiratvasir/digit-recognition-accuracy-99-50-score-in-top-10,Digit Recognizer
11966944,0.97492,0,2,/youssefkizou/digit-recognizer-using-keras,Digit Recognizer
11968832,0.99078,0,0,/manjjimnav/fastai2-mxresnet-ranger,Digit Recognizer
11949480,0.99385,1,1,/alanchn31/mnist-efficientnet-99-in-5-epochs,Digit Recognizer
11953506,0.99235,0,0,/emnikkhil/digit-recognizer-using-cnn,Digit Recognizer
11914085,0.99303,0,1,/jayantawasthi/mnist,Digit Recognizer
11902336,0.96492,0,0,/qrsforever/pytorch-lightning-digit-recognizer-on-squeezenet,Digit Recognizer
11882397,0.99128,1,7,/ayhmrba/build-your-first-cnn-using-keras-99,Digit Recognizer
11858542,0.97553,0,8,/sametevik/cnn-first-step,Digit Recognizer
11650794,0.99489,0,6,/mani97/cnn-99-57-visualise-filters-activation-layers,Digit Recognizer
11821654,0.99407,12,24,/heeraldedhia/mnist-classifier-first-deep-learning-project,Digit Recognizer
11834863,0.98032,0,4,/wickkiey/digit-recognizer-tf2-starter-97-with-10-epochs,Digit Recognizer
11822007,0.8453200000000001,0,4,/aloksahu7478/keras-digit-recognizer,Digit Recognizer
14029574,0.601,0,1,/kutaykutlu/tpus-leaf-disease-simple-inference,Cassava Leaf Disease Classification
14012257,0.9,1,1,/denismetelev/cassava-leaf-disease-tpu-v2-pods-inferece,Cassava Leaf Disease Classification
13792453,0.877,0,1,/shyambhu/copy-of-effnetb4-tf-data-gpu-aug-5x-speedup-tta,Cassava Leaf Disease Classification
13976137,0.843,0,0,/bluesky314/cassavasubmission,Cassava Leaf Disease Classification
13901216,0.897,0,8,/prvnkmr/cassava-leave-disease-pytorch-implementation,Cassava Leaf Disease Classification
13745383,0.369,0,1,/suryaaseran/simple-ensemble-model-template,Cassava Leaf Disease Classification
13287365,0.139,1,8,/abhi8923shriv/dr-arborist-a-doctor-of-leaves,Cassava Leaf Disease Classification
13880662,0.8909999999999999,1,1,/dmitrynokhrin/cassava-predict,Cassava Leaf Disease Classification
13628995,0.899,0,2,/medvedevlev/cassava-leaf-disease-tpu-v2-pods-inference,Cassava Leaf Disease Classification
13906147,0.892,2,0,/shubham108/vision-transformer-tta,Cassava Leaf Disease Classification
13786497,0.893,0,1,/danielwijaya/cassava-submission-notebook,Cassava Leaf Disease Classification
7914095,0.78503,1,3,/kaggleurroad/categorical,Categorical Feature Encoding Challenge II
7874928,0.78542,35,105,/warkingleo2000/first-step-on-kaggle,Categorical Feature Encoding Challenge II
7792984,0.7801600000000001,0,2,/manlunglo/78-acc-a-short-automl-categorical-classification,Categorical Feature Encoding Challenge II
7740713,0.7859999999999999,1,5,/zzy990106/fgcnn,Categorical Feature Encoding Challenge II
7704247,0.61352,3,25,/tunguz/cat-ii-with-rapids-knn,Categorical Feature Encoding Challenge II
7691827,0.77963,6,14,/amoghjrules/encode-like-there-s-no-tomorrow,Categorical Feature Encoding Challenge II
7656618,0.7811100000000001,4,6,/horohoro/try-categoricalnb,Categorical Feature Encoding Challenge II
7525430,0.78574,0,3,/jinbao/embedding-dnn-ii,Categorical Feature Encoding Challenge II
7494405,0.78257,2,3,/bluewizard/logistic-regression-w-onehot-catboost-encoders,Categorical Feature Encoding Challenge II
7481195,0.7835300000000001,5,8,/fkdplc/ensembling-logisticregression-and-catboost,Categorical Feature Encoding Challenge II
7446046,0.78593,2,17,/nandhuelan/let-s-tickle-the-cat-meow,Categorical Feature Encoding Challenge II
7456746,0.78552,1,3,/nicapotato/cat-class-logistic-regression-stack,Categorical Feature Encoding Challenge II
7439376,0.78195,1,6,/tunguz/cat-ii-logistic-regression-baseline,Categorical Feature Encoding Challenge II
7389602,0.7841899999999999,21,64,/subinium/categorical-data-eda-visualization,Categorical Feature Encoding Challenge II
7407918,0.78478,2,4,/nicapotato/categorical-ii-catboost-pool-cv-bayes-opt-gpu,Categorical Feature Encoding Challenge II
7368199,0.7859999999999999,4,22,/lucamassaron/catboost-in-action-with-dnn,Categorical Feature Encoding Challenge II
7360375,0.78581,2,15,/carlodnt/catboost-shap-fastai,Categorical Feature Encoding Challenge II
7359707,0.7855,13,28,/vincentlugat/skf-lightgbm-target-encoding,Categorical Feature Encoding Challenge II
7316820,0.77495,4,26,/drcapa/categorical-feature-engineering-2-xgb,Categorical Feature Encoding Challenge II
7328578,0.76547,0,2,/khulapkosv/lightgbm-simple-starter,Categorical Feature Encoding Challenge II
10083343,0.7226199999999999,0,0,/visiteur/lab-cats-log-regression,Categorical Feature Encoding Challenge II
13432473,0.805,0,0,/azhura/base-model-inference-cnn,Cassava Leaf Disease Classification
13431242,0.883,0,2,/ayushn2000/cassava-leaf-disease-inference,Cassava Leaf Disease Classification
13573389,0.7490000000000001,0,0,/paulorblima/notebookeb6fed02ad,Cassava Leaf Disease Classification
13540920,0.139,0,3,/bruno2siqueira/notebook0557b79a42,Cassava Leaf Disease Classification
13211047,0.597,0,0,/nur988/cassava-pytorch-lightning,Cassava Leaf Disease Classification
13315962,0.898,1,5,/ahmedewida/cassava-resnet,Cassava Leaf Disease Classification
13188808,0.614,0,0,/jkstar/version-test,Cassava Leaf Disease Classification
13334123,0.67,2,1,/tomoakiishii/cassava-eda-and-baseline-with-keras-cnn,Cassava Leaf Disease Classification
13376525,0.88,6,14,/pndeepak/cassava-predict,Cassava Leaf Disease Classification
13345010,0.845,1,13,/harveenchadha/efficientnetb3-baseline-inference-keras-tf2-tta,Cassava Leaf Disease Classification
13327168,0.823,12,41,/harveenchadha/efficientnetb3-keras-tf2-baseline-training,Cassava Leaf Disease Classification
13276151,0.602,0,1,/andreaschandra/exploration,Cassava Leaf Disease Classification
13337020,0.8740000000000001,0,5,/mekhdigakhramanian/cassava-keras,Cassava Leaf Disease Classification
13293951,0.882,0,3,/mekhdigakhramanian/cassava-xception-inference,Cassava Leaf Disease Classification
13322937,0.889,0,5,/tuckerarrants/cassava-tensorflow-starter-inference,Cassava Leaf Disease Classification
13280913,0.8909999999999999,12,39,/szuzhangzhi/vision-transformer-vit-cuda-as-usual,Cassava Leaf Disease Classification
13117879,0.759,0,1,/emreulgac/cassava-leaf-disease-classification,Cassava Leaf Disease Classification
8996949,0.97664,0,0,/harrycode7/mnist-digit-recognizer-deep-learning,Digit Recognizer
12671672,0.98175,0,0,/peternagymathe/notebook-mnist-first-kaggle,Digit Recognizer
12653007,0.99028,0,0,/matlaz/mnist-digit-recognizer-fft-cnn,Digit Recognizer
12704519,0.98182,1,1,/akoraingdkb/dkb-mnist-with-cnn-accuracy-98,Digit Recognizer
10333704,0.98428,0,0,/seemanegi/mnist-datasets,Digit Recognizer
12698465,0.989,0,0,/malechi/notebook51515391bd,Digit Recognizer
12529129,0.9946,0,2,/tanguyperennec/tf-from-scratch-with-exemples-digit-recognizer,Digit Recognizer
12505636,0.95314,0,0,/kentaroh34/mnist-2cnn,Digit Recognizer
12445197,0.99671,0,1,/bartoszpieniak/mnist-cnn-da-ensemble,Digit Recognizer
12081314,0.97603,0,0,/vatshimanshu/mnist-dataset-mlp-gpu-pytorch,Digit Recognizer
12468048,0.99217,0,1,/vikrantrajput/notebook2bdffffaf6,Digit Recognizer
12115975,0.99,0,0,/mabalogun/data-recognition-with-cnn,Digit Recognizer
12444775,0.95185,0,0,/jackstapleton/mnist-mlp-baseline,Digit Recognizer
93236,2.26857,2,2,/drsunilpatil/xgboost-v11,TalkingData Mobile User Demographics
11593231,0.53817,0,0,/ilyasedelnikov/20200905-mercarilightgbm,Mercari Price Suggestion Challenge
11073938,0.53246,0,1,/toshihikok/mercari-price-suggestion-challenge-notebook,Mercari Price Suggestion Challenge
10799224,0.59287,2,8,/cdefreitas/mercari-price-suggestion-2020,Mercari Price Suggestion Challenge
8404878,0.6953600000000001,0,0,/ssingla/mercaripricerecommendation-base-model-0-69,Mercari Price Suggestion Challenge
5902290,0.4697,0,2,/toshinari/mercari-ridge,Mercari Price Suggestion Challenge
4907453,0.42926,0,1,/conformal/bownn2,Mercari Price Suggestion Challenge
4867620,0.42923,0,1,/conformal/bownn,Mercari Price Suggestion Challenge
2140206,0.6315,0,0,/bballsu30/test4,Mercari Price Suggestion Challenge
3496396,0.5486300000000001,0,0,/anhquan0412/mercari-fastai-tabular,Mercari Price Suggestion Challenge
2923513,1.36266,1,0,/unamu1229/keras,Mercari Price Suggestion Challenge
2592211,0.87635,0,0,/ilsersokolov/test-kernel,Mercari Price Suggestion Challenge
2264082,0.45916,0,0,/aschukin/lgb-nosvd-nobrandpredict,Mercari Price Suggestion Challenge
453745,0.66746,0,0,/jennybrown8/mercari-submission-notebook,Mercari Price Suggestion Challenge
545602,0.41405,5,0,/obi1992/ensemble-rnn-wordbatch-linear-model,Mercari Price Suggestion Challenge
870746,0.47635,0,0,/lp3ides/gru-initial-embedding-gensim-v2,Mercari Price Suggestion Challenge
719842,0.41117,2,21,/gspmoreira/cnn-glove-single-model-private-lb-0-41117-35th,Mercari Price Suggestion Challenge
590553,0.58987,0,0,/holfyuen/mercari-prediction-from-median-and-beyond,Mercari Price Suggestion Challenge
11234797,0.281,0,22,/prateekagnihotri/change-image-size-to-get-0-281-lb,Google Landmark Retrieval 2020
11123191,0.2769999999999999,3,45,/nvnnghia/main-0806,Google Landmark Retrieval 2020
11094345,0.008,0,0,/jumpingmandt/see-the-jpg-images-in-different-folders,Google Landmark Retrieval 2020
10964044,0.018,0,6,/chandanverma/resnet-101cosface-softmax,Google Landmark Retrieval 2020
10888296,0.038,8,26,/akensert/glret-triplet-semi-hard-loss-with-distributed-tf,Google Landmark Retrieval 2020
11164899,0.2769999999999999,0,0,/akashsuper2000/main-0806,Google Landmark Retrieval 2020
4890067,0.9398,6,21,/stocks/under-sample-with-multiple-runs,IEEE-CIS Fraud Detection
4890492,0.9373,3,10,/xhlulu/ieee-fraud-efficient-grid-search-with-xgboost,IEEE-CIS Fraud Detection
4880581,0.9351,7,20,/artkulak/use-only-5-of-0-labels-get-negligible-lb-drop,IEEE-CIS Fraud Detection
4866496,0.9459,13,54,/vaishvik25/ensemble,IEEE-CIS Fraud Detection
4832163,0.9394,26,418,/jesucristo/fraud-complete-eda,IEEE-CIS Fraud Detection
4839928,0.9517,41,176,/jazivxt/safe-box,IEEE-CIS Fraud Detection
4845624,0.9049,2,10,/priteshshrivastava/ieee-cis-fraud-random-forest-0-90-lb,IEEE-CIS Fraud Detection
4859604,0.9282,0,1,/om1042/ieee-fraud-xgboost-with-gpu-pca,IEEE-CIS Fraud Detection
4848241,0.7043,0,0,/snakayama/ieee-eda-simple-logisticregression-cv-0-72091,IEEE-CIS Fraud Detection
4831506,0.9361,3,13,/konradb/can-we-beat-it,IEEE-CIS Fraud Detection
4840602,0.9318,3,4,/rstogi896/lightgbmx,IEEE-CIS Fraud Detection
6736207,0.506134,0,0,/titouanluciani/kernel446dd87018,IEEE-CIS Fraud Detection
1659641,0.60235,0,1,/renatoh/keras-convnet-dl-pucp-weak-localization,Dogs vs. Cats Redux: Kernels Edition
1639855,0.43496,0,0,/johnfarrell/dvc-basic-model,Dogs vs. Cats Redux: Kernels Edition
1610005,4.32102,0,0,/ambarish/dogs-and-cats-image-analysis,Dogs vs. Cats Redux: Kernels Edition
1614655,17.06296,0,0,/ambarish/dogs-and-cats-image-analysis-2,Dogs vs. Cats Redux: Kernels Edition
1583645,2.02818,0,5,/mallela432/cats-vs-dogs-cnn-implementation-with-keras,Dogs vs. Cats Redux: Kernels Edition
1489522,4.5558,0,6,/tanmaypandey/dogs-vs-cats-keras-solution,Dogs vs. Cats Redux: Kernels Edition
1448503,0.06107,0,0,/aamnafea/fast-ai-approach-score-0-6524,Dogs vs. Cats Redux: Kernels Edition
1423929,0.35141,1,2,/zhoulingyan0228/cats-dogs-multi-scale-cnn-w-o-pretraining,Dogs vs. Cats Redux: Kernels Edition
1226740,0.58078,0,0,/segfaultalert/cat-vs-dog-v2,Dogs vs. Cats Redux: Kernels Edition
1248735,6.62678,0,1,/ahmednassar/catsvsdogwithresnet,Dogs vs. Cats Redux: Kernels Edition
1036748,0.06891,11,33,/anshulrai/using-fastai-in-kaggle-kernel-updated,Dogs vs. Cats Redux: Kernels Edition
11065731,0.0,2,7,/shivamsouravjha/petfinder-using-ann,PetFinder.my Adoption Prediction
2731776,0.344,0,0,/linhvn/petfinder,PetFinder.my Adoption Prediction
3008818,0.3279999999999999,0,0,/qadfafasfafafafsafa/petfinder-simple-lgbm,PetFinder.my Adoption Prediction
3154191,0.44,0,0,/brandonchan/bran-s,PetFinder.my Adoption Prediction
8131013,0.0,0,2,/darwinwin/humanifyai-petfinder-automl-h2o-package,PetFinder.my Adoption Prediction
7471346,0.0,40,420,/bminixhofer/how-bestpetting-cheated,PetFinder.my Adoption Prediction
7037375,0.0,0,0,/vothikimanh/petfinder-dec2019,PetFinder.my Adoption Prediction
2655586,0.401,0,0,/wakamezake/petfinder-simple-lgbm-baseline,PetFinder.my Adoption Prediction
2745720,0.264,0,0,/amiron/task1,PetFinder.my Adoption Prediction
5346232,0.0,0,0,/bravenoob/submit-mask-rcnn,PetFinder.my Adoption Prediction
5459946,0.0,0,0,/bravenoob/area-hu-moments-maskrcnn-benchmark,PetFinder.my Adoption Prediction
3436527,0.0,0,1,/remisn/petfinder-fastai-ai,PetFinder.my Adoption Prediction
3413989,0.4589999999999999,0,0,/alexfraze/public-ensemble-my-nlp-features,PetFinder.my Adoption Prediction
2546860,0.342,0,0,/narendrashu/pet-finder,PetFinder.my Adoption Prediction
3615077,0.0,0,0,/yangxu1994/kernele581fb42c9,PetFinder.my Adoption Prediction
3541601,0.0,16,116,/bminixhofer/5th-place-solution-code,PetFinder.my Adoption Prediction
3406352,0.457,2,19,/ryches/42nd-solution-nothing-special,PetFinder.my Adoption Prediction
3321823,0.4639999999999999,0,2,/luudactam/darling-lgbm-v14,PetFinder.my Adoption Prediction
3443392,0.0,1,0,/ritik99/xgboost-basic,PetFinder.my Adoption Prediction
3409032,0.44,0,1,/hidehisaarai1213/petfinder-lots-of-images-delete-resc-coding,PetFinder.my Adoption Prediction
3257031,0.335,0,0,/bravenoob/cas-pml-hs18-final-model,PetFinder.my Adoption Prediction
14310043,0.69701,7,26,/shogosuzuki/0-69701-folds-10-lightgbm,Tabular Playground Series - Jan 2021
14011238,0.6985600000000001,1,1,/cchia3/tabular-playground-overview,Tabular Playground Series - Jan 2021
14564926,0.7206100000000001,0,0,/neilgibbons/pi-mu-weighted-average,Tabular Playground Series - Jan 2021
14332086,0.69899,0,0,/oksanashchelkina/jan-tabular-playground-competition,Tabular Playground Series - Jan 2021
14190729,0.69967,0,0,/tomokikmogura/catboost-hyperparameters-tuning-with-optuna,Tabular Playground Series - Jan 2021
14174970,0.71354,0,0,/revathiprakash/jan-tabular-playground-competition-fast-ai,Tabular Playground Series - Jan 2021
14101366,0.7005399999999999,8,9,/harshitt21/tabular-playground-series-eda-model,Tabular Playground Series - Jan 2021
14229595,0.70435,1,1,/mellebrouwer/tabular-baseline,Tabular Playground Series - Jan 2021
14093181,0.73009,0,0,/damoonshahhosseini/tab-data,Tabular Playground Series - Jan 2021
14199994,0.69993,0,3,/aristarhbfg/only-lgb,Tabular Playground Series - Jan 2021
14188542,0.7013,0,5,/chandraroy/lgb-regressor-baseline-model,Tabular Playground Series - Jan 2021
14082785,0.70331,13,12,/mahmoudalaa01010101/eda-tabular-playground,Tabular Playground Series - Jan 2021
14045167,0.69984,0,3,/shyjohn/shy-john-submission,Tabular Playground Series - Jan 2021
14161713,0.6978800000000001,10,6,/bhavikjain/lgbm-xgboost-catboost-tabular-playground-series,Tabular Playground Series - Jan 2021
14130890,0.70415,19,18,/prateekagrawal1405/automl-h20-ktps21,Tabular Playground Series - Jan 2021
14159535,0.91817,0,0,/njelicic/linearregression-from-scratch-pytorch,Tabular Playground Series - Jan 2021
14138753,0.70005,0,1,/aeryss/tabular-playground-jan-2021-catboost-with-fe,Tabular Playground Series - Jan 2021
14097157,0.6994100000000001,5,2,/jarupula/tabular-playground-lgbm-xgboost-catboost,Tabular Playground Series - Jan 2021
14103585,0.70529,0,0,/faraaa/xgboost-tabular-playground,Tabular Playground Series - Jan 2021
14124871,0.7138399999999999,0,0,/dharmendrakondapalli/tabular5,Tabular Playground Series - Jan 2021
6410281,1.12,35,177,/corochann/optuna-tutorial-for-hyperparameter-optimization,ASHRAE - Great Energy Predictor III
6253360,1.39,2,2,/rhodiumbeng/ashrae-energy-simple-average,ASHRAE - Great Energy Predictor III
6409306,3.35,2,3,/hanjoonchoe/ashrae-fe-lgbm-incomplete-ver-1,ASHRAE - Great Energy Predictor III
6394585,1.435,2,1,/basu369victor/need-for-energy-eda-lightgbm-prediction,ASHRAE - Great Energy Predictor III
6308940,1.21,69,90,/caesarlupum/ashrae-ligthgbm-simple-fe,ASHRAE - Great Energy Predictor III
6320788,1.36,0,7,/kumezawa/ashrae-simple-chainer-nn-starter-codes,ASHRAE - Great Energy Predictor III
6238171,1.368,16,152,/ryches/simple-lgbm-solution,ASHRAE - Great Energy Predictor III
6264411,1.37,7,14,/drexpz/ashrae-lightgbm-process-data-visualization,ASHRAE - Great Energy Predictor III
6250757,1.43,3,24,/mlisovyi/no-ml-benchmark,ASHRAE - Great Energy Predictor III
8997223,0.843,2,23,/frtgnn/using-pycaret-on-ion-switch,University of Liverpool - Ion Switching
8976693,0.94,2,10,/shinogi/fe-and-ensemble-mlp-and-lgbm-add-kalmanfilter,University of Liverpool - Ion Switching
8750409,0.91,0,0,/krparekh24/ion-switching,University of Liverpool - Ion Switching
8744763,0.942,7,82,/sggpls/wavenet-with-shifted-rfc-proba,University of Liverpool - Ion Switching
8850464,0.941,15,90,/hirayukis/lightgbm-keras-and-4-kfold,University of Liverpool - Ion Switching
8914253,0.936,0,1,/aryankhatri/predicting-open-ion-channels-with-cleaned-data,University of Liverpool - Ion Switching
8870742,0.94,8,60,/cswwp347724/wavenet-pytorch,University of Liverpool - Ion Switching
8875163,0.292,0,0,/giovannipilloni/test-ionchannels,University of Liverpool - Ion Switching
8585833,0.941,49,207,/siavrez/wavenet-keras,University of Liverpool - Ion Switching
8514862,0.939,0,27,/teejmahal20/a-signal-processing-approach-kalman-filtering,University of Liverpool - Ion Switching
8505118,0.937,9,12,/nxrprime/on-monte-carlo-simulations,University of Liverpool - Ion Switching
8261945,0.937,0,2,/super13579/different-batch-model-and-ensemble,University of Liverpool - Ion Switching
6638716,0.9828,1,1,/arishp/kernel70c8f3681a,Kannada MNIST
6557016,0.9598,2,4,/subinium/pytorch-1-ann-simple-cnn,Kannada MNIST
6566419,0.987,0,7,/datahobbit/grandmaster-solution-adapted-from-mnist,Kannada MNIST
6540146,0.7664,0,0,/chariots17/try-again,Kannada MNIST
6534146,0.967,0,2,/ajayramesh/cnn-for-kannada-mnist,Kannada MNIST
6489493,0.871,0,3,/chariots17/first-submit,Kannada MNIST
6457258,0.9816,0,2,/qz701731tby/kannada-mnist-selfcnn-test,Kannada MNIST
6435296,0.9864,2,8,/kumarvivek9097/crnn-cnn-rnn,Kannada MNIST
6481029,0.9774,0,2,/pranitbankar/keras-conv2d-kannada-digits,Kannada MNIST
6398630,0.97,0,2,/imdevskp/kannada-mnist-classifying-kannada-digits,Kannada MNIST
6146304,0.9838,1,2,/udbhavpangotra/kannada-mnist-code-ver-1,Kannada MNIST
6428472,0.9762,0,1,/humblefool786/mnistkannada,Kannada MNIST
6384470,0.9604,0,0,/manugondssg/kannada-mnist-with-custom-cnn-in-pytorch,Kannada MNIST
6301034,0.9724,5,9,/vikassingh1996/simple-cnn-modeling-kannanda-mnist,Kannada MNIST
5900309,0.9818,0,3,/nubatama/trial-simple-model-with-gelu-and-relu-by-keras,Kannada MNIST
6298764,0.8252,0,2,/lilydong/kannada-mnist-lilydong,Kannada MNIST
6259996,0.934,0,3,/plarmuseau/mnist-cossim,Kannada MNIST
6298456,0.9862,0,0,/zainmar/kannada-kaggle-days-china,Kannada MNIST
6230953,0.9454,0,2,/tunguz/kannada-mnist-xgboost-baseline,Kannada MNIST
6200898,0.9806,0,1,/rubenscastro/kannada-mnist-beginner-by-rubens,Kannada MNIST
6204226,0.9844,4,9,/nicapotato/pytorch-cnn-kanada,Kannada MNIST
6017867,0.9334,0,3,/nicapotato/dense-digit-classifier-kanada-simple-cpu-pytorch,Kannada MNIST
6139528,0.9816,1,2,/pieterblomme/kannada-v0-1,Kannada MNIST
6021051,0.9818,1,3,/cuuuurry/let-s-try-some-dataaugumentation-on-kannada-mnist,Kannada MNIST
6128987,0.9872,0,6,/melissarajaram/mixup-augmentation-tta-fastai,Kannada MNIST
6166997,0.9726,0,3,/iavinas/basics-of-cnn,Kannada MNIST
5958491,0.9818,0,2,/darkrubiks/kannada-mnist-keras-cnn-98,Kannada MNIST
6076923,0.986,0,4,/ragnar123/cnn-baseline-kfold,Kannada MNIST
5880628,0.9854,0,3,/aditya100/kannada-mnist-using-keras,Kannada MNIST
9914650,0.3329999999999999,0,0,/abhi3ichigo/tweet-first-try,Tweet Sentiment Extraction
9951784,0.579,0,1,/senritu/bert-pytorch-starter-v2,Tweet Sentiment Extraction
9909779,0.514,0,0,/freddyyj/roberta-competition,Tweet Sentiment Extraction
9960989,0.654,0,0,/tonyjchen/group-8-refined-work,Tweet Sentiment Extraction
9894272,0.452,0,0,/tonyjchen/group8,Tweet Sentiment Extraction
9872886,0.659,0,2,/swapnilagashe/tweet-sentiment-extraction-spacy-custom-ner-model,Tweet Sentiment Extraction
9938880,0.431,0,0,/jeffster/group8,Tweet Sentiment Extraction
9828718,0.63,0,1,/ramahanishagunda/text-extraction-using-bert-w-sentiment-inference,Tweet Sentiment Extraction
9799749,0.7120000000000001,1,3,/taohoang/tensorflow-roberta,Tweet Sentiment Extraction
9737361,0.5429999999999999,0,0,/pavneettiwana/kernel2e4fd90a7c,Tweet Sentiment Extraction
9433343,0.185,0,1,/taohoang/lstm-tweet-model,Tweet Sentiment Extraction
9682397,0.63,0,1,/charan24/text-extraction-using-spacy,Tweet Sentiment Extraction
9358372,0.148,0,0,/taohoang/first-tweet-model,Tweet Sentiment Extraction
9580983,0.715,61,140,/khoongweihao/tse2020-roberta-cnn-random-seed-distribution,Tweet Sentiment Extraction
9755102,0.664,0,0,/umang123456789/kernel1f84705d35,Tweet Sentiment Extraction
9715821,0.647,0,1,/ramahanishagunda/kernel7926b3924d,Tweet Sentiment Extraction
9592217,0.7140000000000001,53,193,/shoheiazuma/tweet-sentiment-roberta-pytorch,Tweet Sentiment Extraction
9333158,0.665,1,11,/enzoamp/t5-q-a-inference-5-epochs-pytorch,Tweet Sentiment Extraction
597830,0.9772,0,0,/onemoresunday/toxic-comments-nb-svm-strong-linear-baseline,Toxic Comment Classification Challenge
13882987,0.79904,0,0,/maxmuzeau/titanic-model-max-muzeau,Titanic - Machine Learning from Disaster
13905218,0.7751100000000001,0,0,/brettbeaulieu/getting-started-with-titanic,Titanic - Machine Learning from Disaster
13867130,0.77272,0,1,/aeryss/titanic-testing-residual-based-boosting,Titanic - Machine Learning from Disaster
13795550,0.78708,0,0,/hitvarthdiwanji/titanic-challenge-hd,Titanic - Machine Learning from Disaster
4687939,0.78947,0,0,/astropiu/kernelccb476128e,Titanic - Machine Learning from Disaster
13712258,0.78947,32,32,/thiagopanini/pycomp-predicting-survival-on-titanic-disaster,Titanic - Machine Learning from Disaster
13710158,0.48803,0,5,/hibatalat/ht-titanic-first-attempt,Titanic - Machine Learning from Disaster
13814642,0.76794,0,0,/pandekp/analysis-of-titanic-dataset,Titanic - Machine Learning from Disaster
13365684,0.77272,0,5,/onurgule/notebook1a2720b400,Titanic - Machine Learning from Disaster
10357342,0.78947,1,5,/lingyuxiong/preprocessing-hyperparameter-tuning-top-11,Titanic - Machine Learning from Disaster
12084336,0.8109999999999999,0,0,/qaisar95/titanic4-10-2020,Titanic - Machine Learning from Disaster
13425107,0.7703300000000001,0,1,/anandchauhan/titanic-eda,Titanic - Machine Learning from Disaster
13458966,0.7751100000000001,0,0,/rajesharkar/first-work-titanic-data-prediction,Titanic - Machine Learning from Disaster
13735522,0.79186,14,14,/joseguzman/titanic-competition-with-custom-pipelines,Titanic - Machine Learning from Disaster
10027172,1.0,0,1,/tatdatnguyen21/cellular-automata-with-cnn,Abstraction and Reasoning Challenge
9791061,0.98,0,5,/user189546/cellular-automata-learning-with-colorfreqencoder,Abstraction and Reasoning Challenge
9784970,0.921,0,4,/msypetkowski/8-tasks-with-decision-trees-from-8-th-solution,Abstraction and Reasoning Challenge
9744619,0.931,10,36,/golubev/7-solved-tasks-via-trees,Abstraction and Reasoning Challenge
9278255,0.99,0,1,/tarobxl/ca-changing-colors-3-seeds-fix-clean-fast,Abstraction and Reasoning Challenge
9750304,0.95,0,9,/ilialar/5-tasks-part-of-3rd-place-solution,Abstraction and Reasoning Challenge
9719996,0.96,0,7,/andypenrose/macro-dsl-for-arc-with-heuristic-search,Abstraction and Reasoning Challenge
9719153,0.98,0,1,/tunguz/votingclassifier-100-300-300,Abstraction and Reasoning Challenge
8724273,0.99,0,0,/ashora/grid-search-with-xgboost-and-cv,Abstraction and Reasoning Challenge
9215382,0.98,1,16,/roydatascience/ensemble-3-0,Abstraction and Reasoning Challenge
9220801,1.0,0,2,/mattgwwalker/getting-started-with-the-arc,Abstraction and Reasoning Challenge
9042669,0.99,8,46,/zaharch/arc-c-approach,Abstraction and Reasoning Challenge
8814673,1.0,1,3,/hdsk38/matching-manual-coding-and-target-tasks,Abstraction and Reasoning Challenge
8734570,1.0,6,8,/meaninglesslives/reptile-and-transformers-a-meta-learning-attempt,Abstraction and Reasoning Challenge
8452962,1.0,4,38,/seriousran/variational-autoencoder-abstraction-and-reasoning,Abstraction and Reasoning Challenge
680128,0.676971,0,12,/dicksonchin93/collaborative-filtering,Google Cloud & NCAA® ML Competition 2018-Men's
10448221,0.7077,0,1,/jonykarki/yolov5-inference,Global Wheat Detection
10576910,0.7404,1,2,/youshuaiyang/yolov5-pseudo-labeling,Global Wheat Detection
10616021,0.0019,0,1,/rafaeltemirgaliev/kernel69c432ea79,Global Wheat Detection
10518107,0.5798,2,11,/kkhandekar/analysing-those-wheat-heads-object-detection,Global Wheat Detection
10529573,0.0009,0,2,/imakaruamikurah/detect-wheat-results,Global Wheat Detection
10486047,0.7411,38,83,/whurobin/wbf-over-tta-single-model-fasterrcnn-resnest,Global Wheat Detection
10486841,0.6722,2,10,/liranzxc/global-wheat-detection-liran-nachman,Global Wheat Detection
10370002,0.7644,138,370,/nvnnghia/yolov5-pseudo-labeling,Global Wheat Detection
10450123,0.7111,0,0,/liubiyuan/kernel1d24c0866a,Global Wheat Detection
10348146,0.7458,64,80,/wasupandceacar/yolov5-single-model-with-more-tta-lb-0-745,Global Wheat Detection
10341150,0.5463,0,8,/ravi02516/visualize-yolov3-darknet-results-on-test-data,Global Wheat Detection
10219842,0.7359,0,3,/vineeth1999/global-wheat-detection-pseudo-labaling,Global Wheat Detection
10209078,0.5826,0,0,/aycaidil/fft-fasterrcnn-test,Global Wheat Detection
10310678,0.7221,10,18,/vishnukv64/yolov5-inference,Global Wheat Detection
10288268,0.7523,13,62,/orkatz2/yolov5x-k-folds-lb-0-748,Global Wheat Detection
10320624,0.0945,0,1,/muhammadbasilv/with-simplified-yolov3,Global Wheat Detection
10302598,0.677,0,4,/joechenrh/pytorch-yolov3,Global Wheat Detection
10263750,0.6991,2,12,/linyangsdu/detectors-wheat-inference-public,Global Wheat Detection
10193343,0.753,41,121,/orkatz2/yolov5-fake-or-real-single-model-l-b-0-753,Global Wheat Detection
9611728,0.6626,0,0,/sherryzhang000/kernel5462168b24,Global Wheat Detection
9392461,0.6945,0,1,/akashsuper2000/fasterrcnn-pseudo-labeling,Global Wheat Detection
10122090,0.696,0,1,/jerry42tw/gwd-fasterrcnn-with-augmentation-train-inference,Global Wheat Detection
10043916,0.0009,0,1,/balogi85/retinanet-wheat-net-on,Global Wheat Detection
9937338,0.7294,14,149,/shonenkov/wbf-over-tta-single-model-efficientdet,Global Wheat Detection
9882523,0.6864,0,0,/almogstern/pytorch-starter-fasterrcnn-inference,Global Wheat Detection
9819822,0.7162,8,32,/orkatz2/inference-detectron2-resnest,Global Wheat Detection
9738834,0.7375,18,189,/shonenkov/bayesian-optimization-wbf-efficientdet,Global Wheat Detection
9615011,0.7318,34,182,/shonenkov/oof-evaluation-mixup-efficientdet,Global Wheat Detection
9682236,0.6153,0,0,/facevisalilai/kernel54beb4b767,Global Wheat Detection
9609199,0.0009,1,12,/ravirajsinh45/wheat-detection-using-yolo-and-opencv,Global Wheat Detection
64588,0.60624,0,0,/ymcdull/kobe-frank-gbrt-test,Kobe Bryant Shot Selection
59392,0.60582,0,7,/isosnovik/xgbryant,Kobe Bryant Shot Selection
59238,0.90742,23,46,/dixhom/data-analysis-for-beginners,Kobe Bryant Shot Selection
58660,0.6087,0,0,/liziyuann/rf-testing,Kobe Bryant Shot Selection
56672,0.6208899999999999,0,5,/marozet/kobe-bryant-shot-selection-charts-test,Kobe Bryant Shot Selection
14039531,0.7822899999999999,5,6,/jonasschroeder/top-6-eda-and-models-by-a-kaggle-noob,Titanic - Machine Learning from Disaster
13933563,0.77751,0,0,/avirajroy/titanic-ml-updated,Titanic - Machine Learning from Disaster
14018486,0.7751100000000001,0,0,/aymanmaboghonim/titanic-first-trial,Titanic - Machine Learning from Disaster
13154015,0.77751,3,4,/yyuan94/titanic-basic,Titanic - Machine Learning from Disaster
13998051,0.80861,2,3,/faisalahmedbijoy/extremely-randomized-trees,Titanic - Machine Learning from Disaster
13977623,0.7799,1,4,/goodwanghan/titanic-hp-tuning-by-fugue,Titanic - Machine Learning from Disaster
13742065,0.7751100000000001,0,0,/engkangkoh/titanic-ml-for-disaster-management-and-prediction,Titanic - Machine Learning from Disaster
13731175,0.76794,1,0,/mohammedaffan96/titanic-machine-learning-from-disaster,Titanic - Machine Learning from Disaster
13951373,0.76076,3,12,/klmsathishkumar/titanic-data-survival-prediction,Titanic - Machine Learning from Disaster
8733979,0.7751100000000001,2,1,/sebastiangt/titanic-basic-ann-workflow,Titanic - Machine Learning from Disaster
13827148,0.77272,0,1,/sungk95/titanic-practice-with-logistic-regression,Titanic - Machine Learning from Disaster
13924275,0.75358,0,0,/shubham07prasad/titanic-xgbc,Titanic - Machine Learning from Disaster
13911451,0.78468,2,11,/josupra/titanic-essentials-logist-knn-dectree-rforest,Titanic - Machine Learning from Disaster
13974410,0.7751100000000001,0,0,/tracyporter/titanic-numpy-pandas-matplotl,Titanic - Machine Learning from Disaster
13899918,0.7751100000000001,0,0,/gorkemyilmaz/titanic-project,Titanic - Machine Learning from Disaster
499316,0.9772,142,992,/jhoward/nb-svm-strong-linear-baseline,Toxic Comment Classification Challenge
13098573,0.5,0,0,/archingupta/archin-gupta-101703094-coe5,Tweet Sentiment Extraction
6152493,0.98467,0,0,/amir78pgd/fork-of-minimal-lstm-g-f-nb-svm-baseline-ensem,Toxic Comment Classification Challenge
4532139,0.9806,0,0,/barelydedicated/gru-sentiment-analysis,Toxic Comment Classification Challenge
1506687,0.9772,0,0,/louis030195/toxic-comment,Toxic Comment Classification Challenge
726836,0.5,0,0,/maheshak04/basic-nlp-with-toxic-comm,Toxic Comment Classification Challenge
716740,0.9551,0,0,/keshavbalivada/toxic-comment-classification,Toxic Comment Classification Challenge
9360288,0.632,3,5,/maya01/twitter-sentiment-extraction-using-spacy-ner,Tweet Sentiment Extraction
9426751,0.6609999999999999,0,3,/haoxliu/ner-with-spacy,Tweet Sentiment Extraction
9224565,0.705,8,16,/sarthak97/tse-eda-roberta-using-tf-lb-0-705,Tweet Sentiment Extraction
9178961,0.7020000000000001,0,1,/dougtrajano/tweet-sentiment-extraction,Tweet Sentiment Extraction
9390656,0.6970000000000001,0,0,/deskchairlamp/kernel372e24860a,Tweet Sentiment Extraction
9258765,0.6990000000000001,0,0,/adityakpranav/kernel-tweet-sentiment-extraction-3519,Tweet Sentiment Extraction
9310326,0.523,0,1,/ziedbaklouti/bow-logistic-regression-sklearn,Tweet Sentiment Extraction
9186888,0.594,0,0,/binderiyaadishaa/test-submission,Tweet Sentiment Extraction
9130580,0.6509999999999999,2,3,/alexskrn/word-selector,Tweet Sentiment Extraction
9102651,0.594,4,3,/somesh24/twitter-sentiment-analysis,Tweet Sentiment Extraction
9029897,0.7120000000000001,78,143,/al0kharba/tensorflow-roberta-0-712,Tweet Sentiment Extraction
9049702,0.708,6,12,/omegaji/distilbert-updated-score,Tweet Sentiment Extraction
9037877,0.206,0,1,/vishnukanduri/tweet-sentiment-extraction-with-tensorflow-roberta,Tweet Sentiment Extraction
6078277,0.9784,0,1,/greatgamedota/kannada-mnist-cnn,Kannada MNIST
6105453,0.932,0,1,/myasincifci/kernel4676acd02d,Kannada MNIST
5908130,0.9766,2,10,/kurianbenoy/solving-kannada-mnist-with-fastai,Kannada MNIST
6024408,0.9842,5,6,/wojciech1103/kannada-mnist-activation-visualization,Kannada MNIST
5992890,0.9868,5,22,/kaushal2896/kannada-mnist-using-cnn,Kannada MNIST
5945724,0.96,0,4,/pavanbagli/kannadamnist-with-lenet,Kannada MNIST
5984608,0.203,0,2,/shaunthesheep/mnist-kannada,Kannada MNIST
5946673,0.9892,2,8,/chrisluo266/keras-for-kannada-mnist,Kannada MNIST
5966744,0.983,0,2,/melissarajaram/fastai-pytorch-implementation-of-6-layer-convnet,Kannada MNIST
5995136,0.9768,0,3,/huangyifan/kernel3a15c9b2c1,Kannada MNIST
5609840,0.9738,2,4,/revyung2017/kannada-mnist,Kannada MNIST
5935349,0.9774,0,4,/heckinbamboozle/simple-capsule-net-ensemble,Kannada MNIST
5890497,0.9834,0,2,/matheuseduardo/kannada-mnist-convnet,Kannada MNIST
5909658,0.956,0,8,/tunguz/pca-svm-baseline,Kannada MNIST
5883159,0.974,0,2,/zhizuijinmi/kernel2,Kannada MNIST
5897297,0.9878,1,8,/anshumandec94/6-layer-conv-nn-using-adam,Kannada MNIST
5866919,0.977,12,65,/marcovasquez/basic-ml-logistic-dt-pca-xgboost,Kannada MNIST
5878847,0.9546,0,1,/cdonohue/kannadamnist,Kannada MNIST
5878757,0.9414,0,3,/vaidyaprasad84/simple-nn-no-cnn-very-basic-model,Kannada MNIST
5862864,0.9812,4,14,/josephvm/kannada-pytorch-visualizations,Kannada MNIST
5871534,0.9824,0,3,/eeshsingh/mnist-with-keras-and-basic-cnn-lb-0-97,Kannada MNIST
11766516,0.9696,0,0,/luantrinhthanh/mysolution,Kannada MNIST
9906019,0.9734,0,0,/nitishmalagi/kannada-09,Kannada MNIST
7929554,0.9594,0,0,/abbbhishekkk/kannadamnist,Kannada MNIST
6787362,0.9868,0,0,/julianli0220/kannada-mnist,Kannada MNIST
6624476,0.9796,0,0,/flyingmuttus/kannada-mnist-with-fastai,Kannada MNIST
8373560,0.938,0,15,/corochann/permutation-importance-for-feature-selection-part1,University of Liverpool - Ion Switching
8353790,0.938,17,92,/adrianoavelar/3-simple-ideas-lb-0-938,University of Liverpool - Ion Switching
8335441,0.936,2,7,/teejmahal20/ion-550-features-lightgbm,University of Liverpool - Ion Switching
8323743,0.935,1,5,/scirpus/gp-lgb-hybrid,University of Liverpool - Ion Switching
8238540,0.938,0,3,/kalyankkr/fe-pipeline,University of Liverpool - Ion Switching
8234476,0.623,0,0,/reohonda/ion-switching-pls-regression,University of Liverpool - Ion Switching
8202399,0.939,16,79,/hawkey/pytorch-u-net-starter,University of Liverpool - Ion Switching
8234923,0.916,0,2,/ajax0564/keras-1d-cnn,University of Liverpool - Ion Switching
8198208,0.936,4,29,/teejmahal20/simple-eda-model-optimized-rounder,University of Liverpool - Ion Switching
8184671,0.938,15,54,/siavrez/fe-pipeline,University of Liverpool - Ion Switching
8168033,0.938,20,110,/siavrez/simple-eda-model,University of Liverpool - Ion Switching
8169945,0.905,4,42,/xhlulu/ion-switching-simple-bi-gru,University of Liverpool - Ion Switching
8172939,0.936,11,25,/siavrez/fe-pipeline-nn,University of Liverpool - Ion Switching
8134564,0.93983,20,137,/vbmokin/ion-switching-advfe-lgb-wavenet-confmatrix,University of Liverpool - Ion Switching
8141826,0.892,5,31,/ragnar123/first-model-lgbm,University of Liverpool - Ion Switching
8125504,0.91,21,123,/pestipeti/eda-ion-switching,University of Liverpool - Ion Switching
8128236,0.936,29,73,/artgor/eda-and-model-f1-optimization,University of Liverpool - Ion Switching
8125494,0.482,4,45,/tunguz/simple-ion-ridge-regression-starter,University of Liverpool - Ion Switching
8145491,0.3779999999999999,2,2,/maobinfu1988/exploratory-analysis-on-ion-switching-data,University of Liverpool - Ion Switching
8134393,0.575,1,9,/ragnar123/exploratory-data-analysis-and-model-baseline,University of Liverpool - Ion Switching
8131313,0.133,0,4,/gpreda/quick-starter-ion-switching-prediction,University of Liverpool - Ion Switching
6239003,2.908,0,1,/jt120lz/energy-baseline-predict,ASHRAE - Great Energy Predictor III
13209880,1.154,0,0,/mingyusun/mingyu-sun-ashrae-project-by-meter-type,ASHRAE - Great Energy Predictor III
8247479,0.952,0,0,/patrick0302/postprocessed-models,ASHRAE - Great Energy Predictor III
7691379,1.089,0,0,/yunishi0716/k-folds-model,ASHRAE - Great Energy Predictor III
7022589,1.127,0,0,/navidbamdadroshan/unitartu-ml-navid,ASHRAE - Great Energy Predictor III
6384467,1.44,0,0,/uberkinder/elegant-benchmark,ASHRAE - Great Energy Predictor III
6276237,1.15,48,139,/isaienkov/keras-nn-with-embeddings-for-cat-features-1-15,ASHRAE - Great Energy Predictor III
6757852,0.46124,0,0,/starbuucks/first-approach,Airbus Ship Detection Challenge
2381073,0.7255699999999999,0,4,/marvelousninja/kaggle-airbus-3-96-minutes,Airbus Ship Detection Challenge
2113930,0.7086399999999999,0,0,/johnnyjana730/ensemble-lafoss-and-keras-model,Airbus Ship Detection Challenge
2034459,0.628,0,11,/super13579/unet34-predict-result,Airbus Ship Detection Challenge
1950302,0.596,24,41,/kotarojp/first-step-for-submission-u-net-tta,Airbus Ship Detection Challenge
14118496,0.70168,0,1,/code1110/tabplay-linear-stacking-with-gbdt-and-nn,Tabular Playground Series - Jan 2021
14059913,0.7278100000000001,0,0,/chaudharypriyanshu/a-basic-way-to-start-your-journey-of-ml,Tabular Playground Series - Jan 2021
13974600,0.7034199999999999,0,0,/tatakkun/tabular-playground-jan-2021,Tabular Playground Series - Jan 2021
14117071,0.71414,0,0,/yasunorisato/jan00,Tabular Playground Series - Jan 2021
14017196,0.71973,5,25,/sishihara/neural-network-for-tabular-example,Tabular Playground Series - Jan 2021
14038844,0.71383,0,1,/ccollado7/tabular-playground-series-jan-2021-eda,Tabular Playground Series - Jan 2021
13955980,0.70248,1,3,/ashwinrachha1/advanced-ensemble-regression-xgb-lgb-nn,Tabular Playground Series - Jan 2021
13950448,0.6986399999999999,10,56,/ttahara/tps-jan-2021-gbdts-baseline,Tabular Playground Series - Jan 2021
13978279,0.71944,2,2,/imeintanis/tps-jan21-nn-starter-keras-tuner-ensemble,Tabular Playground Series - Jan 2021
13964440,0.7003,1,2,/erelin6613/tps-jan-2021-eda-and-baseline,Tabular Playground Series - Jan 2021
13961531,0.69952,1,10,/mathurinache/tps-jan-2021-xgb-baseline,Tabular Playground Series - Jan 2021
13954446,0.7026100000000001,1,3,/kingabzpro/kaggle-cat-boost-tabular-2021,Tabular Playground Series - Jan 2021
13956250,0.70495,2,3,/niteshhalai/tabular-playground-series-jan-2021,Tabular Playground Series - Jan 2021
12441817,0.233,0,1,/redwankarimsony/cnn-gru-baseline-stage2-train-inference,RSNA STR Pulmonary Embolism Detection
12614363,0.158,0,0,/darraghdog/rsnastr2020-prediction,RSNA STR Pulmonary Embolism Detection
12502087,0.233,0,1,/shubham47/cnn-1,RSNA STR Pulmonary Embolism Detection
12502627,0.1639999999999999,0,0,/keremt/final-inference-2d-3d-sequence-softmax,RSNA STR Pulmonary Embolism Detection
12503424,0.182,4,13,/theoviel/pe-inference-2,RSNA STR Pulmonary Embolism Detection
12475794,0.225,0,2,/zangyangguang/fork-of-cnn-gru-baseline-stage2-train-inference,RSNA STR Pulmonary Embolism Detection
12493678,0.16,1,4,/osciiart/rsna2020-final-sub2,RSNA STR Pulmonary Embolism Detection
12513238,0.183,0,3,/bleaf21/submission-ensemble-02tx-bh,RSNA STR Pulmonary Embolism Detection
12359774,0.233,33,147,/khyeh0719/cnn-gru-baseline-stage2-train-inference,RSNA STR Pulmonary Embolism Detection
5225539,0.9363,0,3,/bootiu/simple-lightgbm-ieee-cis,IEEE-CIS Fraud Detection
5240378,0.8881,0,0,/woaisxxd/keras-nn-starter-shuffle-split,IEEE-CIS Fraud Detection
5201533,0.9057,0,14,/ryches/keras-nn-starter-shuffle-split,IEEE-CIS Fraud Detection
5165903,0.9143,7,79,/ryches/keras-nn-starter-w-time-series-split,IEEE-CIS Fraud Detection
5160565,0.8542,1,1,/mohdsohaib/nn-for-beginners,IEEE-CIS Fraud Detection
5107240,0.9201,32,208,/kabure/almost-complete-feature-engineering-ieee-data,IEEE-CIS Fraud Detection
5120257,0.9272,14,29,/kirankunapuli/ieee-fraud-stacknet-on-gpu-lgb-xgb-cb,IEEE-CIS Fraud Detection
4986651,0.9155,6,27,/vaishvik25/nn-conv1d,IEEE-CIS Fraud Detection
4974620,0.8761,3,7,/prazhant/h2o-automl-tutorial-ieee-cis-fraud-detection,IEEE-CIS Fraud Detection
4940187,0.9429,7,43,/stocks/stacking-higher-and-higher,IEEE-CIS Fraud Detection
4917732,0.9425,2,24,/rajwardhanshinde/stacking,IEEE-CIS Fraud Detection
4921309,0.6929,0,0,/thaer2018/automlclassifierforfrauds-usingtpot,IEEE-CIS Fraud Detection
4876300,0.9386,2,11,/phoenix9032/ieee-fraud-my-first-ml-trial,IEEE-CIS Fraud Detection
12314250,0.8529,0,1,/nehalbandal/airbnb-predicting-user-s-1st-booking-destination,Airbnb New User Bookings
12177093,0.23325,0,0,/gizemayurderi/gizem-airbnb-model-fitting-drop-ndf,Airbnb New User Bookings
7212779,0.68719,0,1,/ayafarrag/eventumtask,Airbnb New User Bookings
6339704,0.8563700000000001,1,1,/ma7555/in-depth-analysis-and-a-keras-dense-model,Airbnb New User Bookings
5553433,0.8639399999999999,0,1,/omarqasim/eventum-task,Airbnb New User Bookings
3165059,0.36933,0,1,/rucrazia/rucrazia-s-airbnb-data-eda,Airbnb New User Bookings
7972348,0.8651,0,0,/xialalala/airbnb-kernel-for-final-project-accuracy-0-86510,Airbnb New User Bookings
5652045,0.0,0,3,/hirokazu12/capsnet-dr,Diabetic Retinopathy Detection
1662531,0.979907,1,2,/nachogarcia88/xgboost-gridsearchcv,Flavours of Physics: Finding τ  →  μμμ
14522401,0.78,0,0,/alinaherderich/submisson-alina-resnet34-v2,HuBMAP - Hacking the Kidney
13883484,0.81,0,2,/sumitjha19/densenet121unet-inference,HuBMAP - Hacking the Kidney
13888768,0.865,22,79,/tivfrvqhs5/global-mask-shift,HuBMAP - Hacking the Kidney
13404065,0.84,9,19,/joshi98kishan/inference-pytorch-tta-sub-0-84,HuBMAP - Hacking the Kidney
13231978,0.828,11,74,/finlay/pytorch-fcn-resnet50-in-20-minute,HuBMAP - Hacking the Kidney
13253190,0.8290000000000001,0,10,/itsuki9180/hubmap-gpu-inference-phase,HuBMAP - Hacking the Kidney
13154843,0.782,0,2,/mistag/inference-hubmap-u-net-512x512,HuBMAP - Hacking the Kidney
13187520,0.795,3,9,/nayuts/hubmap-pytorch-smp-unet-inference,HuBMAP - Hacking the Kidney
738319,0.0,0,0,/dcampeao/using-reg-season-results-logistic-regression,Google Cloud & NCAA® ML Competition 2018-Women's
9357347,0.5489999999999999,0,0,/dzz1th/kernel3de146856b,ALASKA2 Image Steganalysis
9242551,0.799,12,52,/eswarchandt/alaska2-eda-and-efficientnet,ALASKA2 Image Steganalysis
9183068,0.789,1,16,/soham1024/alaska2-inceptionresnetv2-on-tpus,ALASKA2 Image Steganalysis
9158956,0.619,8,41,/pednt9/alaska2-srnet-in-keras,ALASKA2 Image Steganalysis
9172469,0.5,0,0,/grapestone5321/alaska2-image-steganalysis-sample-submission,ALASKA2 Image Steganalysis
13295176,0.754,0,2,/benjibb/xse-resnext50-and-ranger,Cassava Leaf Disease Classification
13260672,0.8490000000000001,2,3,/razatabish/cassava-leaf-disease-classification,Cassava Leaf Disease Classification
13255750,0.6679999999999999,0,0,/j2hoon85/kaggle-for-korean-beginners,Cassava Leaf Disease Classification
13209362,0.884,0,2,/venkat555/cassava-leaf-disease-tpu-tensorflow-inference,Cassava Leaf Disease Classification
13179335,0.805,1,0,/ritik2209/inference,Cassava Leaf Disease Classification
13020876,0.895,0,4,/marcelosanchezortega/pytorch-efficientnet-multiple-tta,Cassava Leaf Disease Classification
13080105,0.897,4,32,/dunklerwald/pytorch-efficientnet-with-tta-inference,Cassava Leaf Disease Classification
13081575,0.9,4,19,/salmaneunus/cassava-leaf-disease-classification-2,Cassava Leaf Disease Classification
13102738,0.631,0,1,/npan1990/simple-eda-inception,Cassava Leaf Disease Classification
13049375,0.8290000000000001,0,5,/anukool89/modelling-and-inference-with-pytorch-resnext,Cassava Leaf Disease Classification
12607235,0.99778,0,1,/tt195361/digit-recognizer-resnet50-only-train-csv-0-99778,Digit Recognizer
13231007,0.99207,0,0,/gsmoura/digits-recognizer,Digit Recognizer
13208178,0.99096,2,2,/mohitkarelia/cnn-on-mnist,Digit Recognizer
13194982,0.98957,0,3,/anirudhjain2712/mnist-digit-recognizer-convolutional-nn,Digit Recognizer
5674715,0.996,0,0,/moudip/mnist-keras-dip,Digit Recognizer
12862208,0.9686,0,0,/qijun5683/digit-recognizer,Digit Recognizer
13071393,0.99339,1,1,/ravijain01/mnist-using-cnn-for-beginners,Digit Recognizer
13106716,0.96892,0,1,/nandhinirajesh29/notebook1ffda58289,Digit Recognizer
13021235,0.9905,3,4,/aicentral/mnist-using-cnn-keras,Digit Recognizer
13036955,0.1140299999999999,1,2,/mitunkantipaul/digit-recognition-tensorflow,Digit Recognizer
13008011,0.96482,0,3,/sagnik1511/how-to-participate-in-a-competition,Digit Recognizer
12974744,0.96757,0,3,/vaibhavkumar808/introduction-to-ann-on-digit-set-keras,Digit Recognizer
12686729,0.99064,1,3,/tharakasampath/digitrecognizer,Digit Recognizer
10048979,0.97271,0,0,/sksahidulislam/mnist-digit-recognizer,Digit Recognizer
12895956,0.97357,0,2,/mahmoud1youssef/logistic-regression-from-scratch,Digit Recognizer
12762404,0.99871,0,2,/heitorbaldo/digit-recognition-with-cnn-using-keras,Digit Recognizer
10345020,0.97371,0,0,/hamiddd/final-test-cnn,Digit Recognizer
10023765,0.72265,0,0,/visiteur/lab-cats-log-regression-with-minmaxscaler,Categorical Feature Encoding Challenge II
1629652,0.8,6,2,/sajinpgupta/u-net-with-resnet-block-1-4-0-8-score,TGS Salt Identification Challenge
1607821,0.802,2,4,/nikhilroxtomar/u-net-with-simple-resnet-blocks-v2-new-loss,TGS Salt Identification Challenge
1593497,0.6459999999999999,0,1,/k8tems/intro-to-seismic-salt-and-how-to-geophysics,TGS Salt Identification Challenge
1543186,0.789,45,99,/shaojiaxin/u-net-with-simple-resnet-blocks,TGS Salt Identification Challenge
1558959,0.465,0,9,/hortonhearsafoo/tgs-salt-identification-fastai-unet-resnet,TGS Salt Identification Challenge
1464854,0.772,3,18,/nikhilroxtomar/u-net-with-image-augmentation,TGS Salt Identification Challenge
1430701,0.794,17,43,/meaninglesslives/apply-crf-unet-resnet,TGS Salt Identification Challenge
1425013,0.731,5,21,/ebberiginal/tgs-salt-keras-unet-depth-data-augm-strat,TGS Salt Identification Challenge
1407285,0.528,0,13,/sanket30/semantic-segmentation-using-u-net-abstract-layer,TGS Salt Identification Challenge
1361493,0.684,7,31,/leighplt/goto-pytorch-fix-for-v0-3,TGS Salt Identification Challenge
1363097,0.6729999999999999,0,1,/avinashrai/unet-with-depth,TGS Salt Identification Challenge
1343536,0.7440000000000001,27,110,/meaninglesslives/apply-crf,TGS Salt Identification Challenge
1354957,0.6459999999999999,0,0,/anondo/fork-of-intro-to-seismic-salt-and-how-to-geophys,TGS Salt Identification Challenge
247642,0.31636,0,0,/avishek/fork-of-naive-xgb-increase-depth,Sberbank Russian Housing Market
246808,0.34702,0,0,/josephbudin/naive-xgb,Sberbank Russian Housing Market
245725,0.32524,0,0,/vabatista/naive-xgb-lb-0-317,Sberbank Russian Housing Market
3900724,2.44464,0,2,/chulongli/tmdb-feat-eng-xgb-tuning,TMDB Box Office Prediction
3917115,3.73247,0,2,/chaojiewang/movie-revenue-xgboost,TMDB Box Office Prediction
3868655,2.75508,0,1,/vivaroma/simple-neural-network,TMDB Box Office Prediction
3814419,1.90717,0,1,/nnick14/box-office-prediction-with-xgboost,TMDB Box Office Prediction
3602292,2.07872,0,0,/kushalnaidu/data-mining-assignment,TMDB Box Office Prediction
3758123,2.66741,0,2,/silverfoxdss/revenues-intuitions-pandasql,TMDB Box Office Prediction
3700614,2.26684,0,2,/oguzkoroglu/tmdb-features-for-catboost-optimization,TMDB Box Office Prediction
3709104,3.99053,0,0,/tmznql1234/tmdb-base-model,TMDB Box Office Prediction
3501544,2.05825,0,2,/milesma/tmdb-box-office-prediction,TMDB Box Office Prediction
3570749,2.01326,0,7,/akumaldo/tmdb-prediction-eda-lgb-xgb-cat-plus-keras-nn,TMDB Box Office Prediction
3543958,3.5092,0,1,/sridharnarasaiahgari/movie-kernel-telugu,TMDB Box Office Prediction
3468429,5.49173,0,10,/dude431/eda-fe-lgbm-no-external-data,TMDB Box Office Prediction
3402874,2.13886,5,16,/dway88/feature-eng-feature-importance-random-forest,TMDB Box Office Prediction
3399876,2.49632,0,0,/tijlkindt/simple-tmdb-prediction-with-linear-regression,TMDB Box Office Prediction
3368490,2.13942,0,1,/oluwaody/where-fun-and-finance-meet,TMDB Box Office Prediction
3356486,2.50304,3,9,/alexandermelde/code-template-for-simple-regression-prediction,TMDB Box Office Prediction
3040729,2.76877,0,0,/anitha136/tmdb-box-office-prediction-xg-boost,TMDB Box Office Prediction
3092736,1.97972,0,7,/amignan/in-parameter-space-no-one-can-hear-you-scream,TMDB Box Office Prediction
14202102,0.602,3,4,/nickuzmenkov/cassava-leaf-disease-na-ve-baseline,Cassava Leaf Disease Classification
13465920,0.78,0,0,/mohamedbouabidi/casava-first-run,Cassava Leaf Disease Classification
13857194,0.897,0,1,/krashennikovalexandr/cassava-leaf-disease-tpu-tensorflow-inference,Cassava Leaf Disease Classification
14217937,0.8490000000000001,0,0,/garycho/notebook3f82f4173a,Cassava Leaf Disease Classification
14111611,0.899,17,15,/durbin164/tpu-bitempered-logistic-loss-inferance,Cassava Leaf Disease Classification
13840366,0.748,0,0,/kunalgaurav18/version-3,Cassava Leaf Disease Classification
13057068,0.883,5,28,/kyoshioka47/psuedo-labeling-with-efficientnet,Cassava Leaf Disease Classification
14109551,0.889,0,0,/enricogrisan/golds-vit-submission,Cassava Leaf Disease Classification
13680985,0.877,0,0,/corrrado/cassava-xception-part1,Cassava Leaf Disease Classification
14060358,0.813,2,4,/prashant2301/cassava-efficientnet,Cassava Leaf Disease Classification
14005807,0.545,0,1,/siddhantsadangi/logreg-baseline-for-beginners,Cassava Leaf Disease Classification
13418167,0.895,0,0,/zekun98/notta-pytorch-efficientnet-baseline,Cassava Leaf Disease Classification
3928867,0.6305,0,0,/jliu104/histopathologic-cancer-detector-lb-0-958,Histopathologic Cancer Detection
3827471,0.9622,0,1,/sdoctor86/kernel68f688de06,Histopathologic Cancer Detection
3352210,0.971,0,0,/benjibb/densenet-resnet-ensemble,Histopathologic Cancer Detection
3306362,0.9607,0,3,/dromosys/fastai-v1-densenet201-heatmap,Histopathologic Cancer Detection
3253784,0.9744,17,26,/jionie/tta-power-densenet169,Histopathologic Cancer Detection
3185776,0.9006,0,0,/mohanamurali/keras-cnn-stratified-k-fold-optimal-lr,Histopathologic Cancer Detection
3125301,0.9656,0,10,/rohitgr/fastaiv1-with-densenet121-and-focal-loss,Histopathologic Cancer Detection
3079817,0.9625,1,3,/sayantandas30011998/fastai-v1-densenet201,Histopathologic Cancer Detection
2929726,0.9556,5,6,/chanhu/residual-attention-network-pytorch,Histopathologic Cancer Detection
2952699,0.971,2,20,/dromosys/fastai-v1-densenet201,Histopathologic Cancer Detection
2643806,0.9452,0,1,/obischenko/nasnetmobile-fully-convolutional-network,Histopathologic Cancer Detection
2846440,0.9678,0,1,/sdeagggg/densenet121-with-fast-ai-v1,Histopathologic Cancer Detection
2559893,0.8641,0,1,/esuchards/keras-cancer-3,Histopathologic Cancer Detection
2712215,0.9419,10,37,/gomezp/complete-beginner-s-guide-eda-keras-lb-0-93,Histopathologic Cancer Detection
2140141,0.9621,0,5,/userdxz/res34-with-pytorch,Histopathologic Cancer Detection
2430869,0.6303,2,20,/dmitrypukhov/cnn-with-imagedatagenerator-flow-from-dataframe,Histopathologic Cancer Detection
2442887,0.7626,1,4,/mrgraeme/histopathologic-keras-conv-v1,Histopathologic Cancer Detection
3462856,0.40235,0,4,/qooqoo/bike-with-randomforestregressor,Bike Sharing Demand
2719562,0.42573,0,0,/hajekim/bike-sharing-demand-20190124-ds-school,Bike Sharing Demand
2475348,0.3961,0,0,/latafa/logy-n-estimators,Bike Sharing Demand
2200876,0.47178,0,0,/sungdoo/i-want-to-ride-my-bicycle,Bike Sharing Demand
2133027,0.57613,0,0,/jbfields/discover-profiles-to-predict,Bike Sharing Demand
2146505,0.413,0,0,/anisio/iesb-python-e-pandas-aula-05-01d396,Bike Sharing Demand
1788208,0.3921699999999999,0,7,/nandobr/python-machine-learning-hands-on,Bike Sharing Demand
1264566,0.41955,0,0,/xhiddu/predicting-bike-demand,Bike Sharing Demand
604509,0.45621,0,4,/liuzhui/bike-demand,Bike Sharing Demand
156375,0.47453,1,11,/yaroshevskiy/bike-rental-predictions-using-lr-rf-gbr,Bike Sharing Demand
11889003,0.71391,0,3,/archingupta/archin-gupta-101703094,Categorical Feature Encoding Challenge
11844834,0.75548,0,2,/diljotwadia/notebookd53bffcf48,Categorical Feature Encoding Challenge
5510698,0.70835,1,2,/martinab/categorical-features-encoding,Categorical Feature Encoding Challenge
8302223,0.69793,0,0,/jarodc/kernelf7e6080591,Categorical Feature Encoding Challenge
8914912,0.71258,0,0,/adarsh415/entity-embedding-tf2-0,Categorical Feature Encoding Challenge
5544039,0.8036800000000001,0,0,/chetanambi/categorical-feature-encoding-challenge,Categorical Feature Encoding Challenge
6742348,0.80819,0,2,/khoongweihao/categorical-feature-encoding-cat-in-dat-top-10,Categorical Feature Encoding Challenge
5508920,0.80152,0,1,/gsdeepakkumar/categorical-encoding-training,Categorical Feature Encoding Challenge
6860439,0.80848,0,4,/tezdhar/lr-v2,Categorical Feature Encoding Challenge
6943845,0.70211,0,2,/sangyunkang/categorical-feature-encoding-challenge,Categorical Feature Encoding Challenge
6882889,0.70428,0,0,/cmcready/final-project,Categorical Feature Encoding Challenge
6898291,0.80793,0,18,/vbmokin/one-hot-stratified-logregress-with-my-tuning,Categorical Feature Encoding Challenge
6905603,0.80793,1,1,/radream/logistic-regression-for-cat,Categorical Feature Encoding Challenge
6842879,0.80804,0,18,/vbmokin/cfec-fe-model-selection-xgb-lgbm-logr-linr,Categorical Feature Encoding Challenge
6830608,0.80202,0,7,/ravijoe/vanilla-logistic-regression,Categorical Feature Encoding Challenge
6812601,0.55847,0,1,/dskagglemt/categorical-feature-encoding-challenge-2,Categorical Feature Encoding Challenge
6749294,0.8085,0,5,/c7934597/on-hot-encoding-for-categorical-encoding,Categorical Feature Encoding Challenge
6666124,0.80266,18,23,/ruchibahl18/categorical-data-encoding-techniques,Categorical Feature Encoding Challenge
6501725,0.64772,0,1,/devkhant24/categorical-encoding-classification-prediction,Categorical Feature Encoding Challenge
6585650,0.79688,0,1,/norm154845/kernel49510b5a77,Categorical Feature Encoding Challenge
6527060,0.80791,0,4,/pavelvpster/cat-in-dat-ohe-logit,Categorical Feature Encoding Challenge
9552424,0.956349,0,8,/mustafabozkurt/fraud-final,IEEE-CIS Fraud Detection
9461978,0.904858,0,3,/mustafabozkurt/fraud-pca,IEEE-CIS Fraud Detection
6022801,0.9444,1,1,/dhyeok1996/xgb-groupkfold,IEEE-CIS Fraud Detection
7962124,0.940107,0,1,/foodaholic/cis-fraud-detection-lightgbm,IEEE-CIS Fraud Detection
7971463,0.828109,1,1,/slatawa/first-model-submission-with-basic-eda,IEEE-CIS Fraud Detection
7340548,0.711692,0,2,/ahmedehabessa/kernel-1,IEEE-CIS Fraud Detection
6530072,0.922273,3,8,/pramod44/fraud-detection-with-lightgbm,IEEE-CIS Fraud Detection
6198235,0.807687,0,1,/ssvitian/simpleieeehack,IEEE-CIS Fraud Detection
5207209,0.9303,0,0,/ajaykgp12/ieee-catboost,IEEE-CIS Fraud Detection
6148311,0.943891,0,3,/muke5hy/modeling-and-training-ieee-cis-fraud-detection,IEEE-CIS Fraud Detection
6252884,0.96191,0,4,/daishu/10th-submission-part-daishu,IEEE-CIS Fraud Detection
5742113,0.8273,0,0,/ruhong/ieee-fraud-detection-logistic,IEEE-CIS Fraud Detection
6016059,0.8982,1,1,/suecooper/tuned-xgboostclassifier,IEEE-CIS Fraud Detection
6023314,0.94388,0,0,/nevret93/xgb-single-model-with-fe,IEEE-CIS Fraud Detection
5885426,0.9053,0,1,/wodlfrh/baseline-wt-useful-feature,IEEE-CIS Fraud Detection
12264216,0.0309899999999999,0,1,/tunguz/cats-vs-dogs-with-eb0-eb7-ns,Dogs vs. Cats Redux: Kernels Edition
12225247,0.032,1,1,/tunguz/cats-vs-dogs-with-eb4-ns,Dogs vs. Cats Redux: Kernels Edition
12224229,0.03217,0,1,/tunguz/cats-vs-dogs-with-eb5-ns,Dogs vs. Cats Redux: Kernels Edition
12175605,0.03661,1,13,/tunguz/cats-vs-dogs-with-eb7-and-catboost,Dogs vs. Cats Redux: Kernels Edition
12173084,0.03526,0,1,/tunguz/xgb-gpu-cats-vs-dogs-with-eb7,Dogs vs. Cats Redux: Kernels Edition
12168456,0.05417,1,2,/tunguz/cats-vs-dogs-with-eb0-pretrained-embeddings,Dogs vs. Cats Redux: Kernels Edition
12170122,0.04035,0,1,/tunguz/cats-vs-dogs-with-eb4-pretrained-embeddings,Dogs vs. Cats Redux: Kernels Edition
12134530,0.03911,0,3,/tunguz/xgb-cats-vs-dogs-with-irv2-pretrained-embeddings,Dogs vs. Cats Redux: Kernels Edition
12128414,0.105,0,2,/rikdifos/dogs-vs-cats-densenet201,Dogs vs. Cats Redux: Kernels Edition
12012238,0.04967,0,1,/tunguz/cats-vs-dogs-with-dn201-pretrained-embeddings,Dogs vs. Cats Redux: Kernels Edition
12011915,0.0522399999999999,0,1,/tunguz/cats-vs-dogs-with-dn121-pretrained-embeddings,Dogs vs. Cats Redux: Kernels Edition
11996542,0.03018,0,3,/tunguz/cats-and-dogs-with-rapids-and-embeddings,Dogs vs. Cats Redux: Kernels Edition
11995364,0.1187,0,1,/tunguz/cats-vs-dogs-with-rn50-pretrained-embeddings-lr,Dogs vs. Cats Redux: Kernels Edition
11589927,0.13967,0,6,/rikdifos/dogs-vs-cats-efficientnetb7,Dogs vs. Cats Redux: Kernels Edition
11027712,0.06406,3,8,/sambitmukherjee/tricks-to-improve-an-image-classifier-fastai,Dogs vs. Cats Redux: Kernels Edition
576064,0.47031,0,0,/laxmikanth/mercari-price-challenge,Mercari Price Suggestion Challenge
575170,0.94843,0,0,/peicozy/mercari-price-suggestion-challenge-try-1,Mercari Price Suggestion Challenge
561378,0.4855399999999999,2,0,/lucenya/mercari-price-nn,Mercari Price Suggestion Challenge
509406,0.4928,0,0,/yicao2/notebookb1b8bf511c,Mercari Price Suggestion Challenge
491991,0.85588,0,0,/maheshsinha/mercari-test1,Mercari Price Suggestion Challenge
464702,0.82478,1,0,/prettyandhuge/notebook70dc216453,Mercari Price Suggestion Challenge
456406,0.53298,0,0,/obi1992/xgboost-3,Mercari Price Suggestion Challenge
455380,0.47216,1,0,/koheiotani/nn-solution-with-keras,Mercari Price Suggestion Challenge
2500746,0.38,20,58,/peterhurford/pets-lightgbm-baseline-with-all-the-data,PetFinder.my Adoption Prediction
2497824,0.348,23,62,/carlolepelaars/eda-and-ensembling,PetFinder.my Adoption Prediction
2508175,0.263,0,3,/xitizzz/eda-lightgbm,PetFinder.my Adoption Prediction
2503637,0.303,6,7,/chriswill/initial-models-and-submission-starter-code,PetFinder.my Adoption Prediction
2499062,0.322,0,2,/cmougan/eda-easyrf,PetFinder.my Adoption Prediction
3184899,0.44,0,0,/divyanshusharma1709/mykernel,PetFinder.my Adoption Prediction
3024250,0.3,0,0,/mohittripathi/catboost-v2,PetFinder.my Adoption Prediction
7109480,0.69314,0,1,/tooshilow/kernel2cf39b7d27,Deepfake Detection Challenge
7317768,0.69235,77,122,/unkownhihi/starter-kernel-with-cnn-model-ll-lb-0-69235,Deepfake Detection Challenge
7219914,0.73219,8,9,/rhysie/deepfake-detection-with-simple-features-0-73,Deepfake Detection Challenge
7213644,0.81215,0,0,/muerbingsha/data-leak-in-metadata,Deepfake Detection Challenge
7066193,0.7904800000000001,3,0,/bootiu/deepfake-trainer,Deepfake Detection Challenge
7044470,17.269379999999998,13,25,/unkownhihi/starter-kernel-with-cnn-model,Deepfake Detection Challenge
7010284,0.69314,39,102,/marcovasquez/basic-eda-face-detection-split-video-and-roi,Deepfake Detection Challenge
7015529,0.71355,2,41,/minhtam/fake-detect-basic,Deepfake Detection Challenge
7017011,0.78032,5,16,/techytushar/frame-size-and-video-length-eda,Deepfake Detection Challenge
7007311,1.06887,0,4,/sriharihumbarwadi/random-baseline,Deepfake Detection Challenge
8212093,0.46788,0,0,/yufeng8607/inference-demo,Deepfake Detection Challenge
13349586,1.365,0,0,/alexandersylvester/ashrae-energy-predictions-with-lightgbm,ASHRAE - Great Energy Predictor III
6772636,1.08,0,0,/alexishchenko/ashare-scoring,ASHRAE - Great Energy Predictor III
7097440,1.217,0,3,/mgiraygokirmak/ashrae-energy-prediction-using-stratified-kfold,ASHRAE - Great Energy Predictor III
8786581,1.065,0,0,/rjings/sunlightsedu-ashrae-lgb,ASHRAE - Great Energy Predictor III
6364596,1.534,0,0,/bootiu/ashrae-lightgbm-baseline,ASHRAE - Great Energy Predictor III
7117024,0.947,1,3,/gpamoukoff/ashrae-subm-stack-fin-3,ASHRAE - Great Energy Predictor III
6862349,0.964,1,1,/yamsam/kfold-lightgbm-sg,ASHRAE - Great Energy Predictor III
6944220,1.039,0,2,/vladimirsydor/bland-by-leak,ASHRAE - Great Energy Predictor III
6920644,1.01,0,1,/vladimirsydor/bland-lgbm-on-leaks,ASHRAE - Great Energy Predictor III
10776140,0.75303,4,6,/soumyasrivastava11/fork-of-otto-group-classification,Otto Group Product Classification Challenge
9232545,6.41922,0,1,/mehradbz/kerneldbcbc137c9,Otto Group Product Classification Challenge
1897711,0.47791,0,1,/wakamezake/otto-simple-xgb,Otto Group Product Classification Challenge
8570966,0.82362,0,0,/wakamezake/otto-simple-lgb,Otto Group Product Classification Challenge
8244656,0.42405,0,4,/masatomatsui/otto-stacking-lgb,Otto Group Product Classification Challenge
8037768,1.28529,0,1,/masatomatsui/otto-knn,Otto Group Product Classification Challenge
7813048,0.4809199999999999,0,4,/wakamezake/tf-keras-otto-neuralnetwork,Otto Group Product Classification Challenge
1980202,0.43567,1,10,/mok0na/tc1-projet-otto-xgboost,Otto Group Product Classification Challenge
859617,7.2305899999999985,0,0,/itsmystyl123/kernel81957c00d8,Otto Group Product Classification Challenge
472302,0.66809,0,0,/chinmay2312/cs514-aai,Otto Group Product Classification Challenge
13780302,0.509,0,0,/alievik/otto-group-product-classification,Otto Group Product Classification Challenge
1941101,1.5728,0,5,/smasar/featureevaluation-randomforest,Google Analytics Customer Revenue Prediction
1922592,1.4139,0,9,/zydaib/life-is-wonderful-with-machine-learning-lb-1-4139,Google Analytics Customer Revenue Prediction
1847253,1.699,0,3,/jitesh03/first-model-2-variables-1-6990,Google Analytics Customer Revenue Prediction
1874216,1.4329,1,4,/nikitayudin/my-homework-v2,Google Analytics Customer Revenue Prediction
1871982,1.4289,0,2,/ssserov/gstore-customers-homework,Google Analytics Customer Revenue Prediction
1856638,1.4357,0,2,/lipann/data-analysis-fe-lgbm-mmp-msu,Google Analytics Customer Revenue Prediction
1859641,1.3233,0,3,/igauty/story-of-a-leak-v01,Google Analytics Customer Revenue Prediction
1808565,1.4176,67,155,/ogrellier/i-have-seen-the-future,Google Analytics Customer Revenue Prediction
1787533,1.4215,19,39,/prashantkikani/ensembling-fe-is-the-answer,Google Analytics Customer Revenue Prediction
1781526,1.4331,0,29,/xavierbourretsicotte/light-gbm-wip,Google Analytics Customer Revenue Prediction
1788428,1.9971,3,5,/aks709/data-cleaning-and-elimination-of-unnecessary-data,Google Analytics Customer Revenue Prediction
1779819,1.4285,7,11,/scirpus/teach-lightgbm-to-sum-predictions-with-gp,Google Analytics Customer Revenue Prediction
9627565,0.713,0,7,/gyanendradas/tweet-sentiment-roberta-pytorch-pseudo-label,Tweet Sentiment Extraction
13197815,0.6985100000000001,0,0,/harryvine/distilbert-qa,Tweet Sentiment Extraction
13099065,0.5077,0,0,/lakshyapandey7/tweet-sentiment-extraction-by-lakshya,Tweet Sentiment Extraction
13105344,0.59451,0,0,/nitinberwal/notebooked5271d326,Tweet Sentiment Extraction
12960580,0.59451,0,0,/eterna1demon/lstm-tweet-sentiment-extraction,Tweet Sentiment Extraction
12880599,0.65194,0,0,/sniperline5/senti-analysis,Tweet Sentiment Extraction
12596273,0.62432,0,0,/bharath150/nn-wordlevel,Tweet Sentiment Extraction
12621720,0.59097,0,0,/bharath150/tse-nn-concat-first,Tweet Sentiment Extraction
12528529,0.69998,0,0,/sais01/tse-roberta,Tweet Sentiment Extraction
12286312,0.58487,0,1,/bharath150/tse-nn-models,Tweet Sentiment Extraction
11730236,0.59451,0,0,/antigravityimport/test-notebook,Tweet Sentiment Extraction
11851432,0.6450899999999999,0,1,/nishant483/simple-solution-with-tf-idf-scores,Tweet Sentiment Extraction
11428975,0.7091,0,4,/deepakat002/tweeter-sentiment-phrase-extraction-roberta,Tweet Sentiment Extraction
11180407,0.65717,0,0,/nthoangcute/tweet-sentiment-extraction-vietbt-mle501,Tweet Sentiment Extraction
9407115,0.466,0,0,/avali21/tweet-bert,Tweet Sentiment Extraction
8564249,0.708,0,1,/narenrathore/kernela2cd255601,Tweet Sentiment Extraction
8872759,0.98473,0,1,/ankitbarai507/toxic-comment-classification-with-bi-lstm,Toxic Comment Classification Challenge
8099181,0.6544399999999999,0,2,/darwinwin/toxic-comments-with-tf-embeddings-and-h2o-automl,Toxic Comment Classification Challenge
620203,0.6149,0,0,/rahullalu/toxic-comment-classifier,Toxic Comment Classification Challenge
6517333,0.97722,0,0,/arifali77/nb-svm-strong-linear-baseline-toxic-comment,Toxic Comment Classification Challenge
6433640,0.987,0,4,/amir78pgd/ensemble-3-blend,Toxic Comment Classification Challenge
6458076,0.98692,0,0,/amir78pgd/ensemble-6-blend,Toxic Comment Classification Challenge
6442367,0.98699,0,0,/amir78pgd/ensemble-lightgbm,Toxic Comment Classification Challenge
6316099,0.98202,0,1,/amir78pgd/bi-gru-lstm-dual-embedding-with-mish,Toxic Comment Classification Challenge
6347034,0.9798,0,0,/amir78pgd/capsule-net-with-gru,Toxic Comment Classification Challenge
6123924,0.98091,0,0,/amir78pgd/improved-lstm-baseline-bi-lstm-dual-embed-dehyp,Toxic Comment Classification Challenge
6157271,0.98472,0,0,/amir78pgd/fork-of-minimal-lstm-g-f-nbsvm-baseline-ensem3,Toxic Comment Classification Challenge
5872321,0.97583,0,2,/jeethathi/toxic-text-classification,Toxic Comment Classification Challenge
5792040,0.98142,0,0,/amir78pgd/improved-lstm-baseline-bi-gru-dual-embedding,Toxic Comment Classification Challenge
3868152,0.98687,1,3,/tunguz/bi-lstm-gru-dual-embedding-new-test-cleaned-1,Toxic Comment Classification Challenge
3857121,0.98656,0,1,/tunguz/bi-gru-dual-embedding-new-test-3,Toxic Comment Classification Challenge
3871666,0.98689,0,1,/tunguz/bi-lstm-gru-dual-embedding-new-test-cleaned-4,Toxic Comment Classification Challenge
4794041,0.97588,2,8,/nikkisharma536/fastai-toxic,Toxic Comment Classification Challenge
4477558,1.188,0,8,/cyberia/eda-set-theory-contingency-tables,Predicting Molecular Properties
4379483,0.632,21,62,/corochann/eda-and-graph-nn-baseline-modeling,Predicting Molecular Properties
4423628,0.039,1,11,/linards/feature-generation-with-molmod-library,Predicting Molecular Properties
4302702,-0.685,5,28,/adrianoavelar/molecule-pred-feature-eng-lb-0-685,Predicting Molecular Properties
4162311,-0.721,46,257,/artgor/using-meta-features-to-improve-model,Predicting Molecular Properties
4181962,-0.63,18,97,/jmtest/molecule-with-openbabel,Predicting Molecular Properties
4128038,1.276,0,3,/lvulliard/regression-approach,Predicting Molecular Properties
4078146,-0.3989999999999999,39,182,/kabure/simple-eda-lightgbm-autotuning-w-hyperopt,Predicting Molecular Properties
4076991,0.373,18,128,/robikscube/exploring-molecular-properties-data,Predicting Molecular Properties
4079065,0.596,3,34,/super13579/simple-eda-and-lightgbm,Predicting Molecular Properties
4096657,0.753,0,5,/scirpus/divide-and-conquer,Predicting Molecular Properties
4094223,1.187,2,4,/seshadrikolluri/simple-one-line-submission,Predicting Molecular Properties
7062637,0.9718,0,0,/ibraheemmoosa/kannada-mnist-fully-conv-with-dropout,Kannada MNIST
9243467,0.9682,0,0,/sneha5gsm/kannada-mnist,Kannada MNIST
13710708,0.9718,1,1,/mischva11/cnn-small-try-on-kannada-mnist,Kannada MNIST
13551880,0.9402,0,0,/gexueren/kannada,Kannada MNIST
13388249,0.9814,0,0,/yusufsalcan/kannada-mnist-classifier,Kannada MNIST
12996616,0.9806,0,0,/vineethvc/kannadadigits,Kannada MNIST
12492510,0.9722,0,0,/yuemengzhang/cnn-version2,Kannada MNIST
12385551,0.975,0,0,/aibeats/kannadamnist-cnn-funjavasf,Kannada MNIST
12073706,0.958,0,1,/chitramdasgupta/desi-mnist,Kannada MNIST
11426572,0.978,0,2,/dongshengcheng/kannadamnistcnn,Kannada MNIST
7436952,0.983,0,0,/thetrueharvey/kannada-mnist,Kannada MNIST
10389588,0.9796,0,0,/barracoda/kannada-mnist,Kannada MNIST
9798744,0.9728,0,0,/epdrumond/simple-cnn-classification,Kannada MNIST
9495233,0.9836,0,0,/pradneshmhatre/kannada-mnist-cnn-with-keras,Kannada MNIST
9151517,0.9864,0,0,/achyutsrivastava/toyresnet-svm,Kannada MNIST
9348442,0.9902,0,0,/dmitriybaranov/kernel42113ced1b,Kannada MNIST
9251673,0.9878,0,0,/gaponeo/kernel7b0330bf6e,Kannada MNIST
6824110,0.9672,0,9,/syamkakarla/kannada-mnist,Kannada MNIST
5927964,0.9594,1,3,/abhishekhegde1998/kannada-mnist-v1-0,Kannada MNIST
6386019,0.9684,0,0,/niteshksingh/kannada-mnist-acc-0-97,Kannada MNIST
7822975,0.9534,0,0,/yazeenyuvvh/kannada-mnist,Kannada MNIST
10878859,0.7594,0,0,/jiangbaoxiang/fork-of-fork-of-yolov5-pseudo-labeling,Global Wheat Detection
13845384,0.7282,0,0,/kevinlu2240/yolov5-inference,Global Wheat Detection
13835610,0.7503,0,0,/kevinlu2240/yolov5-pseudo,Global Wheat Detection
13826409,0.7144,0,0,/chia56028/yolov5-stable,Global Wheat Detection
13346291,0.7059,0,0,/jacobmorin/fasterrcnn-pseudo-labeling,Global Wheat Detection
13404706,0.6708,0,0,/ikaynov/detectron2-retinanet-inference,Global Wheat Detection
11022529,0.7245,0,0,/chenzecharya/yolo-tta-ensemble-48e-weights,Global Wheat Detection
10910608,0.7415,0,0,/kyushuxin/kernel1483a64e17,Global Wheat Detection
12014212,0.7309,0,0,/davidmagny/efficientdet-pseudo-labeling-tta,Global Wheat Detection
9981004,0.6036,0,0,/surenj/detectron2-wheat-detection-gpu,Global Wheat Detection
11031238,0.72,0,0,/torikun3/kernel712aef1747,Global Wheat Detection
10510835,0.7457,0,0,/ffares/ensemble-3-tta-efficientdet557-1-tta-yolov5x,Global Wheat Detection
8582972,1.08627,0,0,/osciiart/covid19-catboost,COVID19 Global Forecasting (Week 1)
8576601,1.07112,0,0,/sergeyverbitskiy/kernel1b192b4d35,COVID19 Global Forecasting (Week 1)
8535551,1.37957,1,0,/sazinsamin/covid-19-prediction,COVID19 Global Forecasting (Week 1)
8584754,0.93861,0,0,/puneetbhateja93/covid-forecast-by-stepfunction-v1-3-etr,COVID19 Global Forecasting (Week 1)
8540716,0.7639100000000001,0,0,/artem99/covid-19-scipy,COVID19 Global Forecasting (Week 1)
8538115,0.92958,0,0,/robotc142/test-kernel1,COVID19 Global Forecasting (Week 1)
8513641,1.28305,0,1,/taherhaggui/covid-forecasting,COVID19 Global Forecasting (Week 1)
8512621,2.33671,0,2,/gpiyama2119/naive-lstm-model-test-for-covid19,COVID19 Global Forecasting (Week 1)
8521699,0.57777,1,2,/yashagrawal300/covid-19-week-1,COVID19 Global Forecasting (Week 1)
8564509,0.72807,0,0,/egorokm/rfc-covid,COVID19 Global Forecasting (Week 1)
8482859,1.19022,8,30,/opanichev/covid19-global-simple-model,COVID19 Global Forecasting (Week 1)
8487287,1.81995,2,18,/sulianova/covid-19-data-visualization-and-eda,COVID19 Global Forecasting (Week 1)
8542237,1.2477200000000002,0,0,/iamabhi1/kernel3223bc1650,COVID19 Global Forecasting (Week 1)
14577228,0.78708,0,1,/martinmarenz/fast-ai-v2-for-titanic-w-o-and-w-feature-eng,Titanic - Machine Learning from Disaster
14428678,0.0,0,1,/sankrish/titanic-eda-model-compare-tuning,Titanic - Machine Learning from Disaster
14418549,0.78468,0,1,/malavika19mds/titanic-dataset,Titanic - Machine Learning from Disaster
13943249,0.7751100000000001,0,1,/aviralb13/titanic-ml,Titanic - Machine Learning from Disaster
14613292,0.76076,0,0,/ninotomo/notebook-titanic-reg,Titanic - Machine Learning from Disaster
14356555,0.78708,0,0,/monica1996/mc-draft-1,Titanic - Machine Learning from Disaster
14566823,0.7751100000000001,0,0,/sdtrklse/titanic-notebook-002,Titanic - Machine Learning from Disaster
13420831,0.7799,0,0,/onispellsword/passanger-survival-probability-on-titanic,Titanic - Machine Learning from Disaster
14466475,0.76315,0,0,/dhawalsoni/titanic-sur,Titanic - Machine Learning from Disaster
14391608,0.77272,0,0,/siva2020s/titanic-new,Titanic - Machine Learning from Disaster
8948812,0.7751100000000001,0,0,/dilyanpenev/titanic-start,Titanic - Machine Learning from Disaster
8586627,0.95547,2,3,/enriqueabad/exponential-and-logistic-like-fit,COVID19 Global Forecasting (Week 1)
8570950,1.48777,0,7,/jlgleason/covid-19-global-forecasts-using-probabilistic-rnns,COVID19 Global Forecasting (Week 1)
8583427,1.1282,1,1,/manojkrmahato/covid19-global-forecasting,COVID19 Global Forecasting (Week 1)
8571748,0.74246,0,1,/pritha21/covid-19-global-forecasting,COVID19 Global Forecasting (Week 1)
8545730,1.50844,1,5,/lisphilar/combination-of-curve-fitting-and-sir-f-model,COVID19 Global Forecasting (Week 1)
8578094,1.40166,1,2,/franlopezguzman/covid19-minimalist-polynomial-regressor-proposal,COVID19 Global Forecasting (Week 1)
8527081,1.21794,0,2,/twhelan/covid19-rf-with-enriched-data,COVID19 Global Forecasting (Week 1)
8522366,0.70768,1,3,/binhlc/sars-cov-2-voting-regressor,COVID19 Global Forecasting (Week 1)
8573755,0.7764,0,1,/salamalqerem/covid19-global-forecasting,COVID19 Global Forecasting (Week 1)
8566841,1.02469,1,6,/sixteenpython/covid-19-transmission-rates-and-factors,COVID19 Global Forecasting (Week 1)
8560835,0.75925,0,4,/egorokm/covid-regressionrf,COVID19 Global Forecasting (Week 1)
8540948,0.7077100000000001,5,10,/sanasam/covid19-top-10-confirmedcase-countries-predictn,COVID19 Global Forecasting (Week 1)
8553063,1.53288,0,0,/andreikhropov/catboost-with-randomized-search,COVID19 Global Forecasting (Week 1)
8552456,0.77336,0,0,/sugiyama34/linear-regression-of-growth-rate,COVID19 Global Forecasting (Week 1)
8521508,0.74824,2,3,/ranjithks/ran-covid-19-week1,COVID19 Global Forecasting (Week 1)
6842643,0.7751100000000001,0,0,/anki112279/titanic,Titanic - Machine Learning from Disaster
14344676,0.78708,0,5,/ankitkhedar/beginning-with-titanic-kaggle-competition,Titanic - Machine Learning from Disaster
14285642,0.7703300000000001,2,7,/lukeraw/titanic-eda-and-prediction,Titanic - Machine Learning from Disaster
14314039,0.75358,0,0,/leandrofusco88/titanic-dataset,Titanic - Machine Learning from Disaster
14311197,0.75837,58,40,/andreshg/titanic-dicaprio-s-safety-guide,Titanic - Machine Learning from Disaster
14122846,0.77272,0,0,/danielsafiski/titanicml,Titanic - Machine Learning from Disaster
14646784,0.80143,0,0,/x2020fjv/titanic-survival-prediction-21,Titanic - Machine Learning from Disaster
14615524,0.75598,0,0,/ninotomo/notebook-titanic-forest,Titanic - Machine Learning from Disaster
14544344,0.7751100000000001,0,0,/lgybas/notebooke178009445,Titanic - Machine Learning from Disaster
14449492,0.7751100000000001,0,0,/navneetpanda/notebook6ccbf8135f,Titanic - Machine Learning from Disaster
14395164,0.79186,0,0,/youcefbenghorbal/notebookbc94f8a6f2,Titanic - Machine Learning from Disaster
14274625,0.7751100000000001,1,0,/midiazm/getting-started-with-titanic,Titanic - Machine Learning from Disaster
14261262,0.7822899999999999,3,6,/itslisu/titanic-ensemble-learning,Titanic - Machine Learning from Disaster
14269685,0.79186,13,29,/coder212hal2020/titanic-0-7918,Titanic - Machine Learning from Disaster
14287016,0.78468,1,2,/davidovidiunae/titanic-with-xgboost,Titanic - Machine Learning from Disaster
14274504,0.77751,0,0,/karolkotynski/titanic-ann-model-predict,Titanic - Machine Learning from Disaster
5263846,-1.131,2,10,/manojprabhaakr/base-model-molecular-properties-catboost,Predicting Molecular Properties
4539091,0.6629999999999999,0,1,/prashantmahamuni/measuringmagneticinteractions,Predicting Molecular Properties
4441781,0.657,1,2,/hiteshsom/predicting-molecular-properties-first-kernel,Predicting Molecular Properties
4093067,5.081,0,1,/darkrai007/kernel68f2cb76f6,Predicting Molecular Properties
4916411,-1.3730000000000002,8,13,/rajwardhanshinde/blend-of-blends,Predicting Molecular Properties
4818806,1.35005,0,2,/marciodelima/predicaomolecular,Predicting Molecular Properties
4754464,-1.3019999999999998,13,59,/scaomath/no-memory-reduction-workflow-for-each-type-lb-1-28,Predicting Molecular Properties
4769449,-1.359,10,11,/vaishvik25/blend,Predicting Molecular Properties
4739077,-1.186,12,31,/scaomath/lgb-giba-features-qm9-custom-objective-in-python,Predicting Molecular Properties
4722913,-1.281,11,40,/titericz/blend-or-not-to-blend-that-is-the-question,Predicting Molecular Properties
4638442,1.182,0,2,/basu369victor/lets-try-out-regression-with-deep-learning,Predicting Molecular Properties
4426806,0.632,12,24,/basu369victor/a-deep-dive-into-atoms-and-molecules,Predicting Molecular Properties
4220769,0.97262,0,0,/ajita15/lstm-cnn-based-classifier2,Toxic Comment Classification Challenge
3991583,0.98122,1,2,/owen1226/toxic-comment-classification-challenge,Toxic Comment Classification Challenge
3877361,0.98553,6,39,/keitakurita/bert-with-fastai-example,Toxic Comment Classification Challenge
3237297,0.97056,1,6,/lsjsj92/toxic-nlp-with-keras-lstm,Toxic Comment Classification Challenge
3065933,0.7952,0,4,/harshel7/toxic-comment-classification-with-keras,Toxic Comment Classification Challenge
2954009,0.9764,0,4,/sameerdev7/toxic-sentence-classification-using-cnn-lstm,Toxic Comment Classification Challenge
2800931,0.9779,0,0,/nrr1509/improved-lstm-baseline-glove-dropout,Toxic Comment Classification Challenge
2593497,0.9771,0,0,/yshubham/bi-gru-and-attention-fork,Toxic Comment Classification Challenge
2391012,0.9772,0,0,/shaojiewei/fork-of-nb-svm-strong-linear-baseline-668a5e,Toxic Comment Classification Challenge
2338128,0.966,0,0,/classtag/toxic-2-position-embedding-and-attention,Toxic Comment Classification Challenge
2152958,0.9723,0,0,/zz2k17/simple-cnn-gru-baseline-classifier,Toxic Comment Classification Challenge
513035,1.5219999999999998,0,0,/miguelcpsbrito/supermarket-predictions,Corporación Favorita Grocery Sales Forecasting
518664,0.514,3,12,/sbongo/lgbm-xgb-lr-weighted-average-lb-0-514,Corporación Favorita Grocery Sales Forecasting
504213,0.519,1,9,/aharless/stage-3-validation-and-submission,Corporación Favorita Grocery Sales Forecasting
462277,0.644,2,2,/cbrioso/optimal-weights-of-prior-sales-lb-0-644,Corporación Favorita Grocery Sales Forecasting
412140,1.27,0,2,/deeplake/favorita-grocery-data-analysis-model-test,Corporación Favorita Grocery Sales Forecasting
11769294,0.4872399999999999,0,4,/kweonwooj/kc03-advanceddataaug,State Farm Distracted Driver Detection
11344675,1.4402,0,0,/hidebu/200830-prediction-try,State Farm Distracted Driver Detection
3891883,1.92549,0,0,/bhanotkaran22/identifying-distracted-drivers,State Farm Distracted Driver Detection
2025535,21.08304,0,0,/amjadm/kernel9bc0204df9,State Farm Distracted Driver Detection
1616070,3.97937,2,0,/ambarish/state-farm-image-analysis,State Farm Distracted Driver Detection
12549628,5.11859,0,0,/adityav1810/predict-driver-detection,State Farm Distracted Driver Detection
1756010,1.4285,87,190,/ogrellier/teach-lightgbm-to-sum-predictions,Google Analytics Customer Revenue Prediction
1767397,1.4563,1,8,/xaviermaxime/light-gbm-with-simple-engineered-features,Google Analytics Customer Revenue Prediction
1695320,1.4469,9,20,/qwe1398775315/eda-lgbm-bayesianoptimization,Google Analytics Customer Revenue Prediction
1714132,1.4467,66,100,/ogrellier/using-classification-for-predictions,Google Analytics Customer Revenue Prediction
1681906,1.5892,0,2,/ezornow/gstore-skeleton,Google Analytics Customer Revenue Prediction
1714249,1.4475,4,42,/jpmiller/go-with-the-flow-lb-1-447,Google Analytics Customer Revenue Prediction
1716697,1.5323,4,7,/cican17/gstore-prediction-bayesian-optimization,Google Analytics Customer Revenue Prediction
1702457,1.5218,2,14,/ashishpatel26/permutation-importance-feature-imp-measure-gacrp,Google Analytics Customer Revenue Prediction
1699792,1.5336,2,16,/scirpus/a-bit-of-gp-clustering,Google Analytics Customer Revenue Prediction
1689174,1.5198,0,3,/wesleyjr01/ga-challenge-xgboost-permutation-importance,Google Analytics Customer Revenue Prediction
1670944,1.6605,5,16,/vishalbajaj2000/google-analytics-first-try-lgbm-lb-1-5986,Google Analytics Customer Revenue Prediction
1677510,1.5764,0,3,/cican17/gstore-revenue-prediction-first-shot,Google Analytics Customer Revenue Prediction
1638462,1.4453,161,1013,/sudalairajkumar/simple-exploration-baseline-ga-customer-revenue,Google Analytics Customer Revenue Prediction
1637020,1.7722,3,20,/tunguz/simple-mean,Google Analytics Customer Revenue Prediction
8280093,0.91605,0,0,/jb13579/simple-submission-edited,Deepfake Detection Challenge
8156829,0.91629,3,1,/muerbingsha/deepfake-prob,Deepfake Detection Challenge
8069381,0.44859,4,13,/sidharthkumar/xception-resnext-ensemble-inference-cleaner,Deepfake Detection Challenge
8053853,0.45103,9,21,/pankymathur/xception-resnext-ensemble-inference-cleaner,Deepfake Detection Challenge
8003261,0.46788,0,1,/kimyoh/deepfake-demo-20190217,Deepfake Detection Challenge
7972294,0.53775,4,24,/greatgamedota/xception-binary-classifier-inference,Deepfake Detection Challenge
7711748,0.69326,3,0,/akihirokkkkk/why-submission-score-is-17-269386,Deepfake Detection Challenge
7799054,0.46776,13,42,/mmmarchetti/inference-demo-ii,Deepfake Detection Challenge
7743441,1.24615,9,10,/zaharch/public-test-errors,Deepfake Detection Challenge
7690202,0.46788,59,288,/humananalog/inference-demo,Deepfake Detection Challenge
7668213,0.67799,3,19,/neiromantik/dfdc-dlib-based-fake-prediction-pipeline-using-cnn,Deepfake Detection Challenge
3207803,9.64904,0,1,/adamlouisky/predict-the-next-ncaa-winner-using-svm,Google Cloud & NCAA® ML Competition 2019-Men's
3023156,0.17825,2,24,/omniactio/basic-logistic-regression-with-cross-validation,Google Cloud & NCAA® ML Competition 2019-Men's
2988379,0.6823600000000001,0,0,/siddharthasharan/eda-and-initial-models,Google Cloud & NCAA® ML Competition 2019-Men's
2956448,0.57812,5,27,/artgor/ncaa-men-s-eda-and-models,Google Cloud & NCAA® ML Competition 2019-Men's
2954746,2.8908400000000003,8,32,/jazivxt/courtside-seat-2019m-competitiveness,Google Cloud & NCAA® ML Competition 2019-Men's
2498332,0.8220000000000001,91,280,/seesee/siamese-pretrained-0-822,Humpback Whale Identification
2452443,0.001,4,0,/konstantinmeskhidze/whale-tail-recognition,Humpback Whale Identification
2469002,0.2339999999999999,1,3,/whatvermawhat/resnet50-128x128,Humpback Whale Identification
2337139,0.262,4,11,/joydeb28/cnn-model-without-new-whale,Humpback Whale Identification
2344997,0.282,4,9,/ashirahama/pytorch-simple-cnn-split-new-whale,Humpback Whale Identification
2333285,0.7659999999999999,1,14,/hung96ad/ensembling-algorithm-for-average-precision-0-766,Humpback Whale Identification
2297079,0.574,0,9,/matthewa313/resnet50,Humpback Whale Identification
2279563,0.59,11,55,/satian/seresnext101-pytorch-starter,Humpback Whale Identification
2269579,0.276,0,15,/ashishpatel26/vgg-19-for-humpback-classification,Humpback Whale Identification
2252779,0.296,0,2,/sukhadj/humpback-whale-identification,Humpback Whale Identification
2251456,0.344,14,84,/artgor/pytorch-whale-identifier,Humpback Whale Identification
2254165,0.289,3,10,/sanikamal/whale-identification-using-cnn,Humpback Whale Identification
2250361,0.336,6,24,/satian/keras-mobilenet-starter,Humpback Whale Identification
2251446,0.281,0,7,/truocpham/keras-vgg-baseline,Humpback Whale Identification
2557959,0.27889,0,0,/sujoykg/keras-xception-pretrained-dataaugment,Humpback Whale Identification
26839,0.0,3,21,/omarelgabry/airbnb-user-bookings,Airbnb New User Bookings
26455,0.0,0,1,/weruioghvn/baseline-if-date-first-booking-is-null,Airbnb New User Bookings
6076985,0.926219,0,4,/naikparag/multi-model-xgb-average-fraud-detection,IEEE-CIS Fraud Detection
5594359,0.955263,0,7,/plasticgrammer/ieee-cis-fraud-detection-gbdt,IEEE-CIS Fraud Detection
5756660,0.9241,0,0,/ruhong/ieee-fraud-detection-xgb,IEEE-CIS Fraud Detection
6045464,0.808171,0,9,/dyyalex/what-feature-we-should-create-maybe-pnn-can-do-it,IEEE-CIS Fraud Detection
5775312,0.9474,5,10,/makin119/ieee-fraud-detection-novice,IEEE-CIS Fraud Detection
5902447,0.9526,14,15,/zeus75/easy-blending,IEEE-CIS Fraud Detection
5899155,0.9477,14,104,/tolgahancepel/lightgbm-single-model-and-feature-engineering,IEEE-CIS Fraud Detection
5802801,0.6729,0,0,/himaoka/rough-data-cleaning-and-prediction,IEEE-CIS Fraud Detection
5803014,0.9516,6,8,/errolpereira/lgbm-blend,IEEE-CIS Fraud Detection
5726302,0.9119,0,2,/priteshshrivastava/ieee-pipeline-3-stacking-with-meta-model,IEEE-CIS Fraud Detection
5817071,0.9381,0,0,/errolpereira/ieee-fraud-detection-eda,IEEE-CIS Fraud Detection
7854996,0.62065,0,0,/ghh8000/basic-regression,Bike Sharing Demand
6898734,0.65384,0,0,/giuliocc/projeto-am,Bike Sharing Demand
6774944,0.80318,2,2,/eduardooo/bike-renting-regression,Bike Sharing Demand
6702048,0.44085,0,1,/flordelais/aula3-random-forest,Bike Sharing Demand
6602010,0.44245,0,0,/clperin/cp-iesb-aula-02-arvore-de-decisao,Bike Sharing Demand
6602052,0.44245,0,1,/atherx/iesb-aula-02-random-forest,Bike Sharing Demand
6348345,0.4936899999999999,0,0,/genpychan/kernel345e250a3c,Bike Sharing Demand
5710622,0.41706,0,3,/kongnyooong/bike-sharing-demand-for-korean-beginners,Bike Sharing Demand
3674594,0.5463,0,0,/sidiclei/aula02-ml-2-aluguel-bike-decision-tree,Bike Sharing Demand
4823309,0.39191,0,0,/terminate9298/bike-sharing-predictions-top-10-score-0-39,Bike Sharing Demand
4392459,0.48782,3,25,/fatmakursun/bike-sharing-feature-engineering,Bike Sharing Demand
4205919,0.4223699999999999,0,0,/xinyouren1995/midterm-for-ppt,Bike Sharing Demand
3930917,0.4773199999999999,0,3,/rotemshalev/bike-sharing-challenge,Bike Sharing Demand
1838449,0.85808,0,1,/rajatkatiyar/bag-of-words-with-svm,Bag of Words Meets Bags of Popcorn
1579705,0.85756,0,0,/annatu/movie-sentiment-analysis,Bag of Words Meets Bags of Popcorn
2205127,0.9446,10,9,/fadhli/starter-code-keras-resnet50-0-9275-lb,Histopathologic Cancer Detection
2141224,0.9247,19,62,/fmarazzi/baseline-keras-cnn-roc-fast-10min-0-925-lb,Histopathologic Cancer Detection
2127070,0.8963,6,24,/hrmello/base-cnn-classification-from-scratch,Histopathologic Cancer Detection
3352355,0.9636,0,0,/pascalnoble/fastai-v1-densenet201-488de9,Histopathologic Cancer Detection
2915570,0.9459,0,0,/zhing001/jorge-zs-vhl,Histopathologic Cancer Detection
10718458,0.3498199999999999,0,7,/aakashveera/bosch-production-line-performance,Bosch Production Line Performance
5228627,0.4163,0,0,/choithuthoi/my-solution,Bosch Production Line Performance
14199366,0.41251,0,0,/pointerfly/easy-catboost,Click-Through Rate Prediction
14318308,0.99378,0,0,/ninotomo/notebook-digitrecognizer-cnn,Digit Recognizer
14523553,0.98642,7,12,/manojkumars00/digit-recognition-tensorflow,Digit Recognizer
10586643,0.98764,0,1,/bhaveshgupta3421/image-recognition-using-cnn-keras,Digit Recognizer
14185659,0.97289,4,6,/ishantkukreti/getting-starting-with-tensorflow,Digit Recognizer
14403737,0.99392,6,5,/songrise/simple-lenet-5-keras-data-augmentation,Digit Recognizer
14542096,0.99485,4,4,/shashankrajput9/digit-recognizer-ml,Digit Recognizer
14568394,0.99592,6,3,/sytuannguyen/99-6-with-a-basic-cnn-model,Digit Recognizer
14595493,0.99182,0,2,/nguynvnphong/mnist-with-cnn-basic,Digit Recognizer
14335264,0.99164,1,2,/tonytrieu/mnsit-cnn-classifier,Digit Recognizer
14601739,0.96932,0,0,/aymenkhaled/neural-net-from-scratch,Digit Recognizer
14468122,0.99282,0,0,/ashishpapanai/mnist,Digit Recognizer
13816969,0.98435,0,0,/nelsongomesneto/digit-recognizer-tcc,Digit Recognizer
14355205,0.99267,12,12,/lakshita2002/implementing-cnn-with-keras-data-augmentation,Digit Recognizer
11516406,0.99196,7,12,/marionhesse/cnn-for-digit-recognition,Digit Recognizer
14245980,0.99089,7,16,/bryanb/pytorch-cnn-for-mnist-digit-recognition-with-gpus,Digit Recognizer
14259513,0.99657,0,2,/marcoisajoke/pytorch-small-model-to-high-approach,Digit Recognizer
14596146,0.99632,0,0,/sytuannguyen/ensemble-convnet-model,Digit Recognizer
14568179,0.98646,0,0,/sytuannguyen/basic-convnet-model,Digit Recognizer
2912002,2.03925,8,7,/jiegeng94/simple-tmdb-prediction-with-gradient-boosting,TMDB Box Office Prediction
2925157,2.44383,0,1,/marcocarnini/feature-engineering-iii,TMDB Box Office Prediction
2876927,2.57248,0,1,/marcocarnini/adding-features-i,TMDB Box Office Prediction
2860103,2.12178,0,7,/gravix/gradient-in-a-box,TMDB Box Office Prediction
1639801,1.6051,9,83,/ashishpatel26/now-you-see-me,TMDB Box Office Prediction
4360227,1.79276,1,0,/honglou/kernel3b7653cf77,TMDB Box Office Prediction
14554310,0.602,0,0,/mohneesh7/what-happens-if-i-predict-the-majority-class,Cassava Leaf Disease Classification
13488744,0.696,0,0,/dhanyasabari/cassava-leaf-disease-gpu-version,Cassava Leaf Disease Classification
14156126,0.821,0,3,/darknesszx/just-a-normal-start,Cassava Leaf Disease Classification
14308121,0.629,0,0,/lokeshduvvuru/squeeze-for-leaves,Cassava Leaf Disease Classification
14234035,0.847,9,13,/ayuraj/tensorflow-inference-no-tta,Cassava Leaf Disease Classification
13197588,0.048,0,0,/soumochatterjee/inference-cassava-leaf-detection,Cassava Leaf Disease Classification
14051403,0.618,1,7,/yuvalnavot/cassava-leaf-disease,Cassava Leaf Disease Classification
14240018,0.895,2,5,/darknesszx/darknesszxzxx,Cassava Leaf Disease Classification
1331771,0.38,0,3,/fpeccia/tensorflow-unet-benchmark,TGS Salt Identification Challenge
1319088,0.672,29,140,/bguberfain/unet-with-depth,TGS Salt Identification Challenge
1736420,0.812,0,0,/nikhilroxtomar/introduction-to-u-net-with-simple-resnet-blocks,TGS Salt Identification Challenge
1328936,0.326,0,0,/manishavenger/tgs-salt-identification,TGS Salt Identification Challenge
87441,2.26508,0,1,/evanwang1028/a-linear-model-on-apps-and-labels,TalkingData Mobile User Demographics
1660469,0.746,0,0,/hengreen/unet-deepunet-ensemble,TGS Salt Identification Challenge
1641890,0.544,0,0,/timsonrisa/using-pre-trained-model-to-predict-and-submit,TGS Salt Identification Challenge
1860657,0.8540000000000001,22,93,/meaninglesslives/getting-0-87-on-private-lb-using-kaggle-kernel,TGS Salt Identification Challenge
1722139,0.8340000000000001,12,44,/waltmay/u-net-with-simple-resnet-blocks-and-mosaic,TGS Salt Identification Challenge
1817858,0.816,2,12,/dromosys/tgs-fastai-resnet34-unet-v1,TGS Salt Identification Challenge
1770554,0.805,0,17,/tcapelle/tgs-fastai-resnet34-unet,TGS Salt Identification Challenge
1794345,0.402,0,0,/tcapelle/fork-of-tgs-fastai-resnet18-dynamicunet,TGS Salt Identification Challenge
1665958,0.823,18,16,/jimmy2002916/unet-resnet34,TGS Salt Identification Challenge
1578322,0.812,0,4,/dingdiego/new-merger,TGS Salt Identification Challenge
1338040,0.706,0,0,/dingdiego/u-net-dropout-augmentation-stratification,TGS Salt Identification Challenge
1344530,0.56,0,0,/dingdiego/fork-of-u-net-dropout-augmentation-stratificati,TGS Salt Identification Challenge
1510361,0.774,0,0,/dingdiego/baseline-v6,TGS Salt Identification Challenge
1709543,0.815,12,52,/deepaksinghrawat/introduction-to-u-net-with-simple-resnet-blocks,TGS Salt Identification Challenge
14039098,2.56246,0,1,/omarbhular/tmdb-eda-cross-validation,TMDB Box Office Prediction
12180158,2.04338,0,0,/nehalbandal/tmdb-revenue-prediction-eda-ml-pipeline,TMDB Box Office Prediction
5382637,1.77895,0,0,/eladdv/elad-tmdb,TMDB Box Office Prediction
4005850,1.91972,0,1,/brendanhasz/box-office-revenue-prediction,TMDB Box Office Prediction
4131879,1.88879,0,1,/lvwenlong/neural-network-with-mc-dropout-and-embedding,TMDB Box Office Prediction
4068244,2.63517,0,3,/shwetagoyal4/predicting-revenue-of-box-office,TMDB Box Office Prediction
3518094,3.19944,0,0,/joshnarani/xgradient-booster-box-ofc-prediction,TMDB Box Office Prediction
13720425,0.99567,0,0,/startover205/fastai-2-digit-recognizer,Digit Recognizer
13783879,0.99192,0,0,/kaggledis/mnist-cnn-v1,Digit Recognizer
13570098,0.99232,0,0,/amirsher/cnn-digit-recognizer-using-boosted-lenet5,Digit Recognizer
13657467,0.96464,0,0,/aquaregis32/digit-recognition,Digit Recognizer
13601591,0.98485,0,0,/nvatuan/2020dec17-digit-recognizer,Digit Recognizer
13593310,0.96571,0,0,/shubham07prasad/mnist,Digit Recognizer
13515461,0.99467,4,8,/josephassaker/cnn-mnist-digit-classification,Digit Recognizer
13485002,0.99021,0,0,/ekshusingh/my-torch-implementation,Digit Recognizer
13467185,0.98571,0,1,/jaekisenagarwal/digitrecognizer-cnn,Digit Recognizer
13429062,0.97564,0,0,/shohel1/handwritten-digit-recognition-problem-solving,Digit Recognizer
13214038,0.98785,0,0,/peterpetrov826/digit-recognizer,Digit Recognizer
13404090,0.99346,4,8,/paulrohan2020/tutorial-kernel-dimensionality-reduction-and-pca,Digit Recognizer
13371489,0.9896,0,5,/thoolihan/keras-cnn-mnist,Digit Recognizer
13324252,0.99728,4,4,/anastasiiablyzniuk/digital-recognizer-cnn,Digit Recognizer
11870050,0.948,5,14,/akarshu121/cancer-detection-with-cnn-for-beginners,Histopathologic Cancer Detection
10155168,0.9619,0,3,/shachi01/complete-productionized-fastai-model,Histopathologic Cancer Detection
8898625,0.6064,0,0,/robbiebeane/cancer-detection-v01,Histopathologic Cancer Detection
8192562,0.9443,1,0,/philieseg/keras-cnn-cancer-detection,Histopathologic Cancer Detection
8038793,0.3753,0,2,/rohitgadhwar/histopathologic-cancer-detection-notebook,Histopathologic Cancer Detection
4644115,0.9489,0,0,/junior100/fork-of-huanglvqicnn,Histopathologic Cancer Detection
3412487,0.9558,0,0,/praxitelisk/histopathologic-cancer-detection-keras,Histopathologic Cancer Detection
3908298,0.8843,0,0,/samarthsarin/detecting-cancer-with-convolution,Histopathologic Cancer Detection
4560086,0.9572,0,1,/msteger93/best-swagoverflowkernel-clr,Histopathologic Cancer Detection
4441937,0.9663,0,1,/rpeer333/pytorch-densenet-for-cancer-detection,Histopathologic Cancer Detection
4176949,0.9669,0,2,/vishal22/histopathology,Histopathologic Cancer Detection
14215414,0.939495,0,2,/thejravichandran/fraud-detection-v10-pipeline,IEEE-CIS Fraud Detection
12647634,0.923118,0,0,/shafqaatahmad/ieee-cis-fraud-detection-lgb-with-fe,IEEE-CIS Fraud Detection
12277439,0.674347,1,2,/vaishnavkapil/ieee-cis-fraud-detection,IEEE-CIS Fraud Detection
5666422,0.9485,0,0,/jobzhf88/ieee-train,IEEE-CIS Fraud Detection
9839461,0.936546,4,10,/mervebdurna/ieee-fraud-detection-model,IEEE-CIS Fraud Detection
7997944,0.05434,0,2,/phylake1337/99-by-simple-inception-transfer-learning,Dogs vs. Cats Redux: Kernels Edition
7879992,2.08893,0,2,/ankk199/project-ai-ankk,Dogs vs. Cats Redux: Kernels Edition
7681118,0.0688,0,5,/bootiu/dog-vs-cat-transfer-learning-by-pytorch-lightning,Dogs vs. Cats Redux: Kernels Edition
7026730,0.0670299999999999,0,4,/bootiu/dog-vs-cat-transfer-learning-vgg16-by-pytorch,Dogs vs. Cats Redux: Kernels Edition
4884472,0.99524,1,2,/aravindsairam1995/pytorch-alexnet,Dogs vs. Cats Redux: Kernels Edition
5811221,0.28948,0,2,/hilla4/simple-cnn-with-vgg-accuracy-87,Dogs vs. Cats Redux: Kernels Edition
5277392,0.3513,0,0,/hung96ad/dogs-vs-cats-pytorch-cnn-without-transfer-learning,Dogs vs. Cats Redux: Kernels Edition
4418616,1.5888,1,5,/stephanedc/tutorial-cnn-partie-2-reconnaissance-chien-chat,Dogs vs. Cats Redux: Kernels Edition
6371672,0.78672,0,0,/ajithvallabai/categorical-ecoding-cat-challange-solved,Categorical Feature Encoding Challenge
6444517,0.80399,0,2,/nicapotato/categorical-catboost-pool-cv-bayes-opt,Categorical Feature Encoding Challenge
6233562,0.67675,0,1,/jirakst/categorical-data-with-target-encoding,Categorical Feature Encoding Challenge
6192227,0.7923100000000001,3,2,/vladlee/categorical-feature-encoding-ohe-nn,Categorical Feature Encoding Challenge
6129475,0.80788,4,7,/purist1024/minimalist-ohe-and-cv-blend-21-lines-for-top-20,Categorical Feature Encoding Challenge
6097431,0.8042699999999999,0,6,/purist1024/a-minimalist-baseline-19-lines-for-top-50,Categorical Feature Encoding Challenge
5790726,0.7844899999999999,3,7,/praxitelisk/categorical-feature-encoding-challenge-eda-ml,Categorical Feature Encoding Challenge
6020002,0.8043100000000001,1,4,/donkeys/scripting-pickled-catboost,Categorical Feature Encoding Challenge
5942818,0.80818,13,87,/superant/oh-my-cat,Categorical Feature Encoding Challenge
5950729,0.7915,1,6,/jeongyoonlee/embeddingencoder-autolgb-in-kaggler,Categorical Feature Encoding Challenge
5914586,0.77881,2,3,/errolpereira/catboost-model,Categorical Feature Encoding Challenge
5909475,0.71158,5,10,/navneetkr123/eda-targetencoding-logistic,Categorical Feature Encoding Challenge
5767391,0.80152,3,6,/ossinova/simple-catboost-0-8-auc-v2-0,Categorical Feature Encoding Challenge
5705707,0.80765,0,3,/pavelvpster/cat-in-dat-stack,Categorical Feature Encoding Challenge
5774108,0.65189,0,1,/alexanderdbooth/fun-with-catboost,Categorical Feature Encoding Challenge
5483130,0.80791,6,24,/martin1234567890/logistic-regression,Categorical Feature Encoding Challenge
1692139,0.55052,0,0,/hudanivy/ai-camp-neural-networks-otto-homework,Otto Group Product Classification Challenge
509502,0.57853,0,0,/prashant10/lr-gbm-rf-ensemble,Otto Group Product Classification Challenge
204313,0.58754,1,0,/changjian/otto-product-classification-predictions,Otto Group Product Classification Challenge
123076,0.58754,0,0,/xgwang/otto-product-classification-predictions,Otto Group Product Classification Challenge
32825,0.60968,1,0,/soniatul83/otto-sa,Otto Group Product Classification Challenge
10890411,0.0,0,1,/ahmedmurad1990/google-analytics-customer-revenue-prediction,Google Analytics Customer Revenue Prediction
1845476,1.4264,0,0,/yakolle/gs-target-test,Google Analytics Customer Revenue Prediction
1855556,1.5293,0,0,/baoanh/try-to-improve-1-55,Google Analytics Customer Revenue Prediction
2495948,0.0,0,2,/manyregression/fastai-0-7-2-random-forest-feature-importance,Google Analytics Customer Revenue Prediction
1852127,1.4286,0,0,/shireennagdive/shireen-ssecondnotebook,Google Analytics Customer Revenue Prediction
2123142,0.0,3,14,/qnkhuat/base-model-v2-with-with-full-features,Google Analytics Customer Revenue Prediction
2099611,0.0,2,6,/geoffpidcock/joke-submission-workbook,Google Analytics Customer Revenue Prediction
2039529,1.291,6,77,/zikazika/google-predictions,Google Analytics Customer Revenue Prediction
1794482,1.5431,0,2,/lituokobe/ga-customer-revenue-prediction-data-exploration,Google Analytics Customer Revenue Prediction
8131069,0.158,1,2,/liu123/the-first-30-is-public-leaderboard,University of Liverpool - Ion Switching
8127443,0.319,2,3,/chariots17/decisiontreeclassifier,University of Liverpool - Ion Switching
8128113,0.152,2,2,/agileteam/simple-lightgbm-starter,University of Liverpool - Ion Switching
9457203,0.944,0,0,/akashsuper2000/uofliv-ensemble,University of Liverpool - Ion Switching
9214804,0.942,0,0,/ashora/wavenet-with-shifted-rfc-proba-and-cbr,University of Liverpool - Ion Switching
8919485,0.941,0,0,/akashsuper2000/wavenet-keras,University of Liverpool - Ion Switching
8354144,0.921,0,0,/kalyankkr/eda-and-model-f1-optimization,University of Liverpool - Ion Switching
6751273,1.23,0,0,/vladimirsydor/randomforestbaseline,ASHRAE - Great Energy Predictor III
6686349,1.07,0,0,/teeyee314/best-single-half-half-lgbm-1-07,ASHRAE - Great Energy Predictor III
6234391,1.388,2,24,/gunesevitan/ashrae-lightgbm-1-048-no-leak,ASHRAE - Great Energy Predictor III
7067056,1.193,0,0,/madisj/kernel3377148266,ASHRAE - Great Energy Predictor III
6964047,1.163,0,1,/clementut/kernel4e51c0227f,ASHRAE - Great Energy Predictor III
6725052,1.331,2,3,/geochatz/ashrae-building-energy-prediction-lightgbm,ASHRAE - Great Energy Predictor III
7036203,1.124,0,0,/navidbamdadroshan/unitartu-ml-submit,ASHRAE - Great Energy Predictor III
6853462,1.08,12,40,/ragnar123/another-1-08-lb-no-leak,ASHRAE - Great Energy Predictor III
6850466,0.97,2,29,/khoongweihao/leak-validation-constrained-heuristic-search-i,ASHRAE - Great Energy Predictor III
6458577,1.13,0,1,/amitkishore/feature-engineering-lgb,ASHRAE - Great Energy Predictor III
6778909,0.97,26,113,/khoongweihao/ashrae-leak-validation-bruteforce-heuristic-search,ASHRAE - Great Energy Predictor III
6803544,1.24,0,2,/jeeperscreepers/ashrae-gep-iii-feature-engineering-try-1,ASHRAE - Great Energy Predictor III
6783822,2.23,0,1,/samihadouaj/ashrae-half-and-half,ASHRAE - Great Energy Predictor III
13850219,0.95875,0,1,/anirbansen3027/jtcc-multilabel-lstm-keras,Toxic Comment Classification Challenge
13797424,0.93591,0,6,/anirbansen3027/jtcc-bag-of-words,Toxic Comment Classification Challenge
6224371,0.97823,0,0,/amir78pgd/improved-lstm-baseline-fasttext-dropout-pl,Toxic Comment Classification Challenge
12972508,0.97112,1,5,/muhammadrehan444/bidirectional-lstm-toxic-comment-classification,Toxic Comment Classification Challenge
11732024,0.9656,0,3,/sathishkumarsg10/beginner-bidirectional-lstm-using-glove-vectors,Toxic Comment Classification Challenge
10862359,0.7096899999999999,0,5,/naimur978/pytorch-gpu-inference-5-fold,Tweet Sentiment Extraction
10603895,0.7108,0,2,/norrsken/roberta-submission,Tweet Sentiment Extraction
10534628,0.53577,0,0,/narimanelsamadony/pytorch-lightning-data-cleaning-8f5cce,Tweet Sentiment Extraction
10299899,0.65146,0,0,/mohamedhany13/bi-lstm-glove-sentiment-separation-pre-post-proces,Tweet Sentiment Extraction
10387948,0.7149300000000001,0,3,/jinheonbaek/pytorch,Tweet Sentiment Extraction
10317519,0.52496,0,1,/salmacmpeg/bert-with-lstm-classifier,Tweet Sentiment Extraction
9659252,0.7140000000000001,0,0,/kennethrithvik/tweet-sentiment-key-sub-text-extraction,Tweet Sentiment Extraction
10146195,0.72,1,2,/hamishdickson/fork-of-w-space-weighted2,Tweet Sentiment Extraction
10135427,0.71,0,1,/behcetsenturk/roberta-q-a-ner-in-s-e-char-level,Tweet Sentiment Extraction
10221669,0.18874,0,4,/sevashasla/try-tweet-sentiment,Tweet Sentiment Extraction
10129553,0.594,0,0,/antongolubev5/marker-full-text,Tweet Sentiment Extraction
9698554,0.593,0,1,/viiids/dnn-v1,Tweet Sentiment Extraction
10127348,0.726,4,30,/naivelamb/roberta-base-ensemble,Tweet Sentiment Extraction
10187235,0.722,0,3,/tkm2261/best-public-kernel-with-magic-pre-post-process,Tweet Sentiment Extraction
10075597,0.7120000000000001,1,4,/samyakkala/tse-visualization-prediction-tf-roberta,Tweet Sentiment Extraction
6649807,0.9826,0,1,/utsavnandi/k-mnist-first-kernel,Kannada MNIST
8083460,0.985,0,1,/abhisheksinghblr/kannada-mnist-using-fast-ai,Kannada MNIST
7941711,0.9708,0,1,/tejaskhanna/tk-update,Kannada MNIST
7992400,0.9414,0,0,/xerous/weareready,Kannada MNIST
7982996,0.9844,0,0,/dimafurs1337/kannada-mnist-cnn,Kannada MNIST
7897208,0.9532,0,1,/vahidsa/kaanada-pca-svc,Kannada MNIST
7861235,0.8012,0,1,/arpithaananth/my-first-cnn-project-kannada-mnist,Kannada MNIST
7878939,0.9246,0,0,/sasa99/kernel1a9e717818,Kannada MNIST
7821696,0.9842,0,0,/kimeg7/kannada-mnist-data-recognizer,Kannada MNIST
6702854,0.9888,0,0,/jjbuchanan/full-dataset-training-kannada-mnist,Kannada MNIST
5900308,0.982,1,3,/vitorgamalemos/using-cnn-in-kannada-digits-resolution,Kannada MNIST
7628345,0.99,0,7,/joshuajhchoi/kannada-mnist-cnn-tutorial,Kannada MNIST
7592424,0.9888,3,4,/bobbyscience/kannada-mnist-cnn-keras,Kannada MNIST
7241313,0.882,0,1,/felipeapgarcia/kannada-mnist-classification,Kannada MNIST
7353379,0.8862,0,0,/cyzhou99/coincidance-xgboost,Kannada MNIST
7336630,0.934,0,0,/mlosthread/kernelsvm-hog,Kannada MNIST
7359502,0.9662,1,0,/scirpus/begin-with-tensorflow-2-but-use-dct,Kannada MNIST
6887942,0.985,0,1,/chenchanggen/kernel1a934f9e51,Kannada MNIST
7225760,0.9378,0,1,/knnagele/first-competition-mnist,Kannada MNIST
7206620,0.9776,0,0,/anayad/baseline-cnn,Kannada MNIST
7200641,0.983,0,0,/devesh2707/kannada-mnist,Kannada MNIST
7099866,0.961,0,0,/fernandoeac/kannada-mnist,Kannada MNIST
5875751,0.9854,0,0,/grecs2001/kannada-mnist-keras-cnn,Kannada MNIST
7072082,0.9852,0,0,/leonyangyu/kannada-mnist-leonyu,Kannada MNIST
7042242,0.9886,0,1,/ricardoamferreira/tf-keras-cnn-with-kannada-mnist-top-9,Kannada MNIST
7136639,0.9892,0,0,/tt195361/my-best-private-score-model-for-kannada-mnist,Kannada MNIST
7053648,0.9866,0,0,/pikkupr/kannadamnist-cnn-dataaugmentation,Kannada MNIST
10954017,0.7732,0,4,/chanhu/wheat-efficientdet7-pseudo-label,Global Wheat Detection
10919071,0.7607,0,1,/raufyagfarov/mmdetection-yolov4-pipeline-with-tta,Global Wheat Detection
11032254,0.77,11,52,/alexanderliao/effdet-d6-pl-s-bn-r-bb-a3-usa-eval-94-13-db,Global Wheat Detection
10490731,0.7622,0,3,/kaushal2896/yolov5x-pseudo-labeling-tta-ensemble,Global Wheat Detection
11028656,0.7444,0,1,/markpeng/yolov4-infer-gpu-v3-tta-fold2-pseudo-label-final,Global Wheat Detection
11046720,0.7714,0,12,/chanhu/wheat-efficientdet7-with-pl,Global Wheat Detection
11011224,0.7582,0,1,/doanquanvietnamca/efficientdet-pseudolabeling-bayersianopti-16ffef,Global Wheat Detection
10761788,0.7505,0,3,/orkatz2/pseudo-efficientdet-d7-multiscale-tta-pb-0-708,Global Wheat Detection
10799348,0.5129,0,1,/devendratapdia/wheat-detection-using-mask-rcnn,Global Wheat Detection
10514615,0.6815,0,1,/iamprateek/wheat-head-detection-yolov4,Global Wheat Detection
10863288,0.7103,0,1,/idozada/wheat-detection-with-fasterrcnn,Global Wheat Detection
10850242,0.6696,0,1,/kuriyaman1002/pytorch-starter-fasterrcnn-train,Global Wheat Detection
11002582,0.7007,0,1,/rickyd/inference-models-that-went-to-trash,Global Wheat Detection
10909752,0.0105,0,3,/adunuthulan/wheat-head-detection-with-mrcnn,Global Wheat Detection
10994346,0.7543,0,0,/jingcchen/efficientdet-tta-pl-wbf-oof,Global Wheat Detection
9828561,0.7193,0,1,/akashsuper2000/bayesian-optimization-wbf-efficientdet,Global Wheat Detection
10910799,0.7086,0,1,/akashsuper2000/inference-efficientdet,Global Wheat Detection
11856934,0.04323,0,1,/vinayvishwkarma/fastai-for-tabular-data,Mechanisms of Action (MoA) Prediction
11799994,0.01977,0,3,/nikilreddy/moa-fastai,Mechanisms of Action (MoA) Prediction
11840719,0.01919,0,1,/sudokill/simple-resnet-10-fold-ensemble,Mechanisms of Action (MoA) Prediction
11759051,0.01917,0,26,/sarthak97/tf-keras-5-folds-nn-starter,Mechanisms of Action (MoA) Prediction
11637549,0.11027,0,0,/aguirremimoun/moa-rf-xgboost,Mechanisms of Action (MoA) Prediction
11790431,0.01975,0,7,/krisho007/moa-fastai-kfold,Mechanisms of Action (MoA) Prediction
11624563,0.10972,0,1,/yamanity/xgb-multioutputclassifier-moa,Mechanisms of Action (MoA) Prediction
11756414,0.0206599999999999,8,92,/gogo827jz/kernel-logistic-regression-one-for-206-targets,Mechanisms of Action (MoA) Prediction
11766436,0.02087,0,7,/demetrypascal/catboost-and-logreg,Mechanisms of Action (MoA) Prediction
11602481,0.0343399999999999,2,3,/iamabhishekdas/moa-prediction-pytorch-cnn,Mechanisms of Action (MoA) Prediction
11736954,0.01955,0,6,/soerendip/fastai-2-0-12-starter,Mechanisms of Action (MoA) Prediction
11657843,0.01989,1,5,/shishu1421/fastai-for-tabular-data,Mechanisms of Action (MoA) Prediction
11739099,0.69314,1,4,/aman2000jaiswal/moi-1133a-base-anmol,Mechanisms of Action (MoA) Prediction
11725124,0.02816,0,2,/avivlevi815/baseline-multi-xgboost,Mechanisms of Action (MoA) Prediction
11657925,0.0197199999999999,0,0,/doguskidik/mechanisms-of-action-moa-prediction,Mechanisms of Action (MoA) Prediction
11674416,0.11469,1,5,/riadalmadani/ensamble-model,Mechanisms of Action (MoA) Prediction
11676026,0.01956,4,39,/maunish/moa-super-cool-eda-and-pytorch-baseline,Mechanisms of Action (MoA) Prediction
11636904,0.01969,0,10,/sarthak97/lish-moa-simple-eda-baseline-model,Mechanisms of Action (MoA) Prediction
11620414,0.02017,0,2,/alturutin/moa-logreg-rapids,Mechanisms of Action (MoA) Prediction
11607607,0.01935,2,25,/prasunmishra/nn-kfold-targets-noscored-wnorm-adamw,Mechanisms of Action (MoA) Prediction
9486167,0.8265299999999999,4,9,/xiu0714/very-simple-svm-to-reach-0-82,Natural Language Processing with Disaster Tweets
9467068,0.7842399999999999,6,3,/urayukitaka/predict-classification-tweets-real-or-not,Natural Language Processing with Disaster Tweets
9320835,0.80171,2,3,/nikithasrikanth/disaster-tweets-classification,Natural Language Processing with Disaster Tweets
9423889,0.84615,1,4,/ahmedattia143/nlp-disaster-pytorch-bert-kfold,Natural Language Processing with Disaster Tweets
8907840,0.79895,0,0,/ioanmaracineanu/nlp-chats-hw,Natural Language Processing with Disaster Tweets
9381108,0.80416,1,3,/starkking07/superfast-tpu-training-and-inference,Natural Language Processing with Disaster Tweets
9238453,0.54459,0,1,/medhinishetty/nlp-beginner,Natural Language Processing with Disaster Tweets
7347326,0.78608,0,0,/kumaresanmanickavelu/fasttext-based-disaster-tweets-classifier,Natural Language Processing with Disaster Tweets
9281243,0.6196699999999999,0,3,/aadityasinghal/disaster-tweet-prediction-using-bidirectional-lstm,Natural Language Processing with Disaster Tweets
8557325,0.83695,0,0,/lucca9211/nlp-disaster-bert,Natural Language Processing with Disaster Tweets
9144664,0.82899,0,0,/sunnyville01/real-or-not-tensorflow-with-bert-2,Natural Language Processing with Disaster Tweets
9155589,0.80753,4,14,/dhruv1234/pre-trained-glove,Natural Language Processing with Disaster Tweets
9055606,0.7290800000000001,0,0,/yadavhimanshu/prediction-nlp,Natural Language Processing with Disaster Tweets
8510454,0.79374,0,0,/pritify/kernel6f2e87b071,Natural Language Processing with Disaster Tweets
14602777,0.7822899999999999,54,56,/adamml/titanic-to-beginner,Titanic - Machine Learning from Disaster
8950656,0.7822899999999999,30,37,/guecoraph/titanic-data-cleaning-model-fitting,Titanic - Machine Learning from Disaster
14635724,0.78468,0,4,/yuankang731/taitanic-problem,Titanic - Machine Learning from Disaster
14576008,0.7368399999999999,4,7,/yashsharmabharatpur/titanic-basic-solution-using-adaboost,Titanic - Machine Learning from Disaster
14525173,0.78468,2,4,/lacieemai/titanic-explorations,Titanic - Machine Learning from Disaster
14509209,0.78708,0,2,/chumajin/pytorch-neural-network-starter-detail,Titanic - Machine Learning from Disaster
13302335,0.79665,2,12,/gtatiana/titanic-dataset,Titanic - Machine Learning from Disaster
14357840,0.7751100000000001,2,4,/connorpuhala/titanic-neural-net-77-5-accurate,Titanic - Machine Learning from Disaster
14467496,0.56698,0,4,/huzeyfedegirmenci/predictception,Titanic - Machine Learning from Disaster
14250350,0.7751100000000001,0,0,/folarteca/begginer-titanic-several-models,Titanic - Machine Learning from Disaster
14553831,0.73205,0,2,/rajeev064/titanic-survival-prediction-using-ann,Titanic - Machine Learning from Disaster
9729659,0.79904,0,3,/gtatiana/getting-started-with-titanic-dataset,Titanic - Machine Learning from Disaster
14578345,0.78947,0,0,/x2020fjv/titanic-survival-prediction,Titanic - Machine Learning from Disaster
8455808,0.8265299999999999,0,0,/sjrsjr/200327-submission-v1-0,Natural Language Processing with Disaster Tweets
7591969,0.7036399999999999,0,0,/bcantt/word-column-classifier,Natural Language Processing with Disaster Tweets
8687032,0.81642,0,0,/andyden/lstm-crawl-and-glove,Natural Language Processing with Disaster Tweets
8221073,0.8378700000000001,0,2,/sawarn69/multilingual-encoder-support-vector-machine,Natural Language Processing with Disaster Tweets
7212747,0.8004899999999999,0,0,/amarpandey/real-or-not-nlp-with-disaster-tweets,Natural Language Processing with Disaster Tweets
8372259,0.5368,0,0,/sawarn69/disaster-nlp-keras-bert-using-tfhub,Natural Language Processing with Disaster Tweets
8699431,0.77444,8,18,/mohitsital/eda-fe-logistic-reg-svm,Natural Language Processing with Disaster Tweets
8722679,0.8378700000000001,1,13,/sofieneressifi/nlp-with-disaster-tweets-complete-eda-and-bert,Natural Language Processing with Disaster Tweets
8582294,0.8378700000000001,0,0,/lucca9211/fork-of-nlp-disaster-bert,Natural Language Processing with Disaster Tweets
8743335,0.0,0,0,/lucca9211/tweetanalysis-tf-hub,Natural Language Processing with Disaster Tweets
8663420,0.77566,0,0,/utsavporwal/real-or-not,Natural Language Processing with Disaster Tweets
8628971,0.78332,0,2,/mashiat/nlp-lstm,Natural Language Processing with Disaster Tweets
8577011,0.6775899999999999,0,0,/mashiat/nlp-rnn,Natural Language Processing with Disaster Tweets
7643093,0.83236,0,0,/montimirko/prova-nlp-disaster-tensorflow-con-bert,Natural Language Processing with Disaster Tweets
8549087,0.82439,1,3,/kirilkuzmin/svm-with-words2vectors-mapping,Natural Language Processing with Disaster Tweets
8527602,0.78976,2,2,/aninda/nlp-disaster-fastai,Natural Language Processing with Disaster Tweets
10912951,0.75197,2,8,/datawarriors/covid19-forecasting-week1-using-random-forest,COVID19 Global Forecasting (Week 1)
8578036,2.20254,0,0,/rajoriyas/covid19-global-forecasting-week-1-rajoriyas,COVID19 Global Forecasting (Week 1)
8509977,0.73935,0,0,/yilmazalp/covid-19,COVID19 Global Forecasting (Week 1)
8530709,0.73961,0,0,/etgreen/kernel1ad3ba5db0,COVID19 Global Forecasting (Week 1)
8579686,2.113,0,0,/mohres/covid-19-extract-important-features,COVID19 Global Forecasting (Week 1)
8542268,0.7571,0,0,/kishanbala/covid19-predictions-bestmodel,COVID19 Global Forecasting (Week 1)
8523754,0.5195,0,3,/akashsuper2000/sarimax-baseline-starter-prediction,COVID19 Global Forecasting (Week 1)
8541250,0.7379399999999999,0,0,/lawrencechen98/covid-19-world-forecast,COVID19 Global Forecasting (Week 1)
8500919,1.66284,0,0,/skeller/sprawling-cov-nb,COVID19 Global Forecasting (Week 1)
8518382,1.19022,0,0,/xscripter/kernel65a82aa42a,COVID19 Global Forecasting (Week 1)
8553923,0.6731699999999999,0,0,/lilylong/kernel855ba0a16f,COVID19 Global Forecasting (Week 1)
8571134,0.67025,0,0,/autokad/kernel128a229670,COVID19 Global Forecasting (Week 1)
8488478,2.15595,0,4,/mdmahmudferdous/covid-19-global-forecasting-1,COVID19 Global Forecasting (Week 1)
8581065,1.0143799999999998,1,31,/osciiart/covid19-lightgbm,COVID19 Global Forecasting (Week 1)
8528868,2.50817,0,0,/mehdi16/simple-straight-forward-covid-19-forecast,COVID19 Global Forecasting (Week 1)
8538309,0.69787,0,0,/sarthakpawar/first-attempt-to-a-forecasting-problem,COVID19 Global Forecasting (Week 1)
11639054,0.0192,9,98,/gogo827jz/moa-stacked-tabnet-baseline-tensorflow-2-0,Mechanisms of Action (MoA) Prediction
11663016,0.01894,0,4,/hengzheng/gpu-split-neural-network-approach-tf-keras,Mechanisms of Action (MoA) Prediction
11646444,0.02001,0,7,/parmarsuraj99/fork-of-neural-mechanism-for-moa-keras,Mechanisms of Action (MoA) Prediction
11638365,0.02398,2,7,/ilikedeeplearning/basic-data-exploration-benchmark,Mechanisms of Action (MoA) Prediction
11582250,0.06111,0,2,/nelsonewert/moa-multioutput-classification,Mechanisms of Action (MoA) Prediction
11628603,0.02337,0,0,/akashkr/moa-v2,Mechanisms of Action (MoA) Prediction
11584106,0.01896,1,26,/ravy101/experimenting-with-gaussian-noise-aug-tf-keras,Mechanisms of Action (MoA) Prediction
11550943,0.01938,4,30,/nakulsingh1289/moa-with-keras-for-beginner-s,Mechanisms of Action (MoA) Prediction
11579229,0.01894,1,13,/anmolkumar/mechanisms-of-action-moa-prediction,Mechanisms of Action (MoA) Prediction
11531849,0.02104,0,12,/tatsuya214355/lightgbm-beginner-moa,Mechanisms of Action (MoA) Prediction
11545101,0.0197,44,233,/fchmiel/xgboost-baseline-multilabel-classification,Mechanisms of Action (MoA) Prediction
11526912,0.01895,45,204,/simakov/keras-multilabel-neural-network-v1-2,Mechanisms of Action (MoA) Prediction
11575322,0.01941,0,6,/barteksadlej123/dnn-in-keras,Mechanisms of Action (MoA) Prediction
11567579,0.01894,3,16,/shaitender/moa-prediction,Mechanisms of Action (MoA) Prediction
11542103,0.02252,0,4,/nicohrubec/206-ridges-baseline,Mechanisms of Action (MoA) Prediction
11564255,0.1224799999999999,4,11,/avloss/i-map-umap,Mechanisms of Action (MoA) Prediction
11560307,0.03786,0,1,/rdekou/random-forest-model-h2o,Mechanisms of Action (MoA) Prediction
10978583,0.6779999999999999,0,0,/koroko1/wheat-mask-detectron2,Global Wheat Detection
10935103,0.6428,11,13,/gooogr/gwd-predicts-with-yolov4-and-gpu-darknet,Global Wheat Detection
10075355,0.7402,0,0,/akashsuper2000/global-wheat-detection-with-pseudo-labelling,Global Wheat Detection
10911210,0.7294,2,8,/pohanlin/augmentation-with-efficientdet-d5,Global Wheat Detection
10826797,0.3046,1,6,/kuzn137/unet-with-data-augmentation-and-sliced-masks,Global Wheat Detection
10662867,0.7101,0,3,/m1nglei/mmdetectionv2-cascadercnn-resnest101,Global Wheat Detection
10626778,0.7714,14,64,/chaonanxi/since-yolov5-is-out,Global Wheat Detection
10282385,0.6226,0,1,/fengyun0720/new-mmdetection-submit,Global Wheat Detection
10786732,0.7413,1,4,/leoisleo1/yolov5-pseudo-labeling-oof-evaluation-lb-0-741,Global Wheat Detection
10700115,0.7209,2,9,/doanquanvietnamca/insect-augmentation-with-efficientdet-d7,Global Wheat Detection
10772957,0.5608,0,0,/dhavalkumar/wheat-detection,Global Wheat Detection
10679803,0.6839,0,4,/rubustjay/yolov5-inference,Global Wheat Detection
10252206,0.5531,0,0,/matanarobas/gwd-test,Global Wheat Detection
10731368,0.7088,0,4,/jonykarki/inference-fasterrcnn-resnet101-fold-3,Global Wheat Detection
9417239,0.6592,0,0,/vincentyong97/efficientdet-inference,Global Wheat Detection
10571423,0.7068,0,1,/rachit07rawat/predict-rajnain,Global Wheat Detection
10665202,0.6524,0,1,/rickyd/inference-fasterrcnn,Global Wheat Detection
10591400,0.7686,46,161,/hawkey/yolov5-pseudo-labeling-oof-evaluation,Global Wheat Detection
4099006,1.239,0,1,/massyl/predicting-molecular-properties,Predicting Molecular Properties
6803172,-2.2004,0,1,/levanlinh/dual-mpnn,Predicting Molecular Properties
6908419,0.9284,0,0,/scirpus/genetic-programming-with-whitening,Kannada MNIST
6836140,0.9844,0,1,/tippu89/kannada-mnist-keras-cnn-y-network,Kannada MNIST
6829070,0.9828,0,0,/tippu89/kannada-mnist-keras-cnn-starter,Kannada MNIST
6848146,0.9882,0,1,/simonthesidekick/a-grid-search-like-approach,Kannada MNIST
6804296,0.735,0,2,/scirpus/gp-clustering-with-knn-classifier,Kannada MNIST
6819462,0.9686,0,0,/mswieton/2019-11-29-kannada-simple-cnn,Kannada MNIST
6752724,0.9688,0,0,/larandaa/aryzhevich-nn-2-lenet,Kannada MNIST
6728226,0.9572,0,3,/tunguz/kannada-mnist-mlpclassifier-baseline,Kannada MNIST
6696644,0.9882,0,7,/c7934597/cnn-in-tensorflow-for-kannada-digits,Kannada MNIST
6496676,0.9608,0,1,/kristogj/basic-cnn-with-kannada-mnist,Kannada MNIST
6692761,0.9696,0,2,/lgh7654/kernel5700d56d55,Kannada MNIST
6686191,0.9174,0,2,/barabashkkka/kernel55d927b991,Kannada MNIST
6625675,0.9894,10,21,/yonminma/keras-easy-with-0-9892-score,Kannada MNIST
6622995,0.9866,0,3,/marstebi/cnn-with-batch-normalization-and-data-augmentation,Kannada MNIST
6611702,0.9848,1,8,/ilyamich/kannada-mnist-choosing-the-right-optimizer,Kannada MNIST
9671064,0.708,0,0,/akashsuper2000/tf-roberta-cnn-head,Tweet Sentiment Extraction
9987471,0.521,0,0,/freddyyj3/roberta-comp,Tweet Sentiment Extraction
9994456,0.711,0,3,/meenakshiramaswamy/tweet-sentiment-roberta-tpu-inference,Tweet Sentiment Extraction
10142436,0.652,0,0,/dmitri9149/tweet-sentiment-extraction-word-counts-only,Tweet Sentiment Extraction
10153599,0.442,0,2,/srijamacherla/baymacs-nlp1,Tweet Sentiment Extraction
8605919,0.6729999999999999,0,0,/khanalkiran/tweet-sentiment-extraction-kk1,Tweet Sentiment Extraction
9629301,0.715,0,0,/akashsuper2000/tweet-sentiment-roberta-pytorch,Tweet Sentiment Extraction
9906843,0.695,0,0,/kimjinhyeon/kernel359407d2c8,Tweet Sentiment Extraction
9794322,0.696,0,0,/josealways123/bertweet-from-colab-2,Tweet Sentiment Extraction
10098862,0.6,0,2,/pranaydate/improving-bert-model,Tweet Sentiment Extraction
10085454,0.713,1,4,/riyajm/attari,Tweet Sentiment Extraction
10076969,0.7020000000000001,0,2,/pranaydate/commented-bert-using-pytorch,Tweet Sentiment Extraction
10049702,0.6509999999999999,0,1,/justinparks/count-vectorizer-plus,Tweet Sentiment Extraction
10097425,0.395,0,0,/anishadatta/kernel681a554f4d,Tweet Sentiment Extraction
10029587,0.594,0,2,/nicholasgeorgekiddle/word-embedding,Tweet Sentiment Extraction
10028868,0.6459999999999999,0,1,/nicholasgeorgekiddle/lemma,Tweet Sentiment Extraction
8955340,0.287,0,4,/gangakrish/keyword-extraction-from-tweets,Tweet Sentiment Extraction
10050377,0.6509999999999999,0,0,/ghaithmshan/kernel2d015ca966,Tweet Sentiment Extraction
9966497,0.389,0,0,/freddyyj/tensorflow-roberta-compet,Tweet Sentiment Extraction
6314587,2.44,0,0,/amaity0/ashrae3-first-try,ASHRAE - Great Energy Predictor III
6706026,1.08,48,233,/aitude/ashrae-kfold-lightgbm-without-leak-1-08,ASHRAE - Great Energy Predictor III
6696519,1.01,1,7,/wentixiaogege/ashrae-maybe-this-can-make-public-lb-some-useful,ASHRAE - Great Energy Predictor III
6626706,1.61,3,10,/mdanielson/model-less-median-prediction,ASHRAE - Great Energy Predictor III
6596070,1.08,23,120,/yamsam/new-ucf-starter-kernel,ASHRAE - Great Energy Predictor III
6332576,1.38,0,3,/ishaan45/starter-code,ASHRAE - Great Energy Predictor III
6368238,1.36,22,74,/vikassingh1996/ashrae-great-energy-insightful-eda-fe-lgbm,ASHRAE - Great Energy Predictor III
6307086,1.11,8,21,/hiteshsom/ashrae-3-lightgbm,ASHRAE - Great Energy Predictor III
6518899,1.1,76,392,/rohanrao/ashrae-half-and-half,ASHRAE - Great Energy Predictor III
6472099,1.244,7,36,/gouherdanishiitkgp/ashrae-basic-eda-and-feature-engineering,ASHRAE - Great Energy Predictor III
6240477,4.699,2,4,/grapestone5321/ashrae-sample-submission-and-data-leakage-exercise,ASHRAE - Great Energy Predictor III
6239562,1.207,39,143,/kimtaegwan/what-s-your-cv-method,ASHRAE - Great Energy Predictor III
6422772,1.11,27,158,/nz0722/aligned-timestamp-lgbm-by-meter-type,ASHRAE - Great Energy Predictor III
58318,2.18468,0,0,/saatetyi/theano-lasange-starter,State Farm Distracted Driver Detection
6190293,3.0380000000000003,0,0,/vladimirn/the-nature-conservancy-fisheries-monitoring,The Nature Conservancy Fisheries Monitoring
7736225,0.4516,0,0,/nizamuddin/fa-c-k-e-detector,Deepfake Detection Challenge
8252685,0.43846,4,6,/revanthrex/gcloud-ensembling-learning-learning-rates,Deepfake Detection Challenge
8604393,0.95883,0,1,/manyregression/fastai-big-inference,Deepfake Detection Challenge
8153241,0.68725,0,1,/ramanareddyeceb/kernel54151e7253,Deepfake Detection Challenge
8666237,0.37463,4,8,/carlolepelaars/efficientnet2xb5-b6200-b6finetuned-b4-2xres-01clip,Deepfake Detection Challenge
7694669,0.31601,0,5,/ims0rry/inference-demo,Deepfake Detection Challenge
8531644,0.32224,0,0,/timesler/face-sequence-ensemble-inference,Deepfake Detection Challenge
8546370,0.43767,2,12,/nxrprime/mixing-up-ensembles,Deepfake Detection Challenge
8526543,0.43793,1,6,/pankymathur/gcloud-ensembling-learning-learning-rates,Deepfake Detection Challenge
8342909,0.45151,23,28,/bootiu/efficientnet-single-model,Deepfake Detection Challenge
426677,0.0026899999999999,0,0,/am1to2/random-assignment-using-naive-statistics,Cdiscount’s Image Classification Challenge
12832756,0.16032,0,0,/cycadring/new-whale-2,Humpback Whale Identification
3943988,0.93493,0,1,/sunny0528/whale-identification-snn,Humpback Whale Identification
3086765,0.866,0,4,/alokevil/whale-ensemble-lb-0-866,Humpback Whale Identification
2983925,0.2789999999999999,0,1,/ortempo/whale-recognition-1-keras-starter,Humpback Whale Identification
2979671,0.621,0,2,/frkhit/triplet-loss-resnet50-bounding-box-0-621,Humpback Whale Identification
2797848,0.428,0,4,/rgoodman/whale-identification-challenge-pytorch,Humpback Whale Identification
2850636,0.264,0,1,/rmihir96/whale-identification,Humpback Whale Identification
2824850,0.295,0,3,/game1level2/cnn-whalev2,Humpback Whale Identification
2760216,0.368,0,2,/ayalamann/siamese-net-with-bb,Humpback Whale Identification
2822132,0.052,0,0,/ayalamann/simple-cnn-classification-no-new-whale,Humpback Whale Identification
2603306,0.2769999999999999,2,3,/paulsantonastaso/pytorch-whale-classifier,Humpback Whale Identification
2507674,0.49,0,2,/akshaysub99/whale-pretrained-with-val,Humpback Whale Identification
4030918,0.17029,1,4,/varnez/dogs-vs-cats-kernel-simple-keras-cnn,Dogs vs. Cats Redux: Kernels Edition
2153237,0.24413,0,0,/jmourad100/dogsvscats-transfer-learning-with-resnet50,Dogs vs. Cats Redux: Kernels Edition
3679703,3.21756,1,1,/yellowduck/transfer-learning-pytorch,Dogs vs. Cats Redux: Kernels Edition
3535572,0.41598,0,1,/mnk812/dogs-cats-keras-baseline,Dogs vs. Cats Redux: Kernels Edition
2883715,0.06957,1,2,/anjanatiha/classification-using-keras-accuracy-100,Dogs vs. Cats Redux: Kernels Edition
2507016,0.05064,4,8,/toshikazuwatanabe/fast-ai-latest-dogs-vs-cats,Dogs vs. Cats Redux: Kernels Edition
2247005,0.5011,0,1,/angelgmedina/cats-vs-dogs-transfer-learning-inceptionresnetv2,Dogs vs. Cats Redux: Kernels Edition
1942011,0.06495,0,1,/vincentpommier/resnet50-with-fastai-library,Dogs vs. Cats Redux: Kernels Edition
1640384,0.08774,1,2,/johnfarrell/dvc-pretrained-model-finetune,Dogs vs. Cats Redux: Kernels Edition
1675640,0.70199,4,7,/risingdeveloper/dogs-vs-cats-keras-implementation,Dogs vs. Cats Redux: Kernels Edition
5479275,0.8073899999999999,23,133,/peterhurford/why-not-logistic-regression,Categorical Feature Encoding Challenge
5509688,0.79359,0,15,/arashnic/cats-vs-lgbm,Categorical Feature Encoding Challenge
5486406,0.8055399999999999,1,14,/neibyr/mean-target-encoding,Categorical Feature Encoding Challenge
5476668,0.80145,0,10,/sugawarya/h2o-automl-30000sec,Categorical Feature Encoding Challenge
6551349,0.75285,0,0,/scottolson/kernel58610bf71d,Categorical Feature Encoding Challenge
14071625,0.40922,0,3,/dogdriip/bike-sharing-demand,Bike Sharing Demand
13341840,0.4641399999999999,1,4,/adielsa/bike-sharing-prediction-score,Bike Sharing Demand
12980177,0.39518,0,1,/divya00/team-pa,Bike Sharing Demand
13003319,0.70349,0,2,/abdullapathan/base-version,Bike Sharing Demand
12889505,0.47339,0,1,/abdullapathan/bike-sharing-demand-version-2-0,Bike Sharing Demand
4070829,0.36154,0,0,/suvinlee/kernel77d7dbc37e,Bike Sharing Demand
10931352,0.5142399999999999,1,2,/sidagar/well-commented-code-using-random-forest,Bike Sharing Demand
10624076,0.4443899999999999,0,0,/daisukeakiyama/kernela689cfc38a,Bike Sharing Demand
10352121,1.34801,4,9,/vibeeshk/bike-sharing-demand-prediction,Bike Sharing Demand
5893705,0.3771,0,0,/seahur/bicycle-attempt,Bike Sharing Demand
9085060,0.4388,0,0,/bartoszwyporkiewicz/eda-ml-model-0-2888-rmsle-in-validation-set,Bike Sharing Demand
4748957,0.3941699999999999,0,0,/sjun4530/bike-sharing-demand,Bike Sharing Demand
14143595,0.95376,0,1,/blighpark/imdb-mlwave,Bag of Words Meets Bags of Popcorn
13156893,0.8538,1,2,/ravijain01/bag-of-words-sing-random-forest-classifier,Bag of Words Meets Bags of Popcorn
8254659,0.8452,0,0,/kongnyooong/imdb-review-nlp-tutorial-part-3,Bag of Words Meets Bags of Popcorn
8072985,0.8532799999999999,0,1,/kongnyooong/imdb-review-nlp-tutorial-part-1,Bag of Words Meets Bags of Popcorn
7371399,0.79896,0,1,/adage14175/natural-language-processing,Bag of Words Meets Bags of Popcorn
7016184,0.8442,0,1,/bilalshahidqureshi/nlp-word2vec-07934b,Bag of Words Meets Bags of Popcorn
6592926,0.8807200000000001,0,2,/yeonseokjeong/imdb-movie-reviews-yeonseok,Bag of Words Meets Bags of Popcorn
5056544,1.0,2,6,/noi031/sentiment-analysis-with-self-attention,Bag of Words Meets Bags of Popcorn
4439488,0.49868,0,0,/minjipark/bagofwords-bilstm,Bag of Words Meets Bags of Popcorn
3710605,0.99,7,55,/alexcherniuk/imdb-review-word2vec-bilstm-99-acc,Bag of Words Meets Bags of Popcorn
2944172,0.8562,0,0,/puneetsingla17/bow-preprocessing-beautifulsoup,Bag of Words Meets Bags of Popcorn
1950280,0.8827200000000001,0,0,/xinruchen/my-nlp-excercises-on-movie-sentiment,Bag of Words Meets Bags of Popcorn
1969600,0.87556,0,6,/amitkvikram/bag-of-words,Bag of Words Meets Bags of Popcorn
14281984,0.8759999999999999,1,4,/krisho007/simple-pytorch-lightning-inference,Cassava Leaf Disease Classification
14476793,0.901,20,64,/capiru/cassavanet-inference-tta-easy-submission,Cassava Leaf Disease Classification
14527205,0.9,13,29,/ragnar123/effb5-cv-0-9007-single-model-tf,Cassava Leaf Disease Classification
14543601,0.139,2,5,/homiarafarhana/cassava-2nd,Cassava Leaf Disease Classification
14520819,0.9,1,13,/alekseyeliseev/pytorch-vit-baseline-inference-tta,Cassava Leaf Disease Classification
14388801,0.6579999999999999,5,11,/sm00ther/cassava-keras-test,Cassava Leaf Disease Classification
14462070,0.892,0,3,/leonshangguan/inference-test,Cassava Leaf Disease Classification
13139779,0.451,5,3,/arka1993/ab-cassava-leaf-disease-classification,Cassava Leaf Disease Classification
14372770,0.602,0,2,/abduljabbar110/sequential-cnn-model,Cassava Leaf Disease Classification
13798349,0.99042,3,1,/phsaikiran/mnist-digit-lenet,Digit Recognizer
14235927,0.99664,0,0,/ouba64/digit-recognizer-cnn-ensemble,Digit Recognizer
14238959,0.99457,0,0,/jokyeongmin/mnist-resnet18-in-pytorch,Digit Recognizer
14200987,0.98085,0,0,/natsukitsukamoto/assignment-a-3,Digit Recognizer
14160703,0.98207,1,1,/ahateshambhuiyan/handwritten-digit-classification-with-keras,Digit Recognizer
14137730,0.96557,4,4,/atultyagi2000/mnist-digit-recognition-lr,Digit Recognizer
11825066,0.97903,0,0,/akashkash/sgh-nn-project,Digit Recognizer
13971433,0.99453,2,3,/mathlasker/digits-recognizer,Digit Recognizer
13659408,0.98628,0,0,/tobiasmmmmmm/digitrecognizer,Digit Recognizer
13999596,0.94539,0,1,/kushagratandon12/digit-classification-tensorflow-keras,Digit Recognizer
13822918,0.9665,0,0,/tomoshimo/mnist-tensorflow,Digit Recognizer
13883499,0.98085,0,0,/azuremis/digit-recogniser,Digit Recognizer
13923447,0.92775,0,0,/shwetabhujbal/digit-recognizer-a-26,Digit Recognizer
13867423,0.97521,5,5,/craigmthomas/beginner-guide-to-digit-recognition,Digit Recognizer
13862328,0.98967,3,5,/mischva11/cnn-mnist,Digit Recognizer
166245,35974.57634,0,0,/octavianh/can-we-improve-by-increasing-variance,Santa's Uncertain Bags
9574162,0.613,0,2,/simrankucheria/fastai,Jigsaw Multilingual Toxic Comment Classification
9671353,0.8677,0,0,/ych1997ych/lstm-1,Jigsaw Multilingual Toxic Comment Classification
8970738,0.9149,0,0,/tapaskd123/english-test,Jigsaw Multilingual Toxic Comment Classification
9321590,0.5033,0,2,/joydeb28/using-distilbert-with-huggingface-please-upvote,Jigsaw Multilingual Toxic Comment Classification
9481861,0.5094,0,0,/plarmuseau/simple-logistic,Jigsaw Multilingual Toxic Comment Classification
9130285,0.9279,0,9,/chittalpatel/jigsaw-toxic-comment,Jigsaw Multilingual Toxic Comment Classification
9130068,0.9459,67,254,/shonenkov/tpu-inference-super-fast-xlmroberta,Jigsaw Multilingual Toxic Comment Classification
9084716,0.8244,0,0,/plarmuseau/jigsaw-spacy,Jigsaw Multilingual Toxic Comment Classification
8990961,0.9058,0,1,/adityasinghgoliya/pytorch-tpu,Jigsaw Multilingual Toxic Comment Classification
9004686,0.7379,0,0,/gauravsharma11/jigsaw-model-using-cnn,Jigsaw Multilingual Toxic Comment Classification
8787833,0.8961,0,2,/anusvar/kernel6386f46944,Jigsaw Multilingual Toxic Comment Classification
79393,2.38809,34,174,/dvasyukova/brand-and-model-based-benchmarks,TalkingData Mobile User Demographics
4691417,10.24737,0,0,/shubham505/imitation-game-2f436a,Generative Dog Images
5156747,121.88942,0,0,/dranzer/pixelshuffle-ralsgan,Generative Dog Images
5074575,66.62578,5,17,/shaygu/dogs-gan-starter-organized,Generative Dog Images
5029215,96.36924,2,6,/javiermzll/gan-keras-dog-generator,Generative Dog Images
4912871,69.26111999999999,5,30,/sakami/ralsgan-dogs-cropping-random,Generative Dog Images
4945377,128.81893,0,2,/qinhui1999/fork-of-vq-vae2-0-for-dog-image-generation,Generative Dog Images
4867926,75.13707,8,44,/mpalermo/pytorch-rals-c-sagan,Generative Dog Images
4893700,7.0004100000000005,6,50,/timetraveller98/memorizer-cgan-pytorch-version,Generative Dog Images
4604703,177.1451,0,4,/neonninja/keras-dcgan,Generative Dog Images
4673940,7.2595399999999985,5,89,/jesucristo/memorizer-cgan-for-dummies,Generative Dog Images
4665673,7.28145,45,217,/cdeotte/dog-memorizer-gan,Generative Dog Images
4650166,105.82446000000002,7,25,/cmalla94/dcgan-generating-dog-images-with-tensorflow,Generative Dog Images
4643103,248.91792,0,1,/akashs2021/pytorch-beginner-code,Generative Dog Images
4645861,599.1831900000002,2,6,/snakayama/simple-keras-dcgan-v1,Generative Dog Images
4597853,200.52603,0,12,/francoisdubois/dcgan-keras,Generative Dog Images
4610287,34.13305,0,6,/shubham505/imitation-game-step-by-step,Generative Dog Images
4435480,278.12283,7,123,/wendykan/gan-dogs-starter,Generative Dog Images
3422089,0.785,0,1,/benjibb/automated-60-models-ensemble-h2o,Don't Overfit! II
3233326,0.706,1,1,/gdmacmillan/data-expo,Don't Overfit! II
2913373,0.802,3,9,/tboyle10/feature-selection,Don't Overfit! II
3363129,0.8490000000000001,15,28,/iavinas/simple-short-solution-don-t-overfit-0-848,Don't Overfit! II
3055888,0.85,4,4,/vukglisovic/iterative-modelling,Don't Overfit! II
3284587,0.856,12,25,/plasticgrammer/don-t-overfit-i-try,Don't Overfit! II
3273431,0.848,4,11,/mitjasha/don-t-overfit-2-linear-models-with-hyperopt,Don't Overfit! II
3246873,0.86,11,34,/melondonkey/bayesian-spike-and-slab-in-pymc3,Don't Overfit! II
2941405,0.8390000000000001,0,0,/shahaffind/over-generalizing,Don't Overfit! II
3145579,0.843,2,2,/pidem2501/bayesian-approach-with-pymc3,Don't Overfit! II
3058299,0.662,0,0,/siddhesh25/classification-using-xgbclassifier,Don't Overfit! II
3009572,0.8029999999999999,13,30,/gpreda/overfitting-the-private-leaderboard,Don't Overfit! II
2950601,0.828,3,19,/tboyle10/hyperparameter-tuning,Don't Overfit! II
2971179,0.7240000000000001,0,2,/somaktukai/72-roc-how-not-to-overfit,Don't Overfit! II
2913595,0.85,4,7,/rishrk007/don-t-overfit-contest,Don't Overfit! II
2986258,0.8440000000000001,0,0,/tomehta/lasso-for-feature-selection-and-logreg,Don't Overfit! II
2896952,0.848,126,506,/artgor/how-to-not-overfit,Don't Overfit! II
2924836,0.737,0,6,/braquino/neural-net-experimentation,Don't Overfit! II
2922286,0.847,0,1,/gravix/elastic-fit,Don't Overfit! II
2909920,0.6809999999999999,0,1,/xsakix/first-overfit,Don't Overfit! II
2903308,0.5710000000000001,0,0,/karthikkannan/baseline,Don't Overfit! II
2883052,0.8440000000000001,9,27,/miroslavsabo/auc-0-844-in-11-loc,Don't Overfit! II
2882788,0.841,2,19,/robikscube/don-t-overfit-ii-first-look-and-eda,Don't Overfit! II
2875164,0.7859999999999999,0,2,/lukebassett/do-2-corr-logistic-regression-experimentation,Don't Overfit! II
2879973,0.7829999999999999,0,1,/bigkizd/lightgbm-with-cross-validation,Don't Overfit! II
2872950,0.648,2,0,/wakamezake/my-first-submit,Don't Overfit! II
3782461,0.6729999999999999,0,0,/alejandrocano/don-t-overfit-tpot,Don't Overfit! II
11312432,0.4854,0,1,/animesh2099/google-landmark-recognition-2020,Google Landmark Recognition 2020
11819471,0.0,0,0,/oliverhansen/oliver-hansen-notebook,Google Landmark Recognition 2020
11549103,0.4852,0,0,/chriszg/google-recognition,Google Landmark Recognition 2020
11883822,0.5414,2,8,/tangshuyun/lb-0-54-effnetb6b7-global-feature,Google Landmark Recognition 2020
11838005,0.4844,0,4,/alifrahman/landmark-prediction-2020-top-35,Google Landmark Recognition 2020
11413746,0.5227,0,0,/raufyagfarov/efficientnet-inference,Google Landmark Recognition 2020
11841990,0.4905,5,9,/tangshuyun/efficientnetb6-and-b7,Google Landmark Recognition 2020
11692481,0.2259,0,8,/jagadish13/efficientnetb0-submission-training-test,Google Landmark Recognition 2020
11834794,0.4832,0,3,/alifrahman/google-landmark-prediction-eda,Google Landmark Recognition 2020
11837994,0.4849,0,1,/akashsuper2000/organizer-s-code-submission,Google Landmark Recognition 2020
11766184,0.108,0,4,/alifrahman/successful-landmark-recognition-submission,Google Landmark Recognition 2020
11605906,0.4844,0,8,/penchalaiah123/code-submission,Google Landmark Recognition 2020
11377868,0.1044,0,3,/siddamravi/notebook7cdbbd9261,Google Landmark Recognition 2020
1146889,0.28169,0,0,/dinaldoap/perda-total,Porto Seguro’s Safe Driver Prediction
958322,0.23999,0,0,/luancaius/porto-seguro-kernel,Porto Seguro’s Safe Driver Prediction
904106,-0.00612,0,1,/nikhil04/safe-driver-prediction,Porto Seguro’s Safe Driver Prediction
564565,0.27443,0,1,/ymittal23/safe-driver-prediction-exploration,Porto Seguro’s Safe Driver Prediction
484104,0.26365,0,0,/joshiankur/lgboost-model,Porto Seguro’s Safe Driver Prediction
2403038,0.941,0,2,/kaerunantoka/0-943-cv-0-52974-features-136,PLAsTiCC Astronomical Classification
2390609,1.063,1,7,/amitkumarjaiswal/boost-plasticc,PLAsTiCC Astronomical Classification
2164631,1.039,0,0,/jimpsull/improveclass99fordartsmoteset,PLAsTiCC Astronomical Classification
2294649,1.0759999999999998,0,0,/jimpsull/closethegapbetweencvandlb,PLAsTiCC Astronomical Classification
2094759,1.135,28,102,/iprapas/ideas-from-kernels-and-discussion-lb-1-135,PLAsTiCC Astronomical Classification
1993534,1.3630000000000002,4,31,/rooshroosh/fork-simple-mlp-for-time-series-classification,PLAsTiCC Astronomical Classification
1877042,1.686,6,49,/meaninglesslives/lgb-parameter-tuning,PLAsTiCC Astronomical Classification
1871397,2.081,0,25,/darbin/weighted-naive-benchmark-lb-2-081,PLAsTiCC Astronomical Classification
831664,0.807,1,8,/anmour/svm-using-mfcc-features,Freesound General-Purpose Audio Tagging Challenge
806853,0.777,1,2,/miklgr500/catboost-mfcc,Freesound General-Purpose Audio Tagging Challenge
802474,0.73,5,10,/amlanpraharaj/random-forest-using-mfcc-features,Freesound General-Purpose Audio Tagging Challenge
812752,0.64889,0,0,/pexea12/forest-cover-draft,Forest Cover Type Prediction
6017848,0.7931699999999999,0,0,/pendras/lgbm-with-bayesian-optimization,Home Credit Default Risk
1471793,0.75459,0,0,/praxitelisk/home-credit-default-risk-competition-gentle-intro,Home Credit Default Risk
1081382,0.743,0,0,/shenba/home-credit-v2-07jun-2018,Home Credit Default Risk
14560611,4096.781,0,5,/dhyeok1996/simple-baseline-lightgbm,Jane Street Market Prediction
14416849,9766.47,49,103,/chixujohnny/try-to-use-nn-baseline,Jane Street Market Prediction
14490430,8448.468,0,5,/aimind/janestreet-1dcnn-for-feature-extraction-train,Jane Street Market Prediction
14516375,10677.152,2,17,/leejingwan/own-jane-street-with-keras-nn-ensemble-date85,Jane Street Market Prediction
14263882,10607.512,5,26,/chienhsianghung/jane-street-simple-nn-mlp-w-purgedgroupts-cv,Jane Street Market Prediction
14278310,10607.512,5,25,/manavtrivedi/mlp-nn-utility,Jane Street Market Prediction
14390970,4867.021,6,22,/snippsy/jane-street-tensorflow-probability-starter,Jane Street Market Prediction
14388156,350.836,8,18,/snippsy/no-gpu-time-sell-at-10-a-m-play-golf,Jane Street Market Prediction
14648618,10658.078,0,2,/sapthrishi007/pytorch-embeddingsnn-resnet-tensorflow,Jane Street Market Prediction
14379694,3897.973,0,4,/yshiml/simple-lgbm-baseline-with-optuna-beginners,Jane Street Market Prediction
14188581,2540.327,1,3,/bhanwarsaini/market-prediction,Jane Street Market Prediction
13303772,6962.585,0,3,/pratiksharm/jane-street-pk-attempt,Jane Street Market Prediction
83893,0.6796399999999999,0,0,/yiyihuijia/second,Grupo Bimbo Inventory Demand
12737378,0.28059,0,0,/ashiqueeelahi/moa-model-and-validation,Mechanisms of Action (MoA) Prediction
12883686,0.0191199999999999,0,1,/lililycai/fastai-moa-with-drugstratification,Mechanisms of Action (MoA) Prediction
12874581,0.01865,2,16,/ash1706/moa-randomoversampler-drug-id-fixed-imbalance,Mechanisms of Action (MoA) Prediction
12872514,0.01995,0,2,/garrettwankel/mechanism-of-action-onevsrest-sgd,Mechanisms of Action (MoA) Prediction
12806511,0.01956,0,0,/yzgast/moa-minimal-kfold-dnn-keras,Mechanisms of Action (MoA) Prediction
12784439,0.01844,3,5,/junyan01/moa-predictions-overfitting-with-tabnet-3233e3,Mechanisms of Action (MoA) Prediction
12798614,0.019,0,0,/salonisethiya/moa-kagggle,Mechanisms of Action (MoA) Prediction
12630360,0.01841,5,27,/ash1706/sampling-moa-mlsmote-pytorch-rankgauss-tsvd,Mechanisms of Action (MoA) Prediction
12240871,0.03355,0,4,/matejhorvat/bla-droge,Mechanisms of Action (MoA) Prediction
12818125,0.05335,0,0,/rtindru/notebook8d3ad46e04,Mechanisms of Action (MoA) Prediction
12811708,0.01952,0,0,/alexandervc/moa36-3-logreg-blend-v3,Mechanisms of Action (MoA) Prediction
12249923,0.01877,0,0,/striker7/mechanisms-of-action-moa-predicti-fastai,Mechanisms of Action (MoA) Prediction
12816903,0.01845,0,0,/art6745/notebookf7f9bc2e32,Mechanisms of Action (MoA) Prediction
12718838,0.01917,0,10,/lhagiimn/random-ensemble-neural-networks,Mechanisms of Action (MoA) Prediction
12680397,0.02025,1,7,/lhagiimn/t-test-with-logistic-regression,Mechanisms of Action (MoA) Prediction
12640620,0.02157,0,1,/awwaldiekaramapepple/fork-of-stacked-xgboostclassifier-nn-lgb-10b19,Mechanisms of Action (MoA) Prediction
12518214,0.01871,0,0,/hasan7/moa-nn-ensamble,Mechanisms of Action (MoA) Prediction
12723396,0.01883,0,0,/art6745/notebookea1c1594ba,Mechanisms of Action (MoA) Prediction
12643833,0.01849,3,7,/snooptosh/save-gpu-quota-during-commit-fork,Mechanisms of Action (MoA) Prediction
12651129,0.10535,0,1,/wittmannf/quick-eda-and-baseline-submission-with-knn,Mechanisms of Action (MoA) Prediction
5019289,0.85674,41,196,/xhlulu/severstal-simple-keras-u-net-boilerplate,Severstal: Steel Defect Detection
5008261,0.63054,14,98,/ateplyuk/keras-starter-segmentation,Severstal: Steel Defect Detection
5022545,0.85674,2,11,/jazivxt/ballistic-report,Severstal: Steel Defect Detection
5002527,0.85674,1,22,/paulorzp/eda-and-opencv-starter,Severstal: Steel Defect Detection
4997726,0.85674,8,13,/vaishvik25/3-line-for-lb-0-85674,Severstal: Steel Defect Detection
6291179,0.90213,0,0,/negi009/fork-of-test-one-image-835c91,Severstal: Steel Defect Detection
1212475,0.79172,0,1,/bogikidd/nltk-and-gru-on-donorschoose-org,DonorsChoose.org Application Screening
881639,0.79159,0,2,/bigzhao/hierarchical-model-with-attention,DonorsChoose.org Application Screening
865690,0.7975899999999999,2,9,/tommyod/mastering-the-basics-80-using-scikit-learn-v2-0,DonorsChoose.org Application Screening
862873,0.72331,0,1,/holfyuen/too-simple-sometimes-naive-bayes,DonorsChoose.org Application Screening
804048,0.7750100000000001,3,7,/thumbsnail/coming-from-google-s-machine-learning-crash-course,DonorsChoose.org Application Screening
777255,0.80795,7,15,/fizzbuzz/the-all-in-one-model,DonorsChoose.org Application Screening
752553,0.73617,0,6,/matthewa313/naive-bayes-svm-on-vocabulary,DonorsChoose.org Application Screening
749170,0.79776,2,16,/nicapotato/abc-s-of-tf-idf-boosting-0-798,DonorsChoose.org Application Screening
746228,0.65536,0,2,/ftlftw/thinking-like-a-data-scientist-donorschoose-eda,DonorsChoose.org Application Screening
735546,0.7959,0,10,/fizzbuzz/a-pure-nlp-approach,DonorsChoose.org Application Screening
714695,0.81177,30,74,/hoonkeng/how-to-get-81-gru-att-lgbm-tf-idf-eda,DonorsChoose.org Application Screening
709350,0.7127399999999999,3,3,/anu0012/eda-ensemble,DonorsChoose.org Application Screening
703759,0.7632,1,7,/a45632/keras-baseline-feature-hashing-cnn-with-graph,DonorsChoose.org Application Screening
696021,0.60743,0,7,/mmi333/beat-the-benchmark-with-one-feature,DonorsChoose.org Application Screening
12637385,0.01895,4,7,/yohannesmelese/moa-prediction-notebook-2,Mechanisms of Action (MoA) Prediction
12631475,0.01881,0,8,/keremt/moa-tabular-starter,Mechanisms of Action (MoA) Prediction
11907539,0.01918,1,2,/muellerzr/fastai-tabular,Mechanisms of Action (MoA) Prediction
12630696,0.01874,0,0,/sasasagagaga1/nn-top3-ensemble-with-cv-7,Mechanisms of Action (MoA) Prediction
12607196,0.01858,0,0,/dariapetrenko/submission-pytorch,Mechanisms of Action (MoA) Prediction
12611141,0.01873,5,5,/nayuts/moa-train-nn-with-swa,Mechanisms of Action (MoA) Prediction
12652366,0.03411,0,0,/wittmannf/quick-eda-and-baseline-submission-with-mlk,Mechanisms of Action (MoA) Prediction
12572839,0.0198,0,3,/acapricorni/moa-stacking-nn-lgbm,Mechanisms of Action (MoA) Prediction
12646796,0.10449,0,0,/barcarum/moa-pytorch-feature-engineering-0-01846,Mechanisms of Action (MoA) Prediction
12498753,0.03073,0,0,/fedorlebed/local-coords,Mechanisms of Action (MoA) Prediction
12557800,0.01974,0,0,/fedorlebed/boss-nn,Mechanisms of Action (MoA) Prediction
12534651,0.02234,0,0,/fedorlebed/histo,Mechanisms of Action (MoA) Prediction
12564999,0.01876,0,0,/pavellukianov/eda-pzad,Mechanisms of Action (MoA) Prediction
12559729,0.0208199999999999,0,0,/sasasagagaga1/gb-predictions,Mechanisms of Action (MoA) Prediction
12626838,0.0191,0,0,/rftexas/moa-model-start-here,Mechanisms of Action (MoA) Prediction
12378537,0.02132,23,30,/damoonshahhosseini/aggregated-neural-networks,Mechanisms of Action (MoA) Prediction
12535493,0.02363,2,19,/titericz/plain-average-baseline,Mechanisms of Action (MoA) Prediction
12415157,0.01839,21,141,/riadalmadani/pytorch-cv-0-0145-lb-0-01839,Mechanisms of Action (MoA) Prediction
12585233,0.01913,0,0,/crafterkolyan/bndl-elu-2048-1024-2-5-5-smoth2e-4-pca-pzad,Mechanisms of Action (MoA) Prediction
12581126,0.01938,0,0,/skryzhanovskaya/moa-prediction-nn,Mechanisms of Action (MoA) Prediction
12409497,0.0203099999999999,0,7,/jagdmir/moa-eda-model,Mechanisms of Action (MoA) Prediction
12422152,0.02132,0,0,/alekseyeliseev/moa-ann-baseline,Mechanisms of Action (MoA) Prediction
11624804,0.0187,1,8,/samratthapa/tabnet-implementation,Mechanisms of Action (MoA) Prediction
12450196,0.02009,0,2,/skryzhanovskaya/moa-prediction,Mechanisms of Action (MoA) Prediction
12445264,0.01967,0,0,/alexanderkholodov/solution-pzad,Mechanisms of Action (MoA) Prediction
12447012,0.02166,0,2,/japandata509/moa-simple-nn,Mechanisms of Action (MoA) Prediction
12439251,0.01881,0,2,/tigersay/moa-1st-try-nn,Mechanisms of Action (MoA) Prediction
232823,0.58321,0,0,/verowulf/neural-network-w-feat-engineering-0-583lb,Two Sigma Connect: Rental Listing Inquiries
11726568,0.5720000000000001,0,0,/gzl0506/inference-0914-oof6,Cornell Birdcall Identification
11023038,0.002,0,0,/wabalubdubdub/birdsong-making-a-prediction,Cornell Birdcall Identification
1086270,0.785,6,24,/ashishpatel26/home-crerit-default-risk-002,Home Credit Default Risk
1085108,0.785,9,56,/ashishpatel26/home-credit-default-analysis,Home Credit Default Risk
1085720,0.785,12,29,/ashishpatel26/home-credit-default-risk-001,Home Credit Default Risk
1085463,0.779,0,1,/sukhyun9673/basic-baseline-with-lgb-v3-one-hot-encoder,Home Credit Default Risk
1081790,0.733,0,1,/itliuodong/deep-learning-in-tf-with-upsampling-lb-742,Home Credit Default Risk
1012040,0.778,0,2,/ezornow/credit-skeleton,Home Credit Default Risk
1072962,0.7809999999999999,0,5,/kosovanolexandr/ligthgbm-0-781-home-credit-default-risk,Home Credit Default Risk
1056158,0.7709999999999999,0,10,/nafisur/nr-home-credit-default-risk,Home Credit Default Risk
1027171,0.6659999999999999,0,0,/xianglong/only-nonan-features,Home Credit Default Risk
1046068,0.773,0,1,/shayak94/lb-0-773-baseline-score,Home Credit Default Risk
1025021,0.774,4,41,/cast42/lightgbm-model-explained-by-shap,Home Credit Default Risk
1022021,0.7120000000000001,7,23,/mlisovyi/15-lines-plb-0-712-ext-source-x-lgbm,Home Credit Default Risk
1014634,0.578,0,3,/shenba/home-credit-eda-23may2018,Home Credit Default Risk
1018232,0.67,0,2,/drribeiro/lgbmclassifier,Home Credit Default Risk
993573,0.772,66,221,/shivamb/homecreditrisk-extensive-eda-baseline-0-772,Home Credit Default Risk
993138,0.7120000000000001,39,79,/artgor/eda-basic-fe-and-lgb,Home Credit Default Risk
13464561,0.7240000000000001,0,0,/semenedel/notebook9524f1c5e3,"Ghouls, Goblins, and Ghosts... Boo!"
12257059,0.64083,1,6,/terrifictitan12/ghouls-goblins-or-ghosts,"Ghouls, Goblins, and Ghosts... Boo!"
11883421,0.7240000000000001,0,0,/lovishjindal/101703312,"Ghouls, Goblins, and Ghosts... Boo!"
11876895,0.73156,0,0,/haggarwal/notebook80317d0e7e,"Ghouls, Goblins, and Ghosts... Boo!"
10860844,0.71455,5,2,/julianbenny/ghouls-goblins-ghosts-keras,"Ghouls, Goblins, and Ghosts... Boo!"
9822338,0.65973,0,0,/dhruvgupta2801/neural-networks,"Ghouls, Goblins, and Ghosts... Boo!"
8331453,0.72022,0,2,/iqrar99/ghouls-goblins-and-ghosts-voting-classifier,"Ghouls, Goblins, and Ghosts... Boo!"
5097113,0.73156,0,0,/kaledata/ghouls-goblins,"Ghouls, Goblins, and Ghosts... Boo!"
3751839,0.7429100000000001,0,0,/falconi/onevsrest-logisticregression,"Ghouls, Goblins, and Ghosts... Boo!"
2690956,0.7240000000000001,0,1,/iamcylee/kernela678bf8139,"Ghouls, Goblins, and Ghosts... Boo!"
1898082,0.71833,0,0,/ritesaluja/halloweenspecial,"Ghouls, Goblins, and Ghosts... Boo!"
386750,0.32325,0,0,/a1058514/notebookac2236a5ae,"Ghouls, Goblins, and Ghosts... Boo!"
219858,0.6465,0,0,/mandeep94/halloween,"Ghouls, Goblins, and Ghosts... Boo!"
186359,0.7240000000000001,0,0,/danchyy/ghosts-ghouls-goblins-test,"Ghouls, Goblins, and Ghosts... Boo!"
431708,0.71455,0,0,/salsamarisha/ghoul-goblin-and-ghost-boo,"Ghouls, Goblins, and Ghosts... Boo!"
402295,0.28456,67,257,/aharless/xgboost-cv-lb-284,Porto Seguro’s Safe Driver Prediction
403658,0.28298,9,20,/aharless/lightgbm-cv-lb-282,Porto Seguro’s Safe Driver Prediction
395778,0.2702,34,64,/sudhirnl7/simple-logistic-model-porto,Porto Seguro’s Safe Driver Prediction
8099102,1.56358,0,2,/darwinwin/lanl-earthquake-with-h2o-automl,LANL Earthquake Prediction
4048539,1.5659999999999998,0,0,/hark99/lanlep,LANL Earthquake Prediction
4071046,1.392,0,0,/vipul97/lanl-earthquake-prediction-model,LANL Earthquake Prediction
3482945,1.556,0,7,/mgiraygokirmak/simple-baseline-with-xgboost,LANL Earthquake Prediction
5235123,2.48758,0,0,/captainkore/lanl-earthquake-prediction-freq-domain-analysis,LANL Earthquake Prediction
4410746,1.63875,0,0,/vanquan/lanl-earthquake-catboostregressor,LANL Earthquake Prediction
4136827,1.5280200000000002,0,0,/hark99/best-lanl,LANL Earthquake Prediction
3991178,1.5119999999999998,0,0,/shihyu/earthquake-competition-without-creating-data,LANL Earthquake Prediction
3662545,1.595,0,0,/lorevala/1d-cnn-testing1,LANL Earthquake Prediction
3892166,1.835,0,0,/madadinga/model-tuning-adaboost,LANL Earthquake Prediction
3813317,1.494,0,0,/dobosp/lgbm-tunning-bayes-search,LANL Earthquake Prediction
4156078,1.43173,2,5,/ahmedengu/lanl-master-s-features-autosklearn,LANL Earthquake Prediction
3506440,1.52,0,4,/isaacshannon/isaac-fast-ai-evo,LANL Earthquake Prediction
4129395,1.3130000000000002,6,16,/alexsemenov/130th-private-lb-ideas-35th-on-public-lb,LANL Earthquake Prediction
4121480,1.389,0,8,/robikscube/lanl-simulating-the-test-set,LANL Earthquake Prediction
4052652,1.5759999999999998,0,2,/cuteliudong/rolling-rnn,LANL Earthquake Prediction
3994275,1.614,0,0,/maicon1981/lanl-earthquake-prediction,LANL Earthquake Prediction
4028460,1.858,0,0,/isaranja/lanl-earthquake-conv1d-series,LANL Earthquake Prediction
4099027,1.4820799999999998,1,1,/ahmedengu/lanl-master-s-features-h2o-automl,LANL Earthquake Prediction
4092067,1.594,7,22,/pnussbaum/dwt-earthquake-w-lto-v01,LANL Earthquake Prediction
4103180,1.53237,0,0,/scaomath/lanl-lgb-selective-data-augmentation,LANL Earthquake Prediction
2708187,2.28117,0,3,/acauveri/prediction-model-selection,Telstra Network Disruptions
4570947,111.65136,4,18,/titericz/ralsgan-dogs-vladislav-bakhteev-kernel,Generative Dog Images
4566682,143.19608,0,13,/super13579/dcgan-with-dog-generation-gif,Generative Dog Images
5307015,55.50563,0,0,/dranzer/gan-baseline-v2-v4,Generative Dog Images
5292730,59.05518000000001,0,0,/jadeblue/dogdcgan-v7-ksize-dropout-sub,Generative Dog Images
5283223,7.21776,0,0,/joek47/introduction-to-generative-adversarial-networks,Generative Dog Images
5254795,57.60009,0,0,/kshen3778/gan-57-crop-nz128-crop-randomapply,Generative Dog Images
5055121,64.91085,0,0,/sorzhe/gan-custom-layers,Generative Dog Images
4806996,7.03637,0,0,/jobzhf88/generative-dog-images-memorizer-gan-commit,Generative Dog Images
4566508,119.13018,0,0,/mnpinto/gan-dogs-starter,Generative Dog Images
13373352,0.4404899999999999,0,1,/willsonbritto/cia028-costa-rica-c-desbalanceadas-wilson-b,Costa Rican Household Poverty Level Prediction
12788547,0.43873,0,1,/vhfleury/victor-hugo-trabalho-final-costa-rica,Costa Rican Household Poverty Level Prediction
11563559,0.44117,0,2,/marcilonsilvacunha/marcilon-1931133129,Costa Rican Household Poverty Level Prediction
11373394,0.43305,0,1,/camilamarques/dataset-costa-rica,Costa Rican Household Poverty Level Prediction
11138985,0.4391199999999999,0,1,/giovannilourenzatto/trabalhomining,Costa Rican Household Poverty Level Prediction
11055283,0.43643,0,1,/simonesymon/1931133125,Costa Rican Household Poverty Level Prediction
7935237,0.4371899999999999,2,5,/marcosvafg/iesb-miner-ii-aula-05-random-forest,Costa Rican Household Poverty Level Prediction
7967599,0.44656,0,0,/jxtz518/understanding-the-problem-is-the-key,Costa Rican Household Poverty Level Prediction
6481869,0.39253,0,1,/eenglin/kernel716462da47,Costa Rican Household Poverty Level Prediction
6278945,0.42135,0,6,/ybabakhin/errored-kaggledays-china-tabular,Costa Rican Household Poverty Level Prediction
4607997,0.36901,0,0,/ksw10203/kernel8a68777a58,Costa Rican Household Poverty Level Prediction
4555280,0.36901,0,0,/chisong/kernel2a109f3bef,Costa Rican Household Poverty Level Prediction
4380043,0.42398,0,0,/yaangjun/yaangjun,Costa Rican Household Poverty Level Prediction
4065823,0.37378,2,7,/gauravlogical/knn-costa-rican-poverty-prediction-beginners,Costa Rican Household Poverty Level Prediction
4059048,0.4401899999999999,0,1,/wqd180113/umcoursework,Costa Rican Household Poverty Level Prediction
4046296,0.19482,0,0,/muksamrat/costa-rica-predicting-poverty-with-estimators,Costa Rican Household Poverty Level Prediction
3583630,0.22173,0,8,/nageshanumala/costa-rican-modelling-with-different-estimators,Costa Rican Household Poverty Level Prediction
3071004,0.40134,0,1,/treybean/costa-rican-household-poverty-level-prediction-2,Costa Rican Household Poverty Level Prediction
2886333,0.411,0,1,/akverma/costa-rican-poverty-analysis,Costa Rican Household Poverty Level Prediction
2772457,0.376,0,1,/woshichendu/costa-rica,Costa Rican Household Poverty Level Prediction
9478670,0.9317,0,0,/quanncore/pytorch-tpu-inference,Jigsaw Multilingual Toxic Comment Classification
10343552,0.6834,0,0,/gakoesataria1/re-hell-notebook-vanilla-submission,Jigsaw Multilingual Toxic Comment Classification
8954383,0.9331,0,3,/shaitender/jigsaw-tpu-roberta,Jigsaw Multilingual Toxic Comment Classification
9466835,0.9488,0,0,/translucent/easy-ensemble,Jigsaw Multilingual Toxic Comment Classification
10220759,0.9414,0,1,/aiaiooas/parcor-regularised-classification,Jigsaw Multilingual Toxic Comment Classification
10176027,0.8658,0,1,/mahmudds/jigsaw-multilingual-toxic-comment-classification,Jigsaw Multilingual Toxic Comment Classification
9975680,0.8592,0,9,/googoogoojoob/simple-efficient-catboost-classifier,Jigsaw Multilingual Toxic Comment Classification
10023373,0.9289,0,0,/vallabhreddy/eda-and-roberta-using-huggingface-trasformers,Jigsaw Multilingual Toxic Comment Classification
10044078,0.9399,7,32,/yeayates21/xlm-roberta-augmentation-ssl-0-9417-pub-lb,Jigsaw Multilingual Toxic Comment Classification
9834497,0.9384,1,8,/roydatascience/xlm-roberta-with-dense-layers,Jigsaw Multilingual Toxic Comment Classification
9801178,0.6592,3,15,/ayushimishra2809/multilingual-comment-classification,Jigsaw Multilingual Toxic Comment Classification
9866608,0.8841,0,0,/vgodie/first-baseline,Jigsaw Multilingual Toxic Comment Classification
9934467,0.5915,1,4,/anasofiauzsoy/multilingual-toxic-comments-with-tf2-bert,Jigsaw Multilingual Toxic Comment Classification
9946290,0.6963,0,0,/yashsudhakardevikar/kernel349f34ba28,Jigsaw Multilingual Toxic Comment Classification
9817603,0.5797,0,0,/ych1997ych/final-project,Jigsaw Multilingual Toxic Comment Classification
9669936,0.5351,0,0,/kekesssss/kernel443d3bbd19,Jigsaw Multilingual Toxic Comment Classification
9698531,0.9332,7,27,/riblidezso/tpu-custom-tensoflow2-training-loop,Jigsaw Multilingual Toxic Comment Classification
8554187,0.9362,0,0,/iamprateek/jigsaw-toxicity-detection,Jigsaw Multilingual Toxic Comment Classification
8990476,0.8392,0,0,/stanleychu/jigsaw-toxic-comment,Jigsaw Multilingual Toxic Comment Classification
248376,0.32865,7,36,/optidatascience/use-partial-pca-for-collinearity-lb-0-328-w-xgb,Sberbank Russian Housing Market
245075,0.31683,46,117,/bguberfain/naive-xgb-lb-0-317,Sberbank Russian Housing Market
245485,0.41262,2,1,/cparmet/notebook1745988256,Sberbank Russian Housing Market
39359,0.51146,0,3,/omarelgabry/predict-search-relevance-by-count-words,Home Depot Product Search Relevance
143725,33045.33547,0,5,/bellar/filling-bags-with-simulated-annealing,Santa's Uncertain Bags
1563900,0.31,0,0,/galopes/pmr3508-2018-906c67c924-costa-rica,Costa Rican Household Poverty Level Prediction
1563243,0.308,0,0,/pedrocarvalhaes/pmr3508-2018-c19b8e5731,Costa Rican Household Poverty Level Prediction
1497189,0.213,1,0,/fehitasi/pmr3508-2018-7dd3037079,Costa Rican Household Poverty Level Prediction
1560202,0.381,0,1,/esdonto/pmr3508-2018-1165842557,Costa Rican Household Poverty Level Prediction
1560551,0.196,0,0,/otvmonteiro/pmr3508-2018-d7465a13af-costarican,Costa Rican Household Poverty Level Prediction
1559930,0.1939999999999999,0,0,/luizffs2/pmr3508-costa-rica-predictions,Costa Rican Household Poverty Level Prediction
1553908,0.2189999999999999,0,0,/dueiras/costa-rica-knn-prediction,Costa Rican Household Poverty Level Prediction
1545549,0.2739999999999999,0,0,/rejaili/pmr3508-householdincomeclassifier,Costa Rican Household Poverty Level Prediction
1502202,0.426,6,11,/jeppbautista/eda-feature-engineering-lgbm,Costa Rican Household Poverty Level Prediction
1534742,0.3339999999999999,0,0,/mguinezi/pmr3508-2018-91dc8aec81pmr3508-knn-costaricahhi,Costa Rican Household Poverty Level Prediction
1515575,0.335,0,1,/guilhermecmarques/pmr3508-2018-7b2def90a2-knn,Costa Rican Household Poverty Level Prediction
1515713,0.405,0,3,/praveentn/506cr-feature-reduction-xtreme-gradient-boosting,Costa Rican Household Poverty Level Prediction
1512061,0.147,0,6,/anirudhmurali/costa-rican,Costa Rican Household Poverty Level Prediction
1500903,0.373,0,0,/mvsandilya/costa-rican-household-part-2,Costa Rican Household Poverty Level Prediction
1489828,0.387,0,2,/victorhz/svm-over-csv-2,Costa Rican Household Poverty Level Prediction
1447153,0.357,0,1,/mvsandilya/costa-rican-household-data-aug-13-2018,Costa Rican Household Poverty Level Prediction
1448232,0.35,0,3,/amitprasad/poverty-prediction-key-results,Costa Rican Household Poverty Level Prediction
1439664,0.4029999999999999,0,1,/fooeta/costarican-dnn,Costa Rican Household Poverty Level Prediction
1401096,0.342,1,6,/anktplwl91/introduction-to-model-stacking,Costa Rican Household Poverty Level Prediction
1421838,0.433,0,1,/debmishra/pda-household-costaricav1-4-voting-clf-tuned,Costa Rican Household Poverty Level Prediction
1418141,0.374,0,1,/stajh05/first-attempt-basic-random-forest,Costa Rican Household Poverty Level Prediction
1348656,0.442,2,8,/skooch/lgbm-with-random-split,Costa Rican Household Poverty Level Prediction
1357850,0.346,0,1,/puneetshekhawat/data-cleaning-handling-missing-data-random-forest,Costa Rican Household Poverty Level Prediction
1348067,0.434,8,12,/aditya1702/data-pipeline-186-features-bayes-optimized-lgb,Costa Rican Household Poverty Level Prediction
1320918,0.427,62,321,/willkoehrsen/a-complete-introduction-and-walkthrough,Costa Rican Household Poverty Level Prediction
5253050,184.59586,0,1,/selfishgene/gmm-in-cnn-ae-space-with-latent-aux-classifier,Generative Dog Images
5301429,204.397,0,0,/tavoglc/classic-dog-imaginarium,Generative Dog Images
4930791,50.60304,0,0,/yk1598/c-sagan-ignite,Generative Dog Images
5292859,62.74861,0,1,/thomashuang2018/fork-of-kernel122785459c,Generative Dog Images
5291871,14.82525,2,7,/dvorobiev/doggies-biggan-sub-final,Generative Dog Images
5306561,39.45836,0,3,/markpeng/small-stylegan-v6-higher-lr-v4-final,Generative Dog Images
5260209,108.12795,0,0,/tianyuz/fork-gan-introduction,Generative Dog Images
5300935,62.84055,1,6,/bitthal/rasl-dogs-gan-pixel,Generative Dog Images
5237107,56.96847,0,1,/joelhanson/dcgan-with-spectralnorm-dropout-avgpool,Generative Dog Images
5163153,14.95516,28,35,/dvorobiev/doggies-biggan-sub-data-aug-3,Generative Dog Images
5295331,54.02982,2,6,/mightyrains/patchdogs-2x2,Generative Dog Images
5150886,605.6632900000002,0,0,/osamaaref/dcgan-keras,Generative Dog Images
5222921,44.69081,1,6,/super13579/ralsgan-dogs-resnet-cbn,Generative Dog Images
5312744,38.9235,5,8,/hirune924/public-lb-38-92350-solution,Generative Dog Images
5238415,55.87249,0,2,/jadeblue/dogdcgan-v6-ksize,Generative Dog Images
5303177,263.98204,1,4,/jpdurham/tensorflow-dcgan,Generative Dog Images
5159643,90.10298,0,1,/prasunroy/generative-dog-images-dcgan-pytorch,Generative Dog Images
4704682,291.83509,0,1,/artpro/vae-gan-hd,Generative Dog Images
4727666,117.67857,0,0,/haataa/dcgan-first-try,Generative Dog Images
4653186,16.4907,0,0,/shubham505/supervised-generative-dog-net,Generative Dog Images
5141728,71.50746,0,0,/vamcochen/kernelbc004572a6,Generative Dog Images
5205630,70.37269,0,0,/jtaglione/woof-1,Generative Dog Images
5276255,89.58891,0,0,/sanjayjalex/ralsgan-dogs,Generative Dog Images
5117999,60.56436,0,0,/asd336655/best-baseline,Generative Dog Images
5086757,89.21822,0,0,/hchaps/let-s-make-some-doggies-ralsgan,Generative Dog Images
11410465,0.1408,1,12,/ragnar123/baseline-dnn-with-delg-global-embeddings,Google Landmark Recognition 2020
11310288,0.0,2,8,/azaemon/effnet-with-tf-records,Google Landmark Recognition 2020
11079237,0.0,0,12,/dimakyn/pre-trained-efficientnet,Google Landmark Recognition 2020
10960768,0.0683,20,58,/akensert/glrec-resnet50-arcface-tf2-2,Google Landmark Recognition 2020
10954761,0.0,6,40,/socathie/pre-trained-mobilenetv2-1000-classes-1-epoch,Google Landmark Recognition 2020
10957894,0.0,5,16,/nischaydnk/landmark-recognition-2020-eda-resnet,Google Landmark Recognition 2020
10956026,0.0,0,8,/socathie/pre-trained-mobilenetv2,Google Landmark Recognition 2020
10952147,0.0,1,8,/koheist/simple-eda,Google Landmark Recognition 2020
12874393,0.511,0,0,/rishabh279/don-t-overfit,Don't Overfit! II
11028769,0.524,0,0,/abdullahzaid/kernelca36454739,Don't Overfit! II
11107032,0.53,0,0,/abdullahzaid/kernele2507ae491,Don't Overfit! II
10347520,0.508,0,2,/snehashis1997/don-t-over-fit-part-2,Don't Overfit! II
8131016,0.512,0,3,/darwinwin/starter-automl-don-t-overfit,Don't Overfit! II
5688115,0.846,0,0,/tamyiuchau/dntoverfit-starter,Don't Overfit! II
6218766,0.635,2,0,/lukemonington/don-t-overfit-ii-my-attempt,Don't Overfit! II
4690297,0.794,0,3,/agrover112/i-overfitted,Don't Overfit! II
4623457,0.706,1,1,/sakaguti1211/lastone,Don't Overfit! II
4526965,0.639,1,1,/lifehacker2601/data-ml,Don't Overfit! II
2997252,0.504,0,0,/sjeetm/dont-overfit,Don't Overfit! II
3996079,0.8490000000000001,0,2,/sameerdev7/84-9-simplest-solution-to-not-overfit,Don't Overfit! II
3934545,0.628,0,0,/kshitijs2014/main-model,Don't Overfit! II
3819440,0.802,8,25,/allunia/don-t-overfit-searching-true-distributions,Don't Overfit! II
3669690,0.741,0,3,/shwetagoyal4/don-t-overfit-ii,Don't Overfit! II
3808285,0.8490000000000001,2,5,/pierresylvain/xgboost-lr-rfe,Don't Overfit! II
3796216,0.586,0,0,/ichabuddaeta/i-tried-again,Don't Overfit! II
3767532,0.8490000000000001,2,10,/adikeshri/logisticregression-dont-overfit-0-849,Don't Overfit! II
3752619,0.8370000000000001,0,3,/jamesdonconley/clustering-improves-logistic-regression,Don't Overfit! II
3723499,0.816,4,6,/jamesdonconley/logistic-regression-and-rfe-pca,Don't Overfit! II
3622796,0.833,5,53,/ateplyuk/dntoverfit-starter,Don't Overfit! II
3577404,0.716,0,0,/himadri1998/don-t-overfit-tryout,Don't Overfit! II
3483439,0.5529999999999999,3,1,/masayakondo/trying-don-t-overfit-ii,Don't Overfit! II
1802455,2.158,0,6,/darbin/naive-benchmark-extended-explanation,PLAsTiCC Astronomical Classification
2398856,1.0908,0,0,/jimpsull/neuralmoredecisive,PLAsTiCC Astronomical Classification
3648938,1.591,0,3,/wangwangsuibinbin/lanl-tcn-trial2,LANL Earthquake Prediction
2611954,2.509,0,0,/reginashay/earthquakes-explore-predict,LANL Earthquake Prediction
3637103,1.7719999999999998,1,3,/attackgnome/basic-feature-benchmark-rfecv-xgboost,LANL Earthquake Prediction
3630916,1.521,2,3,/maneshreyashs/lgbm-xgb,LANL Earthquake Prediction
3617076,1.569,0,4,/pedrormarques/fft-512-frequencies,LANL Earthquake Prediction
3605164,1.577,0,0,/pedrormarques/fft-exp-cv,LANL Earthquake Prediction
3596436,1.526,0,0,/maneshreyashs/kernelcbb1e58da9,LANL Earthquake Prediction
2887707,2.449,0,4,/shobhit18th/earthquake-prediction,LANL Earthquake Prediction
3360427,1.599,2,19,/aperezhortal/cv-splitting-by-earthquake-id,LANL Earthquake Prediction
3313431,1.544,0,4,/tandonarpit6/lanl-earthquake-prediction-fast-ai,LANL Earthquake Prediction
3214411,1.535,2,12,/arkaung/earthquakes-over-feature-engineering-lightgbm,LANL Earthquake Prediction
3235801,1.881,5,3,/lavanyadml/lanl-earthquake-prediction-ls,LANL Earthquake Prediction
3139215,1.547,3,0,/aritrase/earthquake-ver2-top35features,LANL Earthquake Prediction
3104717,1.494,3,39,/harshel7/earthquake-predictions-ensemble-neural-networks,LANL Earthquake Prediction
3101550,1.521,2,1,/ahmedengu/lanl-earthquake-with-h2o-automl,LANL Earthquake Prediction
3049618,1.649,0,13,/adubitskiy/rnn-with-cnn-feature-extraction,LANL Earthquake Prediction
3025167,2.662,0,4,/ahmedengu/lanl-earthquake-simple-svm,LANL Earthquake Prediction
2844311,1.547,3,1,/itsmesunil/lanl-earthquake-prediction-added-features,LANL Earthquake Prediction
2803668,1.821,2,7,/sriram7777/earthquake-detection-baseline-with-fft,LANL Earthquake Prediction
12883434,0.18502,0,1,/franckepeixoto/porto-seguro-s-safe-driver-prediction-colab,Porto Seguro’s Safe Driver Prediction
12232924,0.28163,0,2,/sinamhd9/safe-driver-prediction-a-comprehensive-project,Porto Seguro’s Safe Driver Prediction
10878082,0.27685,0,1,/dmkravtsov/12-insurance,Porto Seguro’s Safe Driver Prediction
9285424,0.27635,0,0,/shanu1988/end-to-end-insurance,Porto Seguro’s Safe Driver Prediction
8099302,0.27202,0,2,/darwinwin/h2o-28800s-portosegro,Porto Seguro’s Safe Driver Prediction
390719,0.28275,0,0,/fesenkod/porto-lightgbm,Porto Seguro’s Safe Driver Prediction
4483557,0.2572,0,0,/matheusfcs/trabalho-de-aprendizagem-de-m-quina-2019-1,Porto Seguro’s Safe Driver Prediction
3880971,0.2634599999999999,0,4,/datajang/xgboost-lgb,Porto Seguro’s Safe Driver Prediction
13867005,0.72918,0,0,/varunsimhareddy/varun-forest-cover,Forest Cover Type Prediction
12190820,0.6078399999999999,0,0,/toppoashish7/knn-1,Forest Cover Type Prediction
10775558,0.7327100000000001,0,1,/rahulkumar234/tutorial-1,Forest Cover Type Prediction
10555571,0.50784,0,4,/sureshmecad/forest-coverype,Forest Cover Type Prediction
10303282,0.73135,0,0,/ramensingh/kernel4d979252bf,Forest Cover Type Prediction
9270347,0.72916,1,1,/dhruvgupta2801/tutorial,Forest Cover Type Prediction
8895321,0.77195,0,1,/hanhdao123/forest-cover-prediction,Forest Cover Type Prediction
8210274,0.73483,0,0,/robbiebeane/forest-cover-01,Forest Cover Type Prediction
7400630,0.62398,0,0,/dhanyasabari/forest-covertype,Forest Cover Type Prediction
6563468,0.53883,0,1,/devkhant24/forest-category-prediction,Forest Cover Type Prediction
5575939,0.72896,0,0,/mehranrafiee/kernel4e74462766,Forest Cover Type Prediction
4064296,0.71015,1,1,/ma7555/knn-from-scratch,Forest Cover Type Prediction
2824153,0.76851,1,2,/tooezy/forest-cover-type-classification,Forest Cover Type Prediction
2750513,0.74436,1,4,/iavinas/forest-cover-type-prediction-0-74436,Forest Cover Type Prediction
899692,0.56643,0,1,/jeonghunyoon/dnn-classifier-for-forest-type,Forest Cover Type Prediction
1476759,0.753,0,3,/debmishra/base-lgb-more-featuresv1-6,Home Credit Default Risk
1499104,0.775,1,12,/ezornow/simple-ffnn-with-rank-gauss-and-early-auc-stopping,Home Credit Default Risk
1194140,0.7709999999999999,0,0,/holfyuen/analyzing-home-credit-default-risk,Home Credit Default Risk
1457238,0.784,4,3,/frizzles7/intro-home-credit-default-risk,Home Credit Default Risk
1426062,0.677,0,0,/xiiiii/homecredit-morefiles,Home Credit Default Risk
1438465,0.7959999999999999,14,53,/hmendonca/lightgbm-predictions-explained-with-shap-0-796,Home Credit Default Risk
1419594,0.66,0,0,/mainakdatageek/simple-exploration-pipeline-imputer-0-7,Home Credit Default Risk
1096088,0.732,0,1,/sonnyto/home-credit-default-risk,Home Credit Default Risk
1335416,0.67,0,1,/aarshshah8/kernal-for-credit,Home Credit Default Risk
1289226,0.7909999999999999,26,102,/willkoehrsen/model-tuning-results-random-vs-bayesian-opt,Home Credit Default Risk
1319042,0.772,0,0,/turbineyang/lightgbm-version-11,Home Credit Default Risk
119277,0.74669,2,8,/hhllcks/neural-net-with-gridsearch,"Ghouls, Goblins, and Ghosts... Boo!"
118439,0.73724,0,0,/chinski99/boo-hoo-2,"Ghouls, Goblins, and Ghosts... Boo!"
115352,0.73724,0,1,/alitvin/neural-networks-with-keras,"Ghouls, Goblins, and Ghosts... Boo!"
13734697,0.0,0,1,/damoonshahhosseini/janest,Jane Street Market Prediction
14550901,1927.958,0,0,/kwht1023/pc-kmeans-lightgbm-regression,Jane Street Market Prediction
14313807,2390.542,9,11,/fernandoramacciotti/janestreet-denoising-rmt,Jane Street Market Prediction
14661501,5852.154,0,0,/pyoungkangkim/autoencoder-batchnorm-dropout-mish-pytorch-model,Jane Street Market Prediction
14379187,8806.119,0,0,/sapthrishi007/feature-featureembeddings-noscal-nn5-160-100-50,Jane Street Market Prediction
14164896,9766.688,115,267,/tarlannazarov/own-jane-street-with-keras-nn,Jane Street Market Prediction
14286581,6724.052,0,5,/code1110/janestreet-mlp-inference-stage3,Jane Street Market Prediction
14249328,4294.976,30,70,/jwilliamhughdore/why-you-want-weighted-training-for-jane-st-update,Jane Street Market Prediction
14266940,5180.698,4,7,/ahmedelhaddad/fastai-submission-custom-ds,Jane Street Market Prediction
14201579,5113.547000000001,7,38,/code1110/janestreet-faster-inference-by-xgb-with-treelite,Jane Street Market Prediction
905135,0.0,0,1,/vishalse/vanilla-clustering-stage2-acc-38,2018 Data Science Bowl
187669,0.7261,0,2,/dbaksi/random-forest-starter-with-numerical-features,Two Sigma Connect: Rental Listing Inquiries
184160,0.74798,0,3,/rgoodman/rental-listings-from-scratch,Two Sigma Connect: Rental Listing Inquiries
180047,0.55209,31,178,/sudalairajkumar/xgb-starter-in-python,Two Sigma Connect: Rental Listing Inquiries
12141122,0.01967,2,4,/quandapro/moa-prediction-using-deep-neural-network,Mechanisms of Action (MoA) Prediction
11947067,0.0191199999999999,0,0,/yutohisamatsu/moa-prediction-pytorch-nn-starter,Mechanisms of Action (MoA) Prediction
12049070,0.01913,2,3,/dhirajchandak04/moa-prediction,Mechanisms of Action (MoA) Prediction
12087336,0.01896,1,6,/fushigen/nn-model-with-keras,Mechanisms of Action (MoA) Prediction
12038735,0.02075,0,1,/shashankpulijala/moa-nn-pytorch-from-and-lunkya,Mechanisms of Action (MoA) Prediction
12157580,0.0203099999999999,0,0,/thienkaka/notebookeda33f617a,Mechanisms of Action (MoA) Prediction
12056747,0.01958,1,16,/bibhabasumohapatra/drug-classification-final,Mechanisms of Action (MoA) Prediction
11835839,0.0209,0,0,/ssclairechen/neural-network-for-beginners,Mechanisms of Action (MoA) Prediction
11639685,0.02005,1,2,/msabr027/neural-network-tensorflow,Mechanisms of Action (MoA) Prediction
12072521,0.0268699999999999,2,4,/akiyoshisutou/notebookc3fdb7de57,Mechanisms of Action (MoA) Prediction
12025723,0.20511,0,1,/pansofluck/xgboost-moa-submit,Mechanisms of Action (MoA) Prediction
11990311,0.0192199999999999,2,14,/morenovanton/moa-pipeline-scaler-pca-dnn-prediction,Mechanisms of Action (MoA) Prediction
11936531,0.02191,0,0,/akshatsharma47/moa-higher-lr,Mechanisms of Action (MoA) Prediction
11767478,0.01978,0,1,/kunduruanil/moa-prediction,Mechanisms of Action (MoA) Prediction
11862242,0.0189,3,14,/alturutin/mlp-onecyclelr-pseudolabeling,Mechanisms of Action (MoA) Prediction
11985543,0.0303,0,2,/hhgami/notebook234b2577af,Mechanisms of Action (MoA) Prediction
11927013,0.03031,0,0,/maxwienandts/a-simple-and-direct-lightgbm-model,Mechanisms of Action (MoA) Prediction
11923127,0.01948,0,0,/epocxy/moa-entity-embedding,Mechanisms of Action (MoA) Prediction
11841846,0.02228,2,23,/tpmeli/visual-guide-to-moa-eda-nn-walkthrough,Mechanisms of Action (MoA) Prediction
11889104,0.01913,4,18,/dimasmunoz/keras-nn-with-cosine-annealing-lr,Mechanisms of Action (MoA) Prediction
11883213,0.02047,0,0,/lumierebatalong/moa-prediction-deep-learning,Mechanisms of Action (MoA) Prediction
11819621,0.02481,0,0,/nikilreddy/moa-xgb-1,Mechanisms of Action (MoA) Prediction
11743462,0.02075,0,27,/domizianostingi/nn-kfold-for-tensorflow,Mechanisms of Action (MoA) Prediction
823616,0.73042,0,0,/amitkumarjaiswal/donorschoose-exploration-submission,DonorsChoose.org Application Screening
3035744,3.5954300000000003,0,0,/mohdsheikibrahim/not-just-ml-who-let-the-dogs-out,Dog Breed Identification
12408173,0.0186,2,8,/riadalmadani/keras-nn-pca-label-smoothing,Mechanisms of Action (MoA) Prediction
12422551,0.01917,0,0,/alexustyuzhanin/solution-pzad,Mechanisms of Action (MoA) Prediction
12346956,0.01866,4,0,/mnk812/moa-pytorch-baseline2-inference,Mechanisms of Action (MoA) Prediction
12399136,0.01964,0,0,/meacca/eda-first-submit,Mechanisms of Action (MoA) Prediction
12380631,0.01985,0,0,/bredonos/notebook016a046dc7,Mechanisms of Action (MoA) Prediction
12358817,0.01872,0,5,/riadalmadani/keras-nn-pca,Mechanisms of Action (MoA) Prediction
12340796,0.01878,2,18,/robertlangdonvinci/lish-moa-kfold-fastai-tabnet-ensemble,Mechanisms of Action (MoA) Prediction
12297066,0.01832,31,63,/domizianostingi/blend-blend-blend,Mechanisms of Action (MoA) Prediction
12331903,0.01864,0,4,/riadalmadani/nn-weighted-sum-cv-0-01477-lb-0-01864,Mechanisms of Action (MoA) Prediction
12313603,0.02029,1,8,/yiqixue/moa-multilabel-lr-model,Mechanisms of Action (MoA) Prediction
12246039,0.01953,4,14,/shlezinger/multi-label-classification-2,Mechanisms of Action (MoA) Prediction
11655550,0.01931,1,3,/mvnewlife/mnl-moa,Mechanisms of Action (MoA) Prediction
12300934,0.0198,0,0,/nur988/moa-pytorch-optuna,Mechanisms of Action (MoA) Prediction
11870978,0.01907,0,3,/sahilmaheshwari/mechanism-of-action-moa,Mechanisms of Action (MoA) Prediction
12228587,0.01953,0,6,/lhagiimn/pytorch-end2end-implementation-using-autoencoder,Mechanisms of Action (MoA) Prediction
12207810,0.01885,8,31,/rahulsd91/moa-autoencoder-features-only-lb-0-01879,Mechanisms of Action (MoA) Prediction
11817186,0.02013,0,1,/tachyon777/moa-tachyon-v2,Mechanisms of Action (MoA) Prediction
12222180,0.02344,0,2,/yassinealouini/one-target-tabnet,Mechanisms of Action (MoA) Prediction
12166678,0.0185,9,29,/mdfahimreshm/explore-the-magic-of-mean-top-2-of-the-publiclb,Mechanisms of Action (MoA) Prediction
11584622,0.25317,0,1,/duttasd28/starter-moa,Mechanisms of Action (MoA) Prediction
12186202,0.01907,0,1,/yfszzx/multi-model,Mechanisms of Action (MoA) Prediction
12175373,0.01987,0,0,/alexzhongyiz007/labels-vanilla-pytorch,Mechanisms of Action (MoA) Prediction
12054408,0.01888,7,20,/yschoe/enjoy-the-moa-competition-with-pytorch,Mechanisms of Action (MoA) Prediction
12147672,0.0199,0,1,/atdata/copy-of-moa-with-keras-for-beginner-s,Mechanisms of Action (MoA) Prediction
11633295,0.0203199999999999,2,4,/hannaliavoshka/mechanisms-of-action-moa-prediction,Mechanisms of Action (MoA) Prediction
92681,0.68535,0,15,/anilnarassiguin/ml-classic-pipeline-python-xgboost,Leaf Classification
791447,0.44122,0,0,/mmohitm/xgboost,New York City Taxi Trip Duration
10196205,0.13079,0,2,/ouranos/point-to-uncertainty-v2,M5 Forecasting - Uncertainty
10345202,0.08745,0,2,/akashsuper2000/concatenate-model-with-grus,M5 Forecasting - Uncertainty
10414223,0.12115,0,1,/mahmudds/m5-forecasting-uncertainty,M5 Forecasting - Uncertainty
10152795,0.1342,3,35,/ulrich07/parallel-linear-regression-silver-medal-v1,M5 Forecasting - Uncertainty
9143476,0.15905,1,1,/jafarib/uncertainty-only-solve,M5 Forecasting - Uncertainty
8906021,0.15905,4,65,/szmnkrisz97/point-to-uncertainty-different-ranges-per-level,M5 Forecasting - Uncertainty
8789625,0.1792099999999999,15,105,/kneroma/from-point-to-uncertainty-prediction,M5 Forecasting - Uncertainty
8619000,0.25705,6,29,/szmnkrisz97/simple-quantiles-of-training-set,M5 Forecasting - Uncertainty
10416428,0.21639,0,0,/matts966/point-to-uncertainty-for-private,M5 Forecasting - Uncertainty
9025336,0.16054,0,0,/akashsuper2000/point-to-uncertainty-different-ranges-per-level,M5 Forecasting - Uncertainty
8771441,0.2483199999999999,0,0,/ghaiyur/coefficient-multiplier,M5 Forecasting - Uncertainty
183411,0.6128,0,0,/hakabuk/random-forest-with-more-numeric-features,Two Sigma Connect: Rental Listing Inquiries
685472,0.69091,0,0,/hyunkyung12/kernel19b52a1b29,Two Sigma Connect: Rental Listing Inquiries
14078794,2837.636,12,69,/a763337092/neural-network-starter-pytorch-version,Jane Street Market Prediction
14104850,3952.504,7,11,/vivekanandverma/hypertuned-lightgbm-classifier,Jane Street Market Prediction
14091332,0.156,0,1,/tarriaza/jane-street-bottleneck-tcn-for-submission,Jane Street Market Prediction
14044581,3.01,0,7,/tarriaza/janestreet-tcn-w-double-stacked-autoencoder,Jane Street Market Prediction
13535008,6005.581999999999,0,3,/mouafekmk/xgb-catboost-with-gpuversion-9,Jane Street Market Prediction
13827562,4295.148,5,6,/sarvesh278/xgboost-trading-classifier,Jane Street Market Prediction
13820450,3916.503,7,9,/jacksmengel/lightgbm-pca-optuna-starter,Jane Street Market Prediction
13808890,8966.403,5,13,/lpachuong/fork-of-fork-of-notebookd9779f8f26-f9b834,Jane Street Market Prediction
13853547,4161.669,1,9,/askolkova/data-prep-eda-baseline-xgb-and-lgbm,Jane Street Market Prediction
13392959,5310.96,0,1,/jsmithperera/nn-starter,Jane Street Market Prediction
13177055,2.514,0,1,/franoisboyer/jane-street-first-random-baseline,Jane Street Market Prediction
122559,0.7448,0,0,/rajatjn/neural-network-tensorflow,"Ghouls, Goblins, and Ghosts... Boo!"
120552,0.71644,0,0,/hhllcks/classification-with-xgboost,"Ghouls, Goblins, and Ghosts... Boo!"
13314238,0.54857,0,9,/jyotsnagamidi/mercedes-benz-with-xgboost-grid-search,Mercedes-Benz Greener Manufacturing
11587047,0.5519,2,5,/oseongchoi/postechai-studya,Mercedes-Benz Greener Manufacturing
10814254,0.54038,0,1,/sankeerthanreddy/eda-and-machine-learning-approach,Mercedes-Benz Greener Manufacturing
10666238,0.58381,0,6,/dmkravtsov/9-mercedes-with-recursive-feature-elimination,Mercedes-Benz Greener Manufacturing
9647540,0.5405300000000001,0,1,/vernondsouza123/cramervcorrandrandomforestregressor,Mercedes-Benz Greener Manufacturing
7542086,0.56005,0,0,/chun1182/mercedes-benz-greener-manufacturing-boost,Mercedes-Benz Greener Manufacturing
6013517,0.55476,2,19,/parulpandey/automating-the-ml-workflow-with-h2o-automl,Mercedes-Benz Greener Manufacturing
5737321,0.55181,0,4,/kulkarnivishwanath/mercedes-benz-green-manufacturing-eda-modelling,Mercedes-Benz Greener Manufacturing
5051621,0.56105,0,0,/terminate9298/mercedez-benz,Mercedes-Benz Greener Manufacturing
3993618,0.5483600000000001,0,2,/krishnaravi17/personal-case-study,Mercedes-Benz Greener Manufacturing
3477320,0.5516399999999999,0,0,/guarouba/mercedes-benz,Mercedes-Benz Greener Manufacturing
1896502,0.55734,0,0,/akhilesh4444/benz-competition-feature-encoded-and-xgboost,Mercedes-Benz Greener Manufacturing
1322008,0.55709,0,8,/deadskull7/78th-place-solution-top-2-private-lb-0-55282,Mercedes-Benz Greener Manufacturing
841533,0.54927,0,1,/asraful70/marcedes-benz-greener-with-xgb,Mercedes-Benz Greener Manufacturing
1310637,0.736,0,0,/turbineyang/lightgbm-version-2,Home Credit Default Risk
1316372,0.7709999999999999,0,0,/turbineyang/lightgbm-version-9,Home Credit Default Risk
1280995,0.7559999999999999,0,2,/rvinamra/comp-3-part-2-python-for-da,Home Credit Default Risk
1276329,0.779,0,1,/nachorovi/homecreditdefaultrisk-mlnd-nachorovi,Home Credit Default Risk
1263623,0.754,0,3,/rvinamra/comp-3-starting-with-python-for-da,Home Credit Default Risk
1259310,0.737,0,0,/queirozfcom/v0-only-main-dataset,Home Credit Default Risk
1233656,0.737,1,4,/sunnynevarekar/home-credit-default-xgboost,Home Credit Default Risk
1248987,0.746,0,1,/sunnynevarekar/fork-of-home-credit-default-xgboost,Home Credit Default Risk
1237830,0.6704100000000001,0,0,/lsiahaan/home-credit-default-risk,Home Credit Default Risk
1203190,0.737,0,9,/ashishpatel26/ultimate-guide,Home Credit Default Risk
1185243,0.774,0,9,/nikitpatel/home-credit-catboost,Home Credit Default Risk
1058534,0.7440000000000001,0,1,/nickel/austral-uni-k1-simple-eda-benchmark,Home Credit Default Risk
1030238,0.711,0,4,/dfoly1/home-credit-eda,Home Credit Default Risk
1147836,0.735,0,1,/crldata/start-here-a-gentle-introduction-312251,Home Credit Default Risk
4052330,1.457,1,3,/ahmedengu/lanl-catboost-mae-vs-rmse,LANL Earthquake Prediction
3987999,3.019,0,2,/mg78838/all-features-mg,LANL Earthquake Prediction
3645995,1.531,0,1,/subham121singh/earthquake-shaking-earth,LANL Earthquake Prediction
3989851,1.58,3,10,/sakagkaggle/only-3-features,LANL Earthquake Prediction
3930796,1.477,2,3,/shihyu/earthquake-prediction,LANL Earthquake Prediction
3885034,1.507,0,0,/madadinga/model-tuning-rf,LANL Earthquake Prediction
3890457,1.964,0,0,/kessido/ido-kess,LANL Earthquake Prediction
3840707,1.4169999999999998,36,90,/zikazika/how-to-score-high,LANL Earthquake Prediction
3825152,1.435,27,84,/bigironsphere/basic-data-augmentation-feature-reduction,LANL Earthquake Prediction
3817610,1.795,2,3,/oguzkoroglu/andrews-features-and-gplearn,LANL Earthquake Prediction
3809471,1.46,3,10,/oguzkoroglu/andrews-new-script-genetic-program-and-gplearn,LANL Earthquake Prediction
3729090,1.6130000000000002,1,3,/scirpus/gp-probability-then-project-on-target,LANL Earthquake Prediction
3707333,1.422,35,75,/scirpus/andrews-new-script-plus-a-genetic-program-model,LANL Earthquake Prediction
3654752,1.639,1,10,/fernandoramacciotti/weibull-time-to-failure,LANL Earthquake Prediction
3661325,1.493,38,126,/bigironsphere/parameter-tuning-in-one-function-with-hyperopt,LANL Earthquake Prediction
3659084,2.85,0,1,/amitkumarjaiswal/lanl-earthquake-with-fastai,LANL Earthquake Prediction
3498012,1.518,0,0,/gus666/earth-quake-predict,LANL Earthquake Prediction
2609095,0.436,1,3,/varwolf/lgbmclassifier,Costa Rican Household Poverty Level Prediction
2583997,0.4029999999999999,0,1,/varwolf/poor-prediction-xgboost,Costa Rican Household Poverty Level Prediction
2223205,0.3989999999999999,0,0,/supermoooonjy/one-pick222,Costa Rican Household Poverty Level Prediction
2035355,0.446,1,5,/jxtz518/applied-machine-learning-is-feature-engineering,Costa Rican Household Poverty Level Prediction
1880620,0.376,0,1,/vuppala/simple-rf-with-no-modifications-to-data,Costa Rican Household Poverty Level Prediction
1418390,0.444,0,9,/ashishpatel26/improve-vision-with-lightgbm,Costa Rican Household Poverty Level Prediction
1665872,0.442,0,8,/nikitsoftweb/costa-rican-household-poverty-level-prediction,Costa Rican Household Poverty Level Prediction
1632643,0.3389999999999999,0,0,/tandonarpit6/costa-rica-household-poverty-challenge,Costa Rican Household Poverty Level Prediction
1752805,0.38,0,2,/jigarsutaria/costa-rica-household-aid-eligibilty-analysis,Costa Rican Household Poverty Level Prediction
1633271,0.417,0,0,/youngdaniel/estimating-costa-rican-household-poverty-levels,Costa Rican Household Poverty Level Prediction
1708584,0.416,0,0,/stupidluca/lastversion-9-23,Costa Rican Household Poverty Level Prediction
1558938,0.445,0,0,/jaybob20/somesome,Costa Rican Household Poverty Level Prediction
1674998,0.31,0,0,/rgrajan/poverty-prediction-using-random-forest,Costa Rican Household Poverty Level Prediction
1589364,0.421,0,4,/puneetgrover/learning-to-make-diff-models-dataclean-feateng,Costa Rican Household Poverty Level Prediction
1609826,0.1939999999999999,0,0,/brayanarrietaalfaro/costa-rican-household-poverty-with-keras,Costa Rican Household Poverty Level Prediction
1371943,0.413,0,1,/tkaleczyc/define-poverty-with-pytorch,Costa Rican Household Poverty Level Prediction
1627999,0.391,0,0,/jaaahaaa/poverty,Costa Rican Household Poverty Level Prediction
1581180,0.416,0,3,/luanho/lgb-feature-engineer-kfold-0-416-lb,Costa Rican Household Poverty Level Prediction
1602896,0.1939999999999999,0,0,/gabrieloliva18/pmr3508-f7551d72e4-household,Costa Rican Household Poverty Level Prediction
1568434,0.34,0,1,/abosol/workbyhogar,Costa Rican Household Poverty Level Prediction
1564805,0.302,0,0,/rmagaldi/pmr3508-household-income-data-set-prediction,Costa Rican Household Poverty Level Prediction
13097626,0.9099,0,0,/olegbezb/logreg-distilbert-baseline,Jigsaw Multilingual Toxic Comment Classification
9351559,0.857,0,1,/ratthachat/jigsaw-gpt2-with-xlm-r-embedding,Jigsaw Multilingual Toxic Comment Classification
11585190,0.8444,0,2,/vaishnavikhilari/jigsaw-multilingual-toxic-comment-classification,Jigsaw Multilingual Toxic Comment Classification
10231709,0.9473,0,0,/ya10ya10/jigsaw-0620,Jigsaw Multilingual Toxic Comment Classification
11046865,0.927,2,9,/kalashnimov/bert-benchmark,Jigsaw Multilingual Toxic Comment Classification
10943819,0.9257,0,1,/falsedmitry/multilingualtoxiccomment-mbert-xlm-r-transformers,Jigsaw Multilingual Toxic Comment Classification
10991271,0.8215,0,0,/yekunwang1/jigsaw-olid-bert-base-cased-freeze,Jigsaw Multilingual Toxic Comment Classification
10860478,0.9148,0,0,/zengm71/jigsaw-olid-solid-xlm-same-tokenizer,Jigsaw Multilingual Toxic Comment Classification
9908585,0.9165,0,0,/nizamuddin/jigsaw,Jigsaw Multilingual Toxic Comment Classification
8863195,0.9303,0,0,/dmitryhcl/xlm-roberta-pseudo-labeling,Jigsaw Multilingual Toxic Comment Classification
10637748,0.5785,0,0,/zengm71/jigsaw-bert-zero-shot-offenseval-t5-few-shots,Jigsaw Multilingual Toxic Comment Classification
10575559,0.9366,1,25,/philippsinger/xlm-roberta-large-pytorch-pytorch-tpu,Jigsaw Multilingual Toxic Comment Classification
8645065,0.916,2,6,/revanthrex/jigsaw-tpu-bert-with-huggingface-and-keras,Jigsaw Multilingual Toxic Comment Classification
10530546,0.8755,0,2,/sophiechampagne/jigsaw-baseline-models-v1,Jigsaw Multilingual Toxic Comment Classification
10433740,0.955,4,21,/rafiko1/1st-place-jigsaw-post-processing-example,Jigsaw Multilingual Toxic Comment Classification
10049061,0.9471,0,0,/akashsuper2000/best-ensemble,Jigsaw Multilingual Toxic Comment Classification
10234805,0.9522,2,35,/xiwuhan/jmtc-2nd-place-solution,Jigsaw Multilingual Toxic Comment Classification
10290296,0.9528,13,29,/hmendonca/jigsaw20-xlm-r-lb0-9487-single-model,Jigsaw Multilingual Toxic Comment Classification
10306781,0.6004,0,0,/mdp1990/toxic-comment-classification-using-gru,Jigsaw Multilingual Toxic Comment Classification
10227634,0.9482,0,18,/roydatascience/silver-medal-solution-9482-9469-private-lb,Jigsaw Multilingual Toxic Comment Classification
245371,0.32295,0,1,/vlarine/naive-xgb-lb-0-32276,Sberbank Russian Housing Market
245155,0.32135,0,3,/rahulvks/naive-score-0-32112,Sberbank Russian Housing Market
246008,0.32449,0,0,/aharless/from-bruno-do-amaral-s-naive-xgb-notebook,Sberbank Russian Housing Market
245891,0.32492,1,0,/tobikaggle/naive-xgb-lb-0-317,Sberbank Russian Housing Market
245491,0.32897,0,0,/sionek/naive-xgb-lb-0-317-dc9ff7,Sberbank Russian Housing Market
245334,0.3221699999999999,0,0,/sionek/naive-xgb-lb-0-317,Sberbank Russian Housing Market
11448221,0.9875,0,2,/phileinsophos/digit-recognizer-kaggle-competition,Digit Recognizer
11454275,0.96196,0,1,/takamotoki/simple-neural-network-from-scratch,Digit Recognizer
11420133,1.0,3,14,/pybear/cnn-100-transfer-learning-data-augmentation,Digit Recognizer
11427773,0.97635,0,1,/adityagamer786/digit-recognition,Digit Recognizer
11410763,0.92514,0,3,/nknshmasaki/notebookc1493f48d8,Digit Recognizer
11368221,0.99228,1,10,/sahidvelji/mnist-digits-with-pytorch,Digit Recognizer
5157418,0.97535,0,0,/arpitp/mnist-ann-pytorch,Digit Recognizer
11361558,0.9655,1,9,/gauravduttakiit/digit-recognizer-using-random-forest,Digit Recognizer
11325292,0.97703,0,2,/khv1999/mnist-digits-recognition-begginer,Digit Recognizer
3354140,0.43772,0,2,/palbha/russian-house-price-prediction,Sberbank Russian Housing Market
8686386,0.8681,0,0,/akashsuper2000/jigsaw-tpu-distilbert-with-huggingface-and-keras,Jigsaw Multilingual Toxic Comment Classification
7657535,0.027,0,3,/iiyamaiiyama/find-crop-flip-images,Peking University/Baidu - Autonomous Driving
6389363,0.038,149,473,/hocop1/centernet-baseline,Peking University/Baidu - Autonomous Driving
8905162,0.973,0,8,/ar2017/plant-panthology-inceptresnetv2-enetb7,Plant Pathology 2020 - FGVC7
8883615,0.943,18,29,/a03102030/plant-pathology-2020-resnet50,Plant Pathology 2020 - FGVC7
8737338,0.974,1,3,/parmarsuraj99/miltiple-plants-on-tpu,Plant Pathology 2020 - FGVC7
8662947,0.953,14,36,/dataraj/fastai-tutorial-for-image-classification,Plant Pathology 2020 - FGVC7
8510766,0.969,6,11,/otzhora/fastai-models-experiments,Plant Pathology 2020 - FGVC7
8640451,0.978,2,13,/seefun/ensemble-top-kernels,Plant Pathology 2020 - FGVC7
8453320,0.49,4,23,/basu369victor/cycle-gan-to-balance-imbalanced-diseased-leaves,Plant Pathology 2020 - FGVC7
8594167,0.942,0,0,/abhishek4273/classification-using-monk-pytorch-keras-mxnet,Plant Pathology 2020 - FGVC7
8506911,0.977,10,94,/ateplyuk/fork-of-plant-2020-tpu-915e9c,Plant Pathology 2020 - FGVC7
8361622,0.96,0,1,/jagannathrk/plant-pathology-2020,Plant Pathology 2020 - FGVC7
8467086,0.953,0,0,/xarshila/plant-pathology-densenet201-pytorch-ipynb,Plant Pathology 2020 - FGVC7
8446670,0.8740000000000001,0,2,/lkatran/basic-guide-for-beginner-using-tf-zoo-models,Plant Pathology 2020 - FGVC7
8445354,0.97,2,11,/miklgr500/plant-pathology-very-concise-tpu-efficientnetb5,Plant Pathology 2020 - FGVC7
8389960,0.8809999999999999,0,1,/ganeshmundra/basic-guide-for-beginner-using-vgg16,Plant Pathology 2020 - FGVC7
8379633,0.975,6,27,/dimakyn/classification-densenet201-efficientnetb7,Plant Pathology 2020 - FGVC7
8351513,0.96,14,40,/lextoumbourou/plant-pathology-2020-eda-training-fastai2,Plant Pathology 2020 - FGVC7
8329882,0.965,6,50,/xhlulu/plant-pathology-very-concise-tpu-efficientnet,Plant Pathology 2020 - FGVC7
8338662,0.838,6,14,/shawon10/plant-pathology-eda-and-deep-cnn,Plant Pathology 2020 - FGVC7
8329788,0.5,0,0,/grapestone5321/plant-pathology-2020-sample-submission,Plant Pathology 2020 - FGVC7
9949007,0.97613,0,0,/iamprateek/plant-is-healthy-or-not,Plant Pathology 2020 - FGVC7
9005681,0.969,0,0,/akashsuper2000/enet-plant-pathology,Plant Pathology 2020 - FGVC7
8912523,0.5,0,0,/akashsuper2000/ensemble-top-kernels,Plant Pathology 2020 - FGVC7
378138,0.27787,2,1,/themachine/porto-trial-score-278,Porto Seguro’s Safe Driver Prediction
377266,0.2690099999999999,0,1,/deepak9001/xgboost,Porto Seguro’s Safe Driver Prediction
376983,0.04659,0,0,/weizhezhang/basic-xgb,Porto Seguro’s Safe Driver Prediction
10259780,0.79067,0,0,/shadiandisheh/project-of-datascience-homecreditdefaultrisk,Home Credit Default Risk
12168843,0.50671,0,0,/anthonymanet/my-try,Home Credit Default Risk
11198944,0.78781,0,0,/rwev0000/features,Home Credit Default Risk
9356808,0.7911,0,2,/meraxes10/lgbm-credit-default-prediction,Home Credit Default Risk
10280864,0.74027,0,3,/volodymyrholomb/xgbmodel-on-base-features,Home Credit Default Risk
8357605,0.74262,0,0,/ganeshn88/home-credit-model,Home Credit Default Risk
2037872,0.3481,0,0,/dasomkang/cnn-practice-using-keras,Statoil/C-CORE Iceberg Classifier Challenge
529963,0.2389,4,8,/hireme/two-inputs-neural-network-using-keras,Statoil/C-CORE Iceberg Classifier Challenge
497305,0.3379999999999999,0,1,/ayanmaity/iceberg-recognition-using-keras,Statoil/C-CORE Iceberg Classifier Challenge
469774,0.2563,11,4,/hcc1995/keras-cnn-model,Statoil/C-CORE Iceberg Classifier Challenge
453818,0.2406,9,13,/fvzaur/iceberg-ship-classification-with-cnn-on-keras,Statoil/C-CORE Iceberg Classifier Challenge
443116,0.1798,0,22,/danieleewww/keras-tf-lb,Statoil/C-CORE Iceberg Classifier Challenge
422438,0.9859,0,0,/plarmuseau/svd-solver,Statoil/C-CORE Iceberg Classifier Challenge
405532,0.2117,45,120,/knowledgegrappler/a-keras-prototype-0-21174-on-pl,Statoil/C-CORE Iceberg Classifier Challenge
409326,0.2848,0,1,/plarmuseau/simple-svd-xgboost-baseline-lb-35-5113d7,Statoil/C-CORE Iceberg Classifier Challenge
10285858,428.6,0,4,/damoonshahhosseini/useful-modules-and-functions,Halite by Two Sigma
10553275,896.0,0,18,/krishnaharish/optimus-mine-agent,Halite by Two Sigma
12758817,742.1,3,28,/jamesmcguigan/random-seed-search-nash-equilibrium-opening-book,"Rock, Paper, Scissors"
5979093,0.99348,0,1,/leesangju92/ism-inceptionresnetv2,Invasive Species Monitoring
1471395,0.98867,0,0,/cooldba/keras-pre-trained-vgg16-kaggle-runnable-version,Invasive Species Monitoring
242104,0.54586,0,0,/sujatar/using-xgboost,Two Sigma Connect: Rental Listing Inquiries
10546202,0.512,2,5,/jonykarki/birdcall-inference-simplecnn,Cornell Birdcall Identification
10572821,0.001,0,0,/gassan117/kernel3a217cf568,Cornell Birdcall Identification
10335943,0.48,2,4,/radek1/esp-starter-pack-v2,Cornell Birdcall Identification
10246479,0.54,0,2,/pawan28a95/xgb-inference,Cornell Birdcall Identification
10176571,0.0,22,128,/cwthompson/birdsong-making-a-prediction,Cornell Birdcall Identification
10147097,0.544,22,136,/frlemarchand/bird-song-classification-using-an-efficientnet,Cornell Birdcall Identification
10144023,0.54,6,80,/artgor/which-bird-is-it,Cornell Birdcall Identification
10171271,0.544,0,1,/grapestone5321/cornell-birdcall-identification-eda,Cornell Birdcall Identification
958967,0.2304,11,57,/dhznsdl/nn-model-adding-variables-step-by-step,Avito Demand Prediction Challenge
953749,0.2297,8,32,/peterhurford/boosting-mlp-lb-0-2297,Avito Demand Prediction Challenge
922759,0.2353,2,9,/dicksonchin93/capsule-networks-on-description,Avito Demand Prediction Challenge
920508,0.2428,7,6,/dicksonchin93/keras-gru-cnn-model-with-fasttext-on-description,Avito Demand Prediction Challenge
915546,0.2317,3,11,/dicksonchin93/lightgbm-with-mean-encode-tfidf-feature-0-231,Avito Demand Prediction Challenge
901555,0.2299,64,403,/sudalairajkumar/simple-exploration-baseline-notebook-avito,Avito Demand Prediction Challenge
901580,0.2606,1,11,/shujian/avito-rf-starter,Avito Demand Prediction Challenge
954103,0.2593,0,0,/skar26/avito-demand-prediction-model-gbm,Avito Demand Prediction Challenge
1763474,0.0,0,0,/pratiush309/ml-project-pneumonia,RSNA Pneumonia Detection Challenge
1955107,0.131,2,3,/aharless/blending-postprocessing-rsna-higher-thresh-stage2,RSNA Pneumonia Detection Challenge
1943891,0.0,0,2,/kmader/sharp-bbox-model-submission,RSNA Pneumonia Detection Challenge
1775368,0.162,7,29,/aharless/fork-v8-henrique-s-model-w-randomly-higher-score,RSNA Pneumonia Detection Challenge
1683842,0.1,65,178,/hmendonca/mask-rcnn-and-coco-transfer-learning-lb-0-155,RSNA Pneumonia Detection Challenge
1713928,0.141,104,153,/seohyeondeok/yolov3-rsna-starting-notebook,RSNA Pneumonia Detection Challenge
1544116,0.077,20,31,/skooch/cnn-segmentation-connected-components-320x320,RSNA Pneumonia Detection Challenge
1583168,0.118,5,5,/aharless/cnn-segmentation-resnet-depth-5-173fd7,RSNA Pneumonia Detection Challenge
1545882,0.109,4,23,/uds5501/cnn-segmentation-resnet-depth-5,RSNA Pneumonia Detection Challenge
11396126,0.20784,3,21,/barkhaverma/house-price-prediction-with-advanced-regre,House Prices - Advanced Regression Techniques
11411876,0.13772,0,1,/navasai/xgboost-model,House Prices - Advanced Regression Techniques
11381082,0.12183,10,25,/edoardo10/house-price-top-14-stacking-regressor,House Prices - Advanced Regression Techniques
11366379,0.14015,3,14,/shrey821/house-price-pred-with-xgboost-regression-rf,House Prices - Advanced Regression Techniques
11346234,0.15198,0,8,/carlmcbrideellis/combining-my-submission-csv-files-for-better-score,House Prices - Advanced Regression Techniques
11231046,0.13045,0,0,/yutohisamatsu/houseprice-elasticnet,House Prices - Advanced Regression Techniques
11272272,0.12519,4,15,/jjmewtw/prices-cleaning-analysis-estimation-in-stages,House Prices - Advanced Regression Techniques
11181990,0.15052,7,19,/brendan45774/house-predict-solution-18-0,House Prices - Advanced Regression Techniques
11255228,0.15023,3,7,/elijah981/house-price-predictions-regression-basics,House Prices - Advanced Regression Techniques
11298999,0.1340299999999999,0,0,/eulisesv/housingnotebook-ev-08202020,House Prices - Advanced Regression Techniques
11268972,1.57434,0,0,/biswaranjanbiswal/house-price-prediction,House Prices - Advanced Regression Techniques
5781378,0.8930600000000001,29,113,/bibek777/heng-s-model-inference-kernel,Severstal: Steel Defect Detection
5817818,0.8417600000000001,0,0,/lokkrish1/kernel281465c7d8,Severstal: Steel Defect Detection
5716768,0.85674,2,7,/rabbitcaptain/keras-resnet50-refinenet,Severstal: Steel Defect Detection
5520072,0.8904299999999999,7,62,/bigkizd/se-resnext50-89,Severstal: Steel Defect Detection
5315229,0.88479,0,5,/asimandia/unet-pytorch-inference-kernel-radam,Severstal: Steel Defect Detection
5381398,0.85619,10,39,/siddhary87/data-understanding-and-visualisation,Severstal: Steel Defect Detection
5292327,0.76625,0,0,/tovvelie/severstal-transfer-learning,Severstal: Steel Defect Detection
5364900,0.87379,1,10,/jian1201/severstal-efficient-u-net-inference,Severstal: Steel Defect Detection
5255751,0.8766,6,46,/xhlulu/severstal-efficient-u-net-inference,Severstal: Steel Defect Detection
5206455,0.8591700000000001,0,3,/saphirox/severstal-simple-2-step-pipeline,Severstal: Steel Defect Detection
5048529,0.86288,59,365,/xhlulu/severstal-simple-2-step-pipeline,Severstal: Steel Defect Detection
13781773,4.8047,0,0,/semenedel/notebook603ef26760,Dog Breed Identification
12350775,0.1890099999999999,1,5,/deepakat002/inception-xception-nasnetlarge-inceptionres,Dog Breed Identification
11211335,4.68377,0,0,/teramera/notebook47efa8e180,Dog Breed Identification
9929159,0.26755,0,0,/kamleshsolanki/dog-breed-classification-load-from-dataframe,Dog Breed Identification
3286640,0.7,0,0,/overload10/surface-prediction-feature-engineering,CareerCon 2019 - Help Navigate Robots
3368728,0.69,0,0,/dogugun/mykernel,CareerCon 2019 - Help Navigate Robots
3652094,0.9876,4,8,/pjofrelora/hybrid-classifier-solution-11th-place,CareerCon 2019 - Help Navigate Robots
3531252,0.6692,0,0,/overload10/xgboost-and-rf,CareerCon 2019 - Help Navigate Robots
3569017,0.9653,6,6,/algorrt/highest-scoring-public-kernel-starter-sol-29,CareerCon 2019 - Help Navigate Robots
3534218,0.71,0,0,/purplejester/the-best-friend-of-an-alchemist,CareerCon 2019 - Help Navigate Robots
3561762,0.66,0,1,/prabhatkumarsahu/helping-robots-career-con,CareerCon 2019 - Help Navigate Robots
3537827,0.47,0,3,/kepure/1d-cnn-lstm,CareerCon 2019 - Help Navigate Robots
3539758,0.68,3,5,/marcushorn/random-forest-ensemble-w-smote,CareerCon 2019 - Help Navigate Robots
3524180,0.73,0,4,/mohi549/lgb-carrercon,CareerCon 2019 - Help Navigate Robots
3364132,0.71,0,1,/andrewzolotarev/cc-2019,CareerCon 2019 - Help Navigate Robots
3484267,0.9272,7,41,/jesucristo/1-smart-robots-complete-compilation,CareerCon 2019 - Help Navigate Robots
3387981,0.72,0,1,/indranilkhedkar/careercon2019-ik,CareerCon 2019 - Help Navigate Robots
3413051,0.61,0,5,/sarmat/baseline-cnn-for-signal-prediction,CareerCon 2019 - Help Navigate Robots
3397022,0.68,11,16,/lockeza/careercon-convneuralnet-starter,CareerCon 2019 - Help Navigate Robots
13238781,0.01831,0,3,/dmitryvyudin/pytorch-transfer-learning-with-post-processing,Mechanisms of Action (MoA) Prediction
13175660,0.01842,0,2,/intwzt/updated-inference-final-mx10-transfer,Mechanisms of Action (MoA) Prediction
13163740,0.0183,0,2,/cdeotte/dae-book3c,Mechanisms of Action (MoA) Prediction
13194320,0.02408,0,1,/ihaifaa/pca-dnn-moa,Mechanisms of Action (MoA) Prediction
12936151,0.01915,0,0,/vkehfdl1/pca-kmeans-nn,Mechanisms of Action (MoA) Prediction
13221370,0.01835,0,8,/bowaka/single-tabnet-private-0-01629-lb-0-01835,Mechanisms of Action (MoA) Prediction
13007885,0.0183,0,1,/aryankhatana/pytorch-transfer-learning-with-k-folds-by-drug-ids,Mechanisms of Action (MoA) Prediction
13206890,0.01815,0,4,/underwearfitting/final-sub-1,Mechanisms of Action (MoA) Prediction
13149435,0.01916,0,0,/alexandremahdhaoui/deep-abstractor-final,Mechanisms of Action (MoA) Prediction
13016112,0.02074,0,0,/thimes/moa-prediction,Mechanisms of Action (MoA) Prediction
12118770,0.01884,0,0,/yxohrxn/mlpclassifier-fit,Mechanisms of Action (MoA) Prediction
13103272,0.01984,0,2,/tarunbisht11/moa-competition-baseline-tensorflow,Mechanisms of Action (MoA) Prediction
13199909,0.12245,0,0,/charlenebrn/notebookgoogle-colab,Mechanisms of Action (MoA) Prediction
12587448,0.01832,0,1,/radadiyamohit/blend-blend-blend,Mechanisms of Action (MoA) Prediction
13176926,0.0183,0,0,/junyan01/inference-blending-pretrained-4-models,Mechanisms of Action (MoA) Prediction
13050462,0.01849,0,0,/aeryss/tabnet-original-hyperparam-tuning-only,Mechanisms of Action (MoA) Prediction
12895679,0.01889,0,0,/aeryss/moa-idk-how-to-fe,Mechanisms of Action (MoA) Prediction
13156180,0.01873,0,7,/pankajdubey87/ensemble-nn-and-xgboost,Mechanisms of Action (MoA) Prediction
13133647,0.01847,0,0,/yongchengmu/moa-project-new,Mechanisms of Action (MoA) Prediction
12999901,0.0199699999999999,0,0,/jb4rogue/simple-nn-keras-pca-optimized,Mechanisms of Action (MoA) Prediction
12083420,0.01841,0,0,/alturutin/moa-resnet,Mechanisms of Action (MoA) Prediction
13165517,0.02167,0,1,/lashamaev/baseline6nn206,Mechanisms of Action (MoA) Prediction
13069732,0.01831,0,0,/tuistan/inference-blending-pretrained-bb6709,Mechanisms of Action (MoA) Prediction
12199934,0.01964,0,0,/wanping7/xgboost,Mechanisms of Action (MoA) Prediction
13015269,0.01841,0,0,/hasan7/kfold-with-dl-gaussian-vt-translearning-ff,Mechanisms of Action (MoA) Prediction
12522728,0.01848,0,0,/promona/tabnet-library,Mechanisms of Action (MoA) Prediction
13108078,0.02081,0,5,/wickkiey/moa-pca-nn-tf,Mechanisms of Action (MoA) Prediction
13090229,0.02223,0,0,/solitariuslykos/moa-solution,Mechanisms of Action (MoA) Prediction
12955800,0.01864,0,2,/sssssssww/transformer,Mechanisms of Action (MoA) Prediction
12905221,0.02158,0,1,/jeeperscreepers/woe-nn-0d92b4,Mechanisms of Action (MoA) Prediction
12930139,0.01845,0,13,/rosarr/fork-of-rapids-svc-nonscored,Mechanisms of Action (MoA) Prediction
12968202,0.02404,0,0,/kevnsan/ensemble,Mechanisms of Action (MoA) Prediction
12994000,0.0183699999999999,1,2,/paantya/lish-moa-notebook,Mechanisms of Action (MoA) Prediction
12595725,0.01835,30,156,/thehemen/pytorch-transfer-learning-with-k-folds-by-drug-ids,Mechanisms of Action (MoA) Prediction
12829507,0.03803,2,1,/yeayates21/moa-basic-fast-ai,Mechanisms of Action (MoA) Prediction
12949848,0.02673,0,1,/nadarsubash/moa-prediction-using-xgboost,Mechanisms of Action (MoA) Prediction
11879944,0.0198,0,0,/plasmaichor/moa-predictions-plasmaichor,Mechanisms of Action (MoA) Prediction
12896513,0.01841,0,5,/martintosstorff/moalibtest,Mechanisms of Action (MoA) Prediction
6579839,0.88184,0,0,/vh1981/severstal-steel-defect-detection-segonly-submit,Severstal: Steel Defect Detection
6376255,0.88805,0,1,/thomasbrandon/severstalsubmission-tta,Severstal: Steel Defect Detection
6344902,0.91201,1,1,/mmmqaq/severstal-mlcomp-catalyst-infer-0-90726,Severstal: Steel Defect Detection
6360497,0.91154,3,6,/nemethpeti/severstal-mlcomp-catalyst-improved-pb0-90147,Severstal: Steel Defect Detection
6176088,0.90726,0,6,/vivekwisdom/severstal-mlcomp-catalyst-0-90726-observations,Severstal: Steel Defect Detection
6227335,0.8932,0,3,/abimannan/steel-detection,Severstal: Steel Defect Detection
6243303,0.82245,0,0,/jiageng/mxnet-gluon-classification-inference,Severstal: Steel Defect Detection
5840246,0.90435,9,12,/jiageng/mxnet-gluon-inference,Severstal: Steel Defect Detection
6011628,0.90726,10,41,/evgenyshtepin/severstal-mlcomp-catalyst-infer-0-90726,Severstal: Steel Defect Detection
6026438,0.83293,0,1,/thehemen/severstal-2-step-pipeline-with-u-net,Severstal: Steel Defect Detection
5983778,0.90689,48,263,/lightforever/severstal-mlcomp-catalyst-infer-0-90672,Severstal: Steel Defect Detection
5824195,0.85674,0,3,/siddhary87/custom-layers-post-pre-process,Severstal: Steel Defect Detection
5849361,0.88526,2,21,/dukhovnik/segmentation-models-pytorch-fpn-unet-inference,Severstal: Steel Defect Detection
5816380,0.8881700000000001,3,62,/bigironsphere/boost-your-score-with-pixel-counts-0-886-0-888,Severstal: Steel Defect Detection
5826697,0.85674,0,1,/huynhtrungnghia/fast-scnn,Severstal: Steel Defect Detection
11249807,0.14019,1,6,/saptarshi96/eda-feature-engineering-modelling,House Prices - Advanced Regression Techniques
11113955,0.1235,5,17,/yashudua/prices-eda-prediction-private-0-00044,House Prices - Advanced Regression Techniques
11218684,0.12074,3,11,/millernicholas/housing-prices-top-5-solution,House Prices - Advanced Regression Techniques
11150688,0.1200599999999999,1,7,/katchupalvarenga/house-prices-top-8-on-leaderboard,House Prices - Advanced Regression Techniques
11077330,0.14436,0,1,/alihanurumov/house-prices-advanced-regression-techniques-ii,House Prices - Advanced Regression Techniques
11186456,0.1515099999999999,0,1,/abdelrahmantarek22/house-prices-advanced-regression,House Prices - Advanced Regression Techniques
11167736,0.12277,2,8,/amanmishra4yearbtech/top-14-eda-p-value-xgb-lgbm-stacking,House Prices - Advanced Regression Techniques
11159434,0.12772,0,6,/dibyansudiptiman/house-prices-prediction-using-advanced-regression,House Prices - Advanced Regression Techniques
11144065,0.36968,0,5,/hrysto97/house-prices-prediction,House Prices - Advanced Regression Techniques
11088774,0.13025,4,22,/sshikamaru/advanced-regression-techniques,House Prices - Advanced Regression Techniques
11107234,0.13826,0,2,/ciscoramond/bakal-m-nas-l-olcek,House Prices - Advanced Regression Techniques
11058694,0.13121,0,0,/viktorpopov/fast-xgb-hyperopt,House Prices - Advanced Regression Techniques
1529187,0.0,9,17,/sanket30/lung-opacity-classification-inceptionv3,RSNA Pneumonia Detection Challenge
4220357,0.72921,0,3,/christianwallenwein/fastai-baseline-model-plant-seedlings,Plant Seedlings Classification
3381439,0.93576,0,0,/asdadadsada/assignment3,Plant Seedlings Classification
3207578,0.767,0,5,/pavanireddyv/plant-seedling-classification-cnn,Plant Seedlings Classification
2871994,0.97858,2,6,/dromosys/fast-ai-v1-focal-loss,Plant Seedlings Classification
1249733,0.93073,1,3,/mehradaria/plant-seedling-classification-aria,Plant Seedlings Classification
725387,0.7953399999999999,0,12,/kmader/pretrained-vgg16-w-attention-for-seedlings,Plant Seedlings Classification
604131,0.95843,7,43,/nikkonst/plant-seedlings-with-cnn-and-image-processing,Plant Seedlings Classification
11346621,0.5670000000000001,2,22,/roguekk007/bird-submission,Cornell Birdcall Identification
11281831,0.5589999999999999,2,11,/tiandaye/infernece-resnet50-with-audio-resample-baseline,Cornell Birdcall Identification
11275387,0.557,0,3,/marcogorelli/cln-esp-starter-pack,Cornell Birdcall Identification
11125493,0.544,12,7,/timothyalexjohn/birdsong-classifier-keras-cnn-part-3-notebook-1,Cornell Birdcall Identification
11171205,0.544,2,3,/eladwar/bert-audio,Cornell Birdcall Identification
10895846,0.568,0,5,/akashsuper2000/inference-birdsong-baseline-resnest50-fast,Cornell Birdcall Identification
11093105,0.578,144,529,/hidehisaarai1213/introduction-to-sound-event-detection,Cornell Birdcall Identification
11074038,0.53,2,9,/jonykarki/birddcall-inference-simplecnn,Cornell Birdcall Identification
10687543,0.564,0,10,/mahmudds/cornell-birdcall-identification,Cornell Birdcall Identification
11290290,0.61274,0,1,/rahulpawade/new-york-city-taxi-trip-duration-xgboost,New York City Taxi Trip Duration
9359767,0.37399,1,2,/jeffreycbw/nyc-taxi-trip-public-0-37399-private-0-37206,New York City Taxi Trip Duration
8803636,0.8925799999999999,0,0,/floooo/submission,New York City Taxi Trip Duration
8411035,1.0259,0,1,/captaincolavin/kernel50ca14687c,New York City Taxi Trip Duration
6218821,0.42172,0,0,/anndd3/keras-nn-data-exploration-v2-0,New York City Taxi Trip Duration
4765276,0.89235,0,0,/ivangord/nyctaxi-fin,New York City Taxi Trip Duration
4173305,0.46219,0,0,/jedtassa/kernel-new-york-city,New York City Taxi Trip Duration
4173462,0.46157,0,0,/ehalifa/kernel-halifael-amin,New York City Taxi Trip Duration
4173535,0.5765100000000001,0,0,/ssf509/kernela45a33a206,New York City Taxi Trip Duration
4173693,0.4451,0,0,/pulsar10130/kernel208b789c11,New York City Taxi Trip Duration
4173992,0.4605,0,0,/cheikhmbaye/ml-ealuation-cheikh-tidiane-mbaye,New York City Taxi Trip Duration
4173526,0.4364,0,0,/djoparasite/kerneldelerayjonathantaxi,New York City Taxi Trip Duration
4189114,0.49913,0,0,/rarara23/kernel-princesse-ngo-billong,New York City Taxi Trip Duration
4173612,0.43548,0,0,/branchard/kernel6e867e1092,New York City Taxi Trip Duration
4173460,0.46302,0,0,/ryagoubi/kerneleff85039b2,New York City Taxi Trip Duration
4173597,0.4355199999999999,0,0,/chaimaa22/kernel5c8d9f9afe,New York City Taxi Trip Duration
3335600,0.41785,0,0,/freel21/antonin-moreno-second-attempt,New York City Taxi Trip Duration
3312340,0.41747,0,0,/chidambara/nyc-taxi-trip-duration,New York City Taxi Trip Duration
2842180,0.40641,0,0,/serhatyildirim/fork-of-datataxi-ny-prediction,New York City Taxi Trip Duration
2858728,0.51635,0,1,/superfadx/nyc-taxi-trip-duration-prediction-fady-hallek,New York City Taxi Trip Duration
2977087,0.39598,0,1,/mnds18/nyc-taxi-eda-mrig,New York City Taxi Trip Duration
2858727,0.46617,0,0,/gregyb/nyc-taxi-gr-goire,New York City Taxi Trip Duration
2866636,0.4195,0,0,/asmasem/new-york-city-taxi-trip-duration-as,New York City Taxi Trip Duration
2860520,0.40197,0,4,/affoumou/taxi-prediction,New York City Taxi Trip Duration
2863037,0.56991,0,1,/njelili/nyc-taxi-duration-nour,New York City Taxi Trip Duration
13110536,4596.709,0,12,/maksymshkliarevskyi/jane-street-market-prediction-baseline-part-2,Jane Street Market Prediction
13068926,4319.503,15,55,/jazivxt/the-market-is-reactive,Jane Street Market Prediction
13071730,4460.821,7,37,/xhlulu/jane-street-cudf-xgboost-with-gpu,Jane Street Market Prediction
13073219,3599.519,2,11,/code1110/janestreet-integration-test-classifier,Jane Street Market Prediction
13062119,1551.808,0,13,/artgor/jane-street-eda-and-simple-model,Jane Street Market Prediction
13064088,0.0,2,9,/jmaslek/logistic-regression-classification-pipeline,Jane Street Market Prediction
13292406,5029.85,0,11,/isaienkov/jane-street-market-prediction-xgb-kfold-rfe,Jane Street Market Prediction
9281463,0.981,1,2,/apthagowda/plant-pathology-2020-tenserflow-tpu,Plant Pathology 2020 - FGVC7
9248419,0.976,1,0,/apthagowda/plant-pathology-2020-pytorch-gpu,Plant Pathology 2020 - FGVC7
9491418,0.94,0,0,/presita/apple-disease-fastai2,Plant Pathology 2020 - FGVC7
9490935,0.948,0,1,/sakshamaggarwal/basic-pytorch-pretrained-models-ensemble,Plant Pathology 2020 - FGVC7
9502954,0.964,1,1,/kuromaguro/plant-pathology-tpu-mixup,Plant Pathology 2020 - FGVC7
9500173,0.882,9,13,/muellerzr/plant-pathology-fastai2-exploration,Plant Pathology 2020 - FGVC7
9353847,0.912,10,20,/aryaprince/pytorch-model,Plant Pathology 2020 - FGVC7
9379496,0.823,0,0,/slipclutch/exception-model-with-image-augmentation,Plant Pathology 2020 - FGVC7
9280205,0.925,0,0,/urayukitaka/comparing-resnet-model,Plant Pathology 2020 - FGVC7
9117198,0.976,6,11,/ronyroy/effnet-fastai-folds-x5,Plant Pathology 2020 - FGVC7
8963333,0.947,0,3,/manyregression/fastai2-in-few-lines,Plant Pathology 2020 - FGVC7
9006346,0.957,0,1,/cqcx64/plant-pathology-2020-mxnet-version,Plant Pathology 2020 - FGVC7
9014076,0.95,2,3,/amankimothi100/plant-pathology-fastai2-starter-with-resnet152,Plant Pathology 2020 - FGVC7
8798031,0.8704,57,777,/tanulsingh077/deep-learning-for-nlp-zero-to-transformers-bert,Jigsaw Multilingual Toxic Comment Classification
8759740,0.8149,0,0,/ahmeriq09/kernel001,Jigsaw Multilingual Toxic Comment Classification
8838144,0.9361,23,121,/mobassir/understanding-cross-lingual-models,Jigsaw Multilingual Toxic Comment Classification
8779853,0.9169,0,1,/siddharthcha519810/toxic-comments-eda-and-xlm-roberta-model,Jigsaw Multilingual Toxic Comment Classification
8685231,0.5049,0,3,/fatmagu/bert-multilingual-transformers-tf-keras,Jigsaw Multilingual Toxic Comment Classification
8629926,0.7488,9,11,/dimitreoliveira/jigsaw-tpu-optimized-training-loops,Jigsaw Multilingual Toxic Comment Classification
8613793,0.9058,11,20,/gopidurgaprasad/stater-pytorch-tpu-google-colab,Jigsaw Multilingual Toxic Comment Classification
8572342,0.9158,22,118,/miklgr500/jigsaw-tpu-bert-with-huggingface-and-keras,Jigsaw Multilingual Toxic Comment Classification
8567027,0.8711,23,94,/xhlulu/jigsaw-tpu-distilbert-with-huggingface-and-keras,Jigsaw Multilingual Toxic Comment Classification
8579474,0.897,3,14,/bamps53/inference-with-translated-test-set,Jigsaw Multilingual Toxic Comment Classification
8552224,0.8965,15,60,/hamditarek/nb-svm-strong-linear-baseline,Jigsaw Multilingual Toxic Comment Classification
8552043,0.931,4,42,/ipythonx/jigsaw-multilingual-quick-eda-tpu-modeling,Jigsaw Multilingual Toxic Comment Classification
8550756,0.6454,8,45,/theoviel/bert-pytorch-huggingface-starter,Jigsaw Multilingual Toxic Comment Classification
8555296,0.8243,2,14,/arvissu/simple-pytorch-bert,Jigsaw Multilingual Toxic Comment Classification
8555118,0.6439,0,2,/vikassingh1996/don-tsay-whatthef-eda-fe-lr,Jigsaw Multilingual Toxic Comment Classification
10874746,0.9438,0,0,/mint101/example-code-of-pseudo-label-on-xlm-r,Jigsaw Multilingual Toxic Comment Classification
10185107,0.9472,0,0,/akashsuper2000/howling-with-wolf-on-l-genpresse,Jigsaw Multilingual Toxic Comment Classification
9672403,0.9139,0,0,/iserya/inference-bert-model,Jigsaw Multilingual Toxic Comment Classification
8146561,0.97885,0,3,/debasisdotcom/digit-recognizer,Digit Recognizer
11601712,0.99178,9,8,/juniorcl/cnn-digit-recognizer-0-99178-score,Digit Recognizer
11718807,0.99078,0,3,/sejalkshirsagar/digit-recognizer-cnn-keras,Digit Recognizer
11696372,0.98467,0,3,/dextermojo/digit-recognizer,Digit Recognizer
11684407,0.99164,6,12,/mrinalsaini/digit-recognizer-mnist-data-cnn,Digit Recognizer
11698912,0.98925,0,7,/pallavisinha12/digit-recognizer,Digit Recognizer
11590103,0.99628,4,13,/pradyut23/cnn-keras-ensemble-model-mnist-99-6-accuracy,Digit Recognizer
11355485,0.96496,0,1,/g9jiggy/digit-recognizer-without-deep-learning,Digit Recognizer
11634714,0.97985,0,3,/stanen/digit-recognizer-mnist,Digit Recognizer
11593117,0.93553,0,0,/georgescriven/digit-recognition,Digit Recognizer
11517111,0.99225,0,0,/rafayelmkrtchyan/mnist,Digit Recognizer
11226330,0.99532,3,10,/manthanbhagat/digitrecognizer-using-keras,Digit Recognizer
11492746,0.9936,1,4,/hoangnguyen719/simple-conv-net-accuracy-0-99360,Digit Recognizer
11499669,0.9945,1,7,/sudiptog81/mnist-digit-predictions,Digit Recognizer
14473273,0.139,0,0,/gianghus/pytorch-augmentation-vit,Cassava Leaf Disease Classification
13863118,0.894,0,0,/maximkalinin/cassava-leaf-disease-submission-kernel,Cassava Leaf Disease Classification
13643882,0.883,0,0,/paulorblima/notebook63977becc3,Cassava Leaf Disease Classification
5060014,0.51031,1,0,/theyoonicon/us-nerve-detection,Ultrasound Nerve Segmentation
12558830,2864.95221,0,2,/marianadehon/walmart-store-sales-forecasting,Walmart Recruiting - Store Sales Forecasting
11589530,2884.29135,0,0,/cfsantos/wallmart-prediction-for-ze-delivery,Walmart Recruiting - Store Sales Forecasting
10037296,5196.186729999999,12,18,/caesarlupum/walmart-store-sales-forecasting-anomaly-analysis,Walmart Recruiting - Store Sales Forecasting
9367694,2782.22259,0,1,/gyongsoksong/walmart-baseline-v0,Walmart Recruiting - Store Sales Forecasting
8666356,2700.06986,0,1,/simonstochholm/walmart-sales-forecast-gammel,Walmart Recruiting - Store Sales Forecasting
8791298,18604.82366,1,4,/cmcoutosilva/zedelivery-python,Walmart Recruiting - Store Sales Forecasting
6457885,4414.56094,0,0,/alexd321/weekly-sales-forecast,Walmart Recruiting - Store Sales Forecasting
13759417,4312.934,7,48,/grafael/fast-predictions-tflite-1h-3x-faster,Jane Street Market Prediction
13787853,3567.218,0,3,/zhenpingfeng/bottleneck-encoder-mlp-keras-tuner-custommse,Jane Street Market Prediction
13740892,2.139,2,17,/backtracking/autoencoder-mlp-cv-multitarget-pytorch-basic,Jane Street Market Prediction
13616933,6813.127,0,3,/matheuspontes/tensorflow-keras,Jane Street Market Prediction
13684945,3596.843,3,22,/manavtrivedi/lstm-rnn-classifier,Jane Street Market Prediction
13686923,3204.118,0,1,/abiolatti/jane-street-market-lgb,Jane Street Market Prediction
13674356,507.754,0,1,/ricardoaraujo/jane-street-basic-template,Jane Street Market Prediction
13625815,276.19,15,25,/carlmcbrideellis/jane-street-tabnet-3-0-0-starter-notebook,Jane Street Market Prediction
13631563,3519.328,0,1,/hardtovary/seed1-6000-model-with-gpu,Jane Street Market Prediction
13616542,109.448,0,1,/legendx/jane-street,Jane Street Market Prediction
13497599,5420.698,1,17,/nakshatrasingh/jane-street-xgboost-using-dmatrix,Jane Street Market Prediction
13388112,6876.781999999998,8,25,/lachlansuter/new-eda-nn-resilience-to-noise-utility-decay,Jane Street Market Prediction
13307803,0.0,2,2,/ironicninja/jane-street-predictions-with-tags,Jane Street Market Prediction
13560686,4021.351,0,1,/zhenpingfeng/jane-street-neural-network-regression-starter,Jane Street Market Prediction
13458498,2924.377,0,1,/tatochen/simple-xgb,Jane Street Market Prediction
13499986,0.0,0,14,/backtracking/pytorch-basic-for-submission,Jane Street Market Prediction
13457548,3596.843,4,15,/harryliyi/js-lstm-baseline,Jane Street Market Prediction
13454583,4992.749,16,136,/marketneutral/purged-time-series-cv-xgboost-optuna,Jane Street Market Prediction
13450428,681.725,3,10,/harshit2708/linear-regression,Jane Street Market Prediction
13446886,4500.907999999999,15,109,/gogo827jz/jane-street-ffill-transformer-baseline,Jane Street Market Prediction
8838350,0.73397,0,0,/ayakhaled2/home-credit-default-risk,Home Credit Default Risk
8740407,0.74493,0,2,/nguyenvlm/home-credit-default-risk,Home Credit Default Risk
5850705,0.75785,0,0,/sanholee/home-credit-feature-engineering-01-kor,Home Credit Default Risk
5714894,0.7531100000000001,0,0,/rizyayemima/homecredit,Home Credit Default Risk
3337146,0.7959999999999999,0,0,/windofdl/kernelf68f763785,Home Credit Default Risk
4870466,0.7856,0,1,/miracle0/home-credit-default-risk,Home Credit Default Risk
4036420,0.74248,0,1,/vaskanman/kernelfadb0573fe,Home Credit Default Risk
4154164,0.6704100000000001,0,0,/seungwanryu/2019-06-05,Home Credit Default Risk
3788366,0.75459,0,0,/zhuicanggaoju/bnu-dataming-2019,Home Credit Default Risk
3711376,0.57727,0,1,/sahil94/home-credit,Home Credit Default Risk
406270,0.2951,1,4,/skhemka/keras-modified-0-29-on-pl,Statoil/C-CORE Iceberg Classifier Challenge
507343,0.1463,0,0,/bowyee/explore-stacking-lb-0-1463,Statoil/C-CORE Iceberg Classifier Challenge
461715,0.451,2,0,/bphlmn/what-can-we-do-with-logistic-regression,Statoil/C-CORE Iceberg Classifier Challenge
37519,0.67305,0,0,/fsharifi/testtesttest,Prudential Life Insurance Assessment
30087,0.55394,0,0,/syroejka/prudential-xgboost,Prudential Life Insurance Assessment
235856,2.0009,6,5,/opanichev/same-data-in-train-and-test,Two Sigma Connect: Rental Listing Inquiries
232914,0.53608,0,0,/jgx020/cv-statistics-better-parameters-and-explaination,Two Sigma Connect: Rental Listing Inquiries
4110218,0.631,0,6,/blondinka/predict-submit-seresnext101,iMet Collection 2019 - FGVC6
3990484,0.556,0,0,/namgalielei/imet-tensorflow,iMet Collection 2019 - FGVC6
4052579,0.235,0,1,/koushikcon/using-pytorch-and-vgg,iMet Collection 2019 - FGVC6
4030122,0.449,0,2,/byrachonok/sigmoid-and-softmax-output-mix-pytorch-resnet18,iMet Collection 2019 - FGVC6
3892742,0.5489999999999999,0,1,/saladjay/densetnet-mixup-focalloss-v11,iMet Collection 2019 - FGVC6
3789949,0.5,0,1,/itwice/kernel266d1c7f1f,iMet Collection 2019 - FGVC6
3770258,0.037,0,1,/parmarsuraj99/pytorch-starter,iMet Collection 2019 - FGVC6
3653784,0.474,2,5,/dimitreoliveira/imet-keras-pretrained-model-as-feature-extractor,iMet Collection 2019 - FGVC6
3593954,0.596,6,10,/demonplus/fastai-resnet152-imet,iMet Collection 2019 - FGVC6
3472408,0.575,12,37,/xiuchengwang/keras-xception-fine-turning-facol-loss,iMet Collection 2019 - FGVC6
3444216,0.47,0,5,/hengzheng/imet-densenet-and-two-weighted-outputs-model,iMet Collection 2019 - FGVC6
3413391,0.488,29,115,/ateplyuk/keras-starter,iMet Collection 2019 - FGVC6
3414874,0.09,0,23,/dimitreoliveira/imet-collection-2019-eda-keras,iMet Collection 2019 - FGVC6
4115924,0.002,0,0,/maxlenormand/act-softmax-incresnetv2,iMet Collection 2019 - FGVC6
1220087,0.2203,8,22,/onodera/beat-the-best-kernel-public-0-2203,Avito Demand Prediction Challenge
1082486,0.2331,0,1,/gautham11/simple-baseline-lightgbm-model,Avito Demand Prediction Challenge
1052876,0.228,7,41,/peterhurford/modified-wordbatch-ridge-fm-ftrl-lgb,Avito Demand Prediction Challenge
923468,0.231,0,0,/twistedtensor/baseline-on-structured-part-with-catboost,Avito Demand Prediction Challenge
1046453,0.2232,0,3,/sukhyun9673/aggregated-features-ridge-image-forked,Avito Demand Prediction Challenge
1021191,0.2285,1,14,/krithi07/baseline-model,Avito Demand Prediction Challenge
987847,0.2266,15,11,/jingqliu/stacked-model-cnn-xgboost,Avito Demand Prediction Challenge
934837,0.2323,0,12,/hugoncosta/tweaked-simple-catboost-tfidf,Avito Demand Prediction Challenge
975256,0.24,8,27,/christofhenkel/self-trained-embeddings-starter-only-description,Avito Demand Prediction Challenge
917126,0.2392,1,4,/jingqliu/fasttext-conv2d-title-keras,Avito Demand Prediction Challenge
10146205,0.02083,0,0,/pratyush1019/fork-of-rsna-kaggle-problem-pneumonia-mask-r-cnn,RSNA Pneumonia Detection Challenge
14430558,0.85521,0,0,/nullsar/loan-default-prediction,Loan Default Prediction - Imperial College London
10720165,1.31846,0,4,/darisdzakwanhoesien2/loan-default-prediction-imperial-college-london,Loan Default Prediction - Imperial College London
3369701,0.26,0,0,/adilbek/svm-with-minimal-data-preprocessing,CareerCon 2019 - Help Navigate Robots
3334577,0.68,29,72,/ishivinal/hyperparamters-optimization-gs-rs-boa-tpe-hb-ga,CareerCon 2019 - Help Navigate Robots
3354429,0.53,0,0,/rjmishra/simple-eda-and-naive-approach,CareerCon 2019 - Help Navigate Robots
3262870,0.7,0,8,/oluwaody/the-humanoid-tier-recognition-with-kfold-nn,CareerCon 2019 - Help Navigate Robots
3312458,0.67,6,8,/itsmesunil/robot-sensor-data-analysis,CareerCon 2019 - Help Navigate Robots
3303632,0.59,0,9,/guntherthepenguin/fastai-lstm,CareerCon 2019 - Help Navigate Robots
3309694,0.51,1,10,/nikitpatel/deep-learning-machine-learning-rf-lgbm-dt-knn-ada,CareerCon 2019 - Help Navigate Robots
3282728,0.64,0,4,/kageyama/predict-by-lightgbm,CareerCon 2019 - Help Navigate Robots
3262369,0.61,3,18,/mohanamurali/conv1d-keras,CareerCon 2019 - Help Navigate Robots
3295916,0.65,0,1,/sivakumard/kaggle-challenge-xgboost,CareerCon 2019 - Help Navigate Robots
3275334,0.66,2,0,/deepdreamx/eda-robot,CareerCon 2019 - Help Navigate Robots
3261499,0.57,6,28,/artgor/basic-pytorch-lstm,CareerCon 2019 - Help Navigate Robots
3262144,0.65,16,40,/artgor/bayesian-optimization-for-robots,CareerCon 2019 - Help Navigate Robots
3276223,0.56,1,5,/ihawks/kernele69c850bd3,CareerCon 2019 - Help Navigate Robots
3258009,0.65,10,38,/jsaguiar/surface-recognition-baseline,CareerCon 2019 - Help Navigate Robots
3254037,0.49,12,59,/theoviel/deep-learning-starter,CareerCon 2019 - Help Navigate Robots
3259045,0.46,0,7,/abefetterman/pytorch-starter,CareerCon 2019 - Help Navigate Robots
13669391,0.76794,0,1,/arielz/titanic-ml,Titanic - Machine Learning from Disaster
12328466,0.7751100000000001,0,0,/swetash/getting-started-with-titanic,Titanic - Machine Learning from Disaster
13440752,0.98803,4,3,/startover205/fastai-2-titanic-rf,Titanic - Machine Learning from Disaster
13672851,0.76076,31,41,/shivadumnawar/titanic-a-complete-tutorial-for-beginners,Titanic - Machine Learning from Disaster
13643237,0.76315,0,0,/gigunlee/titanic-beginner,Titanic - Machine Learning from Disaster
10029649,0.78947,0,1,/yashsingh25/titanic-dataset-analysis,Titanic - Machine Learning from Disaster
13563104,0.76076,0,0,/gabrielloli/titanic-quest,Titanic - Machine Learning from Disaster
13636756,0.79904,3,3,/homayoonkhadivi/titanic-eda-model-hyperparameter-tuning-top-5,Titanic - Machine Learning from Disaster
13644205,0.7822899999999999,0,3,/ahmedy1993/comprehensive-exploratory-data-analysis-of-titanic,Titanic - Machine Learning from Disaster
13608090,0.76555,8,16,/bouweceunen/automl-comparison-titanic-dataset,Titanic - Machine Learning from Disaster
13642352,0.7751100000000001,0,0,/larasluthfiyyah/laras05,Titanic - Machine Learning from Disaster
13635321,0.6411399999999999,0,0,/csachgau/first-attempt-with-titanic-dataset,Titanic - Machine Learning from Disaster
13605209,0.76076,8,15,/raafaq/xgbclassifier,Titanic - Machine Learning from Disaster
12001252,0.7751100000000001,0,0,/priyanshu173/titanic-sail,Titanic - Machine Learning from Disaster
12989316,0.7703300000000001,0,0,/jessy9955/titanic-from-colab,Titanic - Machine Learning from Disaster
13589439,0.78708,1,5,/jackttai/titanic-classification-using-xgboost,Titanic - Machine Learning from Disaster
5747858,0.64513,0,1,/anjanatiha/dog-breeds-classifications-using-keras,Dog Breed Identification
2815474,4.78749,3,3,/hugorcf/dog-breed-identification-using-fastai,Dog Breed Identification
10218996,0.7751100000000001,0,0,/asnuvatanvin/getting-started-with-titanic,Titanic - Machine Learning from Disaster
12931453,0.67224,0,1,/dxkariya/titanic01,Titanic - Machine Learning from Disaster
13429461,0.81339,1,7,/vaishnavikhilari/titanic-survival-prediction,Titanic - Machine Learning from Disaster
13396342,0.75837,13,12,/fayssalelaazouzi/titanic-best-working-classifier,Titanic - Machine Learning from Disaster
13255729,0.78708,0,4,/zhaoyuanhuan/project-of-titanic,Titanic - Machine Learning from Disaster
13397137,0.7751100000000001,5,15,/colearninglounge/titanic-solution-comprehensive-with-explanation,Titanic - Machine Learning from Disaster
13297453,0.76555,0,0,/ryotak12/titanic-sic,Titanic - Machine Learning from Disaster
12110094,0.79425,0,0,/anhtu96/titanic-baseline,Titanic - Machine Learning from Disaster
13379111,0.7751100000000001,4,9,/howeverforever/titanic-lgbm-optuna,Titanic - Machine Learning from Disaster
11903329,0.78468,1,5,/abhayraghuwanshi/kaggle-titanic,Titanic - Machine Learning from Disaster
13334592,0.75358,7,35,/daotan/titanic-using-randomforest,Titanic - Machine Learning from Disaster
13413499,0.7440100000000001,0,0,/suwonkang/hw5-suwon,Titanic - Machine Learning from Disaster
13092061,0.7751100000000001,0,0,/f10rence/3-parameters,Titanic - Machine Learning from Disaster
13371132,0.66028,0,1,/kurukuru8395/notebook4979e3b9c0,Titanic - Machine Learning from Disaster
13105994,0.7822899999999999,0,0,/bohuaxu/cs-100-data-science-1b4492,Titanic - Machine Learning from Disaster
13299129,0.75358,1,0,/sarthakniwate13/titanic-eda-survival-prediction,Titanic - Machine Learning from Disaster
12125072,0.7751100000000001,0,0,/jaeryeong/hw5-titanic,Titanic - Machine Learning from Disaster
10533064,0.76794,0,0,/venkatramnan/differentclassificationmodelsfortitanic,Titanic - Machine Learning from Disaster
11794302,13.24777,0,0,/ridwanolawin/finalsubmission,Mechanisms of Action (MoA) Prediction
12957359,0.01831,0,0,/windmen/score-38,Mechanisms of Action (MoA) Prediction
14301030,0.01905,1,1,/yxohrxn/moa-mixup,Mechanisms of Action (MoA) Prediction
14290447,0.01896,0,0,/hakkoz/ml-project-lish-moa-nn,Mechanisms of Action (MoA) Prediction
12773467,0.0186199999999999,0,0,/yuanyuanyue/pytorch-moa-model-8-6-multihead-epsinadam,Mechanisms of Action (MoA) Prediction
12988155,0.01828,0,2,/a763337092/mlp123-lstm-cnn-blending1120,Mechanisms of Action (MoA) Prediction
12764400,0.01835,1,2,/hghghghgh1234/pytorch-nn-0-01625-private-score,Mechanisms of Action (MoA) Prediction
12595735,0.01844,0,3,/c7934597/moa-pytorch-feature-engineering-0-01846,Mechanisms of Action (MoA) Prediction
13550404,0.01814,0,21,/ttahara/private-0-01599-moa-avg-of-various-stacking,Mechanisms of Action (MoA) Prediction
13518945,0.01829,0,7,/ttahara/stacking-1d-cnn-drugcv,Mechanisms of Action (MoA) Prediction
13552946,0.01952,0,1,/maithiltandel/moa-submission,Mechanisms of Action (MoA) Prediction
13485833,0.01924,0,0,/govindajith/draft-1,Mechanisms of Action (MoA) Prediction
12002940,0.01894,4,7,/mukuldsagupta/moa-keras-2,Mechanisms of Action (MoA) Prediction
13494846,0.14214,0,0,/shishirccr/notebookc0f3938ed5,Mechanisms of Action (MoA) Prediction
13216093,0.01808,7,25,/nischaydnk/fork-of-blending-with-6-models-5old-1new,Mechanisms of Action (MoA) Prediction
13331276,0.01896,0,0,/samsendelbach/predictive-final-proj,Mechanisms of Action (MoA) Prediction
12670449,0.69314,0,0,/seanmh/notebook1cb2b8cdaf,Mechanisms of Action (MoA) Prediction
13260824,0.0177699999999999,14,64,/cdeotte/moa-post-process-lb-1777,Mechanisms of Action (MoA) Prediction
13051176,0.01849,5,7,/philippsinger/moa-starter-13,Mechanisms of Action (MoA) Prediction
13091422,0.01804,11,37,/cdeotte/3rd-place-public-lb-1805,Mechanisms of Action (MoA) Prediction
13192833,0.01877,2,8,/aerdem4/moa-xgb-svm-solution,Mechanisms of Action (MoA) Prediction
13126199,0.01822,1,7,/vladimirsydor/lishmoa-baseline-inference,Mechanisms of Action (MoA) Prediction
2385311,0.35707,4,8,/jcesquiveld/transfer-learning-for-dog-breed-classification-ii,Dog Breed Identification
1716753,10.7278,2,5,/moosecat/fastai-dog-breed-identification,Dog Breed Identification
2290059,0.5886100000000001,0,0,/nikhilpandey360/transfer-learning-on-inception,Dog Breed Identification
2233235,4.984380000000002,0,1,/karthikuppam/dog-breed-basic-cnn,Dog Breed Identification
2186502,0.5876399999999999,0,0,/roboanil/dog-breed-identification-by-inceptionv3,Dog Breed Identification
2013195,5.29046,1,5,/chdhatri/dog-breed-identification-using-vgg-model,Dog Breed Identification
1885129,3.96959,2,3,/amneves/pupper-keras-cnn,Dog Breed Identification
1667142,0.38103,0,1,/raajtilaksarma/dog-breed-identification-using-fastai-resnet,Dog Breed Identification
1467619,0.3446,6,14,/stefanbuenten/dog-breed-test-with-fastai,Dog Breed Identification
937159,11.62853,0,2,/funkyfrankie/transfer-learning,Dog Breed Identification
14359016,0.88665,2,1,/komakizzz/ssdd-resnet18-unet,Severstal: Steel Defect Detection
12298528,0.73039,0,0,/amrkhaledaziz/notebook70b6b8d6f0,Severstal: Steel Defect Detection
9155863,0.91467,0,4,/steveroberts/steel-submission,Severstal: Steel Defect Detection
5905940,0.8321700000000001,0,0,/araj890105/sec-kernel-heng,Severstal: Steel Defect Detection
6370013,0.91368,1,0,/chuong98vt/jitsubmission,Severstal: Steel Defect Detection
5815597,0.9135,0,1,/quandapro/severstal-unet-inference,Severstal: Steel Defect Detection
5934303,0.8732200000000001,0,0,/gefanzhang/firstsub,Severstal: Steel Defect Detection
7563202,0.85674,0,2,/knightwisdom/unet-pytorch-inference-kernel-58c83d,Severstal: Steel Defect Detection
6618092,0.85674,0,0,/shreeshiv/deep-learning-assignment,Severstal: Steel Defect Detection
13238914,0.7751100000000001,0,0,/tarakamer/getting-started-with-titanic,Titanic - Machine Learning from Disaster
13205451,0.77751,0,0,/jaredfeeley/titanic-predictions,Titanic - Machine Learning from Disaster
13149868,0.5334899999999999,2,2,/ariomer/titanic-v1-2,Titanic - Machine Learning from Disaster
13175342,0.76794,0,0,/farshanafathima/titanic-dataset,Titanic - Machine Learning from Disaster
13187813,0.76555,0,0,/nagakalyan2784/titanic-machine-learning-from-disaster,Titanic - Machine Learning from Disaster
13151860,0.41866,1,2,/leugurdesarrio/titanic-with-pandas-profiling,Titanic - Machine Learning from Disaster
12745648,0.77272,0,0,/artemkuksenko/titanic,Titanic - Machine Learning from Disaster
13133687,0.7751100000000001,0,3,/bsoyka3/my-submission-for-the-titanic-competition,Titanic - Machine Learning from Disaster
13087087,0.7751100000000001,0,0,/prakhar2000/titanic-kaggle-starter,Titanic - Machine Learning from Disaster
13094870,0.69138,0,0,/gateishion/titanic-first-kernel,Titanic - Machine Learning from Disaster
13080665,0.7751100000000001,0,0,/arslanhameed/arslan-submission-notebook,Titanic - Machine Learning from Disaster
12816792,0.8062199999999999,0,0,/kenhayahara/titanic-lightgbm,Titanic - Machine Learning from Disaster
9949450,0.76555,0,0,/yusukearai/rev-xgboost-parametertuning-manual,Titanic - Machine Learning from Disaster
13539282,0.76076,0,2,/kabira12698/titanicprediction,Titanic - Machine Learning from Disaster
13517417,0.77272,0,1,/mehmetkaygusuz/titanic-eda,Titanic - Machine Learning from Disaster
13386018,0.77751,0,1,/yanngarcia/titanic-generic-approach-of-machine-learning,Titanic - Machine Learning from Disaster
12783987,0.7822899999999999,1,2,/sharmasalil/titanic-dataset,Titanic - Machine Learning from Disaster
13496059,0.78947,0,0,/andyfoo/titanic-competition,Titanic - Machine Learning from Disaster
13509273,0.78468,0,2,/mrinalsaini/titanic-survival-predictions,Titanic - Machine Learning from Disaster
13504093,0.78947,0,1,/sashirin/titanic-analysis,Titanic - Machine Learning from Disaster
13390492,0.77751,0,0,/orlovmikhail/titanic-data-playground,Titanic - Machine Learning from Disaster
13346141,0.0,0,0,/angrewm/titanic-pt-2,Titanic - Machine Learning from Disaster
13463757,0.76555,0,1,/japandata509/titanic-predicting-by-gender,Titanic - Machine Learning from Disaster
13459641,0.78468,0,0,/blackhurt/approach-to-be-in-top-10,Titanic - Machine Learning from Disaster
13407226,0.78947,6,12,/sneves/titanic-walkthrough-from-top-54-to-top-11,Titanic - Machine Learning from Disaster
10487078,42.34949,0,2,/matthewmrpyton/google-web-traffic-forecasting-models,Web Traffic Time Series Forecasting
7463061,47.11656,0,2,/gautham11/web-traffic-prediction-baseline,Web Traffic Time Series Forecasting
12160481,23.72626,0,1,/aeryss/how-much-did-it-rain,How Much Did It Rain? II
2914977,24.15693,0,0,/goshaq/bidirectional-rnn-in-pytorch,How Much Did It Rain? II
2891252,23.92961,0,2,/adolgushev/rnn-rain,How Much Did It Rain? II
63897,24.06968,0,0,/cuttlefish/first-attempt,How Much Did It Rain? II
13857043,0.9005,0,0,/rijuvaish/plant-seedlings-12-classes-classification,Plant Seedlings Classification
11212072,0.95088,0,1,/bishalkumarshaw/plant-seedling-classification-xception,Plant Seedlings Classification
10833023,0.94206,0,0,/vickyskarthik/resnet34,Plant Seedlings Classification
8475751,0.96977,0,4,/niteshksingh/transfer-learning-xception-96,Plant Seedlings Classification
7224015,0.83249,0,0,/kavyajeet/plant-seedling-classification,Plant Seedlings Classification
4521472,0.95843,0,0,/lemondante/seedling-classification-using-cnn,Plant Seedlings Classification
11872408,0.565,0,7,/enukuro/108th-place-solution-birdcall-keras-tpu,Cornell Birdcall Identification
11736583,0.628,5,36,/hidehisaarai1213/birdcall-resnestsed-effnet-b0-ema-all-th04,Cornell Birdcall Identification
11220000,0.429,0,0,/akashsuper2000/introduction-to-sound-event-detection,Cornell Birdcall Identification
11756517,0.585,0,14,/kneroma/the-power-of-postprocessing-resnest50-at-its-best,Cornell Birdcall Identification
11749951,0.5329999999999999,0,6,/gopidurgaprasad/birdcall-stage3-final,Cornell Birdcall Identification
11448953,0.562,0,1,/truonghoang/se-resnext50-32x4d-inference,Cornell Birdcall Identification
11546240,0.5720000000000001,0,0,/returnofsputnik/bird-submission,Cornell Birdcall Identification
11489110,0.544,2,16,/takamichitoda/birdcall-nocall-prediction-with-denoise,Cornell Birdcall Identification
11423222,0.56,1,5,/shams1/audio-data-analysis-using-librosa,Cornell Birdcall Identification
11208815,0.5539999999999999,0,1,/doanquanvietnamca/inference-eff2w-ogru,Cornell Birdcall Identification
11374731,0.544,11,42,/ishivinal/getting-started-with-audio-analysis,Cornell Birdcall Identification
2854457,0.56713,0,0,/massi006/nyc-taxi-duration-sabi-massi,New York City Taxi Trip Duration
2848673,0.4173699999999999,0,0,/oceanecharlery/oceane-charlery-taxi-trip-duration,New York City Taxi Trip Duration
2805001,0.3769699999999999,2,19,/quentinmonmousseau/ml-workflow-lightgbm-0-37-randomforest-0-39,New York City Taxi Trip Duration
2812733,0.41105,0,1,/darcelvictor/ny-taxi,New York City Taxi Trip Duration
2817231,0.4536699999999999,0,1,/brendanp/poirier-brendan-prediction,New York City Taxi Trip Duration
2817350,0.4905,0,3,/razarocket/nyc-trip-duration-first-try,New York City Taxi Trip Duration
2818100,0.4135399999999999,0,0,/alexlegars/ny-taxi-trip-duration-alexandre-le-gars,New York City Taxi Trip Duration
2813247,0.4362,0,0,/charlespv/taxi-trip-duration-prediction-charlespv,New York City Taxi Trip Duration
2815161,0.5820000000000001,0,0,/beymehdi/taxi-nyc-duration-bey-mehdi,New York City Taxi Trip Duration
2791313,0.43893,0,0,/cgaulier/gaulier-clemence-prediction-test,New York City Taxi Trip Duration
2791315,0.4079,5,2,/sereyvuthc/chum-sereyvuth-nyc-taxi-trip-duration,New York City Taxi Trip Duration
2791376,0.43892,0,0,/cbecret/kernelaf28df944b,New York City Taxi Trip Duration
2138412,0.58199,0,0,/hmshreyas7/nyc-taxi-trip-duration,New York City Taxi Trip Duration
1031912,0.49077,0,0,/rdcmdev/fork-of-2016-nyc-taxi-trip-xgboost,New York City Taxi Trip Duration
352885,0.41355,3,4,/kivaschenko/trick-with-the-store-flags-xgboost,New York City Taxi Trip Duration
10136118,0.73983,0,3,/yerbatry/fork-of-final-ensemble-clean-0-65,TensorFlow 2.0 Question Answering
8727485,0.7391300000000001,0,2,/mahmudds/tensorflow-2-0-q-a,TensorFlow 2.0 Question Answering
10121754,0.63962,0,0,/peterzhoubot/final-ensemble-e7ea95,TensorFlow 2.0 Question Answering
7609979,0.34,0,1,/hakeem/nain-submission,TensorFlow 2.0 Question Answering
7395292,0.69,2,14,/user189546/tfqa-bert-train,TensorFlow 2.0 Question Answering
7237337,0.74,14,83,/seesee/submit-full,TensorFlow 2.0 Question Answering
6578506,0.65,2,9,/siriuself/tf-qa-wwm-verifier-forked,TensorFlow 2.0 Question Answering
7631951,0.6,2,8,/rohitagarwal/rank-93-solution-fork-of-bert-joint-14-changes-37,TensorFlow 2.0 Question Answering
7416975,0.0,1,7,/msheriey/empty-submission,TensorFlow 2.0 Question Answering
7068539,0.52,13,56,/yihdarshieh/inference-use-hugging-face-models,TensorFlow 2.0 Question Answering
7076406,0.09,0,0,/skylord/on-the-professor-and-the-madman,TensorFlow 2.0 Question Answering
6993857,0.0,0,0,/jonathandickson/my-submission-code,TensorFlow 2.0 Question Answering
6668448,0.57,29,188,/mmmarchetti/tensorflow-2-0-bert-yes-no-answers,TensorFlow 2.0 Question Answering
6783412,0.48,0,10,/ymcdull/tensorflow-2-0-edited,TensorFlow 2.0 Question Answering
6626294,0.19,10,40,/petrov/first-long-paragraph,TensorFlow 2.0 Question Answering
6442458,0.23,12,106,/opanichev/tf2-0-qa-binary-classification-baseline,TensorFlow 2.0 Question Answering
6423260,0.17,8,104,/dimitreoliveira/using-tf-2-0-w-bert-on-nq-translated-to-tf2-0,TensorFlow 2.0 Question Answering
6391865,0.15,14,146,/philculliton/using-tensorflow-2-0-w-bert-on-nq,TensorFlow 2.0 Question Answering
2259191,0.6704100000000001,0,0,/omkarpawaskar/kernel7c8856c435,Home Credit Default Risk
1933490,0.74465,1,5,/rquintino/minimal-pipeline-lightgbm-lb-744-auc,Home Credit Default Risk
1207412,0.7490000000000001,0,0,/abimannan/home-value-prediction-light,Home Credit Default Risk
1245855,0.78,0,0,/garylai91/inefficient-feature-engineering,Home Credit Default Risk
1325141,0.758,0,0,/medsriha/second-attempt,Home Credit Default Risk
1537787,0.77733,1,6,/sudhirnl7/exploratory-data-analysis-gbm-model,Home Credit Default Risk
1590137,0.74327,0,0,/tiagoxdxf/capstone-project-notebook,Home Credit Default Risk
1129698,0.757,0,0,/ejrueda95/firts-test,Home Credit Default Risk
1537568,0.8029999999999999,3,19,/ashishpatel26/different-basic-blends-possible,Home Credit Default Risk
1526075,0.782,0,1,/snehithatiger/classification-using-random-search-xgboost,Home Credit Default Risk
113972,0.7391300000000001,4,8,/gauravjoshi1986/ghostbuster-data,"Ghouls, Goblins, and Ghosts... Boo!"
113246,0.74858,14,35,/oysteijo/ghosts-n-goblins-n-neural-networks-lb-0-74858,"Ghouls, Goblins, and Ghosts... Boo!"
2037642,0.064,0,0,/liaobowen/nomad2018-predicting-transparent-conductor,Nomad2018 Predicting Transparent Conductors
897024,0.0547,0,1,/xagor1/nomad-competition-base-models,Nomad2018 Predicting Transparent Conductors
505763,0.0571,1,5,/kzhoulatte/kernel-ridge-regression-lb0-0571-rbf-laplacian,Nomad2018 Predicting Transparent Conductors
500194,0.0572,9,19,/giginim/tensorflow-neural-network,Nomad2018 Predicting Transparent Conductors
498818,0.0569,2,14,/johnfarrell/nomad2018-simple-lgbm-starter,Nomad2018 Predicting Transparent Conductors
537147,0.0583,0,0,/opanichev/basic-lightgbm-model,Nomad2018 Predicting Transparent Conductors
13403399,6895.138000000001,12,40,/mukuldsagupta/jane-street-eda-model-neural-network,Jane Street Market Prediction
13435349,3352.434,0,1,/samkamarfua/samka-xgboost,Jane Street Market Prediction
13098546,6005.581999999999,0,4,/kelink/jane-street-baseline1,Jane Street Market Prediction
13272518,6876.781999999998,89,314,/gogo827jz/jane-street-neural-network-starter,Jane Street Market Prediction
13121983,4816.782,9,34,/haozhuai/expanding-window-sharp-weighted-return,Jane Street Market Prediction
13129245,722.2869999999998,1,7,/njelicic/linear-model,Jane Street Market Prediction
2679301,1.5630000000000002,0,1,/aquaintel/earthquake-time-prediction-at-lab-scale,LANL Earthquake Prediction
2742783,1.5530000000000002,3,12,/devilears/rnn-starter-kernel-with-notebook,LANL Earthquake Prediction
2691150,2.039,0,6,/harshitholmes/testing-each-every-regressor-with-own-instances,LANL Earthquake Prediction
2635281,1.535,2,20,/alinealmeida/basic-feature-benchmark-with-quantiles-augmenting,LANL Earthquake Prediction
2627879,1.527,1,32,/wimwim/rolling-quantiles,LANL Earthquake Prediction
2607579,1.638,42,359,/artgor/seismic-data-eda-and-baseline,LANL Earthquake Prediction
2605383,0.141,33,294,/inversion/basic-feature-benchmark,LANL Earthquake Prediction
2607848,1.509,13,62,/jazivxt/aftershock,LANL Earthquake Prediction
2612710,2.143,0,1,/jpiyush3008/aftershock,LANL Earthquake Prediction
3810010,1.881,0,0,/wajnryt/basic-feature-benchmark,LANL Earthquake Prediction
3667661,1.881,0,0,/gus666/basic-feature-benchmark,LANL Earthquake Prediction
8706155,0.968,0,0,/ibraheemmoosa/plant-pathology-fgvc7-fastai,Plant Pathology 2020 - FGVC7
13052624,0.9301,1,2,/sonujha090/plant-pathology,Plant Pathology 2020 - FGVC7
13095129,0.9438,0,1,/anku5hk/tpu-plant-pathology-baseline,Plant Pathology 2020 - FGVC7
13080913,0.96161,0,1,/ikaynov/denesnet-tta,Plant Pathology 2020 - FGVC7
12912942,0.97954,0,0,/normall777/tf-zoo-models-on-tpu-efficientnetb7,Plant Pathology 2020 - FGVC7
12623838,0.58048,0,0,/evgenh76434/plant-pathology-keras-inceptionresnetv2-baseline,Plant Pathology 2020 - FGVC7
12443219,0.52063,0,1,/alekseyeliseev/plant-pathology-keras-inceptionresnetv2-baseline,Plant Pathology 2020 - FGVC7
12370309,0.93873,0,0,/chitramdasgupta/plant-pathology-xception-93-873-accuracy,Plant Pathology 2020 - FGVC7
11879644,0.97639,0,0,/stardust87/plant-pathology-2020-inference,Plant Pathology 2020 - FGVC7
11128017,0.94717,0,7,/carlolepelaars/evaluating-mobile-cnn-architectures-with-w-b,Plant Pathology 2020 - FGVC7
8937541,0.975,0,0,/akashsuper2000/plant-pathology-enetb7-on-tpus,Plant Pathology 2020 - FGVC7
8860815,0.97,0,0,/iamsdt/plants-tpu-classifications,Plant Pathology 2020 - FGVC7
13055692,0.893,0,6,/moeinshariatnia/pytorch-better-normalization-onecycle-lr-inference,Cassava Leaf Disease Classification
13029975,0.861,4,39,/artgor/cassava-disease-identification-with-lightning,Cassava Leaf Disease Classification
13032283,0.601,2,7,/chekoduadarsh/starter-code-cassava-leaf-disease-cam,Cassava Leaf Disease Classification
13021276,0.893,7,51,/abhishek/leaf-disease-inference-using-tez,Cassava Leaf Disease Classification
13048268,0.889,0,2,/bootiu/cassava-baseline,Cassava Leaf Disease Classification
13044945,0.568,1,5,/khlevnov/efficientnetb0-training,Cassava Leaf Disease Classification
13035386,0.898,2,10,/awsaf49/efficientnetb6-512-cutmixupdropout-tpu-infer,Cassava Leaf Disease Classification
12984461,0.614,56,361,/ihelon/cassava-leaf-disease-exploratory-data-analysis,Cassava Leaf Disease Classification
12984721,0.888,64,177,/frlemarchand/efficientnet-aug-tf-keras-for-cassava-diseases,Cassava Leaf Disease Classification
13015290,0.855,0,6,/wuliaokaola/tensorflow-resnet50-train-with-new-tfrecords,Cassava Leaf Disease Classification
12989301,0.848,1,18,/zzy990106/pytorch-efficientnet-baseline,Cassava Leaf Disease Classification
12999257,0.634,3,7,/shivanandmn/cnn-pytorch-lightning-beginners-model,Cassava Leaf Disease Classification
12984353,0.614,5,15,/drcapa/cassava-leaf-disease-classification-starter-keras,Cassava Leaf Disease Classification
12991479,0.614,0,2,/bjoernjostein/cassava-leaf-disease-classification-using-tf,Cassava Leaf Disease Classification
1355358,0.431,1,2,/skooch/lgbm-with-k-fold-early-stopping,Costa Rican Household Poverty Level Prediction
1360339,0.421,0,2,/wangyije/feature-engineer-baseline-lgb,Costa Rican Household Poverty Level Prediction
1354118,0.43,0,1,/skooch/lgbm-with-random-split-2,Costa Rican Household Poverty Level Prediction
1335546,0.391,1,14,/ashishpatel26/catboost-approach,Costa Rican Household Poverty Level Prediction
1332999,0.382,0,11,/ashishpatel26/catboost-better,Costa Rican Household Poverty Level Prediction
1320207,0.367,0,1,/shubchat/start-costarican-data-simple-eda,Costa Rican Household Poverty Level Prediction
1329917,0.439,1,14,/ashishpatel26/svc-rf-lgbm-xgb,Costa Rican Household Poverty Level Prediction
1329993,0.422,0,3,/nathanliitt/imputation-eda-logit-model-top-10,Costa Rican Household Poverty Level Prediction
1328064,0.356,0,1,/gobert/data-selection-with-randomforest,Costa Rican Household Poverty Level Prediction
1314256,0.4039999999999999,11,45,/katacs/data-cleaning-and-random-forest,Costa Rican Household Poverty Level Prediction
1322408,0.365,0,4,/mukeshbisht/povertykernel,Costa Rican Household Poverty Level Prediction
1315722,0.436,15,35,/youhanlee/3250feats-532-feats-using-shap-lb-0-436,Costa Rican Household Poverty Level Prediction
1320573,0.409,1,5,/amitkumarjaiswal/beginner-s-tutorial-to-costa-rican-poverty,Costa Rican Household Poverty Level Prediction
1318060,0.384,0,5,/maheshdadhich/baseline-model-and-feature-importance-lb-0-38,Costa Rican Household Poverty Level Prediction
1316608,0.3929999999999999,0,7,/heena34/lgb-model,Costa Rican Household Poverty Level Prediction
1315350,0.395,7,7,/ishaan45/eda-feature-removal,Costa Rican Household Poverty Level Prediction
1316401,0.3929999999999999,0,2,/thachhoang2410/predicting-target-by-using-household-feature,Costa Rican Household Poverty Level Prediction
4892637,0.27199,0,0,/yjlee8899/my-first-kernel-stacking,Costa Rican Household Poverty Level Prediction
4670804,0.22453,0,0,/jjungeunzzu/costa-rica-poverty-exploration-kernel,Costa Rican Household Poverty Level Prediction
1563697,0.2689999999999999,0,0,/caiofreitas/pmr3508-household-icome-costa-rica,Costa Rican Household Poverty Level Prediction
3544896,0.0,0,1,/sharmilaupadhyaya/kernelc7d1be8af7,Gendered Pronoun Resolution
3577796,0.0,1,14,/tks0123456789/offsets-model,Gendered Pronoun Resolution
3652506,0.0,5,1,/harshitholmes/final-shot,Gendered Pronoun Resolution
3423827,0.4864,4,17,/chanhu/bert-score-layer-kfold-weightdecay-0-486,Gendered Pronoun Resolution
3338077,0.71895,4,8,/negedng/extracting-features-from-spacy-dependency,Gendered Pronoun Resolution
3309888,0.52279,10,44,/ceshine/pytorch-bert-endpointspanextractor-kfold,Gendered Pronoun Resolution
3211534,0.92955,6,28,/mateiionita/visualizing-bert-plus-an-unsupervised-solution,Gendered Pronoun Resolution
2999422,0.8393700000000001,6,17,/sattree/2-reproducing-gap-results,Gendered Pronoun Resolution
2895649,0.69418,10,102,/keyit92/coref-by-mlp-cnn-coattention,Gendered Pronoun Resolution
2896544,0.92217,7,48,/shujian/ml-model-example-with-train-test,Gendered Pronoun Resolution
2895967,0.0,0,4,/fschilder/simple-baseline-based-on-prior-distribution,Gendered Pronoun Resolution
2846432,0.0,6,21,/eliseygusev/perfect-lb-score-in-5-lines-of-code-1,Gendered Pronoun Resolution
13277527,1172.88052,0,0,/summershan/allstate-car-claims-severity,Allstate Claims Severity
8816116,1718.1418,0,7,/mdmahmudferdous/allstate-claims-severity-prediction-regression,Allstate Claims Severity
8031677,1260.56953,0,0,/arpytanshu/allstate-claims-severity-1260-mae,Allstate Claims Severity
6546779,1131.42948,0,1,/harshitt21/allstate-claims-severity-eda-and-baseline,Allstate Claims Severity
6083297,1320.33401,0,0,/ptaroo/allstate-claims-severity-regression,Allstate Claims Severity
3513083,1119.98162,0,0,/deepdreamx/lgbm-only-featureinteraction-selected,Allstate Claims Severity
11838984,0.15506,0,7,/godwinmadho/housing-prices-in-progress,House Prices - Advanced Regression Techniques
10610510,0.18875,0,1,/subhrajitbordoloi/first-submission-by-a-net-developer,House Prices - Advanced Regression Techniques
11831193,0.13446,0,1,/felipefiorini/house-prices-xgboost-outlier-detect,House Prices - Advanced Regression Techniques
11648055,0.53733,0,0,/adityaprabaswara/house-pricing-predictions,House Prices - Advanced Regression Techniques
11790823,0.1561,1,6,/ssampab/comprehensive-guide-through-regression-modeling,House Prices - Advanced Regression Techniques
11823089,0.39611,0,6,/dipankarsrirag/decision-tree-ensemble-with-adaboost-housing,House Prices - Advanced Regression Techniques
5933647,0.1481,0,0,/shourabhpayal/predict-housing-price-and-improve-model,House Prices - Advanced Regression Techniques
11454529,0.15319,2,8,/felipefiorini/house-prices-cnn,House Prices - Advanced Regression Techniques
11752139,0.00044,0,7,/misalraj/house-price-prediction,House Prices - Advanced Regression Techniques
11338399,0.1248,0,3,/thepinokyo/regularized-linear-model-for-house-price,House Prices - Advanced Regression Techniques
11252496,0.21949,0,1,/iambca/house-price-eda-and-gradboost,House Prices - Advanced Regression Techniques
2199551,0.56784,2,4,/sivaadi92/linear-regression-on-market-data,Two Sigma: Using News to Predict Stock Movements
2117598,0.66382,6,10,/takafumitakizawa/moving-average-and-news,Two Sigma: Using News to Predict Stock Movements
2132037,0.36546,0,0,/sivaadi92/model-market-data-random-forests,Two Sigma: Using News to Predict Stock Movements
1983052,0.0,0,0,/zhangdecheng/random-confidence-value-as-baseline,Two Sigma: Using News to Predict Stock Movements
2023209,0.5895,0,1,/denisvodchyts/two-sigma-tf-2-classification,Two Sigma: Using News to Predict Stock Movements
1856054,0.63865,0,4,/shikha130vv/event-based-trading,Two Sigma: Using News to Predict Stock Movements
2010462,2.6348,4,46,/fabiendaniel/my-2cents-to-2sigma,Two Sigma: Using News to Predict Stock Movements
1986328,0.24172,1,4,/amitabhac/market-news-nn,Two Sigma: Using News to Predict Stock Movements
1923769,0.56119,17,72,/dmitrypukhov/eda-and-lstm-with-generator-for-market-and-news,Two Sigma: Using News to Predict Stock Movements
1934417,0.6684,19,63,/kazuokiriyama/tuning-hyper-params-in-lgbm-achieve-0-66-in-lb,Two Sigma: Using News to Predict Stock Movements
1927868,0.64075,1,35,/zikazika/neural-networks-2sigma,Two Sigma: Using News to Predict Stock Movements
1943956,0.6608,5,7,/jairomateo/kernel795c6ac4e1,Two Sigma: Using News to Predict Stock Movements
1893118,0.65692,7,31,/arunkumarramanan/market-data-nn-baseline,Two Sigma: Using News to Predict Stock Movements
363803,0.5588,0,2,/trion129/lightgbm-version,Personalized Medicine: Redefining Cancer Treatment
11371174,-6.8614,14,67,/mattbast/feature-engineering-with-a-linear-model,OSIC Pulmonary Fibrosis Progression
11429401,-8.5848,0,4,/twinklesebastian/osic-tabular-version2,OSIC Pulmonary Fibrosis Progression
11405308,-6.822,1,10,/dipampaul17/tweak-engineer,OSIC Pulmonary Fibrosis Progression
11382918,-6.9402,0,7,/paritoshkr30/osic-pulmonary-fibrosis-progression,OSIC Pulmonary Fibrosis Progression
11344972,-6.9402,0,2,/msafi04/osic-pulmonaryfibrosis-baseline,OSIC Pulmonary Fibrosis Progression
11392538,-8.417,0,2,/raj713335/notebook03d59db7ed,OSIC Pulmonary Fibrosis Progression
11329839,-8.1277,0,8,/shams1/osic-pulmonary-fibrosis-basic-eda-dicom-full,OSIC Pulmonary Fibrosis Progression
11326103,-6.9584,0,4,/jonykarki/inference-pytorch-qr-9ss4,OSIC Pulmonary Fibrosis Progression
11034886,-6.8688,0,8,/varunyadav17/osic-starter,OSIC Pulmonary Fibrosis Progression
11313466,-7.232,0,3,/jjinho/simple-gradientboostregression-with-quantile-loss,OSIC Pulmonary Fibrosis Progression
11275499,-9.286,1,14,/jonykarki/begineers-pytorch-qntl-reg-cv,OSIC Pulmonary Fibrosis Progression
11279628,-6.8424,0,1,/ghaiyur/efficientnets-quantile-regression-inference,OSIC Pulmonary Fibrosis Progression
11247514,-6.8596,0,2,/nike0good/osic-fianl-project,OSIC Pulmonary Fibrosis Progression
11161627,-6.8219,11,82,/leoisleo1/efficientnets-quantile-regression-inference,OSIC Pulmonary Fibrosis Progression
9446959,0.00085,2,1,/ouwyukha/cpp-surprise,Coupon Purchase Prediction
9451300,0.0052899999999999,0,0,/ouwyukha/cpp-turi-rfr-adagrad,Coupon Purchase Prediction
12247085,6614776.0,6,48,/ajcostarino/ingv-volcanic-eruption-prediction-lgbm-baseline,INGV - Volcanic Eruption Prediction
12247453,6555932.0,10,29,/nayuts/ingv-data-exploration-and-good-feature-search,INGV - Volcanic Eruption Prediction
12256670,11288579.0,4,11,/carlmcbrideellis/baseline-the-mean-volcano,INGV - Volcanic Eruption Prediction
10212764,0.43216,0,4,/confirm/xfeat-catboost-cpu-only,BNP Paribas Cardif Claims Management
55017,0.45474,0,0,/fmarinp/test01,BNP Paribas Cardif Claims Management
46399,0.4535899999999999,0,0,/yangnanhai/extratrees,BNP Paribas Cardif Claims Management
39296,0.4703699999999999,4,11,/omarelgabry/bnp-correlation-predictions,BNP Paribas Cardif Claims Management
9339964,0.3064199999999999,0,0,/ouwyukha/imba-turicreate-itemsim-cosine-pol,Instacart Market Basket Analysis
9339828,0.30645,0,0,/ouwyukha/imba-turicreate-fr-als,Instacart Market Basket Analysis
9353274,0.06206,0,0,/drainvers/instacart-sandbox-lmf,Instacart Market Basket Analysis
9336870,0.062,0,0,/drainvers/instacart-sandbox-bpr,Instacart Market Basket Analysis
5776404,0.37278,0,0,/errolpereira/light-gradient-boosting,Instacart Market Basket Analysis
4120142,0.31529,0,0,/vasilikimastrog/instacart-ml-2-notebook-42d609-ace1ec,Instacart Market Basket Analysis
3909468,0.31465,0,0,/sarantou/instacart-ml-2-notebook,Instacart Market Basket Analysis
4018612,0.37653,0,1,/mohabdiab/instacart,Instacart Market Basket Analysis
2935029,0.38447,0,4,/kokovidis/ml-instacart-f1-0-38-part-two-xgboost-f1-max,Instacart Market Basket Analysis
2563371,0.3765330999999999,1,10,/mandan/lightgbm-benchmark-implementation,Instacart Market Basket Analysis
942407,0.0657072,0,0,/maizespark/categorization-and-lightgbm-0-0657,Zillow Prize: Zillow’s Home Value Prediction (Zestimate)
6526303,0.01411,0,1,/joocheol/fe-lecture-20191109,NFL Big Data Bowl
6528225,0.01242,3,7,/harshitholmes/nfl-trial6,NFL Big Data Bowl
6586200,0.0146699999999999,2,9,/kaushal2896/nfl-big-data-bowl-fe-catboost,NFL Big Data Bowl
6590089,0.01702,0,0,/funaki/cat-boost-regressor-of-funaki,NFL Big Data Bowl
6394137,0.01242,9,88,/bestpredict/location-eda-8eb410,NFL Big Data Bowl
6184736,0.0143699999999999,1,6,/mconway/sarimax,NFL Big Data Bowl
6525481,0.01403,0,1,/yukomiya/komiya-nfl-class3-submit,NFL Big Data Bowl
6509485,0.01554,0,1,/funaki/random-forest-classifier-of-funaki-nfl-3,NFL Big Data Bowl
6480776,0.01384,1,36,/vbmokin/lgbm-multiple-classifier-with-max-depth-6,NFL Big Data Bowl
6447549,0.0127,4,10,/axzhang/nn-no-player-specific-feature,NFL Big Data Bowl
6464818,0.0139099999999999,0,0,/yukomiya/komiya-nfl1-class-submit,NFL Big Data Bowl
6455358,0.01247,0,1,/shubham2306/nfl-big-bowl-data-iteration-for-good-model,NFL Big Data Bowl
6417968,0.01368,2,14,/manojprabhaakr/hybrid-gp-nn-with-feature-engineering,NFL Big Data Bowl
6453069,0.01866,0,0,/funaki/fork-of-funaki-nfl-3,NFL Big Data Bowl
6396023,0.01372,24,50,/scirpus/hybrid-gp-and-nn,NFL Big Data Bowl
6395603,0.0138699999999999,2,7,/scirpus/location-eda,NFL Big Data Bowl
6319549,0.01381,18,157,/mrkmakr/neural-network-with-mae-objective-0-01381,NFL Big Data Bowl
6204649,0.01381,5,51,/ryancaldwell/location-eda,NFL Big Data Bowl
1348061,1.78,0,0,/mogamin/simple-but-out-of-the-approach-ishaan-jain,Santander Value Prediction Challenge
1318186,0.69,21,72,/johnfarrell/baseline-with-lag-select-fake-rows-dropped,Santander Value Prediction Challenge
1324457,1.48,4,5,/nicapotato/lstm-on-giba-s-time-series-features,Santander Value Prediction Challenge
1307662,1.39,0,13,/bhaveshthaker/svp-exploratory-data-analysis-eda-and-modeling,Santander Value Prediction Challenge
1286294,1.39,4,31,/ajarmstrong/separate-aggregates-for-dense-and-sparse-features,Santander Value Prediction Challenge
1284734,1.47,0,1,/lcltopismine/compare-two-method-of-feature-extraction,Santander Value Prediction Challenge
1263703,1.5,0,5,/meaninglesslives/vecstack-ensemble-10models,Santander Value Prediction Challenge
1182668,1.79,1,5,/antoniom85/modelo-keras-simple,Santander Value Prediction Challenge
1217790,1.41,5,22,/hmendonca/keras-regressor-neural-network,Santander Value Prediction Challenge
1220686,1.46,0,11,/amarjeet007/random-forest-feature-engineering-lightgbm,Santander Value Prediction Challenge
1208885,1.49,0,3,/tc171995/santander-value-prediction-insights-from-the-eda,Santander Value Prediction Challenge
1203602,1.44,12,21,/scirpus/cut-and-run,Santander Value Prediction Challenge
1196156,1.4909,23,66,/samratp/aggregates-sumvalues-sumzeros-k-means-pca,Santander Value Prediction Challenge
1199079,1.48,0,7,/ishida/santander-value-lightgbm-for-beginner,Santander Value Prediction Challenge
2193393,0.885,0,5,/sheboke93/how-i-colored-strokes,"Quick, Draw! Doodle Recognition Challenge"
2122608,0.634,0,0,/supers80/rvgis-exam,"Quick, Draw! Doodle Recognition Challenge"
2015263,0.818,0,6,/vinayaks/mobilenetv2-with-submission-v1,"Quick, Draw! Doodle Recognition Challenge"
2145428,0.884,9,51,/leighplt/pytorch-starter-kit,"Quick, Draw! Doodle Recognition Challenge"
2104093,0.0559999999999999,0,3,/mgreene02/bounded-1-d-array-of-google-quick-drawings,"Quick, Draw! Doodle Recognition Challenge"
2032807,0.835,4,35,/huyenvyvy/bidirectional-lstm-using-data-generator-lb-0-825,"Quick, Draw! Doodle Recognition Challenge"
1935317,0.895,3,15,/adarsh1012/mobilenet-lb-0-895,"Quick, Draw! Doodle Recognition Challenge"
1806245,0.682,0,16,/avinashrai/quickdraw-with-wavenet-classifier-tunning,"Quick, Draw! Doodle Recognition Challenge"
1746955,0.5539999999999999,14,171,/kmader/quickdraw-baseline-lstm-reading-and-submission,"Quick, Draw! Doodle Recognition Challenge"
1741072,0.605,15,97,/jpmiller/image-based-cnn,"Quick, Draw! Doodle Recognition Challenge"
3953162,0.8955200000000001,0,0,/meloncha0205/greyscale-mobilenet,"Quick, Draw! Doodle Recognition Challenge"
224288,0.5323100000000001,0,4,/darrellulm/quora-pairs-1st-pass,Quora Question Pairs
222867,0.35372,0,0,/tinkleing/start-of-this-competition,Quora Question Pairs
230188,0.34478,0,4,/tinkleing/feature-test,Quora Question Pairs
7235032,0.6643399999999999,0,0,/harishvutukuri/microsoft-mp-vowpal-wabbit,Microsoft Malware Prediction
6476489,0.61052,0,8,/ravijoe/microsoft-malware-prediction-using-lightgbm,Microsoft Malware Prediction
5276110,0.6781,0,0,/terminate9298/microsoft-malware-predictions,Microsoft Malware Prediction
3336315,0.70215,16,35,/cdeotte/high-scoring-lgbm-malware-0-702-0-775,Microsoft Malware Prediction
3297880,0.67138,9,4,/praxitelisk/microsoft-malware-detection-xgboost-tuning,Microsoft Malware Prediction
3246401,0.67508,3,3,/praxitelisk/microsoft-malware-detection-xgboost-blends,Microsoft Malware Prediction
3156250,0.6679999999999999,2,2,/subhamsharma96/malware-prediction-eda-fe-lightgbm,Microsoft Malware Prediction
3011977,0.6779999999999999,0,1,/sheriytm/msft-malware-fm-starter,Microsoft Malware Prediction
2934661,0.645,0,0,/gpucloud/microsoft,Microsoft Malware Prediction
2862341,0.69,45,147,/guoday/nffm-baseline-0-690-on-lb,Microsoft Malware Prediction
4406293,0.94617,0,0,/tabayu/homesite-quote-conversion,Homesite Quote Conversion
28253,0.59083,0,0,/theiya/improved-rf,Homesite Quote Conversion
27968,0.96143,0,2,/gustavodemari/homesite-home-insurance,Homesite Quote Conversion
25686,0.93299,0,1,/ceruleus/homesite-quote-conversion,Homesite Quote Conversion
24052,0.9614,0,15,/omarelgabry/homesite-customer-predictions,Homesite Quote Conversion
7301694,0.9646,3,35,/bibek777/heng-starter-inference-kernel,Bengali.AI Handwritten Grapheme Classification
7244755,0.9663,59,256,/corochann/bengali-seresnext-prediction-with-pytorch,Bengali.AI Handwritten Grapheme Classification
7134050,0.9396,1,3,/user123454321/resnet18-base-inference,Bengali.AI Handwritten Grapheme Classification
7159814,0.9289,24,126,/khoongweihao/resnet-34-pytorch-starter-kit,Bengali.AI Handwritten Grapheme Classification
7119911,0.0614,3,28,/seriousran/bengali-data-analysis-handwritten-classification,Bengali.AI Handwritten Grapheme Classification
7119744,0.0614,0,0,/grapestone5321/bengali-a-sample-submission,Bengali.AI Handwritten Grapheme Classification
8408452,0.9735,0,0,/poojaarora014/using-ghostnet-and-densenet,Bengali.AI Handwritten Grapheme Classification
8259480,0.9696,0,0,/ludongliang/version1-0-9696,Bengali.AI Handwritten Grapheme Classification
8024866,0.9519,0,0,/hunminyang/bengali-graphemes-starter-eda-multi-output-cnn,Bengali.AI Handwritten Grapheme Classification
7893255,0.0614,0,0,/chriscc/grapheme-fast-ai-inference-stacking,Bengali.AI Handwritten Grapheme Classification
1460440,0.93655,0,3,/dawgwelder/rossman-xgb-solution,Rossmann Store Sales
1286176,0.11257,7,20,/xwxw2929/rossmann-sales-top1,Rossmann Store Sales
1140262,0.1585099999999999,1,20,/stefanozakher94/eda-and-forecasting-with-rfregressor-final-updated,Rossmann Store Sales
245242,0.13161,2,5,/sergotail/rossman-store-sales-kernel,Rossmann Store Sales
244905,0.1738299999999999,0,1,/kapitonov/hw3-kapitonov-technosphere,Rossmann Store Sales
244626,0.1406599999999999,0,10,/dmitry103/notebook50b1ee36b5,Rossmann Store Sales
7797843,0.52655,0,0,/raymant/eda-model-building,Rossmann Store Sales
874590,0.14019,0,0,/afetisov/hw11-ts,Rossmann Store Sales
10867220,0.15937,0,3,/aaroha33/trends-neuroimaging-baggingregressor-rapids,TReNDS Neuroimaging
10421214,0.15936,0,5,/tunguz/rapids-ensemble-for-trends,TReNDS Neuroimaging
10228154,0.15882,0,10,/roydatascience/bronze-medal-solution-0-15925-on-private-lb,TReNDS Neuroimaging
9386083,0.16036,0,1,/tunguz/trends-ridge-2,TReNDS Neuroimaging
9548939,0.177,0,2,/tunguz/trends-with-sklearn-mlpregressor,TReNDS Neuroimaging
10359601,0.1597,1,3,/akashsuper2000/neuroimage-lightgbm-scikit-learn,TReNDS Neuroimaging
9831751,0.1587,0,2,/joatom/trends-ensemble,TReNDS Neuroimaging
10305836,0.2174,0,1,/akashsuper2000/ensemble-notebook-trends,TReNDS Neuroimaging
10217959,0.2224,9,11,/hrfhgrthdyrd/3d-cnn-with-keras,TReNDS Neuroimaging
10170650,0.1636,0,1,/dhuang718/svr-linear-loading-and-fnc-submission,TReNDS Neuroimaging
9938589,0.162,17,72,/tanulsingh077/achieving-sota-results-with-tabnet,TReNDS Neuroimaging
9975159,0.1651,0,8,/dhuang718/domain-explanation-visualization-modeling,TReNDS Neuroimaging
9718859,0.1594,0,11,/hamditarek/trends-neuroimaging-blend,TReNDS Neuroimaging
9741973,0.1669999999999999,1,14,/tunguz/rapids-randomforest-on-trends-neuroimaging,TReNDS Neuroimaging
9418259,0.159,0,13,/roshan03/svm-model,TReNDS Neuroimaging
1190392,1.48,0,9,/ashishpatel26/fork-of-santander-value-prediction-xgb-lightgbm-ca,Santander Value Prediction Challenge
1181649,1.44,12,62,/nicapotato/lgbm-cv-tuning-and-seed-diversification,Santander Value Prediction Challenge
1187498,1.53,4,12,/thomasnelson/an-idiots-guide-to-a-not-horrible-score-lb-1-53,Santander Value Prediction Challenge
1180647,1.49,0,15,/ashishpatel26/santander-challange-rf-baseline,Santander Value Prediction Challenge
1179759,1.67,19,45,/ishaan45/simple-but-out-of-the-box-approach,Santander Value Prediction Challenge
1174789,1.48,10,59,/samratp/santander-value-prediction-xgb-and-lightgbm,Santander Value Prediction Challenge
1178728,1.55,1,18,/samratp/beginner-guide-to-stacking,Santander Value Prediction Challenge
1170717,1.49,8,42,/rooshroosh/lightgbm-baseline-1-49-lb,Santander Value Prediction Challenge
1173357,1.48,1,7,/danofer/baseline-lightgbm-model-1-48lb,Santander Value Prediction Challenge
6241953,0.01868,0,0,/econjt/first-submission,NFL Big Data Bowl
6247215,0.01399,6,42,/rooshroosh/fork-of-neural-networks-different-architecture,NFL Big Data Bowl
6274763,0.01494,0,0,/errolpereira/fork-of-starter-submission,NFL Big Data Bowl
6198111,0.0141699999999999,11,59,/prashantkikani/nfl-starter-mlp-feature-engg,NFL Big Data Bowl
6201911,0.01423,0,15,/hamditarek/fork-of-neural-networks-feature-luck-computer,NFL Big Data Bowl
6188586,0.0130099999999999,0,4,/xwxw2929/nn-kfold,NFL Big Data Bowl
6172840,0.01617,2,37,/prashantkikani/nfl-starter-lgb-feature-engg,NFL Big Data Bowl
6169335,0.01464,3,14,/truenikita/gradbm11,NFL Big Data Bowl
6735095,0.01308,0,0,/oliveia/nfl-big-data-bowl-with-random-forests-k-fold,NFL Big Data Bowl
14545454,0.79282,0,0,/drscarlat/disaster-tweets-fastai-nlp,Natural Language Processing with Disaster Tweets
14361684,0.7821,0,2,/baekseungyun/gpt-2-with-huggingface-pytorch,Natural Language Processing with Disaster Tweets
14400624,0.79895,0,1,/aswinkumar0472/notebook06bdc531ab,Natural Language Processing with Disaster Tweets
14217203,0.81765,0,0,/swimbeginner/simple-embedding-spacyvstensorflow,Natural Language Processing with Disaster Tweets
14123119,0.78087,0,0,/vishubandari/disaster-tweets,Natural Language Processing with Disaster Tweets
13252858,0.83726,1,5,/acadaiaca/tf-idf-word2vec-lr-bert,Natural Language Processing with Disaster Tweets
12849670,0.83615,1,3,/carlmcbrideellis/histogram-gradient-boosting-classifier-example,Santander Customer Satisfaction
12569771,0.77308,0,1,/funxexcel/santander-voting-classifier,Santander Customer Satisfaction
4513376,0.83395,0,0,/rolandas1369/santander-customer-satisfaction,Santander Customer Satisfaction
3543553,0.80072,0,0,/kamusone/prediction-model-with-xgboost,Santander Customer Satisfaction
1001165,0.512843,0,0,/gauravgupta1991/eda-book-ipynb,Santander Customer Satisfaction
174911,0.8319780000000001,0,0,/enezhadian/gradient-boosted-decision-trees,Santander Customer Satisfaction
11921357,-6.8326,2,6,/ethercoin/osic-multiple-quantile-regression-starter,OSIC Pulmonary Fibrosis Progression
11896128,-6.8069,0,3,/alifrahman/osic-pulmuonary-fibrosis-progression-upgraded,OSIC Pulmonary Fibrosis Progression
11874222,-6.8223,0,7,/aakashveera/osic-eda-quantile-regression,OSIC Pulmonary Fibrosis Progression
11772384,-6.9044,2,3,/gilfernandes/lightgbm-simple,OSIC Pulmonary Fibrosis Progression
11597032,-6.8087,0,18,/salmaneunus/fibrosis-osic-submission-21,OSIC Pulmonary Fibrosis Progression
11602600,-8.3473,2,5,/lhagiimn/first-submission,OSIC Pulmonary Fibrosis Progression
11462103,-6.8283,0,5,/mikloskralik/exponential-decay-nn-quantile-regression,OSIC Pulmonary Fibrosis Progression
11578579,-7.4878,0,9,/elvinagammed/simple-eda-linearity-model-top90,OSIC Pulmonary Fibrosis Progression
11398434,-7.3118,0,6,/atrisaxena/osic-efficientnet-regression-pytorch,OSIC Pulmonary Fibrosis Progression
13526332,0.774,0,3,/shanmukh05/ranzcr-clip-catheter-and-line-position,RANZCR CLiP - Catheter and Line Position Challenge
13577502,0.953,3,14,/sinamhd9/keras-models-tensorflow-data-dataset-tpu-part2,RANZCR CLiP - Catheter and Line Position Challenge
13535079,0.923,27,31,/imranzaman5202/resnet50-model,RANZCR CLiP - Catheter and Line Position Challenge
13518074,0.918,6,71,/xhlulu/ranzcr-efficientnet-gpu-starter-train-submit,RANZCR CLiP - Catheter and Line Position Challenge
13520633,0.5,3,18,/titericz/baseline-mean-average,RANZCR CLiP - Catheter and Line Position Challenge
13537192,0.925,0,3,/slm37102/ranzcr-clip-fastai-starter,RANZCR CLiP - Catheter and Line Position Challenge
14629402,0.809,0,0,/venkat555/ranzcr-clip-tpu-effb0-infe,RANZCR CLiP - Catheter and Line Position Challenge
13533151,0.923,0,0,/ahmedewida/ranzcr-resnext50-32x4d-parameters,RANZCR CLiP - Catheter and Line Position Challenge
2551007,0.59865,0,3,/mutatos/twosigma-memoryoptimisedtemplate4modelling,Two Sigma: Using News to Predict Stock Movements
2504574,2.7857,0,4,/oriormeir/xgboost-2-market-news,Two Sigma: Using News to Predict Stock Movements
2120403,0.87561,3,14,/shikha130vv/kernel-with-news-features,Two Sigma: Using News to Predict Stock Movements
2439967,0.65463,3,3,/oguzkaplan/outlier-detection1-5-iqr-method-nn-on-market,Two Sigma: Using News to Predict Stock Movements
2450217,0.63169,0,0,/berkeoral/gradientboosting,Two Sigma: Using News to Predict Stock Movements
2453362,0.65705,0,4,/hh2720/market-simple-nn-with-binary-confidence-values,Two Sigma: Using News to Predict Stock Movements
2427877,0.14992,0,0,/ahmetdemirel/preprocessedtraindata,Two Sigma: Using News to Predict Stock Movements
2370252,0.24022,0,0,/universome/tsa-model,Two Sigma: Using News to Predict Stock Movements
2162454,0.36695,0,0,/regonn/two-sigma,Two Sigma: Using News to Predict Stock Movements
2346323,0.57824,0,0,/shanqiyang/basic-plot-and-etl,Two Sigma: Using News to Predict Stock Movements
2005090,0.55455,0,2,/leegare/protoject-2-sigma,Two Sigma: Using News to Predict Stock Movements
2130826,0.66972,6,20,/takafumitakizawa/lightgbm-with-online-training,Two Sigma: Using News to Predict Stock Movements
2271493,0.65565,0,0,/teamskiy/xgboost-using-only-numeric-data-from-news,Two Sigma: Using News to Predict Stock Movements
2240576,0.64276,0,0,/dineshramasamy/market-data-alone,Two Sigma: Using News to Predict Stock Movements
2182497,1.42774,0,0,/jsarda/kernel-gama-1,Two Sigma: Using News to Predict Stock Movements
5984287,0.089,21,78,/taindow/pytorch-resnext-101-32x8d-benchmark,RSNA Intracranial Hemorrhage Detection
5900157,0.102,21,75,/taindow/pytorch-efficientnet-b0-benchmark,RSNA Intracranial Hemorrhage Detection
11630452,0.17901,0,0,/manuelbunge/houseprice,House Prices - Advanced Regression Techniques
11616797,0.09526,2,5,/pavan9065/house-prices,House Prices - Advanced Regression Techniques
11670126,0.12913,0,0,/tracyporter/ames-house-prices-ensemble,House Prices - Advanced Regression Techniques
11666308,0.13919,0,0,/rogerche/notebook316597e6e5,House Prices - Advanced Regression Techniques
11647235,0.12555,0,4,/arnabark/house-prices-optuna-catboost,House Prices - Advanced Regression Techniques
11552186,0.12409,0,0,/bkyleli/xgboost-regression,House Prices - Advanced Regression Techniques
10950099,0.13045,0,4,/sahilmaheshwari/top-10-through-30-lines-of-code,House Prices - Advanced Regression Techniques
11186395,0.15045,0,0,/danielizumikatagiri/house-pricing-with-xgboost,House Prices - Advanced Regression Techniques
11532718,0.14841,0,0,/yutohisamatsu/houseprice-elasticnet-finish,House Prices - Advanced Regression Techniques
11502347,0.15921,0,1,/abh8017/data-exploration-visualisation-prediction,House Prices - Advanced Regression Techniques
11407160,0.1373599999999999,0,1,/abhaporwal/advanced-house-prediction,House Prices - Advanced Regression Techniques
11533221,0.13135,0,0,/yutohisamatsu/houseprice-xgboost,House Prices - Advanced Regression Techniques
11066379,0.16012,0,0,/asiaahmedabushawish/house-price-advanced-reg,House Prices - Advanced Regression Techniques
11009640,0.12575,1,8,/arpitsolanki14/eda-basic-models-housing-prices-regression,House Prices - Advanced Regression Techniques
11328800,0.13419,2,8,/rishabhx3/house-price-prediction,House Prices - Advanced Regression Techniques
11445380,0.11986,0,1,/canozer/house-prices-stacked-models,House Prices - Advanced Regression Techniques
4959937,0.78841,0,0,/xieshuhan/santander-prediction-by-logistic-and-random-forest,Santander Customer Transaction Prediction
3887116,0.8538899999999999,0,0,/thexapholox/red-neuronal-keras,Santander Customer Transaction Prediction
46952,0.8654649999999999,0,0,/mujtabaasif/elotry,March Machine Learning Mania 2016
59899,0.8299540000000001,1,0,/oxjlisa/customers-v2,Santander Customer Satisfaction
5981727,0.0,0,0,/mastreips/pytorch-bert-inference,Jigsaw Unintended Bias in Toxicity Classification
3985727,0.9315,0,0,/timidli0n/defaultlstm,Jigsaw Unintended Bias in Toxicity Classification
3913641,0.8889100000000001,0,0,/vnbhat/intro-to-bert-jigsaw-unintended-bias,Jigsaw Unintended Bias in Toxicity Classification
14346377,5184439.0,0,0,/dankiy/vulcanic-lstm,INGV - Volcanic Eruption Prediction
13967404,10522834.0,0,0,/damoonshahhosseini/volcano-xgboost,INGV - Volcanic Eruption Prediction
13663189,5046098.0,0,4,/deepakbhatp/ingv-spectraldensity-stft-pca-xgb-stratifiedkfold,INGV - Volcanic Eruption Prediction
13025582,4989282.0,0,4,/khangtran97/cs675-finals,INGV - Volcanic Eruption Prediction
13548614,7383681.0,0,2,/ruchahemantathavale/file-3-rucha,INGV - Volcanic Eruption Prediction
13139226,7367695.0,0,12,/muhakabartay/ingv-volcano-eda-with-lgbm,INGV - Volcanic Eruption Prediction
13048216,5058307.0,0,9,/josemori/tree-model-with-time-and-freq-domain-feature,INGV - Volcanic Eruption Prediction
13034194,5747537.0,2,7,/obougacha/ingv-xgboost-baseline,INGV - Volcanic Eruption Prediction
12813568,5261806.0,1,14,/davidedwards1/volcano-stft-data-optimisation,INGV - Volcanic Eruption Prediction
12675199,5511047.0,0,8,/ymdhryk/volcano,INGV - Volcanic Eruption Prediction
12596536,5833166.0,8,26,/ajcostarino/ignv-adversarial-validation-cv-lb-differences,INGV - Volcanic Eruption Prediction
12385389,5292649.0,3,15,/patrick0302/ingv-h2o-automl,INGV - Volcanic Eruption Prediction
12328526,11414324.0,2,12,/jagdmir/volcano-eruption-linear-regression,INGV - Volcanic Eruption Prediction
12296555,6318978.0,1,10,/patrick0302/ingv-volcanic-eruption-prediction-add-resamplin,INGV - Volcanic Eruption Prediction
13115961,0.80661,0,0,/natjirachamusri/real-or-not-nlp-with-disaster-tweets-for-206,Natural Language Processing with Disaster Tweets
13069909,0.79742,0,1,/bamnichaporn/real-or-not-disaster-tweets,Natural Language Processing with Disaster Tweets
13013816,0.79865,0,0,/qwantisheku/disaster-classifier,Natural Language Processing with Disaster Tweets
13117768,0.80324,0,0,/kanthichajuntepa/dsi-real-or-not,Natural Language Processing with Disaster Tweets
12952939,0.80447,0,12,/patipanrattanawin/disaster-tweet-text-classification-simplest-way,Natural Language Processing with Disaster Tweets
13027244,0.8240799999999999,0,0,/siriratwichianpanya/realornot,Natural Language Processing with Disaster Tweets
12863701,0.80447,1,7,/yanisagam/basic-nlp,Natural Language Processing with Disaster Tweets
12797313,0.7722899999999999,0,0,/israakhalil/nlp-with-disaster-tweets,Natural Language Processing with Disaster Tweets
12787126,0.78087,0,0,/dshubham777/nlp-disaster-tweets,Natural Language Processing with Disaster Tweets
12653515,0.78087,0,0,/maiselfar/twitter-challenge,Natural Language Processing with Disaster Tweets
12590388,0.79711,0,0,/kiranjames6/project-cxlab-kj-grp6,Natural Language Processing with Disaster Tweets
12485775,0.79405,0,4,/soujanyag/nlp-on-disaster-tweets-data,Natural Language Processing with Disaster Tweets
6649355,0.01252,0,0,/mtodisco10/2019-big-data-bowl-submission,NFL Big Data Bowl
6607214,0.01496,0,1,/blazejd/nfl-fixed-bugs,NFL Big Data Bowl
7723814,0.013087,0,0,/ttya16/nfl-challenge-notebook-latesub,NFL Big Data Bowl
7633380,0.0135139999999999,2,2,/vaibhavsxn/the-talented,NFL Big Data Bowl
6757747,0.0123,0,1,/krammerg/nfl-baseline-nn-v4,NFL Big Data Bowl
6765410,0.0166699999999999,0,0,/sylvainmichel/nfl202-swish,NFL Big Data Bowl
6706730,0.01249,0,0,/yukomiya/komiya-nfl-catboost,NFL Big Data Bowl
6644832,0.01474,0,0,/dmintry/catboost,NFL Big Data Bowl
6746986,0.0138599999999999,0,0,/s13658695/simple-xgb-simple-fe,NFL Big Data Bowl
6549159,0.01458,2,1,/kidrulit/nfl-big-data-feature-engineering-and-encoding,NFL Big Data Bowl
6741182,0.0132,6,5,/oliveia/nfl-big-data-bowl-with-nn-keras,NFL Big Data Bowl
6616900,0.0130599999999999,0,0,/danoff/neural-networks-isaih-divya-charlie-version,NFL Big Data Bowl
6704384,0.01349,0,3,/shozendan/davis-ds-club-a-neural-network-approach,NFL Big Data Bowl
6620470,0.01402,0,1,/holoong9291/nfl-big-data-bowl,NFL Big Data Bowl
6671177,0.01363,0,2,/gdpsgdps/location-eda-8eb410,NFL Big Data Bowl
180294,0.63363,0,0,/justin511/random-forest-starter-with-numerical-features,Two Sigma Connect: Rental Listing Inquiries
11871917,1.99872,1,2,/dskagglemt/santander-value-prediction-challenge-eda,Santander Value Prediction Challenge
11191075,1.47817,0,9,/charlessamuel/santander-value-prediction,Santander Value Prediction Challenge
6838766,0.57021,0,1,/jagannathrk/santander-value-prediction,Santander Value Prediction Challenge
5914177,1.50824,0,1,/zhouhong0/voting-regressor,Santander Value Prediction Challenge
5872621,1.71124,0,0,/daphnetree/stacking,Santander Value Prediction Challenge
1478336,0.56,0,5,/tienen/love-is-the-answer-new-blendwinner,Santander Value Prediction Challenge
1199470,1.82,0,1,/aritrase/santander-deeplearning,Santander Value Prediction Challenge
1190568,1.58,0,0,/aritrase/santander-reg,Santander Value Prediction Challenge
1485678,0.49,40,28,/khahuras/0-49-publiclb-simple-blend-private-lb-rank-126th,Santander Value Prediction Challenge
1485494,0.49,0,7,/sagol79/why-it-was-not-worth-publishing-0-56-0-49-0-54,Santander Value Prediction Challenge
1459144,0.56,31,136,/nulldata/jiazhen-to-armamut-via-gurchetan1000-0-56,Santander Value Prediction Challenge
1470679,1.4,0,1,/praxitelisk/santander-preprocess-model-averaging-by-xgb-lgb,Santander Value Prediction Challenge
1393350,0.65,3,33,/danil328/ligthgbm-with-bayesian-optimization,Santander Value Prediction Challenge
1347853,0.63,3,11,/ashishpatel26/blending,Santander Value Prediction Challenge
216230,6.01888,1,29,/badat0202/estimate-distribution-of-data-in-lb,Quora Question Pairs
216874,9.46615,0,0,/premshah/notebook42c025f2de,Quora Question Pairs
9182652,0.95654,0,1,/ttagu99/ensemble-models-2epoch,"Quick, Draw! Doodle Recognition Challenge"
6513351,0.00524,0,0,/spurdy/test-kernel-output-files,"Quick, Draw! Doodle Recognition Challenge"
6488343,0.66406,0,3,/allenedgarpoe/easy-datapreprocessing,"Quick, Draw! Doodle Recognition Challenge"
6478329,0.00481,0,0,/brightj529/bright-jun-quick-draw,"Quick, Draw! Doodle Recognition Challenge"
6425043,0.7346699999999999,0,3,/sawyerjo/sm-net-cnn-quick-draw,"Quick, Draw! Doodle Recognition Challenge"
6442960,0.29172,0,1,/emphasis10/kernel7cbfa44817,"Quick, Draw! Doodle Recognition Challenge"
6465068,0.36995,0,1,/hw7439/kernel215ac7b54a,"Quick, Draw! Doodle Recognition Challenge"
3927233,0.36823,0,0,/hobbang2/summercoding,"Quick, Draw! Doodle Recognition Challenge"
3958577,0.7299,0,0,/sochi201/cnn-doodle,"Quick, Draw! Doodle Recognition Challenge"
3906113,0.83688,2,1,/jaeboklee/pytorch-transfer-learning-with-densenet,"Quick, Draw! Doodle Recognition Challenge"
2710680,0.65554,0,1,/kmader/quickdraw-baseline-lstm-torch,"Quick, Draw! Doodle Recognition Challenge"
2270619,0.8290000000000001,1,4,/deepchatterjeevns/greyscale-mobilenet-forked-from-beluga,"Quick, Draw! Doodle Recognition Challenge"
2533734,0.664,0,46,/kashnitsky/training-while-reading-vowpal-wabbit-starter,Microsoft Malware Prediction
2476641,0.69799,5,62,/roydatascience/microsoft-malware-solution-silver-medal,Microsoft Malware Prediction
2420153,0.674,6,14,/delayedkarma/let-s-add-some-new-features-lb-0-674,Microsoft Malware Prediction
2392863,0.607,3,14,/nikkisharma536/malware,Microsoft Malware Prediction
2374600,0.674,3,17,/jsaguiar/eda-initial-exploration-and-baseline,Microsoft Malware Prediction
2379176,0.5,1,14,/ashishpatel26/h2o-stacking-for-microsoft-malware-prediction,Microsoft Malware Prediction
2375612,0.596,0,1,/nikkisharma536/beginning-challenge,Microsoft Malware Prediction
213326,0.5541,0,0,/vinotharun89/exploratory-data-analysis,Quora Question Pairs
4329653,6.01888,0,0,/vendredi218/estimate-distribution-of-data-in-lb,Quora Question Pairs
13974261,0.39366,0,0,/carroltuna/rossmann-mlp,Rossmann Store Sales
11013798,0.13174,0,3,/maksimbahdanchyk/rossmann-eda-timefeatures-meanenc-xgboost-draft,Rossmann Store Sales
10701944,0.1428299999999999,0,1,/miorgash/ml-5-1-rossmannstoresales-minimum,Rossmann Store Sales
6511414,0.52027,0,11,/aswathikv/rossmanstoresales,Rossmann Store Sales
6511397,0.51502,0,9,/apoorvaappz/rossmann-store-sales,Rossmann Store Sales
6511492,0.13899,2,8,/sudharsan1297/rossmann-store-sales-final,Rossmann Store Sales
6511394,0.25813,1,4,/saibharath12/rossmann-store-sales,Rossmann Store Sales
6518313,0.51502,2,10,/yashnaik12/rossmann-store-sales-analysis,Rossmann Store Sales
6511460,0.52484,0,7,/aahaan007/store-sales,Rossmann Store Sales
6123238,0.09764,1,7,/smksett11/rossmann-entityembedding,Rossmann Store Sales
5016735,0.11358,0,3,/micmia/rossmann-store-sales-prediction-based-on-xgboost,Rossmann Store Sales
3674829,0.17303,0,2,/manoelverissimo/rossmann-store-keras-model,Rossmann Store Sales
3304557,0.48876,0,0,/therri1227/learning-how-to-kaggle,Rossmann Store Sales
2519060,0.15673,1,1,/hisashikarazu/rossmann-python-forecasting-learning-notebook,Rossmann Store Sales
1633570,0.1044099999999999,0,29,/danspace/rossmann-store-sales-xgboost,Rossmann Store Sales
9423952,0.1639999999999999,0,4,/armenabnousi/site1-vs-site2-features-mat-files-datagen-4-gpu,TReNDS Neuroimaging
9253453,0.16,1,10,/chrisfilo/model-comparison,TReNDS Neuroimaging
9195712,0.1617,15,51,/rohitsingh9990/trends-pycaret-training-inference,TReNDS Neuroimaging
9154926,0.166,7,38,/srsteinkamp/trends-eda,TReNDS Neuroimaging
9116081,0.159,18,96,/rftexas/trends-eda-lightgbm-rapids-ai-svm,TReNDS Neuroimaging
9089558,0.166,30,53,/nischaydnk/beginners-trends-neuroimaging-decent-score,TReNDS Neuroimaging
9139993,0.165,0,2,/currypurin/trends-lgb-custom-metric,TReNDS Neuroimaging
9084991,0.161,2,52,/ttahara/trends-simple-nn-baseline,TReNDS Neuroimaging
4639860,0.7891,0,1,/abimannan/siim-segmentation,SIIM-ACR Pneumothorax Segmentation
4944241,0.8997,45,249,/meaninglesslives/nested-unet-with-efficientnet-encoder,SIIM-ACR Pneumothorax Segmentation
4849421,0.7835,6,30,/ekhtiar/lung-segmentation-cropping-resunet-tf-keras,SIIM-ACR Pneumothorax Segmentation
4704361,0.8295,10,87,/iafoss/postprocessing-for-hypercolumns-kernel,SIIM-ACR Pneumothorax Segmentation
4687973,0.8170000000000001,15,31,/soulmachine/siim-deeplabv3,SIIM-ACR Pneumothorax Segmentation
4592092,0.8234,7,43,/giuliasavorgnan/pneumothorax-models-ensemble-average,SIIM-ACR Pneumothorax Segmentation
4548088,0.7886,6,25,/soumikrakshit/pneumothorax-segmentation-using-unet-in-tensorflow,SIIM-ACR Pneumothorax Segmentation
13923478,0.7548199999999999,3,4,/guidosalimbeni/keras-lstm-for-sentiment-disaster-analysis,Natural Language Processing with Disaster Tweets
13807435,0.83634,2,3,/ritheshsreenivasan/disastertweetv1,Natural Language Processing with Disaster Tweets
13805393,0.8014,0,3,/craigmthomas/catboost-fe,Natural Language Processing with Disaster Tweets
13746276,0.80232,0,10,/shyambhu/80-with-vector-embedding-feature-engineering,Natural Language Processing with Disaster Tweets
13769912,0.77781,0,0,/craigmthomas/simple-features-sgd-lightgbm,Natural Language Processing with Disaster Tweets
13722188,0.7177399999999999,0,0,/erammunawwar/subword-tokenizer-with-lstm,Natural Language Processing with Disaster Tweets
13629965,0.7912899999999999,0,1,/tom99763/classify-by-naive-bayes-model,Natural Language Processing with Disaster Tweets
13481571,0.7919,0,0,/mshivanshu10/disastertweetclassify,Natural Language Processing with Disaster Tweets
13344972,0.79252,0,15,/baakkzlay/disaster-tweets-analysis,Natural Language Processing with Disaster Tweets
13323223,0.71467,0,15,/daotan/tweet-analysis-using-lstm,Natural Language Processing with Disaster Tweets
13284656,0.81428,0,5,/ekshusingh/bert-as-embedding,Natural Language Processing with Disaster Tweets
10987603,0.8014,0,0,/shikhar1617/disaster-or-not,Natural Language Processing with Disaster Tweets
13187807,0.79374,3,3,/plasmaichor/nlp-using-smote-and-tf-idf-vectorizer,Natural Language Processing with Disaster Tweets
13044209,0.8011,0,1,/chalatsingangkura/nlp-with-disaster-tweets-group5,Natural Language Processing with Disaster Tweets
929822,0.0644258,0,0,/cpvirani/draft-random,Zillow Prize: Zillow’s Home Value Prediction (Zestimate)
400024,0.0640721,3,5,/aharless/my-solution-part-v-ensemble-with-fudge-factor,Zillow Prize: Zillow’s Home Value Prediction (Zestimate)
377109,0.0701725,0,1,/djhavera/fork-of-pipeline-grid-search-rf-starter-code,Zillow Prize: Zillow’s Home Value Prediction (Zestimate)
13538275,0.843,0,0,/giorgosanastasakos/ranzcr-catheter,RANZCR CLiP - Catheter and Line Position Challenge
13848868,0.958,1,20,/ttahara/ranzcr-multi-head-model-inference,RANZCR CLiP - Catheter and Line Position Challenge
13884756,0.904,0,1,/user123454321/pytorch-efficientnet7-starter,RANZCR CLiP - Catheter and Line Position Challenge
13874621,0.902,0,0,/jackstapleton/rccl-384-inference,RANZCR CLiP - Catheter and Line Position Challenge
13745306,0.893,3,12,/danofer/ranzcr-chexnet-starter,RANZCR CLiP - Catheter and Line Position Challenge
13796877,-6.9178,0,0,/purnima29/final-model,OSIC Pulmonary Fibrosis Progression
14056066,-6.8882,0,0,/ali313/lstm-with-qloss,OSIC Pulmonary Fibrosis Progression
11908183,-6.8927,0,1,/reyvaz/osic-linear-decay-and-quant-reg-inference,OSIC Pulmonary Fibrosis Progression
12251809,-6.8947,0,4,/c7934597/inference-45-55-600-epochs-tuned-effnet-b5-30-ep,OSIC Pulmonary Fibrosis Progression
12213120,-6.9124,0,0,/veronicaloy/linear-model,OSIC Pulmonary Fibrosis Progression
11995770,-7.7383,0,0,/viacheslavkucherenko/submission-retro,OSIC Pulmonary Fibrosis Progression
12106256,-6.896,9,32,/artkulak/inference-45-55-600-epochs-tuned-effnet-b5-30-ep,OSIC Pulmonary Fibrosis Progression
12070275,-7.0176,0,1,/gunawanmarbun/osic-pulmonary-finetune,OSIC Pulmonary Fibrosis Progression
12140748,-6.8812,2,20,/lhagiimn/solution-for-the-first-place-but-we-didn-t-select,OSIC Pulmonary Fibrosis Progression
12109448,-6.9072,1,4,/artkulak/simple-logreg,OSIC Pulmonary Fibrosis Progression
12120569,-6.8897,2,2,/doctorkael/osic-inference,OSIC Pulmonary Fibrosis Progression
12095703,-7.1124,0,0,/chadansharma/enetsb4ns-qr-inference-wf2-v0-1,OSIC Pulmonary Fibrosis Progression
11806113,-6.9723,0,0,/melvin97n/base-8,OSIC Pulmonary Fibrosis Progression
12088953,-6.9613,0,0,/code1110/osic-cv-tab-cnn-ensemble-ocne-nop-lasts,OSIC Pulmonary Fibrosis Progression
3596493,0.90967,3,17,/samarthsarin/ensemble-network-with-keras-and-embeddings,Jigsaw Unintended Bias in Toxicity Classification
3637086,0.93542,12,7,/julianoliveira/blend-the-blend,Jigsaw Unintended Bias in Toxicity Classification
3635499,0.91393,2,5,/francoisdubois/add-a-gru-layer,Jigsaw Unintended Bias in Toxicity Classification
3633857,0.91006,0,1,/francoisdubois/add-a-lstm-layer,Jigsaw Unintended Bias in Toxicity Classification
3501948,0.9238,0,2,/swarnim97/jigsaw-toxicity-using-cnn1d-and-cudnnlstm,Jigsaw Unintended Bias in Toxicity Classification
3600602,0.8463799999999999,2,10,/francoisdubois/build-your-word-embedding-from-scratch,Jigsaw Unintended Bias in Toxicity Classification
3567709,0.92298,0,2,/dtprksh50/toxic-challenge-v2,Jigsaw Unintended Bias in Toxicity Classification
3572804,0.87272,5,12,/tyagit3/logistic-regression-with-tfidf-word-level,Jigsaw Unintended Bias in Toxicity Classification
3582751,0.64595,0,0,/nikhilroxtomar/simple-keras-lstm,Jigsaw Unintended Bias in Toxicity Classification
3525138,0.88218,12,57,/httpwwwfszyc/bert-keras-with-warmup-and-excluding-wd-parameters,Jigsaw Unintended Bias in Toxicity Classification
3439482,0.8614200000000001,0,0,/tp2422/jigsaw-v1,Jigsaw Unintended Bias in Toxicity Classification
3504969,0.90525,0,1,/ajaykgp12/can-model-learn-its-word-embeddings-keras-and-gru,Jigsaw Unintended Bias in Toxicity Classification
3471301,0.92514,1,2,/harsh306/keval1,Jigsaw Unintended Bias in Toxicity Classification
3465673,0.8993,0,8,/alber8295/nb-svm-linear-baseline,Jigsaw Unintended Bias in Toxicity Classification
3446051,0.86321,0,7,/nevermoi/jigsaw-toxic-prediction-by-simple-linearsvr-tfidf,Jigsaw Unintended Bias in Toxicity Classification
3438141,0.92529,11,107,/taindow/simple-cudnngru-python-keras,Jigsaw Unintended Bias in Toxicity Classification
3428089,0.88128,8,92,/artgor/toxicity-eda-logreg-and-nn-interpretation,Jigsaw Unintended Bias in Toxicity Classification
3429371,0.89144,1,12,/jazivxt/ontological-paradox-civil-bias,Jigsaw Unintended Bias in Toxicity Classification
9980013,0.0,0,0,/tozzig/pytorch-bert-inference,Jigsaw Unintended Bias in Toxicity Classification
2483488,0.94537,0,1,/someadityamandal/predict-using-lgbm,Two Sigma: Using News to Predict Stock Movements
2306190,2.62086,0,1,/harshitsheoran/5-split-lgbm-fe,Two Sigma: Using News to Predict Stock Movements
2084931,2.77277,0,2,/emily2008/two-sigma-stock-news-market,Two Sigma: Using News to Predict Stock Movements
1967704,3.27029,1,4,/suchith0312/multiple-time-based-validation-try-xgb,Two Sigma: Using News to Predict Stock Movements
2207797,1.44655,0,0,/jsarda/kernel-unificado-1,Two Sigma: Using News to Predict Stock Movements
2333240,3.71612,2,7,/mnagao/lightgbm-with-online-training,Two Sigma: Using News to Predict Stock Movements
2523893,0.67186,0,0,/prodigalprodigy/stock-market-movement-prediction,Two Sigma: Using News to Predict Stock Movements
2556854,0.59058,0,0,/love123/sigma-1,Two Sigma: Using News to Predict Stock Movements
2010426,0.02833,0,0,/magedrawash/maged-rawash,Two Sigma: Using News to Predict Stock Movements
3056613,0.8170000000000001,0,0,/danielcorreia/santander-ultra-simple-sklearn-pipeline-baseline,Santander Customer Transaction Prediction
3039415,0.66,5,9,/priteshshrivastava/random-forest-santander-classification-fast-ai,Santander Customer Transaction Prediction
3034152,0.89,4,27,/vinhnguyen/accelerating-xgboost-with-gpu,Santander Customer Transaction Prediction
3017892,0.877,0,9,/karangautam/keras-nn,Santander Customer Transaction Prediction
2996387,0.897,34,195,/fayzur/lgb-bayesian-parameters-finding-rank-average,Santander Customer Transaction Prediction
3014980,0.8959999999999999,2,8,/ldm314/catboosting-to-0-896,Santander Customer Transaction Prediction
2983470,0.889,30,189,/blackblitz/gaussian-naive-bayes,Santander Customer Transaction Prediction
2978787,0.856,4,24,/schock/santander-ootb-fast-ai-tabular-implementation,Santander Customer Transaction Prediction
2980818,0.897,2,13,/nicoduf/gbm-with-grid-search-h2o,Santander Customer Transaction Prediction
2980830,0.848,0,4,/ortempo/1-simple-logistic-regression,Santander Customer Transaction Prediction
2947045,0.787,27,238,/allunia/santander-customer-transaction-eda,Santander Customer Transaction Prediction
2943219,0.899,54,211,/artgor/santander-eda-fe-fs-and-models,Santander Customer Transaction Prediction
2949543,0.897,4,9,/siddharth5mn/santander-starter-eda,Santander Customer Transaction Prediction
2954855,0.7859999999999999,1,4,/benjibb/fastai-implementation-stratified,Santander Customer Transaction Prediction
2944733,0.898,7,60,/wakamezake/starter-code-catboost-baseline,Santander Customer Transaction Prediction
2951322,0.853,0,6,/tomeram/keras-lb-0-853-nn-approach,Santander Customer Transaction Prediction
2942561,0.899,3,26,/deepak525/sctp-lightgbm-lb-0-899,Santander Customer Transaction Prediction
2948393,0.8959999999999999,0,4,/magf46/eda-naive-bayes-dt-lgbm-polyfeatures,Santander Customer Transaction Prediction
2944355,0.8959999999999999,3,12,/karthik7395/key-takeaways-eda-fe-lgbm-10-fold-cv,Santander Customer Transaction Prediction
2962935,0.631,2,0,/alepacheco/basic-xgboost-approach,Santander Customer Transaction Prediction
6252117,0.077,2,8,/muhakabartay/rsna-simple-aggregator-x,RSNA Intracranial Hemorrhage Detection
6309611,1.253,0,0,/fanconic/keras-efficientnet-b3-starter-code,RSNA Intracranial Hemorrhage Detection
6928363,0.12557,0,0,/luisrex15/finalproject-alonso-gs-lct,RSNA Intracranial Hemorrhage Detection
6280974,0.086,50,123,/jhoward/from-prototyping-to-submission-fastai,RSNA Intracranial Hemorrhage Detection
6203008,16.91,2,11,/phantomakame/pytorch-fast-ai-top-1-or-good-results,RSNA Intracranial Hemorrhage Detection
6094371,0.084,21,87,/braquino/pytorch-resnext-32x8d-centercrop,RSNA Intracranial Hemorrhage Detection
6076837,0.149,3,12,/viswajithkn/intracranial-hemorrhage,RSNA Intracranial Hemorrhage Detection
6026913,0.102,6,51,/radek1/fastai-starter-pack-train-basic-model-and-submit,RSNA Intracranial Hemorrhage Detection
5119610,0.0169,0,2,/jiaofenx/santander-product-recommendation,Santander Product Recommendation
1688524,0.023073,0,0,/hanene1/combo-lr-rf,Santander Product Recommendation
1430834,0.0230962,0,1,/hachemsfar/combo-lr-rf,Santander Product Recommendation
1422916,0.0221375999999999,0,2,/hachemsfar/na-ve-bayes,Santander Product Recommendation
1399486,0.0173852,0,2,/hachemsfar/naive-bayes,Santander Product Recommendation
956535,0.0221449999999999,0,1,/hachemsfar/most-probable-product-recent,Santander Product Recommendation
1948643,0.427,3,8,/tcapelle/4-channel-darknet-sz-512,Human Protein Atlas Image Classification
1943760,0.4529999999999999,14,28,/zhugds/resnet34-with-rgby-fast-ai-fork,Human Protein Atlas Image Classification
1806121,0.4529999999999999,276,535,/iafoss/pretrained-resnet34-with-rgby-0-460-public-lb,Human Protein Atlas Image Classification
1857293,0.111,4,24,/rejpalcz/datagenerator-for-fast-data-loading,Human Protein Atlas Image Classification
1809490,0.2689999999999999,44,134,/byrachonok/pretrained-inceptionresnetv2-base-classifier,Human Protein Atlas Image Classification
1797119,0.0409999999999999,167,810,/allunia/protein-atlas-exploration-and-baseline,Human Protein Atlas Image Classification
1803640,0.036,0,2,/nikhilroxtomar/transfer-learning-for-human-protein-submission,Human Protein Atlas Image Classification
1796355,0.108,1,7,/francoiscokelaer/v1-starter-stack-color-channels-vgg16-120-epoch,Human Protein Atlas Image Classification
2373375,0.26,0,0,/anastasb/tensorflow-focal-loss,Human Protein Atlas Image Classification
12680847,0.8952399999999999,0,1,/sarthakrastogi/ensembling-popular-networks-for-high-accuracy,Flower Classification with TPUs
8927061,0.96718,0,0,/redwankarimsony/flower-classification-densenet-effecientnetb7,Flower Classification with TPUs
8989587,0.93357,0,3,/tarunbisht11/flower-classification-tpu,Flower Classification with TPUs
10761950,0.83267,0,1,/mankomyk/kernel4267ecb26e,Flower Classification with TPUs
9690078,0.92563,0,0,/mohammedosama/flower-classification-v1,Flower Classification with TPUs
8208799,0.96306,0,0,/akashsuper2000/fork-of-flowers-on-tpu-ii-b26631,Flower Classification with TPUs
8622992,0.9668,0,0,/taohoang/flower-classification-enet-b7-densenet201,Flower Classification with TPUs
9538578,0.955,1,6,/ibrahimsobh/recipe-flower-classification-tpu-0-95-pub,Flower Classification with TPUs
9196779,0.8499700000000001,0,0,/blaxkdolphin/flower-pytorch-xla,Flower Classification with TPUs
8940620,0.96133,0,2,/namanmehta1/flower-classification-lb-0-964,Flower Classification with TPUs
8142318,0.96467,0,0,/qinhui1999/tpu-enet-b7-densenet-random-cut,Flower Classification with TPUs
9466869,0.89292,0,0,/yaraamin/flowers-classification,Flower Classification with TPUs
9054578,0.98389,8,20,/afshiin/flower-classification-focal-loss-0-98,Flower Classification with TPUs
8075951,0.95785,2,3,/meenakshiramaswamy/tpu-flowers-cutmix-ensemble,Flower Classification with TPUs
9313058,0.97536,1,0,/astzls/flower-tpu-v2,Flower Classification with TPUs
9125711,0.95769,0,0,/lucca9211/flowers-classification-on-tpu,Flower Classification with TPUs
9336977,0.95699,1,7,/zzh13332470923/tpu-104-3-0-99,Flower Classification with TPUs
9334948,0.93389,0,2,/moizhk/beginner-guide-to-classification-step-2-final,Flower Classification with TPUs
9117918,0.97208,1,14,/haveri/efficientnet-with-all-5-imagesets-s1,Flower Classification with TPUs
14053591,0.0567299999999999,0,1,/chromerai/submission01,Don't Get Kicked!
13804125,0.0565,0,0,/sanskrutighadipatil/cars-buy,Don't Get Kicked!
13197796,0.04954,0,0,/semenedel/carbuy,Don't Get Kicked!
12169611,0.0567299999999999,0,0,/ayusheeagarwal/don-t-get-kicked,Don't Get Kicked!
10829611,0.0567299999999999,0,0,/rahulkumar234/kernel7b2d75bf16,Don't Get Kicked!
443093,0.74833,0,0,/eaturner/boo-who-is-it-possible-lb-0-37xx,Spooky Author Identification
6777073,0.497,0,1,/jozefc/stacked-models,2019 Data Science Bowl
1346570,1.16813,0,0,/vraval48/eda-and-feature-engineering-predict-future-sales,Predict Future Sales
1407811,1.25958,0,10,/sanket30/predicting-sales-using-keras-regressor,Predict Future Sales
1237589,1.02985,0,0,/plarmuseau/regressions-until-1-029,Predict Future Sales
1090854,1.03583,13,78,/ashishpatel26/predict-sales-price-using-xgboost,Predict Future Sales
1040530,1.39759,0,0,/jeffdlin/fork-of-run-3,Predict Future Sales
997841,1.37015,1,0,/plarmuseau/peng-go,Predict Future Sales
974464,1.55335,3,5,/plarmuseau/100-seconds-solution,Predict Future Sales
980117,1.34911,0,0,/plarmuseau/triple-exponential-smoothing-still-a-pearl,Predict Future Sales
763342,1.10465,2,3,/sebask/keras-2-0,Predict Future Sales
710973,1.25011,45,300,/minhtriet/a-beginner-guide-for-sale-data-prediction,Predict Future Sales
10800582,-6.9092,17,67,/carlossouza/quantile-regression-pytorch-tabular-data-only,OSIC Pulmonary Fibrosis Progression
10679205,-6.9069,0,2,/mithunp/fvc-progression,OSIC Pulmonary Fibrosis Progression
10759879,-6.8429,3,8,/kishor1210/basic-modeling-in-keras,OSIC Pulmonary Fibrosis Progression
10711363,-7.8835,4,24,/jameschapman19/bayesian-nn-pyro-tabular-fixed,OSIC Pulmonary Fibrosis Progression
10641587,-6.8354,6,44,/leoisleo1/osic-multiple-quantile-regression-starter-fork,OSIC Pulmonary Fibrosis Progression
10642734,-7.1134,3,47,/koheist/osic-eda-simplemodel-overview-with,OSIC Pulmonary Fibrosis Progression
10614402,-6.8322,59,312,/ulrich07/osic-multiple-quantile-regression-starter,OSIC Pulmonary Fibrosis Progression
10639206,-6.9069,5,23,/jagadish13/osic-baseline-elasticnet-eda,OSIC Pulmonary Fibrosis Progression
10645245,-6.907,1,7,/shambubm/osic-eda-and-ard-regressor-baseline,OSIC Pulmonary Fibrosis Progression
10613631,-6.939,3,61,/ttahara/osic-baseline-lgbm-with-custom-metric,OSIC Pulmonary Fibrosis Progression
10616727,-6.872000000000001,11,30,/mahmudds/osic-pulmonary-fibrosis-progression,OSIC Pulmonary Fibrosis Progression
10591055,-6.907,6,30,/yeeshu11/baseline-elasticnet,OSIC Pulmonary Fibrosis Progression
10572426,-6.934,1,13,/him4318/best-local-score,OSIC Pulmonary Fibrosis Progression
10557893,-6.935,3,67,/yasufuminakama/osic-ridge-baseline,OSIC Pulmonary Fibrosis Progression
10588063,-6.96,1,6,/satnam007/osic-0001-up-vote-if-useful,OSIC Pulmonary Fibrosis Progression
10546833,-6.96,14,159,/yasufuminakama/osic-lgb-baseline,OSIC Pulmonary Fibrosis Progression
10577768,-7.0397,0,8,/maksimbahdanchyk/fibrosis-eda-draft,OSIC Pulmonary Fibrosis Progression
10581552,-6.959,0,2,/kushagrawadhwa/eda-1-1c,OSIC Pulmonary Fibrosis Progression
745353,0.1246299999999999,0,10,/ripcurl/feedforward-neural-network-0-12174,WSDM - KKBox's Churn Prediction Challenge
477135,0.14592,0,0,/julienhe/xgb01,WSDM - KKBox's Churn Prediction Challenge
11400571,0.83236,1,5,/ozcan15/nlp-disaster-tweets-with-bert,Natural Language Processing with Disaster Tweets
11340760,0.75237,0,0,/tracyporter/identify-the-disaster-log-reg,Natural Language Processing with Disaster Tweets
11323268,0.57033,1,3,/shams1/adkkhn-h-pytorch-baseline-bert,Natural Language Processing with Disaster Tweets
11360005,0.7814800000000001,0,0,/saanai/nlp-getting-started-tutorial,Natural Language Processing with Disaster Tweets
11311334,0.79773,0,1,/lobodemonte/classifying-disaster-tweets,Natural Language Processing with Disaster Tweets
11230962,0.82347,0,0,/fstcap/bert-classical,Natural Language Processing with Disaster Tweets
11200442,0.79895,0,5,/prasannavijayakumar/nlp-tweet-classification,Natural Language Processing with Disaster Tweets
11131246,0.80294,0,5,/pankajg24/prediction-with-basic-tfidf-and-svm-model,Natural Language Processing with Disaster Tweets
10472387,0.79252,0,6,/katchupalvarenga/nlp-disaster-tweets-sgdclassifier,Natural Language Processing with Disaster Tweets
10870309,0.8381799999999999,0,0,/joswin/bert-cross-validation,Natural Language Processing with Disaster Tweets
10773732,0.8467600000000001,0,1,/joswin/bert-try,Natural Language Processing with Disaster Tweets
11552554,0.03573,0,2,/barteksadlej123/basic-logistic-regression,Mechanisms of Action (MoA) Prediction
11538869,0.01901,4,47,/ravy101/drug-moa-tf-keras-starter,Mechanisms of Action (MoA) Prediction
11545731,0.01903,1,29,/gogo827jz/fork-of-keras-multilabel-neural-network,Mechanisms of Action (MoA) Prediction
11556406,0.0196,1,7,/devanshu125/drug-moa-dl-starter-approach,Mechanisms of Action (MoA) Prediction
11549953,0.019,2,13,/shishu1421/keras-starter-moa,Mechanisms of Action (MoA) Prediction
11543290,0.0276399999999999,0,3,/para24/ovr-vs-multioutput-vs-classifier-chaining,Mechanisms of Action (MoA) Prediction
11545198,0.02331,0,4,/santiviquez/lasso-regressor-with-stratified-kfolds-moa,Mechanisms of Action (MoA) Prediction
11539122,0.04682,0,1,/rdekou/eda-and-simple-random-forest-model,Mechanisms of Action (MoA) Prediction
11544238,0.01874,0,10,/ludovick/inference-moa-baseline-mlp-kfold-10,Mechanisms of Action (MoA) Prediction
11522428,0.02,3,26,/simakov/multilabel-neural-network,Mechanisms of Action (MoA) Prediction
11523469,0.0208,2,22,/robikscube/mechanisms-of-action-moa-prediction-starter,Mechanisms of Action (MoA) Prediction
11541076,0.01903,1,3,/shubham9455999082/keras-multilabel-neural-network-v1-2,Mechanisms of Action (MoA) Prediction
14075499,0.04289,0,0,/raghdaalaa/moa-first,Mechanisms of Action (MoA) Prediction
13230383,0.01828,0,0,/tuistan/inference-blending-pretrained-4-models-80b63a,Mechanisms of Action (MoA) Prediction
13226189,0.0182699999999999,0,0,/jared8920/fork-of-inference-blending-pretrained-4-mod-7f648f,Mechanisms of Action (MoA) Prediction
13165216,0.01834,0,0,/edchencc/pytorch-transfer-learningwith-kfoldsdruginference2,Mechanisms of Action (MoA) Prediction
13131326,0.01835,0,0,/huanghuangzhang/pytorch-transfer-learning-with-k-folds-by-drug-ids,Mechanisms of Action (MoA) Prediction
13052328,0.01909,0,0,/paantya/moa-pytorch-nn-starter,Mechanisms of Action (MoA) Prediction
13048190,0.40763,0,0,/fedniko/baseline6,Mechanisms of Action (MoA) Prediction
13048072,0.40773,0,0,/maximivanov1/baseline6,Mechanisms of Action (MoA) Prediction
6355188,0.01304,0,0,/rutviklathiya/nfl-starter-mlp-feature-engg,NFL Big Data Bowl
7934382,0.0614,7,1,/yeayates21/cant-submit-1s-whats-wrong-please-help,Bengali.AI Handwritten Grapheme Classification
7973582,0.9278,0,1,/ajax0564/kernel7c9c3c386a,Bengali.AI Handwritten Grapheme Classification
7894825,0.9316,2,2,/abraristiak39/starter-wide-resnet50-train-inference,Bengali.AI Handwritten Grapheme Classification
7822363,0.8945,0,0,/naveen2961988/kernel6d7c349150,Bengali.AI Handwritten Grapheme Classification
7734215,0.9391,5,8,/kaushal2896/bengali-graphemes-simple-cnn-with-cutmix,Bengali.AI Handwritten Grapheme Classification
7399542,0.9645,0,1,/liang23333/kernel397167931a,Bengali.AI Handwritten Grapheme Classification
7418703,0.9507,14,56,/amanooo/bengali-ai-multi-output-densenet121-keras,Bengali.AI Handwritten Grapheme Classification
7453437,0.8855,4,4,/timaskr/memory-effective-multioutput-resnet,Bengali.AI Handwritten Grapheme Classification
7469059,0.9124,0,0,/anirbank/bengali-graphemes-starter-eda-multi-channel-cnn,Bengali.AI Handwritten Grapheme Classification
7357663,0.9391,12,16,/amanooo/bengali-ai-multi-output-densenet-keras,Bengali.AI Handwritten Grapheme Classification
2377148,0.69,0,4,/hengzheng/pytorch-kflod,Quora Insincere Questions Classification
2365723,0.6940000000000001,13,85,/hengzheng/pytorch-starter,Quora Insincere Questions Classification
2344676,0.6609999999999999,0,0,/kathy0603/sequence-model-with-embedding,Quora Insincere Questions Classification
2403890,0.619,0,0,/xsakix/bilstm-att-base-classifier-pretrained,Quora Insincere Questions Classification
2352815,0.679,1,26,/dannykliu/lstm-with-attention-clr-in-pytorch,Quora Insincere Questions Classification
2359593,0.6679999999999999,0,1,/harshil10/augmented-data-with-wordnet,Quora Insincere Questions Classification
2358245,0.675,0,1,/bninopaul/cudnngru-cnn-attention,Quora Insincere Questions Classification
2306601,0.6509999999999999,0,0,/mnm813/lstm-test-embendings,Quora Insincere Questions Classification
2366953,0.622,0,0,/xsakix/cnn-base-classifier-pretrained,Quora Insincere Questions Classification
2335212,0.696,6,8,/smokerx/test-others,Quora Insincere Questions Classification
2358939,0.564,0,0,/xsakix/cnn-base-classifier-word2vec,Quora Insincere Questions Classification
2315932,0.667,1,11,/robertke94/pytorch-bi-lstm-attention,Quora Insincere Questions Classification
2310528,0.588,0,1,/marcocarnini/climbing-on-giant-s-shoulders,Quora Insincere Questions Classification
2117500,0.685,0,2,/gmhost/different-cnn-units-based-on-kernel-size,Quora Insincere Questions Classification
2271309,0.517,0,0,/cuberti/quora-nlp-classification-problem,Quora Insincere Questions Classification
2295744,0.631,0,0,/viswajithkn/lstm-with-glove,Quora Insincere Questions Classification
2283131,0.4589999999999999,1,2,/anu0012/fasttext-keras,Quora Insincere Questions Classification
2271452,0.637,0,1,/zsn6034/gru-use-every-word-in-sentence-pytorch,Quora Insincere Questions Classification
2277211,0.635,0,2,/xsakix/all-embeddings-5,Quora Insincere Questions Classification
2234742,0.674,0,0,/tomras/quora-classification-with-ensemble,Quora Insincere Questions Classification
2245887,0.524,0,0,/crudelly/lab4-part2,Quora Insincere Questions Classification
1006576,0.4328,0,0,/ottpeterr/trackml-denoise-then-convnet,TrackML Particle Tracking Challenge
970117,0.2507,4,52,/mindcool/hdbscan-clustering-ii,TrackML Particle Tracking Challenge
961014,0.2099,9,25,/mrbeer/dbscan-benchmark-improvement-0-2099,TrackML Particle Tracking Challenge
11470402,0.80983,0,0,/mmotoki/vanilla-logistic-regression,Instant Gratification
4389017,0.92919,0,0,/asvskartheek/qda-beginner-s-attempt,Instant Gratification
4091370,0.96947,0,0,/rdewes/instant-gratification,Instant Gratification
4197718,0.97125,0,4,/gogo827jz/pseudo-labelling-4-models-stacked-with-lgbm,Instant Gratification
4239990,0.50008,0,0,/ratnesh88/gratification-using-py,Instant Gratification
4813446,0.5441,0,0,/sudevschiz/instantly-gratify-me,Instant Gratification
4714728,0.96814,0,0,/deepaksaharan/quadratic-discriminant-analysis,Instant Gratification
4680331,0.50093,0,2,/billumillu/instant-gratification-practice-update-1,Instant Gratification
4420322,0.8085600000000001,0,1,/rhodiumbeng/logistic-regression,Instant Gratification
4358848,0.97522,3,7,/kongliangyu/public-first-place-and-private-60-place,Instant Gratification
4350568,0.97481,50,196,/cdeotte/3-clusters-per-class-0-975,Instant Gratification
4422563,0.97149,7,19,/wanliyu/how-to-make-stacking-work-with-pseudo-labeling,Instant Gratification
4417342,0.97426,6,13,/siavrez/selecting-best-clusters-no-0-97618,Instant Gratification
4347277,0.97491,1,12,/chocozzz/hyun-stacking,Instant Gratification
4411335,0.97454,2,9,/harangdev/instant-gratification-14th-solution,Instant Gratification
4426259,0.97387,2,3,/vipito/simple-gmm-0-97599,Instant Gratification
4293200,0.97465,0,5,/cashfeg/hoxosh-gaussian-mixture-6-clusters,Instant Gratification
4340698,0.97499,0,5,/graf10a/ig-tuning-1,Instant Gratification
4323861,0.96859,0,0,/suuuuuu/qda-with-ls-feature,Instant Gratification
4365744,0.97024,5,21,/sandeepkumar121995/ensemble-oftop-3-public-kernel,Instant Gratification
4071039,0.96248,0,0,/uysimty/uc-gratification,Instant Gratification
3961870,0.96998,1,4,/luffyluffyluffy/clustering,Instant Gratification
4311731,0.97444,0,0,/merkylove/best-conserative-unfair-cv-with-rf,Instant Gratification
4239340,0.97011,0,0,/vrooom/graphicallasso-gaussianmixture,Instant Gratification
4324396,0.97021,9,77,/rohandeysarkar/instant-gratification-qda,Instant Gratification
4342951,0.8688799999999999,0,1,/kaedekato/neural-network-baseline-updated,Instant Gratification
2247615,0.613,0,0,/xsakix/all-embeddings,Quora Insincere Questions Classification
2240051,0.56,0,0,/cristianossd/count-vectorizer-with-logistic-regression,Quora Insincere Questions Classification
2161702,0.583,2,1,/crudelly/lab4-great-solution,Quora Insincere Questions Classification
2179069,0.636,1,1,/kaggleczs/lgb-with-features-engineering,Quora Insincere Questions Classification
2219305,0.653,0,0,/namakemono/training-gru-w-word2vec,Quora Insincere Questions Classification
2208565,0.677,0,8,/bkkaggle/pytorch-determinism-test,Quora Insincere Questions Classification
2147535,0.667,0,0,/jmoore1/multimodel-singlehead,Quora Insincere Questions Classification
2186718,0.635,0,0,/eyobwg/eeze-s,Quora Insincere Questions Classification
2187723,0.687,0,2,/matveich19/blending-with-linear-regression-0-688-lb,Quora Insincere Questions Classification
2182591,0.653,0,0,/pavelholubik/keras-srnn,Quora Insincere Questions Classification
2176480,0.6920000000000001,23,66,/shujian/single-rnn-with-4-folds-v1-9,Quora Insincere Questions Classification
2121548,0.27,2,5,/younad/data-exploration-and-topic-modeling-lsa-vs-lda,Quora Insincere Questions Classification
2094223,0.685,0,0,/ashishsinhaiitr/different-embeddings-with-attention-fork-fork,Quora Insincere Questions Classification
2164842,0.63,3,14,/oysiyl/starter-notebook-with-simple-neural-networks,Quora Insincere Questions Classification
2155255,0.679,3,16,/shujian/single-rnn-model-with-meta-features,Quora Insincere Questions Classification
2137556,0.6890000000000001,17,96,/shujian/fork-of-mix-of-nn-models,Quora Insincere Questions Classification
2154378,0.6629999999999999,0,6,/silverstone1903/gru-is-all-you-need-with-f1-opt-and-attention,Quora Insincere Questions Classification
2182848,0.675,0,0,/bkkaggle/fork-of-quora-other-attention-relu-95k-words-spacy,Quora Insincere Questions Classification
2120591,0.688,20,137,/suicaokhoailang/blending-with-linear-regression-0-688-lb,Quora Insincere Questions Classification
2121765,0.66,5,22,/shujian/transformer-with-lstm,Quora Insincere Questions Classification
2133426,0.357,2,2,/cristianossd/tf-idf-approach-on-insincere-questions,Quora Insincere Questions Classification
2069261,0.495,0,0,/karangautam/toxic-comment-v1,Quora Insincere Questions Classification
2126583,0.6709999999999999,0,0,/hncuong/lstm-attention-baseline-0-652-lb,Quora Insincere Questions Classification
8353052,0.973,16,30,/vishal1310/efficientnet-b5-on-tpu,Bengali.AI Handwritten Grapheme Classification
8189697,0.8513,0,0,/tanmaymaloo/fork-of-final-bengali-fe31e3,Bengali.AI Handwritten Grapheme Classification
8375217,0.7538,0,0,/stefanstanojevic/kernel2b55603361,Bengali.AI Handwritten Grapheme Classification
8302196,0.9152,0,1,/shawon10/deep-cnn-model-with-mixup-and-cutmix-augmentation,Bengali.AI Handwritten Grapheme Classification
8249619,0.9225,0,1,/pankajdubey223/bengali-handwritten-classification-using-keras-cnn,Bengali.AI Handwritten Grapheme Classification
8151765,0.9531,0,2,/parmarsuraj99/bengaliai-resnext50-inference,Bengali.AI Handwritten Grapheme Classification
8287220,0.9696,0,0,/mopu3263/keras-efficientnet-b3-with-image-preprocessing,Bengali.AI Handwritten Grapheme Classification
7540903,0.8929,0,0,/bergerda/bengaliai,Bengali.AI Handwritten Grapheme Classification
8129594,0.0751,0,1,/abebe9849/kernel556cc92c19,Bengali.AI Handwritten Grapheme Classification
8086724,0.964,1,4,/sheriytm/grapheme-fastai2-starter-inference,Bengali.AI Handwritten Grapheme Classification
8015385,0.9361,1,0,/axiostpc/bengali-fast-inference-single-model,Bengali.AI Handwritten Grapheme Classification
8043733,0.933,2,0,/santosh16k/inference-of-resnext50-bengali,Bengali.AI Handwritten Grapheme Classification
7901404,0.9427,0,0,/amaity0/bengali-grapheme-first-predict,Bengali.AI Handwritten Grapheme Classification
7796898,0.9193,0,0,/jeongjiheon/resnet34-dropout-radam,Bengali.AI Handwritten Grapheme Classification
13044076,0.01846,0,0,/martintosstorff/fork-of-moalibtest-a89cfc,Mechanisms of Action (MoA) Prediction
12968282,0.16905,0,0,/nikolayskryabin/skryabinnv,Mechanisms of Action (MoA) Prediction
12926563,0.15642,0,0,/simonplatonov/baseline6,Mechanisms of Action (MoA) Prediction
12652574,0.02188,0,0,/wittmannf/quick-eda-and-baseline-submission-with-keras,Mechanisms of Action (MoA) Prediction
12493405,0.01923,0,0,/superant/moa-pytorch-succinct,Mechanisms of Action (MoA) Prediction
11828570,0.01978,0,0,/mateusnobresantos/moa-lgb-optuna,Mechanisms of Action (MoA) Prediction
11004523,0.80079,0,1,/yurimuniz/classifying-tweets-step-by-step,Natural Language Processing with Disaster Tweets
7865365,1.0,0,2,/hisudha/real-disastertweets-v0,Natural Language Processing with Disaster Tweets
11037474,0.80386,4,15,/amanmishra4yearbtech/getting-started-tweet-classification-xgb-svm,Natural Language Processing with Disaster Tweets
9138409,0.83052,1,2,/mihirps18/nlp-disaster-tweets-easy-entry-into-bert,Natural Language Processing with Disaster Tweets
10581641,0.78577,4,17,/aceconhielo/nlp-process-explanation-top-28-solution,Natural Language Processing with Disaster Tweets
10584535,0.84125,9,28,/qilinchu/nlp-with-disaster-tweets-prediction-bert,Natural Language Processing with Disaster Tweets
10972154,0.78179,0,10,/razamh/basic-eda-cleaning-and-glove,Natural Language Processing with Disaster Tweets
10532144,0.78271,0,0,/tokudata20g1/kernel2ef5758741,Natural Language Processing with Disaster Tweets
10823483,0.83757,0,1,/boiledfishpot/twitter-classify-by-bertweet,Natural Language Processing with Disaster Tweets
10364674,0.84186,0,9,/pranavkasela/bert-vs-spark-nlp-use-embeddings,Natural Language Processing with Disaster Tweets
10817248,0.57033,0,0,/jjioni/simple-nlp,Natural Language Processing with Disaster Tweets
10823766,0.7943600000000001,0,0,/varshinithatiparthi/nlp-getting-started,Natural Language Processing with Disaster Tweets
10844706,0.79865,0,2,/rishimukunthant/linearsvc-disaster-tweets-classification,Natural Language Processing with Disaster Tweets
11188797,-7.0004,29,99,/carlossouza/probabilistic-machine-learning-a-diff-approach,OSIC Pulmonary Fibrosis Progression
11200547,-8.1277,3,12,/trooperog/my-first-kaggle-project-decoding-the-fibrosis,OSIC Pulmonary Fibrosis Progression
11015405,-6.8722,0,6,/hfutybx/pytorch-osic-multiple-quantile-regression,OSIC Pulmonary Fibrosis Progression
11085176,-6.8243,46,208,/khoongweihao/efficientnets-quantile-regression-inference,OSIC Pulmonary Fibrosis Progression
11111286,-6.8322,1,4,/prem134/osic-multiple-quantile-regression-deep-learning,OSIC Pulmonary Fibrosis Progression
11078153,-8.9999,1,7,/jaideepvalani/updated-pytorch-osic-starter-6-88-6-91,OSIC Pulmonary Fibrosis Progression
10969269,-7.033,0,7,/pontusbrink/osic-nn,OSIC Pulmonary Fibrosis Progression
10985306,-6.9054,5,10,/vgarshin/osic-keras-images-and-tabular-data-inference,OSIC Pulmonary Fibrosis Progression
10720017,-6.8513,0,2,/akashsuper2000/osic-multiple-quantile-regression-starter-fork,OSIC Pulmonary Fibrosis Progression
10675429,-6.895,2,9,/ekintiu/osic-pulmonary-fibrosis-baseline-regression,OSIC Pulmonary Fibrosis Progression
12503055,0.0,0,3,/muhammadrehan444/toxicity-classification-step-by-step,Jigsaw Unintended Bias in Toxicity Classification
4101479,0.92662,0,0,/aminejait/jigsaw-bert,Jigsaw Unintended Bias in Toxicity Classification
3547377,0.92888,0,0,/buntyshah/jigsaw-classification-lstm,Jigsaw Unintended Bias in Toxicity Classification
7862756,0.0,0,0,/mingolovestime/kernel276501e036,Jigsaw Unintended Bias in Toxicity Classification
7340457,0.0,3,4,/bparesh/cnn-5fold,Jigsaw Unintended Bias in Toxicity Classification
6926299,0.0,0,0,/jasmeetkaur/jigsaw-toxic-classification-with-lstm-attention,Jigsaw Unintended Bias in Toxicity Classification
3523263,0.92906,0,0,/sorzhe/pytorch-toxic-detection-with-callbacks,Jigsaw Unintended Bias in Toxicity Classification
3660213,0.93293,0,0,/abimannan/jigsaw-text-classify,Jigsaw Unintended Bias in Toxicity Classification
4250828,0.93423,0,1,/penpen86/pytorch-bert-inference-22ec03,Jigsaw Unintended Bias in Toxicity Classification
4266521,0.91726,0,2,/infoabhitech/jigsaw-toxic-classification-keras-nn,Jigsaw Unintended Bias in Toxicity Classification
5704368,0.0,0,0,/sonali0103/kernel5f6932067d,Jigsaw Unintended Bias in Toxicity Classification
4168389,0.94134,0,0,/yangsaewon/bert-lstm-inference,Jigsaw Unintended Bias in Toxicity Classification
3935020,0.8250299999999999,0,0,/manmohan123/kernel97688d5835,Jigsaw Unintended Bias in Toxicity Classification
4367679,0.93736,0,0,/ayush99rox/bert-lstm-blend,Jigsaw Unintended Bias in Toxicity Classification
4958771,0.0,0,1,/httpwwwfszyc/keras-models-inference,Jigsaw Unintended Bias in Toxicity Classification
4321942,0.94607,9,38,/haqishen/jigsaw-predict,Jigsaw Unintended Bias in Toxicity Classification
4429425,0.94721,3,55,/iezepov/wombat-inference-kernel,Jigsaw Unintended Bias in Toxicity Classification
4532529,0.94301,0,5,/blackitten13/lstm-cp-clip-nb-tw-tfidf-bert4x-gpt-test-best,Jigsaw Unintended Bias in Toxicity Classification
4260199,0.9951,0,1,/dkrivosic/predict-future-sales-full-pipeline,Predict Future Sales
4544925,0.91946,0,1,/shilparpns/feature-engineering-xgboost,Predict Future Sales
4261057,1.16777,1,8,/alexyau/previous-value-benchmark-simple-eda,Predict Future Sales
4145655,0.92859,6,44,/kabure/simple-eda-model-hyperopt-w-easy-code,Predict Future Sales
4013561,0.91838,0,14,/monthepp/predict-future-sales,Predict Future Sales
3953156,2.68122,0,0,/lgh7654/a-beginner-guide-for-sale-data-prediction-bc9761,Predict Future Sales
3675192,1.15487,0,2,/akumaldo/eda-simple-time-series-lgb-cat-xgboost,Predict Future Sales
3685199,1.16795,0,0,/skotty971/kernele075ffe2dc,Predict Future Sales
3511614,1.00302,0,11,/talevy23/a-beginner-s-guide-to-sales-prediction,Predict Future Sales
2959648,1.05449,0,1,/leewind/extract-features-and-train-by-xgb,Predict Future Sales
2725445,0.92624,0,4,/econdata/predicting-future-sales-with-xgboost,Predict Future Sales
2568911,10.50232,1,21,/marcoracer/predict-future-sales-with-randomforest,Predict Future Sales
2161069,1.55635,0,2,/lider123/salesforecasting,Predict Future Sales
1956086,1.02826,2,4,/knly10/exploratory-analysis-and-prediction,Predict Future Sales
1918542,1.28105,0,0,/ikedayu/simple-stacking,Predict Future Sales
7060328,0.48,0,0,/vh1981/catboost-bayesian-voting,2019 Data Science Bowl
6837990,0.53,2,18,/vbmokin/quick-and-dirty-regression-other-pred-coeffs,2019 Data Science Bowl
468673,0.4314699999999999,0,0,/prakashpvss/fork-of-meta-features-for-classification,Spooky Author Identification
463257,0.47634,13,93,/metadist/work-like-a-pro-with-pipelines-and-feature-unions,Spooky Author Identification
458088,0.43267,31,454,/kashnitsky/vowpal-wabbit-tutorial-blazingly-fast-learning,Spooky Author Identification
450693,0.67396,8,41,/vukglisovic/classification-combining-lda-and-word2vec,Spooky Author Identification
455371,0.42296,0,3,/ibadia/good-score-with-simple-classifier,Spooky Author Identification
448803,0.45716,7,41,/ibadia/easy-python-tutorial-basic-for-beginner,Spooky Author Identification
424031,0.39066,29,107,/selfishgene/generating-sentences-one-letter-at-a-time,Spooky Author Identification
422742,1.0354,5,21,/sudhirnl7/simple-naive-bayes-xgboost,Spooky Author Identification
420729,0.56463,6,20,/nicapotato/explore-the-spooky-n-grams-wordcloud-bayes,Spooky Author Identification
412304,0.37176,32,95,/nzw0301/simple-keras-fasttext-val-loss-0-31,Spooky Author Identification
410988,0.38525,0,9,/knowledgegrappler/embeddings-features-tdf-idf-let-s-party,Spooky Author Identification
407901,0.4478399999999999,9,23,/juanumusic/to-predict-or-not-to-predict-python-tutorial,Spooky Author Identification
5715541,0.655,53,139,/samusram/cloud-classifier-for-post-processing,Understanding Clouds from Satellite Images
5412755,0.417,17,87,/ateplyuk/satelite-easy-starter-keras,Understanding Clouds from Satellite Images
5398681,0.477,0,2,/chandyalex/dump-cloud-simple-experiment,Understanding Clouds from Satellite Images
1833573,0.62549,0,1,/igauty/lb-0-6326-tuned-xgboost-baseline-cb915c,Two Sigma: Using News to Predict Stock Movements
1786485,0.65113,23,110,/dmdm02/complete-eda-voting-lightgbm,Two Sigma: Using News to Predict Stock Movements
1794071,0.6207199999999999,3,33,/davero/market-data-only-baseline,Two Sigma: Using News to Predict Stock Movements
1790123,-0.06744,0,2,/patrickhyland/eda-and-submission,Two Sigma: Using News to Predict Stock Movements
1737995,0.62902,168,938,/artgor/eda-feature-engineering-and-everything,Two Sigma: Using News to Predict Stock Movements
1754733,2.89408,2,48,/alluxia/lb-0-6326-tuned-xgboost-baseline,Two Sigma: Using News to Predict Stock Movements
1743189,0.53425,7,16,/bielrv/two-sigma-extensive-eda,Two Sigma: Using News to Predict Stock Movements
1729737,1.51443,8,51,/magichanics/amateur-hour-using-headlines-to-predict-stocks,Two Sigma: Using News to Predict Stock Movements
2937304,0.02437,0,0,/drewlinsley/dscov-tutorial,Human Protein Atlas Image Classification
2648505,0.04028,0,1,/econdata/humanprotiensatlasimageclassification,Human Protein Atlas Image Classification
2572064,0.412,1,5,/achoetwice/4-channel-v2-with-rare-t2-plus-threshold,Human Protein Atlas Image Classification
2472224,0.288,0,2,/thesdfdfbaipo/kernelac0446fbd9,Human Protein Atlas Image Classification
2034071,0.3289999999999999,0,1,/wordroid/keras-inceptionresnetv2-resize139x139-005focal,Human Protein Atlas Image Classification
2339808,0.008,0,1,/gaojunsu/sgd-of-ee258-project2-seresnet50,Human Protein Atlas Image Classification
2347364,0.456,8,13,/hung96ad/resnet34-with-rgby-fast-ai-fork-127e02,Human Protein Atlas Image Classification
1856469,0.055,0,0,/anunnikrishnan/image-classify-v1,Human Protein Atlas Image Classification
2097203,0.2239999999999999,4,4,/amneves/keras-proteins,Human Protein Atlas Image Classification
1831064,0.153,1,3,/humamfauzi/human-protein-atlas,Human Protein Atlas Image Classification
116485,1169.00354,0,0,/nightshade7/allstate-severity-test,Allstate Claims Severity
540156,0.390976,0,1,/denisbubel95/fork-of-fork-of-notebook357e5c0385,Liberty Mutual Group: Property Inspection Prediction
8759839,0.98021,5,6,/haveri/flowerflowerwhoareyou-onlysubmissions-ensembling,Flower Classification with TPUs
8907348,0.96422,0,2,/liangqingyuan/flowertpuwin,Flower Classification with TPUs
9146998,0.96729,0,5,/tusharkendre/tpu-enet-b7-incepention-b6,Flower Classification with TPUs
8935457,0.95852,0,0,/dannymac180/flower-classification-w-tpus,Flower Classification with TPUs
8962472,0.90132,0,0,/alexfi/tpu-resnet-flower-detection,Flower Classification with TPUs
8960703,0.96744,1,5,/pawanverma/flowers-efficient-net-dense,Flower Classification with TPUs
8502174,0.93923,0,0,/tgibbons/gibbons-basic-100flowers,Flower Classification with TPUs
8970294,0.95519,0,0,/danielmorton/enet-with-100-flowers-on-tpu,Flower Classification with TPUs
8867076,0.85711,0,0,/cassandraheide/tpu-getting-started-notebook-xception,Flower Classification with TPUs
8812243,0.93476,1,5,/yasufuminakama/flower-pytorch-xla-se-resnext50,Flower Classification with TPUs
8608445,0.85293,0,0,/harisj/flower-v1,Flower Classification with TPUs
8535940,0.79915,0,2,/qinhui1999/pytorch-xla-for-tpu-with-multiprocessing,Flower Classification with TPUs
8258199,0.96552,5,19,/sebastiankoenig/flower-classification-ensemble,Flower Classification with TPUs
8212198,0.95751,0,2,/liangqingyuan/flowers-on-tpu-densenet,Flower Classification with TPUs
8168142,0.9569,23,200,/cdeotte/cutmix-and-mixup-on-gpu-tpu,Flower Classification with TPUs
8186866,0.96018,0,3,/darwinwin/flower-classification-with-tpu,Flower Classification with TPUs
8199890,0.96487,1,2,/jd81197/kernel4fcb503f8e,Flower Classification with TPUs
8022862,0.96182,0,0,/shank885/tpu-enet-b7-densenet,Flower Classification with TPUs
8020511,0.93911,11,10,/chekoduadarsh/inceptionresnetv2-tpu-vs-gpu-benchmark,Flower Classification with TPUs
8027055,0.95055,2,2,/dmcgow/flower-lr-schedule,Flower Classification with TPUs
7931877,0.9444,0,0,/shank885/flowers-custom-data,Flower Classification with TPUs
11088069,0.99425,1,5,/gabrielmilan/efficientnet-baseline,Digit Recognizer
11086248,0.99057,1,8,/sumitkant/say-hello-world-with-cnns,Digit Recognizer
11075147,0.98846,1,6,/ravindrab/digit-recognition-first-competition,Digit Recognizer
11040563,0.99864,4,27,/abdelwahed43/handwritten-digits-recognizer-0-999-simple-model,Digit Recognizer
9740221,0.99396,0,2,/aryamanmishra/digit-recognizer-using-cnn,Digit Recognizer
10977592,0.992,0,0,/joaossmacedo/mnist-cnn,Digit Recognizer
10996890,0.99067,1,10,/imeintanis/cnn-track-your-experiments-weights-biases,Digit Recognizer
11060881,0.94203,0,0,/kubonoyusuke/kernel229022a239,Digit Recognizer
10951671,0.99607,0,5,/chinmaysalvi/digit-mnist-recognizer,Digit Recognizer
10953526,0.992,0,2,/dibyawantrivedi/first-neural-net,Digit Recognizer
10695343,0.99175,0,9,/niksapraljak/mnist-activation-maps,Digit Recognizer
10932097,0.99364,2,8,/liberifatali/mnist-with-pytorch-basic,Digit Recognizer
10938950,0.9926,0,1,/gpdsec/mnist,Digit Recognizer
10925345,0.98292,0,3,/guilhermesdas/mnist-digit-classification-lenet5,Digit Recognizer
10916524,0.96753,0,1,/quentinfortier/mnist,Digit Recognizer
10965505,0.94203,0,0,/junasa/kernel796c60fb39,Digit Recognizer
10848428,0.98275,0,3,/islammohaisen/digit-recognizer-simple-cnn,Digit Recognizer
9752807,0.87,2,26,/manojprabhaakr/melanoma-tpu-starter-efficientnet-b0,SIIM-ISIC Melanoma Classification
9756756,0.877,6,11,/rohitsingh9990/melanoma-eda-visualizations-ensemble,SIIM-ISIC Melanoma Classification
9743588,0.877,2,25,/yeayates21/siim-keras-efficientnetb3-starter-tfrec-tpu,SIIM-ISIC Melanoma Classification
9745418,0.74,0,2,/yeayates21/siim-baseline-no-deep-learning,SIIM-ISIC Melanoma Classification
9748065,0.5,0,0,/grapestone5321/siim-isic-melanoma-class-sample-submission,SIIM-ISIC Melanoma Classification
11001119,0.9351,0,0,/strikecounter/cancer-2,SIIM-ISIC Melanoma Classification
10786871,0.9464,0,0,/akashsuper2000/triple-stratified-kfold-with-tfrecords,SIIM-ISIC Melanoma Classification
10557277,0.927,0,0,/arachauhan/incredible-tpus-finetune-effnetb0-b6-at-once,SIIM-ISIC Melanoma Classification
7250478,0.51,0,0,/srikarg/regression,2019 Data Science Bowl
3306711,0.36577,0,0,/jupaoqq/ncaaw-prelim,Google Cloud & NCAA® ML Competition 2019-Women's
3525312,0.36313,0,2,/takaishikawa/no-ml-modeling,Google Cloud & NCAA® ML Competition 2019-Women's
3317893,0.0,0,7,/hamidhaghshenas/public-score-0-000000,Google Cloud & NCAA® ML Competition 2019-Women's
3089040,0.10964,0,14,/hamidhaghshenas/adaboostclassifier,Google Cloud & NCAA® ML Competition 2019-Women's
2954254,4.1668,2,20,/jazivxt/courtside-seat-2019w-competitiveness,Google Cloud & NCAA® ML Competition 2019-Women's
9350684,0.3064199999999999,0,0,/ouwyukha/imba-turicreate-itemsim-pearson,Instacart Market Basket Analysis
9339700,0.30644,0,0,/ouwyukha/imba-turicreate-fr-als-pol,Instacart Market Basket Analysis
9339568,0.0930899999999999,0,0,/ouwyukha/imba-surprise-nmf,Instacart Market Basket Analysis
9339479,0.0930899999999999,0,0,/ouwyukha/imba-surprise-baseline-sgd,Instacart Market Basket Analysis
4113540,0.30955,0,0,/bad19006/instacart-ml-2-notebook-to-check,Instacart Market Basket Analysis
10561825,-6.96,0,7,/shishu1421/osic-eda,OSIC Pulmonary Fibrosis Progression
10551054,-7.468,0,4,/arunadeviramesh/osic-pulmonary-fibrosis-progression,OSIC Pulmonary Fibrosis Progression
10543846,-10.741,0,7,/akashram/boosting-models-intro-and-baseline,OSIC Pulmonary Fibrosis Progression
11999248,-6.8087,0,0,/akashsuper2000/higher-lb-score-by-tuning-mloss-upgrade-visual,OSIC Pulmonary Fibrosis Progression
11912659,-6.8143,0,0,/shahkanishk/efficient-nets-on-osic,OSIC Pulmonary Fibrosis Progression
11427481,-6.8246,0,0,/akashsuper2000/efficientnets-quantile-regression,OSIC Pulmonary Fibrosis Progression
4036393,0.73864,0,0,/sanjayroberts1/sklearn-algorithms-toxic-comments,Jigsaw Unintended Bias in Toxicity Classification
3769984,0.9089,0,0,/takafumif/lstm-model,Jigsaw Unintended Bias in Toxicity Classification
3899259,0.92257,0,4,/praxitelisk/jigsaw-toxicity-classification-eda-dl-keras-lstm,Jigsaw Unintended Bias in Toxicity Classification
3956181,0.9202,0,0,/afmartinezt/miia-submit-2,Jigsaw Unintended Bias in Toxicity Classification
3933961,0.93373,32,212,/abhishek/pytorch-bert-inference,Jigsaw Unintended Bias in Toxicity Classification
3947595,0.92491,2,6,/jacobgreen4477/dms-korea-clean-text-glove-fasttext-keras,Jigsaw Unintended Bias in Toxicity Classification
3943274,0.93435,0,4,/ddw02141/simple-lstm-attention,Jigsaw Unintended Bias in Toxicity Classification
3966606,0.90644,0,0,/proyectomineria2019/kernel9b764d76f5,Jigsaw Unintended Bias in Toxicity Classification
3888425,0.93442,0,0,/lfrincon90/kernel3b4efc9320,Jigsaw Unintended Bias in Toxicity Classification
3882972,0.8931899999999999,0,0,/gali1eo/benchmark-kernel,Jigsaw Unintended Bias in Toxicity Classification
3894959,0.93449,5,12,/duykhanh99/introduction-to-nlp-hust,Jigsaw Unintended Bias in Toxicity Classification
3894809,0.85226,1,4,/gjeffroy/simple-model-naive-bayes-and-logistic-reg,Jigsaw Unintended Bias in Toxicity Classification
3875431,0.48339,1,2,/prakhar21god/toxic-classification-bias-visualization,Jigsaw Unintended Bias in Toxicity Classification
3812017,0.93372,0,5,/duykhanh99/bidirectional-lstm-and-attention,Jigsaw Unintended Bias in Toxicity Classification
3799338,0.93384,4,19,/duykhanh99/bidirectional-lstm-attention,Jigsaw Unintended Bias in Toxicity Classification
3671648,0.7149300000000001,0,2,/yeayates21/jigsaw-simple-text-features-and-h2o-v4,Jigsaw Unintended Bias in Toxicity Classification
3717325,0.5063300000000001,0,1,/maxl28618/toxic-comments-lstm-in-pytorch-with-torchtext,Jigsaw Unintended Bias in Toxicity Classification
3742684,0.91235,5,13,/neel783d/keras-lstm-model-without-pretrained-embedding,Jigsaw Unintended Bias in Toxicity Classification
3697284,0.92745,6,52,/christofhenkel/temporal-cnn,Jigsaw Unintended Bias in Toxicity Classification
3717542,0.63179,0,3,/xjie123456/kernel3f7f09718c,Jigsaw Unintended Bias in Toxicity Classification
11892159,0.57033,0,2,/mkahraman19/getting-started,Natural Language Processing with Disaster Tweets
11833505,0.8106,0,1,/navneetsajwan/understanding-text-classification-fastai2-ulmfit,Natural Language Processing with Disaster Tweets
11586295,0.8240799999999999,0,1,/sunnyville01/glove-embeddings-solution-sklearn-custom-class,Natural Language Processing with Disaster Tweets
11570067,0.82255,1,3,/ryanchan911/prediction-on-disaster-tweets-using-bert,Natural Language Processing with Disaster Tweets
11645329,0.81428,0,3,/takanobu0210/baseline-keras-bi-lstm-plot-using-nlplot,Natural Language Processing with Disaster Tweets
11617275,0.8014,5,17,/iljaavadiev/nlp-studying,Natural Language Processing with Disaster Tweets
11515273,0.8133600000000001,2,10,/pawan300/catastrophe,Natural Language Processing with Disaster Tweets
11324397,0.8133600000000001,0,0,/chengham/succinct-baseline,Natural Language Processing with Disaster Tweets
11472940,0.8391,3,4,/sonyrajan/disastertweets,Natural Language Processing with Disaster Tweets
11281493,0.80079,0,2,/sayakpaul/nlp-disaster-tweets-practice,Natural Language Processing with Disaster Tweets
11527263,0.81152,0,5,/willyiamyu/disaster-word-embeddings-svm,Natural Language Processing with Disaster Tweets
9519732,0.83481,0,2,/timgibson/moving-from-scikit-learn-to-transformers,Natural Language Processing with Disaster Tweets
11469718,0.78823,2,4,/georgezakharov/tweet-predictor,Natural Language Processing with Disaster Tweets
11430203,0.78394,8,42,/naim99/disaster-tweets-classification-distilbert-bert,Natural Language Processing with Disaster Tweets
11429820,0.80294,0,4,/laytsw/nlp-with-keras-fasttext,Natural Language Processing with Disaster Tweets
110056,11.308,0,1,/icecube314/merged-keras-for-leaf-classification,Leaf Classification
234592,0.35372,0,0,/uditsaini/data-analysis-xgboost-starter-0-35460-lb,Quora Question Pairs
3947313,0.80878,58,231,/cdeotte/logistic-regression-0-800,Instant Gratification
3970532,0.82926,0,1,/atikur/instagrat-keras-stratifiedkfold,Instant Gratification
3953200,0.63121,2,3,/hyeonho/lightgbm-starter,Instant Gratification
3938574,0.7701100000000001,0,20,/artgor/ig-eda-and-models,Instant Gratification
3945426,0.80878,0,3,/seshadrikolluri/ig-various-approaches-why-and-how,Instant Gratification
3941408,0.73852,0,7,/tunguz/instant-h2o-automl,Instant Gratification
3943113,0.75913,0,2,/lsinev/instant-gratification-catboost-starter,Instant Gratification
3942039,0.5886600000000001,0,1,/wodlfrh/lightgbm-pure-starter,Instant Gratification
3939098,0.57321,0,0,/sanikamal/instant-gratification-eda-to-xgb,Instant Gratification
4268684,0.96977,0,0,/shubham505/flip-y-pseudo-labelling-with-pca-qda,Instant Gratification
6877181,0.5541,0,3,/saeedtqp/quora-duplicate-questions,Quora Question Pairs
6272007,0.46802,0,6,/qqgeogor/starter-with-triditional-features,Quora Question Pairs
1265464,0.38483,0,0,/krishmahajan/quora-duplicate-questions-final,Quora Question Pairs
4304570,0.9695,0,16,/qy2205/pca-variance-qda-knn-4-stacking,Instant Gratification
4270029,0.97004,7,65,/tayorm/pl-lasso-gmm-pca-qda,Instant Gratification
4230818,0.96587,5,14,/kellehermhc/plain-old-pandas-and-numpy,Instant Gratification
4276204,0.92893,2,8,/safavieh/knn-lightgbm,Instant Gratification
4244813,0.96977,9,23,/ilhamfp31/flip-y-pseudo-labelling-with-pca-qda,Instant Gratification
4217476,0.88018,0,0,/joelstan/my-thought-process-svc,Instant Gratification
4208612,0.96972,3,67,/rdekou/pseudo-labelling-with-pca-qda,Instant Gratification
4210738,0.96395,9,16,/binilg/labelspreading-with-pseudo-labeling,Instant Gratification
4142289,0.96816,17,103,/nroman/i-m-overfitting-and-i-know-it,Instant Gratification
4153186,0.96814,2,9,/liu123/i-think-there-is-not-overfitting-fork-from-roman,Instant Gratification
4121252,0.96953,19,79,/gogo827jz/pseudo-labelled-polylr-and-qda,Instant Gratification
4126024,0.96288,4,20,/sairakun/visualize-compare-nusvc-svc-qda-knn,Instant Gratification
4131221,0.52308,0,0,/dashnabanita/instant-gratification-randomforest,Instant Gratification
4171282,0.96619,0,0,/vkat72293/kernel29e1348872,Instant Gratification
4090518,0.96942,13,28,/graf10a/tuning-512-separate-qda-models,Instant Gratification
4057711,0.96642,1,15,/kvdatadragon/ordered-code-pca-log-knn-qda,Instant Gratification
4051425,0.4969,13,9,/mhviraf/there-is-predictive-power-in-the-useless-columns-2,Instant Gratification
4001895,0.95788,0,5,/valteresj/svm-lr-knn-ensemble-result-auc-95-78,Instant Gratification
3989008,0.95015,26,208,/cdeotte/private-lb-probing-0-950,Instant Gratification
3998468,0.83362,0,5,/gouzmi/512-lgb,Instant Gratification
3982106,0.91681,1,22,/sagau5999/512-knn-10-with-lgb-feature-selection-91-6-lb,Instant Gratification
3946485,0.88813,0,7,/baomengjiao/nn-with-magic-1024,Instant Gratification
13707263,0.9579,0,0,/jamesccc/bengali-single,Bengali.AI Handwritten Grapheme Classification
10927942,0.9345,0,7,/amanmishra4yearbtech/bengali-classification-basic-eda-implementation,Bengali.AI Handwritten Grapheme Classification
8004072,0.9488,0,1,/shythm/bengali-recognize,Bengali.AI Handwritten Grapheme Classification
9472219,0.8959,0,0,/mahisoni/bengali-ai-deep-learning,Bengali.AI Handwritten Grapheme Classification
9019977,0.9661,0,0,/garyongguanjie/resnet34-ensemble,Bengali.AI Handwritten Grapheme Classification
8230470,0.9502,0,0,/oooyeeun/multioutput-cnn-ensemble,Bengali.AI Handwritten Grapheme Classification
8879580,0.0614,0,0,/jonathanql/tests,Bengali.AI Handwritten Grapheme Classification
8144440,0.9652,0,0,/larswigger/pytorch-bengali-submission,Bengali.AI Handwritten Grapheme Classification
8479625,0.9703,0,1,/p4rallax/private-0-9557,Bengali.AI Handwritten Grapheme Classification
8507079,0.9665,0,0,/moximo13/spatial-transform-network-bengali,Bengali.AI Handwritten Grapheme Classification
8409193,0.9854,9,70,/haqishen/bengali-predict-with-seen-unseen-models,Bengali.AI Handwritten Grapheme Classification
8424910,0.9482,0,4,/jamesmcguigan/bengali-ai-cnn-data-pipeline-problem-solving,Bengali.AI Handwritten Grapheme Classification
8292617,0.9361,0,0,/mrcooperr/the-submission-notebook-of-team-daemencloudt,Bengali.AI Handwritten Grapheme Classification
96577,0.03112,0,0,/userad/notebook6ce096463b,Leaf Classification
96420,0.03112,0,0,/potterxu/leaf-classfication,Leaf Classification
12357525,0.80202,0,1,/sviatoslavlavrinchuk/disaster-tweets,Natural Language Processing with Disaster Tweets
12078411,0.80386,0,0,/thulesen/nlp-basic,Natural Language Processing with Disaster Tweets
12180487,0.7919,0,1,/laxmivishvkarma/notebook6e97103156,Natural Language Processing with Disaster Tweets
49369,0.8230280000000001,1,1,/aissaelouafi/simple-ensemble-method,Santander Customer Satisfaction
47495,0.839161,0,0,/zhaoxiong/test2,Santander Customer Satisfaction
46613,0.838325,0,1,/techieram/santander-classify-customers-forked,Santander Customer Satisfaction
45878,0.5010180000000001,0,0,/srodriguex/xgboost-with-python,Santander Customer Satisfaction
12981020,0.45255,0,0,/roohisharma/movie-review-sentiment-analysis-lstm,Sentiment Analysis on Movie Reviews
11346829,0.62408,0,13,/aczy156/sentiment-analysis-lstm-or-gru,Sentiment Analysis on Movie Reviews
11266342,0.5765,0,0,/julianbenny/sentimentanalysis,Sentiment Analysis on Movie Reviews
11041026,0.6484300000000001,1,2,/razanabudagen/movie-reviews-prediction,Sentiment Analysis on Movie Reviews
10841833,0.66535,0,2,/benjaminkz/bert-for-sentiment-analysis,Sentiment Analysis on Movie Reviews
8364491,0.67715,5,1,/ade1963/sentence-transformers-for-sentiment-analysis,Sentiment Analysis on Movie Reviews
6385650,0.70071,75,252,/maroberti/fastai-with-transformers-bert-roberta,Sentiment Analysis on Movie Reviews
4295444,0.57227,1,0,/pranjalya/movie-reviews,Sentiment Analysis on Movie Reviews
2898064,0.49444,0,2,/ruchibahl18/cudnnlstm-model,Sentiment Analysis on Movie Reviews
4350140,0.93822,0,1,/dyyalex/jigsaw-starter-blend-with-bert-config,Jigsaw Unintended Bias in Toxicity Classification
4797386,0.0,1,0,/animeshsinha1309/simple-lstm-for-toxicity-classification,Jigsaw Unintended Bias in Toxicity Classification
4536838,0.94078,0,3,/snakayama/fork-of-first-bert-model,Jigsaw Unintended Bias in Toxicity Classification
4136954,0.93417,0,0,/ruhong/jigsaw-unintended-bias-simple-lstm,Jigsaw Unintended Bias in Toxicity Classification
4118500,0.9329,0,0,/dalip98/simple-lstm-keras,Jigsaw Unintended Bias in Toxicity Classification
4137347,0.9315,0,0,/ruhong/jigsaw-unintended-bias-simple-lstm-pytorch,Jigsaw Unintended Bias in Toxicity Classification
4333863,0.74239,0,0,/fanluwu/nb-jigsaw,Jigsaw Unintended Bias in Toxicity Classification
4288786,0.90033,1,5,/sanjeeth/logreg-nb-text-classification,Jigsaw Unintended Bias in Toxicity Classification
4209934,0.93446,0,8,/jasonjensen/remove-identity-words-simple-lstm,Jigsaw Unintended Bias in Toxicity Classification
4214426,0.51568,0,0,/tomkpace/bow-svd-knn-as-a-weak-learner,Jigsaw Unintended Bias in Toxicity Classification
4192486,0.93007,0,1,/aialba/lstm-w-pytorch,Jigsaw Unintended Bias in Toxicity Classification
4206928,0.89066,0,0,/cuijinghuan/kernel9dd7d93aaf,Jigsaw Unintended Bias in Toxicity Classification
4149304,0.91178,1,3,/adrianfvm/complete-eda-fasttext-keras-cudnnlstm,Jigsaw Unintended Bias in Toxicity Classification
4105761,0.93481,0,3,/chriscc/jigsaw-starter,Jigsaw Unintended Bias in Toxicity Classification
4051445,0.93706,8,37,/tanreinama/pretext-lstm-tuning-v3-with-ensemble-tune,Jigsaw Unintended Bias in Toxicity Classification
3985544,0.93672,0,1,/rafay12/jigsaw-competition-12,Jigsaw Unintended Bias in Toxicity Classification
13399006,1747712.9557599996,0,2,/jackttai/revenue-prediction-using-lightgbm,Restaurant Revenue Prediction
12100793,2343168.75443,0,0,/miyanic/ds-stdy-restaurant-1021,Restaurant Revenue Prediction
11765688,2381715.34246,0,0,/shogotakamuro/ds-stdy-restaurant-st,Restaurant Revenue Prediction
11656525,1929245.11373,0,6,/spoorthiuk/restaurant-revenue-prediction-withdifferentmodels,Restaurant Revenue Prediction
11477109,1767783.44667,0,4,/meridk/ms-dos,Restaurant Revenue Prediction
10913030,2157761.8372,0,2,/mohgsam/restaurant-revenue-prediction,Restaurant Revenue Prediction
10682713,2426044.01655,0,1,/fatimaafifi/restaurant-revenue-prediction,Restaurant Revenue Prediction
10154347,1931514.66806,8,15,/jatta3399/revenuerrestr,Restaurant Revenue Prediction
9655329,2583331.17641,0,0,/tansifanzar/gradient-boosting-without-parameter-optimization,Restaurant Revenue Prediction
9620391,1790434.3700400004,2,3,/arifintahu/restaurant-revenue-prediction,Restaurant Revenue Prediction
8382140,1836632.63349,0,0,/batofgotham/gradient-boosting,Restaurant Revenue Prediction
6130621,1976398.3905,0,1,/devkhant24/restaurant-revenue,Restaurant Revenue Prediction
6149603,1862892.75184,0,2,/taruto1215/tfi-scikit-learn-randomforest-mlp-svr-ensemble,Restaurant Revenue Prediction
3786189,1752629.9450299996,1,3,/francispimentel/restaurant-revenue,Restaurant Revenue Prediction
138832,1779833.35426,4,56,/ani310/restaurant-revenue,Restaurant Revenue Prediction
9568647,0.99385,0,0,/sophieb/mnist-using-the-fastai-library,Digit Recognizer
11218446,0.99135,0,0,/vensonchiang/digit-recognizer-capsule-net,Digit Recognizer
11275190,0.99025,0,5,/choubane/cnn-digit-recognizer-99-00-validation-accuracy,Digit Recognizer
10774499,0.9955,0,8,/slm37102/0-9955-using-diff-arch-of-cnn-in-mnist-fastai-v1,Digit Recognizer
11245893,0.99275,3,7,/tunguz/keras-kerastuner-best-practices,Digit Recognizer
11179393,0.94185,0,0,/dohunkim/gnb-1,Digit Recognizer
11241542,0.9861,0,3,/tristansaminadayar/my-digit-recognizer,Digit Recognizer
5866910,0.99214,0,0,/sondregj/digit-recognition,Digit Recognizer
11223578,0.98457,2,5,/iamsvp/digit-recognizer-with-pytorch,Digit Recognizer
10820367,0.97396,0,0,/reviveer/faster-training-using-pca,Digit Recognizer
11192704,0.96392,0,0,/sergeycherepanov/digit-recognizer-kaggle-hello-world,Digit Recognizer
10762909,0.99107,0,0,/archanghosh/deep-cnn-using-tensorflow,Digit Recognizer
8212338,0.99471,0,0,/blakemoore/mnist-aug2-simp,Digit Recognizer
11142293,0.99414,0,0,/rajabhi1/kernel24bf39ba19,Digit Recognizer
11152622,0.99189,4,2,/nazmultakbir/digit-recognizer-using-pytorch-with-gpu,Digit Recognizer
11153080,0.9701,0,3,/maxmar/random-forest-with-data-augmentation,Digit Recognizer
11129033,0.99407,0,4,/katchupalvarenga/mnist-cnn-tensorflow-99,Digit Recognizer
11055227,0.99478,0,8,/katchupalvarenga/mnist-cnn-em-portugu-s,Digit Recognizer
13660413,0.4575899999999999,2,4,/nicapotato/spooky-authors-keras-transformers,Spooky Author Identification
13471745,0.4517,0,2,/nicapotato/bert-for-spooky-author-identification,Spooky Author Identification
10327453,0.5304399999999999,0,3,/raj26000/spooky-author-identification-using-bi-lstm-tfidf,Spooky Author Identification
6517425,0.45736,0,3,/guidosalimbeni/feature-extraction-from-text,Spooky Author Identification
3304038,0.4831899999999999,0,0,/lsjsj92/spooky-nlp-eda-keras-lstm,Spooky Author Identification
2251322,0.36393,0,0,/ebertolo/workshop02-simple-keras-fasttext,Spooky Author Identification
1013393,0.67086,1,0,/rdcmdev/spooky-author-with-markov-chain,Spooky Author Identification
424628,1.11891,0,0,/jratchford/sa-nlp-humauto-lowdata,Spooky Author Identification
481570,0.40783,0,3,/nathanielysee/bayesian-networks-for-author-identification,Spooky Author Identification
6775764,0.36166,0,2,/vinayakasandilya/segmentation-using-fast-ai,Understanding Clouds from Satellite Images
6632007,0.66492,8,27,/cdeotte/without-ensemble-lb-0-665,Understanding Clouds from Satellite Images
6646776,0.6002,2,2,/marcogorelli/submitting-chris-unsupervised-masks-cv-0-60,Understanding Clouds from Satellite Images
5920350,0.6559999999999999,0,0,/avinashrai/cloud-classifier-for-post-processing-inference,Understanding Clouds from Satellite Images
6431477,0.611,10,47,/cdeotte/cloud-bounding-boxes-lb-0-61,Understanding Clouds from Satellite Images
6096333,0.653,33,67,/dimitreoliveira/cloud-segmentation-with-utility-scripts-and-keras,Understanding Clouds from Satellite Images
5901256,0.63,23,157,/dhananjay3/image-segmentation-from-scratch-in-pytorch,Understanding Clouds from Satellite Images
5862707,0.6579999999999999,58,233,/mobassir/keras-efficientnetb2-for-classifying-cloud,Understanding Clouds from Satellite Images
5815864,0.653,21,66,/jpbremer/efficient-net-b4-unet-clouds,Understanding Clouds from Satellite Images
5752343,0.614,8,36,/samusram/gradcam-extracting-masks-from-classifier,Understanding Clouds from Satellite Images
7986639,0.96315,4,14,/gskdhiman/enet-b7-densenet-with-tta,Flower Classification with TPUs
7994303,0.95242,0,0,/dmcgow/flowers-tpu,Flower Classification with TPUs
7966677,0.95959,3,5,/sanikamal/flower-classification-dnet201-enetb7,Flower Classification with TPUs
7983233,0.9312,0,3,/phunghieu/flowers-with-tpu-xception-focalloss,Flower Classification with TPUs
7977192,0.91908,0,3,/phunghieu/flowers-with-tpu-resnet152-focalloss,Flower Classification with TPUs
7971672,0.95637,1,2,/chariots17/flower-classification-using-transfer-learning,Flower Classification with TPUs
7956925,0.0004,7,6,/mpwolke/valentine-s-day-no-tpu,Flower Classification with TPUs
7924234,0.95554,14,94,/ratan123/densenet201-flower-classification-with-tpus,Flower Classification with TPUs
7928206,0.9244,2,10,/rakibilly/flowers-for-beginners-like-me-on-tpu,Flower Classification with TPUs
7925580,0.9373,1,5,/kaushal2896/inceptionv3-100-flowers-classification,Flower Classification with TPUs
7923665,0.94874,4,2,/anubhav1302/tpu-flower-classification,Flower Classification with TPUs
6810482,0.96322,7,35,/mmmarchetti/flowers-on-tpu-ii,Flower Classification with TPUs
9291313,0.97731,0,0,/redwankarimsony/efficientnet-with-all-5-imagesets-s1,Flower Classification with TPUs
8795461,0.96322,0,0,/taohoang/flowertpuwin,Flower Classification with TPUs
8199821,0.96239,0,0,/qinhui1999/fork-of-tpu-enet-b7-incepentionresnetv2-densene,Flower Classification with TPUs
8045094,0.95937,0,0,/akashsuper2000/tpu-enet-b7-densenet,Flower Classification with TPUs
7163909,69017.53,0,14,/dmintry/c-stochastic-product-search-in-few-threads,Santa's Workshop Tour 2019
6942765,69307.8,1,5,/weeezy/santa-mip-ls,Santa's Workshop Tour 2019
6941575,77333.33,1,2,/lucamassaron/my-initial-solution-using-hungarian-algorithm,Santa's Workshop Tour 2019
7280089,90365.44,0,2,/nkoprowicz/only-calculate-changing-elements-of-cost,Santa's Workshop Tour 2019
7238572,69827.7,15,88,/golubev/mip-optimization-preference-cost,Santa's Workshop Tour 2019
7221128,250411.48,1,1,/yasarc4/greedy-starter-public-250411,Santa's Workshop Tour 2019
7078845,72046.79,2,14,/gogo827jz/lucky-choice-search,Santa's Workshop Tour 2019
6971527,77777.77,9,23,/khahuras/the-elegant-prize,Santa's Workshop Tour 2019
6934307,1365368.43,2,4,/karnakarthoorpu/what-is-your-visiting-date,Santa's Workshop Tour 2019
6920137,72150.25,4,30,/hengzheng/santa-s-seed-seeker,Santa's Workshop Tour 2019
6902787,10303043.95,2,1,/ichabuddaeta/santa-2-the-return-of-the-last-place-hero,Santa's Workshop Tour 2019
6843525,76177.27,8,40,/jesperdramsch/intro-to-santa-s-2019-viz-costs-22-s-and-search,Santa's Workshop Tour 2019
6830503,79913.26,6,94,/ghostskipper/visualising-results,Santa's Workshop Tour 2019
6808071,75049.53,6,38,/capiru/santa-s-workshop-eda-sorting-visualization,Santa's Workshop Tour 2019
6804568,339618.3,8,24,/pulkitmehtawork1985/hungarian-algorithm-to-be-continued,Santa's Workshop Tour 2019
6768435,672254.02766,16,187,/inversion/santa-s-2019-starter-notebook,Santa's Workshop Tour 2019
6792920,430074.77,5,21,/deyury/starter-notebook-greedy-initialization,Santa's Workshop Tour 2019
6789598,586850.61,1,8,/pulkitmehtawork1985/ho-ho-ho,Santa's Workshop Tour 2019
13063682,0.79186,1,2,/rahuljha21021998/let-s-save-jack-try-1-basic-approach-to-solve,Titanic - Machine Learning from Disaster
13173972,0.03503,0,5,/knstqq/notebook-prize-annealing-sat,Conway's Reverse Game of Life 2020
12887573,0.22851,0,0,/motivic/reverse-forward-cnn,Conway's Reverse Game of Life 2020
12289491,0.14689,0,0,/jamesmcguigan/reverse-game-of-life-rnn,Conway's Reverse Game of Life 2020
12266838,0.13087,0,1,/robintwhite/iterative-cnn-in-tf-keras,Conway's Reverse Game of Life 2020
12485880,0.13502,1,0,/lakitha/game-of-life-code-cnn-accuracy-84-submission,Conway's Reverse Game of Life 2020
12141564,0.08631,0,3,/jamesmcguigan/game-of-life-image-segmentation-solver,Conway's Reverse Game of Life 2020
11962542,0.13093,0,7,/elvenmonk/pycosat-exploration,Conway's Reverse Game of Life 2020
11691237,0.14502,1,10,/jamesmcguigan/game-of-life-hashmap-solver,Conway's Reverse Game of Life 2020
11658012,0.11117,6,32,/yakuben/crgl-probability-extension-true-target-problem,Conway's Reverse Game of Life 2020
11618332,0.1459599999999999,1,14,/parmarsuraj99/a-neural-cnn-game-of-life-with-keras,Conway's Reverse Game of Life 2020
11489373,0.14689,0,12,/seraphwedd18/application-of-gan-for-predicting-initial-state,Conway's Reverse Game of Life 2020
11490687,0.14469,0,10,/li325040229/simple-lgb-model-model-using-only-0-6-test-data,Conway's Reverse Game of Life 2020
11499274,0.14002,2,14,/li325040229/the-game-of-life-reverse-with-random-forest,Conway's Reverse Game of Life 2020
11487714,0.17146,0,7,/arnimen5/eda-and-sample-submission,Conway's Reverse Game of Life 2020
12320368,0.06189,0,0,/akashsuper2000/genetic-algorithm-submission-kernel,Conway's Reverse Game of Life 2020
14582036,0.0,7,30,/ateplyuk/hpa-pytorch-starter-code,Human Protein Atlas - Single Cell Classification
14482995,0.0,6,27,/koheist/hpa-introduction-simple-eda,Human Protein Atlas - Single Cell Classification
14533120,0.016,4,20,/thedrcat/hpa-baseline-cell-segmentation,Human Protein Atlas - Single Cell Classification
14692886,0.84215,4,22,/tunguz/ensembling-starter-tps-feb-2021,Tabular Playground Series - Feb 2021
14641367,0.8850100000000001,5,15,/maunish/tps-feb-super-cool-eda-autoencoder-pytorch,Tabular Playground Series - Feb 2021
14673324,0.86315,0,1,/tthien/effortless-baseline-with-pycaret,Tabular Playground Series - Feb 2021
14619626,0.8485799999999999,1,9,/gregorycalvez/sklearn-and-tensorflow,Tabular Playground Series - Feb 2021
14604454,0.8561,0,3,/martinmarenz/first-pred-feb-tabular-playground-with-fast-ai,Tabular Playground Series - Feb 2021
14632139,0.86779,0,1,/styagi130/eda-baselinelinearregression,Tabular Playground Series - Feb 2021
14606901,0.8463200000000001,2,0,/yuichikuriyama/lightgbm-baseline-with-label-encoder-and-optuna,Tabular Playground Series - Feb 2021
14678962,0.8438100000000001,1,6,/jamesmcguigan/tps-pycaret2-automl-regression,Tabular Playground Series - Feb 2021
12631609,40.98415,6,9,/unfriendlyai/cyclegan-without-identity-loss,I’m Something of a Painter Myself
14500884,924.4,3,5,/gabrielmilan/crazy-goose,Conway's Reverse Game of Life 2020
10511744,0.25529,0,4,/rishabhjain16/simple-submission-petals-to-the-metal,Petals to the Metal - Flower Classification on TPU
10582803,0.24708,0,1,/noele123/exercise-create-your-first-submission,Petals to the Metal - Flower Classification on TPU
10510465,0.87587,2,12,/salmaneunus/petals-to-the-metals-competition-resnet50v2,Petals to the Metal - Flower Classification on TPU
10509194,0.40248,0,6,/salmaneunus/petals-to-the-metals-competition-vgg16-first,Petals to the Metal - Flower Classification on TPU
10334792,0.2555699999999999,0,1,/grt2707/kernel69a6597f47,Petals to the Metal - Flower Classification on TPU
10264576,0.96226,1,4,/sebastianji/petals-random-blocking-data-augmentation-0-96,Petals to the Metal - Flower Classification on TPU
10241706,0.24719,0,1,/mpwolke/exercise-violets-are-blue-we-love-tpu,Petals to the Metal - Flower Classification on TPU
14014727,0.95684,0,0,/denismetelev/start-with-ensemble-v2,Petals to the Metal - Flower Classification on TPU
13722905,0.04267,0,0,/abhishekchikun/create-your-first-submission,Petals to the Metal - Flower Classification on TPU
13432237,0.91823,0,0,/vladscherbakov/start-with-pre-train,Petals to the Metal - Flower Classification on TPU
13196440,0.92355,0,0,/yuriromamov/more-data-with-nasnetlarge,Petals to the Metal - Flower Classification on TPU
12959204,0.94624,0,0,/sergeyakulich/start-with-ensemble-v2,Petals to the Metal - Flower Classification on TPU
12689410,0.95686,0,0,/lkatran/more-data-with-efficientnetb7,Petals to the Metal - Flower Classification on TPU
12508579,0.94161,0,0,/denismetelev/start-with-pre-train,Petals to the Metal - Flower Classification on TPU
12343265,0.24815,0,0,/himanshuhansaria/hello-tpu-s,Petals to the Metal - Flower Classification on TPU
11362904,0.7721600000000001,0,0,/venkatadj/petal-to-metal,Petals to the Metal - Flower Classification on TPU
11080309,0.53801,0,6,/stephenmugisha/roberta-vs-watson,"Contradictory, My Dear Watson"
11080848,0.33166,0,5,/pawan28a95/fast-ai-quick-baseline,"Contradictory, My Dear Watson"
11053603,0.8115399999999999,2,14,/aditya08/translated-data-augmentation-xlm-roberta-kfold,"Contradictory, My Dear Watson"
11016692,0.62059,2,8,/vpkprasanna/bert-base-uncased-multilingual,"Contradictory, My Dear Watson"
11024985,0.66333,0,7,/kkhandekar/elementary-my-dear-watson,"Contradictory, My Dear Watson"
10996517,0.7027899999999999,2,13,/tkrsh09/nlp-starter-complete-tpu-bert-guide-keras,"Contradictory, My Dear Watson"
10971620,0.8025,16,28,/shahules/contradiction-xlm-kfold-starter,"Contradictory, My Dear Watson"
10981694,0.57016,2,8,/rhtsingh/tpu-training-and-inference-pytorch-distilbert,"Contradictory, My Dear Watson"
10963423,0.7984600000000001,7,21,/jpmiller/augmenting-data-with-translations,"Contradictory, My Dear Watson"
10971911,0.6818,0,4,/ajax0564/xlm-roberta-base,"Contradictory, My Dear Watson"
14548313,0.64273,0,0,/jacobbloodaxe/watson-nb,"Contradictory, My Dear Watson"
12359610,0.59709,0,0,/nurkasimov/start-w-o-pre-train,Petals to the Metal - Flower Classification on TPU
12485091,0.57767,0,0,/denismetelev/start-w-o-pre-512train,Petals to the Metal - Flower Classification on TPU
12400713,0.03134,1,2,/kwjcndocjn/flower-petals-different-models-comparison,Petals to the Metal - Flower Classification on TPU
12313894,0.89584,2,3,/shweta2407/flower-classification-using-xception-network,Petals to the Metal - Flower Classification on TPU
12235136,0.89859,0,0,/sameepshrestha/petals-classification,Petals to the Metal - Flower Classification on TPU
11660299,0.74968,0,0,/vyordanov/densenet101-tpu-224x224-100-epochs,Petals to the Metal - Flower Classification on TPU
12096421,0.95509,0,4,/sabindcoster/flower-classification,Petals to the Metal - Flower Classification on TPU
12028553,0.96576,0,1,/anku5hk/train-efficientnet,Petals to the Metal - Flower Classification on TPU
11857703,0.08125,0,8,/abhishektyagi001/tpu-petals-to-metals,Petals to the Metal - Flower Classification on TPU
10949329,0.93402,0,0,/jaishanker/custom-classification-using-pre-trained-cnn-model,Petals to the Metal - Flower Classification on TPU
11634419,0.93968,2,7,/rianlee/petals-to-the-metal-flower-classification-on-tpu,Petals to the Metal - Flower Classification on TPU
11251122,0.7420399999999999,0,1,/yentsai/exercise-create-your-first-submission,Petals to the Metal - Flower Classification on TPU
11220203,0.95251,0,4,/c7934597/kfold-efficientnet-cutmix-mixup-tta,Petals to the Metal - Flower Classification on TPU
11435804,0.2336099999999999,0,1,/sahabudin9/flower-classification,Petals to the Metal - Flower Classification on TPU
11345721,0.8433799999999999,0,8,/hongym7/single-mode-efficientnetb3-augmetation,Petals to the Metal - Flower Classification on TPU
10878259,0.94927,0,1,/taimour/flower-tpu-densenet201-earlystop-simpleaug-0-94,Petals to the Metal - Flower Classification on TPU
11290882,0.92528,0,0,/shashwatrathod/petals2metal-effnetb7,Petals to the Metal - Flower Classification on TPU
11190716,0.8328200000000001,5,4,/swapkh91/using-transfer-learning-v1,Petals to the Metal - Flower Classification on TPU
11129020,0.8322700000000001,1,8,/rajatkumar794/flowerclassification,Petals to the Metal - Flower Classification on TPU
10764604,0.98222,4,45,/atamazian/fc-ensemble-external-data-effnet-densenet,Petals to the Metal - Flower Classification on TPU
11005424,0.2488699999999999,0,2,/jmarrietar/exercise-create-your-first-submission,Petals to the Metal - Flower Classification on TPU
10977475,0.93116,0,0,/srutimallik/flower-classification-with-weighted-deep-nets,Petals to the Metal - Flower Classification on TPU
10871371,0.94217,0,6,/servietsky/flowers-tpu-multiple-cnn-voting,Petals to the Metal - Flower Classification on TPU
10859686,0.94465,0,5,/arch11/petals-with-tpu-95-accurate-using-efficientnet-b7,Petals to the Metal - Flower Classification on TPU
10769481,0.25661,0,1,/prathmeshchoudhari/exercise-create-your-first-submission,Petals to the Metal - Flower Classification on TPU
14631065,0.79615,4,4,/wchowdhu/hands-on-nli-w-transformers-m-bert-xlm-roberta,"Contradictory, My Dear Watson"
14363237,0.50779,0,0,/jamesmcguigan/nlp-contradictory-laser-embeddings-keras,"Contradictory, My Dear Watson"
12848786,0.62637,0,0,/victororlov/nli-bert-tpu-tf,"Contradictory, My Dear Watson"
13825770,0.7974899999999999,2,5,/sharpshim/dearwatson-xlmroberta-pytorch,"Contradictory, My Dear Watson"
13380346,0.96438,0,1,/sapthrishi007/pytorch-gap-xlmroberta-multiple-datasets,"Contradictory, My Dear Watson"
12453496,0.64369,0,1,/sapthrishi007/train-manually-bert-v1,"Contradictory, My Dear Watson"
12093149,0.923,0,3,/mojammel/inference-with-xlm-roberta,"Contradictory, My Dear Watson"
11841121,0.6323300000000001,0,5,/pawan300/nlp-premise,"Contradictory, My Dear Watson"
11434760,0.6379199999999999,0,0,/jaredmarvel/tutorial-notebook,"Contradictory, My Dear Watson"
11089925,0.80558,0,1,/ahmedalesh/notebook-bert-model,"Contradictory, My Dear Watson"
11235467,0.6408,0,1,/reichenbch/tpu-tutorial-code,"Contradictory, My Dear Watson"
11148955,0.84639,1,10,/mattbast/training-transformers-with-tensorflow-and-tpus,"Contradictory, My Dear Watson"
11283948,0.66333,0,2,/barteksadlej123/start-with-tutorial-notebook,"Contradictory, My Dear Watson"
11164561,0.7930699999999999,0,0,/pkundu25/nlp-contradiction-prediction-xlm-roberta-v2,"Contradictory, My Dear Watson"
11266612,0.66814,1,12,/vbookshelf/basics-of-bert-and-xlm-roberta-pytorch,"Contradictory, My Dear Watson"
11250744,0.65967,2,4,/ravi02516/bert-training-5-fold-cross-validation,"Contradictory, My Dear Watson"
11110879,0.33609,3,14,/narendrageek/nlp-augmenter-5-fold-bert-translator,"Contradictory, My Dear Watson"
11168641,0.405,4,14,/trinadhsingaladevi/contradictory-my-dear-watson,"Contradictory, My Dear Watson"
11142339,0.64254,0,7,/shivanandmn/bert-pytorch-tpu,"Contradictory, My Dear Watson"
11093125,0.93512,7,33,/yihdarshieh/more-nli-datasets-hugging-face-nlp-library,"Contradictory, My Dear Watson"
11113678,0.63965,1,4,/krrai77/predicting-the-sentence-correlation,"Contradictory, My Dear Watson"
14573517,0.8583799999999999,0,0,/datbuidinh/flower-classification-with-tpus-dataming,Petals to the Metal - Flower Classification on TPU
14144790,0.9702,0,1,/akataev96/start-with-pre-train-0895d8,Petals to the Metal - Flower Classification on TPU
14050189,0.82629,1,4,/qramkrishna/petals-to-metal-resnet,Petals to the Metal - Flower Classification on TPU
13501138,0.92477,0,2,/ritik282000/petal-to-the-metals,Petals to the Metal - Flower Classification on TPU
14009408,0.94647,0,0,/safonenkomax/start-with-ensemble-v2,Petals to the Metal - Flower Classification on TPU
13908049,0.35827,0,1,/johnspencer98/create-your-first-submission-2,Petals to the Metal - Flower Classification on TPU
13945021,0.5744199999999999,0,0,/drs251/what-does-the-learning-rate-scheduler-do,Petals to the Metal - Flower Classification on TPU
13709597,0.95044,0,4,/xuanzhihuang/flower-classification-with-efficientnet-b7,Petals to the Metal - Flower Classification on TPU
13589735,0.94427,0,0,/grudindmitry/start-with-ensemble-v2,Petals to the Metal - Flower Classification on TPU
13407009,0.7322,0,3,/shanmukh05/flower-classificaton-tpu,Petals to the Metal - Flower Classification on TPU
13080498,0.93103,0,0,/sneky369/fork-of-start-with-pre-train,Petals to the Metal - Flower Classification on TPU
13367476,0.94931,0,1,/maximkalinin/start-with-ensemble-v2-7711ec,Petals to the Metal - Flower Classification on TPU
13341122,0.95517,5,5,/ahmedhisham73/petal-flowers-usingresnet152v2,Petals to the Metal - Flower Classification on TPU
13312612,0.34464,0,0,/saranyavasudevan/petals-to-the-metals-competition-3,Petals to the Metal - Flower Classification on TPU
12955693,0.91977,0,0,/medvedevlev/start-with-pre-train,Petals to the Metal - Flower Classification on TPU
11212188,0.00075,1,1,/brendan45774/flower-petals-identifier-solution-lowest-score,Petals to the Metal - Flower Classification on TPU
12971780,0.91601,0,0,/varlou23/start-with-pre-train-image-augment-exp,Petals to the Metal - Flower Classification on TPU
12722067,0.8859600000000001,0,1,/nguyenhung1903/tpu-flower,Petals to the Metal - Flower Classification on TPU
12686034,0.94921,0,0,/lkatran/start-with-densenet201,Petals to the Metal - Flower Classification on TPU
12419406,0.92305,0,0,/krashennikovalexandr/start-with-pre-train,Petals to the Metal - Flower Classification on TPU
12622803,0.5596800000000001,0,0,/grudindmitry/start-w-o-pre-train,Petals to the Metal - Flower Classification on TPU
12575584,0.85201,0,0,/arnavmehta710a/90acc-flower,Petals to the Metal - Flower Classification on TPU
12548973,0.56318,0,0,/matveevayulia/start-w-o-pre-train,Petals to the Metal - Flower Classification on TPU
12555181,0.58627,0,0,/varlou23/start-w-o-pre-train,Petals to the Metal - Flower Classification on TPU
12552158,0.93238,0,0,/varlou23/start-with-pre-train,Petals to the Metal - Flower Classification on TPU
14524862,100.042,20,96,/ihelon/indoor-location-exploratory-data-analysis,Indoor Location & Navigation
14525374,108.122,6,33,/tanlikesmath/indoor-location-navigation-eda,Indoor Location & Navigation
14557296,80.92399999999998,9,33,/jiweiliu/dask-with-simple-xgb,Indoor Location & Navigation
14529372,95.556,8,9,/ammarali32/floor-distribution,Indoor Location & Navigation
7338862,0.98085,1,0,/pikkupr/exploring-mnist-using-recurrent-neural-network,Digit Recognizer
9491080,0.76076,0,11,/danoozy44/titanic-with-deep-learning-keras-for-beginners,Titanic - Machine Learning from Disaster
3829911,0.99528,0,6,/yashchoudhary/mnist-eda-and-keras-starter-code-99-53,Digit Recognizer
8368438,0.977,0,0,/fleanend/homegrown-nn-cookbook-pytorch,Digit Recognizer
8151668,0.79904,13,11,/aaroha33/titanic-a-machine-learning-problem,Titanic - Machine Learning from Disaster
4322953,0.7751100000000001,5,11,/parthshxh/machine-learning-with-sklearn-and-pytorch,Titanic - Machine Learning from Disaster
7029449,0.76555,6,3,/jcardenzana/titanic-tensorflow,Titanic - Machine Learning from Disaster
2278710,0.98857,0,5,/geochatz/mnist-digit-recognizer-cnn,Digit Recognizer
5344199,0.99,0,2,/txustice/mnist-with-fastai-resnet18-vs-34-50-w-fp16,Digit Recognizer
11832045,0.7751100000000001,0,6,/dongr0510/pytorch-starter,Titanic - Machine Learning from Disaster
8769951,0.98871,2,3,/nayansolanki2411/simple-model-building-with-keras-98-87-accuracy,Digit Recognizer
7280484,0.99585,3,2,/pcyslm/basic-model-for-mnist-solution-tf-v1-top-10,Digit Recognizer
1811546,0.99728,7,11,/amneves/a-tensorflow-keras-cnn-approach,Digit Recognizer
6583149,0.99914,2,9,/amneves/top-5-with-keras-auto-hypertuning,Digit Recognizer
7204871,1.0,7,4,/louisdelloye/data-recognizer-100-score,Digit Recognizer
10339700,0.98803,0,2,/aizardar/mnist-cnn,Digit Recognizer
581073,0.78947,0,5,/mattsu/titanic-deeplearning-keras-sigmoid,Titanic - Machine Learning from Disaster
2217921,0.99242,0,1,/ilyajob05/mnist-ordinary-digitizer-classification-pytorch,Digit Recognizer
8959201,0.99428,0,0,/lomen0857/explaining-cnn-using-shap,Digit Recognizer
9246679,0.99585,2,2,/gasparavit/digit-recognizer-cnn-99-58-acc,Digit Recognizer
8269961,0.82991,0,12,/guidant/nlpdisasters-benchmarking-ensembling-hfmodels,Natural Language Processing with Disaster Tweets
1275100,0.985,0,0,/perlinwarp/solving-mnist-using-a-more-complex-cnn,Digit Recognizer
4549939,0.78468,0,0,/prtkmeh/titanic-ann-using-keras,Titanic - Machine Learning from Disaster
2171128,0.95485,0,4,/arihant0497/introduction-to-neural-networks-using-pytorch,Digit Recognizer
6958423,0.99228,0,3,/fatmakursun/digit-recognizer-cnn-image-detection,Digit Recognizer
1639629,0.99728,8,12,/shikha130vv/mnist-how-close-can-we-get-to-a-full-100,Digit Recognizer
8766186,0.68703,1,2,/syzymon/covid-19-basic-fast-ai-tabular-model,COVID19 Global Forecasting (Week 3)
4859372,0.99442,6,19,/batuhan35/cnn-exercise-99-50-acc,Digit Recognizer
989740,0.98642,16,138,/shivamb/a-very-comprehensive-tutorial-nn-cnn,Digit Recognizer
12089626,0.77751,6,9,/zephyrzhan522/titanic-prediction-dl-vs-ml,Titanic - Machine Learning from Disaster
4087176,0.94785,2,7,/jmdatasci/number-recognition,Digit Recognizer
8271636,0.8325299999999999,31,74,/dantefilu/keras-neural-network-a-hitchhiker-s-guide-to-nn,Titanic - Machine Learning from Disaster
14651554,0.99185,0,0,/rahulkumarp/digit-recognizer,Digit Recognizer
8777579,0.14841,0,2,/dinasinclair/deep-learning-with-keras-housing-prices,House Prices - Advanced Regression Techniques
4095872,0.97114,2,2,/pikkupr/mnist-exploringsimplednn-withensembling,Digit Recognizer
1515892,0.99485,0,0,/szaitseff/fast-lenet5-cnn-in-keras,Digit Recognizer
4728315,0.76685,0,0,/sanwal092/3-layer-neural-network-from-scratch,Digit Recognizer
5525010,0.98971,1,3,/georgeheinemann/cnist-simple-cnn,Digit Recognizer
7201803,0.81612,4,15,/amiiiney/tweets-classification-pretrained-model,Natural Language Processing with Disaster Tweets
5354386,0.91314,0,0,/saurograndi/tf-dnn-tensorflow-deepnn,Digit Recognizer
5469620,0.14304,2,1,/txustice/house-prices-regression-with-fast-ai,House Prices - Advanced Regression Techniques
3463086,0.97485,0,5,/ankur1401/digit-recognizer-using-dnn,Digit Recognizer
6485130,0.99614,5,10,/sanwal092/fastai-and-mnist-top-10,Digit Recognizer
5816781,0.984,0,4,/rohan9889/keras-augmentation-digit-recognition,Digit Recognizer
2155876,0.99014,5,7,/dn3dry/mnist-digit-recognition-with-keras-and-tensorflow,Digit Recognizer
547744,0.97914,0,0,/kaustubholpadkar/digit-recognizer-cnn2,Digit Recognizer
1238808,0.96742,0,0,/iananich/simplest-keras-model-for-mnist-digit-recognizer,Digit Recognizer
3177034,0.98942,0,1,/walidbachri/digits-recognition-tensorflow-vs-keras,Digit Recognizer
1603678,0.79904,0,0,/brayanarrietaalfaro/titanic-with-keras,Titanic - Machine Learning from Disaster
1600908,0.74641,16,12,/omarayman/the-home-for-future-data-scientists,Titanic - Machine Learning from Disaster
1595346,3.12946,33,226,/karanjakhar/facial-keypoint-detection,Facial Keypoints Detection
7612401,0.79589,44,48,/elcaiseri/nlp-the-simplest-way,Natural Language Processing with Disaster Tweets
7683883,0.80661,0,6,/shishu1421/nlp-with-fastai,Natural Language Processing with Disaster Tweets
7364503,1.0,10,40,/holfyuen/basic-nlp-on-disaster-tweets,Natural Language Processing with Disaster Tweets
7192027,0.8057,124,943,/shahules/basic-eda-cleaning-and-glove,Natural Language Processing with Disaster Tweets
12402993,0.7751100000000001,0,8,/emrzcn/titanic-data-visualization-analysis-and-predict,Titanic - Machine Learning from Disaster
12371117,0.77272,10,17,/leodaniel/my-heart-will-go-on,Titanic - Machine Learning from Disaster
12107345,0.7751100000000001,2,5,/ykushagra/mr-kaggler-titanic-project,Titanic - Machine Learning from Disaster
11963250,0.7799,0,0,/shashankrajput9/titanic-data-analysis,Titanic - Machine Learning from Disaster
10947270,0.12757,1,13,/megr25/house-prices-all-columns-linear-and-xgboost,House Prices - Advanced Regression Techniques
10525083,0.7822899999999999,0,2,/nikhilt2305/titanic-dataset-for-beginners-top-12,Titanic - Machine Learning from Disaster
10463368,0.13366,0,8,/kollidatta/house-price-predictions-rf-xgb-gradient-boosting,House Prices - Advanced Regression Techniques
8439182,11.79127,4,5,/nibukdk93/housing-prices-prediction,House Prices - Advanced Regression Techniques
8416851,0.11937,0,0,/mohammedfurqan/stacked-model-feature-engineering,House Prices - Advanced Regression Techniques
8143656,0.80382,2,2,/vitorsr/titanic-randomforestclassifier,Titanic - Machine Learning from Disaster
8082469,0.12264,1,5,/gabrieltamayo/simple-lasso-ridge-and-dbscan-outliers,House Prices - Advanced Regression Techniques
7590734,0.8280700000000001,54,85,/doomdiskday/full-tutoria-eda-to-ensembles-embeddings-zoo,Natural Language Processing with Disaster Tweets
7578825,0.12694,0,1,/lorenzopagliaro01/models-with-xgboost-zero-to-hero,House Prices - Advanced Regression Techniques
7523426,0.79895,2,2,/alex1204/real-or-not-voteclassifier,Natural Language Processing with Disaster Tweets
6745474,0.11497,1,4,/averkij/top-eda-blending-russian,House Prices - Advanced Regression Techniques
6492454,0.79904,1,4,/datalana/titanic-working-with-data-dictionary,Titanic - Machine Learning from Disaster
6218988,0.76076,2,5,/guidosalimbeni/titanic-classification,Titanic - Machine Learning from Disaster
6196518,0.5885100000000001,12,30,/nidaguler/titanic-survival-detection-using-machine-learning,Titanic - Machine Learning from Disaster
6188326,0.80382,18,82,/vbmokin/automatic-eda-with-pandas-profiling-2-9-09-2020,Titanic - Machine Learning from Disaster
5444956,0.11673,3,6,/purist1024/a-minimal-baseline-26-lines-top-17,House Prices - Advanced Regression Techniques
4667493,0.7799,2,4,/izardy/titanic-survivor-prediction-via-random-forest,Titanic - Machine Learning from Disaster
4046086,0.11627,0,1,/werty12121/house-prices-eda-basic-stacknet,House Prices - Advanced Regression Techniques
2200525,0.12759,0,0,/nikkisharma536/house-price-prediction-label-encoding,House Prices - Advanced Regression Techniques
2096231,0.11934,11,50,/chocozzz/beginner-challenge-house-prices,House Prices - Advanced Regression Techniques
2076314,0.6729999999999999,2,56,/vanshjatana/diffrent-embedding,Quora Insincere Questions Classification
1886841,0.7368399999999999,0,2,/ranjanhunt/titanic-machine-learning-with-fastai,Titanic - Machine Learning from Disaster
1882473,0.79425,0,3,/gbellport/simple-titanic-data-analysis-w-cabin,Titanic - Machine Learning from Disaster
1864067,0.75598,0,0,/aj65461/titanic-analysis,Titanic - Machine Learning from Disaster
1721659,0.12958,0,5,/kabacaly/house-prices-xgboost-0-12958,House Prices - Advanced Regression Techniques
1370221,0.79425,102,210,/samsonqian/titanic-guide-with-sklearn-and-eda,Titanic - Machine Learning from Disaster
1359492,0.79425,1,2,/nagar500/titanic-survivor-predictions,Titanic - Machine Learning from Disaster
1268290,0.7799,1,4,/ashwiniprakash/titanic-survival-prediction,Titanic - Machine Learning from Disaster
1169639,0.13635,0,0,/mahmoudmohamed/house-prices-xgboost,House Prices - Advanced Regression Techniques
1018930,0.78468,5,15,/raviprakash438/titanic-survival-prediction,Titanic - Machine Learning from Disaster
555983,0.7799,0,4,/psangam/titanic-survival-analysis-and-prediction-using-ml,Titanic - Machine Learning from Disaster
547126,0.79904,4,14,/impratiksingh/titanic-survival-prediction,Titanic - Machine Learning from Disaster
9206436,0.1488,4,4,/jagadeesh06/house-price-eda-imputation-random-forest,House Prices - Advanced Regression Techniques
7649711,0.7799,16,37,/kushbhatnagar/guidefornewbie-eda-featureeng-modeling-evaluation,Titanic - Machine Learning from Disaster
9347941,0.78947,8,6,/abhinandanrai/titanic-survival-prediction,Titanic - Machine Learning from Disaster
7679151,0.78577,6,17,/akihironomura/nlp-with-disaster-tweets-eda-nlp-xgboost,Natural Language Processing with Disaster Tweets
8115774,0.12627,0,7,/kaggledroid/housing-prices-data-visualization-and-ensembling,House Prices - Advanced Regression Techniques
9827801,0.6650699999999999,0,1,/bryanlambo/titanic,Titanic - Machine Learning from Disaster
3787676,0.78468,0,1,/rayxie0329/titanic-ensemblingmodels,Titanic - Machine Learning from Disaster
8593904,0.76076,0,0,/charlievbc/survival-prediction-on-titanic-classification,Titanic - Machine Learning from Disaster
12672782,0.77272,1,0,/brendanartley/titanic-notebook-take-2,Titanic - Machine Learning from Disaster
9842587,0.14278,0,3,/akramnarejo/house-prices-for-beginners,House Prices - Advanced Regression Techniques
11951301,0.7751100000000001,0,0,/mishabz4321/titanic-analysis,Titanic - Machine Learning from Disaster
9163665,0.80382,5,3,/nitbrok/titanic-super-ship,Titanic - Machine Learning from Disaster
855755,0.7703300000000001,0,3,/anand0427/classification-for-beginners,Titanic - Machine Learning from Disaster
9335407,0.12785,12,40,/hoangnguyen719/eda-feature-selection,House Prices - Advanced Regression Techniques
11521798,0.7751100000000001,9,27,/ujjwalsharma26/titanic-ml-predictions-deep-nn-predictions,Titanic - Machine Learning from Disaster
7277063,0.11979,0,5,/ritwickf2/eda-encoding-and-blending,House Prices - Advanced Regression Techniques
533257,0.74641,0,5,/shrutisaxena0617/exploring-titanic-dataset-for-survival-if-only,Titanic - Machine Learning from Disaster
11487740,0.80143,0,2,/fassily/titanic-competition-walkthrough-to-top-4,Titanic - Machine Learning from Disaster
8787138,0.78468,3,9,/podsyp/titanic-starter-eda-tsne-kfold-lgb,Titanic - Machine Learning from Disaster
6670250,0.80382,3,14,/neerjajhingan/titanic-first-project,Titanic - Machine Learning from Disaster
847197,0.79425,0,9,/manisood001/titanic-m-l,Titanic - Machine Learning from Disaster
10022452,0.78468,11,10,/rizbaltazar/titanic-survivor-predictions,Titanic - Machine Learning from Disaster
5431004,0.74641,6,6,/pranaysingh25/titanic-dataset-a-decent-beginner-approach,Titanic - Machine Learning from Disaster
6667777,0.75598,5,19,/hazelhe99/titanic-eda,Titanic - Machine Learning from Disaster
5201782,0.78468,5,15,/butfirstcode/using-randomforestclassifier-to-predict-survival,Titanic - Machine Learning from Disaster
691361,0.11935,0,4,/svobodnik86/house-prices-lasso-and-xgboost,House Prices - Advanced Regression Techniques
5455833,0.12482,1,4,/durgaprasad64/house-predictions-with-ml-models-for-beginners,House Prices - Advanced Regression Techniques
10910254,0.79186,0,9,/dhiiyaur/titanic-prediction-auto-models-optuna-eda,Titanic - Machine Learning from Disaster
10001986,0.78708,0,0,/jjuinni/titanic-an-approach-to-top-12,Titanic - Machine Learning from Disaster
11709575,0.77751,2,8,/rajkumarl/exploratory-data-analytics-titanic,Titanic - Machine Learning from Disaster
11436872,0.7799,3,14,/mancysaxena/titanic-dataset-hello-world-of-ml,Titanic - Machine Learning from Disaster
2804369,0.78468,0,0,/tooezy/titanic-prediction,Titanic - Machine Learning from Disaster
5905178,0.11827,6,21,/chmaxx/extensive-data-exploration-modelling-python,House Prices - Advanced Regression Techniques
10570985,0.77751,0,5,/udita3996/titanic-eda-predictions,Titanic - Machine Learning from Disaster
13426452,0.12595,0,0,/hhsmobileapps/house-price-prediction-from-a-z,House Prices - Advanced Regression Techniques
8166755,0.78468,1,11,/medyasun/titanic-best-score,Titanic - Machine Learning from Disaster
8698078,0.91387,3,30,/vaishvik25/titanic-eda-fe-3-model-decision-tree-viz,Titanic - Machine Learning from Disaster
12018672,0.72488,0,5,/gauravduttakiit/predict-the-survival-using-adaboost,Titanic - Machine Learning from Disaster
3610082,0.81818,8,39,/volhaleusha/titanic-tutorial-encoding-feature-eng-81-8,Titanic - Machine Learning from Disaster
12599926,0.7751100000000001,4,7,/mamxlam/my-first-notebook-eda-catboost-83-acc-cv-10,Titanic - Machine Learning from Disaster
9263527,0.80861,38,128,/mviola/titanic-eda-simple-model-0-80622,Titanic - Machine Learning from Disaster
1155388,0.81339,1,7,/sz8416/start-here-titanic-survived-prediction,Titanic - Machine Learning from Disaster
8806020,0.14837,5,4,/kamalnaithani/covid-eda-forecast,COVID19 Global Forecasting (Week 3)
7200751,0.79803,0,1,/imdevskp/exploring-disaster-tweets-eda-vis-class,Natural Language Processing with Disaster Tweets
12185937,0.80143,5,9,/hashimchaudry/titanic-a-basic-ensemble-approach-to-get-top-4,Titanic - Machine Learning from Disaster
10500160,0.13669,9,13,/pratikkejriwal/comprehensive-regression-analysis,House Prices - Advanced Regression Techniques
10751655,0.11955,0,0,/georgehu2004/regularized-linear-regression-and-xgboost,House Prices - Advanced Regression Techniques
1635607,0.7511899999999999,0,0,/erikebrown/titanic-dataframe-learning-from-disaster,Titanic - Machine Learning from Disaster
10628396,0.80861,4,7,/ankur123xyz/titanic-eda-feature-engineering-ensemble-top-5,Titanic - Machine Learning from Disaster
11847502,0.75358,0,0,/abhinavsinha845/titanic-simple-code-submission,Titanic - Machine Learning from Disaster
10562196,0.80143,2,5,/jaidevchittoria/titanic-top-4-using-xgboost-80-143,Titanic - Machine Learning from Disaster
3382545,0.7799,4,12,/aditya100/83-accuracy-titanic-prediction,Titanic - Machine Learning from Disaster
5833729,0.12025,5,12,/rohan9889/xgb-lgbm-pipeline-featureengineering-house-price,House Prices - Advanced Regression Techniques
11984731,0.7822899999999999,0,1,/nehalbandal/titanic-survival-prediction-ensemble-learning,Titanic - Machine Learning from Disaster
694641,0.76076,0,3,/hb20007/from-zero-to-random-forest-classifier-explained,Titanic - Machine Learning from Disaster
13024793,0.77751,0,0,/ivochula/titanic-analysis-rf-and-gradientboost,Titanic - Machine Learning from Disaster
2989436,0.12167,5,11,/subhamsharma96/house-prices-gradientboosting-gridsearchcv,House Prices - Advanced Regression Techniques
8091095,0.7520600000000001,1,12,/datark1/disaster-tweets-eda-tokenisation-xgb-ensemble,Natural Language Processing with Disaster Tweets
9017531,0.1415,11,23,/podsyp/complete-linear-model-guide,House Prices - Advanced Regression Techniques
12462474,0.79186,33,26,/onurserbetci/end-to-end-titanic-project,Titanic - Machine Learning from Disaster
1037578,0.11553,28,105,/jack89roberts/top-7-using-elasticnet-with-interactions,House Prices - Advanced Regression Techniques
12355190,0.77751,0,0,/charliewen/notebook7e3efbe131,Titanic - Machine Learning from Disaster
11300598,0.57368,0,6,/ianmoone0617/panda-effnet-b3-inference-fastai-custom-imagelist,Prostate cANcer graDe Assessment (PANDA) Challenge
10435922,0.98589,0,1,/yashguptaab99/digit-recognizer,Digit Recognizer
10342786,0.932406,0,0,/iloveyyp/catboost-and-eda,IEEE-CIS Fraud Detection
10116977,0.97646,0,2,/rishikts/rishikts-mnist,Digit Recognizer
10036156,0.98814,0,2,/hopereal/writting,Digit Recognizer
9904099,0.99582,2,10,/michalbrezk/digit-recognizer-cnn-tensorflow-2-0-top-10,Digit Recognizer
9835110,0.99,1,0,/fotone/tensorflow2-keras-vgg-style-mnist-99,Digit Recognizer
9782103,0.99271,0,0,/vadimbezdushny/mnist,Digit Recognizer
9743684,0.1553,1,3,/tunguz/housing-prices-with-rapids,House Prices - Advanced Regression Techniques
9645998,0.1254299999999999,0,0,/anshu1595/house-price-prediction-advanced-regression,House Prices - Advanced Regression Techniques
9041790,0.99839,31,36,/soham1024/easy-learn-cnn-keras-99-81-accuracy,Digit Recognizer
8921515,0.99614,8,14,/jmosinski/resnets-are-awesome-state-of-the-art,Digit Recognizer
8743941,0.69317,0,0,/seuvitor/covid-19-week-3,COVID19 Global Forecasting (Week 3)
8685734,0.06734,0,0,/letili0417/covid-19-eda-lstm-v1,COVID19 Global Forecasting (Week 2)
8678113,0.99471,0,1,/aniket12/digit-recognizer-through-cnn,Digit Recognizer
13153262,0.895,1,3,/miharsh/inference-training-cv,Cassava Leaf Disease Classification
11186959,3.43914,0,12,/sshikamaru/keras-cnn-starter,Facial Keypoints Detection
10799576,0.99525,0,2,/brenootsuka/kernel3d4343233c,Digit Recognizer
10728166,0.99303,0,3,/sanikamal/digit-recognition-eda-viz-gradienttape,Digit Recognizer
10720766,0.99521,0,1,/suguruu/kernel154c1b6940,Digit Recognizer
10123364,0.99382,0,0,/leonhackl96/digit-recognition-99-with-cnn-s,Digit Recognizer
9963269,0.0,0,4,/shivanandmn/pytorch-english-handwritten-digit-recognition,Digit Recognizer
9753857,0.25214,0,0,/tachodril/kernel5884e55162,Digit Recognizer
9484758,0.99171,1,6,/wuhao1996/digit-recognizer-with-cnn-in-keras,Digit Recognizer
9451576,0.99157,2,3,/stealthflow/resnest-for-mnist,Digit Recognizer
9056716,0.99367,0,0,/aidiary/mnist-by-keras-part-3,Digit Recognizer
8755892,0.99442,0,0,/ncyjain/mnist-digit-base-accumulate-gradient,Digit Recognizer
8701895,2.4908,1,5,/meenakshiramaswamy/wk2-covid-rf,COVID19 Global Forecasting (Week 2)
12511608,0.76076,0,1,/priyankarao18/titanic-case-study-logistic-regression,Titanic - Machine Learning from Disaster
12094919,0.79186,4,8,/codymccormack/top-7-titanic-model-and-eda,Titanic - Machine Learning from Disaster
11929785,0.78468,8,8,/sinamhd9/titanic-tutorial-machine-learning-from-scratch,Titanic - Machine Learning from Disaster
11287080,0.7799,2,9,/mudithsilva/titanic-0-779-accuracy,Titanic - Machine Learning from Disaster
11260552,0.7440100000000001,1,10,/chanakyavivekkapoor/getting-started-with-titanic-dataset,Titanic - Machine Learning from Disaster
11108348,0.75837,18,29,/amritachatterjee09/predicting-survival-in-titanic-disaster,Titanic - Machine Learning from Disaster
10536929,0.78947,3,6,/kasevgen/titanic,Titanic - Machine Learning from Disaster
10393050,0.79425,1,4,/mrinalchandramishra/titanic-survival-accuracy-approx-80,Titanic - Machine Learning from Disaster
9470014,0.78947,0,0,/kaushil268/titanic-machine-learning-from-disaster,Titanic - Machine Learning from Disaster
9177777,0.79904,56,119,/prestonfan/survived-or-deceased,Titanic - Machine Learning from Disaster
8773429,1.1524299999999998,5,16,/arpandas65/covid-19-projection-using-lstm,COVID19 Global Forecasting (Week 3)
8719787,0.8325299999999999,11,11,/mrmorj/titanic-top-2-different-classifiers,Titanic - Machine Learning from Disaster
8663033,0.06853,2,0,/nonstochastic147/covid-19-week-2,COVID19 Global Forecasting (Week 2)
8561667,0.77751,3,15,/mustafabozkurt/titanic-3-ml,Titanic - Machine Learning from Disaster
8098759,0.81397,2,4,/nanto88/eda-ml-deep-learning-with-sklearn-and-pytorch,Natural Language Processing with Disaster Tweets
8004097,0.7751100000000001,25,10,/sagaramu/titanic-visualization-with-additional-features,Titanic - Machine Learning from Disaster
6766289,0.9198,0,5,/ianmoone0617/kannada-mnist-using-random-forest,Kannada MNIST
5864729,0.64593,12,17,/jsvishnuj/survival-prediction-using-ml-for-beginners,Titanic - Machine Learning from Disaster
4909972,0.76076,2,2,/altair73/titanic-dataset-for-beginners,Titanic - Machine Learning from Disaster
4907015,0.992,1,6,/suneelpatel/digit-recognition-tf,Digit Recognizer
4367749,0.72727,0,0,/ashish2002/first-practice-project,Titanic - Machine Learning from Disaster
11833984,0.77272,0,0,/keithmutamba/titanic-project,Titanic - Machine Learning from Disaster
11609919,0.8636299999999999,2,5,/niksaurabh/titanic-a-to-z,Titanic - Machine Learning from Disaster
11162014,0.622,0,8,/gauravduttakiit/predict-the-survival-using-knn,Titanic - Machine Learning from Disaster
10663866,0.77751,0,2,/rahulpawade/solving-titanic-problem-by-logistic-regression,Titanic - Machine Learning from Disaster
9363347,0.75598,4,7,/basarkayastudent/titanic-eda-bk,Titanic - Machine Learning from Disaster
9054318,0.81339,17,41,/milan400/titanic-survival,Titanic - Machine Learning from Disaster
7962104,0.1166799999999999,6,50,/mariapushkareva/house-prices-advanced-regression-techniques,House Prices - Advanced Regression Techniques
7770899,0.7799,0,0,/sanjogyadav/titanic-solution-glm,Titanic - Machine Learning from Disaster
7753371,0.12428,1,2,/mayurjain/bootstrapping-on-house-price-prediction,House Prices - Advanced Regression Techniques
7495730,0.78118,0,6,/vitaliykoren/lstm-with-two-dimensional-max-pooling-with-pytorch,Natural Language Processing with Disaster Tweets
7420435,0.8026300000000001,1,7,/shubham219/nlp-starter-with-different-models,Natural Language Processing with Disaster Tweets
6107155,0.78947,5,27,/amiiiney/titanic-top-20-with-ensemble-votingclassifier,Titanic - Machine Learning from Disaster
5706078,0.12586,1,9,/himaoka/house-simple-svr-support-vector-regression,House Prices - Advanced Regression Techniques
4968065,0.14198,0,0,/scottyiu/random-forest-sklearn-fastai-on-house-prices,House Prices - Advanced Regression Techniques
4557792,0.6650699999999999,2,13,/rockbt1189/analysis-and-prediction,Titanic - Machine Learning from Disaster
3295693,0.11874,11,33,/kpacocha/top-20-house-prices-regression-techniques,House Prices - Advanced Regression Techniques
2983411,0.7703300000000001,0,2,/rania92/titanic-case-study-using-logestic-regression-rfe,Titanic - Machine Learning from Disaster
2139895,9.45397,0,1,/gideon94/house-prices-with-stacked-regression-models,House Prices - Advanced Regression Techniques
2062509,0.78468,0,2,/geochatz/tackle-the-titanic-dataset-classification-models,Titanic - Machine Learning from Disaster
1598434,0.7799,2,6,/ralo44/titanic-machine-learning-from,Titanic - Machine Learning from Disaster
1261350,0.78947,0,2,/szaitseff/under-the-hood-a-dense-net-on-titanic-dataset,Titanic - Machine Learning from Disaster
994472,0.0,0,0,/aadhi444/quick-analysis-and-model-building,Titanic - Machine Learning from Disaster
977015,0.80382,1,2,/jquerne/titanic-pipelines-simple-features-0-80382,Titanic - Machine Learning from Disaster
860787,9.37986,0,1,/vin1234/feature-engineering-of-house-prices-prediction,House Prices - Advanced Regression Techniques
827545,0.13442,0,0,/willianw/house-prices-regression,House Prices - Advanced Regression Techniques
12674219,0.76315,0,1,/ollyattwood/quick-dirty-titanic-get-started-in-5-minutes,Titanic - Machine Learning from Disaster
12021502,0.7799,0,1,/rahulpawade/predicting-survived-or-not,Titanic - Machine Learning from Disaster
11312907,0.78468,14,23,/roshan77/pyspark-classification-model,Titanic - Machine Learning from Disaster
9250282,0.13302,0,0,/srb2907/house-pricing-advance-regression-xgboost,House Prices - Advanced Regression Techniques
8693288,0.2069,0,9,/muhakabartay/sars-cov-2-exponential-model-week-2,COVID19 Global Forecasting (Week 2)
8445839,0.79904,5,8,/prassanth/titanic-fancyimpute-mice-knn-xgboost-hp-tuning,Titanic - Machine Learning from Disaster
7904086,0.79904,0,0,/zacktack/data-scientist-journey-titanic-survivor,Titanic - Machine Learning from Disaster
7847459,0.78947,0,0,/goodot/titanic-mlp-first-steps-in-ml,Titanic - Machine Learning from Disaster
7667682,0.12118,0,0,/znamensky/house-price-prediction-with-catboost,House Prices - Advanced Regression Techniques
6589006,0.79425,2,8,/antoniorivero/full-on-d-s-approach-to-titanic-votingclassifier,Titanic - Machine Learning from Disaster
6556041,0.78468,1,1,/mucahitaz/titanic-svm-kfold,Titanic - Machine Learning from Disaster
6053002,0.7799,0,0,/manas0991/titanic-multiple-sklearn-classifiers,Titanic - Machine Learning from Disaster
6005076,0.12625,0,0,/kathryndaniele/lassocv-model-house-prices-advanced-reg,House Prices - Advanced Regression Techniques
5802499,0.12818,0,4,/himaoka/house-simple-gradient-boosting-regression,House Prices - Advanced Regression Techniques
4881309,0.81818,1,2,/liuqiantx1221/titanic-only-use-two-features-to-reach-top-5,Titanic - Machine Learning from Disaster
4823018,0.11795,3,8,/kulkarnivishwanath/housing-prices-eda-and-modelling,House Prices - Advanced Regression Techniques
4191551,0.79904,0,20,/prasadpatil99/titanic-lgbm-top-8,Titanic - Machine Learning from Disaster
3646933,0.11707,0,1,/kalorincz/new-approach-to-stacking-easy-tutorial-top-17,House Prices - Advanced Regression Techniques
3164012,0.12511,2,3,/vpatricio/house-prediction,House Prices - Advanced Regression Techniques
2924636,0.73205,8,4,/itsmesunil/titanic-survival-prediction-eda,Titanic - Machine Learning from Disaster
2380026,0.132,1,3,/tandreina24/python-ensemble-models,House Prices - Advanced Regression Techniques
2243098,0.1184099999999999,3,5,/geochatz/housing-sales-predictions-regression-models,House Prices - Advanced Regression Techniques
1897265,0.7799,0,5,/tstomoki/titanic-with-basic-and-ensembles-methods,Titanic - Machine Learning from Disaster
1296610,0.6650699999999999,0,2,/rvillalongar/data-exploration-list-of-models-on-titanic,Titanic - Machine Learning from Disaster
1067710,0.78468,0,0,/marklvl/titanic-cleaning-feature-engineering-modeling,Titanic - Machine Learning from Disaster
412422,0.80382,226,849,/nadintamer/titanic-survival-predictions-beginner,Titanic - Machine Learning from Disaster
11678098,0.76076,6,19,/tamccullough/the-titanic-sank-hopefully-this-notebook-doesn-t,Titanic - Machine Learning from Disaster
11523960,0.76794,13,27,/subhanjandas/titanic-survival-prediction-and-eda,Titanic - Machine Learning from Disaster
11161762,0.75598,0,4,/gauravduttakiit/predict-the-survival-using-naive-bayes,Titanic - Machine Learning from Disaster
11159619,0.78468,7,25,/nikhileshkos/titanic-disaster-prediction-80-acc,Titanic - Machine Learning from Disaster
10376325,0.80382,7,31,/syphax93/interactive-visualization-modelling-0-803-top7,Titanic - Machine Learning from Disaster
10296992,0.99635,4,12,/vadimsokolov/mnist-digits-using-keras,Digit Recognizer
10258210,0.13727,3,12,/christianlillelund/house-prices-xgboost-bayesianoptimization,House Prices - Advanced Regression Techniques
10256019,0.7703300000000001,4,10,/joshijai2/titanic-sklearn-stacking-ensemble-classifier,Titanic - Machine Learning from Disaster
9165709,0.14836,4,12,/sidagar/well-commented-code-xgboosting,House Prices - Advanced Regression Techniques
9097067,0.79425,21,27,/celineterranova/first-ml-project-titanic-data,Titanic - Machine Learning from Disaster
9034271,0.80382,1,0,/countingpigeons/titanic-survival-cross-validated-voting-ensembles,Titanic - Machine Learning from Disaster
8869064,0.07873,47,161,/darkside92/detailed-examination-for-house-price-top-10,House Prices - Advanced Regression Techniques
8065829,0.7703300000000001,3,2,/kaggledroid/titanic-ensembling-and-neural-network,Titanic - Machine Learning from Disaster
7981323,0.40199,2,9,/a45632/2020-starter-kernel-women-improved,Google Cloud & NCAA® ML Competition 2020-NCAAW
7770915,0.7799,3,3,/sanjogyadav/titanic-solution-rfc,Titanic - Machine Learning from Disaster
7206401,0.99557,6,14,/chekoduadarsh/starters-guide-convolutional-xgboost,Digit Recognizer
7199685,0.78947,6,10,/jaspritkaur2920/titanic-disaster-prediction,Titanic - Machine Learning from Disaster
6806365,0.98514,0,0,/kabilan45/mnistpytorch,Digit Recognizer
5256249,0.7751100000000001,4,8,/cvarun/titanic-survival-a-beginner-s-analysis,Titanic - Machine Learning from Disaster
4341267,0.1139,64,175,/shaygu/house-prices-begginer-top-7,House Prices - Advanced Regression Techniques
3976915,0.76555,70,150,/vikumsw/beginners-basic-workflow-introduction,Titanic - Machine Learning from Disaster
14678340,0.7751100000000001,0,0,/johntwen/ml-on-titanic,Titanic - Machine Learning from Disaster
11847709,0.83014,0,7,/sohelranaccselab/titanic-ml-from-disaster-using-xgboost,Titanic - Machine Learning from Disaster
8152441,9.45411,2,5,/arunkumarpyramid/eda-ensemble-regularization,House Prices - Advanced Regression Techniques
8004577,0.7751100000000001,0,0,/uditsharmaai/titanic,Titanic - Machine Learning from Disaster
212259,0.7751100000000001,0,0,/mciesiel/titanic,Titanic - Machine Learning from Disaster
11823480,0.7751100000000001,2,2,/jarvisu/titanic-survive,Titanic - Machine Learning from Disaster
10773116,0.7822899999999999,22,37,/roodrakanwar/eda-model-prediction,Titanic - Machine Learning from Disaster
9918480,0.80143,0,7,/kollidatta/titanic-data-analysis-and-predictions-top-7,Titanic - Machine Learning from Disaster
9250742,0.78468,3,14,/alexandraneagu/titanic-predicting-survival-of-passengers,Titanic - Machine Learning from Disaster
8977342,0.98978,2,4,/franckepeixoto/digit-recognizer-tensorflow-rff-vs-cnn,Digit Recognizer
8973640,0.79425,3,17,/thomaswoolley/rf-and-k-nn-titanic-0-77-score,Titanic - Machine Learning from Disaster
8730981,1.0736,1,3,/ranjithks/ran-covid-19-week3,COVID19 Global Forecasting (Week 3)
8310908,0.12234,1,13,/vbmokin/mm-ncaaw-lgb-xgb-regr,Google Cloud & NCAA® ML Competition 2020-NCAAW
8262215,0.79425,10,46,/surajkumar88/titanic-machine-learning-from-disaster-eda,Titanic - Machine Learning from Disaster
8192299,0.82163,0,7,/guidant/disastersnlp-benchmarking-tfhub-bert-variations,Natural Language Processing with Disaster Tweets
7965743,0.96985,2,3,/sauravmishra1710/digit-recognizer,Digit Recognizer
7812584,0.1340599999999999,4,15,/gauthampughazh/house-sales-price-prediction-svr,House Prices - Advanced Regression Techniques
7165581,0.1192299999999999,0,13,/namanj27/top-2-advanced-regression-house-prices,House Prices - Advanced Regression Techniques
6232601,0.78947,2,15,/codesail/titanic-explore-features-with-explanation,Titanic - Machine Learning from Disaster
6168266,0.80382,18,75,/vbmokin/titanic-top-3-cluster-analysis,Titanic - Machine Learning from Disaster
5716126,0.16167,8,18,/kushbhatnagar/first-competition-kernel-house-pricing-prediction,House Prices - Advanced Regression Techniques
4938907,0.12419,2,23,/suneelpatel/house-price-prediction-regression-model,House Prices - Advanced Regression Techniques
3744105,0.80382,23,63,/iavinas/titanic-top-10-percent-simple-solution-and-eda,Titanic - Machine Learning from Disaster
5419676,0.13034,0,6,/evimarp/house-prices-competition,House Prices - Advanced Regression Techniques
10999930,0.7822899999999999,13,17,/kunjmehta/titanic-competition,Titanic - Machine Learning from Disaster
11423181,0.7751100000000001,10,28,/priyankameena/most-structured-solution-for-titanic-challenge,Titanic - Machine Learning from Disaster
484210,0.79425,4,11,/dprater513/classifying-titanic-survivors-svm-logreg-knn,Titanic - Machine Learning from Disaster
2421064,0.7751100000000001,1,4,/delayedkarma/titanic-h2o-automl,Titanic - Machine Learning from Disaster
5701824,0.13152,2,16,/wrecked22/simple-xgboost-with-feature-engineering-and-eda,House Prices - Advanced Regression Techniques
10673752,0.16507,0,8,/gerlandore/advanced-house-regression-eda-model-comparison,House Prices - Advanced Regression Techniques
11368656,0.7751100000000001,0,1,/mmaxon/my-first-kaggle-notebook-complete-analysis,Titanic - Machine Learning from Disaster
12816495,0.76794,0,5,/vaibhavkumar808/titanic-v2,Titanic - Machine Learning from Disaster
10523423,0.77751,4,21,/edoardo10/titanic-dataset-competition-for-beginners,Titanic - Machine Learning from Disaster
535001,0.7751100000000001,0,6,/pallavisama/titanic-survival-analysis,Titanic - Machine Learning from Disaster
231224,0.99557,49,179,/adityaecdrid/mnist-with-keras-for-beginners-99457,Digit Recognizer
2053254,0.79904,7,55,/rblcoder/titanic-features,Titanic - Machine Learning from Disaster
8437028,0.8014,3,2,/diggee/tweet-analysis-from-the-basics,Natural Language Processing with Disaster Tweets
11391142,0.80861,6,9,/infof4221wang/top2-0-808612-featureengineer-optuna-treemodel,Titanic - Machine Learning from Disaster
10574147,0.1166799999999999,3,11,/kietanhhoang/houseprices-xgbregressor-top-3,House Prices - Advanced Regression Techniques
5033630,0.1192599999999999,1,12,/farzadhabibi/house-prices-stacking-feature-selection-eng,House Prices - Advanced Regression Techniques
10563329,0.7822899999999999,5,9,/gaurav2022/survival-chance,Titanic - Machine Learning from Disaster
10707758,0.7822899999999999,9,19,/alexanderklarge/feature-eng-functions-cross-val-gridsearch,Titanic - Machine Learning from Disaster
10137620,0.78468,0,1,/yagarwal1307/titanic-dataset-linear-ensemble-models,Titanic - Machine Learning from Disaster
6946557,0.12663,2,5,/kajolg/house-sales-prediction-using-regression-techniques,House Prices - Advanced Regression Techniques
2444244,0.80861,0,0,/sabasiddiqi/classification-ensemble-methods-trees,Titanic - Machine Learning from Disaster
2823825,0.12255,4,4,/tooezy/house-price-prediction-eda-xgboost,House Prices - Advanced Regression Techniques
1989426,0.97928,4,15,/moghazy/intro-to-dnns-with-data-augmentation-python,Digit Recognizer
8135206,0.7751100000000001,0,2,/arunkumarpyramid/titanic-survived-ml-and-dl-project-techniques,Titanic - Machine Learning from Disaster
2731378,0.82296,1,21,/ajalnine/titanic-keras-vs-lightgbm-vs-catboost-vs-xgboost,Titanic - Machine Learning from Disaster
937170,0.80861,2,18,/dlarionov/titanic-xgboost,Titanic - Machine Learning from Disaster
3985857,0.7799,9,18,/priteshshrivastava/titanic-random-forest-with-model-explainability,Titanic - Machine Learning from Disaster
454897,0.80861,38,127,/dejavu23/titanic-survival-seaborn-and-ensembles,Titanic - Machine Learning from Disaster
521447,0.80382,5,21,/rafaelgoncalves/analysis-visualization-and-learning-log-reg,Titanic - Machine Learning from Disaster
663373,0.7751100000000001,5,8,/antmarakis/beginner-keras-and-visualization-tutorial,Titanic - Machine Learning from Disaster
3265956,0.94042,4,18,/elcaiseri/mnist-simple-sklearn-model-95-accuracy,Digit Recognizer
11719494,0.78708,0,1,/vinaykakara/feature-selection-and-voting-classifier,Titanic - Machine Learning from Disaster
9428263,0.7799,12,12,/shyam21/titanic-dataset,Titanic - Machine Learning from Disaster
5690919,0.12588,10,33,/pradeepmuniasamy/a-comprehensive-guide-to-house-price-prediction,House Prices - Advanced Regression Techniques
6555780,0.13114,1,5,/drcapa/house-prices-eda-feature-enginneering-xgb,House Prices - Advanced Regression Techniques
8981474,0.1385,0,5,/brunovpm/recursive-feature-elimination-house-prices,House Prices - Advanced Regression Techniques
2751817,0.82296,23,44,/vincentlugat/titanic-data-analysis-lgbm-0-82296,Titanic - Machine Learning from Disaster
10385931,0.79425,79,314,/kenjee/titanic-project-example,Titanic - Machine Learning from Disaster
13070164,0.8096800000000001,0,0,/prouserrr/nutchanon,Natural Language Processing with Disaster Tweets
12203311,0.76794,0,1,/cristianfat/titanic-torch,Titanic - Machine Learning from Disaster
11496503,0.7799,0,6,/sudiptog81/titanic-survival-predictions,Titanic - Machine Learning from Disaster
11433918,59.51565,3,24,/ianmoone0617/fastai-v2-introduction-to-cyclegan,I’m Something of a Painter Myself
10870991,2.3569,0,4,/denisart/facial-keypoint-detection,Facial Keypoints Detection
10869544,0.99407,1,8,/damoonshahhosseini/digitrec,Digit Recognizer
10865688,0.98796,0,4,/socathie/mnist-w-vgg16,Digit Recognizer
10767445,0.7336,7,59,/gofarther/efficientdet-framework-tta-pl-etc,Global Wheat Detection
10737286,0.96939,0,10,/shubhamchauda/digit-recogizer,Digit Recognizer
10539113,0.11904,7,39,/ezeanyi/house-prices-prediction,House Prices - Advanced Regression Techniques
10469589,0.98857,0,2,/kaito1412/mnist-test,Digit Recognizer
10427138,0.99232,0,0,/junooolee/practice-cnn-keras,Digit Recognizer
10403729,0.9921,0,0,/amrit09singh/tensorflow-cnn-mnist,Digit Recognizer
10394678,0.99353,1,11,/ihelon/pytorch-efficientnet-cutout-augmentation,Digit Recognizer
10335710,0.97621,0,4,/andreylh/simple-cnn,Digit Recognizer
10057348,0.99071,0,5,/sayantankarmakar/mnist-digit-recognizer-pytorch-cnn,Digit Recognizer
9946831,0.99467,0,3,/shirishsharma/getting-into-the-top-10-with-a-score-of-0-996,Digit Recognizer
9902733,0.98685,0,2,/target3/digit-recognizer-1,Digit Recognizer
9895998,0.99528,0,2,/bassbone/pytorch-mnist,Digit Recognizer
9671849,0.99292,0,1,/urmisen1202/digit-recognizer-cnn-auc-100-0,Digit Recognizer
9581031,0.99657,0,2,/mahmoudima/introduction-to-cnn-keras-0-997-top-6,Digit Recognizer
9309487,0.993,4,11,/shivamsouravjha/cnn-for-noobs,Digit Recognizer
9281452,0.99582,4,16,/yehyachali/kemnist,Digit Recognizer
9076324,0.7775,0,0,/sunnyville01/real-or-not-tensorflow-with-glove,Natural Language Processing with Disaster Tweets
8823836,1.18237,0,0,/patrickssfuchs/deep-learning-lmu-unit-approach,COVID19 Global Forecasting (Week 3)
8815496,0.05574,0,3,/akashsuper2000/arima-model,COVID19 Global Forecasting (Week 3)
11527704,0.77751,0,2,/christopherwsmith/beginner-exploratory-data-analysis-titanic-and-ml,Titanic - Machine Learning from Disaster
14639437,0.76794,0,1,/shunyasunami/python-kaggle-start-book-ch02-01,Titanic - Machine Learning from Disaster
11147300,0.76794,0,0,/maciejmeler/titanic-xgboost,Titanic - Machine Learning from Disaster
7559140,0.76076,0,1,/thilotosanchez/titanic-machine-learning-from-disaster,Titanic - Machine Learning from Disaster
453133,0.14883,0,0,/dataartist99/my-model-22855d,House Prices - Advanced Regression Techniques
4002090,0.98114,0,2,/casianmiron/1-6k-param-0-98-in-1-min-training,Digit Recognizer
7824085,0.82531,0,0,/manlunglo/81-accuracy-quick-short-nlp-model-using-bert,Natural Language Processing with Disaster Tweets
8089699,0.81581,0,2,/darwinwin/prediction-automl-tf-lstm-lightgbm,Natural Language Processing with Disaster Tweets
8147528,0.99628,0,1,/alberto467/digit-recognizer,Digit Recognizer
7715072,0.8335799999999999,0,1,/iamprateek/real-or-fake-disaster-tweet,Natural Language Processing with Disaster Tweets
1021412,0.994,1,13,/lsc2data/minist,Digit Recognizer
4249570,0.99628,0,1,/shashankdeshpande/tensorflow-cnn-intermediate,Digit Recognizer
4273036,0.7799,0,1,/erinsweet/xgboosttest,Titanic - Machine Learning from Disaster
3916180,0.99442,0,0,/rakeshvd/digit-recognizer-tensorflow-keras,Digit Recognizer
4828938,0.2207,3,0,/akaditya149/predicting-house-prices,House Prices - Advanced Regression Techniques
4829753,0.99285,0,2,/saurabhghosh/mnist-with-cnn,Digit Recognizer
2547283,0.79425,0,2,/casim0/titanic-machine-learning-ticket-casim0,Titanic - Machine Learning from Disaster
4505980,0.11992,0,4,/yushg123/ensembling-gradient-boosting-and-lasso,House Prices - Advanced Regression Techniques
4802297,0.98957,1,4,/infhyroyage/hello-pytorch-resnet18,Digit Recognizer
4116060,0.91657,2,4,/gsdeepakkumar/training-mnist-using-pytorch,Digit Recognizer
3308577,0.14951,0,1,/syosr6/house-pricing-linear-regression,House Prices - Advanced Regression Techniques
1970319,0.99585,0,0,/rayxie0329/keras-cnn-with-simple-explanation,Digit Recognizer
7422573,0.80845,0,2,/avinashtum/distilbert-keyword-embedding-classifier,Natural Language Processing with Disaster Tweets
4442168,0.99542,0,0,/niladmirari/digits-recognizer-with-fast-ai,Digit Recognizer
7995656,0.44187,3,3,/chariots17/artificial-nerual-network-keras,Google Cloud & NCAA® ML Competition 2020-NCAAW
4893231,2.41512,0,0,/zhudongxiao/2nd-based-on-cnn,Facial Keypoints Detection
7390641,0.99457,0,0,/wernerechezuria/digit-recognizer,Digit Recognizer
3729811,0.99385,0,0,/hs39ma/keras-single-input-and-multiple-outputs,Digit Recognizer
7868253,0.79425,0,0,/szett27/titanic-catboost-top-16,Titanic - Machine Learning from Disaster
7915844,0.80355,0,1,/yashobhan/disaster-eda-cleaning-classification,Natural Language Processing with Disaster Tweets
1588286,0.99057,0,0,/dhavaltaunk/digit-recognizer-using-conv-net,Digit Recognizer
5960976,0.98071,0,1,/idasss/kernel1d03be1826,Digit Recognizer
4178004,3.3166300000000004,0,0,/brianheredia/kernel2300dabe7f,Facial Keypoints Detection
6087367,0.81339,1,10,/danielmesa/titanic-kernel-final,Titanic - Machine Learning from Disaster
5664194,0.29708,1,3,/rakin12/house-prices-using-random-forest,House Prices - Advanced Regression Techniques
6402396,0.98814,0,1,/rdenadai/mnist,Digit Recognizer
6500737,0.98885,0,2,/raniphore/digit-recognition-using-keras,Digit Recognizer
4641398,0.7799,0,3,/grapestone5321/sklearn-logistic-regression,Titanic - Machine Learning from Disaster
7704706,0.10655,2,4,/phylake1337/housing-prices,House Prices - Advanced Regression Techniques
6695287,0.99071,2,5,/eddididi/residual-attention-networks-on-mnist,Digit Recognizer
5685288,0.9212,5,13,/cybercat/naive-modeling-using-minimum-analysis,IEEE-CIS Fraud Detection
4439667,0.78947,0,1,/nevinbaiju/eda-data-visualization-and-submission,Titanic - Machine Learning from Disaster
4327229,0.98228,0,0,/jakubwal/pytorch-cnn-digits-recognition,Digit Recognizer
4269364,0.994,0,0,/purvesh7/kernel93a4506675,Digit Recognizer
4270318,0.7751100000000001,0,0,/erinsweet/tianictest,Titanic - Machine Learning from Disaster
3809504,0.99514,1,2,/rivesunder/topless-alexnet-0-9947,Digit Recognizer
5174830,0.7368399999999999,0,1,/thimac/chimtau,Titanic - Machine Learning from Disaster
1897386,0.96371,1,1,/jpdurham/mnist-tensorflow-simple-nn-and-cnn,Digit Recognizer
5250683,0.622,0,0,/arishh/kernelbf699a1be7,Titanic - Machine Learning from Disaster
4996129,0.99385,1,4,/vochicong/fast-ai-mnist,Digit Recognizer
4657296,0.79425,0,6,/billumillu/titanic,Titanic - Machine Learning from Disaster
6757264,0.29093,0,0,/cybercat/first-look-analysis,What's Cooking?
3605350,0.99785,1,2,/jan1892/digit-recognition-with-keras-99-75,Digit Recognizer
3946383,0.99414,1,4,/miketonson1006/pytorch-from-a-beginner-s-view,Digit Recognizer
8328244,0.83021,2,3,/chotch/adaptnlp-easier-bert-based-models,Natural Language Processing with Disaster Tweets
4188803,0.99685,0,2,/freedomhappy/digitrecognizer,Digit Recognizer
2167459,0.7751100000000001,11,12,/rp1611/the-deep-learning-tutorial-90-acc-on-training,Titanic - Machine Learning from Disaster
3811922,0.99185,0,1,/gowrishankarin/tf101-multi-layer-perceptron,Digit Recognizer
7484390,0.8241299999999999,0,3,/rexhaif/roberta-based-approach,Natural Language Processing with Disaster Tweets
3238811,0.79425,2,5,/alefsegura/titanic-prediction-warmstarting,Titanic - Machine Learning from Disaster
3823766,0.995,3,1,/jsrshivam/mnist-digit-recognition-nn,Digit Recognizer
6507217,0.99014,0,1,/pseroul/cnn-on-mnist,Digit Recognizer
6526412,0.984,0,2,/iamsaksham/digit-recognizer-mnist,Digit Recognizer
3566234,1.0,3,9,/t0m0ff3l/top-score-using-nearest-neighbours,Digit Recognizer
2911709,0.99014,0,0,/deepanshgoyal/mnistdeep,Digit Recognizer
3446609,0.99,5,0,/yutaoc/cnn-on-mnist,Digit Recognizer
4795843,0.99285,0,0,/rolfrokseth/digit-recognizer-fastai-resnet50,Digit Recognizer
3838316,0.1335299999999999,0,0,/y120062/kernel1cb1bf470b,House Prices - Advanced Regression Techniques
5272353,0.107,0,0,/rudegs/competition-image-test,iWildCam 2019 - FGVC6
5370669,-1.352,0,31,/iloveyyp/base-model-molecular-properties-catboost,Predicting Molecular Properties
3982537,0.98342,3,4,/guoyang0601/cnn-sample-for-mnist-dataset-use-tensorflow,Digit Recognizer
5056419,0.99371,1,1,/thehelixx/deep-nn,Digit Recognizer
4497291,0.12813,2,4,/code1110/make-a-friend-with-lightgbm-and-her-rivals,House Prices - Advanced Regression Techniques
5275345,0.98185,0,1,/fightant1w1ll/pytorch-resnet,Digit Recognizer
3700787,0.6411399999999999,0,2,/datajang/titanic-analysis-test,Titanic - Machine Learning from Disaster
5277402,-1.68,0,39,/iloveyyp/xgboost-dis,Predicting Molecular Properties
7836969,0.99285,0,1,/yannnnnnnnnnnn/kernel5d66c76231,Digit Recognizer
4223111,0.99557,0,0,/anirudhchak/keras-cnn-for-digit-recognition,Digit Recognizer
4297932,0.99542,0,2,/febriyantojefri/introduction-to-cnn-keras-yassine-ghouzam-s,Digit Recognizer
4534470,0.76555,0,1,/barbetpsg/stacked-into-neural-net,Titanic - Machine Learning from Disaster
1480912,0.99585,0,0,/plasticgrammer/digit-recognizer-playground,Digit Recognizer
2143073,0.952,0,1,/u6yuvi/dl-with-pytorch-mnist-classification,Digit Recognizer
5458334,0.94442,0,2,/kfurudate/multilayer-perceptron-using-tensorflow,Digit Recognizer
4356513,0.99142,0,3,/dhruvgupta2801/digit-recogniser-cnn-99-1,Digit Recognizer
6732887,0.94028,0,0,/cutietechie/model-building-scikit-learn-and-pytorch,Digit Recognizer
10660138,0.70334,2,3,/bryanlambo/titanic-models-selection,Titanic - Machine Learning from Disaster
10572596,0.8390000000000001,1,9,/janmejaisingh/basics-of-image-denoising-using-autoencoders,Digit Recognizer
10298431,0.99228,0,2,/samuelvelasquez/proyecto1-samuel-velasquez,Digit Recognizer
9009055,0.99107,3,6,/morenovanton/cnn-batchnormalization-and-data-addition-pca,Digit Recognizer
8631167,1.1571,0,2,/lomen0857/covid-19-forecasting-with-rnn-lstm,COVID19 Global Forecasting (Week 2)
6589612,0.98985,1,5,/kaitmlee/beginner-cnn-in-pytorch,Digit Recognizer
6166956,0.99378,0,0,/anand0427/recognizer-with-cnn-augmentation-and-annealing,Digit Recognizer
5052155,0.99657,2,9,/jiaofenx/digit-recognizer-cnn-keras-tutorial-top-10,Digit Recognizer
4646477,0.99071,0,0,/dipeshpoudel/mnist-digitrecognition-using-cnn,Digit Recognizer
4439703,0.997,0,9,/prasadpatil99/digit-recognizer-with-cnn,Digit Recognizer
4044753,0.76076,0,2,/anirudhchak/titanic-dataset-neural-network-in-keras,Titanic - Machine Learning from Disaster
3019573,0.75598,0,4,/sigmaset/deep-neural-net-with-optimizer-and-pipeline,Titanic - Machine Learning from Disaster
2722461,0.79425,1,1,/erolsen/titanic-feature-engineering-and-neural-network,Titanic - Machine Learning from Disaster
2285148,0.99657,0,0,/sourav13/digit-recognition-using-cnn-and-keras,Digit Recognizer
1785856,0.1234,0,4,/blaskowitz100/stacked-neural-networks,House Prices - Advanced Regression Techniques
1437082,0.99985,4,26,/drscarlat/mnist-99-74-with-convoluted-nn-and-keras,Digit Recognizer
1407999,0.81842,0,0,/justuser/mnist-with-pytorch-cnn,Digit Recognizer
1278972,0.99171,0,3,/elkilany/keras-cnn,Digit Recognizer
1063818,0.96785,0,0,/rangnerok/digit-recognition-with-vanilla-nn,Digit Recognizer
480900,0.99428,21,109,/raoulma/mnist-image-class-tensorflow-cnn-99-51-test-acc,Digit Recognizer
1195173,0.99414,0,1,/hassanamin/keras-neural-network-for-digit-recognition,Digit Recognizer
10638803,0.78708,7,9,/mithunp/how2notoverfit,Titanic - Machine Learning from Disaster
5636558,0.79425,2,11,/sanyarx/titanic-solution-choosing-the-best-model-top-20,Titanic - Machine Learning from Disaster
551980,0.7799,5,26,/justjun0321/basic-eda-and-modeling-for-titanic-survival,Titanic - Machine Learning from Disaster
7690772,0.13077,0,1,/timgibson/house-prices-dry-pipeline-code,House Prices - Advanced Regression Techniques
7969428,0.83021,4,7,/prachichitnis/stack-tfidf-embedding-xgboost,Natural Language Processing with Disaster Tweets
4851629,0.79904,1,5,/moazmagdy/titanic-survival-prediction,Titanic - Machine Learning from Disaster
9870394,0.7751100000000001,0,1,/vadimsuraev/titanic-exercises,Titanic - Machine Learning from Disaster
3148616,0.99328,0,6,/ranjanhunt/basic-classification-using-tensorflow-s-keras-api,Digit Recognizer
1415708,0.80382,0,0,/babinu/scoring-more-than-80-by-doing-svc-on-names-field,Titanic - Machine Learning from Disaster
5433865,0.11764,9,26,/sonnihs/house-prices,House Prices - Advanced Regression Techniques
9567814,0.7703300000000001,6,12,/ayushikaushik/eda-feature-engineering-classification-models,Titanic - Machine Learning from Disaster
748999,0.71291,5,7,/pliptor/titanic-ticket-only-study,Titanic - Machine Learning from Disaster
8993563,0.7368399999999999,57,152,/frtgnn/pycaret-introduction-classification-regression,House Prices - Advanced Regression Techniques
3621652,0.79425,11,28,/spidy20/get-a-79-accuracy-on-titanic-dataset-shortest,Titanic - Machine Learning from Disaster
399690,0.76076,52,326,/mnassrib/titanic-logistic-regression-with-python,Titanic - Machine Learning from Disaster
3379709,0.1212099999999999,0,2,/mommermi/house-prices-with-simple-ridge-regression,House Prices - Advanced Regression Techniques
2971410,0.81339,6,18,/sagarprasad/titanic-survival-prediction-using-random-forest,Titanic - Machine Learning from Disaster
3678327,0.12376,1,14,/iavinas/house-pricing-simple-solution-top-30,House Prices - Advanced Regression Techniques
1058030,0.78947,0,3,/sparrow0hawk/starting-the-machine-learning-journey-titanic,Titanic - Machine Learning from Disaster
1437747,0.7368399999999999,0,4,/ajaysub110/titanic-random-forest-and-hyperparameter-search,Titanic - Machine Learning from Disaster
2304442,0.80861,0,3,/delayedkarma/grid-search-and-rf-lb-0-80861-top-10,Titanic - Machine Learning from Disaster
1916866,0.1221,3,14,/jazivxt/braveheart-at-home,House Prices - Advanced Regression Techniques
758862,0.11819,2,4,/shep312/linear-ensemble-stack-with-preprocessing,House Prices - Advanced Regression Techniques
624751,0.79425,0,11,/lucabasa/achieve-80-accuracy-with-basic-workflow,Titanic - Machine Learning from Disaster
543500,0.79425,4,14,/bismillahkani/predicting-titanic-survivors,Titanic - Machine Learning from Disaster
7419122,0.6570600000000001,1,5,/hrmello/part-of-speech-tagging,Natural Language Processing with Disaster Tweets
494334,0.78468,11,10,/mukultiwari/titanic-top-14-with-random-forest,Titanic - Machine Learning from Disaster
449069,0.8851600000000001,568,4321,/ldfreeman3/a-data-science-framework-to-achieve-99-accuracy,Titanic - Machine Learning from Disaster
2168037,0.91914,4,9,/junrong/diffractive-networks-for-optical-machine-learning,Digit Recognizer
7904543,0.81182,5,8,/xwalker/glove-bilstm,Natural Language Processing with Disaster Tweets
2732592,0.991,0,0,/mjwheele/mnist-convolutional-neural-network,Digit Recognizer
7816601,0.99128,0,0,/sglee487/tf2-1-w-data-augmentation,Digit Recognizer
3885820,0.99457,0,3,/sdoctor86/keras-cnn-with-image-augmentation,Digit Recognizer
4754243,0.98828,0,4,/wikifed/first-keras-model-mnist,Digit Recognizer
4165577,0.99471,0,0,/ashishshrma/mnist-try-1,Digit Recognizer
4187374,0.99485,0,2,/hiks13/first-cnn,Digit Recognizer
4245343,0.11547,2,4,/zhang0928/house-price-new,House Prices - Advanced Regression Techniques
5237803,0.7177,0,3,/rajuyadav7532/titanic-eda-and-prediction,Titanic - Machine Learning from Disaster
4361589,0.99442,1,0,/xiaoenn/kernel-one,Digit Recognizer
5406631,0.75598,1,1,/diskandar69/titanic-survival-prediction,Titanic - Machine Learning from Disaster
3365490,0.99614,0,1,/smivvla/mnist-kernel,Digit Recognizer
4718015,0.7368399999999999,1,2,/zebranerd/titanic-survival-classification,Titanic - Machine Learning from Disaster
4861518,0.99485,0,2,/akaditya149/digit-recognizer-with-cnn-keras-99-4,Digit Recognizer
4794965,0.97071,0,0,/rolfrokseth/digit-recognizer-fastai-resnet34,Digit Recognizer
2926106,0.11694,0,0,/abrarzshahriar/kernel2cf0ffdd2f,House Prices - Advanced Regression Techniques
5146253,0.99428,0,0,/mchomicz/digitrecognizer-simple-model,Digit Recognizer
4082669,0.99414,0,0,/qingyingliu/kernel858eb5d0f6,Digit Recognizer
4872207,0.97728,0,0,/rozamira/mnist-keras-mlp-no-augmentation,Digit Recognizer
5206845,0.78947,2,4,/amritk10/titanic-voyage,Titanic - Machine Learning from Disaster
6393530,0.99228,0,3,/manimannu/accuracy-saved-by-learning-rate,Digit Recognizer
5229888,0.7703300000000001,5,3,/krishna5555/titanic-dataset,Titanic - Machine Learning from Disaster
7253222,0.8124399999999999,1,1,/gsdeepakkumar/real-or-not-nlp-modelling,Natural Language Processing with Disaster Tweets
6732418,0.99657,0,1,/yunseo47/mnist-12-layer-cnn-classification,Digit Recognizer
7253079,0.82439,0,2,/samarthsarin/bert-with-transformers,Natural Language Processing with Disaster Tweets
3415034,0.7751100000000001,3,3,/jzdsml/ml-p5-predicting-titanic-survival,Titanic - Machine Learning from Disaster
5423901,0.1176799999999999,0,1,/georgeheinemann/regularisation-xgbregressor-stacked-robust,House Prices - Advanced Regression Techniques
7429783,0.99971,3,12,/ganeshmundra/do-not-underestimate-power-of-cnn,Digit Recognizer
4208051,0.99528,3,2,/hanslee01/digit-recognizer-with-cnn-fastai,Digit Recognizer
5844541,0.99971,2,7,/acleon/the-acleon-s-first-competion,Digit Recognizer
5915830,0.7511899999999999,0,1,/shubamsachdeva89/deep-neural-networks,Titanic - Machine Learning from Disaster
4028792,0.09957,0,0,/alextowers123443/digitrecognizer-comp,Digit Recognizer
7471754,0.83634,3,16,/vbmokin/disaster-nlp-keras-bert-using-tfhub-tuning-pca,Natural Language Processing with Disaster Tweets
4557083,0.72727,0,0,/hvardhandixit/titanic-ml-take-1,Titanic - Machine Learning from Disaster
4864231,0.99457,0,1,/zecklar/mnist,Digit Recognizer
6426496,0.7751100000000001,2,2,/clarlooktech/titanic-dataset-using-random-forest-model,Titanic - Machine Learning from Disaster
7997672,0.70813,0,0,/abdelkrimbeg/titanic-compitition-with-random-forest,Titanic - Machine Learning from Disaster
8013275,0.98728,0,0,/anand498/mnist,Digit Recognizer
4220148,0.91685,0,2,/joashjw/conditional-dcgan-cnn-using-32-images,Digit Recognizer
7500544,0.997,0,1,/trungha/improved-lenet5-augmentation-ensemble-0-997,Digit Recognizer
5742147,0.99414,0,0,/erikhass/digit-recognition-cnn-99-4-accuracy,Digit Recognizer
6851433,0.98271,0,1,/guidosalimbeni/handwritten-digits-classification,Digit Recognizer
3999796,0.78947,0,0,/pankeshpatel/titanic-survival-prediction,Titanic - Machine Learning from Disaster
2897299,0.7751100000000001,4,15,/yashchoudhary/titanic-eda-and-model-comparision,Titanic - Machine Learning from Disaster
9895226,0.78468,0,0,/vipuljain17/stackingvsblendingmodels,Titanic - Machine Learning from Disaster
3857852,0.98228,1,5,/anirudhjack/digit-recognising-using-inception-modules-keras,Digit Recognizer
10520730,0.76555,3,12,/db102291/titanic-xgboost-pipeline,Titanic - Machine Learning from Disaster
11196075,0.7703300000000001,4,14,/saumandas/neural-networks-in-tensorflow-with-titanic,Titanic - Machine Learning from Disaster
962979,0.90514,0,1,/manisood001/mnist-tensorflow-99-accuracy,Digit Recognizer
8370213,0.7799,8,8,/ramontanoeiro/titanic-competition,Titanic - Machine Learning from Disaster
4857388,0.7751100000000001,0,2,/dsousa/a-newbie-approach-to-titanic,Titanic - Machine Learning from Disaster
400080,0.94542,0,1,/dedecu/a-simple-neural-network,Digit Recognizer
2139758,0.7703300000000001,0,0,/hmshreyas7/titanic-who-survived,Titanic - Machine Learning from Disaster
2075224,0.96557,0,3,/antonk/mnist-trying-and-tuning-different-models,Digit Recognizer
8171427,0.986,0,0,/danaelisanicolas/digit-recognition,Digit Recognizer
2632078,0.979,0,1,/paulsantonastaso/pytorch-mnist-digit-recognizer,Digit Recognizer
2721831,0.79904,0,0,/harshit12345/titanic-disaster-top-17,Titanic - Machine Learning from Disaster
1080537,0.76555,0,0,/vknguyen/titanic-survival-prediction-based-on-gender,Titanic - Machine Learning from Disaster
1186370,0.996,1,2,/ottpeterr/convolutional-digit-classification-99-acc,Digit Recognizer
1505879,0.79425,0,0,/kevinw326/written-by-a-complete-beginner-random-forest,Titanic - Machine Learning from Disaster
2012125,0.80382,0,2,/acaciopassos/titanic-survivors-prediction,Titanic - Machine Learning from Disaster
1495409,0.7703300000000001,0,7,/devasu/beginner-using-decision-tree-classifier,Titanic - Machine Learning from Disaster
11988821,0.77751,2,3,/lavanyask/titanic-passenger-classfy,Titanic - Machine Learning from Disaster
1002729,0.6932699999999999,1,0,/sujit25/cats-vs-dogs-classification-with-cnn-keras,Dogs vs. Cats Redux: Kernels Edition
8073420,0.81305,1,3,/stefanjo/getting-started-with-nlp-nb-lr-baseline,Natural Language Processing with Disaster Tweets
4566279,0.99285,0,6,/stephanedc/tutorial-cnn-partie-1-mnist-digits-classification,Digit Recognizer
12706400,0.7751100000000001,0,1,/japandata509/titanic-neural-network-minmaxscaler,Titanic - Machine Learning from Disaster
7191399,0.74624,67,146,/marcovasquez/basic-nlp-with-tensorflow-and-wordcloud,Natural Language Processing with Disaster Tweets
4803523,0.7799,0,2,/thrasy/various-trial-knn-svc-rforest-nn-0-62-0-78,Titanic - Machine Learning from Disaster
4066562,0.7799,0,1,/utkarshtiwari/titanic-pandas,Titanic - Machine Learning from Disaster
6794990,0.7703300000000001,0,1,/vikasgoel1/this-won-t-save-titanic-but-help-in-titanic-2-0,Titanic - Machine Learning from Disaster
9686466,0.78468,2,15,/danoozy44/titanic-lightgbm,Titanic - Machine Learning from Disaster
6022931,0.99042,0,0,/manas0991/basic-deep-learning-with-keras,Digit Recognizer
4780662,0.97914,2,9,/slehkyi/image-recognition-building-simple-digit-detector,Digit Recognizer
3212316,0.78947,6,5,/mommermi/gridsearch-randomforest-applied-to-titanic,Titanic - Machine Learning from Disaster
2073771,0.98914,0,0,/ushanemani/digit-recognizer-with-deep-learning,Digit Recognizer
2715710,0.99085,0,0,/megaclick/digit-recognizer-using-conv2d-in-keras,Digit Recognizer
5480969,0.99114,1,4,/xiejialun/triplet-loss-on-mnist-classification,Digit Recognizer
3437991,0.99542,0,0,/dalip98/digit-recognizer,Digit Recognizer
10870425,0.76076,0,2,/bluescrunchie/titanic-survival-prediction-using-xgboost,Titanic - Machine Learning from Disaster
1503290,0.79425,0,0,/syncush/out-of-the-box-different-models-accuracy,Titanic - Machine Learning from Disaster
9813100,0.97135,0,11,/nzhongahtan/beginner-classification-models,Digit Recognizer
434514,0.7799,1,4,/tammyrotem/who-dies-who-lives-you-decide,Titanic - Machine Learning from Disaster
12584804,0.78468,0,1,/vadimgareev/titanic-randomforestclassifier-gridsearchcv,Titanic - Machine Learning from Disaster
1007734,0.80382,0,4,/bahafin/titanic-lgbm-parameter-tuning,Titanic - Machine Learning from Disaster
9103032,0.97857,0,3,/amsharma7/mnist-pytorch-for-beginners-detailed-desc,Digit Recognizer
2249093,0.79425,15,28,/gpreda/tutorial-for-classification,Titanic - Machine Learning from Disaster
570499,0.79425,102,370,/niklasdonges/end-to-end-project-with-python,Titanic - Machine Learning from Disaster
3143250,0.7703300000000001,73,224,/fatmakursun/titanic-classification-regression,Titanic - Machine Learning from Disaster
13626434,0.7912899999999999,0,0,/shigua997/classify-with-naive-bayes-score-79-in-submit,Natural Language Processing with Disaster Tweets
5616085,0.78468,4,20,/abhinandanmukherjee/titanic-a-hello-world-for-machine-learning,Titanic - Machine Learning from Disaster
9690994,0.78468,0,10,/danoozy44/titanic-hybrid-model-with-4-weak-learners,Titanic - Machine Learning from Disaster
8493694,0.8409399999999999,0,5,/highflyingbird/how-to-use-roberta-with-mean-and-max-pooling,Natural Language Processing with Disaster Tweets
9534175,0.7511899999999999,6,6,/hs1214lee/a-simple-tutorial-for-beginners-2-3,Titanic - Machine Learning from Disaster
4540563,0.96842,0,0,/aakashg339/digit-recognizer,Digit Recognizer
7221937,0.78945,0,0,/catris25/basic-ml-classification-tutorial-nlp-for-disaster,Natural Language Processing with Disaster Tweets
5315723,0.78947,15,24,/guidant/a-comprehensive-guide-to-get-to-the-top-15,Titanic - Machine Learning from Disaster
9697036,0.79425,3,7,/sebastianbedu/titanic,Titanic - Machine Learning from Disaster
8264973,0.80382,0,0,/maverick6912/ttianic-beta,Titanic - Machine Learning from Disaster
765212,0.7751100000000001,0,6,/delimixx/beginner-first-analytics-eda-xgboost,Titanic - Machine Learning from Disaster
27369,0.88428,1,8,/kogilvie/simple-perceptron-classifier,Digit Recognizer
9597918,0.7703300000000001,2,10,/danoozy44/titanic-catboostclassifier,Titanic - Machine Learning from Disaster
1172238,0.99342,0,0,/ybaojia/simple-cnn-trained-100-epochs,Digit Recognizer
1191702,0.99457,1,5,/rahul110/first-kaggle-competition-99-45-keras-cnn,Digit Recognizer
10874945,0.70813,0,2,/bluescrunchie/titanic-survival-prediction-using-random-forest,Titanic - Machine Learning from Disaster
9549288,0.76555,16,45,/servietsky/easy-way-titanic-pycaret,Titanic - Machine Learning from Disaster
8991277,0.7703300000000001,2,9,/carlmcbrideellis/logistic-regression-classifier-explained-with-eli5,Titanic - Machine Learning from Disaster
8598181,1.82281,0,1,/andynath/covid19-simple-country-wise-and-worldwide-eda,COVID19 Global Forecasting (Week 2)
8810136,0.54389,0,1,/henrychinaski/covid-19-1st-try,COVID19 Global Forecasting (Week 3)
8746308,0.34001,0,1,/osciiart/covid-19-lightgbm-2nd-place-of-week-1-no-leak,COVID19 Global Forecasting (Week 3)
4085955,0.11697,0,0,/shivaansook/shivaan-kernel2a280269b9,House Prices - Advanced Regression Techniques
12482554,0.78468,4,6,/randommmjy/my-data-analysis-with-titanic-dataset,Titanic - Machine Learning from Disaster
8949645,0.7751100000000001,1,1,/shiravahav/kernel4f7c64e112,Titanic - Machine Learning from Disaster
8163983,0.14955,0,0,/dots9999/jo-kernel2,House Prices - Advanced Regression Techniques
3843403,0.121,0,1,/ndivhuwo05/machine-thirteen,House Prices - Advanced Regression Techniques
8756496,0.48848,1,2,/diamondsnake/covid-19-logistic-curve-fitting-week-3,COVID19 Global Forecasting (Week 3)
12072456,0.8157800000000001,0,2,/matthiasmatulla/titanic-divide-men-women-0-81578,Titanic - Machine Learning from Disaster
3805060,0.13797,0,0,/ruhong/house-prices-advanced-regression-techniques-blend,House Prices - Advanced Regression Techniques
12328591,0.80861,1,1,/pavelbulgakov/melody-of-the-magical-forest-top-3,Titanic - Machine Learning from Disaster
7622970,0.78179,0,0,/harpreetkaurmahant/nlp-getting-started-tutorial,Natural Language Processing with Disaster Tweets
1913816,0.1508,0,0,/qiangge199551/house-prices-advanced-regression-techniques,House Prices - Advanced Regression Techniques
8879365,0.03631,0,1,/abhishekkumkar/xgboost,COVID19 Global Forecasting (Week 3)
12423277,0.7751100000000001,0,0,/apeters11/cs-100-data-science,Titanic - Machine Learning from Disaster
8771412,0.7199800000000001,0,0,/mactom1980/convid19-w3,COVID19 Global Forecasting (Week 3)
8864857,0.09108,2,1,/akshitsharma206/covid-19-3rd-week,COVID19 Global Forecasting (Week 3)
7193563,0.82224,2,17,/bibek777/lstm-baseline,Natural Language Processing with Disaster Tweets
7707431,0.81612,1,4,/ningeen/micro-challenge-vectorizers,Natural Language Processing with Disaster Tweets
9311017,0.74641,0,0,/salihmertszer/mert-s-titanic,Titanic - Machine Learning from Disaster
11822062,0.77272,1,1,/dysonisaac/titanic-ml-starter-dt-rf,Titanic - Machine Learning from Disaster
7507402,0.1178599999999999,7,10,/vicarious11/comprehensive-eda-with-regression-techniques,House Prices - Advanced Regression Techniques
8460401,0.7751100000000001,0,0,/rihim421/titanic,Titanic - Machine Learning from Disaster
3117774,0.76555,1,8,/tylerx/titanic-kernel,Titanic - Machine Learning from Disaster
7692146,0.7751100000000001,0,0,/sayedhashim/titanic,Titanic - Machine Learning from Disaster
4373862,0.76555,0,0,/orion12345/kernela89525333a,Titanic - Machine Learning from Disaster
7638418,0.6555,1,0,/maxwellmendz/myfirstproject-titanic-disaster-dataset,Titanic - Machine Learning from Disaster
8799224,0.78468,0,1,/kaushikmishra/titanicpredictor,Titanic - Machine Learning from Disaster
7637187,0.79904,4,7,/christoalex/xgboost-for-absolute-beginners,Titanic - Machine Learning from Disaster
4309959,0.91571,0,0,/vikrantdh/simple-tensorflow,Digit Recognizer
8611767,0.06853,6,10,/ashora/4ver-arima-2ver-xgboost-newbie,COVID19 Global Forecasting (Week 2)
8677965,0.24468,0,1,/lawrencechen98/covid-19-exponential-decay-forecast-and-analysis,COVID19 Global Forecasting (Week 2)
8699382,0.20435,0,0,/ebbygorg/sars-cov-2-exponential-model-week-2,COVID19 Global Forecasting (Week 2)
8699396,0.32576,0,0,/ebbygorg/covid-19-week-2-xgboost-lightgbm,COVID19 Global Forecasting (Week 2)
8785780,0.28141,0,1,/ilialar/logcurve-w3,COVID19 Global Forecasting (Week 3)
9087268,0.23421,1,2,/gaurangmehra/ames-real-estate-3,House Prices - Advanced Regression Techniques
9234238,0.78947,0,0,/bydanielbenner/titanic-gridsearch-svc-knn-logr-rf-nb-vote,Titanic - Machine Learning from Disaster
7234133,0.62679,0,1,/sabya40/titanic,Titanic - Machine Learning from Disaster
8813629,0.16314,0,0,/uvinetz/week-3-submission-poisson,COVID19 Global Forecasting (Week 3)
8826297,0.03516,0,0,/piterfm/covid-week3-xgboost,COVID19 Global Forecasting (Week 3)
8244389,0.78468,0,0,/yastikakumar/first-data-science-project-2,Titanic - Machine Learning from Disaster
12166595,0.77272,0,2,/luxetsal/titanic-pjh,Titanic - Machine Learning from Disaster
8798029,0.03515,0,0,/blackmantis/covid-19-week3-xgb,COVID19 Global Forecasting (Week 3)
8817034,0.5220199999999999,0,0,/pietromarinelli/revised-script,COVID19 Global Forecasting (Week 3)
8825716,0.05716,0,1,/tunguz/covid-19-w3-a-few-charts-and-a-simple-baseline,COVID19 Global Forecasting (Week 3)
8824396,0.0413199999999999,0,3,/christofhenkel/cv19w3-v3fix-cpmp-oscii-belug-full-8118,COVID19 Global Forecasting (Week 3)
8127995,0.76555,0,0,/gameatro/titanic-dataset,Titanic - Machine Learning from Disaster
8933395,0.70813,0,0,/thanhhungnguyen/titanic-version-2,Titanic - Machine Learning from Disaster
8781260,0.94756,0,0,/laiyanting/text-classification-using-bert,Toxic Comment Classification Challenge
8665003,1.46245,0,0,/sharadkumarjayakumar/ensemble,COVID19 Global Forecasting (Week 2)
11401977,0.80861,0,2,/annabalanik/titanic-analysis-top-5-0-80861,Titanic - Machine Learning from Disaster
8765095,2.07797,3,4,/blackmantis/covid19-week3-decisiontree-model,COVID19 Global Forecasting (Week 3)
8737953,0.8385600000000001,0,2,/kaimingk/covid-transformer,COVID19 Global Forecasting (Week 3)
8606894,0.38038,0,0,/jonaswm/covid-ag,COVID19 Global Forecasting (Week 2)
10116525,0.81339,0,0,/fengfangtao/titanic-final3-0,Titanic - Machine Learning from Disaster
8821275,0.42775,0,0,/lisphilar/combination-of-logistic-sir-f-week-3,COVID19 Global Forecasting (Week 3)
9075252,0.78947,0,0,/egorchernov/gradientboostingclassifier,Titanic - Machine Learning from Disaster
9074840,0.79425,0,0,/egorchernov/decisiontreeclassifier,Titanic - Machine Learning from Disaster
3905749,0.099,0,1,/khiwila/kernelb5c64454eb,iWildCam 2019 - FGVC6
3921809,0.7799,0,0,/willrussell90/titanic-notebook,Titanic - Machine Learning from Disaster
3930928,0.7799,0,0,/willrussell90/titanic-simple-rfc-model-with-3-features,Titanic - Machine Learning from Disaster
8256699,0.7703300000000001,0,0,/hessatmim/titanic,Titanic - Machine Learning from Disaster
7759070,0.1210099999999999,2,12,/gabrielmilan/ames-iowa-house-prices,House Prices - Advanced Regression Techniques
3890342,0.78468,0,0,/mrgui94/kernel52eee7a200,Titanic - Machine Learning from Disaster
4171798,0.1897,1,5,/pierpaolo28/house-prices,House Prices - Advanced Regression Techniques
12454912,0.75598,3,7,/tojohasinaraj/titanic-using-massive-pipeline-99-25-on-test-set,Titanic - Machine Learning from Disaster
7877269,0.76555,0,0,/akashkash/titanic-ml,Titanic - Machine Learning from Disaster
9298760,0.7751100000000001,0,2,/leonvahlkamp/lv-titanic-competition,Titanic - Machine Learning from Disaster
1819258,0.78947,1,3,/thachhoang2410/titanic-first,Titanic - Machine Learning from Disaster
12477276,0.7440100000000001,0,0,/redwankarimsony/titanic-with-rf-lr-knn-svm-ensemble,Titanic - Machine Learning from Disaster
12485520,0.80143,0,0,/redwankarimsony/titanic-the-final-voyage,Titanic - Machine Learning from Disaster
4026243,0.79904,0,3,/careforyourcandy/red-pinedo-chang-velasquez,Titanic - Machine Learning from Disaster
7899547,0.7751100000000001,0,0,/anupchandratre/titanic-basic-solution-with-logistic-regression,Titanic - Machine Learning from Disaster
7899543,0.7751100000000001,0,0,/wasimakram89/titanic-basic-solution-with-logistic-regression,Titanic - Machine Learning from Disaster
7899551,0.7751100000000001,0,0,/skhangjarakpam/titanic-basic-solution-with-logistic-regression,Titanic - Machine Learning from Disaster
7899545,0.7751100000000001,0,0,/ashokdas/titanic-basic-solution-with-logistic-regression,Titanic - Machine Learning from Disaster
4483925,0.12822,0,2,/markonkaggle/ridge-regression-with-enumerated-types,House Prices - Advanced Regression Techniques
4314597,0.76076,0,0,/abhishekchandar/titanic,Titanic - Machine Learning from Disaster
7744850,0.7751100000000001,0,1,/vishal146/titanic-ml-from-disaster,Titanic - Machine Learning from Disaster
8678481,0.69744,0,0,/mohitesh07/fork-of-kernel402aed056d,COVID19 Global Forecasting (Week 2)
8678627,0.11972,0,0,/mohitesh07/fork-of-kernel402aed056d-450424,COVID19 Global Forecasting (Week 2)
6728062,0.98142,2,2,/tunguz/mnist-mlpclassifier-baseline,Digit Recognizer
9165347,0.64593,0,0,/bkonovalov/titanic-bkonovalov-submit,Titanic - Machine Learning from Disaster
7258943,0.98528,0,2,/tunguz/mnist-svc-baseline,Digit Recognizer
4293763,0.80382,1,5,/adelardcollins/predict-survival-chances-in-titanic,Titanic - Machine Learning from Disaster
4108903,0.76076,2,3,/ma7555/titanic-by-logistic-regression,Titanic - Machine Learning from Disaster
8841300,0.7751100000000001,0,0,/ashokpandiyana/titanic-basic-solution-with-logistic-regression,Titanic - Machine Learning from Disaster
8634286,0.28336,1,3,/udaypratapyati/covid-19-predict-state-province-wise,COVID19 Global Forecasting (Week 2)
7860554,0.78947,0,7,/duylamnguyen/titanic-ml-from-disaster,Titanic - Machine Learning from Disaster
3899354,0.1235599999999999,1,1,/mohamed1993/kernel0c578feaef,House Prices - Advanced Regression Techniques
4081675,0.74162,3,2,/minarabbit/min-titanic,Titanic - Machine Learning from Disaster
8164650,0.7751100000000001,0,0,/mikkac/titanic-disaster-classification,Titanic - Machine Learning from Disaster
8147682,0.14959,0,0,/ftamur/house-prices-working-with-only-non-string-columns,House Prices - Advanced Regression Techniques
7373519,0.12729,1,5,/moon2002/house-prices-regression,House Prices - Advanced Regression Techniques
2112195,0.3732,0,3,/micky123/titanic-data-analysis,Titanic - Machine Learning from Disaster
9001881,0.14252,0,1,/nicolasmalloy/housing-predictions-correlation-trimming,House Prices - Advanced Regression Techniques
8822284,0.03721,0,1,/sorobedio/simpledecisiontree,COVID19 Global Forecasting (Week 3)
8221058,0.54715,0,2,/lorenzodenisi/ncaam-prediction-with-overall-rankings-and-dnn,Google Cloud & NCAA® ML Competition 2020-NCAAM
3857772,0.76076,0,0,/jutsux/titanic,Titanic - Machine Learning from Disaster
8319005,0.96857,0,0,/hdotyildiz/big-data-analysis,Digit Recognizer
8291989,0.78468,0,1,/medhinishetty/titanic,Titanic - Machine Learning from Disaster
8776807,0.14684,2,4,/dinasinclair/basic-random-forest-pipeline-housing-prices,House Prices - Advanced Regression Techniques
52414,0.99385,0,1,/m13r23/tensorflow-keras-cnn-with-data-augmentation,Digit Recognizer
8879629,0.7751100000000001,0,0,/ankurgarg04/getting-started-with-titanicc,Titanic - Machine Learning from Disaster
8479702,0.25032,0,0,/thanhhungnguyen/house-project-1,House Prices - Advanced Regression Techniques
7776988,0.80355,1,2,/ezzaldin6/eda-nlp-ml,Natural Language Processing with Disaster Tweets
7899533,0.7751100000000001,0,0,/atripathi3675/titanic-basic-solution-with-logistic-regression,Titanic - Machine Learning from Disaster
7894802,0.1379099999999999,0,0,/uoojin95/intuitive-eda-to-modeling,House Prices - Advanced Regression Techniques
4430782,0.99171,0,1,/aurelienmontmejat/digit-rocognizer-notebook,Digit Recognizer
4571105,0.7703300000000001,0,0,/littlefairy/titanic-survial-prediction-logisticregression,Titanic - Machine Learning from Disaster
7612369,0.7751100000000001,0,0,/raj001/titanic-test-practice-cap-5610,Titanic - Machine Learning from Disaster
7780401,0.7799,0,0,/rraghav5600/titanic-model,Titanic - Machine Learning from Disaster
8934676,0.76555,0,1,/sarthakdixit/jack-might-survive,Titanic - Machine Learning from Disaster
7525468,0.78945,0,0,/sabnina/real-or-not-nlp-tweets-analysis-and-predictions,Natural Language Processing with Disaster Tweets
8617043,1.04685,2,3,/zhiyaoliang/2th-week-prediction,COVID19 Global Forecasting (Week 2)
8662691,0.42731,0,0,/amoghjrules/covid-week-2,COVID19 Global Forecasting (Week 2)
9275403,0.13862,0,0,/mattshober/kernelfe8856b7bc,House Prices - Advanced Regression Techniques
8692575,1.04439,0,0,/woodyshi/covid-19-data-visualization-and-prediction,COVID19 Global Forecasting (Week 2)
8710137,0.26185,0,1,/magnuslarsson/kernel43f96abf19,COVID19 Global Forecasting (Week 2)
8761948,0.03516,0,1,/itsbitan/covid-19-week-3-analysis,COVID19 Global Forecasting (Week 3)
8807084,0.04567,0,0,/liangpang/prediction-week3,COVID19 Global Forecasting (Week 3)
9090093,0.7703300000000001,0,0,/currypurin/titanic-pycaret-starter,Titanic - Machine Learning from Disaster
12074619,0.74162,0,0,/dahi1234/titanic-draft1,Titanic - Machine Learning from Disaster
9287162,0.1397,0,1,/mohamedalthafalio/house-price,House Prices - Advanced Regression Techniques
8627046,3.08704,0,0,/ravishankariyer/covid-19-prediction-using-random-forest-beginner,COVID19 Global Forecasting (Week 2)
2301612,0.971,0,0,/yantraguru/mnist-hadwritten-digits-classification,Digit Recognizer
8593069,1.77235,2,7,/nickteim/covid19-model-2,COVID19 Global Forecasting (Week 2)
7990151,0.4358399999999999,0,2,/ricardo13/ncaam2020-baseline-with-xgb-lgb-blending,Google Cloud & NCAA® ML Competition 2020-NCAAM
7994061,0.35718,7,11,/chariots17/predicting-with-dnn-xgboost-tensorflow,Google Cloud & NCAA® ML Competition 2020-NCAAM
5103938,0.82775,1,0,/vidyabhandary/titanic-eda-hyperparameters,Titanic - Machine Learning from Disaster
7719307,0.79895,6,12,/omfuke123/simple-prediction-with-logisticregression,Natural Language Processing with Disaster Tweets
8075514,0.79425,0,1,/jaeminiman/hi-kaggle-with-titinic-data-random-forest,Titanic - Machine Learning from Disaster
7901621,0.7799,0,0,/shrutiturner/titanic-randomforestregressor,Titanic - Machine Learning from Disaster
7718095,0.7703300000000001,0,3,/sishihara/python-kaggle-start-book-ch02-06,Titanic - Machine Learning from Disaster
8326241,0.5180100000000001,0,1,/nickteim/google-cloud-simple-model,Google Cloud & NCAA® ML Competition 2020-NCAAM
3173291,0.4761399999999999,0,7,/moradnejad/ncaa-mens-made-datasets-public-explanation,Google Cloud & NCAA® ML Competition 2019-Men's
4742804,0.1177,2,12,/jiaofenx/houseprice-top-10-with-ensemble-modeling,House Prices - Advanced Regression Techniques
3017668,0.81339,14,60,/toldo171/titanic-a-beginner-guide-to-top-6,Titanic - Machine Learning from Disaster
4845532,0.99171,0,2,/iamsdt/pytorch-iamsdt,Digit Recognizer
6925490,0.99514,4,9,/ahernandez1/mnist-solution-exploration,Digit Recognizer
10421422,0.79665,5,7,/jaidevchittoria/titanic-xgbclassifier-79-625,Titanic - Machine Learning from Disaster
1646791,0.78947,0,0,/abdelmalek1/kernele9ec2f6509,Titanic - Machine Learning from Disaster
10627293,0.12734,2,4,/nizarhaider/top-25-in-5-steps,House Prices - Advanced Regression Techniques
822844,0.1908099999999999,0,0,/dnt1024/my-first-machine-learning-model,House Prices - Advanced Regression Techniques
446701,0.78947,0,2,/abdzrahim/analysis-on-survival-on-the-titanic,Titanic - Machine Learning from Disaster
4224913,0.15908,5,10,/felipefiorini/house-prices-regression-models,House Prices - Advanced Regression Techniques
11512965,0.8062199999999999,0,8,/larice/top-5-with-simple-cross-validation-and-ensemble,Titanic - Machine Learning from Disaster
1406087,0.73205,0,0,/sanikamal/predict-titanic-survivors,Titanic - Machine Learning from Disaster
9122551,0.34518,0,2,/leela2299/feature-engineering-made-easy-beginner-xgboost,COVID19 Global Forecasting (Week 3)
3861474,0.78947,0,2,/chriszou/titanic-with-pytorch-nn-solution,Titanic - Machine Learning from Disaster
8512706,0.78947,0,1,/nickteim/fastai-tanticni-v3,Titanic - Machine Learning from Disaster
9221607,0.7751100000000001,7,8,/carlmcbrideellis/hyperparameter-grid-search-sample-code,Titanic - Machine Learning from Disaster
5523792,0.80861,1,5,/saifulislamplabon/yet-another-simple-neural-net-solution,Titanic - Machine Learning from Disaster
11372153,0.78947,1,6,/janamachutova/titanic-age-by-title-fare-by-class-explained,Titanic - Machine Learning from Disaster
4557766,0.78947,1,14,/jiaofenx/titanic-tutorial-summary-for-beginners,Titanic - Machine Learning from Disaster
5888223,0.78468,0,3,/kongnyooong/ensemble-practice-for-raw-beginners-to-top-25,Titanic - Machine Learning from Disaster
5267184,0.78947,5,3,/kristoph4822/titanic,Titanic - Machine Learning from Disaster
6346527,0.79425,0,0,/alexniubi/very-simple-feature-processing-0-79425,Titanic - Machine Learning from Disaster
6375067,0.12562,0,2,/ayush51379/my-1st-kaggle-participation-public,House Prices - Advanced Regression Techniques
1919210,0.79904,0,3,/sergioortiz/titanic-competition-a-learning-diary,Titanic - Machine Learning from Disaster
2495527,0.1237099999999999,8,8,/grayphantom/bayesian-optimization-of-xgboost,House Prices - Advanced Regression Techniques
537402,0.78468,0,0,/charbull/titanickernel,Titanic - Machine Learning from Disaster
483538,0.76555,0,0,/yipcma/scikit-learn-ml-from-start-to-finish-modified,Titanic - Machine Learning from Disaster
1049845,0.13226,0,0,/praxitelisk/kagglelearn-ml2-3-learning-to-use-xgboost,House Prices - Advanced Regression Techniques
1306079,0.82296,0,0,/hfujita/titanic-survival-prediction-using-family-status,Titanic - Machine Learning from Disaster
1057020,0.75598,8,11,/liopic/simple-prediction-with-just-age-and-sex-features,Titanic - Machine Learning from Disaster
2692970,0.79425,1,7,/sumitbehal13/titanic,Titanic - Machine Learning from Disaster
2893403,0.75598,1,0,/deepu97nagar/titanic-basic-ml-step-by-step,Titanic - Machine Learning from Disaster
1405996,0.17948,0,2,/lider123/housepricing,House Prices - Advanced Regression Techniques
1287037,0.1473799999999999,10,5,/perlinwarp/xgboost-for-house-pricing,House Prices - Advanced Regression Techniques
1267195,0.18127,0,0,/chhuang0123/scikit-learn-lr-rfr-and-nn,House Prices - Advanced Regression Techniques
8642700,0.96211,0,0,/paralleltree/covid19-simple-logistic-fitting,COVID19 Global Forecasting (Week 2)
11553502,0.7751100000000001,8,14,/iljaavadiev/titanic-eda-hypothesis-and-ml,Titanic - Machine Learning from Disaster
8634550,0.31209,0,1,/sanasam/covid19-first-simple-code-submission,COVID19 Global Forecasting (Week 2)
4613620,0.974,0,3,/akashs2021/using-pytorch-for-beginners,Digit Recognizer
10349429,0.97539,0,3,/shauryaa117/ensemble-learning-digit-recognizer,Digit Recognizer
2745119,0.13463,0,0,/bertslonim/iowa-home-prices-first-time-kaggle-submission,House Prices - Advanced Regression Techniques
4715193,0.12225,1,5,/sonnihs/house-price-prediction,House Prices - Advanced Regression Techniques
8226216,0.1416099999999999,2,4,/matheuscoradini/categorical-correlation-rfr-and-xgboost,House Prices - Advanced Regression Techniques
3630145,0.99114,0,1,/lorenzomnto/digit-recognizer-using-cnn-with-tensorflow-keras,Digit Recognizer
2404415,0.99414,0,1,/shashanksai/cnn-on-mnist-keras-99-4,Digit Recognizer
816910,0.79904,0,3,/wenjiebai/modified-titanic-best-working-classifier,Titanic - Machine Learning from Disaster
1343572,0.7799,0,2,/srisudheera/titanic,Titanic - Machine Learning from Disaster
1242882,0.78947,0,3,/jamesvalencia1/titanic-learning-to-ml,Titanic - Machine Learning from Disaster
1475854,0.7703300000000001,0,1,/deepanshkhurana/titanic-solution-attempt-3,Titanic - Machine Learning from Disaster
1877522,0.99285,0,3,/peisuu/rookie-try-cnn-similar-with-alexnet,Digit Recognizer
10904450,0.12217,7,12,/chen2222/lightgbm-tuning-step-by-step-optuna-0-122-lb,House Prices - Advanced Regression Techniques
460768,0.75598,0,1,/limjingyu/who-survived-the-titanic,Titanic - Machine Learning from Disaster
464031,0.7511899999999999,7,11,/stostain/chronicle-of-a-tragedy,Titanic - Machine Learning from Disaster
548581,0.78468,0,0,/geluxp/q3-project-titanic-disaster,Titanic - Machine Learning from Disaster
1331299,0.99485,0,1,/tango911/digit-recognizer-test-acc-995,Digit Recognizer
10613590,0.76076,0,3,/priy998/titanicsurvival-machine-learning-model,Titanic - Machine Learning from Disaster
167795,0.75598,100,782,/helgejo/an-interactive-data-science-tutorial,Titanic - Machine Learning from Disaster
1301491,0.99757,129,536,/cdeotte/25-million-images-0-99757-mnist,Digit Recognizer
3139705,1.0,102,348,/tarunpaparaju/titanic-competition-how-top-lb-got-their-score,Titanic - Machine Learning from Disaster
202132,0.992,74,303,/toregil/welcome-to-deep-learning-cnn-99,Digit Recognizer
370529,0.76555,66,281,/szamil/where-is-my-output-file,Titanic - Machine Learning from Disaster
553948,0.1142099999999999,50,190,/massquantity/all-you-need-is-pca-lb-0-11421-top-4,House Prices - Advanced Regression Techniques
2531357,0.7799,39,144,/frtgnn/introduction-to-pytorch-a-very-gentle-start,Titanic - Machine Learning from Disaster
4916598,0.72727,44,57,/suneelpatel/learn-ml-from-titanic-disaster,Titanic - Machine Learning from Disaster
6349560,0.01401,10,40,/newbielch/lgbm-regression-view,NFL Big Data Bowl
5248655,0.0426,14,40,/iiyamaiiyama/how-to-submit-prediction,Open Images 2019 - Instance Segmentation
9084950,0.159,21,81,/moradnejad/start-from-here-trends-eda-fe-submissions,TReNDS Neuroimaging
106895,0.1258099999999999,9,56,/pablocastilla/predict-house-prices-with-xgboost-regression,House Prices - Advanced Regression Techniques
1176436,0.66028,3,54,/biswajee/titanic-dataset,Titanic - Machine Learning from Disaster
7242344,115427.5,5,22,/golubev/mip-optimization-preference-cost-santa2019revenge,Santa 2019: Revenge of the Accountants
9756445,0.909,8,32,/rftexas/siim-isic-melanoma-analysis-eda-efficientnetb1,SIIM-ISIC Melanoma Classification
10585604,0.75837,1,8,/rohitkaushik23/random-forest,Titanic - Machine Learning from Disaster
8824741,0.03022,0,10,/cpmpml/covid19-w3-submission,COVID19 Global Forecasting (Week 3)
9726384,0.79425,6,14,/danoozy44/titanic-10-fold-cv,Titanic - Machine Learning from Disaster
8158539,0.01676,1,7,/ash16win/march-madness-ensemble-h2o-xgboost-and-gbm,Google Cloud & NCAA® ML Competition 2020-NCAAM
10585216,0.75598,2,7,/vishukaushik/random-forest,Titanic - Machine Learning from Disaster
8049156,0.0,0,7,/ranjithkumarsamala/titanic-survival-classification-90-accuracy,Titanic - Machine Learning from Disaster
10888828,0.7511899999999999,8,8,/meedley/titanic-survivability-prediction-by-a-newbie,Titanic - Machine Learning from Disaster
10577883,0.78468,2,7,/janninga/starting-with-titanic,Titanic - Machine Learning from Disaster
117312,0.0,2,5,/icinnamon/titanic-preprocessing-ml-tutorial,Titanic - Machine Learning from Disaster
7824947,0.79425,28,5,/williamshin/titanic-williamshin,Titanic - Machine Learning from Disaster
9762575,0.81339,9,8,/nischaydnk/top-3-with-logistic-regression,Titanic - Machine Learning from Disaster
9045211,0.9322,10,48,/shahules/fine-tune-xlm-kfold-cv-0-93-lb,Jigsaw Multilingual Toxic Comment Classification
9963645,0.80861,6,6,/shirowanisan/titanic-classification-using-random-forest-model,Titanic - Machine Learning from Disaster
10050741,0.995,4,5,/zeeniye/simple-digit-recog-mnist-z,Digit Recognizer
8823298,3.20533,2,12,/rohanrao/covid-19-w3-lgb-mad,COVID19 Global Forecasting (Week 3)
9850979,0.7799,15,25,/shirishsharma/from-missing-vals-to-ml-models-with-87-acc,Titanic - Machine Learning from Disaster
10486471,0.98521,2,6,/berkaykurhan/digit-recognizer-cnn,Digit Recognizer
9163415,0.78468,14,13,/digvijayyadav/titanic-codesprediction,Titanic - Machine Learning from Disaster
9988485,0.99328,0,5,/activenikhilg/all-for-0-99757,Digit Recognizer
9327700,0.76315,12,21,/suhedacilek/titanic-machine-learning-from-disaster,Titanic - Machine Learning from Disaster
8872203,0.15616,6,41,/carlmcbrideellis/very-simple-xgboost-regression,House Prices - Advanced Regression Techniques
8842033,0.79904,0,5,/funxexcel/titanic-solution-random-forest,Titanic - Machine Learning from Disaster
10902072,0.7751100000000001,1,5,/dktalaicha/titanic-multiple-models,Titanic - Machine Learning from Disaster
10886131,0.79186,2,10,/mikhailg0/titanic-solution,Titanic - Machine Learning from Disaster
8661538,0.75197,25,49,/amarkumar2/covid-19-easy-forecasting-using-random-forest,COVID19 Global Forecasting (Week 1)
9863898,0.78947,0,4,/target3/titanic-feature-engineering-1,Titanic - Machine Learning from Disaster
8592457,0.06901,5,6,/sarthakpawar/arima-for-forecasting,COVID19 Global Forecasting (Week 2)
10606207,0.14847,0,3,/mahmoudalhallaq/advancedhousepricemahmoud,House Prices - Advanced Regression Techniques
9281998,0.67464,2,2,/syoheihoh/python-kaggle-start-book-ch02-01,Titanic - Machine Learning from Disaster
9279003,0.7440100000000001,7,7,/aadityasinghal/titanic-machine-learning-from-disasters-model,Titanic - Machine Learning from Disaster
10514367,1.0,5,10,/manavtrivedi/using-mnist-in-ml,Digit Recognizer
10493126,0.622,1,5,/rluyck/titanic-disaster,Titanic - Machine Learning from Disaster
9090724,0.34759,0,3,/mrkmakr/ykc-cup-1st-starter,YKC-cup-1st
9465380,0.78468,1,4,/wuhao1996/titanic,Titanic - Machine Learning from Disaster
10498040,0.89281,0,8,/manavtrivedi/predict-future-sales-beginner,Predict Future Sales
2791469,0.0,0,3,/krishnapriya66/titanic-survival-prediction-beginner-logistic-reg,Titanic - Machine Learning from Disaster
8827057,0.03067,0,4,/titericz/covid19-w3-submission-blend-4-models,COVID19 Global Forecasting (Week 3)
8825239,0.03092,0,4,/hossein2015/covid-19-week-3-sarima-x-approach,COVID19 Global Forecasting (Week 3)
10547851,0.76555,1,3,/oliveiralucaseng/titanic-test-v1,Titanic - Machine Learning from Disaster
8705542,0.0825,5,30,/gaborfodor/covid-19-a-few-charts-and-a-simple-baseline,COVID19 Global Forecasting (Week 2)
10490765,0.13296,8,13,/mohtashimnawaz/ongoing-house-price-advanced-regression-techniques,House Prices - Advanced Regression Techniques
10497470,0.75598,0,4,/mksaad/titanic-competition-randomforest,Titanic - Machine Learning from Disaster
10554618,0.1318099999999999,1,4,/kareemaburejila/house-price-advanced-competition,House Prices - Advanced Regression Techniques
9064170,0.7751100000000001,1,2,/joelwhittier/getting-started-with-titanic,Titanic - Machine Learning from Disaster
9644593,0.7703300000000001,0,2,/shrekty/titanic-survival-prediction,Titanic - Machine Learning from Disaster
8970551,0.7751100000000001,1,3,/vishnu691999/titanic-for-beginners,Titanic - Machine Learning from Disaster
10013549,0.7751100000000001,0,4,/arnav8/titanic-challenge-randomforest,Titanic - Machine Learning from Disaster
9359434,0.98542,6,4,/doala0155/0-98-digit-recognizer-train-cnn-with-keras,Digit Recognizer
9377473,0.66028,0,3,/hiroshiimai/python-kaggle-start-book-ch02-01,Titanic - Machine Learning from Disaster
10610081,0.828,2,19,/heyytanay/siim-isic-all-you-need-to-get-started,SIIM-ISIC Melanoma Classification
9125986,1.0,11,15,/jafarib/titanic-competition-how-to-get-top-score-100,Titanic - Machine Learning from Disaster
9162397,0.7751100000000001,4,3,/hinamxx/titanic-machine-learning,Titanic - Machine Learning from Disaster
1780500,0.0,1,2,/arindamkumar/solution-2-0-titanic,Titanic - Machine Learning from Disaster
2579663,0.0,0,2,/nagulapatianusha369/myfirst-code,Titanic - Machine Learning from Disaster
6532156,0.0,0,3,/umang5916/titanic-data-analysis-logistic-regression,Titanic - Machine Learning from Disaster
9964670,0.98342,1,1,/briansxu/digit-recognition-w-keras-cnn,Digit Recognizer
9002820,0.14314,2,1,/urayukitaka/house-prices-eda-and-rf-xgb-ensemble-prediction,House Prices - Advanced Regression Techniques
10590455,0.1209799999999999,0,2,/aiueonoff/house-prices-26,House Prices - Advanced Regression Techniques
10579290,0.14329,0,1,/kenyabando/kernel345dd6b731,House Prices - Advanced Regression Techniques
10626502,0.1309,0,4,/vet516lec/ensemble-xgboost-lightgbm,House Prices - Advanced Regression Techniques
10608216,0.33782,0,1,/zintun/logistics-step-by-step-approach,[Open] Shopee Code League - Logistics
9799511,0.91819,1,2,/ikorobkov/text-classification-iad-intro-ds,Text classification
9260286,0.8610200000000001,0,4,/blackitten13/texts-classification-baseline-full,Text classification
8937266,0.7751100000000001,1,2,/amanarora/first-notebook-titanic-beginner-ml,Titanic - Machine Learning from Disaster
9996046,0.7751100000000001,0,1,/missjaanii/getting-started-with-titanic,Titanic - Machine Learning from Disaster
9387125,0.76794,0,1,/halsmit/kagglewintext-ch01,Titanic - Machine Learning from Disaster
9148009,0.14298,3,10,/abhijithchandradas/stacked-xgb-linear-regression-random-forest-svm,House Prices - Advanced Regression Techniques
7191313,12824478.24,0,1,/grapestone5321/santa-2019-sample-submission,Santa 2019: Revenge of the Accountants
8820802,0.06285,0,1,/tbuturishvili/kernel553d59c05a,COVID19 Global Forecasting (Week 3)
8824079,0.46923,0,2,/prashant268/covid19-forecasting-with-country-information,COVID19 Global Forecasting (Week 3)
9431594,0.7511899999999999,0,2,/oliversluk/ui-1-oliver-slusnak,Titanic - Machine Learning from Disaster
9457557,0.7751100000000001,0,1,/tobiask88/titanic-rf-py,Titanic - Machine Learning from Disaster
9335792,0.55219,0,1,/krithi07/fastai-lesson1-2-application,Mercedes-Benz Greener Manufacturing
10550675,0.61517,0,2,/julianbenny/forest-type-knn,Forest Cover Type Prediction
10514061,0.71728,0,2,/biswajitsarangi/house-price-prediction-i,House Prices - Advanced Regression Techniques
9881116,0.12884,1,12,/chandrimad31/house-price-prediction-xgboost-tuned-by-hyperopt,House Prices - Advanced Regression Techniques
10487273,0.7751100000000001,1,1,/nadaboulares/kernel7dfe3ba450,Titanic - Machine Learning from Disaster
10554946,0.7057399999999999,1,2,/abhinavbanisetti/titanic-logistic-regression,Titanic - Machine Learning from Disaster
10560267,0.75358,4,21,/nakulsingh1289/catboost-with-randomized-search-on-titanic,Titanic - Machine Learning from Disaster
9755311,0.96671,1,9,/bpkapkar/digit-identifier-pytorch,Digit Recognizer
10044098,0.77751,0,0,/igorilic94/titanic-starter,Titanic - Machine Learning from Disaster
10554855,11.93714,0,0,/ahmedmurad1990/fork-of-house-price-advanced-competition,House Prices - Advanced Regression Techniques
9460050,0.75837,0,0,/ankykaushik/titanic-survival-predictions,Titanic - Machine Learning from Disaster
9718856,0.969,0,0,/wojciechmigda/classification-with-pytsetlini-classifier,Digit Recognizer
9289204,389.53681,0,0,/benmoskowitz/t8-the-chads,ASN10e Final Submission - Detect COML Faces
8923801,0.7799,2,8,/carlmcbrideellis/very-simple-neural-network-for-classification,Titanic - Machine Learning from Disaster
9744611,0.73923,0,0,/stevenz20/template-for-predictive-modeling,Titanic - Machine Learning from Disaster
9925504,0.73444,2,3,/kaverikr/titanic-survivours,Titanic - Machine Learning from Disaster
9753055,0.7751100000000001,0,0,/mdshadabhussain/titanic-data-science-solutions,Titanic - Machine Learning from Disaster
9280318,0.25787,0,0,/rohitsharma0206/kernel2cf120a541,House Prices - Advanced Regression Techniques
9278970,0.7751100000000001,0,0,/emilyohq/titanic-machine-learning-from-disaster,Titanic - Machine Learning from Disaster
10052025,0.0,0,0,/vervit/titanic,Titanic - Machine Learning from Disaster
3348250,18332.9324,0,3,/prudhvi9999/exercise-machine-learning-competitions,House Prices - Advanced Regression Techniques
8801768,0.65632,0,0,/kirichenko17roman/decision-tree-baseline,COVID-19 diagnostic
10530983,0.76555,0,0,/glorythesky/kernel5633e69f16,Titanic - Machine Learning from Disaster
8808571,0.1715799999999999,0,0,/buitri91/kernel3271dd3032,COVID19 Global Forecasting (Week 3)
10541016,0.1451599999999999,0,0,/ahmedmurad1990/house-price-advanced-competition,House Prices - Advanced Regression Techniques
10540439,0.76315,0,0,/ahmedmurad1990/titanic,Titanic - Machine Learning from Disaster
9471785,0.7177,0,1,/scatteredflo/2-titanic-tutorial-for-study-200514,Titanic - Machine Learning from Disaster
9473382,0.78947,0,0,/iamnibedita/welcome-to-titanic-my-first-kaggle-contest,Titanic - Machine Learning from Disaster
9479015,0.7703300000000001,0,0,/jeckyoh/get-start-with-titanic,Titanic - Machine Learning from Disaster
8797562,1.21849,0,0,/sudhamshsuraj/kernel2c53599420,COVID19 Global Forecasting (Week 3)
8797454,1.23339,0,0,/sudhamshsuraj/kernel6fc57d24dc,COVID19 Global Forecasting (Week 3)
9507324,0.78947,0,0,/rishikts/rishikts-titanic,Titanic - Machine Learning from Disaster
9610055,0.1527,1,6,/carlmcbrideellis/catboost-regression-minimalist-script,House Prices - Advanced Regression Techniques
9019944,0.12465,0,0,/ee33hhee/house-prices,House Prices - Advanced Regression Techniques
9348603,0.67464,0,0,/felipeleonardi/titanic-survival-predict,Titanic - Machine Learning from Disaster
9015972,0.2045,0,0,/abhishekrath1995/arhouse,House Prices - Advanced Regression Techniques
10021539,0.7751100000000001,0,1,/bhavinmoriya/titanic-starts-off,Titanic - Machine Learning from Disaster
8986817,0.7751100000000001,0,1,/pramodmalla/kernel6c0e66a41f,Titanic - Machine Learning from Disaster
8984533,0.7751100000000001,0,0,/xuanedx/titanic-dataset-eda-prediction,Titanic - Machine Learning from Disaster
9656503,0.64593,0,0,/yusukemigitera/2layernet,Titanic - Machine Learning from Disaster
9641213,0.7440100000000001,0,5,/krsna540/a-complete-machine-learning-practise,Titanic - Machine Learning from Disaster
9026601,0.17601,0,0,/uday44/house-price-prediction,House Prices - Advanced Regression Techniques
8855224,0.72248,0,0,/suyogsarda/getting-started-with-titanic,Titanic - Machine Learning from Disaster
8856175,0.78947,0,0,/ssandorov/titanic-competition,Titanic - Machine Learning from Disaster
9310019,0.15916,0,0,/rishikts/rishikts-houseprices,House Prices - Advanced Regression Techniques
9310679,0.7751100000000001,0,0,/cbackes000/cbackes-titanic-comp,Titanic - Machine Learning from Disaster
9040010,0.76555,0,0,/ryu581/kernelee2050440c,Titanic - Machine Learning from Disaster
8906403,0.76555,0,1,/amuhammedmadhih/titanic-journey-ml-from-disaster,Titanic - Machine Learning from Disaster
8903523,0.14881,0,0,/prashant268/covid-19-week3-sarima,COVID19 Global Forecasting (Week 3)
10588557,0.19794,0,0,/syotogo/kernel2c146252dd,House Prices - Advanced Regression Techniques
10069527,0.12299,0,0,/kasayu/regularized-linear-models,House Prices - Advanced Regression Techniques
10613870,0.20661,0,1,/kaggluserjp/kernel4136fd523d,House Prices - Advanced Regression Techniques
9264149,0.98585,0,0,/smiteshp/mnist-digits-cnn,Digit Recognizer
9269178,0.2042,0,0,/deffro/baseline-model,Find me that fish
9244682,0.9431,0,0,/zhuangliu1939/ensemble,Jigsaw Multilingual Toxic Comment Classification
9832832,0.13566,0,0,/kasayu/beginner-s-stop-xgb-lgbm-blend,House Prices - Advanced Regression Techniques
9806045,0.8590000000000001,0,1,/writuparnabanerjee/digit-recognizer,Digit Recognizer
9829342,0.7751100000000001,0,0,/kevinarajomestrinel/titanicaccuracy,Titanic - Machine Learning from Disaster
9959337,0.9921,0,10,/an0utlier/digit-recognizer-using-cnn,Digit Recognizer
8781929,0.40409,0,1,/bedantaprotimdeka/covid-19-week-3,COVID19 Global Forecasting (Week 3)
9953241,0.7751100000000001,0,0,/vishnukaushik/titanic-ml,Titanic - Machine Learning from Disaster
8790907,0.1474,0,0,/dkozlov/kernelke4w7xp98yn7uwvb,COVID19 Global Forecasting (Week 3)
10007221,0.77751,3,3,/tarundaram1/titanic-rfcmodel,Titanic - Machine Learning from Disaster
8941776,0.66985,0,0,/keitarodev/upura-kaggle-tutorial-01-first-submission,Titanic - Machine Learning from Disaster
10000160,0.93624,0,0,/maciejkasprzyk/fork-of-prezenty-mikolaja-a5a662,Santa Gift Matching Challenge
3646279,0.0,0,0,/thamcw/titanicsinking-predicting-survivors,Titanic - Machine Learning from Disaster
218066,0.0,0,0,/xupanda/an-interactive-data-science-tutorial,Titanic - Machine Learning from Disaster
211229,0.0,0,0,/pyobro/titanic-survival-classification,Titanic - Machine Learning from Disaster
206079,0.0,0,0,/squaresurf/an-interactive-data-science-tutorial,Titanic - Machine Learning from Disaster
227921,0.0,0,0,/andyhu/an-interactive-data-science-tutorial,Titanic - Machine Learning from Disaster
225885,0.0,0,0,/terryli/an-interactive-data-science-tutorial-b3e00d,Titanic - Machine Learning from Disaster
168632,0.0,0,0,/rabben/an-interactive-data-science-tutorial,Titanic - Machine Learning from Disaster
203994,0.0,0,0,/noremac/an-interactive-data-science-tutorial,Titanic - Machine Learning from Disaster
176474,0.0,0,0,/wangzehao/an-interactive-data-science-tutorial,Titanic - Machine Learning from Disaster
174153,0.0,0,0,/vivekwisdom/an-interactive-data-science-tutorial,Titanic - Machine Learning from Disaster
969725,0.0,0,0,/hannahccarson/an-interactive-data-science-tutorial-346588,Titanic - Machine Learning from Disaster
914319,0.0,0,0,/nababasky/an-interactive-data-science-tutorial,Titanic - Machine Learning from Disaster
822643,0.0,0,0,/zhaogj/titanic-explore,Titanic - Machine Learning from Disaster
1299746,0.0,0,0,/sakinapetiwala/an-interactive-data-science-tutorial,Titanic - Machine Learning from Disaster
262193,0.0,0,0,/jrcarr/an-interactive-data-science-tutorial,Titanic - Machine Learning from Disaster
262080,0.0,0,0,/ym591276727/an-interactive-data-science-tutorial,Titanic - Machine Learning from Disaster
249804,0.0,1,0,/matianjun1/an-interactive-data-science-tutorial-f3365b,Titanic - Machine Learning from Disaster
664351,0.0,0,1,/mmdeluna/titanic-survival-using-crisp-data-mining-process,Titanic - Machine Learning from Disaster
588152,0.0,0,0,/anuragn4/an-interactive-data-science-tutorial,Titanic - Machine Learning from Disaster
525609,0.0,0,0,/codesail/a-fork-from-an-interactive-data-science-tutorial,Titanic - Machine Learning from Disaster
6437324,0.0,0,0,/shuhengma/titanic-1,Titanic - Machine Learning from Disaster
9892617,0.7751100000000001,0,0,/mollywiener/getting-started-titanic,Titanic - Machine Learning from Disaster
9893499,0.7751100000000001,0,0,/mollywiener/titanic-take-2,Titanic - Machine Learning from Disaster
9894779,0.7751100000000001,0,2,/micky26/titanic-competition,Titanic - Machine Learning from Disaster
5056107,0.0,0,0,/hankbo/an-interactive-data-science-tutorial,Titanic - Machine Learning from Disaster
10512336,0.13108,0,0,/nomotofusei/kernel335fcf624d,House Prices - Advanced Regression Techniques
9340823,0.13491,0,0,/nikemayer/adilgazym-houseprice,House Prices - Advanced Regression Techniques
6926509,0.0,0,0,/gr0ceries/lab-6-titanic-submission,Titanic - Machine Learning from Disaster
8708675,0.06853,0,2,/muhammad4hmed/getting-into-top-ranks-very-easily,COVID19 Global Forecasting (Week 2)
9327908,123.67383999999998,0,0,/rebutler/t4-lonely-team,ASN10e Final Submission - Detect COML Faces
9326374,1.28579,0,0,/zionxu/kernel7c850b0e60,COVID19 Global Forecasting (Week 2)
9863354,0.79904,0,14,/chandrimad31/titanic-survival-prediction-decision-region-plot,Titanic - Machine Learning from Disaster
9861207,0.96614,0,0,/daniilgerasimenko/digit-recognizer-da-ml,Digit Recognizer
9391660,0.72248,1,0,/uedaryo/titanic-random-forest-practice,Titanic - Machine Learning from Disaster
9390314,0.78708,0,0,/ahmedmlabib/titanic-using-logistic-regression,Titanic - Machine Learning from Disaster
9146030,0.92128,0,0,/dmitri9149/pytorch-simple-model-s-from-scratch-v1,Digit Recognizer
9432566,3.81747,0,2,/sureshmecad/covid-19-week-3,COVID19 Global Forecasting (Week 3)
9141481,0.639,0,0,/ashishnnnnn/knnn-2,Don't Overfit! II
8686225,2.86935,0,0,/andrewcharles/forecasting-week-2,COVID19 Global Forecasting (Week 2)
8688381,1.90398,0,2,/avnikabhati/covid19-week-2-exponentialsmoothing,COVID19 Global Forecasting (Week 2)
8689318,2.94119,0,0,/mehvishashiq/covid19-global-forecasting-w-2,COVID19 Global Forecasting (Week 2)
8705528,0.16953,0,0,/vishvambarpanth/kernel463e140e39,COVID19 Global Forecasting (Week 2)
9940582,0.7751100000000001,0,1,/marinaborges/titanic-machine-learning-exercise,Titanic - Machine Learning from Disaster
8681959,2.97385,0,0,/sudiptapurkayastha/covid19-forecasting,COVID19 Global Forecasting (Week 2)
8826842,0.11325,0,0,/therealroman/kernel73535a4d66,COVID19 Global Forecasting (Week 3)
8826215,0.50113,0,6,/sasrdw/gbt3n,COVID19 Global Forecasting (Week 3)
8827694,2.82519,0,0,/swami84/kernel52ceaad666,COVID19 Global Forecasting (Week 3)
8824002,3.77643,0,0,/pakornrtn/covid-19-newbie-step-for-logistic-curve-fitting,COVID19 Global Forecasting (Week 3)
8823285,0.4765899999999999,0,1,/ericfreeman/basic-math-pessimist,COVID19 Global Forecasting (Week 3)
8825650,0.05716,0,0,/mystery/covid-19-w3-a-few-charts-and-a-simple-b-4c8afd,COVID19 Global Forecasting (Week 3)
8824671,0.05522,1,0,/rohitmidha23/covid3-stats,COVID19 Global Forecasting (Week 3)
8824956,0.0564,0,0,/philippsinger/cv19w3-2-v2-play-2-v3fix-sub-last6dayopt,COVID19 Global Forecasting (Week 3)
8825111,0.05575,0,0,/rohitmidha23/covid-week3-final,COVID19 Global Forecasting (Week 3)
8822923,0.2782,0,0,/magnuslarsson/kernel38d43c9260,COVID19 Global Forecasting (Week 3)
8711562,0.08319,0,0,/keedong/covid19-exponential-model2-kee,COVID19 Global Forecasting (Week 2)
8814819,0.44798,0,0,/yaroshevskiy/covid-19-lightgbm-2nd-place-of-week-1-no-leak,COVID19 Global Forecasting (Week 3)
8813914,0.05184,0,0,/sayan341/latent-component-model-v3-covid-19,COVID19 Global Forecasting (Week 3)
8706858,1.13664,0,1,/kimberlyvan/covid-19-global-forecasting-wk-2,COVID19 Global Forecasting (Week 2)
8707432,1.19053,0,0,/regismagnus/covid19-week2,COVID19 Global Forecasting (Week 2)
8815465,1.2921,0,0,/akashsuper2000/xgboost-prediction-model,COVID19 Global Forecasting (Week 3)
8820720,0.5231899999999999,1,12,/darshanjain29/covid-19-week-3,COVID19 Global Forecasting (Week 3)
8815751,0.43066,0,0,/ekzemplaro/covid19-week3-apr08,COVID19 Global Forecasting (Week 3)
8818855,3.20533,1,0,/appian/covid19-week3-3,COVID19 Global Forecasting (Week 3)
8819836,0.03633,0,1,/vtaquet/covid19-week-3-logistic-regression,COVID19 Global Forecasting (Week 3)
9059975,0.78468,0,0,/mujinkeikakupro/kernel-newstart,Titanic - Machine Learning from Disaster
9074140,0.1156799999999999,0,0,/walzer55/house-prices,House Prices - Advanced Regression Techniques
9067724,0.79904,0,1,/mervatnabil1/titanic-data-science-solutions-modified-copy,Titanic - Machine Learning from Disaster
8773240,0.994,6,36,/amarkumar2/digit-recognizer-detailed-step-wise,Digit Recognizer
8775443,0.07701,0,0,/myh0307/covid-19-arima-lstm,COVID19 Global Forecasting (Week 3)
8770216,0.79904,0,0,/nayonika/titanic-analysis,Titanic - Machine Learning from Disaster
8769444,0.26498,0,1,/kashish2801/covid-19-predict-arimax-xgboost-and-prophet-by-fb,COVID19 Global Forecasting (Week 3)
9099970,9.45301,0,0,/rohandawar/fork-of-house-price-prediction,House Prices - Advanced Regression Techniques
8667455,0.69875,0,0,/chantalolieman/fhl2020,COVID19 Global Forecasting (Week 2)
6488916,0.6555,0,0,/kaleedfox/titanic-survival-prediction-using-python,Titanic - Machine Learning from Disaster
10874311,0.70481,0,1,/sakazu310/addzipnull-seedave-cvave-for-second-season,Homework for Students
9118888,0.98557,0,0,/sidharkal/digit-recognizer,Digit Recognizer
10871222,0.15884,0,0,/kubonoyusuke/kernel5bd63b914a,House Prices - Advanced Regression Techniques
9138437,0.1348599999999999,0,0,/gb00000/house-gridsearch,House Prices - Advanced Regression Techniques
10905997,0.97367,0,0,/engnadersarsour/digit-recognizer-competition,Digit Recognizer
10897604,0.70375,0,1,/smatsuyama/cv-averaging-50-lgbm,Homework for Students
10897891,0.70462,0,1,/horisuke13e/2nd-kh-5models-ensemble-02,Homework for Students
10884311,0.7703300000000001,0,3,/amgdhussein/titanic-compete,Titanic - Machine Learning from Disaster
10883222,0.13979,0,0,/yukiyoneda/kernel17fdeed0dc,House Prices - Advanced Regression Techniques
10893728,0.7056600000000001,0,1,/kkomiya/fork-of-best-score-start-18798798719kkdkajkljfdks,Homework for Students
10894264,0.67703,0,0,/chengbufan/kernel12f7284f19,Titanic - Machine Learning from Disaster
10891624,0.70281,0,1,/horinem/ai-academy-4th-homework-04,Homework for Students
10889365,0.14802,0,0,/maimahdi/houseprice-ad,House Prices - Advanced Regression Techniques
3816111,0.9999,9,19,/benjaminwarner/aerial-cactus-prediction-using-fast-ai-resnet-34,Aerial Cactus Identification
3805886,0.9615,1,3,/rblcoder/keras-cnn-in-tf-coursera-course-cactus,Aerial Cactus Identification
3801445,0.9989,0,1,/sdoctor86/simple-keras-cnn,Aerial Cactus Identification
3695994,1.0,6,25,/frlemarchand/simple-cnn-using-keras,Aerial Cactus Identification
3691490,0.9999,0,0,/autuanliuyc/aerial-cactus-identification,Aerial Cactus Identification
3395207,0.9998,0,1,/yeekit24/detecting-cactus-with-kekas,Aerial Cactus Identification
3209761,0.9997,0,1,/vovaekb90/aerial-cactus-simple-vgg-like-cnn,Aerial Cactus Identification
3611654,0.9999,0,0,/narendrashu/cactus-identification-fastai,Aerial Cactus Identification
3618182,0.9985,3,1,/ralhadeff/keras-cnn-cactus-identification,Aerial Cactus Identification
3614756,0.9833,0,1,/cibi075/kernel-cb-i,Aerial Cactus Identification
3566763,0.9926,2,1,/qq1065507891/prediction-with-keras-vgg16,Aerial Cactus Identification
3529314,0.9999,0,3,/gbellport/pre-trained-densenet,Aerial Cactus Identification
3487499,0.9926,0,0,/souvikchanda/aerial-cactus-identification-using-fastai,Aerial Cactus Identification
3468349,0.9675,0,0,/vimlord/cnn-cactus-classifier-c3,Aerial Cactus Identification
3485658,0.9985,0,1,/aniruddhas435/kernel17f23aed87,Aerial Cactus Identification
3235970,0.9997,0,0,/njelicic/resnet-transfer-learning,Aerial Cactus Identification
3319541,0.999,0,0,/abhijit96/detect-cactus-keras,Aerial Cactus Identification
3262419,0.9998,0,0,/elvinmirze/simple-deep-cnn-different-batch-size-and-epochs,Aerial Cactus Identification
3337789,0.9998,0,2,/hnt4499/data-augmentation-model-benchmark,Aerial Cactus Identification
3302556,0.9999,0,1,/anuragshas/densenet-fastai,Aerial Cactus Identification
3263937,0.9806,0,3,/alperkoc/cactus-identification-with-lgbm,Aerial Cactus Identification
3253921,0.9996,10,27,/bonhart/simple-cnn-on-pytorch-for-beginers,Aerial Cactus Identification
3228641,0.9994,1,1,/swwintels/aerial-cactus-identification-with-resnet50,Aerial Cactus Identification
3189811,1.0,12,63,/kenseitrg/simple-fastai-exercise,Aerial Cactus Identification
3203266,0.9933,0,3,/tarunpaparaju/cactus-identifier-vgg16-imgdatagen,Aerial Cactus Identification
3189572,0.9996,4,17,/mnpinto/cactus-identification-fastai-v1-0-46-ensemble,Aerial Cactus Identification
10416820,0.9915,0,0,/moisesamadojr/cnn-vgg16-senac-2020,Aerial Cactus Identification
3644302,3.4618900000000004,0,1,/shravankp/ny-fare-prediction,New York City Taxi Fare Prediction
2952873,3.63759,0,0,/vashwar/nyc-taxi-interference-dnn,New York City Taxi Fare Prediction
2572454,4.01592,0,0,/nikhilmittal/new-york-city-taxi-fare-prediction-1,New York City Taxi Fare Prediction
2198961,4.103219999999999,0,0,/sameesiddiqui/linear-model,New York City Taxi Fare Prediction
2333080,3.34436,0,0,/sameesiddiqui/nn-keras,New York City Taxi Fare Prediction
2200090,3.4894800000000004,0,0,/dineshramasamy/geohashing-and-time-of-day-features,New York City Taxi Fare Prediction
1550080,3.4348,0,1,/yugagrawal95/nyc-regressor-prediction-analysis-for-beginner,New York City Taxi Fare Prediction
1443618,3.21579,0,0,/santhoshbala18/fork-of-taxi-fare-prediction,New York City Taxi Fare Prediction
1925071,4.97227,0,0,/jalbert0906/taxi-rides-manhattan-manhattan-and-jfk-manhattan,New York City Taxi Fare Prediction
1506807,4.21298,0,0,/ritesaluja/007ny,New York City Taxi Fare Prediction
1827891,3.12459,3,3,/corvuslee/nyc-cab-random-forest-practice,New York City Taxi Fare Prediction
1577323,3.98929,0,0,/tandonarpit6/new-york-city-taxi-fare-challenge,New York City Taxi Fare Prediction
1753840,3.84109,1,1,/mikelkn/nyc-taxi-fare-predictions,New York City Taxi Fare Prediction
1748544,4.10393,0,0,/mogady/analysis-using-basemap-to-visualize-spatial-data,New York City Taxi Fare Prediction
1720610,3.88091,3,20,/namakaho/nyctaxi,New York City Taxi Fare Prediction
1723571,3.72419,0,0,/parulm/nyc-taxi-fare-xgboost,New York City Taxi Fare Prediction
1709782,3.13991,0,3,/sudheerkumar2592/newyorkcitytaxifareprediction-keras-dl,New York City Taxi Fare Prediction
1448126,3.03933,1,5,/ffedericoni/nyc-taxi-fare-data-pipeline-with-tensorflow,New York City Taxi Fare Prediction
1589598,3.44872,0,0,/sas2018/nyc-taxi-fare-not-a-learning,New York City Taxi Fare Prediction
3384289,0.8901899999999999,0,1,/harishreddy18/santander-customer-transaction-prediction,Santander Customer Transaction Prediction
4081304,0.5,0,1,/shotaku/prediction-using-neural-network,Santander Customer Transaction Prediction
4004480,0.89559,0,0,/sachin619/santander,Santander Customer Transaction Prediction
3976353,0.85398,0,1,/abrahamchang/red-neuronal-keras-04357d,Santander Customer Transaction Prediction
3945074,0.8541200000000001,0,2,/arturojose19/red-neuronal-keras,Santander Customer Transaction Prediction
3256993,0.899,0,0,/society765/kernel-xgboost-cv,Santander Customer Transaction Prediction
3967851,0.8547,0,0,/geer1997/red-neuronal-keras-717339,Santander Customer Transaction Prediction
3894671,0.8543700000000001,0,0,/earama/red-neuronal-keras,Santander Customer Transaction Prediction
3957814,0.85062,0,0,/alemvangrieken/red-neuronal-keras-alejandro-marcano,Santander Customer Transaction Prediction
3869586,0.89807,0,0,/palbha/santander-customer-transaction-prediction,Santander Customer Transaction Prediction
3247575,0.5,0,1,/niyipop/simple-2-layer-nn-in-pytorch,Santander Customer Transaction Prediction
3713007,0.92218,12,8,/jganzabal/cnn-independence-counts-magic-0-92174-private,Santander Customer Transaction Prediction
3640019,0.92416,0,1,/vamshigunji/kernel6b0e74b753,Santander Customer Transaction Prediction
3085738,0.564,0,0,/hiro310/santander-tree,Santander Customer Transaction Prediction
3566690,0.91211,7,7,/subrahmanyamv/santander-target-encoding-0-910,Santander Customer Transaction Prediction
3548500,0.92022,173,460,/cdeotte/200-magical-models-santander-0-920,Santander Customer Transaction Prediction
3556477,0.92576,22,124,/nawidsayed/lightgbm-and-cnn-3rd-place-solution,Santander Customer Transaction Prediction
3556667,0.92244,23,79,/dott1718/922-in-3-minutes,Santander Customer Transaction Prediction
3560082,0.91334,5,32,/philippsinger/frequency-features-without-test-data-information,Santander Customer Transaction Prediction
3558371,0.92375,2,19,/nagiss/9-solution-nagiss-part-2-2-weight-sharing-nn,Santander Customer Transaction Prediction
3551861,0.775,0,1,/matheuspush/santander-my-keras-model,Santander Customer Transaction Prediction
3522188,0.92239,2,16,/christofhenkel/nn-with-magic-augmentation,Santander Customer Transaction Prediction
3451042,0.92358,6,21,/whitebird/0-923-in-n-5-aug,Santander Customer Transaction Prediction
3152500,0.8440000000000001,0,7,/shubham311994/santander-new,Santander Customer Transaction Prediction
14365653,0.05625,34,79,/marto24/beginners-prediction-top3,House Prices - Advanced Regression Techniques
14505494,0.14593,4,5,/thalesbruno/regression-house-prices-using-pipeline,House Prices - Advanced Regression Techniques
14428047,0.14749,3,3,/mohdsohelqureshi/house-sale-prediction-randomforest-regressor,House Prices - Advanced Regression Techniques
14449664,0.12425,2,1,/joolousada/house-prices-prediction,House Prices - Advanced Regression Techniques
12724583,0.15854,0,0,/tquantsolutions/thinhqn94-housepriceart,House Prices - Advanced Regression Techniques
14327425,1.6758400000000002,0,0,/weslatimarwen/house-prices-beginner,House Prices - Advanced Regression Techniques
14185932,0.14474,0,0,/hoangdang89/house-price-art,House Prices - Advanced Regression Techniques
14323339,0.14632,3,6,/akarachaisaeteaw/house-prices-in-stack-and-voting,House Prices - Advanced Regression Techniques
14546108,0.14335,0,1,/satyammodi/notebook462ddda703,House Prices - Advanced Regression Techniques
14308536,0.12236,0,1,/firebee/notebookd57e16a930,House Prices - Advanced Regression Techniques
13113713,0.7559999999999999,15,40,/gilfernandes/tabnet-fastai-linear-lgb-ensemble-starter,Riiid Answer Correctness Prediction
12983412,0.76,16,70,/berobillard/riid-shared-modeling,Riiid Answer Correctness Prediction
12899802,0.583,0,6,/daihaoxue/walk-through,Riiid Answer Correctness Prediction
12945919,0.735,0,2,/brendanartley/pandas-riiid-attempt,Riiid Answer Correctness Prediction
12866169,0.759,0,5,/wuwenmin/lgb-hyper-opt,Riiid Answer Correctness Prediction
12469107,0.746,0,1,/omega1996/randomforest,Riiid Answer Correctness Prediction
12810994,0.715,48,140,/claverru/demystifying-transformers-let-s-make-it-public,Riiid Answer Correctness Prediction
12815951,0.5,0,1,/tchaye59/riiid-just-submit,Riiid Answer Correctness Prediction
12769308,0.643,0,4,/cshorten30/starter,Riiid Answer Correctness Prediction
12327058,0.748,0,1,/marcinstasko/capstone-project,Riiid Answer Correctness Prediction
12597254,0.614,0,5,/shivanandmn/cnn-lstm-pytorch-riiid,Riiid Answer Correctness Prediction
12591393,0.541,2,10,/leadbest/sakt-self-attentive-knowledge-tracing-submitter,Riiid Answer Correctness Prediction
12575709,0.7509999999999999,2,29,/rar4dx/two-feature-model,Riiid Answer Correctness Prediction
12535134,0.6990000000000001,0,6,/tusharcode/riiid-answer-correctness-prediction-full-pipeline,Riiid Answer Correctness Prediction
10002376,0.8913,6,46,/mariapushkareva/melanoma-classification-tensorflow-efficientnetb7,SIIM-ISIC Melanoma Classification
10854529,0.4651,1,12,/amneves/random-classifier-because-why-not,SIIM-ISIC Melanoma Classification
10838371,0.6238,0,1,/bluescrunchie/random-forest-melanoma-malignancy-classification,SIIM-ISIC Melanoma Classification
10853663,0.5529,1,3,/amneves/bigtransfer-bit-transfer-on-steroids,SIIM-ISIC Melanoma Classification
10849253,0.8197,0,8,/zefirchik/table-predict-0-8270,SIIM-ISIC Melanoma Classification
10843018,0.6985,0,7,/shubham9455999082/xgboost-cross-validation,SIIM-ISIC Melanoma Classification
10558061,0.8703,3,9,/vaidicjain/siim-deeplearning-basic-model-best-score-of-87,SIIM-ISIC Melanoma Classification
10803201,0.8861,5,12,/dndxiii/multimodal-with-efficient-net-and-xgb-ensemble,SIIM-ISIC Melanoma Classification
10810830,0.9151,2,7,/amneves/tensorflow-densenet-transfer-learning,SIIM-ISIC Melanoma Classification
10730963,0.9513,106,392,/datafan07/analysis-of-melanoma-metadata-and-effnet-ensemble,SIIM-ISIC Melanoma Classification
10752856,0.9039,0,9,/orionpax00/melanoma-inference,SIIM-ISIC Melanoma Classification
10740251,0.9074,8,25,/ipythonx/training-cv-melanoma-starter-ghostnet-tta,SIIM-ISIC Melanoma Classification
10756736,0.7112,0,4,/estau2020/melanoma-result-view,SIIM-ISIC Melanoma Classification
10666322,0.921,3,11,/ttt2209181/melanoma-classification-with-uniform-augment-x256,SIIM-ISIC Melanoma Classification
10724176,0.7802,0,5,/sgrahkgg/simple-cnn-baseline-from-scratch-10min-training,SIIM-ISIC Melanoma Classification
10072586,0.862,0,3,/sanyam83/siim-melanoma-classification,SIIM-ISIC Melanoma Classification
9801878,0.8503,0,1,/synysterjeet/image-classification-using-monkai,SIIM-ISIC Melanoma Classification
8469862,1.23906,0,0,/rajneeshkumar0509/future-sales-prediction-using-deep-learning,Predict Future Sales
8148462,0.92893,0,1,/pierricm/predict-sales-catboost-and-bayesian-optimization,Predict Future Sales
8223643,1.16813,1,3,/caneradil/predict-future-sales-analysis,Predict Future Sales
7924209,0.89281,6,34,/lonewolf45/coursera-final-project,Predict Future Sales
7960537,0.93017,0,0,/dimonrtm/averaging,Predict Future Sales
6445781,0.95465,0,2,/holoong9291/predict-future-sales,Predict Future Sales
7469607,1.16781,0,0,/lonewolf45/coursera-course,Predict Future Sales
7727385,0.91793,5,9,/saga21/future-sales-comp-time-series-prediction-with-lgb,Predict Future Sales
7373920,1.23646,0,1,/aaroha33/predict-future-sales-time-series,Predict Future Sales
7222221,1.1958,0,0,/katsuragitetsuya/futuresales,Predict Future Sales
5541930,0.6829999999999999,0,0,/winterchroma/fast-ai-resnet152-with-progressive-resizing,APTOS 2019 Blindness Detection
5616638,0.805,0,0,/chopinforest1986/efficientnetb6-noweights-before-20200310,APTOS 2019 Blindness Detection
5094572,0.736,0,0,/maureenliu/densenet-aug-external,APTOS 2019 Blindness Detection
8098225,0.114533,0,2,/darwinwin/blindness-prediction-automl,APTOS 2019 Blindness Detection
4884450,0.6940000000000001,0,0,/mayankkestwal10/diabetic-retinopathy-fast-ai,APTOS 2019 Blindness Detection
4841395,0.359701,1,1,/hefestion77/kernel-aptos-resnet50,APTOS 2019 Blindness Detection
4713560,0.5760000000000001,2,2,/naushads/aptos-2019-road-to-victory,APTOS 2019 Blindness Detection
6799381,0.7576470000000001,0,0,/braindeadtheory/resnetpretrainedensemble,APTOS 2019 Blindness Detection
6488852,0.772619,0,0,/hieunpd/fork-of-fork-of-fork-of-fork-of-kernel6519ae0776,APTOS 2019 Blindness Detection
4871133,0.735,0,0,/brianrice2/aptos-fast-ai-resnet50,APTOS 2019 Blindness Detection
6552573,0.685862,0,1,/hank60033/resnet50,APTOS 2019 Blindness Detection
5426770,0.792,1,2,/abimannan/aravind-hospital-blindness-detection,APTOS 2019 Blindness Detection
6254726,0.816679,0,3,/laserboxes/aptos2019,APTOS 2019 Blindness Detection
4734983,0.78,0,1,/osciiart/dr-submit,APTOS 2019 Blindness Detection
5659125,0.804,2,2,/salilm23/effi-b0-1-2-3-5-6-aptos-2019-regression-avg,APTOS 2019 Blindness Detection
5833999,0.783996,1,2,/adnanzaidi/starter-kernel-for-0-79,APTOS 2019 Blindness Detection
5431943,0.317,0,0,/ratikvig/kernelcc5ae3f115,APTOS 2019 Blindness Detection
3003473,0.07603,0,3,/aditya100/finish-placement-prediction-in-pubg,PUBG Finish Placement Prediction (Kernels Only)
3025662,0.05907,0,0,/kumaml/kuma-pubg-eda,PUBG Finish Placement Prediction (Kernels Only)
2493383,0.0561,0,0,/rgwegwegwe/first-submission,PUBG Finish Placement Prediction (Kernels Only)
2632281,0.0194599999999999,0,1,/itslek/10th-place-solution-ensemble-lvl2,PUBG Finish Placement Prediction (Kernels Only)
2889200,0.06488,0,0,/rishrk007/pubg-finish-placement-prediction-playground,PUBG Finish Placement Prediction (Kernels Only)
2412444,0.0608,0,1,/iamarjunchandra/part-1-pubg-eda-base-model,PUBG Finish Placement Prediction (Kernels Only)
2775970,0.06185,2,4,/fatrubicc/pubg-finish-placement-prediction,PUBG Finish Placement Prediction (Kernels Only)
2762616,0.0212,0,1,/econdata/pubgml,PUBG Finish Placement Prediction (Kernels Only)
2761987,0.2472,0,2,/econdata/pubgstats8,PUBG Finish Placement Prediction (Kernels Only)
2732900,0.4336,0,0,/sangatar/lgbm-ending-kernel,PUBG Finish Placement Prediction (Kernels Only)
1991333,0.079,0,0,/bassjo/training-of-the-nnerds,PUBG Finish Placement Prediction (Kernels Only)
2644517,0.05707,0,0,/ezzzio/pubg-finish-placement-predictions,PUBG Finish Placement Prediction (Kernels Only)
2680186,0.0329,0,1,/zhoujcc/kernel12d021107a,PUBG Finish Placement Prediction (Kernels Only)
2516688,0.0914,0,0,/hitewar/xyzabc,PUBG Finish Placement Prediction (Kernels Only)
2367414,0.0913,0,0,/siftsingh/linear-regression-kernel,PUBG Finish Placement Prediction (Kernels Only)
2448365,0.0586,0,0,/praneethvarmaalluri/next-try,PUBG Finish Placement Prediction (Kernels Only)
2294807,0.0272,3,16,/masurte/really-big-kernel-eda-lightgbm-pictures,PUBG Finish Placement Prediction (Kernels Only)
2432693,0.0649,0,1,/harshul23/kerneld070e89f5b,PUBG Finish Placement Prediction (Kernels Only)
2397098,0.0603,0,0,/praneethvarmaalluri/model-2,PUBG Finish Placement Prediction (Kernels Only)
2383043,0.0327,1,2,/alucard1177/pubg-nn,PUBG Finish Placement Prediction (Kernels Only)
2075290,0.0266,0,0,/huma97/pubg-competition,PUBG Finish Placement Prediction (Kernels Only)
2257419,0.0914,0,0,/disguise666/my-pubg-placement-predict,PUBG Finish Placement Prediction (Kernels Only)
14567539,0.8440000000000001,15,40,/reppic/mean-teachers-find-more-birds,Rainforest Connection Species Audio Detection
13951336,0.7959999999999999,0,17,/duythanhng/rfcx-torchvision-models-augmentation,Rainforest Connection Species Audio Detection
13889911,0.861,22,75,/kneroma/inference-tpu-rfcx-audio-detection-fast,Rainforest Connection Species Audio Detection
13886190,0.8109999999999999,1,8,/kilimannejaro/rfcx-train-resnet50-with-tpu,Rainforest Connection Species Audio Detection
13681175,0.8240000000000001,6,16,/mekhdigakhramanian/rfcx-resnet50-tpu,Rainforest Connection Species Audio Detection
13522839,0.747,4,3,/duythanhng/all-in-one-rfcx-baseline-model-avg,Rainforest Connection Species Audio Detection
12962848,0.214,0,2,/eladwar/earthforest,Rainforest Connection Species Audio Detection
13163022,0.748,20,116,/fffrrt/all-in-one-rfcx-baseline-for-beginners,Rainforest Connection Species Audio Detection
13162524,0.466,1,8,/pluceroo/feature-engineering-using-vectorization-cupy,Rainforest Connection Species Audio Detection
10218294,0.0,10,13,/arunima24/using-lstm-embedding-layer,Natural Language Processing with Disaster Tweets
10193562,0.79528,0,1,/kaushalk/real-and-fake-tweet-nlp-eda-bof-td-idf-glove,Natural Language Processing with Disaster Tweets
10087696,0.8403299999999999,40,118,/datafan07/disaster-tweets-nlp-eda-bert-with-transformers,Natural Language Processing with Disaster Tweets
10092260,0.7618699999999999,0,2,/abhimanyubhadauria/tweetv2,Natural Language Processing with Disaster Tweets
9984035,0.8057,0,2,/iamsdt/nlp-eda-flair,Natural Language Processing with Disaster Tweets
9901967,0.78976,0,0,/abhimanyusethi/disaster-tweets,Natural Language Processing with Disaster Tweets
9876295,0.0,0,2,/mohamedberrimi/b-lstm-with-soft-attention,Natural Language Processing with Disaster Tweets
7250264,0.85044,0,6,/chardo/top-5-winning-automl-submission,Natural Language Processing with Disaster Tweets
8191554,0.80723,0,0,/dubeyji/disaster-prediction,Natural Language Processing with Disaster Tweets
9258819,0.79466,0,0,/felixhaba/nlp-hello-world,Natural Language Processing with Disaster Tweets
9262693,0.51842,4,13,/bpkapkar/forecasting-accuracy,M5 Forecasting - Accuracy
9295432,0.6341899999999999,7,14,/binhlc/m5-top-down-forecast,M5 Forecasting - Accuracy
9342708,0.4745,10,15,/suuuuuu/three-shades-of-dark-darker-magic-change-num-iter,M5 Forecasting - Accuracy
9288746,0.6990000000000001,2,11,/eakdag/lstm-with-keras-0-7,M5 Forecasting - Accuracy
9290591,0.4745,2,9,/kamalnaithani/modelwithimprovedaccuracy,M5 Forecasting - Accuracy
9116437,0.63463,7,43,/binhlc/forecasting-multiple-time-series-using-prophet,M5 Forecasting - Accuracy
8967396,0.47506,125,609,/kyakovlev/m5-three-shades-of-dark-darker-magic,M5 Forecasting - Accuracy
10784932,2.97492,2,5,/makhloufsabir/higgs-boson-classification-physics-rnn,Higgs Boson Machine Learning Challenge
10381487,0.87,3,5,/manavtrivedi/panda-inference,Prostate cANcer graDe Assessment (PANDA) Challenge
10435129,0.8,0,2,/mdp1990/prostate-cancer-challenge-model-building,Prostate cANcer graDe Assessment (PANDA) Challenge
10129067,-0.03,4,7,/razamh/panda-eda-better-visualization-simple-baseline,Prostate cANcer graDe Assessment (PANDA) Challenge
10211402,0.35,0,4,/tottinfish/for-test-submission,Prostate cANcer graDe Assessment (PANDA) Challenge
10163576,0.45,0,0,/naomiding/panda-stain-norm-12x128x128-inference,Prostate cANcer graDe Assessment (PANDA) Challenge
10045921,-0.03,0,0,/tgibbons/cis-6115-unit-5-chap-10-11,Prostate cANcer graDe Assessment (PANDA) Challenge
9905805,0.6,0,0,/lvulliard/predict-panda-test-set,Prostate cANcer graDe Assessment (PANDA) Challenge
9263327,0.76,0,1,/norrsken/panda-prediction,Prostate cANcer graDe Assessment (PANDA) Challenge
9823674,0.58,0,1,/pmwaniki/finetune-resnet50,Prostate cANcer graDe Assessment (PANDA) Challenge
9704440,0.73,0,3,/nobletp/panda-keras-baseline,Prostate cANcer graDe Assessment (PANDA) Challenge
9677237,0.76,2,0,/fanconic/panda-inference-for-effnetb0-regression,Prostate cANcer graDe Assessment (PANDA) Challenge
9502946,0.78,2,7,/virajbagal/panda-se-resnext50-tpu-weights-inference,Prostate cANcer graDe Assessment (PANDA) Challenge
2587076,3.692,0,0,/abhishekaarush/lightgbm-gbdt-rf-baysian-ridge-reg-lb-3-61,Elo Merchant Category Recommendation
2540508,0.3389999999999999,0,0,/abhilashganji/text-classification,Quora Insincere Questions Classification
2539372,0.6709999999999999,0,1,/xsakix/cnn-base-classifier-fold-unfreeze-meta,Quora Insincere Questions Classification
2479542,0.618,0,0,/maguirebrian/kernel994a1c3216,Quora Insincere Questions Classification
2548163,0.6609999999999999,0,0,/xsakix/cnn-base-classifier-fold-meta-v3,Quora Insincere Questions Classification
2350122,0.418,0,0,/skathirmani/ml-with-word-embeddings,Quora Insincere Questions Classification
2516760,0.6920000000000001,6,17,/nicke1/fine-text-preproc-concat-embedding-lstm-gru-att,Quora Insincere Questions Classification
2531345,0.6679999999999999,0,0,/xsakix/bilstm-att-base-res-clr-unfreeze,Quora Insincere Questions Classification
2532571,0.614,0,0,/xsakix/glove-base-fold-unfreeze,Quora Insincere Questions Classification
2518259,0.389,0,1,/gordonbry/practice-kaggle,Quora Insincere Questions Classification
2534489,0.657,0,0,/xsakix/separable-cnn-base-classifier-fold-unfreeze,Quora Insincere Questions Classification
2532326,0.665,0,0,/xsakix/cnn-base-classifier-fold-unfreeze,Quora Insincere Questions Classification
2526421,0.667,0,0,/xsakix/bilstm-base-classifier-fold,Quora Insincere Questions Classification
2484004,0.647,0,4,/bmmidei/a-simple-bidirectional-lstm-with-glove,Quora Insincere Questions Classification
2502126,0.631,1,5,/david26694/nb-svm-baseline-trigrams,Quora Insincere Questions Classification
2498259,0.6829999999999999,1,2,/raylee12/different-embeddings-with-attention-fork-fork,Quora Insincere Questions Classification
2427074,0.6970000000000001,31,248,/hung96ad/pytorch-starter,Quora Insincere Questions Classification
2475452,0.669,0,0,/mastermindbraveson/data-mining-project,Quora Insincere Questions Classification
2436842,0.5670000000000001,0,3,/oaref92/quora-text-trial,Quora Insincere Questions Classification
2458728,0.614,0,0,/shubham0101/a-basic-nlp-approch,Quora Insincere Questions Classification
2418866,0.5770000000000001,0,0,/jeevananne/basic-logistic-regression,Quora Insincere Questions Classification
2465235,0.635,0,0,/xsakix/wiki-filter-bilstm-att,Quora Insincere Questions Classification
2434233,0.466,0,0,/xsakix/cnn-baseline-spacy,Quora Insincere Questions Classification
2432477,0.5660000000000001,0,4,/jayashreesridhar/text-classification-lstm-cnn-glove-embeddings,Quora Insincere Questions Classification
2456891,0.635,0,0,/xsakix/filter-bilstm-base,Quora Insincere Questions Classification
2141435,0.664,0,1,/manojasaithambi/simple-lstm-with-glove,Quora Insincere Questions Classification
2310566,0.662,0,0,/younad/using-randomized-search-cv-with-keras,Quora Insincere Questions Classification
2325148,0.515,0,1,/raziahmed/tfidf-with-full-fit-on-lgbm,Quora Insincere Questions Classification
2120270,0.107,0,0,/alexandruuu/constant-submission,Quora Insincere Questions Classification
2357174,0.682,1,11,/mlwhiz/learning-text-classification-attention,Quora Insincere Questions Classification
2352127,0.6709999999999999,5,6,/mlwhiz/learning-text-classification-bidirectionalrnn,Quora Insincere Questions Classification
8885185,0.03275,0,0,/akashsuper2000/lightgbm-regressor-week-4,COVID19 Global Forecasting (Week 4)
8868955,0.01135,0,0,/pradeepkumarrajkumar/covid19-submission1,COVID19 Global Forecasting (Week 4)
8814644,0.11464,0,0,/yaroshevskiy/covid-19-fit-logistic-curves-and-submit-week-3,COVID19 Global Forecasting (Week 4)
8901950,0.08489,0,0,/ee257sp20nivedha/kernel74981b9c74,COVID19 Global Forecasting (Week 4)
8836105,0.07682,3,10,/kirichenko17roman/dsanet-approach,COVID19 Global Forecasting (Week 4)
8834329,0.48423,1,12,/syzymon/covid19-tabnet-fast-ai-baseline,COVID19 Global Forecasting (Week 4)
8845799,0.03639,0,1,/mldlai/covid19globalw4,COVID19 Global Forecasting (Week 4)
8729949,0.08217,111,249,/anshuls235/covid19-explained-through-visualizations,COVID19 Global Forecasting (Week 4)
9539570,0.0,0,0,/pradeepkumarrajkumar/m3-xgboost-djp,COVID19 Global Forecasting (Week 4)
9207552,0.50565,0,0,/kirderf/ensemble-short-long-term-models-indeweight-pp-lock,COVID19 Global Forecasting (Week 4)
9206098,0.7217899999999999,0,0,/kirderf/ensemble-of-the-top-5,COVID19 Global Forecasting (Week 4)
9006167,0.01135,0,0,/pragyarathore/model-2-polynomial-fit-xgb,COVID19 Global Forecasting (Week 4)
8958461,0.0299,0,0,/kazanova/fork-of-script-with-commented-train-counts-for-zer,COVID19 Global Forecasting (Week 4)
8950103,0.93417,0,0,/pietromarinelli/china-pp-15-85-cummax-regression-vs-poisson-vs,COVID19 Global Forecasting (Week 4)
8949826,0.08485,0,0,/jagdishkatariya/covid-19-week-4,COVID19 Global Forecasting (Week 4)
8949005,0.03934,1,0,/appian/covid19-week4-2,COVID19 Global Forecasting (Week 4)
8948480,0.03563,0,0,/yatinece/sub2-submission-covid19-w4,COVID19 Global Forecasting (Week 4)
8946236,0.03734,0,0,/yaroshevskiy/covid19-forecasting-xgboost,COVID19 Global Forecasting (Week 4)
8937016,0.2688,0,0,/ronakbadhe/fork-of-logisticcovid,COVID19 Global Forecasting (Week 4)
8931902,0.91784,0,0,/hamsterherder/covid-19-forecasting-with-an-rnn,COVID19 Global Forecasting (Week 4)
8902236,0.7316600000000001,0,0,/pragyarathore/xgb-regression,COVID19 Global Forecasting (Week 4)
5789856,0.0,0,53,/gaborfodor/eda-3d-object-detection-challenge,Lyft 3D Object Detection for Autonomous Vehicles
13887891,0.7915300000000001,0,0,/xenakas/notebook4fbc84d81b,What's Cooking? (Kernels Only)
7440305,0.77363,0,0,/vilceanumihnea97/kernel4164d67650,What's Cooking? (Kernels Only)
6454261,0.82139,10,49,/tanulsingh077/what-s-cooking,What's Cooking? (Kernels Only)
5602477,0.76377,0,1,/stasler/nom-nom-nom-nn-edition,What's Cooking? (Kernels Only)
1286184,0.74225,0,0,/waltster/cleaning-the-citchen-before-cooking,What's Cooking? (Kernels Only)
3450755,0.77956,0,1,/kuni2018/sklearn-cheat-sheet-is-useful,What's Cooking? (Kernels Only)
3441836,0.80923,0,0,/choijieun/predict-cuisine-type,What's Cooking? (Kernels Only)
3121579,0.77876,0,0,/sarthakpawar/kernel05b1676f50,What's Cooking? (Kernels Only)
2935254,0.77795,0,1,/acauveri/simple-cuisine-classification,What's Cooking? (Kernels Only)
2880385,0.81958,0,1,/emily2008/is-this-your-favorite-food,What's Cooking? (Kernels Only)
2616041,0.8145600000000001,0,2,/shivamsarawagi/cookingprediction,What's Cooking? (Kernels Only)
2291780,0.81074,0,0,/evians/cousines,What's Cooking? (Kernels Only)
2279238,0.82119,0,1,/akshaysiras/kernela1db0d290f,What's Cooking? (Kernels Only)
1912683,0.7800600000000001,0,0,/jayjoshi31/ann-approach-for-what-is-cooking,What's Cooking? (Kernels Only)
2088911,0.7628699999999999,0,0,/dblmok/kernel901624e42b,What's Cooking? (Kernels Only)
2000077,0.2681,0,0,/clairegao/whats-cooking-classification,What's Cooking? (Kernels Only)
1580546,0.82119,0,0,/kukulkan/tf-idf-ovr,What's Cooking? (Kernels Only)
1626395,0.72737,0,0,/alphadraco/submission-cooking,What's Cooking? (Kernels Only)
1628347,0.82099,0,0,/brochinejr/whatiscooking-brochinejr-latest,What's Cooking? (Kernels Only)
1711215,0.75955,0,2,/shineatml/cooking,What's Cooking? (Kernels Only)
1686087,0.77886,0,2,/sidjhanji/identify-the-cuisine-comparing-models,What's Cooking? (Kernels Only)
1668558,0.79414,0,0,/tarunpaparaju/what-s-cooking-cuisine-prediction-with-dnns-v2,What's Cooking? (Kernels Only)
1632259,0.8226,0,2,/srcecde/xg-svm-tf-cooking-svm-0-82260-xgb-0-77383,What's Cooking? (Kernels Only)
1618624,0.78841,0,2,/tarunpaparaju/what-s-cooking-cuisine-prediction-with-gb-trees,What's Cooking? (Kernels Only)
1588620,0.78489,0,1,/john850512/what-s-cooking-compare-different-model,What's Cooking? (Kernels Only)
74447,0.98652,1,10,/albadr/shelter-animal-exploration,Shelter Animal Outcomes
55422,0.76974,0,4,/potterxu/dog-and-cat,Shelter Animal Outcomes
2699759,0.691,0,1,/econdata/quora-insincere-question-classification,Quora Insincere Questions Classification
2681757,0.675,0,6,/anebzt/quora-preprocessing-model,Quora Insincere Questions Classification
2672772,0.6579999999999999,0,1,/frngo3/quora-insecure-questions-predictions,Quora Insincere Questions Classification
2640775,0.613,0,0,/xsakix/torch-lstm-word2vec,Quora Insincere Questions Classification
2679622,0.69,1,0,/wangggong/quora-bilstm-attention-kfold-fork-jannen,Quora Insincere Questions Classification
2649500,0.6990000000000001,23,31,/paulorzp/compressing-a-binary-submission-within-a-string,Quora Insincere Questions Classification
2638616,0.639,0,0,/jialinzhang/text-cnn-quora-question,Quora Insincere Questions Classification
2616817,0.565,4,0,/jialinzhang/dnn-quora-question,Quora Insincere Questions Classification
2651722,0.629,0,0,/tboyle10/lstm-attention,Quora Insincere Questions Classification
2613566,0.14,0,1,/lordskloore2/quora-compo,Quora Insincere Questions Classification
2622573,0.6559999999999999,0,0,/xsakix/torch-lstm-mean,Quora Insincere Questions Classification
2609097,0.5870000000000001,0,0,/xsakix/torch-train-pretrianed,Quora Insincere Questions Classification
2589012,0.687,4,7,/jf2333/how-should-we-validate-our-models,Quora Insincere Questions Classification
2627447,0.6759999999999999,0,0,/red8012/gru-2-epochs,Quora Insincere Questions Classification
2621859,0.665,0,0,/benedictldm/quora-comp,Quora Insincere Questions Classification
2525409,0.6759999999999999,6,13,/eligijus/rnn-spell-checker,Quora Insincere Questions Classification
2096346,0.672,0,0,/kanishk01234/a-look-at-different-embeddings,Quora Insincere Questions Classification
2536980,0.655,0,1,/bmmidei/using-convolution-for-nlp-text-classification,Quora Insincere Questions Classification
2594434,0.605,0,0,/xsakix/torch-clr-train-pretrianed,Quora Insincere Questions Classification
2592247,0.624,0,0,/yshubham/attention-on-bi-lstm-hidden-states-forked,Quora Insincere Questions Classification
2582441,0.6509999999999999,0,0,/reatank/convnns,Quora Insincere Questions Classification
2568098,0.685,0,0,/strifonov/avg-on-embeddings-simple-rnn-with-attention,Quora Insincere Questions Classification
2548178,0.67,0,9,/ziliwang/pytorch-text-cnn,Quora Insincere Questions Classification
2569495,0.574,0,0,/xsakix/torch-first,Quora Insincere Questions Classification
2734980,3.778,1,3,/s7anmerk/ee-collective,Elo Merchant Category Recommendation
2665175,3.695,0,3,/kiyo22/fork-of-lightgbm-gbdt-rf-analyst-in-japanese,Elo Merchant Category Recommendation
8993222,0.79711,0,2,/danofer/prophet-ts-starter-multiprocessing,M5 Forecasting - Accuracy
8941412,0.52585,0,1,/izotov/m5-under-0-50-optimized,M5 Forecasting - Accuracy
8919595,0.49608,5,47,/ar2017/m5-forecasting-lightgbm,M5 Forecasting - Accuracy
8892312,0.4887399999999999,13,88,/poedator/m5-under-0-50-optimized,M5 Forecasting - Accuracy
8832242,0.5394100000000001,27,106,/ragnar123/simple-lgbm-groupkfold-cv,M5 Forecasting - Accuracy
8736803,0.0,17,69,/mayer79/m5-forecast-poisson-loss-top-10,M5 Forecasting - Accuracy
8737832,0.59939,7,30,/ragnar123/asymmetric-loss-functions-lgbm,M5 Forecasting - Accuracy
8605412,0.62366,0,13,/mfjwr1/for-japanese-beginner-with-wrmsse-in-lgbm,M5 Forecasting - Accuracy
8554995,0.52056,0,14,/lkatran/first-step-eda-and-baseline,M5 Forecasting - Accuracy
8435286,0.8377,0,6,/qcw171717/other-naive-forecasts-submission-score,M5 Forecasting - Accuracy
9429620,0.75,13,47,/vgarshin/panda-keras-baseline,Prostate cANcer graDe Assessment (PANDA) Challenge
9059126,0.21,3,25,/ibtesama/simple-baseline-keras-vgg16,Prostate cANcer graDe Assessment (PANDA) Challenge
9377014,0.0,1,7,/jakobw/minimal-submission-script,Prostate cANcer graDe Assessment (PANDA) Challenge
9262882,0.68,1,6,/prateekagnihotri/2-hrs-tpu-training-lb-0-68,Prostate cANcer graDe Assessment (PANDA) Challenge
9230194,0.68,0,2,/spidyweb/panda-efficientnetb7-on-tpu-tensorflow,Prostate cANcer graDe Assessment (PANDA) Challenge
9088783,0.47,0,1,/tongluocq/panda-5fold-inference,Prostate cANcer graDe Assessment (PANDA) Challenge
9057568,0.0,2,13,/raviyadav2398/panda-eda-visualization-and-predictions,Prostate cANcer graDe Assessment (PANDA) Challenge
9182704,0.68,1,2,/karrak3256/panda-test-file,Prostate cANcer graDe Assessment (PANDA) Challenge
9106434,0.67,6,59,/tanlikesmath/prostate-cancer-grading-intro-starter-kernel,Prostate cANcer graDe Assessment (PANDA) Challenge
9111288,0.2,0,11,/prateekagnihotri/efficientnet-keras-infernce-tta,Prostate cANcer graDe Assessment (PANDA) Challenge
9068047,0.59,11,86,/yasufuminakama/panda-se-resnext50-classification-baseline,Prostate cANcer graDe Assessment (PANDA) Challenge
10589850,0.69935,1,4,/yosukehasimoto/real-or-not,Natural Language Processing with Disaster Tweets
10599004,0.7732100000000001,5,6,/xwalker/bert-features-logireg,Natural Language Processing with Disaster Tweets
10457372,0.79252,0,5,/maksimbahdanchyk/text-preprocessing-worth-or-not,Natural Language Processing with Disaster Tweets
10242017,0.8381799999999999,1,4,/yingxuhe/identify-disaster-tweets,Natural Language Processing with Disaster Tweets
10504037,0.7726,3,10,/hkubra/real-or-not,Natural Language Processing with Disaster Tweets
10554819,0.78394,0,0,/yumakomoto/kernel58fb589469,Natural Language Processing with Disaster Tweets
10467786,0.53754,0,6,/doanquanvietnamca/multi-dropout-kfold-with-roberta-on-gpu,Natural Language Processing with Disaster Tweets
10373224,0.81305,0,6,/goutham794/distill-bert-fine-tuning-huggingface-and-pytorch,Natural Language Processing with Disaster Tweets
10251147,0.7722899999999999,11,24,/mitramir5/nlp-visualization-eda-glove,Natural Language Processing with Disaster Tweets
10325413,0.0,1,6,/vibeeshk/tweet-prediction-real-or-not-nlp-with-disaster,Natural Language Processing with Disaster Tweets
10281703,0.79681,5,13,/manjeetsingh/how-to-start-with-natural-language-processing,Natural Language Processing with Disaster Tweets
10271792,0.7885300000000001,0,2,/akhilkasare/prediction-fake-or-not-nlp,Natural Language Processing with Disaster Tweets
5543364,0.8,0,0,/a1pacaz/fastai-efficientnet-b5-preprocessing,APTOS 2019 Blindness Detection
5157160,0.667,0,1,/baranwal/blindness-detection-5,APTOS 2019 Blindness Detection
5697155,0.6779999999999999,1,2,/andyandy/aptos-inception-v4,APTOS 2019 Blindness Detection
5646119,0.594,0,3,/srg9000/aptos-resnet50-3,APTOS 2019 Blindness Detection
5686462,0.7290000000000001,0,2,/mayank17/efficientnetb5-o,APTOS 2019 Blindness Detection
5691408,0.723,0,0,/mehranrafiee/blindness-detection-resnet150,APTOS 2019 Blindness Detection
5713381,0.575,0,0,/marcushorn/pytorch-resnet50-old-new-data,APTOS 2019 Blindness Detection
5344221,0.0579999999999999,11,49,/dataraj/aptosincepttionv3pretrained,APTOS 2019 Blindness Detection
4588622,0.685,1,4,/darshanpatel11/aptos-2019-blindness-detection-transfer-learning,APTOS 2019 Blindness Detection
5522311,0.789,3,5,/nnezhxw/ensemble-models-by-vote,APTOS 2019 Blindness Detection
5453591,0.79,1,2,/caiyutiansg/efficientnetb5-keras-added-preproc-aptos-2019,APTOS 2019 Blindness Detection
4695625,0.7090000000000001,0,1,/kbolaris/keras-diabetic-retinopathy-detector,APTOS 2019 Blindness Detection
5482056,0.713,0,2,/bhargav5040/aptos-fastai-0p681,APTOS 2019 Blindness Detection
4713836,0.701,0,0,/sthuthyevangelinem/aptos19-fastai-vgg16-bn,APTOS 2019 Blindness Detection
5142096,0.685,0,6,/surbhibhardwaj/aptos-imageresizing-resnet-101-kappa-optimization,APTOS 2019 Blindness Detection
5384866,0.623,0,0,/mayankkestwal10/fastai-model-preparation,APTOS 2019 Blindness Detection
7495165,0.05969,0,0,/zhengpushi/nmsx1916021-random-forest,PUBG Finish Placement Prediction (Kernels Only)
3624744,0.02064,0,0,/raphalencar/pubg-finish-placement-prediction-v3-0,PUBG Finish Placement Prediction (Kernels Only)
7461309,0.0665,0,0,/nmsf1916019/nmsf1916019,PUBG Finish Placement Prediction (Kernels Only)
7086679,0.06025,0,1,/pauloelkers/kernel627d0b4622,PUBG Finish Placement Prediction (Kernels Only)
7521609,0.05677,0,0,/liruowei/kernel7fda72b4fe,PUBG Finish Placement Prediction (Kernels Only)
7466986,0.06266,0,0,/hypnosx99/pubg-rf-with-model-interpretation-fast-ai,PUBG Finish Placement Prediction (Kernels Only)
2068245,0.0341,0,0,/memesrizedxd/pubgick,PUBG Finish Placement Prediction (Kernels Only)
7199653,0.0205699999999999,0,0,/nmbl1903502/nmbl1903502,PUBG Finish Placement Prediction (Kernels Only)
6319056,0.06081,0,0,/keyurparalkar/pubg-winner-prediction,PUBG Finish Placement Prediction (Kernels Only)
6011590,0.05745,0,0,/gubyb91/random-forrest-submission,PUBG Finish Placement Prediction (Kernels Only)
1842835,0.0318,0,0,/mak4alex/pubg-placement-prediction,PUBG Finish Placement Prediction (Kernels Only)
3747619,0.04292,0,2,/rucrazia/rucrazia-s-pubg-korean,PUBG Finish Placement Prediction (Kernels Only)
4409052,0.35018,0,0,/terminate9298/pubg-model-predictions,PUBG Finish Placement Prediction (Kernels Only)
4241578,0.12802,0,0,/arshadgeek/puubg,PUBG Finish Placement Prediction (Kernels Only)
2622231,0.0699,0,0,/ikalinich/kernel4704713fe0,PUBG Finish Placement Prediction (Kernels Only)
3815541,0.04573,0,0,/wendyyangmin/kernel96adbd45f9,PUBG Finish Placement Prediction (Kernels Only)
3746124,0.02047,0,1,/brijrokad/lgbmregression-in-pubg,PUBG Finish Placement Prediction (Kernels Only)
2606670,0.0603,0,0,/tanyeejet/pubg-finish-placement-prediction-rf,PUBG Finish Placement Prediction (Kernels Only)
3281913,0.1615,0,0,/venkateshdeepak/pubg-linear-r,PUBG Finish Placement Prediction (Kernels Only)
3438765,5401.14811,0,0,/ababakri/ai-pubg-aba-bakri-ibragimov,PUBG Finish Placement Prediction (Kernels Only)
3335058,0.36841,0,1,/ikarostech/kernelfa72455ca7,PUBG Finish Placement Prediction (Kernels Only)
85771,0.939483,0,1,/apartmentguru/boost-the-red,Predicting Red Hat Business Value
808273,0.0,2,5,/amardeepbansal/gimagerecognigtion,Google Landmark Recognition Challenge
595662,0.0,0,10,/kmader/baseline-landmark-model,Google Landmark Recognition Challenge
919384,0.0,0,0,/safavieh/random-landmark-based-on-train-set-frequency,Google Landmark Recognition Challenge
5628991,1.03583,0,0,/mohamedalhawi/pre-model123,Predict Future Sales
6835336,0.90684,0,1,/jagannathrk/predict-future-sales-xgboost,Predict Future Sales
6703258,1.68723,1,3,/alangarrickbercero/predict-future-sales,Predict Future Sales
6688708,0.90684,0,2,/minhey11/bigdata,Predict Future Sales
6604165,1.02047,0,0,/ruixuerub/by-lstm-model,Predict Future Sales
6620089,0.93131,1,5,/akshayt19nayak/classification-vs-regression-gbdt-and-ensembling,Predict Future Sales
6644121,2573675.38201,0,2,/olatundesodiq/kernel1490f4c540,Predict Future Sales
6276745,0.94049,0,1,/kwatanwa/future-sales-predict-xgboost-and-rf,Predict Future Sales
5908381,0.90781,0,6,/basselkassem/introduction-about-predicting-monthly-sales,Predict Future Sales
4260814,1.16777,0,0,/gundamb2/eda-feature-engineering-selection-model-select,Predict Future Sales
5612305,1.01998,0,6,/ifashion/predict-future-sales-with-lstm,Predict Future Sales
4944369,0.91946,4,11,/revanthrex/predict-future-sales-xgboost-feature-selection,Predict Future Sales
4903699,0.94941,0,0,/alanxiao/final-project-a-longer-meta-training-period,Predict Future Sales
12373274,0.7140000000000001,0,1,/radema/sub-notebook,Riiid Answer Correctness Prediction
12470813,0.754,5,26,/mamun18/riiid-lgbm-lii-hyperparameter-tuning-optuna,Riiid Answer Correctness Prediction
12349625,0.7559999999999999,6,34,/mikel1/mike-simple-predictor,Riiid Answer Correctness Prediction
12434607,0.693,1,1,/sayedathar11/riiid-lgbm-hyperopt-paramstuning,Riiid Answer Correctness Prediction
12347684,0.677,0,0,/maosengao/lgb-framework-cookly,Riiid Answer Correctness Prediction
12374937,0.742,0,10,/aralai/riiid-classification-using-naive-bayes,Riiid Answer Correctness Prediction
12338031,0.75,78,591,/erikbruin/riiid-comprehensive-eda-baseline,Riiid Answer Correctness Prediction
12181856,0.754,1,18,/duythanhng/riiid-answer-correctness-notebook-collection,Riiid Answer Correctness Prediction
12304609,0.7240000000000001,10,61,/datafan07/riiid-challenge-eda-baseline-model,Riiid Answer Correctness Prediction
12398655,0.557,0,0,/yutinghanmg/notebook7f3ceff127,Riiid Answer Correctness Prediction
12252993,0.7509999999999999,9,30,/yaroslavmavliutov/riiid-prediction-cnn-keras-0-751,Riiid Answer Correctness Prediction
12218117,0.74,10,55,/spacelx/2020-r3id-incremental-learning-pytorch-creme,Riiid Answer Correctness Prediction
12217284,0.6990000000000001,0,0,/mayank7080/notebookda718bac31,Riiid Answer Correctness Prediction
12216020,0.6559999999999999,0,13,/alexj21/riiid-tabnet-starter-kernel,Riiid Answer Correctness Prediction
12185415,0.746,4,48,/pavelvpster/riiid-fe-target-encoding-keras,Riiid Answer Correctness Prediction
12195846,0.753,5,36,/code1110/riiid-lgb-hyperparameter-tuning,Riiid Answer Correctness Prediction
12199117,0.745,0,7,/code1110/riiid-keras-logisitc-regression-for-analytics,Riiid Answer Correctness Prediction
10607683,0.9418,3,22,/shivam17818/test-all-efficientnet-model-b0-b7,SIIM-ISIC Melanoma Classification
10587614,0.945,260,573,/cdeotte/triple-stratified-kfold-with-tfrecords,SIIM-ISIC Melanoma Classification
10627930,0.9462,3,36,/blurredmachine/siim-isic-an-ensemble-beginner-s-approach,SIIM-ISIC Melanoma Classification
10643012,0.8497,0,0,/gaaauravvv/siim-isic-melanoma-hemansai-g,SIIM-ISIC Melanoma Classification
10492293,0.907,0,0,/akashsuper2000/ensemble-notebook,SIIM-ISIC Melanoma Classification
10513818,0.8170000000000001,4,29,/kittlein/xgboost-tabular-data-ml-cv-86-lb-787,SIIM-ISIC Melanoma Classification
10332970,0.8813,0,4,/samuelvedrik/pytorch-melanoma-efficientnet,SIIM-ISIC Melanoma Classification
10456823,0.8759999999999999,0,0,/amirarqand/albumentations-in-keras-cv,SIIM-ISIC Melanoma Classification
10431989,0.939,8,67,/vbhargav875/efficientnet-b5-b6-b7-tf-keras,SIIM-ISIC Melanoma Classification
10433786,0.921,0,3,/matthewchung/melanoma-pytorch-starter-efficientnet-standardiz,SIIM-ISIC Melanoma Classification
8153201,0.8884799999999999,0,0,/aibaend/sadu-aibyn,Santander Customer Transaction Prediction
8190836,0.88786,0,3,/darwinwin/sklearn-model-exploration,Santander Customer Transaction Prediction
7519290,0.8532,0,0,/hassanamin/santander-transaction-prediction,Santander Customer Transaction Prediction
7459184,0.5,1,1,/saeedtqp/customer-transaction-predict,Santander Customer Transaction Prediction
7239921,0.89423,0,0,/thduik/kernel2fd2288455,Santander Customer Transaction Prediction
6891548,0.78573,0,2,/jlfdatascience/santander-bank-prediction-model,Santander Customer Transaction Prediction
6670186,0.67519,0,4,/cketant/transaction-prediction-eda-gaussian-naive-bayes,Santander Customer Transaction Prediction
5531187,0.67589,0,0,/rashmiek99/kernel963e2ea302,Santander Customer Transaction Prediction
3101226,0.898,0,0,/rsinghal757/santandar-code-experiment,Santander Customer Transaction Prediction
5313225,0.89025,1,6,/bcosta12/santander-customer-transaction-lightgb,Santander Customer Transaction Prediction
3537332,0.90066,0,0,/yoheiii/almost-final-submission,Santander Customer Transaction Prediction
4522370,0.89686,0,1,/shilparpns/bayesian-parameters-finding-lgb,Santander Customer Transaction Prediction
3199219,0.899,0,0,/ajithvajrala/santander-customer-transaction-prediction,Santander Customer Transaction Prediction
3494506,0.899,0,0,/tasnimfatima/only-kmeansclustering-and-lgbm,Santander Customer Transaction Prediction
4566827,0.9997,0,0,/yoonacha/kernel8977786447,Aerial Cactus Identification
4119033,0.9999,0,0,/pieterblomme/cactus-v2,Aerial Cactus Identification
3503299,0.9871,0,0,/zhuhai/vgg19-imagegenerator,Aerial Cactus Identification
1640526,3.03311,4,21,/justjun0321/exploratory-geoclustering-to-modeling,New York City Taxi Fare Prediction
1477391,3.37691,1,6,/pnprabakaran/ny-city-taxi-fare-exploration-with-dask-and-dxgb,New York City Taxi Fare Prediction
1349612,3.94091,0,1,/mrrajeshreddy/google-new-york-city-taxi-fare-prediction,New York City Taxi Fare Prediction
1498975,3.50606,15,34,/dimitreoliveira/tensorflow-dnn-coursera-ml-course-tutorial,New York City Taxi Fare Prediction
1345310,8.30512,0,0,/varshneydevansh/google-fare-price-prediction,New York City Taxi Fare Prediction
1459525,3.38993,0,1,/matt29/clean-train-random-forest-and-lgbm,New York City Taxi Fare Prediction
1383592,4.83935,5,11,/ojones3/feature-engineering-corrected-manhattan-distance,New York City Taxi Fare Prediction
1374905,3.03767,15,67,/btyuhas/bayesian-optimization-with-xgboost,New York City Taxi Fare Prediction
1375592,4.6767,1,4,/donniedarko/the-fare-next-door,New York City Taxi Fare Prediction
1387744,3.80715,0,0,/hancelpv/nyc-taxi-fare-prediction,New York City Taxi Fare Prediction
1384009,3.11875,0,2,/danofer/bayesian-optimization-with-xgboost-3-11-lb,New York City Taxi Fare Prediction
1370780,3.21447,3,4,/tejaeduc/newyorktaxifare,New York City Taxi Fare Prediction
1354754,3.5071,5,4,/akosciansky/using-ml-for-data-exploration-feat-engineering,New York City Taxi Fare Prediction
1339428,5.74184,27,291,/dster/nyc-taxi-fare-starter-kernel-simple-linear-model,New York City Taxi Fare Prediction
1347183,7.78808,0,0,/manojvijayan/feature-engineering-and-xgboost,New York City Taxi Fare Prediction
1342171,4.68065,8,16,/judesen/fare-prediction,New York City Taxi Fare Prediction
1345491,4.08951,1,4,/jlochter/nyc-taxi-fare-dnn-xgb-ensemble,New York City Taxi Fare Prediction
10890045,3.09737,0,0,/ahmedmurad1990/nyc-taxi-fare-prediction,New York City Taxi Fare Prediction
13960342,5.68916,0,0,/varunsimhareddy/taxi-fare-new-york-city-varun-predictions,New York City Taxi Fare Prediction
13103676,5.68923,0,0,/roohisharma/nyc-taxi-fare,New York City Taxi Fare Prediction
12340802,3.0177400000000003,0,1,/allenkong/nyc-taxi-fare,New York City Taxi Fare Prediction
11565938,3.30424,0,0,/afrinp/nyc-taxi-fare-prediction-5-lakh-rows,New York City Taxi Fare Prediction
11170051,0.15738,2,7,/salazarslytherin/intro-to-ml-full-kaggle-mini-course,New York City Taxi Fare Prediction
10675130,3.18415,0,1,/ryanlambert/my-first-notebook,New York City Taxi Fare Prediction
10435773,5.74184,0,1,/mahmudds/new-york-city-taxi-fare-prediction,New York City Taxi Fare Prediction
9515587,5.68915,1,0,/dhruvgupta2801/taxi-fare-prediction-eda-linear-regression,New York City Taxi Fare Prediction
9203638,4.87021,1,1,/nishantpatyal/nyc-taxi-fare,New York City Taxi Fare Prediction
1568887,3.63072,0,0,/hbajaj/nyc-taxi-fare-xgboost,New York City Taxi Fare Prediction
5618212,4.19822,0,0,/omarelejla/fork-of-newyyork-taxi-fare-xgboosting2,New York City Taxi Fare Prediction
1617324,3.4746300000000003,0,1,/amitsh6604/nyc-taxi-fare-prediction-with-xgboost,New York City Taxi Fare Prediction
4669179,3.37198,0,1,/harsh22/taxi-fare-prediction-regression,New York City Taxi Fare Prediction
2316955,3.1770400000000003,0,0,/amitamb/using-rf,New York City Taxi Fare Prediction
1729495,4.21995,0,0,/pibieta/taxi-fare-w-pandas-input-fn-datetime,New York City Taxi Fare Prediction
113401,0.0,0,0,/mikraig/santander,Santander Product Recommendation
14239369,0.13762,0,0,/yutoricstar/house-price,House Prices - Advanced Regression Techniques
14245306,0.1323099999999999,0,0,/hekkta/house-prices-ensemble,House Prices - Advanced Regression Techniques
14294784,0.22868,0,0,/lc4311/acada-tnp-mod6-sample-notebook,House Prices - Advanced Regression Techniques
14122902,0.15953,4,3,/zayanmakar/use-gradientboostingregressor-with-parameters,House Prices - Advanced Regression Techniques
13870322,0.1193299999999999,0,2,/chrisbradley/house-prices-eda-optuna-stacking-top7,House Prices - Advanced Regression Techniques
14066604,0.23267,1,2,/eschibli/house-prices-automl,House Prices - Advanced Regression Techniques
14056120,0.16071,0,0,/simplya/notebook-ac,House Prices - Advanced Regression Techniques
14039412,0.12351,0,1,/homayoonkhadivi/significant-eda-preprocessing-result-house-price,House Prices - Advanced Regression Techniques
13959853,0.15434,0,1,/mohitkarelia/hpp1-00,House Prices - Advanced Regression Techniques
13733404,0.19905,0,1,/ruchitb/house-prices-prediction-using-linear-regression,House Prices - Advanced Regression Techniques
13987426,0.15037,0,0,/sanskrutighadipatil/xg-saleprice,House Prices - Advanced Regression Techniques
13923358,0.11968,0,0,/shwetabhujbal/house-prices-advanced-regression,House Prices - Advanced Regression Techniques
13870975,0.13767,0,3,/ivanseredenko/house-pricing-linear-prediction,House Prices - Advanced Regression Techniques
13928830,0.11968,0,0,/sumeetspisal/notebookdd8558431c,House Prices - Advanced Regression Techniques
12634243,0.14118,4,8,/ruiyap/0-8929-score-eda-data-processing-model-building,House Prices - Advanced Regression Techniques
13797883,0.80675,1,1,/zayanmakar/house-prices-prediction-with-linear-regression,House Prices - Advanced Regression Techniques
13729401,0.13127,0,0,/feamaika/predict-sales-prices-hello-world,House Prices - Advanced Regression Techniques
13911520,0.018,7,20,/frlemarchand/maskrcnn-for-chest-x-ray-anomaly-detection,Predict Future Sales
10024034,0.804,0,9,/tunguz/melanoma-with-h2o-automl,SIIM-ISIC Melanoma Classification
10021739,0.695,1,2,/ajax0564/bit-transformer,SIIM-ISIC Melanoma Classification
9952961,0.82,7,12,/piantic/tf-keras-tpu-burn,SIIM-ISIC Melanoma Classification
9950994,0.833,3,3,/sarques/siim-pytorch-ignite-simple-baseline-starter,SIIM-ISIC Melanoma Classification
9908776,0.9464,5,28,/redwankarimsony/siim-isic-melanoma-classification-ensemble,SIIM-ISIC Melanoma Classification
9861835,0.5870000000000001,0,5,/akashram/cnn-baseline-pytorch,SIIM-ISIC Melanoma Classification
9862355,0.835,4,14,/zzy990106/lgb-xgb-meta-data-image,SIIM-ISIC Melanoma Classification
9758480,0.8959999999999999,0,3,/shubhamai/melanoma-classification,SIIM-ISIC Melanoma Classification
9854395,0.701,0,1,/p4rallax/pytorch-siim-isic-inference,SIIM-ISIC Melanoma Classification
9798202,0.6609999999999999,1,27,/louise2001/beginner-lightgbm-on-patient-only-information,SIIM-ISIC Melanoma Classification
9754337,0.933,34,100,/khoongweihao/siim-isic-multiple-model-training-stacking,SIIM-ISIC Melanoma Classification
13110014,0.747,0,0,/niaibrahim/cnn-lr-0001-dense-1-epoch-40-batch-60000,Riiid Answer Correctness Prediction
12925224,0.5920000000000001,0,0,/niaibrahim/fork-of-fork-of-basic-karas-nn-made-epoch-s-178786,Riiid Answer Correctness Prediction
12617386,0.7559999999999999,0,25,/isaienkov/lgbm-optuna-rfe,Riiid Answer Correctness Prediction
12465373,0.5,0,0,/mamasinkgs/submission-with-7000-limit,Riiid Answer Correctness Prediction
12245455,0.743,0,36,/isaienkov/riiid-answer-correctness-prediction-keras-nn,Riiid Answer Correctness Prediction
10092165,1.05788,1,7,/krishnaheroor/predict-future-sales,Predict Future Sales
9508095,0.89915,0,1,/rrrrrikimaru/create-model-simple-e-to-e-eda-to-ensemble,Predict Future Sales
9065768,1.00699,0,0,/venkat555/how-to-win-a-data-science-competition,Predict Future Sales
9194708,0.89333,0,12,/sal001/google-trends-for-sale-prediction-xgboost,Predict Future Sales
13966489,0.745983,0,0,/rushali2406/dense121-or-169,PUBG Finish Placement Prediction (Kernels Only)
13523968,0.758699,0,0,/ggg999bbb666/notebook8cb8dbb420,PUBG Finish Placement Prediction (Kernels Only)
13496444,0.0,0,0,/nctuvrdl/notebook318fe13da6,APTOS 2019 Blindness Detection
13385716,0.8375020000000001,0,1,/jyruan/notebook0f0f494ac4,PUBG Finish Placement Prediction (Kernels Only)
13289508,0.60869,0,0,/wmloh98/notebook59b83e7ebf,PUBG Finish Placement Prediction (Kernels Only)
5623158,0.748776,1,1,/vh1981/aptos-2019-blindness-detection-2019-data-only,APTOS 2019 Blindness Detection
5614860,0.545,0,0,/marinan67/kernel-densenet,APTOS 2019 Blindness Detection
5665440,0.83,0,0,/quandapro/aptos-2019-submission,APTOS 2019 Blindness Detection
5558127,0.7659999999999999,0,0,/mjurewicz3/aptos2019-blindness-detection,APTOS 2019 Blindness Detection
13075654,0.599,1,13,/salmaneunus/rainforest-audio-species-detection,Rainforest Connection Species Audio Detection
13019660,0.589,0,13,/mehrankazeminia/audio-detection-soliset-201,Rainforest Connection Species Audio Detection
12945652,0.6,12,53,/kneroma/inference-resnest-rfcx-audio-detection,Rainforest Connection Species Audio Detection
128686,0.6956,0,0,/alexionby/first-attempt,Leaf Classification
116383,0.04153,1,1,/gauravjoshi1986/leaf-classification,Leaf Classification
10727299,0.914,0,1,/drhabib/ens-xie-2fold-drhb-igor-ru-se50-ru-efnet-5,Prostate cANcer graDe Assessment (PANDA) Challenge
10683041,0.8690000000000001,0,1,/nikhilbartwal001/panda-inference,Prostate cANcer graDe Assessment (PANDA) Challenge
9922490,0.917,0,0,/iafoss/panda-init-class-128,Prostate cANcer graDe Assessment (PANDA) Challenge
10924019,0.5432600000000001,0,6,/dararc/prostate-cancer-detection-end-to-end-ml-project,Prostate cANcer graDe Assessment (PANDA) Challenge
11105228,0.8896799999999999,0,4,/kyoshioka47/5-fold-effb0-with-cleaned-labels-pb-0-935,Prostate cANcer graDe Assessment (PANDA) Challenge
10314606,0.8759999999999999,0,0,/thomasx/test-the-big-size,Prostate cANcer graDe Assessment (PANDA) Challenge
10790126,0.907,0,1,/qitvision/jj-2020-07-21-all-models,Prostate cANcer graDe Assessment (PANDA) Challenge
10747835,0.908,0,5,/arroqc/tile-model-ensemble,Prostate cANcer graDe Assessment (PANDA) Challenge
9873440,0.33,0,1,/arkajyotib/lbp-cca-features-tune-upsample-better-image-tiles,Prostate cANcer graDe Assessment (PANDA) Challenge
10536133,0.6,1,9,/arpcode/pandas-and-bamboos-learning-rate-scheduling,Prostate cANcer graDe Assessment (PANDA) Challenge
9823227,0.5542600000000001,0,0,/jordai/rausnaus-lightgbm-per-category-best-notebook,M5 Forecasting - Accuracy
9025292,0.47506,0,0,/akashsuper2000/m5-three-shades-of-dark-darker-magic,M5 Forecasting - Accuracy
8472093,0.7498100000000001,0,0,/akashsuper2000/m5-forecast-keras-with-categorical-embeddings-v2,M5 Forecasting - Accuracy
3301792,0.70093,0,0,/noexittv/embeddings-keras-v04,Quora Insincere Questions Classification
3302628,0.49413,0,1,/anshulmishra/quora-logistics,Quora Insincere Questions Classification
2591111,0.695,0,1,/moshizhiyinof401/moshi-s,Quora Insincere Questions Classification
3302046,0.5274399999999999,0,0,/kavyarall/quora-text-classification-493662,Quora Insincere Questions Classification
3178034,0.66395,0,0,/evgeneus/quara-rnn-baseline,Quora Insincere Questions Classification
2590032,0.7020000000000001,0,1,/zsn6034/2gru-2attention-v1,Quora Insincere Questions Classification
2809878,0.14417,1,8,/akhileshrai003/quora-text-classification,Quora Insincere Questions Classification
2823459,0.695,0,7,/mlwhiz/multimodel-ensemble-clean-kernel,Quora Insincere Questions Classification
2458138,0.6970000000000001,0,1,/zengyao/cnn-lstm-atten-res,Quora Insincere Questions Classification
2591986,0.70248,1,3,/genyuan/lstm-emsemble-deep-learning-pytorch,Quora Insincere Questions Classification
2913972,0.98847,5,13,/isikkuntay/gru-with-attention,Quora Insincere Questions Classification
2780071,0.703,3,37,/canming/ensemble-mean-iii-64-36,Quora Insincere Questions Classification
2836021,0.69897,2,11,/mschumacher/44th-place-add-all-the-randomness,Quora Insincere Questions Classification
3618614,0.80691,0,0,/architpatil23/kernelae570edc1c,What's Cooking? (Kernels Only)
1493671,0.78469,0,0,/madhavambati/what-s-cooking-a-basic-simple-model,What's Cooking? (Kernels Only)
1302671,0.75402,0,0,/ibraheemmoosa/bag-of-trees,What's Cooking? (Kernels Only)
8913639,0.15721,0,0,/folk85/covid-19-forecast-test-week-4,COVID19 Global Forecasting (Week 4)
8834249,0.03639,0,1,/nitbrok/covid-19-w4,COVID19 Global Forecasting (Week 4)
8948755,0.1430799999999999,0,0,/egissys/gb-stack-v1,COVID19 Global Forecasting (Week 4)
8942515,1.09236,0,0,/shainy/covid-19-linear-regression-with-python,COVID19 Global Forecasting (Week 4)
8938649,0.07254,0,1,/lomen0857/covid-19-prediction-using-only-train-data-week4,COVID19 Global Forecasting (Week 4)
8891014,0.10633,0,1,/paralleltree/covid19-simple-logistic-fitting-week4,COVID19 Global Forecasting (Week 4)
8922765,0.4145,0,0,/henrifroese/final-notebook-seircd,COVID19 Global Forecasting (Week 4)
8932463,0.4686,0,1,/williamsabodunrin/kernel29abb02853,COVID19 Global Forecasting (Week 4)
8936336,0.75593,0,0,/ethankolkmeier/kernel607dcf502a,COVID19 Global Forecasting (Week 4)
8949715,0.06599,0,0,/jaehooncha/kernel553798e182,COVID19 Global Forecasting (Week 4)
8950332,0.03516,0,0,/varsinibalasundaram/kernel6a5daeed2b,COVID19 Global Forecasting (Week 4)
8938997,0.33501,0,1,/alexkhrystoforov/kernel2e512d4a60,COVID19 Global Forecasting (Week 4)
8923403,0.6564800000000001,0,0,/lomen0857/covid-19-forecasting-w4,COVID19 Global Forecasting (Week 4)
8899452,0.04638,2,2,/holfyuen/predicting-coronavirus-cases-with-logistic-curve,COVID19 Global Forecasting (Week 4)
8929919,0.06105,0,0,/ee257sp20arjunmadhu/kernel172938695f,COVID19 Global Forecasting (Week 4)
8942660,0.72825,0,0,/ashrithsher/kernel50f8c26470,COVID19 Global Forecasting (Week 4)
8877379,0.99853,0,0,/sayanotsu/covid-19-week4-holt-s-method,COVID19 Global Forecasting (Week 4)
8911127,0.0335899999999999,1,7,/abhijithchandradas/linearregressionmodel,COVID19 Global Forecasting (Week 4)
8946409,0.36668,0,0,/andrewshevelev/kernel4d51baf36e,COVID19 Global Forecasting (Week 4)
8918106,0.06109,0,0,/seaofstars/kernel480807e23c,COVID19 Global Forecasting (Week 4)
8910174,0.28163,2,4,/lisphilar/combination-of-logistic-sir-f-week-4,COVID19 Global Forecasting (Week 4)
8933331,0.68789,0,0,/anushreemanoharan/kernel23ed1e897a,COVID19 Global Forecasting (Week 4)
8914867,0.1362,0,1,/akshitsharma206/covid-19-week-4-trying-to-get-in-top-10,COVID19 Global Forecasting (Week 4)
8913787,0.05268,0,1,/hossein2015/xgboost-algorithm-covid-19-week-4,COVID19 Global Forecasting (Week 4)
8915333,0.696,0,0,/akshitsharma206/deploying-lgbm-covid-19-week-4,COVID19 Global Forecasting (Week 4)
8846608,0.09808,0,0,/gauravbrills/covid-week-4-forecasting,COVID19 Global Forecasting (Week 4)
8947652,0.22364,0,0,/titericz/week-4-blend-ideas,COVID19 Global Forecasting (Week 4)
8911710,2.91226,0,0,/tejaspatel1094/covid-19-gobal-forecasting,COVID19 Global Forecasting (Week 4)
8946235,1.04894,0,0,/clodette0071/kernel74af024417,COVID19 Global Forecasting (Week 4)
8834895,0.44487,0,1,/deshmane/kernel6cd1e69544,COVID19 Global Forecasting (Week 4)
8949177,0.0351,0,0,/ashimak01/kernel654fb5d918,COVID19 Global Forecasting (Week 4)
8901233,0.52254,0,0,/shaolong2019/covid-19-prediction-with-random-forest-regressor,COVID19 Global Forecasting (Week 4)
8935020,3.2859199999999995,0,0,/kojimar/kernel728e65ae76,COVID19 Global Forecasting (Week 4)
8898441,0.07649,0,0,/mystery/covid19-week4,COVID19 Global Forecasting (Week 4)
8950553,0.9433,0,0,/ee257sp20subhashree/covid-19-global-forecasting,COVID19 Global Forecasting (Week 4)
8869671,0.03639,0,11,/milan400/covid19-forecasting,COVID19 Global Forecasting (Week 4)
8897459,4.61536,0,0,/pragyarathore/model-1-simple-linear-regression,COVID19 Global Forecasting (Week 4)
8840676,0.03737,1,3,/kaimingk/covid-week4-transformer,COVID19 Global Forecasting (Week 4)
8891208,1.15641,0,2,/wjholst/prediction-with-a-gamma-pdf,COVID19 Global Forecasting (Week 4)
8872642,0.01135,1,18,/mdmahmudferdous/covid-19-global-forecasting-4-lasso-polynomial,COVID19 Global Forecasting (Week 4)
8878638,0.11747,0,0,/basselkassem/covid19-week4,COVID19 Global Forecasting (Week 4)
8887495,0.10036,0,1,/dkozlov/rrgnwndlg2wd23ru2beutvmrsuv5beuy,COVID19 Global Forecasting (Week 4)
8925441,1.4552,0,2,/shubhankartiwari/covid-19-forecast,COVID19 Global Forecasting (Week 4)
8854515,0.0788,0,25,/darshanjain29/xgboost-regressor-solution-covid19-week-4,COVID19 Global Forecasting (Week 4)
8872946,0.03639,0,2,/milantripathi/covid19-forecasting,COVID19 Global Forecasting (Week 4)
8873584,4.22169,0,1,/sohaibmian/bi-dir-lstm-time-series-forecasting-for-new-york,COVID19 Global Forecasting (Week 4)
8843750,0.4218699999999999,1,21,/darshanjain29/bagging-regressor-solution-covid19-week-4,COVID19 Global Forecasting (Week 4)
8865512,0.14364,0,3,/artem99/scipy,COVID19 Global Forecasting (Week 4)
8842834,0.76231,0,0,/khanalkiran/covid19-global-week4-sir-model,COVID19 Global Forecasting (Week 4)
8907279,0.70514,0,0,/peteralaoui/3-regimes-logistic-exponential-and-linear-w4,COVID19 Global Forecasting (Week 4)
8907995,0.92505,0,0,/ggiuffre/sigmoidal-fit,COVID19 Global Forecasting (Week 4)
1598451,0.77896,0,1,/alluxia/what-s-cooking-in-keras,What's Cooking? (Kernels Only)
1395948,0.76337,0,0,/timothycwillard/cooking-with-linear-models,What's Cooking? (Kernels Only)
1572087,0.78992,0,0,/tandonarpit6/cooking-challenge,What's Cooking? (Kernels Only)
1562426,0.8246100000000001,1,3,/mtinti/kernel7fa5f6352e,What's Cooking? (Kernels Only)
1543911,0.72304,0,1,/alfasst/bag-of-ingredients-svd-and-keras,What's Cooking? (Kernels Only)
1367015,0.78791,0,0,/figarrikeisha/cooking-time,What's Cooking? (Kernels Only)
1497313,0.80108,0,2,/bigkirill/cooking,What's Cooking? (Kernels Only)
1524804,0.78268,0,0,/zhaohuang12/tf-idf-nn-tensorflow-py,What's Cooking? (Kernels Only)
1514817,0.71641,0,0,/chandan2495/guess-the-cuisine,What's Cooking? (Kernels Only)
1386651,0.78127,0,0,/bjjoy2009/tf-idf-lr,What's Cooking? (Kernels Only)
1396445,0.79847,0,2,/belousych/tfidf-lg-lgbm-stack,What's Cooking? (Kernels Only)
1394088,0.78942,0,0,/muxxer/baking-brainies,What's Cooking? (Kernels Only)
1383964,0.75683,0,0,/bjjoy2009/bag-rf,What's Cooking? (Kernels Only)
1376856,0.7972600000000001,0,4,/hengzheng/a-test-on-tf-idf-with-mlp-and-textcnn,What's Cooking? (Kernels Only)
1382317,0.5038199999999999,0,0,/plarmuseau/marcelo,What's Cooking? (Kernels Only)
1341543,0.82119,1,9,/nafisur/cooking,What's Cooking? (Kernels Only)
1329740,0.76377,0,1,/aalchemist/word-embedding-logistic-regression,What's Cooking? (Kernels Only)
1318545,0.82119,0,2,/andreaschandra/svm-and-gridsearchcv,What's Cooking? (Kernels Only)
1321245,0.79123,0,2,/dzkaggle/chef-keras-deep-cooking,What's Cooking? (Kernels Only)
1300656,0.75331,0,0,/sarahjaynekessler/cuisine-guessing,What's Cooking? (Kernels Only)
1306629,0.78992,0,0,/abhinav08/what-s-cooking-tfidf-bow-features,What's Cooking? (Kernels Only)
1304030,0.78298,1,0,/esipenko/first-try-countvectorizer-linearsvc,What's Cooking? (Kernels Only)
1290889,0.71621,0,0,/ibraheemmoosa/bernoulli-naive-bayes,What's Cooking? (Kernels Only)
1294448,0.72475,0,0,/ibraheemmoosa/multinomial-naive-bayes,What's Cooking? (Kernels Only)
1277808,0.54927,0,0,/plarmuseau/xtratrees,What's Cooking? (Kernels Only)
1260791,0.7719199999999999,0,0,/qgh1223/xgboost-lightgbm-for-cooking,What's Cooking? (Kernels Only)
1235330,0.76639,0,9,/ashishpatel26/hyper-parameter-tuning-using-svm,What's Cooking? (Kernels Only)
1205690,0.8039,17,84,/ash316/what-is-the-rock-cooking-ensembling-network,What's Cooking? (Kernels Only)
8142716,1.68723,0,0,/carlobpy/sales-product,Predict Future Sales
5436072,0.9512,0,0,/leoromanovich/i-wish-to-predict-v3,Predict Future Sales
2684178,0.7040000000000001,1,1,/peining/third-place-model-for-toxic-comments-in-pytorch,Quora Insincere Questions Classification
2241059,0.6940000000000001,0,1,/tks0123456789/projection-meta-embedding-and-ema,Quora Insincere Questions Classification
2808639,0.6940000000000001,0,1,/tarunpaparaju/quora-attention-bigru-capsule-weightdrop-padam,Quora Insincere Questions Classification
2830102,0.6990000000000001,1,1,/blacksix/quora-34th-place-bilstm-gru-single-model,Quora Insincere Questions Classification
2646394,0.63,0,0,/n2cholas/preprocessing-and-cnn-attention-in-tensorflow,Quora Insincere Questions Classification
2838713,0.705,24,31,/alsaco/is-this-a-desirable-competition-format,Quora Insincere Questions Classification
2796334,0.516,0,0,/noamfrank/quora-classification-with-random-forest-model,Quora Insincere Questions Classification
2818414,0.5441,0,1,/stevexyu/fasttext-from-keras,Quora Insincere Questions Classification
2362410,0.601,0,0,/anjumanoj/project2,Quora Insincere Questions Classification
2820934,0.6759999999999999,0,1,/loktev/3x-gru-w-attn,Quora Insincere Questions Classification
2785077,0.093,0,2,/mirodil/quora-insincere-qc-with-fastai,Quora Insincere Questions Classification
2788821,0.7,2,20,/zeus75/quora-with-bilstm,Quora Insincere Questions Classification
2124664,0.672,0,0,/asquarek/fork-of-word2vec-cnn,Quora Insincere Questions Classification
2794721,0.6459999999999999,0,2,/infinitylogesh/the-curious-case-of-siamese-twins,Quora Insincere Questions Classification
2797476,0.682,0,0,/johnkyon/char-embeddings,Quora Insincere Questions Classification
2746764,0.696,0,7,/sfzero/focal-loss-feature-0-99994,Quora Insincere Questions Classification
2513242,0.69,0,0,/ncsuahlusar/single-rnn-with-4-fold-cv-and-using-attention,Quora Insincere Questions Classification
2360919,0.619,0,0,/cuberti/quora-baseline-nbsvm-model,Quora Insincere Questions Classification
2691756,0.424,0,0,/wangggong/quora-prediction-by-lgb,Quora Insincere Questions Classification
2733280,0.63,1,8,/hamishdickson/char-level-only-lb0-630,Quora Insincere Questions Classification
2731803,0.6709999999999999,0,5,/ruijiezheng2022/single-lstm,Quora Insincere Questions Classification
2748645,0.6859999999999999,0,0,/xsakix/bilstm-meta-v2,Quora Insincere Questions Classification
2501414,0.4629999999999999,0,0,/grantsw82/simple-tfidf-without-stem-lem-token,Quora Insincere Questions Classification
2702047,0.0,1,9,/amitabhac/rl-nlp-first-attempt,Quora Insincere Questions Classification
2409525,0.6729999999999999,0,1,/isikkuntay/keeping-it-insincere,Quora Insincere Questions Classification
8267136,0.8445799999999999,0,0,/iamtalos/machine-learning-end-to-end,Shelter Animal Outcomes
1238570,1.02546,1,0,/zhoulingyan0228/shelter-animal-outcome-prediction-w-nn,Shelter Animal Outcomes
595738,0.1164579999999999,0,23,/kmader/transfer-learning-with-inceptionv3,IEEE's Signal Processing Society - Camera Model Identification
506010,0.127708,1,1,/rishabhiitbhu/keras-cnn-starter,IEEE's Signal Processing Society - Camera Model Identification
504560,0.175,0,17,/merryhunter/very-simple-pca-baseline,IEEE's Signal Processing Society - Camera Model Identification
8484814,1.07978,2,11,/robertburbidge/statistical-benchmarks-wrmsse-stochastic-ensemble,M5 Forecasting - Accuracy
8471498,0.75238,4,16,/chrisrichardmiles/simple-model-avg-last-28-days-grouped-by-weekday,M5 Forecasting - Accuracy
8426841,1.92428,0,0,/harupy/simple-baseline-using-average-over-past-years,M5 Forecasting - Accuracy
8336193,0.64127,1,9,/resistance0108/digging-in-correlation-between-sales-of-items,M5 Forecasting - Accuracy
8320614,0.7836,8,34,/georsara1/fbprophet-try2-python-multiprocessing,M5 Forecasting - Accuracy
8302589,3.43848,2,2,/ajax0564/keras-with-embeddings,M5 Forecasting - Accuracy
8254683,1.06696,29,106,/tpmeli/visual-guide-3-m5-baselines-eda-sarima,M5 Forecasting - Accuracy
8288619,5.44561,0,0,/grapestone5321/m5-forecasting-accuracy-sample-submission,M5 Forecasting - Accuracy
8263651,0.7836,3,5,/georsara1/fbprophet-try1,M5 Forecasting - Accuracy
8249814,1.1602,3,11,/kmatsuyama/m5-forecasting-a-starter-model-by-simple-nn,M5 Forecasting - Accuracy
8225923,1.08216,18,59,/rdizzl3/eda-and-baseline-model,M5 Forecasting - Accuracy
8230199,1.05659,8,27,/kongnyooong/the-m5-competition-baseline-for-korean,M5 Forecasting - Accuracy
8232833,1.15868,2,7,/tomkelly0/price-and-demand,M5 Forecasting - Accuracy
13239803,0.73648,0,0,/avivlevi815/final-solution-726th-place,M5 Forecasting - Accuracy
11629220,1.34621,0,0,/swetajoshi/sample-submission-score-1-28,M5 Forecasting - Accuracy
133549,0.69324,0,0,/jinrui/notebook0cff30484a,Leaf Classification
4837622,3.70086,0,0,/a18974761777/merchant,Elo Merchant Category Recommendation
4161080,3.88965,0,0,/ceci2017/03-elo-merchant,Elo Merchant Category Recommendation
2765286,3.688,2,14,/roydatascience/combining-your-model-with-a-model-without-outlier,Elo Merchant Category Recommendation
171635,0.73558,1,3,/ctrlaltdel/west-nile-virus-prep-and-model,West Nile Virus Prediction
7108077,0.6904399999999999,0,2,/genericurl/demo-ds-project-based-on-the-winning-solution,VSB Power Line Fault Detection
3888590,0.01882,0,1,/francispimentel/working-with-large-datasets,VSB Power Line Fault Detection
3391408,0.68008,12,118,/mark4h/vsb-1st-place-solution,VSB Power Line Fault Detection
2828836,0.627,0,1,/silvernine/vbs-power-line-fault-detection,VSB Power Line Fault Detection
2774001,0.665,0,10,/dzabalar/5-fold-lstm-attention-fully-commented,VSB Power Line Fault Detection
2892169,0.48,0,2,/yatzhash/smote-to-learn-from-a-few-anomaly-sample,VSB Power Line Fault Detection
3299602,0.7,8,24,/roydatascience/attention-feature-engineering-augmentation,VSB Power Line Fault Detection
3141724,0.637,0,2,/donkeys/learning-to-lstm,VSB Power Line Fault Detection
3040749,0.655,10,62,/junkoda/handmade-features,VSB Power Line Fault Detection
3020418,0.4529999999999999,2,20,/pnussbaum/adversarial-cnn-of-ptp-for-vsb-power-v12,VSB Power Line Fault Detection
2844857,0.574,3,21,/yatzhash/non-neural-baseline-with-summarized-features,VSB Power Line Fault Detection
2779328,0.672,0,44,/suicaokhoailang/transformer-baseline-0-672-lb,VSB Power Line Fault Detection
2695159,0.506,0,2,/fernandoramacciotti/cnn-with-class-weights,VSB Power Line Fault Detection
2670990,0.618,24,65,/suicaokhoailang/5-fold-lstm-with-threshold-tuning-0-618-lb,VSB Power Line Fault Detection
2456270,0.3329999999999999,24,82,/miklgr500/flatiron,VSB Power Line Fault Detection
14031191,0.84467,0,1,/hidekiizumi/submit,Recruit Restaurant Visitor Forecasting
9205631,0.51644,0,0,/simonstochholm/recruit-restaurant-visitor-forecasting,Recruit Restaurant Visitor Forecasting
6397182,0.49777,0,1,/mimorisoki/first-charenge,Recruit Restaurant Visitor Forecasting
2419655,0.521,0,0,/mhyodo/baseline-model-1219,Recruit Restaurant Visitor Forecasting
978627,0.546,0,0,/peeyushsahu/visitorforecast-nn-rt,Recruit Restaurant Visitor Forecasting
604256,0.514,2,36,/osciiart/denoising-autoencoder,Recruit Restaurant Visitor Forecasting
577960,0.519,0,3,/johnfarrell/groupby-mean-median-feature-baselines,Recruit Restaurant Visitor Forecasting
541325,0.479,13,60,/nitinsurya/surprise-me-2-neural-networks-keras,Recruit Restaurant Visitor Forecasting
524817,0.479,5,35,/tejasrinivas/surprise-me-4-lb-0-479,Recruit Restaurant Visitor Forecasting
520487,0.5,6,6,/scirpus/time-series-not-optimized,Recruit Restaurant Visitor Forecasting
505287,0.914,3,6,/asindico/recruit-restaurant-data-understanding,Recruit Restaurant Visitor Forecasting
502587,0.501,0,2,/johannesss/forecasting-using-light-gbm,Recruit Restaurant Visitor Forecasting
481205,0.498,0,5,/jeru666/rrv-modelling-trials,Recruit Restaurant Visitor Forecasting
14083709,0.0495699999999999,0,0,/nuaazhouweiwei/notebook7b8f4c3f96,PUBG Finish Placement Prediction (Kernels Only)
7497212,0.05871,0,0,/xushanjie/kernel75be911559,PUBG Finish Placement Prediction (Kernels Only)
13700300,0.01528,3,0,/zzy248/fudan-sds-bigdata,PUBG Finish Placement Prediction (Kernels Only)
12974490,0.12958,0,3,/pontiacboy/pubg-competition,PUBG Finish Placement Prediction (Kernels Only)
12105907,0.04459,0,0,/ankitsharmax/pubg-pred,PUBG Finish Placement Prediction (Kernels Only)
3129386,0.02872,0,0,/stasdeep/2nn-duo-squad-solo-v2-with-postprocess,PUBG Finish Placement Prediction (Kernels Only)
12087146,0.2762199999999999,0,1,/mynextstep16/beginner-1st-model,PUBG Finish Placement Prediction (Kernels Only)
11319720,0.09772,0,2,/elgendy5576/pubg-analysis-and-prediction,PUBG Finish Placement Prediction (Kernels Only)
10179595,0.06627,0,1,/sevashasla/pubg-win-of-not,PUBG Finish Placement Prediction (Kernels Only)
9710277,0.05857,0,0,/shimomatomoya/pubg-ranking-regressor,PUBG Finish Placement Prediction (Kernels Only)
5931520,0.0557799999999999,0,3,/ljh9885/pubg-eda-by-lee,PUBG Finish Placement Prediction (Kernels Only)
8705684,0.04575,0,0,/shjas94/kernel6e57a3355c,PUBG Finish Placement Prediction (Kernels Only)
7945849,0.06289,0,0,/macchi57/aprendizado-supervisionado-pubg-winplaceperc,PUBG Finish Placement Prediction (Kernels Only)
7610790,0.05592,0,0,/dmitryrebrik/kernel15a3c1b030,PUBG Finish Placement Prediction (Kernels Only)
7540143,0.11497,0,0,/nmsf1916044/nmsf1916044,PUBG Finish Placement Prediction (Kernels Only)
7513653,0.02916,1,2,/muyun99/nmsc1916003-rf-lightgbm-dnn-model-ensembling,PUBG Finish Placement Prediction (Kernels Only)
10343622,1.41241,0,1,/ufukilik/170202096,Predict Future Sales
10316572,1.90783,0,7,/mertuzan/kou-b-y-k-veri-final-160202009,Predict Future Sales
10221012,0.93901,1,8,/davikrause/predict-future-sales-xgboost-e-rf-com-lagfeatures,Predict Future Sales
10341245,0.931,8,16,/jagadish13/melanoma-detection-efficientnetb7-tpu-eda,SIIM-ISIC Melanoma Classification
10124634,0.8320000000000001,11,77,/allunia/don-t-turn-into-a-smoothie-image-statistics,SIIM-ISIC Melanoma Classification
10230551,0.9533,17,46,/truonghoang/stacking-ensemble-on-my-submissions,SIIM-ISIC Melanoma Classification
10291159,0.927,16,43,/apthagowda/melanoma-efficientnet-b6-tpu-tta,SIIM-ISIC Melanoma Classification
10125182,0.763,0,1,/viiids/inception-resnetv2-with-oversampling-malignant,SIIM-ISIC Melanoma Classification
10060046,0.785,0,3,/mushaya/melanoma-class-1,SIIM-ISIC Melanoma Classification
10140213,0.937,22,107,/ragnar123/efficientnet-x-384,SIIM-ISIC Melanoma Classification
10106272,0.922,45,88,/graf10a/efficientnet-bn-tabular-features-tf-cv5-512x512,SIIM-ISIC Melanoma Classification
10188483,0.7929999999999999,0,0,/allenljw/skin-cancer-image-classification-3,SIIM-ISIC Melanoma Classification
10045768,0.921,0,20,/ragnar123/catboost-metadata,SIIM-ISIC Melanoma Classification
12161154,0.753,11,113,/jsylas/riiid-lgbm-starter,Riiid Answer Correctness Prediction
12171782,0.735,0,0,/gautham11/riiid-become-one-with-the-data-eda,Riiid Answer Correctness Prediction
12179420,0.711,4,13,/yutohisamatsu/riiid-lgbm-simple-baseline,Riiid Answer Correctness Prediction
12152761,0.7390000000000001,2,18,/dwit392/simple-lgbm-3-feature-model,Riiid Answer Correctness Prediction
12120485,0.7240000000000001,23,180,/ilialar/simple-eda-and-baseline,Riiid Answer Correctness Prediction
12132134,0.7240000000000001,1,6,/eudmar/riiid-eda-comparing-models,Riiid Answer Correctness Prediction
12145012,0.568,0,8,/rautaki0127/riiid-lgbm-xgb-catboost-sklearn-ensemble,Riiid Answer Correctness Prediction
12115681,0.539,0,85,/sishihara/riiid-lgbm-5cv-benchmark,Riiid Answer Correctness Prediction
12139794,0.732,1,8,/lgreig/simple-logistic-baseline,Riiid Answer Correctness Prediction
12129110,0.539,0,20,/tamilsel/riiid-exploration-lightgbm-beginner-kit,Riiid Answer Correctness Prediction
12135505,0.705,0,11,/paulorzp/mean-of-answered-correctly-with-all-rows,Riiid Answer Correctness Prediction
12133422,0.504,2,5,/yohannwattiez/example-of-prediction-with-randomforest,Riiid Answer Correctness Prediction
12122485,0.505,2,8,/swarajshinde/riiid-answer-correctness-eda-baseline,Riiid Answer Correctness Prediction
14102993,0.7879999999999999,0,0,/azamat25/riiid-lgbm-0-782-sakt-0-777-0-789-on-35m-data,Riiid Answer Correctness Prediction
14073837,0.7859999999999999,0,0,/tarique7/v2-2-fork-of-riiid-lgbm-bagging-new,Riiid Answer Correctness Prediction
13488477,0.75,0,0,/moradmah/riiid-pfa,Riiid Answer Correctness Prediction
13436134,0.7659999999999999,0,0,/zekun98/sakt-with-state-updates,Riiid Answer Correctness Prediction
13303911,0.76,0,0,/zekun98/riid-modelineng,Riiid Answer Correctness Prediction
13263731,0.7509999999999999,0,0,/niaibrahim/cnn-reduced-learning-rate,Riiid Answer Correctness Prediction
13118414,0.7509999999999999,0,0,/niaibrahim/fork-of-cnn-lr-001-epoch-40-conv1d-128,Riiid Answer Correctness Prediction
13747559,0.1197,19,21,/awwalmalhi/hyperparameter-tuning-with-optuna-and-gridsearch,House Prices - Advanced Regression Techniques
13769375,0.14135,0,0,/suneelkumarkanthala/house-price-prediction-suneel,House Prices - Advanced Regression Techniques
13755588,5.7800400000000005,2,2,/akhilesh94/house-price-using-linear-regression,House Prices - Advanced Regression Techniques
13709906,0.1330599999999999,0,0,/mavillan/spike-hands-on,House Prices - Advanced Regression Techniques
13736417,0.14125,0,0,/tomonorisasaki/hpar-with-lasso-regressionn,House Prices - Advanced Regression Techniques
13649191,0.14274,3,3,/raafaq/advanced-regression-xgbregressor-simple-dataprep,House Prices - Advanced Regression Techniques
13615002,0.13363,0,0,/kishinhoshima/baseline,House Prices - Advanced Regression Techniques
13441568,0.12717,0,2,/batprem/house-price-prediction-after-eda,House Prices - Advanced Regression Techniques
13275134,0.14479,0,0,/nathanporter/house-prices-final,House Prices - Advanced Regression Techniques
13360528,0.12252,0,0,/tianyangzhaodtu/house-prices-advanced-regression-techniques,House Prices - Advanced Regression Techniques
13162820,0.13883,0,0,/virajkadam/notebookfa6a9143c9,House Prices - Advanced Regression Techniques
13349315,0.1581,1,2,/mkudlick/house-prices-random-forest,House Prices - Advanced Regression Techniques
12509385,0.14728,0,2,/fcaner/house-price-prediction-with-random-forest,House Prices - Advanced Regression Techniques
13489232,0.8629399999999999,0,0,/drcapa/santander-customer-transaction,Santander Customer Transaction Prediction
10754790,0.7960699999999999,0,2,/dorauierp1/santander-transaction-prediction,Santander Customer Transaction Prediction
8282475,0.9002,0,1,/jagannathrk/santander-eda-and-prediction,Santander Customer Transaction Prediction
8369941,0.8884799999999999,0,0,/orazkarl/karl-ml,Santander Customer Transaction Prediction
8372337,0.52265,0,0,/kossaibek/secondtry,Santander Customer Transaction Prediction
8363991,0.77328,0,0,/nikitaminakov61/da3-midterm,Santander Customer Transaction Prediction
8384003,0.8877700000000001,0,0,/muhamedali/muhamed-ali,Santander Customer Transaction Prediction
8137686,0.5001,0,1,/ali19158/xgboostmid,Santander Customer Transaction Prediction
7814529,0.389,1,1,/kirsin/bert-base-pretrained-models,Google QUEST Q&A Labeling
7781499,0.161,0,0,/aninda/sklearn-model-rf,Google QUEST Q&A Labeling
7725796,0.365,11,31,/coolcoder22/simple-lstm-with-cv,Google QUEST Q&A Labeling
7677158,0.294,9,36,/abhishek/bert-inference-of-tpu-model,Google QUEST Q&A Labeling
7089272,0.373,4,7,/tanlikesmath/quest-bert-base-pytorch-minimalistic-training,Google QUEST Q&A Labeling
7595130,0.001,0,0,/nikhilpinn/simple-bert,Google QUEST Q&A Labeling
7432185,0.384,10,26,/vinaydoshi/tfbert-with-4-hidden-layers-and-preprocessing,Google QUEST Q&A Labeling
6836133,0.266,0,0,/vinaydoshi/google-q-a-rcnn,Google QUEST Q&A Labeling
7038559,0.281,0,0,/vinaydoshi/google-q-a-lstm,Google QUEST Q&A Labeling
7397799,0.377,20,63,/phoenix9032/pytorch-bert-plain,Google QUEST Q&A Labeling
7344524,0.361,2,19,/manyregression/fastai-no-spacy-transformers-here,Google QUEST Q&A Labeling
7257384,0.38,8,38,/ykojima1989/fine-tune-bert-use-on-sklearn-pipeline,Google QUEST Q&A Labeling
7188277,0.285,0,4,/jessormazaespin/kernel-with-word-embeddings,Google QUEST Q&A Labeling
7130791,0.158,0,0,/saahmathworks/google-quest,Google QUEST Q&A Labeling
7099909,0.384,10,32,/bibek777/bert-base-tf2-0-minimalistic-iii,Google QUEST Q&A Labeling
6853865,0.3,1,2,/meghakapoor/fastai-with-bert,Google QUEST Q&A Labeling
7108704,0.3229999999999999,0,0,/rohitsingh9990/use-metafeatures-oof,Google QUEST Q&A Labeling
6949312,0.352,14,25,/dimitreoliveira/google-quest-eda-and-use-baseline,Google QUEST Q&A Labeling
6746875,0.301,0,3,/jagannathrk/tfidf-swem-approach,Google QUEST Q&A Labeling
6817764,0.382,171,414,/akensert/quest-bert-base-tf2-0,Google QUEST Q&A Labeling
5860577,0.9908,0,0,/bhabie/cactusnotebooksubmission,Aerial Cactus Identification
5801854,0.9988,0,1,/antimony18/pytorch-cnn-model,Aerial Cactus Identification
5703488,0.9816,0,0,/eiosifov/aerial-cactus-identification,Aerial Cactus Identification
5565611,0.9925,0,2,/shubhams9k96/cactus-baseline,Aerial Cactus Identification
5570827,0.9775,0,2,/harshitt21/playing-with-cnns,Aerial Cactus Identification
5355349,0.9883,0,1,/dishingoyani/train-cnn-network-pytorch-beginner,Aerial Cactus Identification
5205249,0.9973,8,13,/sadilkhan786/aerial-cactus-classification-using-pytorch,Aerial Cactus Identification
5211968,0.997,0,0,/nuldiego/baseline-cactus-cnn-with-keras,Aerial Cactus Identification
4781335,0.9376,0,2,/aurelienmontmejat/cactus-personal-model,Aerial Cactus Identification
5144562,1.0,1,2,/horohoro/aerial-cactus-identification-simplecnn,Aerial Cactus Identification
5094933,0.9927,0,0,/sajjadmanal/kernel19546b3b89,Aerial Cactus Identification
4773813,0.9965,0,0,/zhangzhch/kernel7128fee394,Aerial Cactus Identification
4763053,0.996,1,3,/sahilchoudhary/cactus-detection-tensorflow-2-0-gpu-beta,Aerial Cactus Identification
4743759,0.9997,0,1,/albertsheldon/test-m1,Aerial Cactus Identification
4167857,0.9999,0,1,/njwangyuting/kernel5065e63c60,Aerial Cactus Identification
4721710,0.9982,0,1,/ahkhalwai55/simple-fastai-exercise-squeezenet1,Aerial Cactus Identification
4721881,0.9999,0,0,/aanubhav/cactus-data-analysis,Aerial Cactus Identification
4695411,0.9608,0,2,/snehashis1997/aerial-cactus-custom,Aerial Cactus Identification
4637940,0.9999,0,2,/sajaldeb25/cactus-identification,Aerial Cactus Identification
4639654,0.9988,0,1,/muhamamdasim/fed-up-with-model-tuning-autokeras-to-the-rescue,Aerial Cactus Identification
4576916,0.9999,0,2,/elgatodelbosque/aerial-cactus-keras,Aerial Cactus Identification
4626339,0.9998,1,8,/pheaboo/simple-cnn-trained-from-scratch,Aerial Cactus Identification
4675019,0.9995,0,0,/honglou/kernel57a7dc59b3,Aerial Cactus Identification
4615958,0.9999,1,2,/neeraj17/fastai-cactus,Aerial Cactus Identification
4557360,0.9307,0,1,/truematrix/true-kernel-aci,Aerial Cactus Identification
4543493,0.989,0,1,/kesha18/cactus-classification-keras,Aerial Cactus Identification
4512708,0.9911,0,1,/barbetpsg/convnet-with-lr-reduction,Aerial Cactus Identification
4299194,0.4991,6,1,/lijeshshetty/aerial-cactus-lijesh-shetty,Aerial Cactus Identification
1638721,3.13176,0,0,/garylai91/fork-of-feature-engineering-xgboost,New York City Taxi Fare Prediction
3439000,0.504,2,9,/soumikdg/a-simple-clustering-based-pipe-line,Santander Customer Transaction Prediction
3439867,0.8909999999999999,7,7,/scirpus/how-does-this-work-at-all,Santander Customer Transaction Prediction
3413392,0.901,17,74,/darbin/hierarchical-clustering-approach,Santander Customer Transaction Prediction
3386597,0.9,6,15,/blade001/accuracy-is-0-900185,Santander Customer Transaction Prediction
3367939,0.9,40,159,/hjd810/keras-lgbm-aug-feature-eng-sampling-prediction,Santander Customer Transaction Prediction
3248888,0.8884799999999999,2,3,/plasticgrammer/santander-customer-transaction-gaussiannb,Santander Customer Transaction Prediction
3370297,0.8390000000000001,2,13,/matthew886/easy-nn-for-santander,Santander Customer Transaction Prediction
3348538,0.8809999999999999,10,18,/shivani1711/eda-feature-eng-lgb-gpu-cb-gpu-ranking,Santander Customer Transaction Prediction
3312948,0.9,0,2,/ssslarry/2-layers-lightgbm,Santander Customer Transaction Prediction
3017453,0.898,0,1,/akshay235/sctp-kernel,Santander Customer Transaction Prediction
3353977,0.645,2,2,/gokultalele/different-model-comparison-for-beginners,Santander Customer Transaction Prediction
3306136,0.9,3,16,/aawanghui/catboost,Santander Customer Transaction Prediction
12225653,0.31504,0,0,/jaydoncobb/notebook82f6218899,House Prices - Advanced Regression Techniques
12146446,0.1214799999999999,1,4,/jirakst/house-price,House Prices - Advanced Regression Techniques
11376586,0.1592599999999999,0,1,/fercampos19/house-prices-regression-example,House Prices - Advanced Regression Techniques
11738929,0.12102,20,49,/codymccormack/top-11-house-pricing-model-and-eda,House Prices - Advanced Regression Techniques
11860580,0.12407,0,0,/aymen311/weightedaverged-regression,House Prices - Advanced Regression Techniques
9783977,0.14438,0,0,/abikshitnayak/advance-regression,House Prices - Advanced Regression Techniques
11986329,0.18365,2,6,/jayantawasthi/advanceregression-ml-used-to-find-missing-val,House Prices - Advanced Regression Techniques
12030378,0.142,3,4,/koussayabdouli/house-price-prediction-xgboost-model,House Prices - Advanced Regression Techniques
11987093,0.14429,0,5,/vitkov/just-try-to-solve,House Prices - Advanced Regression Techniques
11595990,0.12232,2,6,/infof4221wang/houseprice-optuna-framework-blending-model,House Prices - Advanced Regression Techniques
802756,0.9582,7,10,/gowrishankarin/rnn-gru-4-layers-modified-nn-arch-of-kireev,TalkingData AdTracking Fraud Detection Challenge
715720,0.921,0,0,/robinchao/try-xgboosting,TalkingData AdTracking Fraud Detection Challenge
710328,0.9161,4,27,/vchoubey/random-forest-mean-encoding-for-app-channel,TalkingData AdTracking Fraud Detection Challenge
707959,0.8931,2,12,/shujian/rf-starter,TalkingData AdTracking Fraud Detection Challenge
948977,0.9579,0,0,/raulguarini/timed-rf,TalkingData AdTracking Fraud Detection Challenge
823668,0.9214,0,0,/dspider/talkingdata-first-shot-with-xgboost,TalkingData AdTracking Fraud Detection Challenge
721950,0.9256,0,0,/bytestorm/mean-field-approximation-of-joint-dist,TalkingData AdTracking Fraud Detection Challenge
708397,0.5,0,0,/yufengdata/dirty-xgboost-sample-balancing,TalkingData AdTracking Fraud Detection Challenge
27155,26.28993,0,1,/xdurana/knn-simple,San Francisco Crime Classification
14075448,0.76,0,0,/avaniv/riiid-threefields-noai,Riiid Answer Correctness Prediction
13897299,0.703,4,11,/mrbeancoder/saint-implemented-in-keras,Riiid Answer Correctness Prediction
13415272,0.767,0,0,/zekunn/ensemble-riiid-submit,Riiid Answer Correctness Prediction
13094448,0.765,0,0,/tongx93/submission,Riiid Answer Correctness Prediction
13475686,0.763,0,12,/sirano1004/feature-engineering-lgbm-dkt-saks-saint,Riiid Answer Correctness Prediction
13826394,0.782,1,2,/zekun98/blend-another,Riiid Answer Correctness Prediction
13402350,0.752,0,0,/zefirchik/nn-version-1,Riiid Answer Correctness Prediction
13794880,0.78,31,41,/zyy2016/0-780-lgbm-0-780,Riiid Answer Correctness Prediction
13316074,0.746,0,0,/zekun98/riid-modeling-cv,Riiid Answer Correctness Prediction
13560308,0.77,0,0,/zekun98/tuneof-sakt-with-randomization-state-updates,Riiid Answer Correctness Prediction
13773427,0.774,0,7,/satorushibata/riiid-optimized-lightgbm-with-optuna,Riiid Answer Correctness Prediction
13534112,0.7709999999999999,0,0,/zekun98/sakt-with-randomization-state-updates,Riiid Answer Correctness Prediction
13767785,0.413,1,0,/rohithansdah/riiid-lgbm-classifier-bagging-ensemble-learning,Riiid Answer Correctness Prediction
13690560,0.7709999999999999,0,15,/scaomath/riiid-is-there-a-magic-for-sakt,Riiid Answer Correctness Prediction
13644985,0.7559999999999999,0,4,/tchaye59/riiid-work-with-the-full-state-using-sqlalchemy,Riiid Answer Correctness Prediction
12493641,0.741,0,0,/niaibrahim/naive-bayes-algorithm-group-1a,Riiid Answer Correctness Prediction
13163483,0.7490000000000001,0,0,/niaibrahim/cnn-more-layers,Riiid Answer Correctness Prediction
11331672,0.9203,1,8,/doncalculator/tensorflow-vs-pytorch-part-1-tensorflow,SIIM-ISIC Melanoma Classification
11046365,0.9346,0,0,/debashissanyal/siim-tf2-augmentation-with-tfa-effnets,SIIM-ISIC Melanoma Classification
11258063,0.9385,7,35,/vladimirsydor/solution-private-score-0-9498-not-selected,SIIM-ISIC Melanoma Classification
11264164,0.954,2,8,/gopidurgaprasad/optimize-auc-using-oof,SIIM-ISIC Melanoma Classification
10840632,0.9618,2,14,/sheriytm/melanoma-clf-xgb-missed-opportunity-4th-0-9482,SIIM-ISIC Melanoma Classification
10703675,0.9547,0,7,/aziz69/how-to-pseudo-labelling-create-tf-records,SIIM-ISIC Melanoma Classification
11247371,0.9507,0,6,/itsuki9180/private-0-9406-bfl-ganaug,SIIM-ISIC Melanoma Classification
11249366,0.8983,0,2,/samklein/learning-from-multiple-embeddings,SIIM-ISIC Melanoma Classification
10992366,0.9654,5,3,/redwankarimsony/siim-scic-melanoma-classification-ensemble,SIIM-ISIC Melanoma Classification
11228405,0.6544,0,1,/ilosvigil/lgb-only-metadata-public-private-0-6524-0-7156,SIIM-ISIC Melanoma Classification
10677452,0.9469,0,0,/ziliwang/stack-models,SIIM-ISIC Melanoma Classification
10829037,0.8,0,0,/estau2020/melanoma-elihu-resnet,SIIM-ISIC Melanoma Classification
11173875,0.895,0,4,/shaitender/pytorch-efficientnet,SIIM-ISIC Melanoma Classification
11097596,0.9474,0,0,/foodaholic/efficientnetb6-with-metadata-2019-2020,SIIM-ISIC Melanoma Classification
11062419,0.9519,0,0,/shikha130vv/amazingly-fast-kernel-0-9402,SIIM-ISIC Melanoma Classification
11171241,0.8601,0,2,/fuyixing/melanoma-tpu-keras-tuner-no-tabular-data,SIIM-ISIC Melanoma Classification
11233699,0.6012,0,2,/aman2000jaiswal/notebook669a50d2bb,SIIM-ISIC Melanoma Classification
11083399,0.8203,0,1,/praveen648/siim-isic-densenet,SIIM-ISIC Melanoma Classification
11151221,0.9183,1,2,/wessam611/siim-melanoma-classification,SIIM-ISIC Melanoma Classification
11182913,0.8643,0,0,/krisho007/fork-of-melanoma-with-pylightning-256-b0,SIIM-ISIC Melanoma Classification
11517805,363.567,0,8,/fnands/the-most-basic-of-baselines,Lyft Motion Prediction for Autonomous Vehicles
11440379,187.179,0,12,/tuckerarrants/lyft-inference-resnet18,Lyft Motion Prediction for Autonomous Vehicles
11421398,62.503,11,32,/paulorzp/multi-mode-models-ensemble,Lyft Motion Prediction for Autonomous Vehicles
11409960,134.091,5,38,/corochann/save-your-time-submit-without-kernel-inference,Lyft Motion Prediction for Autonomous Vehicles
11364244,113.26,34,121,/kool777/lyft-level5-eda-training-inference,Lyft Motion Prediction for Autonomous Vehicles
13495162,897.288,0,0,/logaritm/submission-pixel-0-25-raster-122,Lyft Motion Prediction for Autonomous Vehicles
12970719,23.087,0,0,/akashsuper2000/lyft-submission-kernel,Lyft Motion Prediction for Autonomous Vehicles
12738913,46.18899999999999,0,0,/tusharjain1003/combining-lyft-multimode-models,Lyft Motion Prediction for Autonomous Vehicles
12001365,46.18899999999999,0,0,/akashsuper2000/combining-lyft-multimode-models,Lyft Motion Prediction for Autonomous Vehicles
11682322,46.18899999999999,0,0,/ssaisuryateja/combining-lyft-multimode-models,Lyft Motion Prediction for Autonomous Vehicles
5376761,0.0,3,14,/naivelamb/process-test-images-parallelly,APTOS 2019 Blindness Detection
5305047,0.008,12,65,/dataraj/apotosrf1,APTOS 2019 Blindness Detection
5364705,0.777,0,8,/jian1201/eye-inference-num-class-1-ver3,APTOS 2019 Blindness Detection
5355364,0.0,0,1,/noth99y/first-submission-resnet50,APTOS 2019 Blindness Detection
5240720,0.774252,108,234,/carlolepelaars/efficientnetb5-with-keras-aptos-2019,APTOS 2019 Blindness Detection
4626458,0.67,0,0,/sunilsj99/diabetic-retinopathy-resnet50,APTOS 2019 Blindness Detection
5241106,0.7240000000000001,0,2,/jayasoo/keras-densenet121-tta,APTOS 2019 Blindness Detection
5190500,0.695,1,2,/phantomakame/mobilenet-and-vgg16-keras-lrfinder,APTOS 2019 Blindness Detection
4642835,0.63075,2,2,/necrobs/starter-code-0-5-with-resnet-50-keras,APTOS 2019 Blindness Detection
5163173,0.0,1,0,/nanditab35/vgg-3,APTOS 2019 Blindness Detection
5100723,0.738,0,0,/daisuke0209/densenet-random-forest-keras,APTOS 2019 Blindness Detection
5176853,0.0,0,0,/nanditab35/vgg19-with-under-sampling,APTOS 2019 Blindness Detection
5037576,0.622,0,2,/samarthsarin/resnet-50-pytorch,APTOS 2019 Blindness Detection
4973092,0.563,0,5,/mistag/image-classification-with-tensorflow-estimator-api,APTOS 2019 Blindness Detection
4940037,0.7509999999999999,0,4,/amardeepganguly/fork-of-dense161-with-mixup-8e434e,APTOS 2019 Blindness Detection
5073460,0.406,0,1,/chopinforest/mobilenet-olddata-20190731,APTOS 2019 Blindness Detection
4814952,0.736,0,8,/rushabhvasani24/resnet101-using-only-this-competition-s-data,APTOS 2019 Blindness Detection
5083507,0.7240000000000001,0,0,/hirate12345/densenet-keras-wj,APTOS 2019 Blindness Detection
4908933,0.794894,11,39,/muhakabartay/can-boost-to-0-798-with-starter-kernel-for-0-79,APTOS 2019 Blindness Detection
5012536,0.532,0,0,/marcogorelli/from-pytorch-transfer-learning-tutorial,APTOS 2019 Blindness Detection
4939160,0.705,1,0,/akashshastri/solutions-fastai,APTOS 2019 Blindness Detection
4971731,0.236,0,0,/smartsn123/aptos-submission-v1,APTOS 2019 Blindness Detection
4830213,0.695,4,9,/aminyakubu/aptos-2019-blindness-detection-fast-ai,APTOS 2019 Blindness Detection
4968269,0.281,0,0,/debangshu16/eda-convnet-classification,APTOS 2019 Blindness Detection
1954598,0.0272,3,9,/bengwalt/pubg-exploration-and-lightgbm,PUBG Finish Placement Prediction (Kernels Only)
2011375,0.0605,1,8,/fadhli/pubg-fast-ai-approach,PUBG Finish Placement Prediction (Kernels Only)
2015806,0.0683,0,0,/rubakdabbas/pubg-finish-placement-prediction-eda,PUBG Finish Placement Prediction (Kernels Only)
2011514,0.0491,0,1,/benshentist/pubg-lightgbm-xentropy,PUBG Finish Placement Prediction (Kernels Only)
1996289,0.0209,24,19,/harshitsheoran/mlp-and-fe,PUBG Finish Placement Prediction (Kernels Only)
1827190,0.065,0,0,/itstaredback/pubg-mlpregressor,PUBG Finish Placement Prediction (Kernels Only)
1976338,0.0587,0,1,/drsenri/randomforest-baseline,PUBG Finish Placement Prediction (Kernels Only)
1826500,0.1633,0,0,/ottpeterr/winner-winner-machine-learning-dinner-xgboost,PUBG Finish Placement Prediction (Kernels Only)
1929522,0.0763,0,0,/drpeolo/main-pol-2,PUBG Finish Placement Prediction (Kernels Only)
1919196,0.0562,0,0,/jagratkhandelwal/first-kernel,PUBG Finish Placement Prediction (Kernels Only)
1930044,0.0889,0,3,/mathfour/pubg-linear-reg-on-5-features-rf,PUBG Finish Placement Prediction (Kernels Only)
1923502,0.0914,0,0,/tienham/pubg-train,PUBG Finish Placement Prediction (Kernels Only)
1929805,0.1252,11,11,/mathfour/pubg-prediction-with-one-feature,PUBG Finish Placement Prediction (Kernels Only)
1925669,0.0865,0,0,/banivyom/eda-and-lightgbm,PUBG Finish Placement Prediction (Kernels Only)
1921141,0.3908,0,0,/alesandrus/constaant-prediction-loco,PUBG Finish Placement Prediction (Kernels Only)
1861245,0.0318,0,6,/pavelvpster/pubg-random-forest-baseline,PUBG Finish Placement Prediction (Kernels Only)
1888256,0.0883,0,0,/kasmithh/baseline-pubg-competition-model,PUBG Finish Placement Prediction (Kernels Only)
1893870,0.0456,3,2,/tarunpaparaju/pubg-placement-prediction-nn-regression,PUBG Finish Placement Prediction (Kernels Only)
1874164,0.0833,0,2,/tandonarpit6/pubg-game-winner-predictions,PUBG Finish Placement Prediction (Kernels Only)
1826829,0.0614,0,0,/deltanullnull/neural-chicken-dinner-network,PUBG Finish Placement Prediction (Kernels Only)
1821308,0.0408,0,2,/nicapotato/playerunknown-s-ai,PUBG Finish Placement Prediction (Kernels Only)
1879102,0.0571,0,0,/dhavaltaunk/xgboost-wins-pubg,PUBG Finish Placement Prediction (Kernels Only)
1874384,0.0649,0,0,/beta4ever/pubg-finish-placement-prediction,PUBG Finish Placement Prediction (Kernels Only)
1848924,0.3015,0,0,/ankurcricket109/kernel4b79d72062,PUBG Finish Placement Prediction (Kernels Only)
1857252,0.0376,0,3,/afgonczol/feat-engineering-player-insights,PUBG Finish Placement Prediction (Kernels Only)
173454,2.62004,0,0,/ngarciasantos/rookie-applies-pca-and-analyses-4-models,Leaf Classification
165414,1.45365,0,0,/mitchfrump/leaf-classification,Leaf Classification
10783247,0.48701,0,0,/fayrouzfarra/leaf-classification-ranodmforest-gridsearch,Leaf Classification
10324364,0.73246,0,0,/ritvik29/prophet-univariate-final-submission,M5 Forecasting - Accuracy
10368580,1.08216,0,4,/mahmudds/m5-forecasting-accuracy-analysis-models,M5 Forecasting - Accuracy
10384536,1.06108,0,0,/leonzz/light-gbm,M5 Forecasting - Accuracy
10349309,0.0,1,10,/shaitender/light-gbm-m5-forecasting-accuracy-forcasting,M5 Forecasting - Accuracy
9945796,0.0,0,17,/chandrimad31/m5-forecasting-moving-avg-lightgbm-with-hyperopt,M5 Forecasting - Accuracy
8837820,0.4887399999999999,0,0,/akashsuper2000/m5-forecast-accuracy,M5 Forecasting - Accuracy
9817381,0.75193,0,2,/tchaye59/m5-acc-lstm-model,M5 Forecasting - Accuracy
9733710,0.58643,0,0,/hingencity/mlip-daemencloudt-lightgbm-notebook-wrmsse,M5 Forecasting - Accuracy
9731213,0.5745100000000001,0,0,/lvmoos/catboost1,M5 Forecasting - Accuracy
10017443,0.5542600000000001,0,0,/fbergh/category-models-evaluation-submission,M5 Forecasting - Accuracy
9993690,2.46041,0,0,/cyrilromain/final-clg,M5 Forecasting - Accuracy
11886260,0.2522,19,90,/nasirkhalid24/cnn-transformer-enc-rnn-feature-eng-data-aug,OpenVaccine: COVID-19 mRNA Vaccine Degradation Prediction
11761348,0.28381,4,6,/abhinaythurlapati/reaching-the-rna,OpenVaccine: COVID-19 mRNA Vaccine Degradation Prediction
11853613,0.3014699999999999,0,10,/prasunmishra/openvaccine-simple-lgb-baseline-additional-feature,OpenVaccine: COVID-19 mRNA Vaccine Degradation Prediction
11847648,0.32204,0,3,/fernandoramacciotti/lgbm-bpp-features,OpenVaccine: COVID-19 mRNA Vaccine Degradation Prediction
11837456,0.29354,0,4,/code1110/openvaccine-mlp-regression-with-bpps,OpenVaccine: COVID-19 mRNA Vaccine Degradation Prediction
11794907,0.2581699999999999,7,66,/vbmokin/gru-lstm-mix-custom-loss-tuning-by-3d-visual,OpenVaccine: COVID-19 mRNA Vaccine Degradation Prediction
11754552,0.25847,9,32,/itsuki9180/mvan-covid-mrna-vaccine-analysis-notebook-268,OpenVaccine: COVID-19 mRNA Vaccine Degradation Prediction
11714774,0.27087,1,24,/xhlulu/openvaccine-gru-with-keras-tuner,OpenVaccine: COVID-19 mRNA Vaccine Degradation Prediction
11730603,0.26311,37,131,/aestheteaman01/mvan-covid-mrna-vaccine-analysis-notebook-268,OpenVaccine: COVID-19 mRNA Vaccine Degradation Prediction
11699371,0.26272,10,31,/thedrcat/openvaccine-lstm-fastai,OpenVaccine: COVID-19 mRNA Vaccine Degradation Prediction
11737090,0.26709,1,6,/anzhemeng/openvaccine-gru-lstm-with-custom-loss-function,OpenVaccine: COVID-19 mRNA Vaccine Degradation Prediction
11755699,0.47755,2,4,/santiviquez/openvaccine-linear-regression-kfold,OpenVaccine: COVID-19 mRNA Vaccine Degradation Prediction
11712752,0.25729,9,61,/albernard/openvaccine-gru-lstm-gkf-augs,OpenVaccine: COVID-19 mRNA Vaccine Degradation Prediction
11729224,0.26919,5,10,/ajaykumar7778/openvaccine-gru-lstm-noiselevel-7352f3,OpenVaccine: COVID-19 mRNA Vaccine Degradation Prediction
11720834,0.27049,6,12,/alicia183/openvaccine-ensemble,OpenVaccine: COVID-19 mRNA Vaccine Degradation Prediction
9054433,0.36,1,26,/super13579/panda-classifier-starter-pytorch-eff-b0,Prostate cANcer graDe Assessment (PANDA) Challenge
9054304,0.0,0,5,/grapestone5321/panda-challenge-sample-submission,Prostate cANcer graDe Assessment (PANDA) Challenge
11955994,0.79028,0,0,/eddiefroufrou/arutema-base-model-w-logits-a-s-36x128x128,Prostate cANcer graDe Assessment (PANDA) Challenge
10718910,0.875,0,0,/digvijayyadav/panda-inference-w-36-tiles-256,Prostate cANcer graDe Assessment (PANDA) Challenge
10544334,0.85,0,0,/akashsuper2000/pandas-42x256x256x3-inference,Prostate cANcer graDe Assessment (PANDA) Challenge
10265201,0.87,0,0,/takashinemoto/panda-inference-w-36-tiles-256,Prostate cANcer graDe Assessment (PANDA) Challenge
14343875,0.5431199999999999,0,4,/saadsebai/group-n-13-quora,Quora Insincere Questions Classification
13188140,0.57429,1,3,/shishirkumar/fasttext-classification,Quora Insincere Questions Classification
12952286,0.4702699999999999,0,0,/demetreuridia/notebookbf48eaf2f1,Quora Insincere Questions Classification
2309757,0.691,0,0,/fredma/glove-quora-insincere-model,Quora Insincere Questions Classification
11281928,0.37161,0,0,/rohitr4307/quora,Quora Insincere Questions Classification
10503430,0.58306,0,2,/raj26000/question-classification-randomizedsearchcv-used,Quora Insincere Questions Classification
10456546,0.57649,0,0,/vermadev54/kernel4214c6837b,Quora Insincere Questions Classification
2335640,0.52,0,0,/sid2073/quora-kaggle-competition,Quora Insincere Questions Classification
9549396,0.6661100000000001,0,0,/prashanthbodduna/train-with-tensorflow,Quora Insincere Questions Classification
9517992,0.46665,0,0,/halftru/kernel5cd708ea59,Quora Insincere Questions Classification
9323565,0.7079300000000001,0,0,/siriusz/kernel457249d274,Quora Insincere Questions Classification
2757077,0.7,0,2,/someadityamandal/classifying-quora-questions-using-pytorch,Quora Insincere Questions Classification
2727475,0.672,0,1,/mitya8128/gru-capsule,Quora Insincere Questions Classification
2320456,0.69,0,0,/fesenkod/quora-3,Quora Insincere Questions Classification
7716285,0.53905,1,0,/mohammadfakhrani/kernel28fccaf313,Quora Insincere Questions Classification
5466021,0.68488,0,0,/vinaydoshi/text-preprocessing-load-embeddings-and-cnn-lstm,Quora Insincere Questions Classification
9023347,0.97132,0,1,/cristianfat/looking-for-covid,COVID19 Global Forecasting (Week 4)
10716550,0.80318,0,0,/luyoucong/rf-covid19-week4,COVID19 Global Forecasting (Week 4)
10558251,0.04238,0,0,/aakashveera/polynomial-regression,COVID19 Global Forecasting (Week 4)
9513603,4.04392,0,0,/pradeepkumarrajkumar/m1-linear-djp,COVID19 Global Forecasting (Week 4)
8930445,2.98437,0,0,/dennischhun/covid19-week-4-beginner-machine-learning-project,COVID19 Global Forecasting (Week 4)
9233252,0.74695,0,0,/gorewa/covid-19-predictions-xgboost,COVID19 Global Forecasting (Week 4)
9226048,0.03034,0,0,/kantapongv/covid19-forecasting,COVID19 Global Forecasting (Week 4)
8895554,0.18189,0,0,/robikscube/robs-covid19-week-4-blender,COVID19 Global Forecasting (Week 4)
9150155,0.58549,0,0,/gauss1809/predicting-covid19-using-vars-vector-autoreg,COVID19 Global Forecasting (Week 4)
9229603,2.26247,1,1,/kensakamoto/kernel39fff6669c,COVID19 Global Forecasting (Week 4)
9152913,0.68777,0,1,/domchanjl/nowsave,COVID19 Global Forecasting (Week 4)
9206678,0.8770899999999999,0,0,/kusanag1/kernel1fb1c2c16a,COVID19 Global Forecasting (Week 4)
9088450,0.01135,0,1,/shunsuke218/covid19-global-forecasting,COVID19 Global Forecasting (Week 4)
2038796,0.579,3,26,/peterhurford/lgb-baseline,Quora Insincere Questions Classification
2040731,0.5539999999999999,2,8,/milesh1/simple-logistic-regression-model-beating-baseline,Quora Insincere Questions Classification
2039034,0.4029999999999999,1,9,/coronate/where-to-start,Quora Insincere Questions Classification
7518582,0.64482,0,0,/miracle0/quora-insincere-question-classification,Quora Insincere Questions Classification
6853582,0.0,0,0,/mathiazhagan/cycliclr-and-k-fold,Quora Insincere Questions Classification
5875301,0.7055,0,0,/therabiulawal/insincere-approach-3-1,Quora Insincere Questions Classification
4765455,0.66704,0,0,/athiats/ptresvvc,Quora Insincere Questions Classification
3972994,0.53465,0,0,/gurucharanreddy/quora-gurucharan,Quora Insincere Questions Classification
3297342,0.51469,0,0,/darapanenihari/tm-hyd-mar11,Quora Insincere Questions Classification
3026530,0.6107600000000001,0,0,/amokrane/quora-classification-with-logistic-regression,Quora Insincere Questions Classification
2839595,0.6,0,0,/shivamkushwaha/cnn-with-tanh-and-sigmoid,Quora Insincere Questions Classification
2725387,0.7,0,0,/qhd0081/qhd-qiqc-final,Quora Insincere Questions Classification
2636940,0.68,0,0,/jmourad100/fork-of-nlp-text-analytics-quora-insincere-quest,Quora Insincere Questions Classification
2553016,0.685,0,0,/splacorn/better-embeddings,Quora Insincere Questions Classification
2402261,0.657,0,0,/xsakix/cnn-base-classifier-all-emb-vall-acc,Quora Insincere Questions Classification
2325923,0.637,0,0,/taohoang/simple-lstm-that-does-the-job,Quora Insincere Questions Classification
2197085,0.183,0,0,/ivtsygankov/fork-of-fork-of-try1-for-toxic,Quora Insincere Questions Classification
8098224,0.00112,0,2,/darwinwin/cellular-stacking-1-5,Recursion Cellular Image Classification
5937926,0.943,9,54,/hmendonca/fold1h4r3-arcenetb4-2-256px-rcic-lb-0-9759,Recursion Cellular Image Classification
5714638,0.129,0,17,/christopherberner/hungarian-algorithm-to-optimize-sirna-prediction,Recursion Cellular Image Classification
5612997,0.046,0,1,/christianwallenwein/beginners-guide-to-rcic-with-fast-ai,Recursion Cellular Image Classification
5178314,0.129,2,22,/bonhart/eda-efficientnet-creating-video-pytorch,Recursion Cellular Image Classification
5073160,0.174,0,13,/antgoldbloom/doing-inference-using-google-automl,Recursion Cellular Image Classification
4820812,0.141,7,25,/chandyalex/recursion-cellular-keras-densenet,Recursion Cellular Image Classification
5095790,0.181,0,0,/rivesunder/recursion-2-headed-cnn-and-training-in-2-stages,Recursion Cellular Image Classification
2089085,0.617,0,4,/noklamchan/starter-classic-linear-model-nb-logistic-3mins,Quora Insincere Questions Classification
2109659,0.6729999999999999,0,2,/nikhilroxtomar/single-model-with-embeddings,Quora Insincere Questions Classification
2080023,0.639,0,5,/bhasha4995dushara/quora-insincere-questions-classification-beginner,Quora Insincere Questions Classification
2089495,0.544,0,5,/hoangpham51/getting-start-with-nltk,Quora Insincere Questions Classification
2051250,0.66,6,5,/alber8295/bigru-w-attention-visualized-for-beginners,Quora Insincere Questions Classification
2092890,0.5429999999999999,0,3,/alecthekulak/quora-insincere-questions-basic-models,Quora Insincere Questions Classification
2075204,0.674,6,35,/satian/a-look-at-different-embeddings-with-attention,Quora Insincere Questions Classification
2087928,0.624,0,0,/jackiewu/text-cnn,Quora Insincere Questions Classification
2086568,0.5770000000000001,0,0,/zukangy/quora-insincere-questions-classification,Quora Insincere Questions Classification
2075691,0.6729999999999999,0,4,/nikhilroxtomar/playing-with-embeddings-using-lstm-and-cnn,Quora Insincere Questions Classification
2063644,0.67,1,13,/youhanlee/avengers,Quora Insincere Questions Classification
2077093,0.61,0,1,/argonalyst/simple-and-fast-approach-lb-0-61,Quora Insincere Questions Classification
2038582,0.639,11,65,/nicapotato/eli5-shap-lgbm-lr-interpretable-ml,Quora Insincere Questions Classification
2050466,0.60798,46,97,/frtgnn/beginner-s-stop-to-text-data-an-introduction,Quora Insincere Questions Classification
2050522,0.621,2,13,/peterhurford/applying-mercari-state-of-the-art-mlp-to-quora,Quora Insincere Questions Classification
2038729,0.601,11,104,/tunguz/just-some-simple-eda,Quora Insincere Questions Classification
2058185,0.289,0,0,/mohanrao/all-models-are-wrong-some-are-ok,Quora Insincere Questions Classification
2044295,0.62,2,13,/hippskill/vowpal-wabbit-starter-pack,Quora Insincere Questions Classification
2051244,0.147,0,2,/abosol/first-observations,Quora Insincere Questions Classification
2050091,0.3,0,2,/tanreinama/finding-negative-words,Quora Insincere Questions Classification
2042113,0.604,0,10,/mlisovyi/vowpal-wabbit-linear-baseline,Quora Insincere Questions Classification
4486569,0.0,0,0,/neonninja/fast-mfcc-rf-golf,Freesound Audio Tagging 2019
11664051,0.4752,35,160,/robikscube/openvaccine-covid-19-mrna-starter-eda,OpenVaccine: COVID-19 mRNA Vaccine Degradation Prediction
11683196,0.35614,1,18,/nxrprime/train-infer-catalyst-pytorch-rnn-baseline,OpenVaccine: COVID-19 mRNA Vaccine Degradation Prediction
11666458,0.37976,3,39,/matthewmasters/pytorch-nn-starter-baseline-inference-0-380,OpenVaccine: COVID-19 mRNA Vaccine Degradation Prediction
11673196,0.43216,0,13,/amiiiney/covid-19-mrna-vaccine-eda-baseline,OpenVaccine: COVID-19 mRNA Vaccine Degradation Prediction
11663981,0.64846,4,13,/sajikim/baseline-vaccine-degradation-prediction,OpenVaccine: COVID-19 mRNA Vaccine Degradation Prediction
12121494,0.25515,0,0,/sishihara/inference-of-data-aug43k-of-covid-ae,OpenVaccine: COVID-19 mRNA Vaccine Degradation Prediction
11951497,0.24673,0,0,/sishihara/covid-ae-pretrain-gnn-attn-cnn,OpenVaccine: COVID-19 mRNA Vaccine Degradation Prediction
9675262,1.32246,0,4,/patrykradon/m5-forecasting-accuracy,M5 Forecasting - Accuracy
9975682,1.0765,3,9,/mpware/n-beats-basics,M5 Forecasting - Accuracy
9638506,0.64157,0,0,/rodrigoignacioperez/m5-forecast-keras-with-categorical-embeddings-v2,M5 Forecasting - Accuracy
10012999,1.36127,0,0,/pkulczak/m5-forecasting-accuracy,M5 Forecasting - Accuracy
9600010,0.45863,23,79,/marutama/m5-dark-witch-time-by-store,M5 Forecasting - Accuracy
9743998,0.71696,3,20,/christoffer/pandas-multi-indices-for-hts-fast-loading-etc,M5 Forecasting - Accuracy
9549303,0.0,161,411,/anshuls235/time-series-forecasting-eda-fe-modelling,M5 Forecasting - Accuracy
9435067,0.46193,51,157,/kyakovlev/m5-witch-time,M5 Forecasting - Accuracy
9489026,0.8645799999999999,0,0,/amansanghvi/walmart-sales-aman,M5 Forecasting - Accuracy
196462,1.24582,0,0,/momentaj/10-classifier-showdown-in-scikit-learn,Leaf Classification
4933156,0.261,0,4,/kmader/attention-lr-inceptionv3-for-blindness,APTOS 2019 Blindness Detection
4930836,0.687,1,1,/virajbagal/densenet-aptos-cropped,APTOS 2019 Blindness Detection
4687586,0.6609999999999999,0,0,/ge5288/kernel6b88de7674,APTOS 2019 Blindness Detection
4862932,0.7959999999999999,16,89,/ahoukang/aptos-vote,APTOS 2019 Blindness Detection
4863369,0.757,4,4,/rabbitcaptain/kernelb5d71b3d06,APTOS 2019 Blindness Detection
4845852,0.701,12,18,/esmaeil391/fast-ai-densenet201,APTOS 2019 Blindness Detection
4827779,0.655,0,1,/generalerror/aptos-inception-v3-training-submission-demo,APTOS 2019 Blindness Detection
4801453,0.601,0,3,/davidtsaturyan/simple-pytorch-solution,APTOS 2019 Blindness Detection
4796150,0.726,4,12,/amardeepganguly/resnet-fast-ai-starter,APTOS 2019 Blindness Detection
4742561,0.703,15,117,/ratthachat/aptos-augmentation-visualize-diabetic-retinopathy,APTOS 2019 Blindness Detection
4601855,0.146,0,0,/k4ni5h/cnn-model,APTOS 2019 Blindness Detection
4703239,0.659,4,5,/jayasoo/baseline-keras-with-circle-crop-and-augmentation,APTOS 2019 Blindness Detection
4642247,0.6809999999999999,1,2,/nivedas/densenet121-fully-trained-network,APTOS 2019 Blindness Detection
4698354,0.691,0,2,/yangfan556677/submit-resnet152,APTOS 2019 Blindness Detection
4657175,0.69,6,11,/aritrase/aptos-pytorch-with-cohenkappaforearlystopping,APTOS 2019 Blindness Detection
2347884,0.0206,0,1,/henupark/henu-park-version-lightgbm-baseline,PUBG Finish Placement Prediction (Kernels Only)
1848935,0.1007,0,0,/louis030195/pubg-placement,PUBG Finish Placement Prediction (Kernels Only)
2295345,0.0598,0,0,/praneethvarmaalluri/main-model,PUBG Finish Placement Prediction (Kernels Only)
2324414,0.0572,0,0,/cvelas11/kernel0a7af6e49d,PUBG Finish Placement Prediction (Kernels Only)
2286679,0.064,0,0,/dsuper/pubg-test,PUBG Finish Placement Prediction (Kernels Only)
2137822,0.0691,1,2,/hariprakashamk/pubg-eda-vif-gradient-boost-tuning,PUBG Finish Placement Prediction (Kernels Only)
2205653,0.0694,0,0,/cuberti/pubg-evaluation-python,PUBG Finish Placement Prediction (Kernels Only)
2070430,0.0884,0,0,/yipcyj/pubg-eda,PUBG Finish Placement Prediction (Kernels Only)
1882441,0.0649,0,0,/dhavaltaunk/pubg-win-by-neural-networks,PUBG Finish Placement Prediction (Kernels Only)
2159304,0.0517,0,0,/neelkamal692/my-approach,PUBG Finish Placement Prediction (Kernels Only)
2188015,0.0564,0,1,/snakayama/pubg-eda-lightgbm-v1,PUBG Finish Placement Prediction (Kernels Only)
2184337,0.0678,0,3,/beaubellamy/pubg-predictions,PUBG Finish Placement Prediction (Kernels Only)
2170980,0.072,0,5,/abusiddik/pubg-randomforest,PUBG Finish Placement Prediction (Kernels Only)
2193503,0.3199,0,0,/beaubellamy/pubg-predictions-random-forest,PUBG Finish Placement Prediction (Kernels Only)
1970844,0.0203,29,189,/plasticgrammer/pubg-finish-placement-prediction-playground,PUBG Finish Placement Prediction (Kernels Only)
2153109,0.0587,0,1,/teemingyi/pubg-my-first-lightgbm-submission,PUBG Finish Placement Prediction (Kernels Only)
2149871,0.0683,0,0,/ayushsaklani/pubg-kernel,PUBG Finish Placement Prediction (Kernels Only)
2150116,0.0486,0,0,/sachinjchorge/pubg-light-gbm-part-2,PUBG Finish Placement Prediction (Kernels Only)
1865184,0.0556,0,0,/bigkirill/pubg-prediction,PUBG Finish Placement Prediction (Kernels Only)
1938374,0.0205,28,63,/chocozzz/lightgbm-baseline,PUBG Finish Placement Prediction (Kernels Only)
2074607,0.0279,0,8,/nitinaggarwal008/rf-output-feature-eng,PUBG Finish Placement Prediction (Kernels Only)
2028876,0.0576,0,0,/ywleung/pubg-eda-rf-prediction,PUBG Finish Placement Prediction (Kernels Only)
1982139,0.0378,0,0,/pschale/treating-winplaceperc-as-a-categorical-variable,PUBG Finish Placement Prediction (Kernels Only)
14560914,0.8597,9,13,/saadbinmanjuradit/predict-future-sales-with-light-gbm-top-1,Predict Future Sales
14600896,1.01911,0,0,/emorkrin/ml-intensive,Predict Future Sales
14168225,1.17956,0,0,/abdullahcantekin/171307006-abdullahcantekin,Predict Future Sales
13275093,1.7334900000000002,0,8,/aeryss/predict-future-sales-training-inference,Predict Future Sales
14149820,1.33489,0,0,/bulentay/161307011-yusufb-lentay,Predict Future Sales
13919052,1.04368,0,0,/zeynepnilsuergin/171307041-zeynepnilsuergin,Predict Future Sales
13720678,1.04348,0,0,/major1991/predict-future-sales-major1991,Predict Future Sales
13038170,1.0359200000000002,0,0,/lmarcelactapasco/coursera-challenge-predicting-future-sales,Predict Future Sales
13208951,1.24239,0,0,/stephenyang0215/time-series-prediction,Predict Future Sales
3161334,0.96269,1,1,/wwu651/lightgbm-with-sklearn-interface,Predicting Red Hat Business Value
136284,0.831578,0,0,/sharmin/with0-0,Predicting Red Hat Business Value
13267545,0.759,0,0,/saijasthi/lightgbm-lr-025,Riiid Answer Correctness Prediction
13595353,0.775,35,62,/zephyrwang666/riiid-lgbm-bagging2-1,Riiid Answer Correctness Prediction
13498821,0.7809999999999999,26,185,/ammarnassanalhajali/riiid-lgbm-bagging2-sakt-0-781,Riiid Answer Correctness Prediction
13548986,0.743,0,1,/oym8012/lgb-xgb-cat-ensemble-baseline,Riiid Answer Correctness Prediction
13484703,0.78,33,139,/leadbest/sakt-riiid-lgbm-bagging2,Riiid Answer Correctness Prediction
13468542,0.773,53,258,/ragnar123/riiid-model-lgbm,Riiid Answer Correctness Prediction
13492614,0.772,3,37,/scaomath/riiid-sakt-baseline-minimal-inference,Riiid Answer Correctness Prediction
13419502,0.762,0,1,/bitcrunch/feature-engineering-predicitons-0-762,Riiid Answer Correctness Prediction
13449847,0.767,2,11,/gannonreynolds/fastai-tabular-with-state,Riiid Answer Correctness Prediction
13427206,0.515,0,2,/bws620/add-features-and-pre-process,Riiid Answer Correctness Prediction
13397938,0.76,0,13,/manikanthr5/riiid-lgbm-single-model-ensembling-training,Riiid Answer Correctness Prediction
13390483,0.745,0,3,/slungile/riiid-answer-correctness-prediction,Riiid Answer Correctness Prediction
13147434,0.5870000000000001,2,6,/itokianarafidinarivo/xgboost-riid-answer-prediction,Riiid Answer Correctness Prediction
12936105,0.732,1,4,/buin6319/riiidi,Riiid Answer Correctness Prediction
13217198,0.752,23,53,/mpware/sakt-fork,Riiid Answer Correctness Prediction
12977627,0.752,0,2,/vivektewari2000/modeltest,Riiid Answer Correctness Prediction
13188753,0.7559999999999999,0,4,/tsukasa0000/riiid-lgbm-optuna-rfe,Riiid Answer Correctness Prediction
13218264,0.738,2,6,/fatmakursun/riid-data-preparing-and-modelling,Riiid Answer Correctness Prediction
11098980,0.946,14,51,/ipythonx/optimizing-metrics-out-of-fold-weights-ensemble,SIIM-ISIC Melanoma Classification
11124992,0.8759999999999999,0,6,/zainahmad/stacked-ensemble-with-deep-learning-tf,SIIM-ISIC Melanoma Classification
11124327,0.5196,0,3,/sudhanshuraheja/siim-isic-dataset,SIIM-ISIC Melanoma Classification
11094146,0.931,21,43,/niteshx2/full-pipeline-dual-input-cnn-model-with-tpus,SIIM-ISIC Melanoma Classification
10207865,0.8784,1,7,/stephenfenel/torch-melanoma-gpu,SIIM-ISIC Melanoma Classification
10826715,0.9619,27,54,/paklau9/minmax-highest-public-lb-9619,SIIM-ISIC Melanoma Classification
10977987,0.7812,0,1,/olucasferreira/siim-isic-melanoma-ensemble-xgboost-effcentnet,SIIM-ISIC Melanoma Classification
10992615,0.8876,2,11,/shaitender/efficientnet-pytorch,SIIM-ISIC Melanoma Classification
11037982,0.9255,0,4,/tikoboss/test-augmentation,SIIM-ISIC Melanoma Classification
10988453,0.9507,6,70,/ajaykumar7778/efficientnet-cv,SIIM-ISIC Melanoma Classification
10796283,0.9237,0,1,/ajaykumar7778/melanoma-siim-isic-2020-fast-ai-efficientnetb2,SIIM-ISIC Melanoma Classification
10894905,0.9498,12,63,/rajnishe/rc-fork-siim-isic-melanoma-384x384,SIIM-ISIC Melanoma Classification
10955315,0.7273,4,19,/zainahmad/eda-melanoma-classification-using-tensorflow,SIIM-ISIC Melanoma Classification
10414411,0.9133,1,8,/sayantankarmakar/siim-isic-efficientnet-metadata-pytorch,SIIM-ISIC Melanoma Classification
10840546,0.8607,3,14,/sachin93/melanoma-cnn,SIIM-ISIC Melanoma Classification
10498708,0.935,0,0,/akashsuper2000/efficientnet-b6-on-tpus,SIIM-ISIC Melanoma Classification
10881012,0.7754,0,10,/shisham/transfer-learning-using-resnet50,SIIM-ISIC Melanoma Classification
10852958,0.9306,2,19,/foodaholic/effnet-0-6-with-tabular-data,SIIM-ISIC Melanoma Classification
10889906,0.8424,2,16,/zefirchik/tabular-data-0-8313,SIIM-ISIC Melanoma Classification
10862226,0.9475,3,26,/iwatatakuya/siim-isic-efficientnet-b6-single-model-lb-0-9475,SIIM-ISIC Melanoma Classification
10727899,0.8909,2,12,/miaadk/melanoma-classification-0-90lb-efficientnetb0-b5,SIIM-ISIC Melanoma Classification
947931,13.45556,0,0,/kullapat/naive-bayes-model-prediction,San Francisco Crime Classification
65720,27.62709000000001,0,0,/ymcdull/frank-second-test,San Francisco Crime Classification
3231118,125084774.87523998,0,0,/saayan/travelling-santa-splitting-with-theta,Traveling Santa 2018 - Prime Paths
2596731,1515561.63,10,38,/sharthz23/frunk-optimization,Traveling Santa 2018 - Prime Paths
2599926,1515584.45,1,3,/mpware/concorde-lkh-local-optimization-numba-9h,Traveling Santa 2018 - Prime Paths
2600038,1515558.42,0,1,/zfturbo/frunk-optimization,Traveling Santa 2018 - Prime Paths
2543459,1515572.48,23,147,/blacksix/dp-shuffle,Traveling Santa 2018 - Prime Paths
2534429,1516093.81,3,16,/ubarredo/k-opt-algorithm,Traveling Santa 2018 - Prime Paths
2482058,1515656.62,37,77,/kostyaatarik/shame-on-me,Traveling Santa 2018 - Prime Paths
2473820,1812550.5,0,3,/wizmik12/are-prime-cities-important-greedy,Traveling Santa 2018 - Prime Paths
2441429,1516774.51,2,3,/neodelphis/optimization-for-the-prime-twist,Traveling Santa 2018 - Prime Paths
2395191,446884407.52,0,0,/frkhit/traveling-santa-2018-prime-paths,Traveling Santa 2018 - Prime Paths
2320726,1516749.13,8,44,/blacksix/flip-n-roll-fast-python-scorer,Traveling Santa 2018 - Prime Paths
2313443,1642203.92,0,4,/lopezgd/farthest-insertion-tour-construction,Traveling Santa 2018 - Prime Paths
2272763,1516780.69,4,72,/jsaguiar/lkh-solver,Traveling Santa 2018 - Prime Paths
2298077,1524465.88,0,1,/southman/concorde-tsp-solver,Traveling Santa 2018 - Prime Paths
2246029,1810034.82,1,4,/dan3dewey/reindeer-rebellion-traveling-santa-2018,Traveling Santa 2018 - Prime Paths
2239396,196478811.25,0,0,/nickcanny/lost-santa,Traveling Santa 2018 - Prime Paths
2193213,1533472.89,2,21,/ultragamza/compute-cost-accelerator-by-tensorflow-gpu,Traveling Santa 2018 - Prime Paths
2165948,1533177.23,15,77,/andresionek/object-oriented-santa-s-route-concorde-solver,Traveling Santa 2018 - Prime Paths
3447212,0.923,2,6,/super13579/lgbm-with-duplicate-flag-value-0-923,Santander Customer Transaction Prediction
3506346,0.903,0,1,/nadare/catboost-with-gpu,Santander Customer Transaction Prediction
3261622,0.92,0,4,/super13579/lgbm-model-only,Santander Customer Transaction Prediction
3349757,0.909,1,3,/super13579/fork-of-fork-of-concate-nn-46ae3a,Santander Customer Transaction Prediction
3549535,0.8972100000000001,0,2,/mks2192/model-that-overfitted-the-data,Santander Customer Transaction Prediction
3257918,0.899,0,0,/djordjenedeljkovic/santander-customer,Santander Customer Transaction Prediction
3070337,0.897,0,0,/mayankkestwal10/santandar,Santander Customer Transaction Prediction
3525414,0.889,8,12,/elcaiseri/simple-gnb-and-pipeline-model-with-eda,Santander Customer Transaction Prediction
3531551,0.9,1,2,/tasnimfatima/lgbm-with-no-augmentation,Santander Customer Transaction Prediction
3383099,0.897,1,2,/darbin/lgb-with-some-pca-features,Santander Customer Transaction Prediction
3423729,0.8959999999999999,0,4,/silantev/santander-prediction-based-on-the-statistics,Santander Customer Transaction Prediction
3328186,0.901,4,8,/justlookinxd/baliz,Santander Customer Transaction Prediction
3427500,0.89,2,5,/praxitelisk/santander-customer-transaction-prediction-eda-ml,Santander Customer Transaction Prediction
3461050,0.893,6,9,/mommermi/resampling-gridsearch-lightgbm-magic,Santander Customer Transaction Prediction
3364676,0.9,0,1,/zeus75/santander-with-lgbm,Santander Customer Transaction Prediction
3464125,0.894,1,2,/m3yrin/santander-with-pfnet-autogbt,Santander Customer Transaction Prediction
3425944,0.9,1,3,/sandeepkumar121995/train-auc-0-930-valid-auc-0-917-lb-0-898,Santander Customer Transaction Prediction
3467227,0.633,0,0,/dpamgautam/santander-prediction,Santander Customer Transaction Prediction
3424779,0.89725,0,3,/bhaveshthaker/sctp-exploratory-data-analysis-eda-modeling,Santander Customer Transaction Prediction
3353538,0.898,0,8,/blackblitz/radius-neighbors-naive-bayes,Santander Customer Transaction Prediction
12039800,0.14156,0,3,/moussasacko/basic-notebook-for-beginner,House Prices - Advanced Regression Techniques
12413750,0.14662,0,1,/mayanklad/house-prices,House Prices - Advanced Regression Techniques
12424672,0.13979,0,0,/siriasadeddin/house-price,House Prices - Advanced Regression Techniques
12423468,9.45925,1,1,/ayesha0616/my-final-submission,House Prices - Advanced Regression Techniques
12398677,0.41502,0,0,/ayesha0616/hw-4-part-c,House Prices - Advanced Regression Techniques
12393612,0.1275599999999999,0,0,/johannesbruch/exploring-and-fitting-data-on-house-sales,House Prices - Advanced Regression Techniques
12364373,0.12392,2,2,/levnovitskiy/house-prices-feature-engineering-stacking,House Prices - Advanced Regression Techniques
12308263,0.41502,0,0,/ayesha0616/hw-4-for-inst414-part-b,House Prices - Advanced Regression Techniques
12362296,0.13771,0,1,/shuditkumar/notebook4814359f9b,House Prices - Advanced Regression Techniques
12273088,0.21172,0,0,/jmregs/house,House Prices - Advanced Regression Techniques
11688859,0.14661,0,6,/shrirangdixit/house-price-advance-regression-technique,House Prices - Advanced Regression Techniques
4366989,0.9999,3,4,/shrutimechlearn/learn-fast-ai-from-zero,Aerial Cactus Identification
4429660,0.9999,0,0,/theivaprakasham/fastai-resnet152-kernal,Aerial Cactus Identification
3809084,0.9948,0,6,/iluvmahheart/simple-beginner-cactus-identification,Aerial Cactus Identification
4382992,0.7403,0,1,/princetyagi193/cactus-recognition-cnn-with-keras,Aerial Cactus Identification
4355034,0.9999,0,2,/maliabbas/pretrainedresnet101fastai,Aerial Cactus Identification
4350430,0.9541,0,2,/mouri11/identify-columnar-cactus-using-deep-learning,Aerial Cactus Identification
4327671,0.9999,1,5,/phoenix9032/cactus-identification-with-keras-cnn-ensemble,Aerial Cactus Identification
4306469,0.497,0,3,/priteshshrivastava/cactus-identification-using-cnn-resnet34,Aerial Cactus Identification
4244177,0.9999,2,1,/bhabani2077/aerial-cactus-tensorflow-keras,Aerial Cactus Identification
4079813,0.9999,0,1,/anilcr/simple-cnn-augmentation,Aerial Cactus Identification
4205988,0.9984,0,2,/aplayer98/cactus-recognizer-with-predict-generator,Aerial Cactus Identification
4205107,0.9446,0,3,/niranjankumarc/aerialcactusclassification-pytorch-vgg-imagenet,Aerial Cactus Identification
4172324,0.9996,1,28,/arjunrao2000/beginners-guide-efficientnet-with-keras,Aerial Cactus Identification
4132554,0.9999,0,0,/aswinharitharan/aerial-cactus-inception,Aerial Cactus Identification
4006542,0.9981,0,0,/fycher/aerial-cactus-recognition-with-keras-and-vgg16,Aerial Cactus Identification
4059711,0.5,0,3,/vpkprasanna/aerial-cactus-identification,Aerial Cactus Identification
4111939,0.9925,0,0,/s3chwartz/aerial-cactus-identification,Aerial Cactus Identification
4031204,0.97,0,0,/rahool214/aerial-cactus-identification-keras-3c3d,Aerial Cactus Identification
4085137,0.9681,0,0,/divyanshshekhar/aerial-cactus-identification-by-cnn-tensorflow,Aerial Cactus Identification
3469285,0.9998,0,0,/labcoat/kernel4e7436a182,Aerial Cactus Identification
4040294,0.9999,0,6,/sarthakbatra/fastai-tutorial,Aerial Cactus Identification
3819362,0.9954,0,0,/vvijaybabu/2-class-classification-resnet-18-kernel,Aerial Cactus Identification
3890495,0.9989,0,1,/adityajn105/aerial-cactus-identification-0-9989-lb,Aerial Cactus Identification
3973533,0.9999,19,18,/umangjpatel/aerial-cactus-cnn,Aerial Cactus Identification
3957153,0.9695,0,0,/adityagarimella/aerial-cactus-identification-using-keras-cnn,Aerial Cactus Identification
3935716,0.9914,0,0,/danielmakc/cactus,Aerial Cactus Identification
3931478,0.9999,0,4,/gaurav2796/detailed-approach-with-fastai,Aerial Cactus Identification
3810767,0.9998,0,2,/grecs2001/cactus-identification-keras-cnn,Aerial Cactus Identification
3861382,0.9999,3,3,/iavinas/simple-fastai-aerial-cactus-identification,Aerial Cactus Identification
6858560,0.385,28,142,/mobassir/jigsaw-google-q-a-eda,Google QUEST Q&A Labeling
6838549,0.0,0,1,/mihais/googleqa-lstm-word2vec,Google QUEST Q&A Labeling
6840004,0.3329999999999999,8,10,/codehax41/google-qa-by-keras-nn,Google QUEST Q&A Labeling
6835623,0.267,0,6,/saivarunk/basic-modelling-for-quest-q-a-using-xgboost,Google QUEST Q&A Labeling
6739628,0.271,0,6,/alonalon/hirarchical-attention-with-text-augmentation,Google QUEST Q&A Labeling
6787351,0.034,1,22,/axel81/pytorch-bert-baseline,Google QUEST Q&A Labeling
6767833,0.311,5,26,/tunguz/quest-ridge-regression-with-word-char-ngrams,Google QUEST Q&A Labeling
6763542,0.307,1,11,/atikur/google-quest-fastai-v1,Google QUEST Q&A Labeling
6727890,0.31,15,99,/phoenix9032/get-started-with-your-questions-eda-model-nn,Google QUEST Q&A Labeling
6734413,0.247,7,16,/artgor/eda-and-baseline-in-keras,Google QUEST Q&A Labeling
6731345,0.291,2,18,/hamditarek/tfidf-benchmark,Google QUEST Q&A Labeling
6658132,0.389,6,26,/mmmarchetti/jigsaw-google-q-a-eda-iii,Google QUEST Q&A Labeling
7830049,0.391,0,0,/takbull/bert-base-pretrained-models,Google QUEST Q&A Labeling
5186320,0.42496,2,13,/akashdeepjassal/tensorflow-hub-inception-resnet-v2-submission,Open Images 2019 - Object Detection
5034426,1e-05,0,2,/gipark2001/baseline-predictions-using-inception-resnet-v2,Open Images 2019 - Object Detection
4965068,6e-05,1,1,/gagandeep16/object-detection-script,Open Images 2019 - Object Detection
4162884,1e-05,18,66,/vikramtiwari/baseline-predictions-using-inception-resnet-v2,Open Images 2019 - Object Detection
4335101,1e-05,0,0,/akashsuper2000/baseline-predictions-using-inception-resnet-v2,Open Images 2019 - Object Detection
5100130,0.68138,0,3,/terminate9298/music-recommandation-system,WSDM - KKBox's Music Recommendation Challenge
487518,0.65663,9,10,/freshwater/basic-of-lgbm,WSDM - KKBox's Music Recommendation Challenge
477856,0.5110399999999999,3,1,/toorkp/wsdm-recommendations,WSDM - KKBox's Music Recommendation Challenge
3798600,0.9835,0,1,/rivesunder/headless-densenet-with-keras,Aerial Cactus Identification
14302663,0.9999,0,0,/baehyunchul/aerial-cactus-identification-by-trajanus,Aerial Cactus Identification
4266611,0.5920000000000001,0,0,/rodrigoga/kernel4833e980f9,Aerial Cactus Identification
12685151,0.9883,0,0,/gusjohnson/cactus,Aerial Cactus Identification
11775023,0.9941,1,3,/jinyongnan/review-of-cactus-recognition-basic-cnn,Aerial Cactus Identification
11595487,0.4948,0,0,/ljh415/20200908,Aerial Cactus Identification
10171228,0.9991,0,0,/ljh415/200617,Aerial Cactus Identification
10482482,0.9836,0,4,/thanhdatnv2712/aerial-cactus-identification-pytorch,Aerial Cactus Identification
10445132,0.4883,0,3,/ayeongjeong/detecting-cactus-using-keras,Aerial Cactus Identification
3354072,0.9776,0,0,/marinan67/kernel-vgg,Aerial Cactus Identification
3816514,0.9649,0,0,/nathanielbo/cactus-identification,Aerial Cactus Identification
8640653,0.9906,0,0,/paulorenatocastro/cactus-proj-01,Aerial Cactus Identification
8872358,1.0,0,0,/hadayo/finalcactustron,Aerial Cactus Identification
4045087,0.9902,0,0,/akhileshk1/aercactus,Aerial Cactus Identification
4516817,0.9846,0,7,/jabertuhin/aerial-cactus-identification-with-pytorch,Aerial Cactus Identification
7984661,0.9685,0,0,/jugglingsnakeboarder/cactusidentificationcnn,Aerial Cactus Identification
7604943,0.9961,0,0,/szyjak/cactus-done-right,Aerial Cactus Identification
7618288,0.9828,0,1,/abhishek4273/classification-using-monk-design-a-custom-network,Aerial Cactus Identification
4649023,0.9809,0,0,/nazmulshuvo03/aerial-cactus-identification,Aerial Cactus Identification
6967699,1.0,0,0,/marcelorbsousa/kernel6062d6a639,Aerial Cactus Identification
6863681,0.5,0,1,/jusikyun/kaggle-cactus-ex,Aerial Cactus Identification
6830872,0.0,0,0,/rammsesprateat/cactus-aprendizado,Aerial Cactus Identification
6648033,0.9895,0,0,/nazarii/aerial-cactus-identification,Aerial Cactus Identification
5201563,0.6101,0,0,/sriramyakannepalli/keras-vgg16-transfer-learning-with-auc-roc-plot,Aerial Cactus Identification
6153465,0.9616,0,3,/eddididi/gonnasting-with-one-inception-layer,Aerial Cactus Identification
6061912,0.9999,0,2,/d94niel/2019-10-04-cactus-detection,Aerial Cactus Identification
5764674,0.9989,0,3,/spandan28/vggnet-based-transfer-learning,Aerial Cactus Identification
7548275,0.429,0,0,/bluexleoxgreen/google-quest-inference-kernel,Google QUEST Q&A Labeling
9978871,0.40135,0,2,/varunsaproo/xlnet-based,Google QUEST Q&A Labeling
9950952,0.37435,0,1,/rajprakhar/bert-huggingface,Google QUEST Q&A Labeling
10172990,0.1831799999999999,0,2,/mansi3296choudhary/simple-model,Google QUEST Q&A Labeling
9672072,0.13257,0,0,/rajprakhar/google-quest-architecture-2,Google QUEST Q&A Labeling
7686707,0.38,0,1,/misterion777/qa-bert,Google QUEST Q&A Labeling
7849105,0.489,0,6,/robin111/bert-use-fe-stacking-full-pipeline-v2,Google QUEST Q&A Labeling
7264315,0.35902,0,0,/abhijeetbiswasiitb/kernel107f123349,Google QUEST Q&A Labeling
8329925,0.4419399999999999,0,1,/shoheiazuma/google-quest-bert-xlnet,Google QUEST Q&A Labeling
7250258,0.367,0,1,/nilsine11202/distilbert-use-features-oof,Google QUEST Q&A Labeling
7997349,0.44792,0,8,/alexsumt/trying-to-learn-by-reading-and-editing,Google QUEST Q&A Labeling
7902674,0.46275,34,174,/ddanevskyi/1st-place-solution,Google QUEST Q&A Labeling
7924731,0.40992,3,51,/sakami/google-quest-single-lstm,Google QUEST Q&A Labeling
7935284,0.44792,4,4,/takamichitoda/26th-place-solution,Google QUEST Q&A Labeling
7634282,0.385,0,1,/tangchengshun/jigsaw-google-q-a-eda-1-3repeat-by-leomessi10,Google QUEST Q&A Labeling
7253130,0.386,0,1,/tangchengshun/bert-tensorflow2-0-6,Google QUEST Q&A Labeling
7872794,0.457,2,12,/guchio3/refactored-bert2-robert1-xlnet1-blend-pseudo1,Google QUEST Q&A Labeling
7914747,0.425,0,7,/jionie/models-without-optimization-v3,Google QUEST Q&A Labeling
7902890,0.3879999999999999,0,1,/buntyshah/google-quest-q-a-labeling-8-fold,Google QUEST Q&A Labeling
7573212,0.39,0,0,/daigohirooka/huggingface-tfbert-for-full-prediction,Google QUEST Q&A Labeling
7382095,0.3905,1,7,/m10515009/customizedbert-pytorch-version-training,Google QUEST Q&A Labeling
7910001,0.4029999999999999,0,0,/buntyshah/tfbert-ensemble-preprocess-v1,Google QUEST Q&A Labeling
7806347,0.391,35,62,/nxrprime/bert-base-pretrained-models,Google QUEST Q&A Labeling
12602414,0.89154,0,0,/georgezoto/talkingdata-adtracking-comp-categorical-encoding,TalkingData AdTracking Fraud Detection Challenge
6547123,0.52558,2,2,/atashnezhad/knn-means-clustering-model,TalkingData AdTracking Fraud Detection Challenge
5348429,0.5118699999999999,0,0,/hemantdata/stacking,TalkingData AdTracking Fraud Detection Challenge
910152,0.9504,0,1,/ranliu/wik-workshop-on-light-bgm-with-answers,TalkingData AdTracking Fraud Detection Challenge
897177,0.9426,0,2,/jingqliu/nn-with-word2vec-large-embeddings,TalkingData AdTracking Fraud Detection Challenge
732679,0.967,0,1,/aishanhang/lightgbm-second,TalkingData AdTracking Fraud Detection Challenge
888206,0.978,4,8,/propanon/simple-mix-lb-09780-sub-log,TalkingData AdTracking Fraud Detection Challenge
714756,0.9398,0,1,/aishanhang/fraud-optimation-3-10,TalkingData AdTracking Fraud Detection Challenge
877882,0.9659,0,1,/asraful70/feature-engineering-of-talkingdata-with-lightgbm,TalkingData AdTracking Fraud Detection Challenge
861449,0.9683,2,22,/antmarakis/deep-learning-approach-validation-lb-0-9684,TalkingData AdTracking Fraud Detection Challenge
838018,0.971,0,2,/k872892/simple-linear-stacking-lb-9730-fa924d,TalkingData AdTracking Fraud Detection Challenge
13327882,0.13305,0,2,/aicentral/house-price-prediction-using-xgboost,House Prices - Advanced Regression Techniques
13247362,0.13788,0,0,/blackhurt/notebook7d53690501,House Prices - Advanced Regression Techniques
13247731,0.13347,0,0,/entrpn/multiple-regressors-feeding-a-neural-network,House Prices - Advanced Regression Techniques
13156285,0.1753299999999999,0,0,/kevinliaoo/house-prices,House Prices - Advanced Regression Techniques
13130610,0.11599,0,6,/kami634/top-3-on-new-leaderboard-0-11599,House Prices - Advanced Regression Techniques
12872322,0.16313,1,8,/nachobergad/house-prices-xgboost,House Prices - Advanced Regression Techniques
9393166,0.13493,0,1,/mihirsharma3/housing-prices-prediction,House Prices - Advanced Regression Techniques
13083097,0.1227299999999999,0,0,/adujohn/beginner-top-15-20,House Prices - Advanced Regression Techniques
12456957,0.14694,12,19,/mahmoudalaa01010101/regularized-linear-models,House Prices - Advanced Regression Techniques
13003271,0.12202,0,0,/virtual888/prices-prediction-lightgbm-xgboost-lb-14,House Prices - Advanced Regression Techniques
12983164,0.13467,0,0,/virtual888/house-prices-prediction-with-randomforests-public,House Prices - Advanced Regression Techniques
12465240,0.1437,0,2,/leodaniel/wanna-buy-my-house,House Prices - Advanced Regression Techniques
12928916,0.13979,0,0,/batprem/house-price-eda,House Prices - Advanced Regression Techniques
12911683,5.2801800000000005,1,1,/mahmutsamierolu/house-prediction,House Prices - Advanced Regression Techniques
3319789,0.901,12,58,/omgrodas/lightgbm-with-data-augmentation,Santander Customer Transaction Prediction
2996141,0.888,0,0,/davidesangalli/dataset-analysis-and-classifier-comparison,Santander Customer Transaction Prediction
3313181,0.898,3,25,/pnussbaum/santander-genetically-engineered-features-v07,Santander Customer Transaction Prediction
3324891,0.889,0,0,/dursunkoc/rapska,Santander Customer Transaction Prediction
3311919,0.892,0,1,/mueno115/randomly-shuffle-python,Santander Customer Transaction Prediction
3282562,0.895,1,20,/lockeza/convolutional-nn-model,Santander Customer Transaction Prediction
3245811,0.9,0,5,/wwu651/auto-feature-engineering-lgb,Santander Customer Transaction Prediction
3295490,0.888,0,3,/silantev/santander-prediction-using-gaussiannb,Santander Customer Transaction Prediction
3280875,0.899,3,4,/graf10a/overshuffling,Santander Customer Transaction Prediction
3283376,0.885,0,2,/omgrodas/baseline-nn-with-fast-ai,Santander Customer Transaction Prediction
3265670,0.887,1,4,/itslek/automl-easy-with-h2o-santander-transaction-v3,Santander Customer Transaction Prediction
3267489,0.847,4,6,/arnabdan/nn-dropouts-early-stopping-acc-loss-plots,Santander Customer Transaction Prediction
3184137,0.9,5,16,/rednivrug/dae-with-lightgbm,Santander Customer Transaction Prediction
3246886,0.634,2,2,/vnbhat/santander-binary-classification,Santander Customer Transaction Prediction
3255002,0.6659999999999999,0,0,/trvanand/light-gb-prediction,Santander Customer Transaction Prediction
3220685,0.9,0,18,/jazivxt/blender,Santander Customer Transaction Prediction
3155023,0.6679999999999999,0,3,/samfiddis/simple-keras-model,Santander Customer Transaction Prediction
3101922,0.895,0,0,/isaranja/santander-time-distributed-dense-layer,Santander Customer Transaction Prediction
3223668,0.9,3,0,/arulrajj/gridsearchcv,Santander Customer Transaction Prediction
3139109,0.898,0,1,/ibozkurt79/santander-lightgbm-a-boost-with-bayesiansearch,Santander Customer Transaction Prediction
3196092,0.599,0,0,/danimelv/modelo-xgboost,Santander Customer Transaction Prediction
3203877,0.8390000000000001,0,0,/akihirosanada/20190310-baseline-model,Santander Customer Transaction Prediction
13390346,0.5167,0,3,/drcapa/melanoma-classifier-tpu-tutorial,SIIM-ISIC Melanoma Classification
11094016,0.9096,0,1,/ramjib/melonoma-tpu,SIIM-ISIC Melanoma Classification
13281899,0.9473,0,1,/system32/melanoma-2-fold,SIIM-ISIC Melanoma Classification
13016636,0.8082,1,0,/annasofielunde/machinelearning-tdt4173-cnn-model2,SIIM-ISIC Melanoma Classification
12840472,0.7809,0,2,/annasofielunde/xgboost-on-metadata-for-ml,SIIM-ISIC Melanoma Classification
12547823,0.7795,0,5,/annasofielunde/machinelearning-tdt4173-cnn,SIIM-ISIC Melanoma Classification
12479429,0.8631,0,9,/tunguz/melanoma-with-embeddings-and-rapids,SIIM-ISIC Melanoma Classification
11027838,0.9201,0,3,/tikoboss/tko-okt,SIIM-ISIC Melanoma Classification
10721342,0.8534,0,0,/bssmsi/melanoma-classification,SIIM-ISIC Melanoma Classification
9959489,0.784,0,0,/blueturtle/siim-inference-of-enet-b4,SIIM-ISIC Melanoma Classification
11608543,0.8640000000000001,3,2,/captaintyping/fork-of-siim-isic-melanoma-random-forest-cnn,SIIM-ISIC Melanoma Classification
10470056,0.9643,0,3,/medmostapha/adias-kernel,SIIM-ISIC Melanoma Classification
13741334,0.7809999999999999,0,1,/c7934597/riiid-lgbm-bagging2-sakt-0-781,Riiid Answer Correctness Prediction
13571942,0.679,0,0,/filipinogambino/riiid-neural-net,Riiid Answer Correctness Prediction
14084190,0.7859999999999999,0,0,/gangnam277/fork-of-riiid-lgbm-bagging2-1-471152-96a878,Riiid Answer Correctness Prediction
14350913,0.815,0,0,/letranduckinh/riiid-model-submission-4th-solution,Riiid Answer Correctness Prediction
14269407,0.649,0,0,/khatrimohit/knowledge-tracing-prediction,Riiid Answer Correctness Prediction
14095966,0.8170000000000001,31,161,/mamasinkgs/public-private-2nd-place-solution,Riiid Answer Correctness Prediction
14104034,0.8140000000000001,4,23,/mamasinkgs/public-private-2nd-place-solution-single-fold,Riiid Answer Correctness Prediction
14068874,0.809,2,25,/shujun717/encoder-only-model-20th-solution,Riiid Answer Correctness Prediction
14098431,0.807,0,10,/nadare/32nd-transformer-dcn-v2-model,Riiid Answer Correctness Prediction
14093335,0.799,1,15,/yihdarshieh/r3id-transformer,Riiid Answer Correctness Prediction
13550162,0.7959999999999999,0,12,/a763337092/lgb1215,Riiid Answer Correctness Prediction
12754742,0.664,0,0,/yangam/riiid-keras-starter-with-additional-data,Riiid Answer Correctness Prediction
13943465,0.785,0,0,/luffy521/bert-like-model,Riiid Answer Correctness Prediction
14057573,0.71,1,7,/zephyrwang666/riiid-saint-tf-transformer-6-inference,Riiid Answer Correctness Prediction
13408986,0.7390000000000001,15,7,/adityaecdrid/fork-of-saint-inference-ea970c,Riiid Answer Correctness Prediction
13789070,12.584,0,0,/sai11fkaneko/lyft-late-submission-study,Lyft Motion Prediction for Autonomous Vehicles
13458761,138.78799999999998,0,0,/hbparman/mix-match,Lyft Motion Prediction for Autonomous Vehicles
12778584,10.272,4,1,/charmq/private-submit-without-kernel-inference-bd2592,Lyft Motion Prediction for Autonomous Vehicles
13171080,23.61,0,2,/vigneshkkumar127/temwirk,Lyft Motion Prediction for Autonomous Vehicles
13049826,21.343000000000004,6,10,/ashusma/inference-lyft-tensorflow-tpu-multi-mode,Lyft Motion Prediction for Autonomous Vehicles
12737142,23.622,3,6,/kneroma/lyft-track-id-does-not-matter,Lyft Motion Prediction for Autonomous Vehicles
12314635,23.622,26,36,/louis925/lyft-complete-train-and-prediction-pipeline,Lyft Motion Prediction for Autonomous Vehicles
12089683,293.782,0,5,/etareduce/lyft-av-pytorch-resnet-baseline-with-l5kit-1-1-0,Lyft Motion Prediction for Autonomous Vehicles
11994793,18665.253,0,8,/shayantaherian/lyft-pytorch-implementation,Lyft Motion Prediction for Autonomous Vehicles
11914770,37.017,13,33,/doanquanvietnamca/tpu-resnet50-faster-better,Lyft Motion Prediction for Autonomous Vehicles
11813671,23.622,71,167,/huanvo/lyft-complete-train-and-prediction-pipeline,Lyft Motion Prediction for Autonomous Vehicles
11767220,53.482,8,37,/kneroma/inference-motion-prediction-with-pointnet,Lyft Motion Prediction for Autonomous Vehicles
11657245,46.18899999999999,11,48,/kneroma/combining-lyft-multimode-models,Lyft Motion Prediction for Autonomous Vehicles
11486482,52.438,5,6,/forwet/lyft-ensemble-and-submission,Lyft Motion Prediction for Autonomous Vehicles
11601522,49.948,7,15,/paulorzp/sort-multi-mode-submission-by-confidence,Lyft Motion Prediction for Autonomous Vehicles
11526094,342.254,8,24,/zaharch/kalman-filter-baseline,Lyft Motion Prediction for Autonomous Vehicles
93455,0.989905,2,2,/tanlikesmath/xgboost-with-leak,Predicting Red Hat Business Value
10799170,0.91803,0,2,/mekhdigakhramanian/predict-future-sales,Predict Future Sales
10872334,0.8826299999999999,28,76,/gordotron85/future-sales-xgboost-top-3,Predict Future Sales
1844716,0.0389,4,5,/cloudchng/pubg-catboost,PUBG Finish Placement Prediction (Kernels Only)
1835592,0.0615,2,2,/martchellop/first-run-with-xgboost,PUBG Finish Placement Prediction (Kernels Only)
1838431,0.0741,0,1,/jaysn1/simple-xgboost,PUBG Finish Placement Prediction (Kernels Only)
1824727,0.1007,0,4,/simonandersen/pubg-linear-regression,PUBG Finish Placement Prediction (Kernels Only)
1824078,0.0676,0,2,/amoatasem/playernowknown-s-battlegrounds,PUBG Finish Placement Prediction (Kernels Only)
1814080,0.0473,0,2,/tarunpaparaju/pubg-placement-predictor-catboost,PUBG Finish Placement Prediction (Kernels Only)
1812681,0.0792,0,0,/varunsharmaml/pubg-finish-placement-prediction,PUBG Finish Placement Prediction (Kernels Only)
1801952,0.0432,3,14,/mlisovyi/relativerank-of-predictions,PUBG Finish Placement Prediction (Kernels Only)
1799497,0.0607,3,10,/mlisovyi/pubg-survivor-kit,PUBG Finish Placement Prediction (Kernels Only)
1797435,0.0653,0,1,/akashdeepjassal/pubg-chicken-dinner-starter,PUBG Finish Placement Prediction (Kernels Only)
7506109,0.05018,0,0,/nmsf1916009ml/feature-engineering-and-model-stacking,PUBG Finish Placement Prediction (Kernels Only)
3621581,0.05971,0,0,/prkprime/kernelbfdcc05395,PUBG Finish Placement Prediction (Kernels Only)
2539433,0.0226,0,0,/yzfaiyxj/pubg-finish-placement-prediction-learning,PUBG Finish Placement Prediction (Kernels Only)
2426752,0.0253,0,0,/goldenbat/pubg-finish-placement-prediction-playground,PUBG Finish Placement Prediction (Kernels Only)
2415041,0.0246,0,0,/wang1997/ttest,PUBG Finish Placement Prediction (Kernels Only)
1061157,0.3796199999999999,4,28,/ashishpatel26/whale-prediction-using-resnet-50,Humpback Whale Identification Challenge
661865,0.32875,30,83,/gimunu/data-augmentation-with-keras-into-cnn,Humpback Whale Identification Challenge
537607,0.15722,0,5,/cristianpb/using-similarity-of-train-and-test-for-predictions,Humpback Whale Identification Challenge
63459,0.21508,0,0,/gautamsihag/withallattempt1-1,Expedia Hotel Recommendations
12966962,2.93004,0,1,/yutatakaba/notebookf2c19fb947,M5 Forecasting - Accuracy
13069752,0.0,0,0,/takumikawashima/m5-alignandsubmit,M5 Forecasting - Accuracy
8237925,0.68957,0,0,/drcapa/m5-forecasting-accuracy,M5 Forecasting - Accuracy
8960517,0.4887399999999999,0,0,/fredcaram/m5-under-0-50-optimized,M5 Forecasting - Accuracy
10890393,0.0,0,1,/ahmedmurad1990/m5-forecasting-accuracy,M5 Forecasting - Accuracy
10717771,0.26795,0,8,/aakashveera/m5-accuracy,M5 Forecasting - Accuracy
9411509,0.5002300000000001,1,0,/zenonn/m5-predict,M5 Forecasting - Accuracy
10412413,0.0,0,1,/akashsuper2000/m5-lightgbm-model,M5 Forecasting - Accuracy
12009174,0.24883,0,0,/qizhengqi/covid-19-vaccine-degradation-prediction-qizhengqi,OpenVaccine: COVID-19 mRNA Vaccine Degradation Prediction
11693092,0.26036,0,0,/brodzik/openvaccine-lstm-in-pytorch,OpenVaccine: COVID-19 mRNA Vaccine Degradation Prediction
12021299,0.25467,0,3,/vatsalparsaniya/openvaccine-model-training-augmentation,OpenVaccine: COVID-19 mRNA Vaccine Degradation Prediction
12149865,0.2358699999999999,3,7,/gdonchyts/probing-private-scores,OpenVaccine: COVID-19 mRNA Vaccine Degradation Prediction
12143234,0.23894,1,7,/nyanpn/eternafold-open-waccine-pytorch-ae-pretrain,OpenVaccine: COVID-19 mRNA Vaccine Degradation Prediction
12058356,0.248,0,2,/manchunhui/openvaccine-gru-lstm-with-cnn,OpenVaccine: COVID-19 mRNA Vaccine Degradation Prediction
12140894,0.24731,0,3,/underwearfitting/lessonslearned-d,OpenVaccine: COVID-19 mRNA Vaccine Degradation Prediction
12039198,0.27419,0,0,/suzukiseiya/lightgbm-augmented,OpenVaccine: COVID-19 mRNA Vaccine Degradation Prediction
12022607,0.2455699999999999,42,161,/takadaat/openvaccine-pytorch-ae-pretrain,OpenVaccine: COVID-19 mRNA Vaccine Degradation Prediction
11933058,0.25842,0,1,/danofer/cnn-transformer-rnn-feature-eng-data-aug,OpenVaccine: COVID-19 mRNA Vaccine Degradation Prediction
12041414,0.26338,0,7,/gopidurgaprasad/covid-ae-gnn-attn-cnn-pytorch,OpenVaccine: COVID-19 mRNA Vaccine Degradation Prediction
11935941,0.24738,87,372,/mrkmakr/covid-ae-pretrain-gnn-attn-cnn,OpenVaccine: COVID-19 mRNA Vaccine Degradation Prediction
11964483,0.25505,0,8,/nancy392/mix-gru-lstm-oop-refactoring,OpenVaccine: COVID-19 mRNA Vaccine Degradation Prediction
12178539,0.02988,0,1,/dskagglemt/1-leaf-classification-understanding-dataset,Leaf Classification
11268356,0.64875,0,4,/rahulpawade/solving-leaf-classification-problem-by-xgboost,Leaf Classification
10909365,0.67291,0,1,/mohgsam/leaf-classification,Leaf Classification
10890388,0.67291,1,7,/abdalazez/final-leaf-classification,Leaf Classification
10866293,0.95953,0,2,/aqifilyaskhan/leaf-cls-95-acc-on-test-csv,Leaf Classification
10772033,0.70526,0,1,/fayrouzfarra/leaf-classification-xgboost-gridsearch,Leaf Classification
5943728,0.13415,0,2,/thanhtra/leaf-classification-randomforest-svc-kneighbor,Leaf Classification
4998731,0.4098199999999999,0,0,/venkatarathnam/leaf-classification-custom-features,Leaf Classification
4310341,5.08427,0,1,/imranmik/leaf-classification,Leaf Classification
1855708,1.20692,0,0,/ai2prx/leaf-classification,Leaf Classification
538476,0.71926,2,4,/guywhowantstolearnml/leaf-classification,Leaf Classification
240230,0.63443,0,1,/luikna/eda-and-tensorflow-implementation,Leaf Classification
195674,0.0086,1,3,/tobikaggle/keras-model-average,Leaf Classification
195897,0.01842,0,0,/alexionby/notebookcbc6345b8f,Leaf Classification
186883,1.2485,0,0,/tobikaggle/10-classifier-showdown-in-scikit-learn,Leaf Classification
7092599,0.59103,0,0,/alexejdrosdov/lb6nlp,Quora Insincere Questions Classification
7154594,0.57295,0,0,/thisisyana/kernel6224785f16,Quora Insincere Questions Classification
6956451,0.70373,0,2,/arinaruck/gru-madness,Quora Insincere Questions Classification
6855337,0.6761,0,7,/saeedtqp/quora,Quora Insincere Questions Classification
6809848,0.5878,0,0,/ksotillo/proyecto-3-quora-insincere-questions,Quora Insincere Questions Classification
6684164,0.6055,0,0,/luism1228/proyecto-3-ce-torres-eskenazi-nu-ez,Quora Insincere Questions Classification
2565330,0.695,0,0,/qibajiu/nlpproject,Quora Insincere Questions Classification
6489528,0.62997,0,0,/majeedaskari/w2v-lstm,Quora Insincere Questions Classification
6480653,0.68303,0,1,/dipta007/glove-para,Quora Insincere Questions Classification
6154612,0.57494,0,0,/tysonpatrick/final-model,Quora Insincere Questions Classification
5789486,0.6158,0,0,/obrunet/quora-insincere-questions-classification,Quora Insincere Questions Classification
4736343,0.69109,0,0,/nurakib/proposed-model,Quora Insincere Questions Classification
2392512,0.598,0,0,/agtadarsh/tfidf-linearsvm-1,Quora Insincere Questions Classification
2415388,0.46,0,0,/rishabhjain2764/quora-insincere-tfidf,Quora Insincere Questions Classification
5035193,0.70043,0,0,/ronaksvijay/qiqc-nn,Quora Insincere Questions Classification
2503442,0.497,0,0,/tgregory98/quora-notebook-naive-bayes-attempt,Quora Insincere Questions Classification
9145636,0.01135,0,0,/mano2891/kernel5701ff262b,COVID19 Global Forecasting (Week 4)
9005602,0.01135,0,1,/pradeepkumarrajkumar/m3-xgb-djp,COVID19 Global Forecasting (Week 4)
8885820,0.10035,0,10,/muhakabartay/covid19-forecasting-xgboost,COVID19 Global Forecasting (Week 4)
8986466,2.22074,0,2,/nicolasmalloy/covid-19-ml-models,COVID19 Global Forecasting (Week 4)
8853768,0.01135,0,1,/aniket165/covid-19-forecasting-xgboost-week-4,COVID19 Global Forecasting (Week 4)
8891156,0.53158,2,4,/vincentwang25/seir-hcd-model-age-distribution,COVID19 Global Forecasting (Week 4)
8974683,0.44651,0,3,/shaitender/forked,COVID19 Global Forecasting (Week 4)
8848207,0.03279,0,0,/havihavish/csce-5300-1,COVID19 Global Forecasting (Week 4)
8896310,0.06376,0,2,/priteshraj10/covid-19-cases,COVID19 Global Forecasting (Week 4)
8858850,0.07078,0,0,/mathnery/covid19-pred-xgboosting-week4,COVID19 Global Forecasting (Week 4)
8838528,2.24974,0,1,/niteshx2/week-4-covid-using-extra-features,COVID19 Global Forecasting (Week 4)
8865171,0.5943,0,0,/themichaelkam/covid19-kernel,COVID19 Global Forecasting (Week 4)
8925360,0.04541,0,1,/cchaojie/covid19-global-forecasting-week-4,COVID19 Global Forecasting (Week 4)
8901850,0.66077,0,0,/sithlord0121/steve-w-rnn-model,COVID19 Global Forecasting (Week 4)
8843913,0.12195,0,1,/esotericazzo/week-4-analysis-model,COVID19 Global Forecasting (Week 4)
8900849,0.09531,0,0,/sophiasusanraju/covid-week-4-v2-randomforest,COVID19 Global Forecasting (Week 4)
8932218,0.0610799999999999,0,0,/johnprasanth/covid-boost-forecasting,COVID19 Global Forecasting (Week 4)
8930634,0.19855,0,0,/ee257sp20madhuarjun/knn-week4,COVID19 Global Forecasting (Week 4)
8844314,0.91247,0,0,/silverstorm/covid-forecasting-script,COVID19 Global Forecasting (Week 4)
8932557,0.35763,0,0,/ee257sp20hinashruti/covid-19-week4,COVID19 Global Forecasting (Week 4)
8858359,0.0375,0,0,/lexcalibur/learning1,COVID19 Global Forecasting (Week 4)
8909041,0.16217,0,0,/harshpalsingh/kernel-10,COVID19 Global Forecasting (Week 4)
8932929,0.01845,1,10,/tunguz/covid-19-week-4,COVID19 Global Forecasting (Week 4)
8950393,0.7953899999999999,0,1,/anatidae/kernel45a758d627,COVID19 Global Forecasting (Week 4)
8852132,0.03639,0,1,/itsbitan/covid-19-week-4,COVID19 Global Forecasting (Week 4)
8950581,0.08741,0,0,/petersorensen360/zoom360,COVID19 Global Forecasting (Week 4)
8937958,0.04007,0,1,/seaofstars/fork-of-kernel16e9ef2435,COVID19 Global Forecasting (Week 4)
8938366,0.29609,0,3,/stealthflow/week4-osciiart-happiness,COVID19 Global Forecasting (Week 4)
8950999,0.04502,0,0,/luweijia2heather/covidsigmoid,COVID19 Global Forecasting (Week 4)
8888866,0.37161,3,2,/meenakshiramaswamy/covid-wk4-multiple-data-sources-xgboost,COVID19 Global Forecasting (Week 4)
8942366,0.03303,0,0,/pyotam/covid-week-4-blend-modified,COVID19 Global Forecasting (Week 4)
8947839,0.1272299999999999,0,4,/christofhenkel/cv19w4-full-pdd-oscii-david-vopa-sub1,COVID19 Global Forecasting (Week 4)
8950392,0.03456,0,7,/paulorzp/covid19-w4-blend6,COVID19 Global Forecasting (Week 4)
8832221,0.03639,0,0,/datahadi/covid19-global-forecasting-week-4,COVID19 Global Forecasting (Week 4)
8940288,0.23839,0,0,/ilialar/logcurve-w4,COVID19 Global Forecasting (Week 4)
8932884,0.06485,0,0,/sophiaray/covid-19-forecast-phophet-week4,COVID19 Global Forecasting (Week 4)
8833828,0.31494,0,0,/amitstei/covid19-forecasting-state-space-lgbm,COVID19 Global Forecasting (Week 4)
8859464,0.37533,0,1,/henrychinaski/covid-19-week-4-polynomial,COVID19 Global Forecasting (Week 4)
8950841,0.0038299999999999,0,4,/usamanathani/covid19-xbg,COVID19 Global Forecasting (Week 4)
8866466,0.14452,0,0,/paulmorganaus/simple-sigmoid-week4,COVID19 Global Forecasting (Week 4)
8927626,0.24474,0,0,/sadeka007/wk4-covid-19-forecasting-with-lgb-goss,COVID19 Global Forecasting (Week 4)
8913263,0.04857,0,0,/thilakg/covid19-global-forecasting-week-4-tg,COVID19 Global Forecasting (Week 4)
8917644,0.0525399999999999,0,2,/sadakmed/lgbm-baseline-short-elegant,COVID19 Global Forecasting (Week 4)
8939499,0.0343399999999999,4,8,/mrmorj/covid-19-sarima-week4,COVID19 Global Forecasting (Week 4)
8860999,0.03677,0,0,/nkiith/covid19-week4,COVID19 Global Forecasting (Week 4)
8880445,3.15416,0,1,/worldkeeping/week4-lgb,COVID19 Global Forecasting (Week 4)
8832998,0.03018,0,0,/wdyreborn/xgboost-regression,COVID19 Global Forecasting (Week 4)
8948192,1.26911,0,0,/patrickssfuchs/deep-learning-lmu-unit-week-4,COVID19 Global Forecasting (Week 4)
8881509,0.03436,0,1,/robertolandi/sequence-to-sequence-model,COVID19 Global Forecasting (Week 4)
8951781,2.80593,0,0,/apoorvm/covid19-week-4,COVID19 Global Forecasting (Week 4)
8915023,0.0364,0,0,/kdosyr/covid19-global-wk4,COVID19 Global Forecasting (Week 4)
8941054,0.38708,0,0,/timriggins/week-4,COVID19 Global Forecasting (Week 4)
4764988,0.62984,0,2,/atul0204/quora-insincere-using-nn,Quora Insincere Questions Classification
4707782,0.6213,0,0,/zoomelectrico/competicion-quora-emergente-p3,Quora Insincere Questions Classification
4735281,0.65833,0,0,/abrahamchang/proyecto-3-emergente-chang-pinedo-velasquez,Quora Insincere Questions Classification
4703492,0.68023,0,0,/earama/proyecto-3-quora-arama-d-lacoste-de-castro,Quora Insincere Questions Classification
4669045,0.65417,0,1,/saqib96mobin/bilstm-clr-embedding-glove-paragram-in-keras,Quora Insincere Questions Classification
4657113,0.69644,0,0,/a2020s/quora-classification-test-script,Quora Insincere Questions Classification
4627839,0.70112,0,1,/danielisanchez/computacionemergente-proyecto3,Quora Insincere Questions Classification
4462124,0.11432,0,0,/quasikris/tf-idf,Quora Insincere Questions Classification
4078165,0.69808,0,0,/wpan2019/questions-classification-in-pytorch,Quora Insincere Questions Classification
3973002,0.53465,0,0,/skathirmani/tm-bng-may-2019,Quora Insincere Questions Classification
3849064,0.68386,0,0,/cyy2427/quora-bilstm,Quora Insincere Questions Classification
3802180,0.6443,0,0,/shilpiagrawal1/kernelce525abffc,Quora Insincere Questions Classification
2193907,0.688,0,0,/pavelholubik/sliced-rnn-keras,Quora Insincere Questions Classification
3389835,0.63089,0,0,/jialanz/kernel657b8edba7,Quora Insincere Questions Classification
3542773,0.68074,0,1,/yairh3/ltsm-gru-on-gpu-text-prep-avg-embedding,Quora Insincere Questions Classification
3567576,0.64062,0,0,/xfffrank/gru-noembeddings,Quora Insincere Questions Classification
3547521,0.65255,1,10,/samarthsarin/simple-guide-for-lstm-and-glove-embeddings,Quora Insincere Questions Classification
2836491,0.48815,0,0,/ratnesh88/predict-insincere-questions,Quora Insincere Questions Classification
3447979,0.20315,0,0,/ashisht08/logistic-regression-with-tfidf-vectorisation,Quora Insincere Questions Classification
3305531,0.67235,17,9,/ratthachat/workshop-text-classification-quora,Quora Insincere Questions Classification
3402525,0.55808,0,0,/cajorgen57/quora-insincere-questions-cjorg,Quora Insincere Questions Classification
2237195,0.636,0,1,/yokolet/quora-sentiment-analysis-by-pytorch,Quora Insincere Questions Classification
3357606,0.50567,0,3,/xfffrank/tfidf-stemming,Quora Insincere Questions Classification
4098499,0.71554,0,2,/toldo171/fat-2019-submit-curated-mixup-tta-kfold,Freesound Audio Tagging 2019
3862129,0.3989999999999999,0,0,/piyush28/simple-cnn-freesound-audio-detection,Freesound Audio Tagging 2019
4409763,0.0,5,13,/ebouteillon/12th-public-lb-inference-kernel-using-fastai,Freesound Audio Tagging 2019
4387837,0.0,0,0,/vzaguskin/fat-2019-80-place-public-lb-solution-lwlrap-70-2,Freesound Audio Tagging 2019
4183048,0.654,0,1,/shubham505/simple-2d-cnn-pytorch,Freesound Audio Tagging 2019
4134335,0.408,2,7,/kunstmord/look-ma-no-deep-learning,Freesound Audio Tagging 2019
4039513,0.6609999999999999,1,24,/vinayaks/2d-cnn-high-score-fast-ai,Freesound Audio Tagging 2019
3890117,0.072,0,0,/jramcast/random-predictions-baseline,Freesound Audio Tagging 2019
3845617,0.35,1,8,/pluceroo/new-approach-wavelet-packet-decomposition-in-ml,Freesound Audio Tagging 2019
3776983,0.518,0,3,/hariswidjaja/freesound-audio-tagging-mfcc-cnn-solution,Freesound Audio Tagging 2019
3760508,0.501,0,2,/lonelygo/cnn-2d-basic-solution-powered-by-fast-ai,Freesound Audio Tagging 2019
3714482,0.41,0,10,/rio114/keras-cnn-with-lwlrap-evaluation,Freesound Audio Tagging 2019
3693760,0.63,3,46,/peining/simple-cnn-classifier-with-pytorch,Freesound Audio Tagging 2019
3670962,0.502,12,38,/titericz/giba-keras-2-folds-from-scratch-lb-0-501,Freesound Audio Tagging 2019
3654152,0.61,17,153,/mhiro2/simple-2d-cnn-classifier-with-pytorch,Freesound Audio Tagging 2019
3497074,0.495,8,36,/voglinio/keras-2d-model-5-fold-log-specgram-curated-only,Freesound Audio Tagging 2019
3525043,0.356,1,6,/htopper/improving-on-baseline-with-bidirectional-model,Freesound Audio Tagging 2019
57206,0.30366,2,23,/omarelgabry/explore-expedia-search-data,Expedia Hotel Recommendations
2298620,0.0594,0,0,/yanyiming137/regression-data-0-2,PUBG Finish Placement Prediction (Kernels Only)
2184809,0.192,0,0,/hyerimpak/submission-christmas-eve,PUBG Finish Placement Prediction (Kernels Only)
1929918,0.1146,0,0,/mathfour/pubg-prediction-with-three-features,PUBG Finish Placement Prediction (Kernels Only)
1838583,0.066,0,0,/tarunpaparaju/pubg-placement-prediction-with-bagging-and-ranking,PUBG Finish Placement Prediction (Kernels Only)
1818151,0.1291,0,0,/tarunpaparaju/pubg-placement-predictor-adaboost,PUBG Finish Placement Prediction (Kernels Only)
4632181,0.597,0,0,/rahultp97/aptos-2019-blindness-detection-using-fastai,APTOS 2019 Blindness Detection
4664345,0.677,0,1,/kalikichandu/aptos-blindness-detection-fastai-starter-resnet152,APTOS 2019 Blindness Detection
4666504,0.677,10,23,/takamichitoda/resnet50-with-ben-graham-s-preprocessing-fast-ai,APTOS 2019 Blindness Detection
4628058,0.7490000000000001,30,144,/ratan123/aptos-2019-keras-baseline,APTOS 2019 Blindness Detection
4633408,0.176,0,0,/harshavardhanbabu/kerasclassifierresnet50,APTOS 2019 Blindness Detection
4596681,0.708,3,47,/lextoumbourou/blindness-detection-resnet34-ordinal-targets,APTOS 2019 Blindness Detection
4595791,0.726,26,119,/hmendonca/efficientnetb4-fastai-blindness-detection,APTOS 2019 Blindness Detection
4556515,0.722,37,459,/tanlikesmath/intro-aptos-diabetic-retinopathy-eda-starter,APTOS 2019 Blindness Detection
4576851,0.68,3,15,/hmendonca/efficientnet-pytorch-ignite-aptos19-submission,APTOS 2019 Blindness Detection
4560511,0.758,87,417,/abhishek/pytorch-inference-kernel-lazy-tta,APTOS 2019 Blindness Detection
4557844,0.731,78,330,/xhlulu/aptos-2019-densenet-keras-starter,APTOS 2019 Blindness Detection
4563194,0.451,5,65,/ateplyuk/aptos-pytorch-starter-rnet50,APTOS 2019 Blindness Detection
4558445,0.759,25,73,/demonplus/fast-ai-starter-with-resnet-50,APTOS 2019 Blindness Detection
4564131,0.14,2,14,/arjunrao2000/baseline-keras-model,APTOS 2019 Blindness Detection
4559930,0.6659999999999999,4,22,/kageyama/fastai-blindness-detection-resnet34,APTOS 2019 Blindness Detection
4569213,0.0,0,4,/kageyama/keras-blindness-detection-simple-resnet50,APTOS 2019 Blindness Detection
4558534,0.6859999999999999,5,5,/adkarhe/blindness-detection,APTOS 2019 Blindness Detection
4607952,0.183,0,0,/lifengnan/kernel1c22bb6303,APTOS 2019 Blindness Detection
13726142,0.71455,0,0,/jerry00914/notebook6ce33caed9,APTOS 2019 Blindness Detection
5786940,0.7899609999999999,0,0,/pltibles/aptos-2019-blindness-detection-v2,APTOS 2019 Blindness Detection
12915008,1.02241,1,3,/avhisheksingh/predict-future-sales-project,Predict Future Sales
12563412,1.10943,2,3,/ngc224/predict-future-sales-eda-and-lstm-prediction,Predict Future Sales
12597183,0.92092,0,0,/curibe10/final-project-curibe-co-part-2-2,Predict Future Sales
11687718,1.61689,0,1,/abhishekanand101/predicting-sales,Predict Future Sales
12183193,1.41241,8,6,/jayantawasthi/rnn-used-to-predict-future-sales,Predict Future Sales
12122701,0.95208,0,0,/taidopurason/randomforest-model,Predict Future Sales
12101535,0.93184,0,0,/dimitry2ishenko/eda-train-and-predict-using-xgboost,Predict Future Sales
12085891,0.92064,0,0,/rikdifos/timeseries-gbms-via-gpu,Predict Future Sales
11924045,0.8826299999999999,0,4,/abdalazez/predict-future-sales-2020,Predict Future Sales
11649889,0.92367,0,0,/gandagorn/data-exploration-and-price-prediction-ensemble,Predict Future Sales
11597085,1.02345,0,0,/akhilkasare/predict-future-sales-using-lstm-simple-and-easy,Predict Future Sales
11543154,0.9979,0,1,/defaultv007/coursera-predict-future-sale,Predict Future Sales
11354472,1.16457,0,0,/kgehring/bayesridge,Predict Future Sales
12743102,26.73665,0,0,/roohisharma/san-francisco-crime,San Francisco Crime Classification
11727187,27.20626,0,1,/aloksahu7478/san-francisco-crime-data-classification-using-knn,San Francisco Crime Classification
11303554,6.96272,0,1,/rahulpawade/san-francisco-crime-classification,San Francisco Crime Classification
10911902,26.86597,0,3,/julianbenny/sf-crime,San Francisco Crime Classification
9991836,2.3407400000000003,0,3,/munmun2004/san-francisco-crime-classification,San Francisco Crime Classification
9366350,2.2354700000000003,0,1,/mohitsital/sf-crime-classification,San Francisco Crime Classification
9349217,2.95649,0,1,/tabikh/kernel378479132f,San Francisco Crime Classification
8068263,2.42442,0,0,/doublepoi/lightgbm-test-2,San Francisco Crime Classification
4924018,2.63975,0,1,/cdf255/hackatinho,San Francisco Crime Classification
3819428,2.46054,0,0,/shubham2306/crime-classification,San Francisco Crime Classification
4363927,2.42145,0,0,/xodud2881/xgboost-a,San Francisco Crime Classification
2486446,2.49136,0,0,/yannisp/sf-crime-analysis-prediction-base-model,San Francisco Crime Classification
1720010,2.50732,0,0,/ludi666/lr-300freatures,San Francisco Crime Classification
1200799,2.36808,0,0,/aurelienkd/final-projet-pinquier-mohamed-khefifderain,San Francisco Crime Classification
921215,20.35059,0,1,/kullapat/knn-model-prediction,San Francisco Crime Classification
12891860,0.3603699999999999,0,0,/orvindemsy/housing-prices-ordinalencoder-for-non-num-feature,House Prices - Advanced Regression Techniques
12873303,0.24165,0,1,/geekspeakerdeep/try-best-score,House Prices - Advanced Regression Techniques
12573236,0.1307299999999999,0,0,/iraseidman/ames-house-prices-submission,House Prices - Advanced Regression Techniques
12804852,0.1851,0,2,/kazuyakusumi/result,House Prices - Advanced Regression Techniques
12743921,0.16188,0,2,/alistairdouglas/house-pricing-prediction-neural-net,House Prices - Advanced Regression Techniques
12521618,0.15636,0,0,/poramintrlimpaporn/houseprices,House Prices - Advanced Regression Techniques
12682264,0.12013,0,2,/ashishsingh226/stacking-blending-weighted-average-top-10-model,House Prices - Advanced Regression Techniques
12685268,1.16539,0,1,/saeedarisha/notebook12f3d57324,House Prices - Advanced Regression Techniques
12603639,0.00044,4,3,/mohammedfahadvp/house-price,House Prices - Advanced Regression Techniques
6766146,0.90746,2,1,/marcossantanauff/planet-eda-fastai,Planet: Understanding the Amazon from Space
6425805,0.92523,0,0,/stellargo/three3tres,Planet: Understanding the Amazon from Space
3543117,0.93,0,1,/stevenvoo/planet-amazon,Planet: Understanding the Amazon from Space
2886187,0.9275,0,1,/danielnbarbosa/fast-ai-v1-2019-on-planet,Planet: Understanding the Amazon from Space
1777531,0.93075,0,2,/golddigger1943/golddigger-s-planet,Planet: Understanding the Amazon from Space
3178793,0.889,6,25,/scirpus/santander-gp,Santander Customer Transaction Prediction
3158672,0.9,19,33,/ruby33421/quick-start-0-9-lgb-with-new-features,Santander Customer Transaction Prediction
3082500,0.898,7,12,/abhigupta4981/fastai-baseline-public-lb-0-898,Santander Customer Transaction Prediction
3144973,0.899,4,18,/canonwu/save-intermediate-results-for-stacking,Santander Customer Transaction Prediction
3127602,0.8959999999999999,37,103,/graf10a/logistic-regression-with-new-features-feather,Santander Customer Transaction Prediction
3141941,0.894,0,1,/marcocarnini/lightgbm-model-optimizing-learning-reate,Santander Customer Transaction Prediction
3139313,0.887,3,3,/karangautam/everything-overfits,Santander Customer Transaction Prediction
3003424,0.899,18,46,/kwonyoung234/too-many-names-of-params-in-lgbm,Santander Customer Transaction Prediction
3116135,0.895,1,4,/zakraicik/ensemble-gnb-xgboost-lb-0-895,Santander Customer Transaction Prediction
3123104,0.879,1,8,/axel81/nn-model-using-fastai-1-0-lb-score-0-879,Santander Customer Transaction Prediction
3108572,0.898,4,27,/dromosys/gpu-accelerated-lightgbm-full,Santander Customer Transaction Prediction
3122780,0.887,2,2,/marcospcsj/simple-xgboost-test,Santander Customer Transaction Prediction
3077305,0.8740000000000001,0,0,/amishgadigi/blind-kfold-stack-xgboost-prediction,Santander Customer Transaction Prediction
3112742,0.8440000000000001,0,2,/rahul516/santander-decisiontree,Santander Customer Transaction Prediction
3016212,0.899,14,32,/mytymohan/sct-prediction-eda-smote-lgbm,Santander Customer Transaction Prediction
3096541,0.897,1,4,/x8x8d3n9/simple-lightbgm-baseline-model,Santander Customer Transaction Prediction
3086967,0.878,1,0,/jialinzhang/xgb-bayesianoptimization,Santander Customer Transaction Prediction
3025964,0.747,0,0,/ashirahama/simple-random-forest,Santander Customer Transaction Prediction
3031453,0.898,1,1,/psnjiki/gboosters-lb-898,Santander Customer Transaction Prediction
3073065,0.682,0,0,/jialinzhang/dataexploration-randomforest-bayesianoptimization,Santander Customer Transaction Prediction
3040314,0.79,0,1,/zakraicik/fast-ai-starter-w-smote-resample,Santander Customer Transaction Prediction
2160105,1811953.68,24,213,/seshadrikolluri/understanding-the-problem-and-some-sample-paths,Traveling Santa 2018 - Prime Paths
2167585,1811964.66,0,38,/zikazika/greedy-algorithm-approach,Traveling Santa 2018 - Prime Paths
2156469,446884407.52,5,22,/yukikubo123/how-to-calculate-the-score,Traveling Santa 2018 - Prime Paths
2154955,215829922.41,0,9,/miklgr500/simple-visualization,Traveling Santa 2018 - Prime Paths
2154711,446884407.52,0,5,/naivelamb/sample-submission,Traveling Santa 2018 - Prime Paths
12327863,0.99467,0,0,/nitinranjansharma/mnist-try-1,Digit Recognizer
12305349,0.99528,1,2,/arturnrnberg/digit-recognizer-keras-cnn-0-993,Digit Recognizer
11955185,0.9915,1,1,/elmetejon/digit-recognizer-using-pytorch-from-scratch,Digit Recognizer
12242884,0.99553,0,6,/e194235/mnist-digit-recognition-with-cnn-keras,Digit Recognizer
12263000,0.9705,0,0,/swastik25/mnist-digit-recognizer-with-pytorch,Digit Recognizer
12233077,0.99389,1,2,/alok10/number-classification-cnn,Digit Recognizer
12234781,0.97675,0,1,/agentauers/demo-mnist,Digit Recognizer
12216311,0.99178,0,0,/taiguang/simple-cnn-model-for-mnist,Digit Recognizer
12099647,0.99625,10,7,/toyox2020/mnist-using-keras-cnn-ensembling-0-996-accuracy,Digit Recognizer
12178567,0.96914,2,5,/gauravduttakiit/digit-recognizer-using-catboost,Digit Recognizer
12219286,0.98914,0,0,/pranoseedavoor/notebook48e809666f,Digit Recognizer
13773813,0.901,13,36,/shubham108/cassava-ensemble-efnetb3-resnet50,Cassava Leaf Disease Classification
13685132,0.894,0,7,/dimitreoliveira/cassava-supervised-contrastive-learning-inference,Cassava Leaf Disease Classification
13753285,0.899,2,1,/hinamimi/efficientnet-baseline-inference-tta,Cassava Leaf Disease Classification
13684511,0.8909999999999999,0,0,/nurkasimov/cassava-leaf-disease-submission,Cassava Leaf Disease Classification
13550912,0.8759999999999999,0,0,/surayuthpintawong/inference-cassava,Cassava Leaf Disease Classification
13460607,0.895,0,0,/qq1623620766/pytorch-efficientnet-baseline-no-tta,Cassava Leaf Disease Classification
13615431,0.845,0,0,/andrcaruso/vgg11-bn-primeira,Cassava Leaf Disease Classification
13273599,0.9,44,90,/dimitreoliveira/cassava-leaf-disease-tpu-v2-pods-inference,Cassava Leaf Disease Classification
13587861,0.879,0,1,/bruno2s/notebook-20201912-v0,Cassava Leaf Disease Classification
13555023,0.847,1,14,/sayooj98/cassava-leaf-classification-simple-cnn,Cassava Leaf Disease Classification
13567367,0.868,2,2,/jeevanbhoot/cassava-classification-fastai,Cassava Leaf Disease Classification
13499132,0.898,1,7,/kanruwang/ensemble-efficientnet-and-resnext-inference,Cassava Leaf Disease Classification
8153802,0.58757,0,6,/achintyatripathi/categorical-feature-encoding-by-lr,Categorical Feature Encoding Challenge II
9250741,0.78438,0,0,/parlin987p/labels-encoding-abishek-video,Categorical Feature Encoding Challenge II
7731399,0.78562,0,1,/itsbitan/encode-categorical-variable,Categorical Feature Encoding Challenge II
8630111,0.67533,0,0,/xanthate/categorical-encoding,Categorical Feature Encoding Challenge II
8444927,0.78451,2,2,/nickteim/categorical-feature-ii-fastai-v3,Categorical Feature Encoding Challenge II
8583815,0.67374,1,3,/dalsa17/comprehensive-feature-encoding-beginner,Categorical Feature Encoding Challenge II
8595982,0.76218,0,0,/hi2de5/random-state-334,Categorical Feature Encoding Challenge II
8541103,0.7448,0,2,/devicharith/categorical-features-xgbclassifer-quicklook,Categorical Feature Encoding Challenge II
8546984,0.78518,0,2,/worldkeeping/benchmark-lightgbm-model-feature-importance,Categorical Feature Encoding Challenge II
8495011,0.78623,0,1,/ricopue/keras-embedding-catboost,Categorical Feature Encoding Challenge II
8355518,0.7863899999999999,0,1,/shubhamlekhwar/categorical-feature-encoding-challenge-ii,Categorical Feature Encoding Challenge II
7561101,0.557,0,0,/pjrenzov/categorical-encoding-ii,Categorical Feature Encoding Challenge II
8160345,0.78556,1,6,/adarshsng/target-encoding-neural-network-easy-peasy,Categorical Feature Encoding Challenge II
8120993,0.78522,0,2,/darwinwin/cats-ii-with-h2o-automl,Categorical Feature Encoding Challenge II
8014133,0.78606,3,9,/scirpus/complicated,Categorical Feature Encoding Challenge II
7995631,0.74436,1,2,/vahidsa/simple-way-v2,Categorical Feature Encoding Challenge II
7959225,0.78669,7,24,/siavrez/libffm-stratified-with-blend,Categorical Feature Encoding Challenge II
7996863,0.78533,0,0,/scirpus/categorical-done-the-right-way,Categorical Feature Encoding Challenge II
7913720,0.76197,0,1,/tunguz/cat-ii-et-regressor-baseline-sklearn,Categorical Feature Encoding Challenge II
5168791,0.773,4,17,/serengil/finding-relative-faces,Northeastern SMILE Lab - Recognizing Faces in the Wild
5040596,0.703,0,6,/shubhendumishra/modified-siamese-network-pytorch,Northeastern SMILE Lab - Recognizing Faces in the Wild
4895255,0.5,0,1,/shubhendumishra/recognizing-faces-in-the-wild-vggface-pytorch,Northeastern SMILE Lab - Recognizing Faces in the Wild
4713401,0.746,0,1,/nikhil1011/similar-blood-relationship-challenge,Northeastern SMILE Lab - Recognizing Faces in the Wild
4216092,0.872,27,31,/hsinwenchang/vggface-baseline-197x197,Northeastern SMILE Lab - Recognizing Faces in the Wild
4151725,0.636,0,5,/jiangstein/a-very-simple-siamese-network-in-pytorch,Northeastern SMILE Lab - Recognizing Faces in the Wild
4152966,0.8240000000000001,1,20,/janpreets/just-another-feature-extractor-0-824-lb,Northeastern SMILE Lab - Recognizing Faces in the Wild
3974276,0.58,0,2,/khiwila/kernel992c53437e,Northeastern SMILE Lab - Recognizing Faces in the Wild
3901211,0.792,4,15,/caseyworks/open-kimono-kinship-notebook-0-792lb,Northeastern SMILE Lab - Recognizing Faces in the Wild
3912391,0.5,0,2,/forhadsidhu/sample-solution-kinship,Northeastern SMILE Lab - Recognizing Faces in the Wild
4559652,0.894,0,0,/supportvectordevin/liangde-model,Northeastern SMILE Lab - Recognizing Faces in the Wild
645905,0.44025,0,1,/jayspeidell/predictions-as-features,Mercari Price Suggestion Challenge
647712,0.46979,0,1,/cychang/price-prediction-by-keras-ann-v1,Mercari Price Suggestion Challenge
604220,0.52198,0,0,/alexeysrus/fork-of-fork-of-fork-of-notebooke19d21d65c-026057,Mercari Price Suggestion Challenge
626872,0.4407,0,0,/jayspeidell/stacked-ridge-and-lightgbm,Mercari Price Suggestion Challenge
626871,0.4781899999999999,0,0,/danieleewww/forked-from-only-model-tflearn-13-by-gouthamand,Mercari Price Suggestion Challenge
591222,0.7548600000000001,0,1,/nareshsrikakulapu/sub-median,Mercari Price Suggestion Challenge
582472,0.47378,0,1,/jayspeidell/eda-and-simple-ridge-regression,Mercari Price Suggestion Challenge
547559,0.74264,5,6,/girlduck/item-description-cnn-word2vec,Mercari Price Suggestion Challenge
563309,0.50133,2,0,/warpri81/mercari-first-nn-lb-0-50974,Mercari Price Suggestion Challenge
569879,0.70377,0,0,/fhzh123/practice,Mercari Price Suggestion Challenge
569678,0.96158,0,0,/shwetasahu/notebook34175d1c4a,Mercari Price Suggestion Challenge
555364,0.4271,15,115,/valkling/mercari-rnn-2ridge-models-with-notes-0-42755,Mercari Price Suggestion Challenge
539854,0.45497,0,1,/leansa/lerenbeslissen17-629d2e-fc42b1,Mercari Price Suggestion Challenge
523727,0.65691,0,0,/yoichi7yamakawa/mercari-base-model1,Mercari Price Suggestion Challenge
512959,0.47789,0,2,/afetisov/mercari-ensemble-regression-hw8,Mercari Price Suggestion Challenge
503263,0.7338600000000001,0,1,/ytsai43/first-trail-svr,Mercari Price Suggestion Challenge
504745,0.4487899999999999,0,1,/zhangruinan9652/catboost-model-0,Mercari Price Suggestion Challenge
488294,0.44646,20,84,/tilii7/cross-validation-weighted-linear-blending-errors,Mercari Price Suggestion Challenge
37346,0.8655799999999999,0,0,/lisenegger/script-2,Airbnb New User Bookings
35269,0.8655799999999999,0,0,/satomacoto/script-0-1,Airbnb New User Bookings
29721,0.8506,0,5,/totalrecall/getting-started-1-0,Airbnb New User Bookings
27260,0.0,0,0,/totalrecall/getting-started,Airbnb New User Bookings
5877343,0.5009,0,0,/hemantdata/trail-1,IEEE-CIS Fraud Detection
5871427,0.9048,6,7,/scirpus/50-parameters-with-frequency-analysis,IEEE-CIS Fraud Detection
5814118,0.9145,0,2,/naikparag/simple-xgb-fraud-detection,IEEE-CIS Fraud Detection
5741302,0.8063,0,4,/kartikathale/fraud-detection-eda-basic-logistic-regression,IEEE-CIS Fraud Detection
5803113,0.8963,9,19,/scirpus/50-features-with-target-encoding,IEEE-CIS Fraud Detection
5833026,0.7132,0,0,/deeplearn1/logistic-kfold,IEEE-CIS Fraud Detection
5612619,0.8962,0,2,/mubasharnazarawan/cis-fraud-detection,IEEE-CIS Fraud Detection
5788922,0.9415,1,1,/xwxw2929/lgb-kfold,IEEE-CIS Fraud Detection
5816131,0.9351,0,3,/nikhilbalwani/fraud-detection-xgb,IEEE-CIS Fraud Detection
5755487,0.9205,0,7,/kulkarnivishwanath/ieee-cis-fraud-detection,IEEE-CIS Fraud Detection
5624008,0.9219,0,5,/eligijus/lgb-boosting-using-a-custom-pipeline,IEEE-CIS Fraud Detection
5695133,0.9527,31,224,/paulorzp/gmean-of-low-correlation-lb-0-952x,IEEE-CIS Fraud Detection
5656703,0.9407,0,7,/aussie84/quick-xgb-model-diagnostics,IEEE-CIS Fraud Detection
5662673,0.9176,0,1,/harshitt21/ieee-cis-fraud-detection,IEEE-CIS Fraud Detection
5572180,0.948,24,132,/kyakovlev/ieee-simple-lgbm,IEEE-CIS Fraud Detection
4374092,0.5,0,2,/hoodini/amazon-employee-access-random-forest,Amazon.com - Employee Access Challenge
3414311,0.90589,0,32,/dmitrylarko/kaggledays-sf-3-amazon-supervised-encoding,Amazon.com - Employee Access Challenge
2698176,0.8533,0,15,/lucamassaron/kaggle-days-paris-skopt-catboost-solution,Amazon.com - Employee Access Challenge
3373035,0.387,3,15,/masterscrat/crash-course-what-you-need-to-know-to-compete,PetFinder.my Adoption Prediction
3333119,0.4579999999999999,17,44,/rookzeno/ensemble-other-people-kernel,PetFinder.my Adoption Prediction
3326181,0.106,0,0,/szuhuas/vgg16,PetFinder.my Adoption Prediction
3291573,0.3289999999999999,0,0,/irischiba/kerneld311276057,PetFinder.my Adoption Prediction
3245148,0.315,0,0,/artula/forest-and-gradient-boosting,PetFinder.my Adoption Prediction
2731372,0.257,0,0,/longfin/kernela31ef89928,PetFinder.my Adoption Prediction
3165425,0.321,0,0,/vineeladumpa/pet-finder-predictions,PetFinder.my Adoption Prediction
3132650,0.003,1,48,/ateplyuk/keras-starter-images-only,PetFinder.my Adoption Prediction
3076355,0.319,0,0,/olivbau/petfinder-olivbau,PetFinder.my Adoption Prediction
2747385,0.0,1,3,/aniketkatyal/pet-adoption-analysis,PetFinder.my Adoption Prediction
3139359,0.312,0,0,/kushpal/kernel73971049d2,PetFinder.my Adoption Prediction
2988649,0.354,0,1,/phongnguyen1906/simple-nn-model,PetFinder.my Adoption Prediction
3075454,0.367,0,1,/joonasmaanonen/practice-rf-by-a-new-kaggler,PetFinder.my Adoption Prediction
2974585,0.431,7,17,/chriskeown/baselinemodeling-keown,PetFinder.my Adoption Prediction
2548323,0.322,0,0,/s3rg388/kernel4337896d9e,PetFinder.my Adoption Prediction
2936015,0.427,31,269,/wrosinski/baselinemodeling,PetFinder.my Adoption Prediction
2937955,0.375,1,13,/anhquan0412/build-nn-model-with-early-stopping-using-fastai,PetFinder.my Adoption Prediction
2961115,0.195,0,0,/parmeet1992/logistic-regresssion,PetFinder.my Adoption Prediction
2901523,0.414,9,62,/myltykritik/simple-lgbm-image-features,PetFinder.my Adoption Prediction
2895355,0.336,3,2,/deepbilal/using-fastai-tabular-on-petfinder,PetFinder.my Adoption Prediction
2803079,0.33,2,2,/conradws/don-t-put-cats-and-dogs-in-the-same-teapot,PetFinder.my Adoption Prediction
14687644,0.69548,0,8,/somayyehgholami/0-69470-results-driven-january-tabular-301,Tabular Playground Series - Jan 2021
14587054,0.69691,4,20,/fatihozturk/models-stacking-3rd-place-solution,Tabular Playground Series - Jan 2021
14412255,0.6984199999999999,15,20,/hyperbeam/simple-stacking-with-ensemble-regressors,Tabular Playground Series - Jan 2021
14463946,0.72782,2,14,/ankitverma2010/tubular-playground-regression,Tabular Playground Series - Jan 2021
14495071,0.7039300000000001,4,9,/nguyncaoduy/fastai-tabular-regression-model-nn-xgb,Tabular Playground Series - Jan 2021
14438581,0.70063,6,8,/chandraroy/ensemble-techniques-xgb-lgb-regressor,Tabular Playground Series - Jan 2021
14527832,0.70106,1,7,/tunguz/jan-21-tps-h2o-automl,Tabular Playground Series - Jan 2021
14461056,0.70409,10,6,/henseljahja/h2o-automl-python,Tabular Playground Series - Jan 2021
14515126,0.69802,0,5,/marionhesse/hyperopt-xgboost-parameter-tuning,Tabular Playground Series - Jan 2021
14546409,0.7025,3,3,/gvyshnya/h2o-automl-with-additional-features,Tabular Playground Series - Jan 2021
14384808,0.71423,3,4,/jamesmcguigan/fast-ai-tabular-solver,Tabular Playground Series - Jan 2021
14194052,0.6967800000000001,0,3,/hal1001k/tabular-playground-jan-2021-eda,Tabular Playground Series - Jan 2021
14428754,0.70243,2,3,/ca3295/lightgbm-basics,Tabular Playground Series - Jan 2021
14555764,0.7288399999999999,0,2,/ezzzio/tabular-playgroud-series,Tabular Playground Series - Jan 2021
14497208,0.71386,0,2,/tunguz/tps-jan-2021-with-mlpregressor,Tabular Playground Series - Jan 2021
14330582,0.7279899999999999,3,2,/padmanabhabanerjee/gymkhana,Tabular Playground Series - Jan 2021
14149162,0.70134,0,1,/cafalchio/simple-tabular-with-autogluon,Tabular Playground Series - Jan 2021
14570320,0.6989,0,0,/nicholaskarlson/tps-hyperopt-run-jan2021,Tabular Playground Series - Jan 2021
13987553,0.70957,0,0,/kagglepij/tabular-playground-xgboost,Tabular Playground Series - Jan 2021
14239535,0.7935800000000001,0,0,/kingabzpro/tf-regression-approach,Tabular Playground Series - Jan 2021
12604636,0.93424,0,2,/divyansh22/lightning-fast-xgboost-classifier-with-rapids,University of Liverpool - Ion Switching
8207533,0.938,0,0,/brodzik/lightgbm-ion-switching,University of Liverpool - Ion Switching
8698902,0.922,0,1,/nizamuddin/ionchannels,University of Liverpool - Ion Switching
10769060,0.93904,0,2,/anycode/simple-mlp,University of Liverpool - Ion Switching
9146145,0.944,0,0,/akashsuper2000/wavenet-with-shifted-rfc-proba-and-cbr,University of Liverpool - Ion Switching
9849396,0.94376,0,17,/titericz/randomforest-on-gpu-in-3-minutes,University of Liverpool - Ion Switching
9741150,0.94719,2,30,/cdeotte/top-solutions-ensemble-0-947,University of Liverpool - Ion Switching
9496442,0.252,0,0,/nekkittay/ion-switching-notebook,University of Liverpool - Ion Switching
8658377,0.944,0,2,/graf10a/is-knn-313-cv5-sine-filtered,University of Liverpool - Ion Switching
9588066,0.945,5,32,/gunesevitan/ion-switching-26-model-gbdt-ensemble,University of Liverpool - Ion Switching
9711351,0.93471,0,1,/neomatrix369/54-features-model-clean-datasets,University of Liverpool - Ion Switching
9634271,0.929,0,7,/dragonsan17/simple-single-feature-svm,University of Liverpool - Ion Switching
9506824,0.926,0,2,/omesan619/single-feature-models,University of Liverpool - Ion Switching
9408128,0.938,17,21,/nischaydnk/ion-switching-with-rapids-visualization,University of Liverpool - Ion Switching
9296304,0.924,0,2,/sidneyng/liverpool-ion-switching-inceptiontime-fast-ai,University of Liverpool - Ion Switching
13921061,0.77986,0,0,/janurbanek/notebook51f12119e9,What's Cooking?
13312303,0.77956,0,0,/semenedel/notebookffee6c6549,What's Cooking?
8845857,0.60378,0,0,/sidagar/what-s-cooking,What's Cooking?
6050549,0.77956,0,0,/hgimonet/cs5785-a2-p1p2,What's Cooking?
1266167,0.77242,1,0,/zhoulingyan0228/cuisine-type-classification,What's Cooking?
219265,0.70233,0,1,/mandeep94/finding-cuisine,What's Cooking?
95813,0.7866,0,0,/darshanms1991/msd-notebook,What's Cooking?
1197620,0.9132,0,1,/limitpointinf0/eda-and-naive-bayes,Toxic Comment Classification Challenge
1054412,0.9852,0,8,/tunguz/glove-and-fasttext-blender,Toxic Comment Classification Challenge
1022878,0.8447,0,1,/gavarnamarn/toxicity-using-doc2vec,Toxic Comment Classification Challenge
820538,0.9784,7,65,/larryfreeman/toxic-comments-code-for-alexander-s-9872-model,Toxic Comment Classification Challenge
751542,0.9786,2,3,/arturlacerda/nbsvm-with-classifierchain-ensemble-lb-0-9786,Toxic Comment Classification Challenge
750995,0.9809,0,3,/abhi111/naive-bayes-baseline-and-logistic-regression,Toxic Comment Classification Challenge
733917,0.9838,0,4,/harshaneigapula/cnn-lstm,Toxic Comment Classification Challenge
734280,0.9738,2,1,/byrachonok/keras-gru-toxic-comments-classificator,Toxic Comment Classification Challenge
729600,0.979,0,1,/shihabshahriar/regressionv1,Toxic Comment Classification Challenge
715363,0.9809,2,19,/tottenham/ridge-with-words-and-char-n-grams-lb-0-9809,Toxic Comment Classification Challenge
714659,0.9821,36,185,/chongjiujjin/capsule-net-with-gru,Toxic Comment Classification Challenge
716125,0.9772,0,1,/rajathmc/toxic-comment-classifier,Toxic Comment Classification Challenge
689999,0.9725,0,1,/mattwills8/no-bullsh-t-tfidf-sgd-no-blend-run-on-kaggle,Toxic Comment Classification Challenge
8621579,0.631,4,9,/mohamedkhamis/bi-lstm-glove-preprocessing-lb-0-605,Tweet Sentiment Extraction
8591257,0.142,1,2,/rahulvks/key-term-s-extraction-using-nlp,Tweet Sentiment Extraction
8590234,0.662,10,36,/gskdhiman/bert-baseline-starter-kernel-ner-approach,Tweet Sentiment Extraction
8572383,0.696,13,55,/vaishvik25/albert-train-infer,Tweet Sentiment Extraction
8558878,0.708,24,162,/shahules/complete-eda-baseline-model-0-708-lb,Tweet Sentiment Extraction
8582205,0.0,3,10,/parthplc/starter-code-for-bert,Tweet Sentiment Extraction
8556458,0.0,2,12,/nandhuelan/tweet-extraction-simple-approaches,Tweet Sentiment Extraction
8551335,0.589,0,4,/jmourad100/tweet-sentiment-extraction-baseline,Tweet Sentiment Extraction
13100956,0.47908,0,0,/amittal1be17/notebookebe921b54f,Tweet Sentiment Extraction
10528445,0.70065,0,0,/hassanelmenier/nnets-final-project-qa-distilbert-ep2,Tweet Sentiment Extraction
9659519,0.708,0,0,/kishor1210/tse2020-roberta-cnn-random-seed-distribution,Tweet Sentiment Extraction
9345967,0.708,0,0,/yashsharma9897/tensorflow-roberta-cnn-head-lb-v2,Tweet Sentiment Extraction
8876620,0.703,0,0,/akashsuper2000/bert-base-with-tf2-1-mixed-precision,Tweet Sentiment Extraction
8789148,0.62,0,0,/akashsuper2000/tweet-sentiment-eda-pre-processing-extractor,Tweet Sentiment Extraction
14272371,0.7822899999999999,2,0,/mahmoudyasser/getting-started-with-titanic,Titanic - Machine Learning from Disaster
13870647,0.78708,0,0,/liweijun/final-version,Titanic - Machine Learning from Disaster
13463794,0.77272,0,0,/fuki29/eda-titanic,Titanic - Machine Learning from Disaster
10612176,0.77272,0,0,/rohitroychowdhury0/kernel36239a5421,Titanic - Machine Learning from Disaster
14225396,0.7751100000000001,1,3,/frecklebars/ml-flow-based-visual-coding-using-ryven,Titanic - Machine Learning from Disaster
14218617,0.77751,2,6,/tombowe/titanic-ml-notebook,Titanic - Machine Learning from Disaster
9230711,0.7751100000000001,0,0,/chernenkoruslan/titanic-ml-model,Titanic - Machine Learning from Disaster
7407205,0.79904,0,0,/leledan/titanic2,Titanic - Machine Learning from Disaster
11705038,0.76794,0,0,/satyads/post-pruned-decision-trees,Titanic - Machine Learning from Disaster
14074051,0.79425,3,1,/abhisr/getting-started-with-titanic,Titanic - Machine Learning from Disaster
13634920,0.77751,0,0,/danilorosmaninho/danilo-titanic-n2,Titanic - Machine Learning from Disaster
14204208,0.76555,0,0,/nitinbhore3/titanic-machine-learning-from-disaster,Titanic - Machine Learning from Disaster
8041190,1.0,0,3,/super13579/arc-color-change-detect-by-in-output-with-cv,Abstraction and Reasoning Challenge
7993147,1.0,1,9,/jazivxt/the-river,Abstraction and Reasoning Challenge
9395709,0.98,0,0,/akashsuper2000/decision-tree-smart-data-augmentation,Abstraction and Reasoning Challenge
8921372,0.99,0,0,/akashsuper2000/lgbmclassifier-for-arc-challenge,Abstraction and Reasoning Challenge
9294494,0.0009,0,1,/grapestone5321/global-wheat-detection-sample-submission,Global Wheat Detection
11019385,0.6962,0,0,/youngsteringabout/fork-of-fasterrcnn-resnet101-tta-inference,Global Wheat Detection
10965184,0.7709999999999999,0,0,/akashsuper2000/yolov5-pseudo-labeling-oof-evaluation-4322a2,Global Wheat Detection
10700347,0.7244,0,0,/zekunn/inference-efficientdet4,Global Wheat Detection
10525991,0.7316,0,0,/akashsuper2000/yolov5-pseudo-labeling,Global Wheat Detection
9914096,0.7223,0,0,/akashsuper2000/global-wheat-detection-pseudo-labelling,Global Wheat Detection
9455891,0.479,11,25,/superkevingit/faster-rcnn-with-mmdetection-without-internet,Global Wheat Detection
9421951,0.6009,1,24,/abhishek/w-t-f-ml-wheat-inference,Global Wheat Detection
9406914,0.527,6,18,/kyoshioka47/pytorch-centernet-inference-with-tta,Global Wheat Detection
9371498,0.6267,0,10,/nvnnghia/keras-centernet-inference,Global Wheat Detection
9343046,0.5749,2,50,/ateplyuk/gwd-starter-efficientdet-inference,Global Wheat Detection
9376790,0.0009,0,2,/aviralpamecha/kernel51ee132115,Global Wheat Detection
9285304,0.1647,8,32,/bendvd/global-wheat-detection-first-submission,Global Wheat Detection
9288903,0.0044,5,33,/pednt9/gwd-keras-unet-starter,Global Wheat Detection
12278835,0.6057600000000001,4,1,/ktykohbe/my-study-kobe-bryant-shot-selection,Kobe Bryant Shot Selection
1193119,0.63054,0,0,/myxue4869/kobe-keras-loocv,Kobe Bryant Shot Selection
247002,0.6016,3,19,/aakashkerawat/exploring-and-predicting-lb-score-0-60160,Kobe Bryant Shot Selection
620779,0.84991,0,0,/ctuckercva/messing-with-kobe-again,Kobe Bryant Shot Selection
8450934,1.77239,0,6,/benhamner/no-new-cases-baseline-covid-19-week-1-global,COVID19 Global Forecasting (Week 1)
8574200,0.77812,0,0,/akashsuper2000/covid19-global-forecasting,COVID19 Global Forecasting (Week 1)
8558602,0.8285299999999999,0,0,/jaygaud1/jay123,COVID19 Global Forecasting (Week 1)
8522725,1.56908,0,0,/skeller/sigmoid-per-country,COVID19 Global Forecasting (Week 1)
13480826,0.79186,0,0,/mellebrouwer/titanic-baseline,Titanic - Machine Learning from Disaster
14113286,0.76555,1,1,/abhivadali35/titanic-beginner-s-ml-w-logistic-regression,Titanic - Machine Learning from Disaster
13838894,0.77751,0,0,/davidcanozafra/titanic-analysis,Titanic - Machine Learning from Disaster
14133000,0.78947,0,3,/christinezh0u/titanic-data-prediction,Titanic - Machine Learning from Disaster
14100672,0.7368399999999999,0,0,/krishna2196/titanic,Titanic - Machine Learning from Disaster
14143502,0.77272,1,2,/denisziborov/titanic-stepik-introduction-to-data-science-method,Titanic - Machine Learning from Disaster
14140133,0.75358,0,0,/torstenknop/titanic-beginner,Titanic - Machine Learning from Disaster
14135882,0.7751100000000001,0,0,/subhamsagarpaira/beginners-logistic-regression-titanic,Titanic - Machine Learning from Disaster
14123777,0.76794,0,2,/dkumar12/titanic-using-xgboost,Titanic - Machine Learning from Disaster
14126650,0.7822899999999999,0,0,/sanskrutighadipatil/titanic-logistic-regression,Titanic - Machine Learning from Disaster
14117012,0.7751100000000001,4,2,/brainjar/titanic-tutorial,Titanic - Machine Learning from Disaster
14161529,0.78468,1,3,/harshavarshney/notebookcb0497dca0,Titanic - Machine Learning from Disaster
14097212,0.81339,1,4,/xinyuanzuo/titanic-pytorch,Titanic - Machine Learning from Disaster
13794910,0.78468,0,1,/ubiratanfilho/titanic-with-randomforestclassifier,Titanic - Machine Learning from Disaster
14073582,0.78708,0,1,/zavarzindmitry/simplecalcwithnoageandother,Titanic - Machine Learning from Disaster
8876993,0.695,1,3,/keremt/tse-transformers-q-a-with-fastai-inference,Tweet Sentiment Extraction
8987893,0.7120000000000001,0,2,/imdevskp/tweet-sentiment-extraction,Tweet Sentiment Extraction
8801108,0.5770000000000001,0,20,/bpkapkar/sentiment-extraction,Tweet Sentiment Extraction
8916215,0.7090000000000001,199,363,/cdeotte/tensorflow-roberta-0-705,Tweet Sentiment Extraction
8964894,0.703,0,1,/drhouse3/norm-mltsmpldrp,Tweet Sentiment Extraction
8869454,0.649,0,0,/saurabh1999/j034-j059-nlp-m3,Tweet Sentiment Extraction
8877984,0.43,0,0,/shankarjsharat/nlp-m3-j006-j025-j045-textblob,Tweet Sentiment Extraction
8873836,0.616,0,0,/aaryanjethva77/code-lad,Tweet Sentiment Extraction
8874022,0.604,0,1,/vishrutjai/kernel38136b6866,Tweet Sentiment Extraction
8802347,0.65194,7,9,/rajaram1988/ignored-stop-words-using-only-word-counts,Tweet Sentiment Extraction
8806297,0.486,1,6,/parmarsuraj99/inference-of-tweetspan,Tweet Sentiment Extraction
8758093,0.0,48,279,/abhishek/bert-base-uncased-using-pytorch,Tweet Sentiment Extraction
8749866,0.0,2,12,/maxjon/robreta-large-with-tricks,Tweet Sentiment Extraction
8723604,0.606,1,2,/schrammsm/tweets-simple-string-manipulation,Tweet Sentiment Extraction
8705907,0.6970000000000001,11,24,/raghaw/roberta-baseline-starter-test,Tweet Sentiment Extraction
8646643,0.7020000000000001,54,126,/doomdiskday/full-tutorial-eda-to-dnns-all-you-need,Tweet Sentiment Extraction
8676129,0.0,1,2,/kilseungahn/sensitivity-score-based-classification,Tweet Sentiment Extraction
679249,0.9595,0,12,/sanghan/attention-with-fasttext-embeddings,Toxic Comment Classification Challenge
551084,0.9776,0,0,/yehorsomov/notebook008c7f8fa3,Toxic Comment Classification Challenge
657419,0.8972,4,9,/anu0012/toxic-comments-classification-using-keras,Toxic Comment Classification Challenge
604201,0.9423,3,19,/umbertogriffo/cnn-yoon-kim-s-model-and-google-s-word2vec-model,Toxic Comment Classification Challenge
547643,0.9766,0,3,/twistedtensor/fork-of-basic-word2vec-using-gensim,Toxic Comment Classification Challenge
592473,0.9487,2,3,/vlasoff/keras,Toxic Comment Classification Challenge
577072,0.9825,0,9,/prashantkikani/toxic-simple-blending,Toxic Comment Classification Challenge
550896,0.9747,1,18,/tunguz/logistic-regression-tfidf,Toxic Comment Classification Challenge
519126,0.9772,12,57,/sudhirnl7/logistic-regression-tfidf,Toxic Comment Classification Challenge
512595,0.9779,39,38,/demesgal/lstm-glove-lr-decrease-bn-cv-lb-0-047,Toxic Comment Classification Challenge
500070,0.9792,1,19,/iamprateek/can-i-classify-toxic-comments,Toxic Comment Classification Challenge
4086190,1.239,0,5,/bhavesh09/simple-submission,Predicting Molecular Properties
5599813,-1.57229,2,0,/anermakov/rfs-with-distance-features-only-lb-1-57,Predicting Molecular Properties
5514608,-1.664,0,0,/filemide/keras-neural-net-and-distance-features,Predicting Molecular Properties
1885001,0.0,0,3,/frlhell/ship-detection-with-pytorch-unet,Airbus Ship Detection Challenge
1571658,0.6990000000000001,58,153,/iafoss/unet34-submission-tta-0-699-new-public-lb,Airbus Ship Detection Challenge
1379694,0.517,14,111,/kmader/transfer-learning-for-boat-or-no-boat,Airbus Ship Detection Challenge
1376411,0.847,8,47,/npatta01/naive-model,Airbus Ship Detection Challenge
6374294,0.5209,0,0,/maxboels/airbus-ship-detection-from-satellite-imagery,Airbus Ship Detection Challenge
2738770,0.287,0,7,/sun4gh/one-hot-encoding-categorical-data-using-sklearn,PetFinder.my Adoption Prediction
2792223,0.306,0,1,/mukesh62/lgbm-with-catboost,PetFinder.my Adoption Prediction
2727788,0.3,0,3,/nandals/getting-started-using-adaboostclassifier,PetFinder.my Adoption Prediction
2712726,0.3289999999999999,0,0,/dongwonl/petfinderpredictions-xgboost-and-random-forest,PetFinder.my Adoption Prediction
2718066,0.4,1,9,/tayyabali55/complete-pet-finder-analysis-with-lgbm-4-0,PetFinder.my Adoption Prediction
2709697,0.317,0,4,/kgeorge/tabular-data-pipeline,PetFinder.my Adoption Prediction
2730397,0.3,0,0,/gennadylaptev/petfinder-mlp-on-categorical-features,PetFinder.my Adoption Prediction
2697147,0.371,2,9,/takuok/word2vec,PetFinder.my Adoption Prediction
2697930,0.272,0,1,/abhineethmishra/using-random-forest,PetFinder.my Adoption Prediction
2670444,0.362,0,2,/husnusensoy/as-simple-as-it-gets,PetFinder.my Adoption Prediction
2648655,0.3339999999999999,0,0,/cloverdharmendra/hard-lgb,PetFinder.my Adoption Prediction
2509017,0.282,0,0,/erlichsefi/petfinder,PetFinder.my Adoption Prediction
2627553,0.306,1,1,/patricknguyen/machine-learning-for-noobs,PetFinder.my Adoption Prediction
2601905,0.32,2,11,/debkings/basic-python-prediction,PetFinder.my Adoption Prediction
2579090,0.0289999999999999,6,8,/timsonrisa/pytorch-fully-connected-net-starter,PetFinder.my Adoption Prediction
2587419,0.336,0,1,/matthewsparr/notebook-xgb-classifier,PetFinder.my Adoption Prediction
2560911,0.33,1,8,/andraszsom/shap-intro-actionable-insights-from-tabular-data,PetFinder.my Adoption Prediction
2555616,0.352,0,1,/mitjasha/simple-blending-petfinder,PetFinder.my Adoption Prediction
2541087,0.026,1,5,/gaborvecsei/adoption-speed-from-images,PetFinder.my Adoption Prediction
2543180,0.259,0,4,/mgreene02/random-forest-pet-adoption-speed-prediction,PetFinder.my Adoption Prediction
2528463,0.345,2,3,/hsankesara/petfinder-exploring-everything,PetFinder.my Adoption Prediction
2527013,0.331,0,1,/karunakars/petfinder,PetFinder.my Adoption Prediction
2521057,0.288,0,3,/asdftoger/quick-and-dirty-random-forest-petfinder,PetFinder.my Adoption Prediction
2516851,0.34,0,5,/amitrajan012/eda-and-lightgbm-only-train-data,PetFinder.my Adoption Prediction
2502708,0.358,8,31,/kikexclusive/curiosity-didn-t-kill-the-cat-all-in-one,PetFinder.my Adoption Prediction
11913896,0.465,9,52,/orkatz2/resnext-pulmonary-embolism-inference,RSNA STR Pulmonary Embolism Detection
11824336,0.585,0,7,/penchalaiah123/my-submission-with-mean-baseline-and-eda,RSNA STR Pulmonary Embolism Detection
11796169,0.434,0,18,/rezwan249/keras-model-creation-and-pe-detection-submission,RSNA STR Pulmonary Embolism Detection
12387205,0.233,0,0,/akashsuper2000/cnn-gru-baseline-stage2-train-inference,RSNA STR Pulmonary Embolism Detection
12273848,0.295,0,0,/akashsuper2000/monai-3d-cnn-inference,RSNA STR Pulmonary Embolism Detection
5521587,0.9232,0,2,/wrecked22/xgboost-tutorial,IEEE-CIS Fraud Detection
5521251,0.9056,5,8,/kimchiwoong/simple-fast-check-xgboost-prediction-performance,IEEE-CIS Fraud Detection
5465840,0.8951,0,4,/yuewangmoophy/fraud-detection-adaboost,IEEE-CIS Fraud Detection
5464235,0.9468,2,34,/kyakovlev/ieee-experimental,IEEE-CIS Fraud Detection
5458986,0.9381,0,1,/dinu1763/ieee-fraud-detection,IEEE-CIS Fraud Detection
5423072,0.9467,28,50,/gunesevitan/ieee-cis-fraud-detection-lightgbm,IEEE-CIS Fraud Detection
5275737,0.6932,0,0,/venkateshprabhug/fraud-detection,IEEE-CIS Fraud Detection
5274636,0.9373,0,5,/luisfredgs/ieee-cis-fraud-detection-catboost,IEEE-CIS Fraud Detection
5333769,0.9055,18,54,/carlolepelaars/ensembling-with-stacknet,IEEE-CIS Fraud Detection
5185061,0.9433,26,129,/plasticgrammer/ieee-cis-fraud-detection-eda,IEEE-CIS Fraud Detection
5316345,0.9408,8,33,/kyakovlev/ieee-ground-baseline,IEEE-CIS Fraud Detection
5282513,0.9442,12,34,/krishonaveen/xtreme-boost-and-feature-engineering,IEEE-CIS Fraud Detection
5256675,0.9218,10,92,/abazdyrev/keras-nn-focal-loss-experiments,IEEE-CIS Fraud Detection
477344,0.52879,1,0,/larui529/xgboost-2-fold-cv-lb-0-52879,Mercari Price Suggestion Challenge
451129,0.95749,0,0,/chabo130/first,Mercari Price Suggestion Challenge
480409,0.82478,0,0,/mnakajima75/first-commit,Mercari Price Suggestion Challenge
468790,0.55983,0,3,/ambar003/mercari-challenge,Mercari Price Suggestion Challenge
467168,0.45231,0,6,/omsairam/ensemble-of-lgbm-ridge-lb-0-45231,Mercari Price Suggestion Challenge
459788,0.60596,0,0,/motivic/basic-lightgbm-model-on-top-brands-and-category,Mercari Price Suggestion Challenge
449732,0.48368,85,280,/knowledgegrappler/a-simple-nn-solution-with-keras-0-48611-pl,Mercari Price Suggestion Challenge
447101,0.62851,11,22,/jkkphys/category-tf-idf-linear-regression,Mercari Price Suggestion Challenge
455736,0.79505,1,1,/anubhavbhardwaj/notebook8fae100d19,Mercari Price Suggestion Challenge
446832,0.55798,1,36,/bguberfain/naive-catboost,Mercari Price Suggestion Challenge
448142,0.7195699999999999,0,2,/jeru666/mercari,Mercari Price Suggestion Challenge
447090,0.58444,0,9,/tunguz/simple-blend,Mercari Price Suggestion Challenge
994585,0.4509899999999999,0,0,/alaahamzah/kerneld62fe542ce,Mercari Price Suggestion Challenge
642201,0.7360399999999999,0,0,/yuichielectric/mecari-competition,Mercari Price Suggestion Challenge
624719,0.47001,0,0,/soneo0/fork-of-a-simple-nn-solution-with-keras-0-48611,Mercari Price Suggestion Challenge
598683,0.54575,0,0,/tao416/tfidf-xgbregression,Mercari Price Suggestion Challenge
591176,0.82478,0,0,/nareshsrikakulapu/sub-mean,Mercari Price Suggestion Challenge
3526020,0.47207,0,0,/amelnozieres/bike-sharing-demand-rmsle-0-3194,Bike Sharing Demand
12980793,0.836,56,171,/iafoss/hubmap-pytorch-fast-ai-starter-sub,HuBMAP - Hacking the Kidney
13014716,0.742,4,9,/hypocrites/hubmap-sm-unet-256x256,HuBMAP - Hacking the Kidney
12951706,0.813,39,53,/leighplt/pytorch-fcn-resnet50,HuBMAP - Hacking the Kidney
12954560,0.8109999999999999,10,29,/joshi98kishan/hubmap-keras-pipeline-training-inference,HuBMAP - Hacking the Kidney
10781314,0.922,0,0,/jaidevchittoria/alaska-jd-2,ALASKA2 Image Steganalysis
10811881,0.94,6,24,/wuliaokaola/alaska2-best-b6-inference-private-lb-0-929,ALASKA2 Image Steganalysis
10799855,0.925,0,3,/tunguz/best-b1-inference,ALASKA2 Image Steganalysis
9861973,0.921,0,1,/akashsuper2000/train-inference-gpu-baseline-with-ensemble,ALASKA2 Image Steganalysis
10313776,0.741,0,6,/makhloufsabir/alaska2-image-steganalysis-with-efficientnetb3,ALASKA2 Image Steganalysis
10500977,0.922,16,52,/demesgal/train-inference-gpu-baseline-tta,ALASKA2 Image Steganalysis
10479403,0.921,5,27,/mahmudds/alaska2-image-eda-understanding-and-modeling,ALASKA2 Image Steganalysis
10400618,0.5579999999999999,3,15,/servietsky/alaska2-lgbm-classifier-with-hyperparameter-tuning,ALASKA2 Image Steganalysis
10308975,0.584,0,6,/urvishp80/tf-tpu-custom-training,ALASKA2 Image Steganalysis
10193292,0.921,0,18,/raviyadav2398/image-steganaysis,ALASKA2 Image Steganalysis
9824781,0.921,62,316,/shonenkov/train-inference-gpu-baseline,ALASKA2 Image Steganalysis
9612113,0.893,10,47,/khoongweihao/alaska2-stacking-gpu-tpu-models-a-starter-kit,ALASKA2 Image Steganalysis
9309739,0.614,0,2,/manyregression/alaska2-simple-fastai2,ALASKA2 Image Steganalysis
9286171,0.764,0,11,/rainmaker29/alaska2-efficientnet-on-tpus,ALASKA2 Image Steganalysis
1086159,2.30946,0,4,/kylingu/talkingdata-learning,TalkingData Mobile User Demographics
144515,2.26508,0,0,/xieyufish/gender-predictor,TalkingData Mobile User Demographics
12086307,0.98335,0,0,/mehmetlaudatekman/mnist-dataset-cnn-with-keras,Digit Recognizer
12004549,0.9915,0,0,/thulesen/mnist-basic,Digit Recognizer
12012054,0.98707,0,2,/quantumsnowball/digit-recognizer,Digit Recognizer
12006608,0.98014,0,1,/thibautlemarchand/mnist-data,Digit Recognizer
12012172,0.98367,0,0,/alexinicab/keras-lenet5,Digit Recognizer
11503477,0.98642,0,0,/tchristie/mnist-current,Digit Recognizer
11964083,0.99496,0,10,/toyox2020/mnist-with-keras-cnn-0-995-accuracy,Digit Recognizer
8042252,0.99614,0,4,/spiiiii/digit-recognizer-nn,Digit Recognizer
11839729,0.8654200000000001,6,7,/sahilmaheshwari/very-important-beginner-plots-with-mnist,Digit Recognizer
11907515,0.9661,0,1,/jaydewz/digit-recognizer-using-mlpclassifier,Digit Recognizer
11883776,0.97028,2,4,/alexinicab/neural-network-scikit-learn,Digit Recognizer
3015140,0.96857,0,0,/geoffreygeo/mnist-digit-recognizer,Digit Recognizer
10181118,0.98296,1,0,/olivergardiner/digit-recogniser-cnn-keras,Digit Recognizer
11813917,0.99814,0,3,/sohelranaccselab/digit-recognizer-using-cnn-top-4,Digit Recognizer
14015712,0.888,4,7,/saurabh2mishra/cassava-leaf-disease-inference-label-smoothing,Cassava Leaf Disease Classification
14102111,0.8809999999999999,0,0,/virajkadam/notebook5f7e85ec6e,Cassava Leaf Disease Classification
13945558,0.8059999999999999,1,1,/rijuvaish/leaf-disease-classification-using-residual-network,Cassava Leaf Disease Classification
13183814,0.773,7,13,/mohamedhanyyy/cassava-leaf-dc-using-cnn,Cassava Leaf Disease Classification
14012642,0.8390000000000001,0,0,/andrewtomich/cldc-resnet50-fastai,Cassava Leaf Disease Classification
13958924,0.9,8,17,/liustrong/efficientnet-model-ensemble-single-tta-inference,Cassava Leaf Disease Classification
14040094,0.882,0,0,/atsushiiwasaki/pytorch-cassava-baseline-efficientnet-b0,Cassava Leaf Disease Classification
13480186,0.585,0,0,/wumumu/notebookfc5c3a6220,Cassava Leaf Disease Classification
13000336,0.139,4,10,/anandsm7/starter-code-pytorch-efficientnetb4-0-87,Cassava Leaf Disease Classification
13875603,0.8270000000000001,12,39,/nozarchos/the-shortest-way-to-tensorflow-baseline,Cassava Leaf Disease Classification
13898823,0.765,10,90,/ateplyuk/simplest-starting-code-cassava-leaf-pytorch,Cassava Leaf Disease Classification
13978606,0.8909999999999999,0,0,/alekseyeliseev/cassava-disease-efficientnet-tf-inference,Cassava Leaf Disease Classification
13178735,0.884,0,0,/louisheublein/cassava-leaf-disease-classification-eda-pytorch,Cassava Leaf Disease Classification
13872051,0.898,11,46,/khoongweihao/insect-augmentation-et-al,Cassava Leaf Disease Classification
13903056,0.892,0,3,/yerramvarun/pytorch-efficientnetb4-svm-top-inference,Cassava Leaf Disease Classification
13628842,0.863,2,3,/shubham219/experiment-with-models-in-keras-inference,Cassava Leaf Disease Classification
13840280,0.8420000000000001,0,2,/user123454321/pytorch-densenet121-starter-inference,Cassava Leaf Disease Classification
7926822,0.78392,0,1,/nayuts/cats-ii-xgboost-with-simple-encodings,Categorical Feature Encoding Challenge II
7890260,0.7852,0,3,/kaggleurroad/cats-ii-with-rapids-ridge-regression,Categorical Feature Encoding Challenge II
7886229,0.78493,9,31,/ogrellier/libffm-model,Categorical Feature Encoding Challenge II
7863893,0.78557,2,1,/davidbnn92/imputation-by-regression-of-woes,Categorical Feature Encoding Challenge II
7832710,0.78563,3,5,/davidbnn92/weight-of-evidence-encoding,Categorical Feature Encoding Challenge II
7714000,0.78579,21,19,/siavrez/comparing-imputation-for-ordinal-data,Categorical Feature Encoding Challenge II
7528989,0.7850600000000001,1,7,/itsbitan/encode-categorical-features,Categorical Feature Encoding Challenge II
7456554,0.78259,0,3,/nyk510/categorical-encoding-by-simple-target-encoding,Categorical Feature Encoding Challenge II
7624547,0.78378,1,3,/lsinev/catboost-with-bayes-opt,Categorical Feature Encoding Challenge II
7594732,0.78474,8,17,/vadbeg/pytorch-nn-with-embeddings-and-catboost,Categorical Feature Encoding Challenge II
7547024,0.78077,3,4,/aanubhav/almost-fastai-defaults-crossvalidation,Categorical Feature Encoding Challenge II
7531726,0.78205,1,6,/alexandervc/simple-catboost-cat-in-dat-ii,Categorical Feature Encoding Challenge II
7468352,0.78033,2,4,/nicapotato/categorical-tabular-pytorch-classifier,Categorical Feature Encoding Challenge II
7407695,0.77217,1,10,/nicapotato/whats-new-sklearn-0-22-1-cat-classifier-stack,Categorical Feature Encoding Challenge II
7446812,0.7858,4,8,/horohoro/categorical-feature-encoding2-logisticregression,Categorical Feature Encoding Challenge II
7435731,0.7841,0,4,/alexandervc/pure-lightgbm-with-tuned-params-0-78410,Categorical Feature Encoding Challenge II
7381053,0.78455,2,1,/horohoro/categorical-feature-encoding2-eda-catboost-train,Categorical Feature Encoding Challenge II
7337560,0.78623,11,169,/abhishek/same-old-entity-embeddings,Categorical Feature Encoding Challenge II
7317035,0.65494,14,23,/marcovasquez/basic-eda-categoricals-values,Categorical Feature Encoding Challenge II
7339655,0.69197,1,1,/ryohgy/simply-drop-columns-with-high-cardinality,Categorical Feature Encoding Challenge II
7331068,0.5,1,1,/grapestone5321/categorical-feature-sample-submission,Categorical Feature Encoding Challenge II
10082925,0.7287899999999999,0,0,/visiteur/lab-cats-gr-boosting-with-minmaxscaler,Categorical Feature Encoding Challenge II
13603554,0.6579999999999999,0,0,/paulorblima/notebook80a7b1d2d3,Cassava Leaf Disease Classification
13469610,0.88,1,26,/ipythonx/tf-keras-cassava-advanced-training-mechanism,Cassava Leaf Disease Classification
13453615,0.883,26,59,/harveenchadha/effnetb4-tf-data-gpu-aug-5x-speedup-tta,Cassava Leaf Disease Classification
13478565,0.84,0,0,/taichinakabeppu/tutorial-create-model-train-submit,Cassava Leaf Disease Classification
13437860,0.882,1,11,/benjibb/resnet50-pretrained-fastai,Cassava Leaf Disease Classification
13368254,0.865,0,1,/negeek/fastai-xresnet18-mixup,Cassava Leaf Disease Classification
13300987,0.879,4,12,/anukool89/efficientnet-with-pytorch-lightning-train-infer,Cassava Leaf Disease Classification
13256167,0.858,0,6,/pranavuikey/fastai-training-inference,Cassava Leaf Disease Classification
13337205,0.893,0,3,/kami2suukyi/pytorch-efficientnet-train-infer-easy-to-improve,Cassava Leaf Disease Classification
13314900,0.88,3,12,/shariarriday/keras-efficientnet-cnn-with-k-fold,Cassava Leaf Disease Classification
13323125,0.602,0,0,/shrutisaxena/resnet50-e10,Cassava Leaf Disease Classification
13345799,0.8859999999999999,0,0,/pawan28a95/fastai-baseline,Cassava Leaf Disease Classification
13247858,0.903,40,211,/piantic/no-tta-cassava-resnext50-32x4d-inference-lb0-903,Cassava Leaf Disease Classification
12763275,0.95553,0,6,/rajeev064/cnn-keras-accuracy-0-95553-mnist,Digit Recognizer
12617004,0.97907,0,1,/spaghettiidawg666/mnist-arnav-sangamnerkar,Digit Recognizer
12685240,0.98375,0,0,/brunotriebus/notebookaaa955e698,Digit Recognizer
12714797,0.99485,0,0,/viacheeselove/cnn-mnist,Digit Recognizer
12644849,0.98189,0,2,/jagdmir/digit-recognizer-simple-model-with-0-98150-score,Digit Recognizer
12680723,0.97814,0,0,/shuntakinami/practice-digit-recog,Digit Recognizer
12247040,0.99121,0,0,/mohandgamal/cnn-for-begineers,Digit Recognizer
11966240,0.99289,0,0,/anuragiitr1823/digit-recogition,Digit Recognizer
12482444,0.99528,1,4,/iamandrewliao/mnist-cnn,Digit Recognizer
12510073,0.96232,0,1,/ikaynov/kaggle-introduction-digit-recognition,Digit Recognizer
12500723,0.93957,0,0,/kentaroh34/mnist-mlp,Digit Recognizer
12477197,0.99632,0,6,/tqch2020/mnist-torch-cnn-da-bn-dropout-ensemble-acc99-6,Digit Recognizer
12506433,0.99196,0,0,/mabalogun/data-recognition-with-conv-neural-network,Digit Recognizer
12403998,0.99639,0,1,/matthieuplante/digit-recognition-w-ensembling,Digit Recognizer
11422522,0.47355,12,21,/chandrimad31/suggest-automatic-price-advanced-text-processing,Mercari Price Suggestion Challenge
9623199,0.42811,0,0,/yogeeshwari/gru-kaggle,Mercari Price Suggestion Challenge
9945533,0.44016,0,0,/rajarshi253/rajarshi-kernel48ed8e3714,Mercari Price Suggestion Challenge
6740467,0.64242,1,4,/shivashi11/mercary-price-competition,Mercari Price Suggestion Challenge
6199824,0.43308,0,1,/shubhanshi8/price-prediction-models-lb-0-43,Mercari Price Suggestion Challenge
5880362,0.44896,0,2,/jubergandharv/stacked-1ridge-1lgbm,Mercari Price Suggestion Challenge
4860022,0.45631,0,1,/conformal/ridge,Mercari Price Suggestion Challenge
4851694,0.5010100000000001,0,1,/marwanelghitany/mercari-with-eda,Mercari Price Suggestion Challenge
4299141,0.68789,0,0,/akumaldo/mercari-price-lgb-model,Mercari Price Suggestion Challenge
3849557,0.95771,0,0,/ikefuji/sample-random-forest,Mercari Price Suggestion Challenge
3244803,0.5302399999999999,0,0,/badreeshshetty/mercari-price-suggestion-challenge-xgb-lgbm,Mercari Price Suggestion Challenge
535019,0.42876,0,0,/anderstr/my-mercari-notebook,Mercari Price Suggestion Challenge
1452288,0.59234,1,1,/plasticgrammer/mercari-price-suggestion-playground,Mercari Price Suggestion Challenge
2049055,0.6845,0,0,/shubhamshishodia/mercari-price-suggestion-exploratory-analysis,Mercari Price Suggestion Challenge
511120,0.46561,0,0,/mtander/mercari-price-predictions-ridge-ensemble,Mercari Price Suggestion Challenge
1276756,0.44327,0,1,/lintingle/mercari-price-suggestion-challenge,Mercari Price Suggestion Challenge
739490,0.49817,0,0,/lanhpham/linear-regression,Mercari Price Suggestion Challenge
574775,0.68034,0,0,/gosuddin/mercari-predictions-using-random-forest,Mercari Price Suggestion Challenge
11047240,0.067,0,0,/akashsuper2000/enet-on-cosface-with-distributed-tf,Google Landmark Retrieval 2020
11174333,0.278,0,5,/prateekagnihotri/lr-aug-add,Google Landmark Retrieval 2020
11097006,0.0,0,8,/rajivranjansingh/starter-code-pre-trained-keras-inceptionv3,Google Landmark Retrieval 2020
10639277,0.089,0,7,/micheomaano/creating-submission-from-your-own-model,Google Landmark Retrieval 2020
10734980,0.271,0,5,/chandanverma/baseline-submission-retrieval-2020,Google Landmark Retrieval 2020
10881060,0.057,9,31,/akensert/glret-cosface-with-distributed-tf,Google Landmark Retrieval 2020
10777451,0.271,0,17,/vineeth1999/baseline-model-0-271,Google Landmark Retrieval 2020
10687097,0.271,0,5,/mahmudds/glr-2020-eda,Google Landmark Retrieval 2020
4836224,0.9387,182,896,/kabure/extensive-eda-and-modeling-xgb-hyperopt,IEEE-CIS Fraud Detection
4873912,0.9376,15,52,/chizuchizu/japanese-python-first-code,IEEE-CIS Fraud Detection
4846958,0.9417,45,193,/vincentlugat/ieee-lgb-bayesian-opt,IEEE-CIS Fraud Detection
4867140,0.9374,1,5,/danofer/ieee-fraud-features-xgboost-0-934-lb,IEEE-CIS Fraud Detection
4839300,0.9398,18,107,/artkulak/ieee-fraud-simple-baseline-0-9383-lb,IEEE-CIS Fraud Detection
4881126,0.936,0,1,/om1042/lgb-xgb,IEEE-CIS Fraud Detection
4868158,0.9335,0,3,/mastergogo/fraud-detection,IEEE-CIS Fraud Detection
4833297,0.93952,12,73,/tunguz/ieee-with-h2o-automl,IEEE-CIS Fraud Detection
4853266,0.9346,1,0,/frizzles7/fraud-detection-preliminary-analysis,IEEE-CIS Fraud Detection
4831760,0.9201,0,21,/robikscube/baseline-catboost-feat-importance,IEEE-CIS Fraud Detection
4831485,0.927,4,14,/ryches/ieee-ensemble-xgboost-lgbm-w-earlystopping,IEEE-CIS Fraud Detection
4839227,0.907,0,7,/ricardtrinchet/ieee-eda-random-forest-model,IEEE-CIS Fraud Detection
4831695,0.928,0,13,/smerllo/lgbm-baseline-features-importance,IEEE-CIS Fraud Detection
4831798,0.9196,1,5,/vchulski/ieee-simple-models-comparison,IEEE-CIS Fraud Detection
6059453,0.91,0,0,/egolinko/dimension-reduction-and-xgboost-train,IEEE-CIS Fraud Detection
5753416,0.9485,0,0,/dcstang/ieee-lgbm-with-groupkfold-cv-2,IEEE-CIS Fraud Detection
1674761,0.4141399999999999,0,0,/johnfarrell/dvc-advanced-model,Dogs vs. Cats Redux: Kernels Edition
1443020,0.05605,0,5,/aquatiko/dogs-vs-cats-fast-ai,Dogs vs. Cats Redux: Kernels Edition
1438301,0.06963,16,53,/gpreda/cats-or-dogs-using-cnn-with-transfer-learning,Dogs vs. Cats Redux: Kernels Edition
1390653,1.54905,0,0,/aamnafea/dogs-vs-cats-cnn,Dogs vs. Cats Redux: Kernels Edition
909205,0.57785,0,3,/escalante/cats-dogs-alexnet,Dogs vs. Cats Redux: Kernels Edition
1014165,5.626869999999999,0,12,/deadskull7/cats-vs-dogs-84,Dogs vs. Cats Redux: Kernels Edition
917349,0.6543,1,23,/shaochuanwang/keras-warm-up-cats-vs-dogs-cnn-with-vgg16,Dogs vs. Cats Redux: Kernels Edition
531265,0.76513,14,119,/sarvajna/dogs-vs-cats-keras-solution,Dogs vs. Cats Redux: Kernels Edition
3336970,0.3279999999999999,0,0,/hugoboum/petfinder-comp,PetFinder.my Adoption Prediction
5988472,0.0,0,2,/vumichien/ml2-final-projects,PetFinder.my Adoption Prediction
2767224,0.24,0,0,/amiron/task3,PetFinder.my Adoption Prediction
4703768,0.0,0,0,/bravenoob/mas-keras-dataframe,PetFinder.my Adoption Prediction
5524736,0.0,0,0,/bravenoob/sentiment-analyse-mas-image-captioning,PetFinder.my Adoption Prediction
4654084,0.0,0,0,/vite99999/petfinder,PetFinder.my Adoption Prediction
3573605,0.0,0,0,/jky594176/justtrysth,PetFinder.my Adoption Prediction
3273685,0.456,0,1,/kishi001/xgboost-model-updated,PetFinder.my Adoption Prediction
3367883,0.4539999999999999,1,1,/insaff/xgboost-blending-full-model,PetFinder.my Adoption Prediction
3354927,0.45953,0,1,/budiryan/32nd-solution-stacking-of-5-models,PetFinder.my Adoption Prediction
3303276,0.479,0,6,/jmyrberg/final-model-1-best-lb-31-lb,PetFinder.my Adoption Prediction
3397768,0.461,0,0,/matsuik/stratifiedgroupkfold-stratified-groupcv,PetFinder.my Adoption Prediction
3404048,0.465,0,1,/bigswimatom/ensemble-8-model,PetFinder.my Adoption Prediction
3312804,0.457,0,0,/kumarajay/single-xgboost-model,PetFinder.my Adoption Prediction
3486342,0.0,0,0,/dan3dewey/simple-scikit-models-and-stacking,PetFinder.my Adoption Prediction
3170988,0.465,0,5,/axel81/ensemble-of-xgboost-lgbm-59th-place-solution,PetFinder.my Adoption Prediction
14309939,0.69852,20,19,/sakuraandblackcat/leaning-validation-curve-and-optuna-for-gbdts,Tabular Playground Series - Jan 2021
14259415,0.69819,21,35,/sakuraandblackcat/leaning-validation-curve-optuna-for-gbdts,Tabular Playground Series - Jan 2021
14289120,0.69765,0,0,/ashishtop/jan-tabular-playground-competition,Tabular Playground Series - Jan 2021
14248069,0.69688,0,0,/blighpark/merge-results-tabular-playground-series,Tabular Playground Series - Jan 2021
14338693,0.6986100000000001,0,0,/santabudi/jan-tabular-playground-competition,Tabular Playground Series - Jan 2021
14234158,0.69735,50,99,/hamzaghanmi/xgboost-hyperparameter-tuning-using-optuna,Tabular Playground Series - Jan 2021
14262521,0.6991,2,8,/sahintiryaki/xgboost,Tabular Playground Series - Jan 2021
14215799,0.71919,14,12,/neilgibbons/tuning-tabnet-with-optuna,Tabular Playground Series - Jan 2021
14040323,0.69882,5,11,/kingabzpro/automljar,Tabular Playground Series - Jan 2021
14171184,0.69926,2,4,/rmiperrier/tps-jan-easy-lgb-with-pycaret,Tabular Playground Series - Jan 2021
14068166,0.7273,0,2,/elvenmonk/genetic-programming-sample-gplearn,Tabular Playground Series - Jan 2021
14137778,0.70423,2,3,/code1110/tabplay-xgboost-seed-average,Tabular Playground Series - Jan 2021
14129092,0.70394,4,2,/sanikamal/tabular-playground-i-tree-based-algorithms,Tabular Playground Series - Jan 2021
6437505,1.4,0,8,/hanjoonchoe/ashrae-lgbm-with-optuna-demonstration,ASHRAE - Great Energy Predictor III
6360642,1.23,10,31,/kyakovlev/ashrae-catboost,ASHRAE - Great Energy Predictor III
6346627,2.13,0,1,/teeyee314/how-low-can-we-go,ASHRAE - Great Energy Predictor III
6329054,1.31,0,1,/yujaiol/kernel4aa1c5a92e,ASHRAE - Great Energy Predictor III
6236424,1.121,89,289,/corochann/ashrae-training-lgbm-by-meter-type,ASHRAE - Great Energy Predictor III
6241124,1.103,26,80,/kulkarnivishwanath/ashrae-great-energy-predictor-iii-eda-model,ASHRAE - Great Energy Predictor III
6249697,1.38,8,6,/khairulislam/great-energy-predictor-intro-with-lightgbm,ASHRAE - Great Energy Predictor III
6235197,3.813,16,87,/tunguz/simple-linear-regression-benchmark,ASHRAE - Great Energy Predictor III
6260723,1.34,2,2,/georsara1/starter-great-energy-predictor,ASHRAE - Great Energy Predictor III
8722524,0.36,0,0,/manyregression/fastai-v2-tabular,University of Liverpool - Ion Switching
8622503,0.939,0,8,/nxhong93/feature-precess,University of Liverpool - Ion Switching
9080449,0.8270000000000001,1,17,/johnoliverjones/both-sides-now,University of Liverpool - Ion Switching
9040071,0.929,2,7,/ricopue/ion-switching-in-20-lines-9-29,University of Liverpool - Ion Switching
8921146,0.942,40,156,/mobassir/understanding-ion-switching-with-modeling,University of Liverpool - Ion Switching
8953123,0.927,0,12,/ejunichi/for-japanese-one-feature-model,University of Liverpool - Ion Switching
8868713,0.936,4,36,/group16/lb-0-936-1-feature-forward-backward-vs-viterbi,University of Liverpool - Ion Switching
8851543,0.932,1,21,/miklgr500/viterbi-algorithm-without-segmentation-on-groups,University of Liverpool - Ion Switching
8776634,0.941,10,34,/code1110/ion-weighted-voting-ensemble,University of Liverpool - Ion Switching
8637050,0.94,2,32,/teejmahal20/wavenet-keras-kalman-filter-pre-processing,University of Liverpool - Ion Switching
8643496,0.939,5,11,/icarofreire/only-one-model,University of Liverpool - Ion Switching
8564417,0.939,10,28,/ragnar123/single-model-lgbm,University of Liverpool - Ion Switching
8421205,0.94,40,104,/martxelo/fe-and-ensemble-mlp-and-lgbm,University of Liverpool - Ion Switching
8270097,0.5710000000000001,3,20,/johnoliverjones/frequency-domain-filtering,University of Liverpool - Ion Switching
8495344,0.932,13,109,/friedchips/the-viterbi-algorithm-a-complete-solution,University of Liverpool - Ion Switching
8181876,0.8490000000000001,0,0,/varunyadav17/liverpool-ion-switching,University of Liverpool - Ion Switching
8464122,0.93766,0,1,/scirpus/alright-la,University of Liverpool - Ion Switching
8441853,0.613,0,1,/adnaiksachin25/summary-features-knn-model,University of Liverpool - Ion Switching
6618813,0.8578,0,0,/aabhi202/only-submissions,Kannada MNIST
6582045,0.9812,0,2,/amneves/kannada-with-tensorflow-keras-83-7-dig,Kannada MNIST
6520964,0.9904,14,49,/kenanajk/understanding-cnns-with-kannada-mnist,Kannada MNIST
6534712,0.9874,0,9,/alexkct/kannadamnist-fast-ai-alexkct,Kannada MNIST
6242744,0.9888,4,9,/nachiket273/fastai-unfreeze,Kannada MNIST
6440759,0.7022,1,7,/chariots17/my-first-comptition,Kannada MNIST
6492370,0.9784,0,2,/chmarco97/kannada-mnist,Kannada MNIST
6479905,0.9754,0,2,/rdenadai/kannada-mnist,Kannada MNIST
6469869,0.9832,0,3,/airk0126/my-first-kaggle-kernels-only-competition-keras,Kannada MNIST
6430258,0.9848,0,0,/shrishahs/kannadamnistbeginnerstry,Kannada MNIST
6421213,0.8756,1,7,/polyzer/hyperparameters-tuning-with-kerastuner-hyperresnet,Kannada MNIST
6373413,0.9572,1,10,/rhtsingh/tensorflow-2-0-kannada-mnist,Kannada MNIST
6298117,0.9714,0,1,/al4win/kernel-kannada-01,Kannada MNIST
6299122,0.9782,0,2,/yiranupup/kannada-mnist,Kannada MNIST
6042854,0.984,0,1,/sathkrith/cnn-for-digits,Kannada MNIST
6299598,0.9784,0,0,/natavidad/kaggle-day-1,Kannada MNIST
6220325,0.9772,0,1,/thomasschafer97/keras-kannada-mnist,Kannada MNIST
6219218,0.9828,0,4,/douglascamargo/kannada-mnist,Kannada MNIST
6211956,0.1008,2,5,/jsvishnuj/kannada-language-recognizer-deep-learning,Kannada MNIST
6128843,0.9902,21,75,/bustam/cnn-in-keras-for-kannada-digits,Kannada MNIST
6204020,0.9632,0,0,/harshavardhanbabu/kannadamnist-catalyst-pytorch,Kannada MNIST
6023617,0.13719,0,1,/sanath123/easy-and-quick-keras-model-for-kannada-mnist,Kannada MNIST
6118256,0.9854,9,35,/deepakdeepu8978/kannada-mnist-using-cnn-with-acc-98,Kannada MNIST
6077287,0.9876,13,43,/datahobbit/cnn-for-digit-recognition-in-the-kannada-script,Kannada MNIST
6078394,0.9884,0,5,/ragnar123/cnn-baseline-3-shuffle-folds,Kannada MNIST
9631062,0.7020000000000001,0,1,/gopidurgaprasad/tweet-xml-roberta-base-inf,Tweet Sentiment Extraction
9878200,0.703,12,34,/rohitsingh9990/data-augmentation-by-synonym-replacement,Tweet Sentiment Extraction
9890623,0.701,2,13,/vpkprasanna/text-analysis-eda-wordcloud-ngram,Tweet Sentiment Extraction
9878908,0.286,0,1,/zerowith/tweet-sentiment-extraction,Tweet Sentiment Extraction
9757535,0.579,1,25,/anasofiauzsoy/tweet-sentiment-extraction-with-tf2-spanbert,Tweet Sentiment Extraction
9874849,0.713,5,8,/ashoksrinivas/roberta-with-iii-conv-layers,Tweet Sentiment Extraction
9943580,0.235,0,0,/jesusv/kernel7e5289cf60,Tweet Sentiment Extraction
9873544,0.708,0,1,/simonta/tensorflow-roberta-one-model-per-sentiment,Tweet Sentiment Extraction
9835468,0.708,0,0,/jonykarki/pseudo-labeled-data,Tweet Sentiment Extraction
9697618,0.594,0,6,/garbamoussa/tweet-sentiment-extraction-0-594,Tweet Sentiment Extraction
9538677,0.59082,0,1,/salmacmpeg/kernel-attention-with-convolution,Tweet Sentiment Extraction
9575636,0.7120000000000001,0,3,/ram2tayal/tweet-sentiment-eda,Tweet Sentiment Extraction
9695811,0.706,0,27,/ryches/alternate-span-selection-dynamic-padding-apex,Tweet Sentiment Extraction
9578325,0.711,0,0,/laluwalke/kernel571326273e,Tweet Sentiment Extraction
9642287,0.62,7,6,/koushiksahu/roberta-extremely-verbosed-for-beginners,Tweet Sentiment Extraction
9633362,0.7040000000000001,2,6,/saimanojakondi/roberta-0-713,Tweet Sentiment Extraction
9607257,0.594,0,3,/xiu0714/silly-model,Tweet Sentiment Extraction
628493,0.961,0,0,/rahuld/classifying-multi-label-comments-0-9741-lb,Toxic Comment Classification Challenge
603536,0.9224,0,0,/kuntalcse006/sentimentanalysis2,Toxic Comment Classification Challenge
586140,0.97,0,0,/wangsg/hand-by-hand-to-solve-nlp-problem,Toxic Comment Classification Challenge
13913476,0.7751100000000001,0,1,/fanbyprinciple/titanic-2-fastai-v4-kernel-submit,Titanic - Machine Learning from Disaster
13873876,0.7799,0,1,/avirajroy/titanic-ml-hands-on,Titanic - Machine Learning from Disaster
13821516,0.7822899999999999,0,0,/nitiruengcharoen/titanic-survival-prediction,Titanic - Machine Learning from Disaster
8148191,0.76076,0,1,/hassanw65/titanic-machine-learning-from-disaster,Titanic - Machine Learning from Disaster
11901220,0.78708,7,7,/saptarshisit/my-first-kaggle-work-titanic,Titanic - Machine Learning from Disaster
13834670,0.75598,4,8,/stephanbauer/titanic-my-first-dive-into-ml-classification,Titanic - Machine Learning from Disaster
7734599,0.76555,0,0,/debanjalibasu/titanic-ml,Titanic - Machine Learning from Disaster
13663126,0.79186,0,1,/elyousfiomar/top8-titanic-disaster-logistic-regression,Titanic - Machine Learning from Disaster
13773137,0.78708,9,15,/sethnaman/titanic,Titanic - Machine Learning from Disaster
13773786,0.80861,25,39,/serorjb/top-3-extremely-randomized-trees,Titanic - Machine Learning from Disaster
13719069,0.76794,11,20,/utkarshxy/best-algorithms-comparison-xg-ada-grboot-included,Titanic - Machine Learning from Disaster
13753919,0.75598,16,8,/mahmoudyussef/titanic-expecting-survivals,Titanic - Machine Learning from Disaster
13750996,0.7703300000000001,20,16,/kritidoneria/automl-titanic-using-pycaret,Titanic - Machine Learning from Disaster
12769445,0.72009,0,3,/bjoernholzhauer/lightgbm-tuning-with-optuna,Titanic - Machine Learning from Disaster
13756684,0.78708,3,4,/sahintiryaki/titanic,Titanic - Machine Learning from Disaster
13743948,0.7703300000000001,0,6,/spbalameghanashivani/titanic-dataset-prediction-logistic-regression,Titanic - Machine Learning from Disaster
9466862,0.98,0,1,/giangpt/memory-approach-to-arc,Abstraction and Reasoning Challenge
9800433,0.813,0,28,/ilialar/3rd-place-end-to-end-solution,Abstraction and Reasoning Challenge
9772270,0.99,0,7,/yueqiw/pretraining-knowledge-priors-with-transformers,Abstraction and Reasoning Challenge
9639129,0.99,0,3,/jamesmcguigan/arc-oo-framework-xgboost-multimodel-solvers,Abstraction and Reasoning Challenge
9743969,0.96,1,20,/jpbremer/very-simple-dsl-solving-up-to-6-tasks,Abstraction and Reasoning Challenge
9749800,0.95,0,6,/szabo7zoltan/repairingmosaicsandsymmetry,Abstraction and Reasoning Challenge
9344610,0.96,0,3,/bitthal/dsl-ensemble-0-96,Abstraction and Reasoning Challenge
9666173,0.98,1,11,/vbmokin/outliers-analysis-lb-0-98,Abstraction and Reasoning Challenge
9566704,1.0,0,6,/borisov/symmetry-pattern,Abstraction and Reasoning Challenge
9266713,0.98,15,81,/adityaork/decision-tree-smart-data-augmentation,Abstraction and Reasoning Challenge
9252362,1.0,0,0,/isikkuntay/strange-loss-function,Abstraction and Reasoning Challenge
9159997,1.0,6,28,/jamesmcguigan/arc-geometry-solvers,Abstraction and Reasoning Challenge
9096128,0.98,0,33,/backaggle/ensemble-from-public-kernels,Abstraction and Reasoning Challenge
8919585,1.0,0,0,/akashsuper2000/stacking-models-and-new-features-for-arc,Abstraction and Reasoning Challenge
8209868,1.0,0,2,/fernandeslouro/primus,Abstraction and Reasoning Challenge
8457029,0.99,5,35,/aaafgcfg/wow-0-990,Abstraction and Reasoning Challenge
8416825,0.99,10,121,/meaninglesslives/using-decision-trees-for-arc,Abstraction and Reasoning Challenge
8392802,1.0,0,7,/poteman/this-competition-is-so-hard,Abstraction and Reasoning Challenge
735073,0.598023,0,0,/tylerburkett/march-madness-predictions-clean,Google Cloud & NCAA® ML Competition 2018-Men's
698632,0.6232479999999999,7,12,/virtonos/advanced-basketball-analytics,Google Cloud & NCAA® ML Competition 2018-Men's
729419,0.600954,0,0,/naturebalance/submission-fork-of-ncaa-m-v9-selectinput,Google Cloud & NCAA® ML Competition 2018-Men's
54547,0.61778,0,1,/potterxu/sport,Kobe Bryant Shot Selection
10623753,0.6487,0,5,/bhavesjain/gw-infer,Global Wheat Detection
10585246,0.6658,0,8,/sherkt1/broken-framework,Global Wheat Detection
10525886,0.5786,7,12,/moximo13/inference-object-detection-with-transformers-detr,Global Wheat Detection
10545038,0.6812,0,4,/vishalpentakota/gwd-using-pytorch-fastercnn,Global Wheat Detection
10497631,0.5272,7,15,/ravirajsinh45/global-wheat-tensorflow-object-detection-api,Global Wheat Detection
10458725,0.6687,8,18,/raviyadav2398/wheat-detection,Global Wheat Detection
10467520,0.6914,1,4,/yearing1017/faster-rcnn-pytorch,Global Wheat Detection
10426019,0.1319,0,2,/kuzn137/corrected-u-net-starter,Global Wheat Detection
10361009,0.6715,0,1,/liranzxc/tta-rcnn-wbf,Global Wheat Detection
10355173,0.6394,1,6,/jonykarki/fasterrcnn-resnet101-inference,Global Wheat Detection
10217303,0.7389,33,78,/khoongweihao/insect-augmentation-with-efficientdet-d6,Global Wheat Detection
10361491,0.6147,0,0,/jay0118jay/kernel70d11cc72d,Global Wheat Detection
10229753,0.6777,21,26,/jqeric/detectors-new-sota-based-mmdetection,Global Wheat Detection
10255929,0.6297,0,1,/avivlazar/final-project-submission-only,Global Wheat Detection
10175078,0.6488,0,1,/facevisalilai/efficientdet,Global Wheat Detection
9569573,0.6687,0,1,/akashsuper2000/pytorch-starter-fasterrcnn-inference,Global Wheat Detection
10047807,0.5884,0,7,/kenextra/detectron2-first-kernel-with-segmentation-head,Global Wheat Detection
9530704,0.6179,0,6,/karanjakhar/simple-detectron2-prediction,Global Wheat Detection
9733563,0.6815,16,25,/nxhong93/wheat-detectron2,Global Wheat Detection
9651775,0.5542,0,3,/yiyunchen/fork-of-kernel1a58643656,Global Wheat Detection
59574,0.61121,0,2,/liziyuann/feature-selection,Kobe Bryant Shot Selection
55077,0.6087199999999999,0,0,/liziyuann/testing,Kobe Bryant Shot Selection
12220926,0.7751100000000001,0,0,/monishareddy9/getting-started-with-titanic,Titanic - Machine Learning from Disaster
14010291,0.7511899999999999,0,1,/dogdriip/decisiontreeclassifier,Titanic - Machine Learning from Disaster
14022669,0.77272,0,2,/iainmcintosh/usingrandomforestfortitanicdatasetpredictions,Titanic - Machine Learning from Disaster
14004344,0.7751100000000001,0,1,/mariamkotob/titanic-eda-getting-started,Titanic - Machine Learning from Disaster
13875924,0.78708,0,0,/mariacvesterli/portfolio-1b,Titanic - Machine Learning from Disaster
14064838,0.76555,0,0,/abdulwasae/notebookd54bc08cfc,Titanic - Machine Learning from Disaster
13964502,0.7822899999999999,4,5,/aguado/eda-ensemble-modeling-as-a-d-s-student,Titanic - Machine Learning from Disaster
13947810,0.7799,0,0,/aviadbuskila/aviad-s-titanic-project,Titanic - Machine Learning from Disaster
13898362,0.7822899999999999,0,0,/mariacvesterli/portfolio-1b-test,Titanic - Machine Learning from Disaster
12503631,0.7751100000000001,0,0,/amritanshupandey/kaggle1,Titanic - Machine Learning from Disaster
13921351,0.78947,1,6,/xiaokedou/titanic,Titanic - Machine Learning from Disaster
14000756,0.73444,0,0,/hrishikeshdeshmukh06/notebook4c4bc18c36,Titanic - Machine Learning from Disaster
13932128,0.71291,0,0,/leeikgyu/data-analysis-for-titanic,Titanic - Machine Learning from Disaster
12472732,0.7822899999999999,1,1,/loycelorenzo/titanic-machine-learning-from-disaster,Titanic - Machine Learning from Disaster
13920736,0.64593,0,0,/anunnikrishnan/titanic-ml-file-rf,Titanic - Machine Learning from Disaster
499816,0.9765,2,43,/gaussmake1994/word-character-n-grams-tfidf-regressions-lb-051,Toxic Comment Classification Challenge
12392476,0.97299,0,0,/thientoantran/jigsaw-toxic-comment-v1,Toxic Comment Classification Challenge
6570062,0.98215,0,0,/amir78pgd/fork-of-improved-lstm-baseline-fasttext-dropout,Toxic Comment Classification Challenge
6522471,0.98341,0,0,/samson22/bidirectional-lstm-with-convolution,Toxic Comment Classification Challenge
6134373,0.97676,0,0,/amir78pgd/improved-lstm-baseline-glove-dropout,Toxic Comment Classification Challenge
830760,0.9619,0,0,/ritupande/ltsm-for-text-classication,Toxic Comment Classification Challenge
783259,0.977,3,0,/jingqliu/conv2d-with-tensorflow-fasttext-2m,Toxic Comment Classification Challenge
743283,0.9637,0,0,/jayeshrane2107/choose-your-words-carefully,Toxic Comment Classification Challenge
715215,0.9815,0,0,/ritam3144/bi-gru-conv-with-feature-engineering,Toxic Comment Classification Challenge
9648411,0.423,0,1,/aman2000jaiswal/kernel4a949e9d46,Tweet Sentiment Extraction
9572215,0.514,0,2,/ankigupt/keyword-for-sentiment-analysis,Tweet Sentiment Extraction
9614294,0.655,0,0,/ivanl1/training-spacy-ner-with-early-stopping,Tweet Sentiment Extraction
9497304,0.7120000000000001,0,6,/elvictor/training-with-roberta-pretrained,Tweet Sentiment Extraction
9444482,0.364,0,0,/carlochen/submission-notebook,Tweet Sentiment Extraction
9472698,0.5329999999999999,0,0,/gyanaluckydas/w-o-neutral,Tweet Sentiment Extraction
9390988,0.7120000000000001,21,89,/seesee/faster-2x-tf-roberta,Tweet Sentiment Extraction
9184119,0.6859999999999999,0,0,/navesharma9/question-answer-distilbert,Tweet Sentiment Extraction
8997669,0.546,0,2,/xooca1/pytorch-lightning-tweet-sentiment-extraction,Tweet Sentiment Extraction
9307690,0.71,0,10,/rohitgr/roberta-with-pytorch-lightning-train-test-lb-0-710,Tweet Sentiment Extraction
9251329,0.635,0,0,/rajgupt/twitter-sentiment-model-1,Tweet Sentiment Extraction
9132915,0.7120000000000001,0,19,/mohannksr/tensorflow-roberta-cnn-head-lb-v2,Tweet Sentiment Extraction
9099708,0.594,0,0,/alexskrn/simple-nn,Tweet Sentiment Extraction
9004961,0.71,2,65,/enzoamp/commented-roberta-training-with-pytorch,Tweet Sentiment Extraction
9070204,0.6509999999999999,0,3,/meetashok/tweet-sentiment-extraction,Tweet Sentiment Extraction
9049670,0.332,0,1,/savanmorya/pure-pythonic-way-no-lstms-no-bert,Tweet Sentiment Extraction
9044749,0.483,0,6,/anandsubbu007/robert-model-beginners-nlp,Tweet Sentiment Extraction
9045652,0.6990000000000001,0,1,/cseamaoo/albert-large-300,Tweet Sentiment Extraction
6051542,0.9594,1,4,/hanjoonchoe/kannada-mnist-simple-neural-net,Kannada MNIST
6050619,0.918,4,5,/zmey56/kannada-mnist-ver4-randomforestclassifier,Kannada MNIST
5883926,0.974,0,2,/wakamezake/kannada-mnist-efficientnet,Kannada MNIST
6035880,0.9826,0,1,/mak4alex/kannada-mnis-competition,Kannada MNIST
5983181,0.984,3,7,/xwalker/kannada-mnist-minicnn,Kannada MNIST
5949515,0.9872,7,15,/demonplus/kannada-mnist-with-fast-ai-and-resnet,Kannada MNIST
5962267,0.8868,0,2,/tunguz/pca-nusvm-baseline,Kannada MNIST
5948686,0.9762,0,3,/prtkmeh/kannada-mnist-with-keras-cnn,Kannada MNIST
5944345,0.9486,0,1,/plarmuseau/pca-svm-baseline,Kannada MNIST
5917068,0.9822,4,8,/marcthalen/mnist-cnn-99-top-30-using-keras,Kannada MNIST
5915219,0.875,0,2,/cantaoml/lightgbm,Kannada MNIST
5903888,0.9862,0,6,/hocop1/i-can-t-overfit,Kannada MNIST
5895430,0.9432,0,9,/tunguz/truncatedsvd-t-sne-and-decision-tree-baseline,Kannada MNIST
5872933,0.9876,2,35,/hocop1/manifold-mixup-using-pytorch,Kannada MNIST
5888213,0.98,0,3,/robertopreste/kannada-mnist-with-keras-v1,Kannada MNIST
5872060,0.9842,4,8,/rohan9889/kannada-mnist-cnn-keras,Kannada MNIST
5862691,0.9012,3,14,/tunguz/kannada-mnist-logistic-regression-baseline,Kannada MNIST
5862695,0.9824,0,9,/danofer/pytorch-resnet34-starter-97-74-lb,Kannada MNIST
7749041,0.9838,0,0,/gundamb2/simple-eda-and-tf-keras-cnn,Kannada MNIST
6295067,0.9838,0,0,/mrugeles/kannada-mnist,Kannada MNIST
8383808,0.939,10,47,/teejmahal20/3-simple-ideas-ensemble,University of Liverpool - Ion Switching
8358775,0.938,2,4,/vladlee/ion-switching-lgb-regression-model,University of Liverpool - Ion Switching
8304373,0.925,5,9,/scirpus/scousers-genetic-programming,University of Liverpool - Ion Switching
8254819,0.927,119,484,/cdeotte/one-feature-model-0-930,University of Liverpool - Ion Switching
8240081,0.938,3,50,/tunguz/fe-pipeline-with-histgradientboostingregressor,University of Liverpool - Ion Switching
8225108,0.934,3,11,/vladlee/ion-lstm-baseline-feature-selection,University of Liverpool - Ion Switching
8206829,0.937,8,50,/gpreda/ion-switching-advanced-eda-and-prediction,University of Liverpool - Ion Switching
8197203,0.932,0,1,/teejmahal20/ion-switching-lgb-catb-optimized-rounder,University of Liverpool - Ion Switching
8189840,0.934,2,8,/alexfocus/ion-switching-lgb-catb-model,University of Liverpool - Ion Switching
8192210,0.784,0,3,/ragnar123/validation-scheme,University of Liverpool - Ion Switching
8148787,0.916,32,135,/kmat2019/u-net-1d-cnn-with-keras,University of Liverpool - Ion Switching
8158048,0.905,0,1,/anjeetkumar/ion-classification-switching-v-1-0,University of Liverpool - Ion Switching
8130056,0.402,7,57,/suicaokhoailang/an-embarrassingly-simple-baseline,University of Liverpool - Ion Switching
8126420,0.15,7,30,/sudalairajkumar/simple-exploration-notebook-ion-switching,University of Liverpool - Ion Switching
8132500,0.936,4,11,/pavelvpster/ion-switching-fe-lgb,University of Liverpool - Ion Switching
8130490,0.652,1,9,/hengzheng/an-embarrassingly-simple-baseline-0-961-lb,University of Liverpool - Ion Switching
8134886,0.467,2,1,/kunaal0/simple-starter-code-with-logistic-regression,University of Liverpool - Ion Switching
6238013,1.508,7,9,/hukuda222/basic-preprocessing-simple-lightgbm-model,ASHRAE - Great Energy Predictor III
6239800,1.606,7,4,/authman/copycat-ken,ASHRAE - Great Energy Predictor III
6237940,1.847,0,3,/dcaichara/simple-eda,ASHRAE - Great Energy Predictor III
7803913,1.101,0,0,/yunishi0716/half-and-half-model-ashre,ASHRAE - Great Energy Predictor III
6477980,1.13,0,0,/enzoamp/ashrae-eda-fe-fastai-3-fold,ASHRAE - Great Energy Predictor III
6233856,2.053,65,177,/isaienkov/lightgbm-fe-1-19,ASHRAE - Great Energy Predictor III
8507867,0.4418,0,0,/qiuzy2/sub-tversky-qzy,Airbus Ship Detection Challenge
6772640,0.40165,0,0,/choi98/airbus-ship-detection,Airbus Ship Detection Challenge
5260924,0.67593,0,0,/awater1223/unet-resnet34-for-ships,Airbus Ship Detection Challenge
2066981,0.512,0,4,/yassinealouini/airbus-quick-eda-mask-r-cnn-annotated,Airbus Ship Detection Challenge
1780540,0.66798,11,86,/hmendonca/airbus-mask-rcnn-and-coco-transfer-learning,Airbus Ship Detection Challenge
1970077,0.52,0,2,/jeffaudi/create-a-submission-with-no-ship-masks,Airbus Ship Detection Challenge
1968598,0.52,2,4,/alexanderliao/no-mask-prediction,Airbus Ship Detection Challenge
14110259,0.71138,0,1,/ricorauschkolb/fastai-nn,Tabular Playground Series - Jan 2021
14091662,0.69887,2,4,/rmiperrier/tps-jan-xgb-cat-lgb-with-pycaret,Tabular Playground Series - Jan 2021
14106538,0.98189,0,0,/yanngarcia/tps-jan-2021-generic-approach-of-machine-learning,Tabular Playground Series - Jan 2021
14060460,0.69931,11,17,/songrise/beginner-basic-eda-fine-tuning-model-stacking,Tabular Playground Series - Jan 2021
14053839,0.7039,1,5,/deb009/tabular-playground-series-jan-2021,Tabular Playground Series - Jan 2021
14017621,0.69904,6,14,/nyk510/vivid-baseline,Tabular Playground Series - Jan 2021
13991234,0.69824,3,22,/bowaka/tps21-optuna-lgb-fast-hyper-parameter-tunning,Tabular Playground Series - Jan 2021
14000637,0.6984600000000001,0,7,/mathurinache/pseudo-labelling,Tabular Playground Series - Jan 2021
13973114,0.69929,10,25,/oldwine357/tabular-series-eda-xgb-hyperparameter-tuning,Tabular Playground Series - Jan 2021
14054667,0.69957,0,0,/mathurinache/starter-lgb-with-fe,Tabular Playground Series - Jan 2021
13979629,0.69813,0,10,/mathurinache/gbdts-baseline-prevision-io-for-free,Tabular Playground Series - Jan 2021
13986132,0.7020000000000001,0,2,/pierresylvain/tabular-playground-2021-lgbm-xgboost,Tabular Playground Series - Jan 2021
13966573,0.7192,0,2,/nicapotato/keras-nn-tabular-regression-problem,Tabular Playground Series - Jan 2021
12657643,0.879,0,3,/aniketbiprojit/multi-unet,RSNA STR Pulmonary Embolism Detection
12390577,0.233,0,1,/c7934597/cnn-gru-baseline-stage2-train-inference,RSNA STR Pulmonary Embolism Detection
12130641,0.693,0,0,/senabay/rsna-images,RSNA STR Pulmonary Embolism Detection
12604202,0.159,0,1,/kazumax0720/rsna-str-pulmonary-embolism-detection-inference,RSNA STR Pulmonary Embolism Detection
12494179,0.154,0,2,/shimacos/rsna2020-inference-final,RSNA STR Pulmonary Embolism Detection
12403265,0.1889999999999999,4,14,/robikscube/b0b6-cnn-gru-baseline-stage2-train-inference,RSNA STR Pulmonary Embolism Detection
12480165,0.222,3,7,/stanleyjzheng/fast-baseline-with-tta-after-gru-8e13cc,RSNA STR Pulmonary Embolism Detection
12488419,0.161,0,2,/osciiart/rsna2020-final-sub,RSNA STR Pulmonary Embolism Detection
12489964,0.511,0,0,/imnishantg/stage2-prediction,RSNA STR Pulmonary Embolism Detection
12461792,0.233,3,18,/aikhmelnytskyy/cnn-gru-baseline-saving-time-and-gpu,RSNA STR Pulmonary Embolism Detection
12246095,0.295,19,168,/boliu0/monai-3d-cnn-inference,RSNA STR Pulmonary Embolism Detection
12168136,0.325,10,134,/osciiart/baseline-with-no-image,RSNA STR Pulmonary Embolism Detection
12071978,0.434,1,5,/sergei416/mean-baseline-submission,RSNA STR Pulmonary Embolism Detection
12019129,0.435,0,2,/akashsuper2000/mean-baseline,RSNA STR Pulmonary Embolism Detection
12031958,0.693,0,1,/superbo/rsna-pe-submission-notebook,RSNA STR Pulmonary Embolism Detection
5226084,0.9429,31,83,/timon88/lgbm-baseline-small-fe-no-blend,IEEE-CIS Fraud Detection
5220228,0.9303,0,5,/toto07/modeling-xgb-hyperopt-submit-test,IEEE-CIS Fraud Detection
5181148,0.9453,16,73,/nicapotato/gpyopt-hyperparameter-optimisation-gpu-lgbm,IEEE-CIS Fraud Detection
4847372,0.943,0,7,/takedown/starter-code-fraud-detection,IEEE-CIS Fraud Detection
5125964,0.9095,6,45,/ratan123/lgb-data-augmentation-starter,IEEE-CIS Fraud Detection
5098518,0.9419,50,258,/nroman/lgb-single-model-lb-0-9419,IEEE-CIS Fraud Detection
5095465,0.9339,20,69,/kirankunapuli/ieee-fraud-lightgbm-with-gpu,IEEE-CIS Fraud Detection
5047201,0.9392,10,21,/grantgasser/fraud-detection,IEEE-CIS Fraud Detection
4985914,0.9193,3,4,/nicapotato/fraud-shap-xgboost,IEEE-CIS Fraud Detection
4841257,0.93,12,35,/sunilsj99/fraud-detection-ieee,IEEE-CIS Fraud Detection
4878932,0.939,1,10,/terminate9298/ieee-fraud-baseline,IEEE-CIS Fraud Detection
4985159,0.943,1,8,/sanjusci/ieee-cis-fraud-detection,IEEE-CIS Fraud Detection
4977256,0.9112,1,1,/firmai/firmai-20-of-data-used,IEEE-CIS Fraud Detection
4951943,0.9427,5,15,/jolasa/stacking-higher-every-time-0-9427,IEEE-CIS Fraud Detection
4936032,0.9429,3,22,/hamditarek/ensemble-0-9429,IEEE-CIS Fraud Detection
4916505,0.7578,1,4,/tikedameu/anomaly-detection-with-autoencoder-pytorch,IEEE-CIS Fraud Detection
12098648,0.69062,0,1,/gizemcemileelik/airbnbsubmissions,Airbnb New User Bookings
8718289,0.8652299999999999,51,77,/krutarthhd/airbnb-eda-and-xgboost,Airbnb New User Bookings
6796506,0.8650899999999999,0,1,/jagannathrk/airbnb-eda,Airbnb New User Bookings
5936225,0.7114,0,0,/gemyhamed/aibnb-challenge,Airbnb New User Bookings
2478631,0.63667,0,1,/krunal3kapadiya/airbnb-new-user-data,Airbnb New User Bookings
2197725,0.8658899999999999,0,8,/justk1/airbnb,Airbnb New User Bookings
13976904,0.861,2,4,/mistag/inference-hubmap-fpn-single-model-ii,HuBMAP - Hacking the Kidney
13407632,0.8170000000000001,0,0,/jianguolin/notebook,HuBMAP - Hacking the Kidney
13675899,0.838,0,9,/zichengliu0226/pranet-hubmap,HuBMAP - Hacking the Kidney
13427934,0.8270000000000001,0,2,/zhkuo24/pytorch-fcn-resnet50-in-20-minute,HuBMAP - Hacking the Kidney
13268253,0.478,1,10,/igor14497/making-a-successful-submission,HuBMAP - Hacking the Kidney
13272168,0.423,0,1,/antonio5444/hubmap-pytorch-smp-unet-inference,HuBMAP - Hacking the Kidney
13247547,0.8140000000000001,1,11,/mistag/inference-hubmap-u-net-mobilenetv2-256x256,HuBMAP - Hacking the Kidney
728592,0.4822979999999999,0,1,/naturebalance/submission-fork-of-ncaa-w-v9-selectinput,Google Cloud & NCAA® ML Competition 2018-Women's
686618,0.0,1,5,/therealdaita/ncaa-womens-keras-network,Google Cloud & NCAA® ML Competition 2018-Women's
9224393,0.706,20,76,/mobassir/pytorch-tpu-transfer-learning-baseline,ALASKA2 Image Steganalysis
9158631,0.598,34,349,/tanulsingh077/steganalysis-complete-understanding-and-model,ALASKA2 Image Steganalysis
9176158,0.99,42,139,/xhlulu/alaska2-efficientnet-on-tpus,ALASKA2 Image Steganalysis
9173404,0.556,6,55,/tanulsingh077/steganalysis-a-knaive-approach,ALASKA2 Image Steganalysis
9171484,0.54,4,15,/miklgr500/jpeg-compression-rate,ALASKA2 Image Steganalysis
9175905,0.589,1,1,/akashsuper2000/jpeg-compression-rate,ALASKA2 Image Steganalysis
9409095,0.718,0,0,/akashsuper2000/enetb7-on-tpus,ALASKA2 Image Steganalysis
13224341,0.882,71,128,/maksymshkliarevskyi/cassava-leaf-disease-best-keras-cnn,Cassava Leaf Disease Classification
13211577,0.833,1,8,/nachiket273/vit-gpu,Cassava Leaf Disease Classification
13264346,0.8140000000000001,0,0,/sudosudh/cassava-leaf-disease-submission,Cassava Leaf Disease Classification
13217724,0.428,6,21,/anantgupt/cassava-leaf-doctor-eda-keras,Cassava Leaf Disease Classification
13080968,0.8490000000000001,0,0,/sanikamal/cassava-leaf-disease-cnn-prediction,Cassava Leaf Disease Classification
13195577,0.902,12,37,/underwearfitting/clean-inference-kernel-8xtta-lb902,Cassava Leaf Disease Classification
13254671,0.6,0,0,/akshatdevve/notebook21f870c98e,Cassava Leaf Disease Classification
13157373,0.892,13,19,/muellerzr/fastai-abhishek-inference,Cassava Leaf Disease Classification
13022126,0.19,0,0,/volcanoflash/cassava-leaf-disease-classification-fast-ai,Cassava Leaf Disease Classification
13161001,0.826,0,3,/dliend/starter-fastai-pretrained-model-weights-offline,Cassava Leaf Disease Classification
13061963,0.843,2,3,/morenovanton/inceptionv3-leaf-disease-classification,Cassava Leaf Disease Classification
13039974,0.836,1,10,/nroman/cassa-leaf-with-pytorch,Cassava Leaf Disease Classification
13059864,0.853,6,15,/maksymshkliarevskyi/cassava-leaf-disease-keras-cnn-prediction,Cassava Leaf Disease Classification
13388382,0.97407,1,12,/cosmosankur/cnn-keras-mnsit-for-beginners,Digit Recognizer
13307296,0.99307,0,0,/mohdkashifakhtar/notebook336b10bff5,Digit Recognizer
13330495,0.99385,0,0,/ludovicchangeon/digitrecognizer-simplecnn,Digit Recognizer
13255052,0.99435,0,0,/kiainio/digit-recognizer,Digit Recognizer
13278269,0.99046,0,2,/jacquelinehong/easy-to-follow-cnn-digit-recognizer,Digit Recognizer
13315815,0.98939,1,0,/najiaboo/notebook37488bd23f,Digit Recognizer
13244373,0.98282,1,1,/ibnuthoriqh/digit-recognizer-with-cnn-tensorflow-keras,Digit Recognizer
7484184,0.99371,1,1,/ankitkumarsaini/mnist-cnn,Digit Recognizer
13136919,0.99003,0,1,/hankarmostafa/digit-recognizer-using-cnn-model,Digit Recognizer
13182620,0.99925,0,0,/davidabelin/digiteerx,Digit Recognizer
13020289,0.99475,2,1,/tymonhuchla/introduction-to-digit-classification-with-keras,Digit Recognizer
13009821,0.99671,1,1,/mooncrater/mnist,Digit Recognizer
12997499,0.99403,0,0,/rajeevtyagi/digit-recognizer-through-tf2-x-keras,Digit Recognizer
12983127,0.99392,0,0,/chuckedfromspace/image-augmentation-with-preprocessing-layers,Digit Recognizer
9011146,0.98696,0,0,/parthp94/digit-cnn-final,Digit Recognizer
10828001,0.98414,0,0,/aczy156/digit-recognizer-data-argumentation-cnn,Digit Recognizer
12856244,0.98775,1,4,/iamyajat/cnn-digit-recognizer,Digit Recognizer
12838303,0.9901,1,3,/philsaurabh/digit-recognizer-trying-alexnet-mobilenet,Digit Recognizer
12787718,0.97471,0,0,/wevertonmdrs/basic-cnn-digit-recognition,Digit Recognizer
12790887,0.98946,0,0,/martinhaha/practice-cv,Digit Recognizer
1699995,0.7929999999999999,0,1,/hemantime/u-net-with-resnet-block-1-4-0-79,TGS Salt Identification Challenge
1648662,0.6890000000000001,0,6,/kotarojp/first-step-for-submission-u-net-iou-threshold,TGS Salt Identification Challenge
1586640,0.7929999999999999,0,9,/ashishpatel26/change-is-inevitable,TGS Salt Identification Challenge
1593487,0.8079999999999999,183,245,/shaojiaxin/u-net-with-simple-resnet-blocks-v2-new-loss,TGS Salt Identification Challenge
1601682,0.64,2,12,/iamhamzaabdullah/salt-identification-algorithm,TGS Salt Identification Challenge
1588055,0.655,11,31,/jcesquiveld/tgs-vanilla-u-net-baseline,TGS Salt Identification Challenge
1522275,0.722,0,1,/nbhogi/unet-resnet34-rmsprop-optimizer-in-keras,TGS Salt Identification Challenge
1550420,0.765,0,1,/dilip1988/rmsprop-data-analysis-with-u-net,TGS Salt Identification Challenge
1424404,0.767,0,12,/ashishpatel26/u-net-bn-aug-strat-dice,TGS Salt Identification Challenge
1414420,0.763,0,9,/ashishpatel26/unet-bas,TGS Salt Identification Challenge
1436656,0.79,9,28,/lpachuong/apply-crf-unet-bn-diceloss,TGS Salt Identification Challenge
1411530,0.775,0,4,/alexanderliao/lb-0-775-conditional-random-field-for-tgs,TGS Salt Identification Challenge
1420974,0.586,0,1,/zahharovareal/intro-to-seismic-salt-and-how-to-geophysics,TGS Salt Identification Challenge
1406972,0.479,0,10,/sanket30/semantic-segmentation-using-u-net-deep-layer,TGS Salt Identification Challenge
1348690,0.7440000000000001,0,12,/nafisur/crf-nr,TGS Salt Identification Challenge
1360184,0.5760000000000001,2,10,/ashishpatel26/best-try-i-do-for-this,TGS Salt Identification Challenge
1317950,0.63,0,10,/nafisur/keras-tgs-salt,TGS Salt Identification Challenge
247960,0.32387,0,0,/artimous/why-go-naive-when-you-can-go-worldly-wise,Sberbank Russian Housing Market
3814271,2.06802,12,19,/praxitelisk/tmdb-box-office-prediction-eda-ml,TMDB Box Office Prediction
3656873,2.06405,0,1,/ivansv/regression-tmdb-box-office-prediction-challenge,TMDB Box Office Prediction
3676967,1.99143,1,3,/sumitdas84/tmdb-box-office-prediction,TMDB Box Office Prediction
3670564,1.70874,4,11,/takedown/tmdb-box-office-revenue-prediction,TMDB Box Office Prediction
3637988,2.81423,0,0,/raghavendrakotala/first-submision,TMDB Box Office Prediction
3594593,5.26362,0,0,/chenhanhsiao/tmdb-box-office-prediction-xgboost,TMDB Box Office Prediction
3502279,2.39735,0,2,/nakayamar/revenue-prediction-with-posters-using-cnn-keras,TMDB Box Office Prediction
3477509,2.36575,34,58,/shahules/eda-feature-engineering-and-keras-model,TMDB Box Office Prediction
3347476,2.17274,0,1,/a4anandr/tmdb-anand,TMDB Box Office Prediction
3353256,3.1926400000000004,0,3,/donatastamosauskas/first-take-on-tmdb-challenge,TMDB Box Office Prediction
3335851,2.58098,0,0,/drumstasd/the-tmdb-puzzle,TMDB Box Office Prediction
3266851,2.0803700000000003,2,7,/shitalkat/tmdb-box-office-prediction-xg-boost,TMDB Box Office Prediction
3234065,1.87497,0,10,/floarrd/can-you-predict-a-movie-s-revenue,TMDB Box Office Prediction
3016050,2.6449,0,0,/anitha136/tmdb-box-office-prediction-random-forest-regressor,TMDB Box Office Prediction
3143811,1.76783,0,4,/ahmedengu/fork-eda-feature-engineering-lgb-xgb-cat,TMDB Box Office Prediction
2998293,2.07851,1,8,/jiegeng94/machine-learning-beginner-tutorial,TMDB Box Office Prediction
13946374,0.8220000000000001,0,0,/dinesh19aug/notebook-bnet0-trained,Cassava Leaf Disease Classification
14120292,0.602,0,0,/jjmachan/casava-classification-eda,Cassava Leaf Disease Classification
14089437,0.901,0,33,/smirnyaginandr/cassava-leaf-disease-tpu-v2-pods-inference,Cassava Leaf Disease Classification
13722319,0.8909999999999999,0,0,/grudindmitry/sportanaliz,Cassava Leaf Disease Classification
14006248,0.8809999999999999,0,4,/funky15/5-fold-tpu-inference-res50-res101-effb4,Cassava Leaf Disease Classification
13537726,0.8859999999999999,1,2,/retal95/verygood2,Cassava Leaf Disease Classification
13931397,0.888,0,1,/daveccampbell/cassava-bitempered-logistic-loss-save-model,Cassava Leaf Disease Classification
14047923,0.432,2,6,/sd4321/fastai-baseline-model,Cassava Leaf Disease Classification
13584398,0.602,0,0,/zekun98/vit-cuda-as-usual-ensemble-inference,Cassava Leaf Disease Classification
3722372,0.9511,0,4,/frlemarchand/transfer-learning-for-cancer-detection-keras,Histopathologic Cancer Detection
3845070,0.9617,0,3,/vishal22/histo,Histopathologic Cancer Detection
3524990,0.4589,0,2,/aditya100/histopathologic-cancer-detection,Histopathologic Cancer Detection
3275537,0.9738,10,22,/seefun/you-really-need-attention-pytorch,Histopathologic Cancer Detection
3256616,0.9639,7,11,/guntherthepenguin/densenet169-with-wsi-split-and-focalloss,Histopathologic Cancer Detection
3152283,0.8742,0,0,/timk111/competition,Histopathologic Cancer Detection
3101065,0.9648,0,1,/sayantandas30011998/fastai-v1-densenet121,Histopathologic Cancer Detection
3046443,0.9685,3,51,/artgor/cancer-detection-with-kekas,Histopathologic Cancer Detection
3035042,0.7977,0,2,/infoabhitech/cancer-detection-cnn-215k-images-no-crash-2,Histopathologic Cancer Detection
2958209,0.9618,0,2,/tbass134/fastai-demo,Histopathologic Cancer Detection
2962396,0.9496,0,1,/phekima/covnet-review-beginner-lb-0-94,Histopathologic Cancer Detection
2852710,0.6973,0,1,/maxlenormand/my-very-first-cnn,Histopathologic Cancer Detection
2752707,0.9702,0,5,/erikgaasedelen/vhl-starter-code,Histopathologic Cancer Detection
2672586,0.9333,0,0,/tjay4798/histopathologic-cancer-detection-2,Histopathologic Cancer Detection
2534322,0.9606,0,1,/guntherthepenguin/fastai-v1-group-equivariate-cnns,Histopathologic Cancer Detection
2537614,0.9667,1,3,/ingbiodanielh/cancer-detection-with-fastai-v2,Histopathologic Cancer Detection
2464411,0.8847,1,6,/mihaipop/resnet-using-pytorch,Histopathologic Cancer Detection
2193227,0.958,2,35,/greg115/histopathologic-cancer-detector-lb-0-958,Histopathologic Cancer Detection
2293556,0.9694,0,1,/pratik2901/densenet-121-fastai,Histopathologic Cancer Detection
3475398,0.39769,0,0,/draco67p/kernela527e77e5b,Bike Sharing Demand
3012493,0.39633,0,0,/junheo/random-forest-by-jun-bike-sharing-demand,Bike Sharing Demand
2177542,0.44432,0,4,/kwonyoung234/practice-bike-sharing-demand,Bike Sharing Demand
1758085,1.82269,0,0,/erickmuzart/machine-learning-brasilia-aula-11,Bike Sharing Demand
1318352,0.41551,0,0,/rchitic17/ten-million-bicycles,Bike Sharing Demand
941943,0.44053,0,2,/shivani1711/top-10-percentile-xgboost,Bike Sharing Demand
568717,0.52069,0,0,/xanthate/bike-sharing-demand,Bike Sharing Demand
156200,1.58455,0,4,/quantumdamage/bike-sharing-demand,Bike Sharing Demand
10903918,0.42276,0,0,/preejababu/bike-sharing-gradient-boost,Bike Sharing Demand
12576446,0.6198600000000001,0,1,/mahmoud1youssef/categorical-features-study,Categorical Feature Encoding Challenge
11606791,0.76199,0,3,/samhithavadlamani/categorical-encoding-challenge,Categorical Feature Encoding Challenge
5837311,0.78556,0,0,/ipg2014xxx/cat-in-the-dat-kernel,Categorical Feature Encoding Challenge
10078695,0.80197,0,1,/chuvik89/one-hot-encoding-on-raw-data,Categorical Feature Encoding Challenge
8945118,0.75617,0,0,/adarsh415/entity-embedding-pytorch,Categorical Feature Encoding Challenge
5777681,0.7954,0,0,/abhishek9599/not-using-one-hot-encoding,Categorical Feature Encoding Challenge
7503587,0.71834,0,1,/tushvjti/cat-featureencoding2,Categorical Feature Encoding Challenge
6532656,0.5041899999999999,0,0,/pranaiyadav/categorical-encoding,Categorical Feature Encoding Challenge
6392063,0.68766,0,1,/lukemonington/categorical-feature-encoding-challenge,Categorical Feature Encoding Challenge
6484818,0.7991,0,1,/gsdeepakkumar/categorical-encoding,Categorical Feature Encoding Challenge
7003204,0.80807,6,5,/krkirov/minimalist-code-0-80257-private-score,Categorical Feature Encoding Challenge
6963592,0.80781,0,0,/roshninaidu/feature-encoding-logistic-regression,Categorical Feature Encoding Challenge
6835361,0.8071,0,3,/jagannathrk/categorical-feature-entity-embeddings,Categorical Feature Encoding Challenge
6895239,0.8062600000000001,0,16,/vbmokin/logisticregressioncv,Categorical Feature Encoding Challenge
6849524,0.76684,0,7,/ejaz22/simple-xgboost-model,Categorical Feature Encoding Challenge
6799971,0.80289,0,0,/vladlee/categorical-feature-encoding-simple-xgb,Categorical Feature Encoding Challenge
6639786,0.79072,0,0,/ma7555/a-logit-and-a-sparse-matrix,Categorical Feature Encoding Challenge
6489954,0.80819,13,31,/pavelvpster/cat-in-dat-ohe-vs-thermometer-logit,Categorical Feature Encoding Challenge
6573958,0.8075899999999999,0,1,/kaushal2896/cat-in-the-dat-simple-logistic-regression,Categorical Feature Encoding Challenge
9600549,0.921337,0,2,/vedato/card-fraud,IEEE-CIS Fraud Detection
9517016,0.955917,1,7,/mustafabozkurt/fraud-corr,IEEE-CIS Fraud Detection
9270489,0.914501,0,0,/aaron35/transaction-fraud-detection,IEEE-CIS Fraud Detection
9125013,0.96809,1,3,/jafarib/ieee-cis-fraud-detection-good-score,IEEE-CIS Fraud Detection
5873890,0.9075,0,0,/abhishek9599/fraud,IEEE-CIS Fraud Detection
6859756,0.913953,0,0,/hassanamin/modelling-for-cis-fraud-detection,IEEE-CIS Fraud Detection
7790199,0.959413,13,72,/cdeotte/rapids-feature-engineering-fraud-0-96,IEEE-CIS Fraud Detection
7652243,0.7199399999999999,0,2,/homelesssandwich/ml-challenge-day,IEEE-CIS Fraud Detection
5815668,0.921178,0,18,/rimon57/fraudster-with-light-gbm,IEEE-CIS Fraud Detection
7132974,0.926218,0,0,/rohith464/fraud-detection-rf-xgb-lightgbm,IEEE-CIS Fraud Detection
6060871,0.8705280000000001,0,1,/rebeccanbryant/fraud-detection-models,IEEE-CIS Fraud Detection
6470652,0.8892610000000001,0,0,/jcmiii/cc-fraud-one-hot-encoding,IEEE-CIS Fraud Detection
5914690,0.8740000000000001,0,1,/lluissalord/fraud-detection-catboost-and-bayesian-search,IEEE-CIS Fraud Detection
6411330,0.938193,0,2,/davidcairuz/data-1-1-the-power-of-feature-engineering,IEEE-CIS Fraud Detection
5917286,0.936534,0,1,/alexishchenko/iee-cis-fraud-detection,IEEE-CIS Fraud Detection
5888697,0.9521,0,2,/jacobgreen4477/9519-normal,IEEE-CIS Fraud Detection
6132771,0.941313,4,13,/sukrullex/lightgbm-with-downsampling,IEEE-CIS Fraud Detection
6119430,0.96809,2,21,/philippsinger/blend-the-zoo-fraudsquad,IEEE-CIS Fraud Detection
6021645,0.94901,0,1,/nevret93/lgb-single-model-with-fe,IEEE-CIS Fraud Detection
13725899,0.0686799999999999,2,2,/tobiasmmmmmm/dogs-vs-cats-vgg16,Dogs vs. Cats Redux: Kernels Edition
12265065,0.02978,0,3,/tunguz/xgb-lr-gpu-cats-vs-dogs-with-eb7-ns,Dogs vs. Cats Redux: Kernels Edition
12225371,0.03862,0,3,/tunguz/cats-vs-dogs-with-eb1-ns,Dogs vs. Cats Redux: Kernels Edition
12225395,0.03996,0,1,/tunguz/cats-vs-dogs-with-eb0-ns,Dogs vs. Cats Redux: Kernels Edition
12224137,0.03333,1,1,/tunguz/cats-vs-dogs-with-eb6-ns,Dogs vs. Cats Redux: Kernels Edition
12171308,0.03616,0,3,/tunguz/cats-vs-dogs-with-eb7-pretrained-embeddinings,Dogs vs. Cats Redux: Kernels Edition
12169696,0.04122,0,2,/tunguz/cats-vs-dogs-with-eb3-pretrained-embeddings,Dogs vs. Cats Redux: Kernels Edition
12170504,0.03686,0,1,/tunguz/cats-vs-dogs-with-eb6-pretrained-embeddings,Dogs vs. Cats Redux: Kernels Edition
12169597,0.04445,0,1,/tunguz/cats-vs-dogs-with-eb2-pretrained-embeddings,Dogs vs. Cats Redux: Kernels Edition
12135954,0.03784,1,2,/tunguz/xgb-cats-vs-dogs-with-naslarge,Dogs vs. Cats Redux: Kernels Edition
12131327,0.03978,1,2,/tunguz/hgbc-cats-vs-dogs-with-irv2-pretrained-embeddings,Dogs vs. Cats Redux: Kernels Edition
12009697,0.03789,1,2,/tunguz/cats-vs-dogs-with-naslarge-pretrained-embeddings,Dogs vs. Cats Redux: Kernels Edition
12009546,0.09396,0,1,/tunguz/cats-vs-dogs-with-rn152-pretrained-embeddings,Dogs vs. Cats Redux: Kernels Edition
11995201,0.13989,0,3,/tunguz/cats-vs-dogs-with-vgg19-pretrained-embeddings-lr,Dogs vs. Cats Redux: Kernels Edition
11888068,0.14251,0,1,/tunguz/cats-vs-dogs-with-vgg16-pretrained-embeddings-lr,Dogs vs. Cats Redux: Kernels Edition
10508885,0.0631299999999999,13,22,/sanchitvj/cat-or-dog-transfer-learning-using-resnets,Dogs vs. Cats Redux: Kernels Edition
10415518,0.16863,0,3,/ashimdahal/pretrained-cnn-in-keras,Dogs vs. Cats Redux: Kernels Edition
9895757,0.55262,0,1,/franckepeixoto/dogs-x-cats-classifier-using-inceptionv3,Dogs vs. Cats Redux: Kernels Edition
9646533,0.3951199999999999,10,8,/alpaca0984/dog-vs-cat-with-pytorch,Dogs vs. Cats Redux: Kernels Edition
580822,0.7881600000000001,0,0,/safinar/mercari-price,Mercari Price Suggestion Challenge
574117,0.46007,0,0,/ranjanabhay/mercari-data-challenge,Mercari Price Suggestion Challenge
560671,0.42765,0,0,/danieleewww/mercari-rnn-2ridge-models-with-notes-0-fa2c64,Mercari Price Suggestion Challenge
515226,0.80335,0,0,/asi1007/predict-by-shipping-flag,Mercari Price Suggestion Challenge
498480,0.8144600000000001,0,0,/looseleaf0727/notebookdea5e8441d,Mercari Price Suggestion Challenge
459395,0.70555,0,0,/keitashimizu/xg-boost-test-with-2nd-level-category,Mercari Price Suggestion Challenge
452840,0.7548600000000001,0,0,/keitashimizu/basic-input-output,Mercari Price Suggestion Challenge
449616,3.1845,2,0,/luanho/first-simple-approach,Mercari Price Suggestion Challenge
2497701,0.372,18,93,/abhishek/maybe-something-interesting-here,PetFinder.my Adoption Prediction
2494788,0.1639999999999999,2,20,/christofhenkel/keras-baseline-only-train-csv,PetFinder.my Adoption Prediction
2491115,0.337,2,14,/timothycwillard/petfinder-simple-lgbm-model-baseline,PetFinder.my Adoption Prediction
2490169,0.33,1,19,/abhishek/nothing-interesting-here,PetFinder.my Adoption Prediction
2496311,0.2319999999999999,1,0,/ulissesdias/exploratory-data-analysis-and-decision-tree,PetFinder.my Adoption Prediction
2513736,0.332,0,0,/geeknurse/petfinder-h2o-automl-combined,PetFinder.my Adoption Prediction
10112237,0.0,0,0,/mjack94/final-pet-data-cis-6115,PetFinder.my Adoption Prediction
3110208,0.292,0,0,/ritvik169/petfinder-my,PetFinder.my Adoption Prediction
2793428,0.241,0,0,/rodasoares/tree-forest-xgboost-simple-densenn,PetFinder.my Adoption Prediction
7675243,17.269379999999998,0,0,/piteryo/kernel69f291971a,Deepfake Detection Challenge
7586071,0.69319,1,3,/khoongweihao/deepfake-keras-cnn-starter-epoch-lr-eda,Deepfake Detection Challenge
7469803,0.68505,5,18,/climbest/facial-recognition-model-in-pytorch-change-bias,Deepfake Detection Challenge
7356551,16.18176,1,1,/jitendrapurohit/deepfake-detection-model,Deepfake Detection Challenge
7217193,0.69314,6,11,/debanga/submission-with-externally-trained-keras-model,Deepfake Detection Challenge
7194133,0.70039,5,60,/diegojohnson/compute-lb-score-directly-data-leak,Deepfake Detection Challenge
7073327,0.69042,2,29,/andrewgao/facial-recognition-model-in-pytorch,Deepfake Detection Challenge
7031119,0.69314,3,16,/phoenix9032/yet-another-deepfake-starter,Deepfake Detection Challenge
7025346,0.6614800000000001,6,27,/mmmarchetti/deep-fake-chalenge,Deepfake Detection Challenge
7042244,0.7403,3,4,/niranjankumarc/deepfake-detection-challenge-starterguide,Deepfake Detection Challenge
7012809,17.269379999999998,8,40,/jesperdramsch/intro-to-deep-fakes-videos-and-metadata-eda,Deepfake Detection Challenge
7006651,0.69314,6,33,/mmmarchetti/importing-and-basics,Deepfake Detection Challenge
7015273,17.269379999999998,1,4,/grapestone5321/deepfake-sample-submission,Deepfake Detection Challenge
7025633,0.69314,2,2,/loaiabdalslam/kernel67770947f6,Deepfake Detection Challenge
8201023,0.43144,0,0,/yatinece/fork-of-fork-of-fork-of-xception-resnext-ensembl,Deepfake Detection Challenge
7717913,0.46788,2,0,/chariots17/transfer-learning,Deepfake Detection Challenge
12906336,1.065,0,1,/yindachen/ashrae-lgb,ASHRAE - Great Energy Predictor III
10636319,1.198,0,2,/darisdzakwanhoesien2/ashrae-great-energy-predictor-iii,ASHRAE - Great Energy Predictor III
6548948,1.5,0,0,/sergioli212/ashrae-processing,ASHRAE - Great Energy Predictor III
8138718,1.8,1,0,/dots9999/jo-kernel-ash1,ASHRAE - Great Energy Predictor III
6403712,2.59,0,0,/chivalroushv/energy-pred,ASHRAE - Great Energy Predictor III
6666133,1.34,0,0,/knotseaborg/ashrae-attempt,ASHRAE - Great Energy Predictor III
7804458,1.117,0,0,/yunishi0716/3-folds-by-each-meter-type,ASHRAE - Great Energy Predictor III
7705275,1.268,0,0,/evelynchin/ashrae-energy-predictor,ASHRAE - Great Energy Predictor III
6849608,0.966,0,1,/yamsam/meter-split-sg,ASHRAE - Great Energy Predictor III
7612520,1.494,0,2,/vaibhavsxn/light-gbm,ASHRAE - Great Energy Predictor III
11840766,0.50684,0,0,/kintaro1/notebook8f036e19a0,Otto Group Product Classification Challenge
9018451,0.4569899999999999,0,1,/nagomiso/predict-with-lightgbm-optuna,Otto Group Product Classification Challenge
8159755,0.45974,0,1,/masatomatsui/otto-simple-nn,Otto Group Product Classification Challenge
8159064,0.43995,2,3,/masatomatsui/otto-simple-lgb,Otto Group Product Classification Challenge
8244746,0.56053,0,0,/chizuchizu/problem-with-pca,Otto Group Product Classification Challenge
4978967,0.56129,0,0,/xieshuhan/otto-products,Otto Group Product Classification Challenge
1977562,1.3322,0,4,/raajtilaksarma/pytorch-nn-otto-group-product-classification,Otto Group Product Classification Challenge
1684662,0.56289,0,0,/annatu/neural-networks-product-classification-otto,Otto Group Product Classification Challenge
950551,0.5635899999999999,0,0,/reinwzhang/nn-otto-product-classifications-mlp-xgboost,Otto Group Product Classification Challenge
442433,0.55477,0,2,/quamber/random-forest-with-rne-feature-selection,Otto Group Product Classification Challenge
13196759,0.42926,0,0,/hiroshi20180410/otto-12,Otto Group Product Classification Challenge
1985260,1.5057,0,7,/super13579/revisit-one-hot-ranking,Google Analytics Customer Revenue Prediction
1890826,1.4795,0,2,/wutaomicoo/leaky-homework,Google Analytics Customer Revenue Prediction
1959761,1.6179,0,2,/subhankar29/kernel91c558eae0,Google Analytics Customer Revenue Prediction
1870703,1.291,14,51,/karkun/sergey-ivanov-msu-mmp,Google Analytics Customer Revenue Prediction
1869503,1.3626,0,2,/trrrrrrrrrrrrt/bibik-mmp-msu-for-learning-purposes,Google Analytics Customer Revenue Prediction
1857045,1.3233,12,35,/satian/story-of-a-leak,Google Analytics Customer Revenue Prediction
1872795,1.4243,0,2,/glebmaslyak/kernel7573c78c85,Google Analytics Customer Revenue Prediction
1818956,1.4193,0,10,/zzerozz/lgbm-xgboost-fork-of-i-have-seen-the-future,Google Analytics Customer Revenue Prediction
1793107,1.4215,0,23,/mukesh62/lgb-fe-groupkfold-cv-xgb,Google Analytics Customer Revenue Prediction
1755305,1.4542,1,4,/thesoum/google-analytics-data-cleaning-lightgbm-model,Google Analytics Customer Revenue Prediction
1784031,1.4297,2,12,/nikitpatel/lightgbm-with-best-hyperparameter-tuning,Google Analytics Customer Revenue Prediction
1768678,1.4248,27,67,/ashishpatel26/updated-bayesian-lgbm-xgb-cat-fe-kfold-cv,Google Analytics Customer Revenue Prediction
14064208,0.65068,0,2,/ayoubsandali/twitter-sentiment-extraction,Tweet Sentiment Extraction
13207190,0.58353,0,0,/lukeballantyne/tweets-1,Tweet Sentiment Extraction
13110423,0.65194,0,0,/rohitgargkk0/notebookbd4daef7e5,Tweet Sentiment Extraction
13029065,0.59451,0,0,/gouravagg77/twitter-sentiment-assignment,Tweet Sentiment Extraction
12981435,0.39929,0,0,/aekamjotsingh/tweet-sentiment,Tweet Sentiment Extraction
12978935,0.4986,0,0,/ashutosh1229/notebook83bea4d89f,Tweet Sentiment Extraction
12797839,0.71286,0,2,/kanruwang/tweet-sentiment-roberta-pytorch-inference,Tweet Sentiment Extraction
12758022,0.59103,0,0,/bharath150/learning-attention2,Tweet Sentiment Extraction
12566035,0.70994,0,0,/sais01/tse-712-folds,Tweet Sentiment Extraction
11976013,0.6260399999999999,0,1,/dplutcho/tweet-sent-ext-ner,Tweet Sentiment Extraction
12167638,0.77842,1,22,/faressayah/natural-language-processing-with-python,Natural Language Processing with Disaster Tweets
11527644,0.26048,0,1,/ryanchan911/bert-model-saved,Tweet Sentiment Extraction
10041353,0.647,0,0,/jamesyang3/kernel2d823646f0,Tweet Sentiment Extraction
9482689,0.96596,0,0,/reddyml/sentiment-analysis,Toxic Comment Classification Challenge
9070999,0.98005,0,0,/qurb48/kernel2f9724b85c,Toxic Comment Classification Challenge
8891193,0.9804,0,0,/qurb48/glove-gru,Toxic Comment Classification Challenge
8039876,0.93942,0,0,/manishachakraborty/toxic-comment-classification,Toxic Comment Classification Challenge
7362758,0.98429,0,33,/nkaenzig/bert-tensorflow-2-huggingface-transformers,Toxic Comment Classification Challenge
6024151,0.9824,0,3,/samson22/distilbert-in-pytorch,Toxic Comment Classification Challenge
6481379,0.95576,0,0,/magic929/kernel7c1c3ec772,Toxic Comment Classification Challenge
6229818,0.98101,0,0,/amir78pgd/improved-lstm-baseline-bi-lstm-dual-embed-pl,Toxic Comment Classification Challenge
6437952,0.98682,0,0,/amir78pgd/ensemble-4-blend,Toxic Comment Classification Challenge
6123154,0.97421,1,5,/schateau/model-ensembling-part-1-bi-lstm-glove,Toxic Comment Classification Challenge
6206311,0.75707,0,0,/logan1997/multi-label-classification,Toxic Comment Classification Challenge
6047504,0.98169,0,5,/lymenlee/toxic-comments-classification-fast-ai,Toxic Comment Classification Challenge
6082968,0.9106,1,5,/ahayek84/toxic-comment-classification-challenge,Toxic Comment Classification Challenge
5741081,0.98576,0,2,/amir78pgd/minimal-lstm-nb-svm-baseline-ensemble,Toxic Comment Classification Challenge
5740062,0.97722,0,0,/amir78pgd/nb-svm-strong-linear-baseline,Toxic Comment Classification Challenge
3857137,0.98639,0,2,/tunguz/fork-of-bi-gru-lstm-dual-embedding-new-test-5,Toxic Comment Classification Challenge
3871657,0.987,0,1,/tunguz/bi-lstm-dual-embedding-new-test-cleaned-3,Toxic Comment Classification Challenge
3857099,0.98638,0,1,/tunguz/bi-lstm-dual-embedding-new-test-2,Toxic Comment Classification Challenge
5551298,0.97451,3,5,/asrsaiteja/toxic-comments-eda-baselines,Toxic Comment Classification Challenge
5262077,0.96892,0,0,/daidew/toxic-multilabel-classification-bigrus,Toxic Comment Classification Challenge
4326522,-0.8290000000000001,15,145,/adrianoavelar/bond-calculaltion-lb-0-82,Predicting Molecular Properties
4430469,-0.306,0,16,/robertburbidge/distance-features,Predicting Molecular Properties
4443786,-0.3929999999999999,0,8,/robertburbidge/using-estimated-mulliken-charges,Predicting Molecular Properties
4338378,-0.6409999999999999,12,139,/artgor/validation-feature-selection-interpretation-etc,Predicting Molecular Properties
4281736,1.611,4,11,/shivamanhar/molecular-eda-ml,Predicting Molecular Properties
4184451,0.8079999999999999,8,69,/zaharch/quantum-machine-9-qm9,Predicting Molecular Properties
4125997,0.561,0,4,/geoman3/initial-data-foray,Predicting Molecular Properties
4134198,-0.595,72,372,/artgor/brute-force-feature-engineering,Predicting Molecular Properties
4082794,0.2289999999999999,6,33,/jazivxt/all-this-over-a-dog,Predicting Molecular Properties
4108882,0.5870000000000001,1,7,/danofer/fastai-tabular-starter-fork,Predicting Molecular Properties
4083408,1.147,0,7,/joydeb28/ml-models,Predicting Molecular Properties
4086752,0.583,2,16,/rakibilly/fastai-tabular-starter,Predicting Molecular Properties
14200694,0.9916,0,0,/handerblue/kd-minist,Kannada MNIST
13779060,0.9768,0,0,/nadezdascosyrskih/notebookbd02afb681,Kannada MNIST
13457136,0.9618,0,0,/caiqingyang/201212mnist,Kannada MNIST
12439656,0.9856,0,0,/furiouscool/notebook7843a4d57f,Kannada MNIST
12963248,0.9702,0,0,/biswadeep20/notebookb804f19c0b,Kannada MNIST
12475577,0.9838,0,0,/gyengera/kannada-mnist,Kannada MNIST
12292119,0.9194,0,0,/aibeats/kannadamnist-knn-funjavasf,Kannada MNIST
6266310,0.9858,0,0,/duvictorsc/kannada-mnist-convolutional-neural-network,Kannada MNIST
11731459,0.9878,2,2,/fanbyprinciple/kannada-mnist-fastai-pixel-to-image-conv,Kannada MNIST
10912405,0.9902,0,0,/tokuntokum/kernel67c5a2b3ed,Kannada MNIST
10459677,0.9776,0,0,/muhhamedarslanishaq/kernel1303f86d76,Kannada MNIST
10007752,0.9908,0,0,/shengmincui/cnn-model,Kannada MNIST
10036768,0.985,0,2,/alshautsou/shevsubmission,Kannada MNIST
9347957,0.9732,0,0,/swatoplus/kernel330d0bfd5e,Kannada MNIST
8301856,0.9468,0,0,/giniya/giniya-1,Kannada MNIST
9342253,0.9714,0,0,/iljailya/kernel4cdacc46d4,Kannada MNIST
9174543,0.9826,0,0,/saravananoppila/cnn-kannada-mnist,Kannada MNIST
9031020,0.986,0,0,/aladdint/cnn-with-data-augmentation-and-lr-reduction,Kannada MNIST
8282582,0.9866,1,5,/nguyncaoduy/kanada-mnist,Kannada MNIST
13779162,0.6994,0,0,/kevinlu2240/yolo-v4,Global Wheat Detection
13879890,0.5875,0,0,/a871234342/final-project,Global Wheat Detection
13396666,0.6919,0,0,/chia56028/yolov5,Global Wheat Detection
13670589,0.6551,0,0,/wangchilung/detectron2-for-global-wheat-detection,Global Wheat Detection
12954242,0.6705,0,1,/ikaynov/detectron2-wheat-detection,Global Wheat Detection
10879077,0.7371,0,1,/annamel11111/gwd-efficientdet-d5-25e-wbf-over-tta,Global Wheat Detection
12432198,0.6528,0,0,/starktony45/pytorch-rcnn,Global Wheat Detection
12034973,0.6825,0,0,/kaelanlockhart/yolov5works,Global Wheat Detection
10604738,0.7377,0,0,/yongshenghou/effdet,Global Wheat Detection
10011128,0.7366,0,1,/genvsdis/wbf-over-tta-single-model-efficientdet,Global Wheat Detection
10657405,0.7733,0,1,/shengzhan/yolov5-pseudo-labeling-oof-evaluation,Global Wheat Detection
11334010,0.54,0,1,/luciferadmin/yolov3-detector,Global Wheat Detection
10434435,0.6792,0,2,/ganesh562/global-wheat-detection-using-keras-retinanet,Global Wheat Detection
10050208,0.7495,0,2,/kaushal2896/efficientdet-pseudo-labeling-wbf-tta,Global Wheat Detection
8530342,0.3571,2,9,/yatinece/exp-model-to-state-of-city-or-country-all-data,COVID19 Global Forecasting (Week 1)
8542734,2.3661,0,0,/tinurohith18/covid-19,COVID19 Global Forecasting (Week 1)
8536535,2.66933,0,2,/cjh34544/covid-19-forecasting-in-korea,COVID19 Global Forecasting (Week 1)
8538277,0.7507699999999999,0,0,/jasonyikim/covid-19-rf,COVID19 Global Forecasting (Week 1)
8534915,1.03557,0,1,/fzhurd/eda-and-random-forest-score-0-81-for-beginner,COVID19 Global Forecasting (Week 1)
8512222,0.57777,7,23,/yashgoyal401/less-code-accurate-result-best-predictions,COVID19 Global Forecasting (Week 1)
8526978,0.40819,1,4,/carlkirstein/covid19-forecast-global-competition,COVID19 Global Forecasting (Week 1)
8574747,3.07087,0,0,/srikanthnaveen/kernel509955b756,COVID19 Global Forecasting (Week 1)
8512311,0.49466,2,6,/mertcaglar/sarimax-baseline-starter-prediction,COVID19 Global Forecasting (Week 1)
8489649,0.63563,15,41,/group16/sigmoid-per-country-no-leakage,COVID19 Global Forecasting (Week 1)
8473424,0.91052,8,38,/super13579/covid19-global-forcast-simple-eda-pr-model,COVID19 Global Forecasting (Week 1)
8546578,0.79357,0,0,/maximebataille/covid-19-simple-baseline,COVID19 Global Forecasting (Week 1)
8494119,1.4161700000000002,0,0,/thalestozatto/covid-world-forecast-mlp,COVID19 Global Forecasting (Week 1)
14407320,0.7822899999999999,2,1,/saidbouferriche/titanic-notebook,Titanic - Machine Learning from Disaster
14643676,0.7799,0,0,/amoghrajesh1999/titanic-ml-nn,Titanic - Machine Learning from Disaster
14593543,0.7822899999999999,0,0,/sanikad/decision-tree-with-hyper-parameter-tuning,Titanic - Machine Learning from Disaster
14581083,0.76315,0,0,/sunaysawant/titanic-2,Titanic - Machine Learning from Disaster
14387104,0.7751100000000001,0,0,/linuxiest123/titanic-data,Titanic - Machine Learning from Disaster
14547551,0.76555,0,0,/luispatioquiroz/prediction-2,Titanic - Machine Learning from Disaster
14477874,0.78708,0,0,/alexandrumarcau/ml-project-titanic,Titanic - Machine Learning from Disaster
14386165,0.7751100000000001,0,0,/mahadamer99/getting-started-with-titanic-mahad,Titanic - Machine Learning from Disaster
14382536,0.75837,0,0,/zheyingvivi/titanic-classification,Titanic - Machine Learning from Disaster
14128124,0.7751100000000001,0,0,/rashingh/initial-submission-titanic,Titanic - Machine Learning from Disaster
14011505,0.7751100000000001,0,0,/quantumporium/titanic-submission,Titanic - Machine Learning from Disaster
13251454,0.7751100000000001,0,0,/jasony96/my-titanic-solution,Titanic - Machine Learning from Disaster
12586999,0.7751100000000001,0,0,/sehrishilyas/getting-started,Titanic - Machine Learning from Disaster
10442665,0.7751100000000001,0,1,/yacinerouizi/titanic-2,Titanic - Machine Learning from Disaster
8692899,0.76555,0,0,/bdokkkk/kernelanic,Titanic - Machine Learning from Disaster
8581228,0.72124,0,1,/mikestubna/simple-curve-fitting,COVID19 Global Forecasting (Week 1)
8570652,1.0657,1,2,/manasgargv6/covid19-randomforest,COVID19 Global Forecasting (Week 1)
8493324,1.84533,2,1,/kinkpunk/my-forecasting-covid19,COVID19 Global Forecasting (Week 1)
8565767,0.7450899999999999,7,12,/jorijnsmit/sigmoid-fitting-with-known-parameters-for-china,COVID19 Global Forecasting (Week 1)
8583570,0.7818,0,1,/shikhar721/fork-of-my-covid19-94b60a,COVID19 Global Forecasting (Week 1)
8560644,1.70482,5,4,/andynath/covid-19-beginner-eda-random-forest-xgboost,COVID19 Global Forecasting (Week 1)
8578453,0.6795800000000001,2,4,/lakshmitechie/covid-19-analysis,COVID19 Global Forecasting (Week 1)
8520064,1.6136700000000002,0,4,/lookfwd/sir-pools,COVID19 Global Forecasting (Week 1)
8552936,0.70248,1,8,/nickteim/covid19-1-model,COVID19 Global Forecasting (Week 1)
8576768,0.98295,0,0,/singhharshita/covid-19-global-forecast-week-1,COVID19 Global Forecasting (Week 1)
8566972,1.1262,3,2,/puneetbhateja93/covid-forecast-by-stepfunction-v1-0,COVID19 Global Forecasting (Week 1)
8566332,0.77922,2,1,/vjshinde04/basic-data-transformation-forecasting,COVID19 Global Forecasting (Week 1)
8550197,0.4153199999999999,0,0,/lakshpri/data-analysis-and-forecasting-covid-19,COVID19 Global Forecasting (Week 1)
8531204,0.5614399999999999,7,36,/fanconic/covid-19-additional-statistics,COVID19 Global Forecasting (Week 1)
8557484,1.49876,1,3,/casras/covid19-eda-and-exponential-curve-fitting-predict,COVID19 Global Forecasting (Week 1)
8486880,2.27667,0,2,/tsubasatwi/lightgbm-multi-output-regressor-model-covid-19,COVID19 Global Forecasting (Week 1)
8525280,0.68138,17,44,/jasonbenner/lets-try-xgboost-simple-w-added-features,COVID19 Global Forecasting (Week 1)
8569377,0.6938300000000001,0,0,/pararols/lets-try-xgboost-simple-w-added-features,COVID19 Global Forecasting (Week 1)
8535407,1.94527,0,0,/apoorvm/covid19-forcast,COVID19 Global Forecasting (Week 1)
14284433,0.60047,0,1,/firebee/titanic-prediction,Titanic - Machine Learning from Disaster
14349124,0.79186,0,6,/yosrbali/titanic,Titanic - Machine Learning from Disaster
13574015,0.77272,10,7,/christopherangelelli/don-t-panic-titanic-kaggle-competition,Titanic - Machine Learning from Disaster
14657796,0.63636,0,0,/ninotomo/notebook-titanic-knn,Titanic - Machine Learning from Disaster
14647709,0.7703300000000001,0,0,/x2020eua/x2020eua,Titanic - Machine Learning from Disaster
14615207,0.75837,0,0,/ninotomo/notebook-titanic-tree,Titanic - Machine Learning from Disaster
14594885,0.78468,0,0,/sanikad/random-forest-with-hyper-parameter-tuning,Titanic - Machine Learning from Disaster
11968844,0.77751,0,0,/tracyporter/titanic-bagging-classifier,Titanic - Machine Learning from Disaster
14464656,0.79665,0,0,/faisalirzal/notebook8b6ebba021,Titanic - Machine Learning from Disaster
13181704,0.7751100000000001,0,0,/drakedyban/eda-with-titanic,Titanic - Machine Learning from Disaster
14081455,0.76555,0,0,/jpvlerbe/titanic-basic-jasper,Titanic - Machine Learning from Disaster
14319415,0.7799,0,0,/btejkiran/titanic-a2,Titanic - Machine Learning from Disaster
14311009,0.7751100000000001,0,1,/lakshita2002/sailing-with-titanic,Titanic - Machine Learning from Disaster
14295108,0.74162,0,1,/milanbhadja/visualization-to-prediction-from-scratch,Titanic - Machine Learning from Disaster
14250965,0.77751,0,0,/mohdbilalhaider/titanic,Titanic - Machine Learning from Disaster
14301160,0.78708,0,0,/kirllsafronov/titanic,Titanic - Machine Learning from Disaster
13861851,0.7799,8,6,/fethiye/titanic-predict-survival-prediction,Titanic - Machine Learning from Disaster
14257810,0.76794,0,0,/istilllovemyex/competition-titanic-survived-prediction,Titanic - Machine Learning from Disaster
14016485,0.76794,0,0,/tobiasmmmmmm/titanic-survival-prediction-notebook,Titanic - Machine Learning from Disaster
5202636,-1.481,22,198,/criskiev/distance-is-all-you-need-lb-1-481,Predicting Molecular Properties
4223264,1.401,0,1,/ummarda3/insight-in-data,Predicting Molecular Properties
5076610,-0.525,0,3,/everyitfrombit/auto-champs-featuretools-borutapy,Predicting Molecular Properties
4970642,-1.587,1,10,/roydatascience/yet-another-one,Predicting Molecular Properties
4949778,-1.577,5,22,/vaishvik25/keras-nn-s-ieee,Predicting Molecular Properties
4865402,-1.581,4,13,/danmusetoiu/staking-and-stealing-like-a-molecule,Predicting Molecular Properties
4902429,-1.367,0,10,/roydatascience/steal-like-an-electron,Predicting Molecular Properties
4758723,-1.367,2,14,/lpachuong/statstack,Predicting Molecular Properties
4730702,1.263,5,14,/ricardtrinchet/exploratory-analysis-and-clustering-champs,Predicting Molecular Properties
4607667,-1.008,0,9,/vaishvik25/1-r-3-hyperpar-tuning,Predicting Molecular Properties
4608658,-1.073,38,261,/todnewman/keras-neural-net-for-champs,Predicting Molecular Properties
4557929,-0.94,2,28,/kingychiu/inverse-square-law,Predicting Molecular Properties
4761635,0.97994,0,1,/ajithvajrala/word-embeddings-with-tfidf-ensemble,Toxic Comment Classification Challenge
4328568,0.49898,0,1,/iashreya/toxiccommentclassification,Toxic Comment Classification Challenge
3443608,0.98399,0,0,/jason0713/model-cnn-rcnn,Toxic Comment Classification Challenge
3669865,0.96914,1,2,/samarthsarin/lstm-and-convolution1d-ensemble-with-glove,Toxic Comment Classification Challenge
2908401,0.9246,0,2,/abhinav2308/pytorch-toxic-comment-solution,Toxic Comment Classification Challenge
2787590,0.9622,0,0,/ramyapriyadarshini98/keras-toxic-comments,Toxic Comment Classification Challenge
2539599,0.75,0,3,/bcs013/simple-stepwise-solution,Toxic Comment Classification Challenge
2407168,0.9665,0,4,/tunguz/toxic-comments-with-tf-embeddings-and-h2o-automl,Toxic Comment Classification Challenge
505774,0.524,1,5,/aharless/lightgbm-including-unsold-items,Corporación Favorita Grocery Sales Forecasting
483651,1.009,0,0,/singhpari/notebook5754646f19,Corporación Favorita Grocery Sales Forecasting
482970,0.529,4,14,/aharless/dissecting-ceshine-lee-s-lgbm-kernel,Corporación Favorita Grocery Sales Forecasting
453348,0.6659999999999999,0,3,/jaime9/using-the-media-as-estimator-of-the-sales-simple,Corporación Favorita Grocery Sales Forecasting
11736414,0.56729,1,5,/kweonwooj/kc03-day03-driversplit,State Farm Distracted Driver Detection
10583056,7.85833,0,6,/anayantzinp/cnn-state-farm-distracted-driver-detection,State Farm Distracted Driver Detection
2056521,18.88186,0,0,/amjadm/kernelf33377257a,State Farm Distracted Driver Detection
1622809,3.98203,0,0,/ambarish/state-farm-image-analysis-3,State Farm Distracted Driver Detection
2080471,22.79382,0,0,/amjadm/kernel8a4ea7e3ce,State Farm Distracted Driver Detection
1758082,1.4318,19,58,/prashantkikani/teach-lightgbm-to-sum-predictions-fe,Google Analytics Customer Revenue Prediction
1697272,1.426,12,62,/rahullalu/gstore-eda-lgbm-baseline-1-4260,Google Analytics Customer Revenue Prediction
1727777,1.4417,21,85,/youhanlee/which-encoding-is-good-for-time-validation-1-4417,Google Analytics Customer Revenue Prediction
1723946,1.4471,0,12,/ashishpatel26/light-gbm-with-cross-validation-approach,Google Analytics Customer Revenue Prediction
1722064,1.442,5,17,/vkgpt11/lgbm-with-feature-eng-and-cleaning-lb-1-4420,Google Analytics Customer Revenue Prediction
1716151,1.5324,2,18,/chocozzz/bayesian-optimization-for-lightgbm,Google Analytics Customer Revenue Prediction
1720187,1.4480000000000002,0,13,/ajaykgp12/lgb-with-simple-time-related-features,Google Analytics Customer Revenue Prediction
1712006,1.4651,5,19,/artgor/lgb-and-feature-generation,Google Analytics Customer Revenue Prediction
1681497,1.5985,8,33,/artgor/nn-baseline,Google Analytics Customer Revenue Prediction
1638764,0.0,39,88,/plasticgrammer/customer-revenue-prediction-v2-playground,Google Analytics Customer Revenue Prediction
1673505,1.5566,2,43,/dimitreoliveira/lgbm-google-store-revenue-prediction,Google Analytics Customer Revenue Prediction
1666106,1.5033,0,9,/sanket30/eda-lgbm,Google Analytics Customer Revenue Prediction
1645758,1.291,28,73,/ashishpatel26/1-23-pb-first-try-to-think,Google Analytics Customer Revenue Prediction
1658789,1.8347,2,11,/sanket30/gstore-revenue-prediction-using-bayesian-opt-xgb,Google Analytics Customer Revenue Prediction
1659228,1.467,1,1,/johnfarrell/gacrp-simple-lgb-custom-metric,Google Analytics Customer Revenue Prediction
1638912,1.4728,45,159,/artgor/eda-on-basic-data-and-lgb-in-progress,Google Analytics Customer Revenue Prediction
1652740,1.4661,0,1,/sriharinitumu/light-gbm-with-hurdle-modelling,Google Analytics Customer Revenue Prediction
1641522,1.4707,16,46,/ashishpatel26/1-43-plb-feature-engineering-best-model-combined,Google Analytics Customer Revenue Prediction
1645323,1.4727,0,8,/bhavikapanara/lightgbm-looks-good,Google Analytics Customer Revenue Prediction
8288137,2.0888400000000003,0,0,/keremt/deepfake-retinaface-and-faceforensics,Deepfake Detection Challenge
8235794,0.86244,0,0,/kimyoh/kernel1a2c5df377,Deepfake Detection Challenge
8128955,1.0781,17,42,/timesler/facenet-pytorch-decord-process-every-frame,Deepfake Detection Challenge
7355597,0.69294,1,0,/debanga/kernel-150-256x256,Deepfake Detection Challenge
8040512,0.69314,1,0,/dthrone/are-test-labels-wrong,Deepfake Detection Challenge
7463602,0.68466,1,2,/nxhong93/deep-fake,Deepfake Detection Challenge
7927752,0.46445,4,5,/nxrprime/frames-per-video-the-ultimate-helper,Deepfake Detection Challenge
7949122,17.269379999999998,0,0,/prondeau/kernel14c3f0e66b,Deepfake Detection Challenge
7856148,0.69314,0,7,/funxexcel/deepfake-basic-code-with-internet,Deepfake Detection Challenge
7736443,0.6997800000000001,24,41,/phunghieu/deepfake-detection-inference-baseline,Deepfake Detection Challenge
7786127,1.1202,0,0,/wangvincent/kernel2ac7df5e53,Deepfake Detection Challenge
3327694,12.06131,0,21,/akash14/google-march-madness-men-s,Google Cloud & NCAA® ML Competition 2019-Men's
3306205,0.0,0,0,/hamidhaghshenas/public-score-0-00000,Google Cloud & NCAA® ML Competition 2019-Men's
3191520,0.5674100000000001,0,4,/goodspellr/how-to-score-your-own-predictions-and-more,Google Cloud & NCAA® ML Competition 2019-Men's
3048155,0.66268,0,0,/paulreiners/basketball-statistics-101-the-four-factor-model,Google Cloud & NCAA® ML Competition 2019-Men's
3078152,0.5401,0,8,/joseleiva/massey-s-ordinal-s-ordinals,Google Cloud & NCAA® ML Competition 2019-Men's
2972170,0.56672,0,52,/ateplyuk/lgbm-str,Google Cloud & NCAA® ML Competition 2019-Men's
2959670,0.57071,2,50,/addisonhoward/basic-starter-kernel-ncaa-men-s-dataset-2019,Google Cloud & NCAA® ML Competition 2019-Men's
2572072,0.805,236,170,/iafoss/similarity-densenet121-0-805lb-kernel-time-limit,Humpback Whale Identification
2417161,0.249,0,4,/sukhadj/humpback-whale-identification-transfer-learning,Humpback Whale Identification
2419619,0.2239999999999999,4,50,/ateplyuk/keras-triplet-loss-lb-0-224,Humpback Whale Identification
2398904,0.685,3,11,/longkt96/xception-bbox-yolov3-whale,Humpback Whale Identification
2421154,0.317,5,4,/dromosys/radek-fast-ai-whale,Humpback Whale Identification
2358152,0.657,2,14,/hung96ad/bbox-seresnext101-pytorch-0-657,Humpback Whale Identification
2336626,0.7020000000000001,15,18,/axel81/ensemble-resnet50-resnet101-lb-0-702,Humpback Whale Identification
2331701,0.282,0,5,/ashirahama/pytorch-simple-cnn-aug,Humpback Whale Identification
2306386,0.593,2,10,/axel81/resnet101-using-sgd-with-restarts-lb-0-640,Humpback Whale Identification
2302747,0.731,5,36,/matthewa313/ensembling-algorithm-for-average-precision-metric,Humpback Whale Identification
2295745,0.544,7,21,/asanakoev/easy-peasy-resnet50-with-fastai-0-574-lb,Humpback Whale Identification
2267125,0.304,1,12,/ashishpatel26/fast-ai-humpback-resnet-18,Humpback Whale Identification
2252490,0.222,8,59,/ashishpatel26/triplet-loss-network-for-humpback-whale-prediction,Humpback Whale Identification
2248405,0.288,25,111,/pestipeti/keras-cnn-starter,Humpback Whale Identification
2253122,0.278,9,17,/youhanlee/small-data-many-class-data-augmentation,Humpback Whale Identification
2252235,0.281,0,3,/undrallaramesh/humpback-whale-identification-keras-cnn,Humpback Whale Identification
2250402,0.281,0,6,/nikhilroxtomar/keras-pretrained-resnet50,Humpback Whale Identification
3248379,0.27948,0,0,/droid021/pytorch-whale-identifier,Humpback Whale Identification
2879605,0.428,0,0,/hexadd5/simple-resnet50-with-keras,Humpback Whale Identification
26724,0.0,0,0,/qsuire/basic,Airbnb New User Bookings
25814,0.0,0,2,/ceruleus/airbnb-notebook,Airbnb New User Bookings
5749624,0.7184,3,4,/atikahamed/cis-fraud-detection,IEEE-CIS Fraud Detection
6081870,0.954635,0,3,/sheriytm/find-client-by-d1-and-card-leak-use-on-submit,IEEE-CIS Fraud Detection
5916577,0.9473,0,3,/yokotani/ieee-fraud-detection-feature-engineering,IEEE-CIS Fraud Detection
5734818,0.934,0,0,/ruhong/ieee-fraud-detection-lgb,IEEE-CIS Fraud Detection
5726935,0.940516,3,13,/kjkr73/fraud-detection-light-gbm-and-visualizations,IEEE-CIS Fraud Detection
5968221,0.9044,0,0,/nanditab35/ieee-fraud-combine-2models-regression,IEEE-CIS Fraud Detection
5982935,0.924989,0,1,/roshantanisha/simple-easy-features,IEEE-CIS Fraud Detection
5978673,0.935,0,0,/gk5894/xgboost-with-optimized-parameters,IEEE-CIS Fraud Detection
5989616,0.9305,0,2,/xwxw2929/model-training,IEEE-CIS Fraud Detection
5952274,0.9385,0,3,/rohan9889/final-submission-ieee-fraud,IEEE-CIS Fraud Detection
5838723,0.9251,1,5,/gautham11/catboost-xgboost-lightgbm-ensemble,IEEE-CIS Fraud Detection
5926374,0.9178,0,9,/rsmits/keras-cnn-1d-fraud-detector,IEEE-CIS Fraud Detection
5729407,0.9175,0,2,/priteshshrivastava/ieee-pipeline-2-c-model-c-xgboost,IEEE-CIS Fraud Detection
5698646,0.9103,0,5,/aleksthegreat/keras-t-sne,IEEE-CIS Fraud Detection
5892310,0.9427,11,30,/navneetkr123/ieee-fraud-play-with-count-lightgbm,IEEE-CIS Fraud Detection
8137669,0.4342,0,0,/dchak2020/random-forest-and-xgboost-rmsle-0-26,Bike Sharing Demand
7873811,0.4142,0,0,/dhanyasabari/bike-sharing-gradient-boost,Bike Sharing Demand
7945399,0.49623,0,0,/alexschwartz/bike-sharing-dmnd-predict-48-score-32rmsle,Bike Sharing Demand
7559061,0.45681,0,0,/chun1182/bike-shared-xgboost,Bike Sharing Demand
7169533,0.52623,0,2,/adage14175/bike-notebook,Bike Sharing Demand
6908375,1.2293,0,0,/idolaspecus/random-forest,Bike Sharing Demand
6769215,0.44338,0,0,/idolaspecus/submission,Bike Sharing Demand
6575196,0.46366,9,15,/swinalmeshram/bike-sharing,Bike Sharing Demand
6420379,0.4221399999999999,4,7,/drcapa/bike-sharing-demand-rnn,Bike Sharing Demand
6108308,0.4735899999999999,0,2,/jiyoonkim/third,Bike Sharing Demand
6062819,0.45871,2,11,/hanifansari93/bike-sharing-demand-eda-modeling,Bike Sharing Demand
5594627,0.40809,3,1,/kennyinkaggle/bikesharingdemand,Bike Sharing Demand
4702749,0.39125,0,5,/drcapa/bike-sharing-regressor,Bike Sharing Demand
4159668,0.4901,0,0,/cheikhmbaye/resumeregressionlineaire,Bike Sharing Demand
4170074,0.4882899999999999,0,0,/yadechi/kernel4c0bcda4c1,Bike Sharing Demand
3826254,0.43262,0,0,/wdiego/2019-05-07-iesb-miner-ii-aula-04,Bike Sharing Demand
3750732,0.43811,0,0,/wdiego/iesb-miner-ii-aula-03-random-forest,Bike Sharing Demand
3389369,0.42272,0,0,/amelnozieres/random-forest,Bike Sharing Demand
1833662,0.88336,0,1,/lcukerd/rating-predictor,Bag of Words Meets Bags of Popcorn
1826864,0.8538399999999999,0,0,/khushboosrivastava2/assignment-09-oct-2018,Bag of Words Meets Bags of Popcorn
1586313,0.8242799999999999,0,0,/hudanivy/movie-sentiment-baseline-stop-words,Bag of Words Meets Bags of Popcorn
2206014,0.5471,1,6,/winstonvan/python-keras-resnet50-for-cancer,Histopathologic Cancer Detection
2156068,0.9693,8,32,/guntherthepenguin/fastai-v1-densenet169,Histopathologic Cancer Detection
2126994,0.9709,47,149,/CVxTz/cnn-starter-nasnet-mobile-0-9709-lb,Histopathologic Cancer Detection
2121242,0.9257,22,72,/artgor/simple-eda-and-model-in-pytorch,Histopathologic Cancer Detection
3074987,0.9633,0,0,/artgol/cancer-detection-with-resnet34-in-fastai-v1,Histopathologic Cancer Detection
10827226,0.40926,0,2,/aakashveera/bosch-random-forest,Bosch Production Line Performance
11615983,0.43892,0,2,/kkchuchu/logisticregression-practice-0815,Click-Through Rate Prediction
14453230,0.9891,0,1,/sambor1313/cnn-digits-recognition-knowledge-summary,Digit Recognizer
14586885,0.98675,7,3,/gurharkhalsa/digits-pytorch-resnet,Digit Recognizer
14478178,0.97757,5,4,/riteshpatil8998/digit-recognizer-cracking-top-rankings,Digit Recognizer
14276918,0.98342,0,2,/vinayharyan/learningcnn-digit-recongnizer,Digit Recognizer
14382741,0.99092,0,1,/aicentral/image-classification-mnist-with-automl,Digit Recognizer
14318525,0.97485,0,0,/ricardocepedaraza/mnist-competition-draft-1,Digit Recognizer
14556937,0.99171,0,0,/randommmjy/cnn-digit-recognition,Digit Recognizer
7945168,0.99321,0,0,/jouleffect/digit-top,Digit Recognizer
13414325,0.98407,0,0,/tallesviana/basic-pytorch-kornia-mnist,Digit Recognizer
13700974,0.97575,0,0,/krl666/digit-recognizer-nn-keras,Digit Recognizer
14393657,0.92892,0,5,/nguyentienduy/using-svm,Digit Recognizer
14568676,0.97364,0,0,/sytuannguyen/pre-trained-convnet-mobilenet,Digit Recognizer
2957163,1.96073,6,13,/liviuasnash/predict-movies-step-by-step,TMDB Box Office Prediction
2977833,2.01006,0,1,/swwintels/pca-and-svd-to-represent-multi-category-vars,TMDB Box Office Prediction
2892223,1.86307,143,426,/artgor/eda-feature-engineering-and-model-interpretation,TMDB Box Office Prediction
2904852,1.94895,1,8,/palend/ensemble-xgb-lgbm-catboost,TMDB Box Office Prediction
2857023,1.99809,1,11,/rblcoder/box-office-regression,TMDB Box Office Prediction
4350421,1.79434,0,0,/honglou/kernel5df7c2ac5a,TMDB Box Office Prediction
14219767,0.85,0,0,/peterpetrov826/saving-cassava-with-fastai,Cassava Leaf Disease Classification
14667401,0.8959999999999999,0,0,/chengsiyuan1019/cesmooth-fold1,Cassava Leaf Disease Classification
14112617,0.903,1,5,/alekseyeliseev/pytorch-efficientnet-baseline-inference-tta,Cassava Leaf Disease Classification
14378658,0.048,0,0,/researchbntz/xceptionstack-newaug,Cassava Leaf Disease Classification
12993080,0.723,3,4,/josephsha/cassava-leaf-disease-classification-tf,Cassava Leaf Disease Classification
13461755,0.89,0,0,/bitunsen/cassava-leaf-disease-pretrainined-model,Cassava Leaf Disease Classification
1337108,0.67,0,1,/nikhilroxtomar/unet-with-depth,TGS Salt Identification Challenge
1330930,0.38,6,5,/kmader/u-net-with-dice-and-augmentation,TGS Salt Identification Challenge
1316498,0.625,115,769,/jesperdramsch/intro-to-seismic-salt-and-how-to-geophysics,TGS Salt Identification Challenge
1318125,0.6709999999999999,4,29,/ashishpatel26/have-you-check-this-approach,TGS Salt Identification Challenge
1324639,0.637,1,2,/markhacker/salty,TGS Salt Identification Challenge
2212603,0.764616,0,0,/malyada/basicunet-v1,TGS Salt Identification Challenge
1718204,0.8059999999999999,0,0,/kumarajay/unet-resnet-with-two-losses,TGS Salt Identification Challenge
86315,2.26735,0,1,/apapiu/a-linear-boosted-model-on-apps-and-labels,TalkingData Mobile User Demographics
6602534,0.78481,0,3,/saeedtqp/unet-resnet34,TGS Salt Identification Challenge
1723328,0.815,1,1,/peter0749/u-net-with-simple-resnet-blocks-v2-new-loss,TGS Salt Identification Challenge
1367637,0.71,0,0,/saloni0101/notebook-tgs-salt,TGS Salt Identification Challenge
1761929,0.8190000000000001,0,1,/gaelblanch/optimal-unet-beyond-a,TGS Salt Identification Challenge
1606295,0.74,0,0,/sumitdua10/fork-of-salt-images,TGS Salt Identification Challenge
1871180,0.7765489999999999,0,0,/wenjieluo/tgs-wj,TGS Salt Identification Challenge
1810197,0.826,20,54,/abhilashawasthi/unet-with-simple-resnet-blocks,TGS Salt Identification Challenge
1773855,0.8029999999999999,0,1,/hmen97/u-net-with-resnet,TGS Salt Identification Challenge
1765442,0.696,0,14,/leighplt/pytorch-tta-flip-left-right,TGS Salt Identification Challenge
1714703,0.4039999999999999,0,1,/residentmario/structured-unet-model-1,TGS Salt Identification Challenge
1321439,0.6809999999999999,0,0,/dingdiego/unet-with-depth-recurrent,TGS Salt Identification Challenge
1499879,0.755,0,0,/dingdiego/baseline-v5-tmp,TGS Salt Identification Challenge
1480681,0.7609999999999999,0,0,/dingdiego/baseline-v5,TGS Salt Identification Challenge
1460975,0.777,0,0,/dingdiego/baseline-v4,TGS Salt Identification Challenge
1409336,0.769,0,0,/dingdiego/self-baseline-0-760-0-140-u-net-v3,TGS Salt Identification Challenge
12684694,1.78922,0,0,/zimulyu/tmdb-box-office-pred,TMDB Box Office Prediction
11821257,3.4687300000000003,0,2,/dipankarsrirag/decisiontree-tmdb,TMDB Box Office Prediction
10131189,2.05702,0,4,/jjoonhk92/tmdb-jhk-ver3,TMDB Box Office Prediction
10133927,1.97557,0,1,/kodain/tmdb-box-office-prediction-kn,TMDB Box Office Prediction
10046559,2.08379,0,0,/yukomiya/tmbd-ko-na-ki-200611,TMDB Box Office Prediction
9943026,2.94394,0,0,/daku5768/tmdb-box-office-prediction-beginner-approach,TMDB Box Office Prediction
9920947,2.17014,0,0,/yukomiya/tmbd-ko-na-ki-200605,TMDB Box Office Prediction
9800835,2.37585,0,4,/alessiomartello/tmdb-box-office-prediction,TMDB Box Office Prediction
6865144,2.20067,0,2,/texasroh/feature-engineering-eda,TMDB Box Office Prediction
4301727,2.06179,0,2,/brekope/predict-movie-revenue,TMDB Box Office Prediction
5855366,2.02076,0,0,/raval64/tmdb-box-office-pred,TMDB Box Office Prediction
4989138,2.1924900000000003,1,13,/suneelpatel/movie-s-box-office-revenue-prediction,TMDB Box Office Prediction
4246081,1.81686,0,2,/honglou/kernel888e2dec7f,TMDB Box Office Prediction
4032130,2.01442,0,0,/mzlikesb/0602-tmdb-prediction,TMDB Box Office Prediction
3992750,1.85055,0,0,/adrianoavelar/simple-clean-tmdb-box-office-prediction,TMDB Box Office Prediction
4077211,2.25535,0,3,/mommermi/box-office-eda-bayesian-opt-random-forest,TMDB Box Office Prediction
4004909,2.56327,0,4,/akashdhorajiya/box-office-revenue-prediction,TMDB Box Office Prediction
13825967,0.99471,3,9,/direwolf770/mnist-99-4-99-5-accuracy-with-image-augmentation,Digit Recognizer
13423365,0.99614,18,27,/datajameson/mnist-top-5-using-cnn-accuracy-0-997,Digit Recognizer
13673359,0.99478,1,2,/konstanter/mnist-on-keras-publilb-0-99475-by-konstanter,Digit Recognizer
13780555,0.98928,0,1,/hassanw65/digit-recognizer-cnn,Digit Recognizer
13747932,0.92664,0,3,/ouba64/digit-recognizer-cnn-ensemble-source,Digit Recognizer
13752109,0.98896,0,1,/shyam21/mnist-strater-code,Digit Recognizer
13598763,0.96789,2,2,/fengjim/mnist,Digit Recognizer
13642278,0.96792,0,1,/liyilang/digit-recognizer,Digit Recognizer
13447302,0.94975,6,9,/daotan/digitrecognizer-using-cnn,Digit Recognizer
13238746,0.97364,0,0,/mithra06/mnist-using-keras-and-tensorflow,Digit Recognizer
13395603,0.99028,0,3,/yuichikuriyama/simple-minst-for-kaggle,Digit Recognizer
13384771,0.10678,0,1,/amitalexander/digital-recognizer-cnn,Digit Recognizer
13795590,0.9488,1,1,/homayoonkhadivi/medical-diagnosis-histopathologic-cancer-cnn,Histopathologic Cancer Detection
2416400,0.9646,0,1,/aimdata/cancer-detection-using-cnn,Histopathologic Cancer Detection
9821144,0.9496,0,0,/vernondsouza123/beginner-transfer-learning,Histopathologic Cancer Detection
8638755,0.8614,0,0,/giniya/hist-cap2-3,Histopathologic Cancer Detection
3028085,0.8629,0,0,/wbtoms74/histopathologic-cancer-detection-with-cnn,Histopathologic Cancer Detection
3444122,0.9629,0,0,/aadharsh0428/hc-detection-using-pytorch,Histopathologic Cancer Detection
7697287,0.941,0,0,/tomaszgil/image-classification-with-densenet,Histopathologic Cancer Detection
7045991,0.799,1,0,/hanjoonchoe/histopathologic-quick-resnet32-pytorch-no-gpu,Histopathologic Cancer Detection
7001972,0.9265,0,0,/danilaot/pytorch-cnn-from-scratch,Histopathologic Cancer Detection
4548234,0.9455,0,0,/feltonvon1019/cancer-cnn,Histopathologic Cancer Detection
4469558,0.9512,0,1,/rhodiumbeng/histopathologic-cancer-detection-using-cnn,Histopathologic Cancer Detection
4058735,0.9579,0,0,/spichon/cancerdetectionja,Histopathologic Cancer Detection
5007542,0.9435,0,3,/muhakabartay/ieee-fraud-detection-lgbm-stacking-overfit,IEEE-CIS Fraud Detection
13314351,0.925515,0,0,/mrizwansaeed/ieee-cis-fraud-detection-eda-and-models,IEEE-CIS Fraud Detection
12647760,0.8916120000000001,0,0,/shafqaatahmad/ieee-cis-fraud-detection-random-forest,IEEE-CIS Fraud Detection
12647531,0.906929,0,0,/shafqaatahmad/ieee-cis-fraud-detection-eda-lgb,IEEE-CIS Fraud Detection
6495435,3.95946,0,0,/vinaydoshi/dogs-vs-cats-using-resnet50-imagenet-features,Dogs vs. Cats Redux: Kernels Edition
2095146,0.19472,1,1,/jmourad100/fork-of-dogsvscats-keras-convnet-starter,Dogs vs. Cats Redux: Kernels Edition
6903648,1.46444,0,0,/abdulrazzaqraz/transfer-learning-on-dogs-vs-cats-data,Dogs vs. Cats Redux: Kernels Edition
6498656,0.07246,0,1,/thinkingreed/dog-cat-redux-fastai-hw1,Dogs vs. Cats Redux: Kernels Edition
5919667,3.74285,1,4,/brijesh41/dog-vs-cat-mobilenet-transfer,Dogs vs. Cats Redux: Kernels Edition
5220197,2.5641,0,1,/wrecked22/dogonet,Dogs vs. Cats Redux: Kernels Edition
4644663,0.09946,0,0,/timdarcet/dogs-v-cats,Dogs vs. Cats Redux: Kernels Edition
4447251,0.19983,1,1,/stephanedc/tutorial-cnn-partie-3-mod-le-vgg16,Dogs vs. Cats Redux: Kernels Edition
6510345,0.80804,0,5,/bustam/one-hot-stratified-logistic-regression,Categorical Feature Encoding Challenge
6486895,0.76913,3,3,/yutanakamura/25-line-model-of-lightgbm,Categorical Feature Encoding Challenge
6370475,0.79464,0,10,/afajohn/lightgbm-nom-5-9-transformation,Categorical Feature Encoding Challenge
6295119,0.69758,4,10,/caesarlupum/catcomp-split-test,Categorical Feature Encoding Challenge
6230658,0.80743,0,21,/abhishek/entity-embeddings-to-handle-categories-using-mish,Categorical Feature Encoding Challenge
6156056,0.80507,45,66,/caesarlupum/catcomp-simple-target-encoding,Categorical Feature Encoding Challenge
6096584,0.7884800000000001,6,14,/subinium/lightgbm-is-powerful,Categorical Feature Encoding Challenge
6054505,0.79929,2,9,/drcapa/categorical-feature-engineering-xgb,Categorical Feature Encoding Challenge
6054110,0.80801,1,24,/cuijamm/simple-onehot-logisticregression-score-0-80801,Categorical Feature Encoding Challenge
5906645,0.78442,1,4,/egolinko/qgel-embed-lookup,Categorical Feature Encoding Challenge
5904662,0.25172,0,0,/errolpereira/simple-data-exploration,Categorical Feature Encoding Challenge
5916593,0.80758,2,2,/errolpereira/logistic-regression,Categorical Feature Encoding Challenge
5871492,0.78617,2,11,/kulkarnivishwanath/categorical-feature-encoding-challenge-eda-model,Categorical Feature Encoding Challenge
5834866,0.80481,0,1,/merckel/target-encoding-and-lightgbm,Categorical Feature Encoding Challenge
5771450,0.8041699999999999,2,10,/iwanenko/why-not-catboost,Categorical Feature Encoding Challenge
5605362,0.78341,0,3,/maramsofyan94/cat-in-dat,Categorical Feature Encoding Challenge
5695351,0.80604,0,1,/plarmuseau/logistic-regression,Categorical Feature Encoding Challenge
5629317,0.6796,2,6,/thirumani/categorical-feature-encoding-challenge,Categorical Feature Encoding Challenge
5583487,0.76917,1,4,/stephenmugisha/labelencoder,Categorical Feature Encoding Challenge
14071992,0.0,0,2,/owenhuanghao/huanghao-late-submission,Google Analytics Customer Revenue Prediction
10257765,0.0,3,8,/vikrantdeshpande098/gstore-cust-revenue-prediction,Google Analytics Customer Revenue Prediction
2152426,0.0,0,0,/omarmorsli/google-analytics-customer-revenue-prediction,Google Analytics Customer Revenue Prediction
3216683,0.0,0,1,/hjd810/fast-skim-v2-testing-baseline-models,Google Analytics Customer Revenue Prediction
1965305,1.4449,0,1,/shravankp/data-preprocessing-and-fitting-lgb-model,Google Analytics Customer Revenue Prediction
1921857,1.4463,0,0,/urmilkadakia/google-revenue,Google Analytics Customer Revenue Prediction
1849953,1.555,0,0,/baoanh/gstore-revenue-predict-lightgbm,Google Analytics Customer Revenue Prediction
2202326,0.0,1,11,/augustmarvel/base-model-v2-user-level-solution,Google Analytics Customer Revenue Prediction
2240868,0.0,0,0,/ppanchak/gstore-predictions,Google Analytics Customer Revenue Prediction
2007312,0.0,0,20,/manojasaithambi/gstore-eda-with-lgbm-and-lstm,Google Analytics Customer Revenue Prediction
2005080,0.0,0,4,/kalyankkr/google-analytics-customer-revenue-prediction,Google Analytics Customer Revenue Prediction
1849382,1.4753,0,4,/subhamkapoor360/xgbregressor,Google Analytics Customer Revenue Prediction
8127645,0.1169999999999999,2,4,/vaibhavsxn/logisticregcv-new-metric,University of Liverpool - Ion Switching
9247833,0.94,0,0,/ashora/understanding-ion-switching-with-modeling,University of Liverpool - Ion Switching
8961465,0.938,1,0,/scirpus/fork-of-alright-la,University of Liverpool - Ion Switching
8861722,0.94,0,0,/akashsuper2000/pytorch-u-net-model,University of Liverpool - Ion Switching
6745526,1.35,0,0,/vladimirsydor/naivemeanpredictor,ASHRAE - Great Energy Predictor III
7121796,1.32,1,1,/abhinc/lgbm-simple,ASHRAE - Great Energy Predictor III
7043719,2.412,0,0,/pierrematthieupair/ashrae-modeling,ASHRAE - Great Energy Predictor III
7119261,0.96,0,8,/minmyk/blending-with-genetic-algorithm-pipeline,ASHRAE - Great Energy Predictor III
6897787,0.955,5,6,/themonologue/ashrae-manual-weights,ASHRAE - Great Energy Predictor III
7110288,1.906,0,0,/kadriligi/no-weather-data,ASHRAE - Great Energy Predictor III
7107462,1.117,0,0,/tsymbolist/ashrae-kfold-lightgbm,ASHRAE - Great Energy Predictor III
6468270,1.15,0,0,/patelatharva/prediction,ASHRAE - Great Energy Predictor III
7014050,1.075,3,24,/roydatascience/ashrae-stratified-kfold-lightgbm,ASHRAE - Great Energy Predictor III
6948274,1.16,0,0,/enigola/ashrae-ml-hw6-lgbm,ASHRAE - Great Energy Predictor III
6897699,1.074,18,74,/roydatascience/ashrae-energy-prediction-using-stratified-kfold,ASHRAE - Great Energy Predictor III
6956485,2.777,2,5,/cereniyim/save-the-energy-for-the-future-3-predictions,ASHRAE - Great Energy Predictor III
6655362,2.18,0,5,/alonalon/many2many-lstm-using-pytorch,ASHRAE - Great Energy Predictor III
6712535,0.989,14,22,/roydatascience/ashrae-exploiting-leak-site-5,ASHRAE - Great Energy Predictor III
6836857,0.97,10,22,/barnrang/ashrae-leak-validation-gradient-descent-search,ASHRAE - Great Energy Predictor III
6846946,1.4469999999999998,6,8,/yasarc4/baseline-solution-without-model-1-44-publiclb,ASHRAE - Great Energy Predictor III
6805452,1.097,1,20,/iwatatakuya/ashrae-kfold-lightgbm-without-building-id,ASHRAE - Great Energy Predictor III
6769251,1.08,43,202,/purist1024/ashrae-simple-data-cleanup-lb-1-08-no-leaks,ASHRAE - Great Energy Predictor III
6659430,1.159,0,5,/josecarmona/ashrae-lgbm-t2,ASHRAE - Great Energy Predictor III
13853616,0.98329,0,0,/anirbansen3027/jtcc-multilabel-bert-pytorch,Toxic Comment Classification Challenge
13809250,0.80413,0,3,/anirbansen3027/jtcc-fasttext-supervised,Toxic Comment Classification Challenge
6133765,0.98292,0,0,/amir78pgd/minimal-lstm-g-f-nb-svm-baseline-ensemble,Toxic Comment Classification Challenge
6221751,0.97865,0,0,/amir78pgd/improved-lstm-baseline-glove-dropout-pl,Toxic Comment Classification Challenge
12759568,0.838,0,0,/aadeshbaral/toxic-comment-using-lstm,Toxic Comment Classification Challenge
12370141,0.83057,0,0,/rog007/transformers,Toxic Comment Classification Challenge
826475,0.9505,0,1,/alexanderhades/toxic-classification-notebook,Toxic Comment Classification Challenge
11344778,0.97844,0,7,/kalashnimov/weighted-logistic-regression-and-nbsvm,Toxic Comment Classification Challenge
11167460,0.8786299999999999,0,0,/maxjeblick/zero-shot-classification,Toxic Comment Classification Challenge
10998727,0.94739,2,3,/digvijayyadav/getting-started-with-nlp,Toxic Comment Classification Challenge
10842125,0.95781,10,10,/mineshjethva/nlp-text-classification-lstm-conv,Toxic Comment Classification Challenge
9774054,0.92375,0,1,/gcspkmdr/toxiccommentfastai-custom-metrics,Toxic Comment Classification Challenge
9769830,0.7249800000000001,0,0,/varshinithatiparthi/kernel38adeef37c,Toxic Comment Classification Challenge
9523566,0.6990000000000001,0,0,/heng98/twitter-sentiment-extraction-2,Tweet Sentiment Extraction
10525096,0.7149300000000001,0,3,/marcogorelli/cln-tweet-sentiment-roberta-pytorch,Tweet Sentiment Extraction
9591576,0.696,0,0,/jarvispandey/kernel41bc745ce1,Tweet Sentiment Extraction
10054166,0.4039999999999999,0,0,/tumul360/using-tfidf-only,Tweet Sentiment Extraction
10451154,0.6847,0,0,/narimanelsamadony/pytorch-lightning-data-cleaning,Tweet Sentiment Extraction
10014759,0.72,0,2,/wuyhbb/roberta-inference-ensemble-v10,Tweet Sentiment Extraction
9951532,0.7120000000000001,0,0,/ab971631/tweet-sentiment-roberta-pytorch,Tweet Sentiment Extraction
10353499,0.59118,0,7,/mileypiao/ml-pos-neg,Tweet Sentiment Extraction
10161074,0.7288600000000001,0,10,/hiromoon166/inference-8models-seed100101-bucketing-2-ver2,Tweet Sentiment Extraction
10335097,0.5523899999999999,0,1,/salmacmpeg/lstm-model,Tweet Sentiment Extraction
10260652,0.45541,0,0,/salmacmpeg/bert-classifier,Tweet Sentiment Extraction
9059821,0.7120000000000001,0,0,/akashsuper2000/tensorflow-roberta,Tweet Sentiment Extraction
9675118,0.715,0,1,/sajalgoyal/nlp-0-715,Tweet Sentiment Extraction
9820306,0.657,0,2,/kunduruanil/spacy-ner-model,Tweet Sentiment Extraction
9989117,0.715,0,1,/darshanpatel11/inference-code,Tweet Sentiment Extraction
9218971,0.69747,0,0,/nanto88/albert-base-pytorch,Tweet Sentiment Extraction
9931553,0.7140000000000001,2,7,/cwthompson/twitter-sentiment-main-model,Tweet Sentiment Extraction
10097031,0.708,0,2,/yassinealouini/model-inference-only,Tweet Sentiment Extraction
10183065,0.73532,3,67,/theoviel/character-level-model-magic,Tweet Sentiment Extraction
10179715,0.7209,0,4,/ajax0564/singlemodel-tf2-2-tfroberta,Tweet Sentiment Extraction
10119838,0.72,0,0,/hamishdickson/w-space-weighted2,Tweet Sentiment Extraction
10086412,0.7340000000000001,0,6,/aruchomu/no-sampler-ensemble-normal-sub-0-7365,Tweet Sentiment Extraction
9350582,0.7070000000000001,0,1,/agengsetyatutuko/tweet-sent-0-710-final,Tweet Sentiment Extraction
10167701,0.6990000000000001,0,3,/rootofarch/roberta-w-preprocessing,Tweet Sentiment Extraction
7040501,0.9836,0,0,/sophiasusanraju/kannadamnist-v3-team-niso,Kannada MNIST
8129996,0.9576,0,1,/kojiiwase/kernel74c205f3f1,Kannada MNIST
7937425,0.9824,0,3,/xwalker/fork-of-kannada-mnist-minicnn,Kannada MNIST
6655969,0.9628,0,0,/zanin259/kannda-mnist,Kannada MNIST
7922441,0.9814,0,0,/anku5hk/kannada-mnist,Kannada MNIST
7863739,0.8638,0,0,/mchavoshi/rahnema-new-mnist,Kannada MNIST
7800476,0.9496,0,0,/bilokin/kannada,Kannada MNIST
7804043,0.977,0,2,/anjanatiha/kannada-mnist-classification-with-deep-learning,Kannada MNIST
6166226,0.9762,0,0,/aniketmm98/kernel432b47a074,Kannada MNIST
7311154,0.9694,0,2,/nickteim/kannada-mnist-1,Kannada MNIST
6080595,0.9882,0,1,/japanese910/kannnada-mnist,Kannada MNIST
7666638,0.9726,0,3,/toreleon/mnist-kannada-with-cnn,Kannada MNIST
7562636,0.9438,4,2,/mylee2009/kannada-building-some-noob-dnn-on-keras,Kannada MNIST
7523819,0.9184,1,7,/nathanbruzat/base-cnn-in-keras-for-kannada-digits,Kannada MNIST
7079013,0.9704,0,0,/rezedamindubaeva/kernel12cd3c885b,Kannada MNIST
6843831,0.8986,0,0,/cyzhou99/coincidance-lr,Kannada MNIST
7376798,0.9676,0,0,/abhishek4273/building-cnns-while-visualizing-with-monk,Kannada MNIST
7302225,0.9836,0,0,/nitwmanish/kannada-mnist-simple-densenet-in-keras,Kannada MNIST
7339701,0.9434,0,0,/liuyuanzhao/kernel22067ddf63,Kannada MNIST
6798908,0.9626,0,1,/kashyapmehta/ee258-kannada-dataset,Kannada MNIST
7207050,0.9368,0,2,/rahulharlalka/kannada-mnist-svm,Kannada MNIST
7198676,0.933,0,2,/nibba2018/kannada-mnist,Kannada MNIST
6870539,0.9912,5,35,/nnquangw/kannanda-mnist-how-i-get-to-9th-position,Kannada MNIST
7080776,0.9858,2,7,/andrewgao/kannada-mnist-cnn-tutorial-with-app-top-2-cce7db,Kannada MNIST
7039119,0.9856,0,3,/andrewgao/kannada-mnist-cnn-tutorial-with-app-top-2-11a1f5,Kannada MNIST
9773887,0.7452,0,1,/matthewmasters/my-model-inference-tta-pl,Global Wheat Detection
11026366,0.6756,0,0,/yukfaiwong/fastrcnn-wbf,Global Wheat Detection
10795878,0.7499,0,0,/raozhan/eff-off-wbf-pl,Global Wheat Detection
10961406,0.7432,4,11,/stanleyjzheng/yolov4-pseudolabelling-oof,Global Wheat Detection
11029041,0.6687,0,1,/eclipser33/faster-rcnn-resnet50,Global Wheat Detection
11039517,0.7634,0,0,/hisudha/wheat-detection-v1,Global Wheat Detection
10748915,0.713,0,2,/harelazim/fasterrcnn-resnet101-tta-ensemble,Global Wheat Detection
11033542,0.7189,0,1,/harelazim/ensemble-all-model,Global Wheat Detection
10484186,0.6974,0,0,/qingyuanwang/detr-inference,Global Wheat Detection
11040382,0.7145,0,2,/riyajm/riyaj-lb-0-741,Global Wheat Detection
10957946,0.7237,0,1,/idozada/global-wheat-detection-efficientdet-d5,Global Wheat Detection
10930836,0.5920000000000001,1,4,/eclipser33/faster-rcnn-with-pytorch,Global Wheat Detection
11023012,0.7177,0,3,/jonykarki/inference-efficientdet-tta,Global Wheat Detection
10978368,0.6452,2,7,/atrisaxena/wheat-detecton2,Global Wheat Detection
10514879,0.7338,0,0,/beelee4085/bayesian-optimization-wbf-efficientdet,Global Wheat Detection
9622329,0.7216,0,1,/akashsuper2000/oof-evaluation-efficientdet,Global Wheat Detection
11657172,0.02228,6,26,/dan3dewey/moa-or-not-moa,Mechanisms of Action (MoA) Prediction
11808842,0.02049,0,0,/deepakjhanji/moa-prediction-v1-0,Mechanisms of Action (MoA) Prediction
11892512,0.0211699999999999,0,2,/woniuxiao/notebookc005faaaa5,Mechanisms of Action (MoA) Prediction
11625763,0.0186199999999999,2,2,/akashsuper2000/inference-public-only-fast,Mechanisms of Action (MoA) Prediction
11809996,0.02087,0,4,/demetrypascal/lightgbm-and-logreg,Mechanisms of Action (MoA) Prediction
11781284,0.02002,2,22,/saiyanwarrior/moa-train-and-predict-easy-to-understand-code,Mechanisms of Action (MoA) Prediction
11800000,0.01993,1,2,/sudokill/moa-keras-simple-resnet-first-submission,Mechanisms of Action (MoA) Prediction
11849557,0.02214,0,0,/ysk24ok/notebook674b82deae,Mechanisms of Action (MoA) Prediction
11551072,0.01914,7,40,/huntermitchell/moa-k-fold-ensemble-starter-nn-xgboost-logreg,Mechanisms of Action (MoA) Prediction
11757167,0.0207,0,17,/konradb/build-model-svm,Mechanisms of Action (MoA) Prediction
11715462,0.01899,12,126,/gogo827jz/moa-lstm-pure-transformer-fast-and-not-bad,Mechanisms of Action (MoA) Prediction
11765665,0.02159,0,0,/snievan/moa-with-dnn,Mechanisms of Action (MoA) Prediction
11748963,0.02064,0,4,/latong/one-over-the-rest,Mechanisms of Action (MoA) Prediction
11698423,0.01858,28,147,/ludovick/introduction-to-tabnet-kfold-10-inference,Mechanisms of Action (MoA) Prediction
11744482,0.05925,0,2,/krishnakalyan3/moa-catboost-baseline,Mechanisms of Action (MoA) Prediction
11664044,0.0211599999999999,0,0,/ryu581/moa-prediction-with-lightgbm,Mechanisms of Action (MoA) Prediction
11670341,0.01873,67,411,/namanj27/new-baseline-pytorch-moa,Mechanisms of Action (MoA) Prediction
11706229,0.01896,2,13,/shubham328/moa-mechanisms-of-action-prediction,Mechanisms of Action (MoA) Prediction
11653386,0.01866,11,36,/ksouriazer/the-power-of-blend,Mechanisms of Action (MoA) Prediction
11642509,0.01889,42,130,/elcaiseri/moa-keras-multilabel-classifier-nn-starter,Mechanisms of Action (MoA) Prediction
11682130,0.02216,0,3,/subash03/moa-rf,Mechanisms of Action (MoA) Prediction
11639420,0.01895,11,57,/utkukubilay/deep-learning-model-moa,Mechanisms of Action (MoA) Prediction
9447827,0.7885300000000001,4,6,/layahaasini/nlp-disaster-detection-project,Natural Language Processing with Disaster Tweets
9213434,0.78547,1,1,/gautamv/initial-submission,Natural Language Processing with Disaster Tweets
9408840,0.8014,1,3,/maximilianblacher/real-or-not-nlp-with-disaster-tweets,Natural Language Processing with Disaster Tweets
9169773,0.80324,0,0,/dilyanpenev/realdisaster,Natural Language Processing with Disaster Tweets
9288880,0.78057,0,1,/selcukwashere/sel-uk-z-disaster-in-tweets-with-nlp,Natural Language Processing with Disaster Tweets
9251054,0.8262299999999999,0,3,/starkking07/roberta-classifier,Natural Language Processing with Disaster Tweets
9193361,0.79374,3,3,/mattbast/rnn-and-nlp-detect-a-disaster-in-tweets,Natural Language Processing with Disaster Tweets
9141654,0.8351200000000001,1,2,/mikeaalv/bert-huggingface-pytorch,Natural Language Processing with Disaster Tweets
8263586,1.0,2,4,/liangqingyuan/the-secret-of-accuracy-rate-1,Natural Language Processing with Disaster Tweets
8988128,0.78884,0,0,/krparekh24/predict-twitter-comment-aalysis-using-nlp,Natural Language Processing with Disaster Tweets
8597596,0.78425,0,0,/amackcrane/svm-knn,Natural Language Processing with Disaster Tweets
14420076,0.78947,0,1,/alvinyapabidin/the-titanic-project-a,Titanic - Machine Learning from Disaster
14697630,0.76076,0,0,/huikang/lightgbm-example,Titanic - Machine Learning from Disaster
14475058,0.7751100000000001,29,32,/legendsplay/titanic-let-s-compare-all-model-eda-ft,Titanic - Machine Learning from Disaster
14280118,0.77272,0,0,/bibimyeonmaster/titanic-prediction,Titanic - Machine Learning from Disaster
14550817,0.79425,2,5,/sevdatrumanidze/titanic-analysis-and-prediction,Titanic - Machine Learning from Disaster
14457133,0.7751100000000001,0,0,/lucaamore/predict-survival-the-titanic-xgboost,Titanic - Machine Learning from Disaster
14392010,0.7751100000000001,13,14,/zahidmahar/getting-started-with-titanic,Titanic - Machine Learning from Disaster
14341846,0.8109999999999999,60,77,/awwalmalhi/titanic-eda-and-feature-engineering,Titanic - Machine Learning from Disaster
14500336,0.7751100000000001,0,5,/tasneemabdulrahim/getting-started-with-titanic,Titanic - Machine Learning from Disaster
14623635,0.7703300000000001,0,3,/yuankang731/tatanic-rescue-problem,Titanic - Machine Learning from Disaster
14484446,0.7799,0,2,/bluesadi/my-solution-for-titanic,Titanic - Machine Learning from Disaster
14452044,0.76076,2,3,/terrifictitan12/titanic-survival-prediction-84-accuracy,Titanic - Machine Learning from Disaster
14642180,0.78468,0,0,/x2020dxm/titanic-survival-prediction,Titanic - Machine Learning from Disaster
8996306,0.77045,0,0,/pradyu99914/disaster-tweets-lightgbm-tfidf,Natural Language Processing with Disaster Tweets
8029550,0.79497,0,1,/itsbitan/nlpreal-or-not,Natural Language Processing with Disaster Tweets
8849088,0.80508,0,0,/jvpasp/real-or-not-nlp-with-disaster-tweets,Natural Language Processing with Disaster Tweets
8757320,0.82776,2,6,/nachiket273/huggingface-bert-uncased-tweet-classification,Natural Language Processing with Disaster Tweets
8121923,0.80447,0,3,/maroberti/disaster-tweets-roberta-with-fastai,Natural Language Processing with Disaster Tweets
8403941,0.78669,0,1,/sushanth1995/xgboost-for-text-classification,Natural Language Processing with Disaster Tweets
8543887,0.7934399999999999,0,0,/lucca9211/real-or-not-nlp-with-disaster,Natural Language Processing with Disaster Tweets
8664490,0.74379,1,2,/xceptions/twitter-text-classification-with-tensorflow,Natural Language Processing with Disaster Tweets
8584585,0.79681,0,0,/amackcrane/spacy-vectors,Natural Language Processing with Disaster Tweets
8634868,0.81612,0,0,/benfraser/rnn-lstm-implementation,Natural Language Processing with Disaster Tweets
8368180,0.7943600000000001,2,18,/bpkapkar/disasters-sentiments-improved,Natural Language Processing with Disaster Tweets
8672951,0.76218,0,0,/runorz/kernel79dc30567f,Natural Language Processing with Disaster Tweets
8545358,0.80202,0,0,/vincsous/simple-npl-nn,Natural Language Processing with Disaster Tweets
8541936,1.0,3,24,/anushakarthik1991/nlp-with-disaster-tweets-eda-cleaning-and-bert,Natural Language Processing with Disaster Tweets
8546522,0.79711,0,2,/nickteim/disaster-tweets-nlp-with-fastai,Natural Language Processing with Disaster Tweets
8328914,0.80723,0,0,/iosialectus/nlp-basics,Natural Language Processing with Disaster Tweets
8477451,0.79528,2,6,/agrover112/simple-fasttext-disaster-tweets-classification,Natural Language Processing with Disaster Tweets
7733031,0.79619,0,0,/mrugeles/nlp-with-disaster-tweets,Natural Language Processing with Disaster Tweets
7833290,0.81274,0,0,/ananthreddy/disaster-tweet-classification-bert,Natural Language Processing with Disaster Tweets
8492548,1.55836,0,0,/elenabalena/covid19-notebook-2,COVID19 Global Forecasting (Week 1)
10900688,1.03608,0,7,/mekhdigakhramanian/covid19-gbm,COVID19 Global Forecasting (Week 1)
8518603,0.83652,0,0,/natalietech/covid19-forecast-notebook,COVID19 Global Forecasting (Week 1)
9557394,0.69787,0,4,/abhaydhiman/covid-19-week-1st-pred,COVID19 Global Forecasting (Week 1)
8543434,2.66933,0,0,/suvarnaborse/novel-corona-virus-2019,COVID19 Global Forecasting (Week 1)
8511689,0.72121,0,0,/tonygeefus/covid-19-week-1-global,COVID19 Global Forecasting (Week 1)
8566371,1.70038,0,0,/nsaleille/randomforest-classifiers,COVID19 Global Forecasting (Week 1)
8508630,1.7312,0,0,/algonell/covid-19-series-poly-fit-w1,COVID19 Global Forecasting (Week 1)
8531025,0.37256,0,0,/xscripter/covid19-for-understanding,COVID19 Global Forecasting (Week 1)
8534042,0.7783399999999999,0,0,/rollend/kernel350dc8464f,COVID19 Global Forecasting (Week 1)
8522485,0.50547,0,9,/skeller/sarimax-influenza-baseline,COVID19 Global Forecasting (Week 1)
8541199,0.74861,12,24,/dferhadi/global-forecasting-covid-19-random-forest,COVID19 Global Forecasting (Week 2)
8507830,0.3571,0,1,/yatinece/exp-analysis-for-stages-check-weekly,COVID19 Global Forecasting (Week 1)
11672921,0.16153,2,3,/nishimoto/lishmoa-lgbm1st,Mechanisms of Action (MoA) Prediction
11639664,0.1083099999999999,0,0,/kuldeep7688/simple-sklearn-logistic-baseline,Mechanisms of Action (MoA) Prediction
11648329,0.3265699999999999,0,5,/sidharkal/mechanisms-of-action-moa-prediction,Mechanisms of Action (MoA) Prediction
11645146,0.10497,0,3,/raoofnaushad/moa-prediction-baseline,Mechanisms of Action (MoA) Prediction
11627791,0.0206699999999999,4,14,/alphamuth/let-s-do-eda-a-robust-prediction-using-dnn-keras,Mechanisms of Action (MoA) Prediction
11574221,0.02072,0,0,/kongnyooong/moa-prediction-base-fast-ai-for-korean,Mechanisms of Action (MoA) Prediction
11605890,0.02055,2,3,/shirishsharma/jason-mo-moa,Mechanisms of Action (MoA) Prediction
11596995,0.02421,0,1,/alturutin/moa-eda-means-baseline,Mechanisms of Action (MoA) Prediction
11601265,0.0203099999999999,0,9,/sarmat/simple-catalyst-pytorch-some-eda-and-kfold,Mechanisms of Action (MoA) Prediction
11563611,0.02016,8,31,/utkukubilay/drug-prediction-lgbm-model,Mechanisms of Action (MoA) Prediction
11592825,0.12505,0,5,/avloss/vae-start,Mechanisms of Action (MoA) Prediction
11521899,0.02039,1,85,/sishihara/moa-lgbm-benchmark,Mechanisms of Action (MoA) Prediction
11568324,0.01966,0,20,/rohitr4307/moa-prediction-ensemble-randomsearch,Mechanisms of Action (MoA) Prediction
11534018,0.1135099999999999,0,5,/yushg123/moa-from-eda-to-modelling,Mechanisms of Action (MoA) Prediction
11520135,0.02098,4,20,/mannsingh/simple-xgboost-model-for-beginners,Mechanisms of Action (MoA) Prediction
11531439,0.01905,26,352,/yasufuminakama/moa-pytorch-nn-starter,Mechanisms of Action (MoA) Prediction
11580107,0.02319,0,6,/benfraser/basic-exploration-and-submission,Mechanisms of Action (MoA) Prediction
11555337,0.02252,1,2,/rainmaker29/lb-0-022-tabnet-moa,Mechanisms of Action (MoA) Prediction
11563972,0.01931,0,8,/dmarkd/moa-prediction,Mechanisms of Action (MoA) Prediction
11544792,0.01903,0,26,/imeintanis/moa-nn-bayesian-optimisation-skopt,Mechanisms of Action (MoA) Prediction
11559354,0.02628,0,2,/erelin6613/eda-and-baseline-mechanisms-of-action-moa,Mechanisms of Action (MoA) Prediction
11558328,0.1307299999999999,0,17,/carlmcbrideellis/moa-baseline-0-02398-0-s-0-13073,Mechanisms of Action (MoA) Prediction
10944436,0.738,0,4,/qq1623620766/small-rectify,Global Wheat Detection
10923608,0.6826,0,4,/jonykarki/inference2-efficientdet-d4,Global Wheat Detection
10853579,0.7112,0,5,/rickyd/inference-gwd-efficientdet,Global Wheat Detection
10779150,0.1194,0,3,/savanmorya/efficientdet-keras-train-and-test-offline,Global Wheat Detection
10783594,0.7001,7,8,/fmobrj1975/detr50-round19-800-50pct,Global Wheat Detection
10808966,0.6687,0,1,/k4rth33k/pytorch-starter-fasterrcnn-inference,Global Wheat Detection
10089666,0.4469,0,0,/motoight/fork-of-mycenternet,Global Wheat Detection
10765331,0.6817,0,1,/annamel11111/gwd-version3,Global Wheat Detection
10603619,0.7502,0,6,/naimur978/wheat-head-detection-yolov5-pseudo-labelling-oof,Global Wheat Detection
10764908,0.7059,0,14,/jonykarki/ensemble-fasterrcnn-rn101-cv,Global Wheat Detection
10730667,0.6878,12,6,/chrisstan/darknet2pytorch-apache-license-yolov4-inference,Global Wheat Detection
10732880,0.716,0,4,/jonykarki/fasterrcnn-resnet101-tta-fold-3,Global Wheat Detection
10712675,0.7154,0,9,/jonykarki/fork-of-fasterrcnn-resnet101-tta-inference,Global Wheat Detection
10640650,0.6611,2,7,/abhishek4273/starter-code-using-monk-object-detection-library,Global Wheat Detection
4106253,0.5589999999999999,0,3,/vyordanov/molecular-xgboost-gpu,Predicting Molecular Properties
6708307,0.99,1,2,/billynguyen/begin-with-tensorflow-2,Kannada MNIST
6801479,0.9828,1,1,/rugan01/fastai-resnet34and50,Kannada MNIST
6673280,0.9364,0,1,/mswieton/2019-11-28-kannada-densenn,Kannada MNIST
6777882,0.99,2,12,/xiejialun/deep-dive-in-kannadamnist-with-tfkeras,Kannada MNIST
6807338,0.9664,0,0,/toraveng/kernel632409fc29,Kannada MNIST
6749865,0.9752,2,3,/grvgtm/regression-keras-kanada-mnist-cnn,Kannada MNIST
6704383,0.9818,0,2,/abhilashtensor/simple-cnn-rmsprop-learningratereduction,Kannada MNIST
6714708,0.978,1,9,/melissarajaram/model-ensembling-and-transfer-learning,Kannada MNIST
6693020,0.9734,0,3,/chandraroy/vgg-16-mnist-classification,Kannada MNIST
6695669,0.98,2,9,/crysialucifer/kanada-mnist-tensorflow-keras,Kannada MNIST
6692341,0.971,0,3,/emilianogl/kannada-mnist-keras-cnn,Kannada MNIST
6676246,0.953,8,8,/hanjoonchoe/kannada-mnist-classification-with-cnn-pytorch,Kannada MNIST
6572073,0.9588,0,1,/priteshshrivastava/kannada-mnist-simple-cnn-using-fastai,Kannada MNIST
6686522,0.9106,0,1,/hankovich/kernel4b60a7b069,Kannada MNIST
6626310,0.7722,0,0,/nikitaatroshenko/kernel3fff1d0236,Kannada MNIST
6657526,0.9088,0,1,/anastasiyaartsiom/kernel5279381f17,Kannada MNIST
6619447,0.9772,0,1,/flyingmuttus/kannada,Kannada MNIST
9993582,0.7140000000000001,0,1,/marcogorelli/inference-tweet-sentiment-roberta-pytorch,Tweet Sentiment Extraction
9651541,0.6970000000000001,0,0,/akashsuper2000/tweet-sentiment-file-submission-kernel,Tweet Sentiment Extraction
10006260,0.21,0,0,/shinichiishida/kernel5ef081182c,Tweet Sentiment Extraction
10043828,0.58,0,3,/piotrkukielka/distilbert-attempt-polish-assignment,Tweet Sentiment Extraction
10135107,0.7140000000000001,0,0,/dmitri9149/tweet-sentiment-roberta-and-pytorch,Tweet Sentiment Extraction
8714209,0.631,0,3,/nzhongahtan/3-tweet-sentiment-extraction,Tweet Sentiment Extraction
10139960,0.7090000000000001,0,2,/pranaydate/roberta-tpu-increase-s-running-speed,Tweet Sentiment Extraction
9951020,0.695,0,8,/abhiex7/tweet-sentiment-extraction-roberta-base,Tweet Sentiment Extraction
9769815,0.713,0,0,/volody/tse-roberta-v3,Tweet Sentiment Extraction
10102714,0.4679999999999999,0,2,/gabrielmilan/huggingface-s-tfrobertaforquestionanswering,Tweet Sentiment Extraction
9888662,0.69,2,13,/souravkgoyal/tweet-extraction-eda-simple-transformer-squad,Tweet Sentiment Extraction
10087904,0.664,0,2,/mahmudds/tweet-sentiment-extraction,Tweet Sentiment Extraction
9999893,0.665,0,1,/ulisesz/rf-w-subset-probabilities,Tweet Sentiment Extraction
9502935,0.26354,0,1,/hamiddd/first-test,Tweet Sentiment Extraction
9838715,0.626,0,1,/senritu/text-extraction,Tweet Sentiment Extraction
10084770,0.711,0,2,/pallavenkat/adabound-optimizer-with-fine-tuned-roberta,Tweet Sentiment Extraction
10004878,0.526,0,1,/steveblomgren/sklearn-multinomialnb-and-svm,Tweet Sentiment Extraction
10056271,0.594,0,0,/mason001/kernel69bd7b843e,Tweet Sentiment Extraction
9951644,0.7140000000000001,0,7,/ramahanishagunda/roberta-analysis,Tweet Sentiment Extraction
9658070,0.71259,1,12,/mks2192/tse-model-agnostic-ensemble,Tweet Sentiment Extraction
10020802,0.546,0,0,/myho63/test-version2,Tweet Sentiment Extraction
550515,1.172,0,0,/elloumi/test-1,Corporación Favorita Grocery Sales Forecasting
6741675,0.99,22,133,/yamsam/ashrae-leak-validation-and-more,ASHRAE - Great Energy Predictor III
6695114,1.03,0,47,/yamsam/ashrae-highway-kernel-route2,ASHRAE - Great Energy Predictor III
6564182,1.06,6,30,/yixinchen1/ashrae-1-1-to-1-06-with-ucl,ASHRAE - Great Energy Predictor III
6687149,1.34,2,10,/sheriytm/simple-xgboost-regressor-model,ASHRAE - Great Energy Predictor III
6622538,2.17,0,1,/sreelathar/ashrae-meter-reading-prediction,ASHRAE - Great Energy Predictor III
6655733,1.18,0,1,/yixinchen1/simple-lightgbm-lb-1-18,ASHRAE - Great Energy Predictor III
6369000,1.1,2,6,/remisharoon/ashrae-gep-iii-rms-nb,ASHRAE - Great Energy Predictor III
6559468,1.1,7,9,/yixinchen1/ashrae-eda-lgb-doublelgb,ASHRAE - Great Energy Predictor III
6553388,1.04,21,130,/rohanrao/ashrae-divide-and-conquer,ASHRAE - Great Energy Predictor III
6273605,1.42,4,12,/rahullalu/ashrae-eda-and-104-models,ASHRAE - Great Energy Predictor III
8083831,0.45016,3,5,/revanthrex/xception-resnext-ensemble-for-deepfake-0-45,Deepfake Detection Challenge
7281318,0.97887,0,1,/asromhain/dfc-baseline,Deepfake Detection Challenge
8211490,0.29618,0,0,/ustczhq/kernel6debdbe04c,Deepfake Detection Challenge
8190249,0.4736899999999999,0,1,/namanmehta1/deepfake-resnext101,Deepfake Detection Challenge
8670039,0.25322,2,41,/vaillant/dfdc-3d-2d-inc-cutmix-with-3d-model-fix,Deepfake Detection Challenge
8025608,0.71341,0,0,/diamondsnake/deepfake-detection,Deepfake Detection Challenge
8461662,0.97226,0,0,/gmimg9/200318-submit-mtcnn-300frame-video,Deepfake Detection Challenge
8677460,0.30017,0,0,/maralski/fork-of-ensemble-of-5-networks,Deepfake Detection Challenge
8165148,0.69234,0,3,/rohandeysarkar/deepfake-detection-image-eda,Deepfake Detection Challenge
8673533,0.29477,1,11,/vostankovich/efnet6-ensemble-5-clip-1e-15,Deepfake Detection Challenge
8690296,0.29529,5,9,/unkownhihi/a-ton-ensemble,Deepfake Detection Challenge
8468340,1.62875,0,0,/agni147878/kernel3391d9cda9,Deepfake Detection Challenge
7388220,0.69247,3,2,/tothbalazs0920/sequence-based-model-demo,Deepfake Detection Challenge
8414538,0.38069,79,94,/unkownhihi/dfdc-lrcn-inference,Deepfake Detection Challenge
7878112,1.0917,2,7,/manyregression/fastai-inference,Deepfake Detection Challenge
7395504,0.69299,0,0,/mitya8128/cnn-lstm,Deepfake Detection Challenge
8310035,0.69313,0,2,/apoorwanand/codelabs,Deepfake Detection Challenge
3937007,0.93779,0,1,/codegass/siamese-net-with-ensemble,Humpback Whale Identification
3936191,0.8229700000000001,0,0,/cpe695yimeng/fifth,Humpback Whale Identification
3747312,0.2585,0,0,/jweissenberger/keras-cnn-with-image-augmentation,Humpback Whale Identification
3250479,0.90873,1,23,/jaideepvalani/arcface-humpback-customhead-fastai-score919,Humpback Whale Identification
2834258,0.3329999999999999,0,0,/overload10/fishing-whales,Humpback Whale Identification
2624220,0.276,0,3,/tarunpaparaju/resnet-pretrained,Humpback Whale Identification
2995699,0.8420000000000001,8,25,/jionie/esemble-lb-0-842,Humpback Whale Identification
2991409,0.286,0,1,/iishchukov/keras-with-simple-mlp-model,Humpback Whale Identification
2247981,0.0,2,6,/nacicansao/augmentation-mobilenet,Humpback Whale Identification
2802859,0.28,0,2,/harishvutukuri/humpback-whale-fast-ai,Humpback Whale Identification
2655912,0.233,2,3,/iamarjunchandra/keras-cnn-whale-detection-beginner-model,Humpback Whale Identification
2824112,0.2689999999999999,0,1,/smitkiri/humpback-whale-prediction-using-keras,Humpback Whale Identification
2742271,0.2802699999999999,0,1,/sujoykg/with-grayscale-images,Humpback Whale Identification
2479107,0.544,0,2,/akshaysub99/whale-pretrained,Humpback Whale Identification
2642559,0.833,0,6,/xasiimov/siamese-ensemble-lb-0-833,Humpback Whale Identification
3903685,0.41658,0,1,/chriszou/dogs-vs-cats-pytorch-cnn-without-transfer-learning,Dogs vs. Cats Redux: Kernels Edition
2517002,0.16837,0,0,/jmourad100/dogsvscats-keras-convnet,Dogs vs. Cats Redux: Kernels Edition
2429055,0.0717899999999999,0,0,/jianjin/dogs-vs-cats-with-fastai-1-0,Dogs vs. Cats Redux: Kernels Edition
2309282,0.38732,0,2,/singhsatwinder/dog-vs-cat-classification-using-tf-with-keras,Dogs vs. Cats Redux: Kernels Edition
2217224,0.18455,0,0,/bbqlp33/vgg-like-net-for-dog-cat-recognition,Dogs vs. Cats Redux: Kernels Edition
1943881,0.5151600000000001,0,0,/keogh24/dogs-v-cats-keras,Dogs vs. Cats Redux: Kernels Edition
1872050,8.65414,0,0,/upperthrower4/cnn-leo,Dogs vs. Cats Redux: Kernels Edition
5548450,0.8076800000000001,1,22,/asimandia/let-s-try-some-feature-engineering,Categorical Feature Encoding Challenge
5476443,0.78185,44,333,/kabure/eda-feat-engineering-encode-conquer,Categorical Feature Encoding Challenge
5486240,0.80491,7,49,/gogo827jz/catboost-baseline-with-feature-importance,Categorical Feature Encoding Challenge
5478318,0.80716,11,87,/abhishek/entity-embeddings-to-handle-categories,Categorical Feature Encoding Challenge
5503096,0.78482,3,8,/jazivxt/cat-encode,Categorical Feature Encoding Challenge
5473932,0.75168,1,8,/inversion/simple-rf-starter,Categorical Feature Encoding Challenge
5715058,0.8053100000000001,0,0,/atiko9876/mean-target-encoding,Categorical Feature Encoding Challenge
14143387,0.3981099999999999,10,12,/werooring/bike-sharing-demand-top-6-6-solution,Bike Sharing Demand
13730868,1.73891,2,5,/yaminiharikrishnan/bike-sharing-demand,Bike Sharing Demand
13343252,0.40186,0,2,/homayoonkhadivi/xgboost-model-for-bike-prediction,Bike Sharing Demand
13003838,0.47192,0,1,/abdullapathan/bike-sharing-demand-version-1,Bike Sharing Demand
11867820,0.43631,0,0,/marcosvafg/iesb-graduacao-cia028-bike-sharing,Bike Sharing Demand
10644420,0.44728,0,0,/tom727/kernel1cbace2247,Bike Sharing Demand
9485463,0.3875699999999999,0,1,/munmun2004/bike-sharing-demand,Bike Sharing Demand
4946261,0.36154,0,0,/sjun4530/bike-sharing-demand-final,Bike Sharing Demand
8664655,0.38081,1,6,/rbud613/bike-demand,Bike Sharing Demand
14560088,0.9718,16,12,/saadbinmanjuradit/bag-of-words-m-bags-of-popcorn-with-93-accuracy,Bag of Words Meets Bags of Popcorn
14116458,0.8536,0,0,/blighpark/imdb-part1-bow,Bag of Words Meets Bags of Popcorn
12431860,0.8464,1,0,/jsyphil/part-1-bag-of-words-meets-bags-of-popcorn,Bag of Words Meets Bags of Popcorn
9224934,0.8454,0,1,/oscarola/word2vec-tutorial,Bag of Words Meets Bags of Popcorn
7734143,0.8478,0,0,/evelynchin/bag-of-words-meets-bags-of-popcorn,Bag of Words Meets Bags of Popcorn
7202664,0.8764799999999999,0,7,/yepp2411/baseline-model-using-nn-for-movie-review,Bag of Words Meets Bags of Popcorn
6998885,0.84824,0,1,/shrinidhin/bag-of-words-meets-bag-of-popcorn-mykernel,Bag of Words Meets Bags of Popcorn
6403415,0.8717600000000001,0,3,/byhoson/text-classification,Bag of Words Meets Bags of Popcorn
4438966,0.8424799999999999,0,1,/mayankg/kernelf4f8b76d32,Bag of Words Meets Bags of Popcorn
3529045,0.8442,0,0,/justk1/bow-part1,Bag of Words Meets Bags of Popcorn
3445023,0.8135600000000001,0,0,/kuso88/countvectorizer-and-multinomialnb,Bag of Words Meets Bags of Popcorn
3259698,0.88356,0,0,/nmm33342/kernel3988ecf775,Bag of Words Meets Bags of Popcorn
2721854,0.96892,0,8,/jatinmittal0001/word2vec,Bag of Words Meets Bags of Popcorn
2681706,0.8449200000000001,0,0,/aakashjhawar/imdb-bag-of-words,Bag of Words Meets Bags of Popcorn
2348204,0.8734,0,2,/necrospk/kaggle-words-train,Bag of Words Meets Bags of Popcorn
2219468,0.8461200000000001,0,0,/magic9800/kernel2bc4481284,Bag of Words Meets Bags of Popcorn
2169183,0.8458,0,1,/lider123/sentiment-analysis-for-bags-of-popcorn,Bag of Words Meets Bags of Popcorn
1850421,0.8403200000000001,0,0,/aamnafea/nltk-bag-of-words,Bag of Words Meets Bags of Popcorn
114047,0.4158,0,0,/jyp2016/test-variables,Bosch Production Line Performance
14525104,0.8009999999999999,22,34,/mnavaidd/casava-leaf-disease-classification-with-keras,Cassava Leaf Disease Classification
13083880,0.103,3,6,/clatonhendricks/cassava-leaf-disease-keras-and-efficientnetb4,Cassava Leaf Disease Classification
14573474,0.64,12,11,/nelsonwongisme/for-dummies-tf-keras-data-augmentation,Cassava Leaf Disease Classification
14630714,0.602,1,2,/jitshil143/efficientnetb3-model,Cassava Leaf Disease Classification
14431628,0.848,4,4,/iamyajat/cassava-leaf-disease-inceptionresnetv2,Cassava Leaf Disease Classification
13988594,0.885,1,3,/mohneesh7/submission-notebook-for-cassava,Cassava Leaf Disease Classification
14101482,0.888,1,6,/ryohatano/efficientnet-and-bi-tempered-w-pytorch-lightning,Cassava Leaf Disease Classification
14494469,0.9,0,2,/salmaneunus/cassava-classification-10,Cassava Leaf Disease Classification
13661419,0.884,0,1,/manjeetsingh06/cassava1,Cassava Leaf Disease Classification
14315639,0.99475,6,3,/ralphguo/cnn-for-minst-data,Digit Recognizer
14236588,0.96435,5,32,/ateplyuk/pytorch,Digit Recognizer
14227432,0.99592,0,0,/blagoyh/identifying-handwriting-with-ai-99-6,Digit Recognizer
14079394,0.99225,0,3,/alexbas/pytorch-lightening,Digit Recognizer
14110957,0.99339,0,2,/davidblumenstiel/digit-recognition-simple-cnn,Digit Recognizer
14144855,0.9715,0,0,/raghav2002sharma/digit-recognizer-using-neural-networks,Digit Recognizer
13892249,0.97803,0,0,/aryansakhala/cnn-first-step-towards-project,Digit Recognizer
14100127,0.96682,0,0,/tracyporter/nmist-sklearn,Digit Recognizer
14043653,0.99053,1,4,/eschibli/simple-cnn-with-random-hyperparameter-search,Digit Recognizer
13929233,0.97521,0,0,/thirdeyecyborg/simplesvcdigitrecognizer,Digit Recognizer
13921699,0.9286,0,0,/nishantdeshmukh/digit-recognizer-a-43,Digit Recognizer
13858887,0.97871,0,0,/abakamousa/digit-recongnizer-with-cnn,Digit Recognizer
90625,2.26508,0,3,/joaomarcosgris/talking-data,TalkingData Mobile User Demographics
149246,36289.17158,41,92,/breuker/can-we-improve-by-increasing-variance,Santa's Uncertain Bags
2774148,0.5008199999999999,0,2,/yuriicojocari/eda-analysis,Home Depot Product Search Relevance
9642212,0.8701,0,0,/bs2537/bert-multilingual-jigsaw-tpu,Jigsaw Multilingual Toxic Comment Classification
9563835,0.9358,1,4,/akshaykumarray/jigsaw-toxic-comment-using-xlm-roberta,Jigsaw Multilingual Toxic Comment Classification
9570768,0.9189,0,0,/benjaminfontaine/averaged-submissions,Jigsaw Multilingual Toxic Comment Classification
9491170,0.8104,4,5,/parikshitag/bert-tpu-huggingface,Jigsaw Multilingual Toxic Comment Classification
9499096,0.86,1,1,/pushpendraparmar/toxic,Jigsaw Multilingual Toxic Comment Classification
8767469,0.9368,0,4,/namanmehta1/jigsaw-roberta,Jigsaw Multilingual Toxic Comment Classification
9288221,0.9416,74,208,/shonenkov/tpu-training-super-fast-xlmroberta,Jigsaw Multilingual Toxic Comment Classification
9242033,0.6333,0,9,/rainmaker29/jigsaw-bert-transfer-learning,Jigsaw Multilingual Toxic Comment Classification
9242676,0.8366,0,3,/heyytanay/jigsaw-classification-bert,Jigsaw Multilingual Toxic Comment Classification
9193949,0.7662,0,0,/kaushikvit/tpu2-testing,Jigsaw Multilingual Toxic Comment Classification
9068842,0.5617,0,0,/adwaitraghav/logistic-toxic-comments-classification,Jigsaw Multilingual Toxic Comment Classification
9046798,0.9346,0,3,/bruuuuuuce/jigsaw-tpu-xlm-roberta,Jigsaw Multilingual Toxic Comment Classification
77940,2.39114,0,0,/rohansadale/trying-some-analysis,TalkingData Mobile User Demographics
4928489,75.15254,0,0,/kpriyanshu256/pytorch-rals-c-sagan,Generative Dog Images
5101242,78.39478000000003,0,3,/wrosinski/combining-gans,Generative Dog Images
5053716,83.53885,2,3,/jadeblue/dogdeepdcgan-with-spectralnorm-tf-keras,Generative Dog Images
4995153,81.2265,15,31,/cafeal/cropping-dog-faces-using-opencv,Generative Dog Images
4967760,21.13784,0,2,/amanooo/is-memorizer-gan-a-gan,Generative Dog Images
4674226,140.96589,1,13,/francoisdubois/biggan-aaronleong-64,Generative Dog Images
4960733,111.66887,2,1,/gossan/ct-gan-with-pytorch,Generative Dog Images
4764915,178.33188,0,1,/toshikazuwatanabe/keras-generative-dog-images,Generative Dog Images
4677705,7.21588,9,109,/roydatascience/introduction-to-generative-adversarial-networks,Generative Dog Images
4825760,104.80952,10,17,/amanooo/wgan-gp-keras,Generative Dog Images
4574315,148.86912,0,2,/shwetagoyal4/dcgan-using-pytorch,Generative Dog Images
4643385,17.92851,42,232,/cdeotte/supervised-generative-dog-net,Generative Dog Images
4651382,98.33122,0,14,/tenffe/dcgan-with-tensorflow,Generative Dog Images
4659535,106.20659,0,8,/jmcslk/pytorch-dcgan-doggo-generator,Generative Dog Images
4620836,153.42006,1,15,/phoenix9032/fastai-wgan-for-dog-image-generation,Generative Dog Images
4581795,121.63161,18,81,/jesucristo/introducing-dcgan-dogs-images,Generative Dog Images
4573621,267.37146,6,10,/francoisdubois/bidirectionnal-gan-keras,Generative Dog Images
3407568,0.73,0,3,/benjibb/automated-gridsearch-ensemble-for-lazy-people,Don't Overfit! II
3354252,0.851,6,15,/aantonova/851-logistic-regression,Don't Overfit! II
3337470,0.723,0,0,/bharatsingh213/donotrepeat,Don't Overfit! II
3188389,0.723,0,1,/niteshx2/kernel41b644f80d,Don't Overfit! II
3139721,0.723,2,0,/swarnim97/logistic-regression-with-grid-searchcv,Don't Overfit! II
3002671,0.64,0,2,/xsakix/third-overfit,Don't Overfit! II
2999593,0.84,0,0,/tomehta/elastic-net-with-hyper-param-tuning-using-grid,Don't Overfit! II
2938759,0.8140000000000001,4,22,/tunguz/just-some-overfitting-eda,Don't Overfit! II
2920686,0.7120000000000001,3,17,/ashishpatel26/which-model-is-best-check-out,Don't Overfit! II
2910988,0.85,3,43,/vincentlugat/logistic-regression-rfe,Don't Overfit! II
2909058,0.496,0,13,/ashishpatel26/starter-automl-don-t-overfit,Don't Overfit! II
2931783,0.7509999999999999,0,0,/tomehta/underfitting-with-rf-and-lgbm,Don't Overfit! II
2931637,0.498,0,0,/tomehta/underfitting-with-svc,Don't Overfit! II
2878614,0.838,2,18,/nadare/simple-logistic-regression-with-l1-penalty,Don't Overfit! II
2880014,0.8440000000000001,2,6,/takaishikawa/experiment02-corr-select-logistic-reg,Don't Overfit! II
2878391,0.7909999999999999,0,1,/rodasoares/correlation-matrix-randomforestregressor,Don't Overfit! II
1799835,0.756,2,72,/ashishpatel26/model-interpretation-with-voting-classifier-hard,Don't Overfit! II
3092129,0.772,0,0,/samshipengs/why-it-s-overfitting,Don't Overfit! II
11947212,0.4977,0,0,/markpeng/all-pretrained-delg-v1-global,Google Landmark Recognition 2020
11592683,0.5432,1,3,/josealways123/efnet-global-delg-local-34-place-solution,Google Landmark Recognition 2020
11894428,0.4857,0,2,/igorsondors/base-glr-2020-to-submit,Google Landmark Recognition 2020
11875310,0.0017,0,2,/zaccheroni/pytorch-efficientnet-arcface-submission,Google Landmark Recognition 2020
11837984,0.4836,0,1,/akashsuper2000/google-landmark-prediction-eda,Google Landmark Recognition 2020
11660885,0.0,0,1,/tchristie/landmark-recognition-2020-inference,Google Landmark Recognition 2020
11287156,0.0118,0,10,/rajivranjansingh/landmark-recognition-xception-cnn,Google Landmark Recognition 2020
11494904,0.2069,1,3,/chenbaoying/private-efficientnet-submission,Google Landmark Recognition 2020
11244709,0.485,18,55,/jagdmir/google-landmark-prediction-2020,Google Landmark Recognition 2020
538880,0.28436,4,25,/gpreda/porto-seguro-exploratory-analysis-and-prediction,Porto Seguro’s Safe Driver Prediction
528506,-0.00612,0,1,/oshoagyeya/driver-prediction-simple-first-model-1-0,Porto Seguro’s Safe Driver Prediction
429188,0.23156,0,2,/elikplim/xgboost-vs-ann-with-swish,Porto Seguro’s Safe Driver Prediction
456184,-0.00612,5,2,/luckyt/logistic-regression-lb-0-006,Porto Seguro’s Safe Driver Prediction
453328,0.21316,0,0,/hchhabada/striker,Porto Seguro’s Safe Driver Prediction
438150,0.2832699999999999,5,8,/scirpus/a-rank-solution,Porto Seguro’s Safe Driver Prediction
427464,0.27583,0,0,/arthurlpgc/emsemble-try-16-on-porto-seguro,Porto Seguro’s Safe Driver Prediction
420551,0.28458,16,43,/aharless/rgf-or-xgb-k-fold-with-log-odds-averaging,Porto Seguro’s Safe Driver Prediction
2398823,0.939,0,5,/jimpsull/latestneuralfiftyfiftyblendwithmoredecisiveneural,PLAsTiCC Astronomical Classification
2304773,1.01,0,0,/jimpsull/ourpathtowherewearenewparams,PLAsTiCC Astronomical Classification
2381136,0.952,0,0,/jimpsull/fiftyfivefourtyfiveblend,PLAsTiCC Astronomical Classification
2304658,1.037,12,70,/jimpsull/collaboratingwithkagglecommunity-1-037-lb,PLAsTiCC Astronomical Classification
2044815,1.318,8,15,/bulatza/blend-them-all-best-public-score,PLAsTiCC Astronomical Classification
1979628,1.375,51,214,/meaninglesslives/simple-neural-net-for-time-series-classification,PLAsTiCC Astronomical Classification
1848166,2.158,5,15,/nasirislamsujan/eda-prediction-beginner-approach,PLAsTiCC Astronomical Classification
1785089,0.406976,1,8,/kmader/spectrogram-classifier-mobilenet,Freesound General-Purpose Audio Tagging Challenge
1274914,0.099,0,4,/morrisb/freesound-classification,Freesound General-Purpose Audio Tagging Challenge
1097313,0.895,2,28,/ashishpatel26/best-trick-for-audio-data,Freesound General-Purpose Audio Tagging Challenge
818579,0.826,0,2,/tetyanayatsenko/xgb-using-mfcc-opanichev-s-featur-02,Freesound General-Purpose Audio Tagging Challenge
803364,0.8109999999999999,11,30,/amlanpraharaj/xgb-using-mfcc-opanichev-s-features-lb-0-811,Freesound General-Purpose Audio Tagging Challenge
812592,0.58082,0,0,/pavanreddy1998/forest-decession,Forest Cover Type Prediction
30650,0.75178,0,0,/luzhao/random-forests,Forest Cover Type Prediction
7524911,0.75459,0,0,/jleonardt/start-here-a-gentle-introduction,Home Credit Default Risk
1533277,0.7929999999999999,0,0,/luudactam/hc-v600,Home Credit Default Risk
1457566,0.6779999999999999,0,0,/lmzentner/a2-home-credit-default-risk,Home Credit Default Risk
1316642,0.772,3,0,/turbineyang/lightgbm-version-10,Home Credit Default Risk
947295,0.54823,0,0,/vipul92/mercedes-xgboost,Mercedes-Benz Greener Manufacturing
14599927,11170.252,20,55,/mouafekmk/simple-pytorch-tensorflow-mlp,Jane Street Market Prediction
14598354,10609.091,18,48,/mouafekmk/simple-mlp,Jane Street Market Prediction
14448479,9341.295,12,27,/aimind/resnet-or-1dcnn,Jane Street Market Prediction
14612860,6883.857,0,8,/pyoungkangkim/mlp-batchnorm-dropout-linear-pytorch-no-pre-bnorm,Jane Street Market Prediction
14612146,7477.986999999999,2,7,/pyoungkangkim/mlp-batchnorm-dropout-linear-pytorch,Jane Street Market Prediction
14624226,8958.171999999999,2,4,/quincyqiang/tensorflow-resnet-inference,Jane Street Market Prediction
14443207,592.694,3,14,/michaelito170/jane-street-afternoon-shopping-greedy,Jane Street Market Prediction
14353992,2660.843,14,46,/vincentwang25/janestreet-groupkfold-or-purgedgroupcv,Jane Street Market Prediction
14307785,9464.615,1,9,/onurserbetci/jane-street-market-prediction,Jane Street Market Prediction
14489333,8822.151,2,8,/lhagiimn/resnet-training,Jane Street Market Prediction
14613012,6286.366,1,7,/pyoungkangkim/resnet-batchnorm-dropout-linear-pytorch-jstreet,Jane Street Market Prediction
14497521,3365.199,0,3,/ottpocket/cnn-with-generator,Jane Street Market Prediction
14413088,120.525,0,1,/negativegamma/rand-jane-street,Jane Street Market Prediction
14525329,6747.849,7,4,/taherhaggui/lgbm-multilabel,Jane Street Market Prediction
14441485,3005.67,2,1,/oym8012/jane-street-tabnet-model,Jane Street Market Prediction
81806,0.97669,0,0,/yiyihuijia/trying,Grupo Bimbo Inventory Demand
229834,0.53825,0,0,/abverma/cv-statistics-edited-0-5346-score-test-av,Two Sigma Connect: Rental Listing Inquiries
12845424,0.01848,0,1,/tuistan/moa-predictions-overfitting-with-tabnet-3233e3,Mechanisms of Action (MoA) Prediction
12298682,0.02002,0,0,/anshu1595/moa-pca,Mechanisms of Action (MoA) Prediction
12934912,0.01907,0,0,/chaejihan/moa-cjh,Mechanisms of Action (MoA) Prediction
12866502,0.01867,0,3,/ikobzev/moa-keras-nn-baseline-from-ml-newbie,Mechanisms of Action (MoA) Prediction
12797335,0.019,4,34,/tmhrkt/grownet-gradient-boosting-neural-networks,Mechanisms of Action (MoA) Prediction
12803977,0.01948,0,3,/alexandervc/moa36-logreg-blend-v1,Mechanisms of Action (MoA) Prediction
12792388,0.0186099999999999,2,6,/riadalmadani/kubi-pytorch-moa-transfer-pca-rankgauss,Mechanisms of Action (MoA) Prediction
12746012,0.01968,0,4,/awwaldiekaramapepple/moa-xgboostclassifier-feature-engineering,Mechanisms of Action (MoA) Prediction
11897282,0.01964,0,0,/wburchenal/moa-analysis,Mechanisms of Action (MoA) Prediction
12343253,0.0201,0,1,/svobodnik86/tell-me-why-no-transfer,Mechanisms of Action (MoA) Prediction
12641166,0.02454,0,4,/lavanyask/moa-prediction-eda,Mechanisms of Action (MoA) Prediction
12677962,0.01902,4,18,/code1110/moa-new-validation-based-on-drugid,Mechanisms of Action (MoA) Prediction
12647650,0.01864,1,5,/retal95/very-good,Mechanisms of Action (MoA) Prediction
12614920,0.01994,8,47,/jiweiliu/moa-all-rapids,Mechanisms of Action (MoA) Prediction
12703198,0.01847,0,0,/art6745/notebookf0a11d076c,Mechanisms of Action (MoA) Prediction
10458822,0.41718,0,1,/vernondsouza123/predict-biological-response-through-lightgbm,Predicting a Biological Response
1720101,0.59425,0,0,/ludi666/lr-regression,Predicting a Biological Response
5043300,0.83109,52,330,/ekhtiar/resunet-a-baseline-on-tensorflow,Severstal: Steel Defect Detection
5012287,0.01924,10,88,/ateplyuk/keras-starter-u-net,Severstal: Steel Defect Detection
4993040,0.00836,3,48,/titericz/mask-average-benchmark-dice-metric,Severstal: Steel Defect Detection
4995589,0.85674,6,16,/rupeshs/basic-eda-submission,Severstal: Steel Defect Detection
4992306,0.0,0,8,/tunguz/sample-submission-benchmark,Severstal: Steel Defect Detection
7509790,0.82432,0,0,/knightwisdom/13012020-sever-submission,Severstal: Steel Defect Detection
5839622,0.63044,0,0,/naresh31/keras-starter-segmentation-submission,Severstal: Steel Defect Detection
14432420,0.71883,6,6,/paulrohan2020/feature-engineering-naive-bayes-with-bag-of-words,DonorsChoose.org Application Screening
5879937,0.54579,0,1,/iacsddbda/neuralnetwork-approach,DonorsChoose.org Application Screening
1936959,0.7795,0,0,/boomberung/eda-tf-idf-lightgbm,DonorsChoose.org Application Screening
1138733,0.7959,0,9,/ashishpatel26/beginner-s-guide-to-capsule-networks,DonorsChoose.org Application Screening
987160,0.53885,0,1,/timsikes/donors-choose-cleaned-up,DonorsChoose.org Application Screening
790688,0.78296,0,2,/pbnjeff/xgboosting-to-a-better-algorithm,DonorsChoose.org Application Screening
820975,0.69902,0,0,/grjasewe/lgbm-with-selected-columns,DonorsChoose.org Application Screening
745662,0.58096,0,1,/tanank/data-exploration-and-random-forest-result,DonorsChoose.org Application Screening
727620,0.7379,0,2,/blasteraj/donorschoose-org-1,DonorsChoose.org Application Screening
710784,0.67774,1,10,/jgoldberg/donorschoose-eda-text-classification,DonorsChoose.org Application Screening
751100,0.68512,0,3,/ianchute/baseline-model-bernoulli-naive-bayes-lb-0-68,DonorsChoose.org Application Screening
734073,0.73337,8,146,/vlasoff/beginner-s-guide-nn-with-multichannel-input,DonorsChoose.org Application Screening
724015,0.8154399999999999,9,12,/emotionevil/beginners-workflow-meanencoding-lgb-nn-ensemble,DonorsChoose.org Application Screening
709526,0.75376,3,17,/nvhbk16k53/simple-rnn-with-keras,DonorsChoose.org Application Screening
702585,0.74492,1,13,/CVxTz/keras-baseline-feature-hashing-cnn,DonorsChoose.org Application Screening
651490,0.56522,7,65,/skleinfeld/getting-started-with-the-donorschoose-data-set,DonorsChoose.org Application Screening
2716333,0.52788,0,0,/timsikes/donors-choose-cleaned-up-working-version,DonorsChoose.org Application Screening
854938,0.75174,0,0,/phustak/donors-choose-tensorflow-model,DonorsChoose.org Application Screening
12616532,0.0184,9,34,/amateurdesperado/pca-var-cv-simple-nn,Mechanisms of Action (MoA) Prediction
12617626,0.01906,1,1,/kotoro/moa-simple-keras-with-rankgauss-jp,Mechanisms of Action (MoA) Prediction
12605262,0.01874,9,14,/bibhash123/moa-transfer-learning-feature-selection-gaussrank,Mechanisms of Action (MoA) Prediction
12680993,0.01851,0,0,/art6745/notebooka050851501,Mechanisms of Action (MoA) Prediction
12614007,0.01948,0,0,/noelmat/5-fold-cv-with-nn-rankgauss-pca,Mechanisms of Action (MoA) Prediction
12569968,0.01875,0,1,/fedorlebed/tabnetregressor-with-unscored-boost,Mechanisms of Action (MoA) Prediction
12442067,0.02051,0,0,/fedorlebed/nn-baseline,Mechanisms of Action (MoA) Prediction
12537034,0.02363,0,0,/fedorlebed/bosss-pca,Mechanisms of Action (MoA) Prediction
12501453,0.02367,0,0,/fedorlebed/local-pca,Mechanisms of Action (MoA) Prediction
12567554,0.0186099999999999,0,2,/riadalmadani/kubi-pytorch-lb-0-01861,Mechanisms of Action (MoA) Prediction
12443704,0.69314,0,0,/ravasiliev/eda-ravasiliev-pzad,Mechanisms of Action (MoA) Prediction
12346411,0.11139,0,0,/lakitha/moa-data-submission,Mechanisms of Action (MoA) Prediction
12525706,0.01924,20,32,/elcaiseri/moa-keras-newbaseline-initializers-featureseng,Mechanisms of Action (MoA) Prediction
12600727,0.02242,0,0,/rajsinghgaur2000/notebook7accf6d0a6,Mechanisms of Action (MoA) Prediction
12520045,0.01925,0,5,/code1110/moa-lgb-seed-average,Mechanisms of Action (MoA) Prediction
12533078,0.02398,0,6,/vineeth1999/moa-elasticnet,Mechanisms of Action (MoA) Prediction
12591318,0.26792,0,0,/bredonos/notebook2d4f4b99fa,Mechanisms of Action (MoA) Prediction
12547084,0.01894,0,0,/crafterkolyan/fork-of-moa-solution-v6-local-pzad,Mechanisms of Action (MoA) Prediction
12505081,0.0186099999999999,3,9,/ksouriazer/moa-pytorch-0-01861lb-0-01439cv-eda-fe-nn,Mechanisms of Action (MoA) Prediction
12487944,0.0194599999999999,0,7,/rtombs/linear-combinations-of-linear-models-moa,Mechanisms of Action (MoA) Prediction
12162872,0.01849,5,9,/josemori/moa-tabnet,Mechanisms of Action (MoA) Prediction
12485765,0.12286,0,0,/arunamenon/moa-predictions-base-model,Mechanisms of Action (MoA) Prediction
12457212,0.01876,0,6,/evgkol/moa-pzad,Mechanisms of Action (MoA) Prediction
12461658,0.01888,0,1,/ogoldobina/baseline-1,Mechanisms of Action (MoA) Prediction
12431107,0.02005,0,1,/dwchen/moa-lightgbm-baseline-with-feature,Mechanisms of Action (MoA) Prediction
12355957,0.01848,39,173,/hiramcho/moa-tabnet-with-pca-rank-gauss,Mechanisms of Action (MoA) Prediction
12423736,0.0253,0,2,/georgyk/first-attempt-pzad,Mechanisms of Action (MoA) Prediction
11751688,0.568,0,0,/markpeng/blend-public-custom-final-v3,Cornell Birdcall Identification
11394329,0.56,0,0,/hatenahatena/birdcall-resnet-baseline,Cornell Birdcall Identification
75627,0.8272,0,0,/yiyihuijia/first,Grupo Bimbo Inventory Demand
71314,0.6288699999999999,0,0,/gustavodemari/inventory-demand-prediction,Grupo Bimbo Inventory Demand
1096369,0.7879999999999999,7,20,/stardust0/simple-blending-788-lb,Home Credit Default Risk
1080033,0.758,73,679,/willkoehrsen/introduction-to-manual-feature-engineering,Home Credit Default Risk
1110759,0.7490000000000001,0,0,/shenba/home-credit-v6-12jun2018,Home Credit Default Risk
1078102,0.785,28,66,/scirpus/hybrid-jeepy-and-lgb-ii,Home Credit Default Risk
1068284,0.77,0,0,/sukhyun9673/baselin-cleansing-and-merging-data-only-g1-yet,Home Credit Default Risk
1056491,0.7829999999999999,56,113,/scirpus/hybrid-jeepy-and-lgb,Home Credit Default Risk
1059161,0.71,2,21,/darryldias/data-exploration-dd3,Home Credit Default Risk
1034046,0.754,516,2945,/willkoehrsen/start-here-a-gentle-introduction,Home Credit Default Risk
1040571,0.736,0,15,/harunshimanto/introduction-home-credit-default-risk,Home Credit Default Risk
1030120,0.7659999999999999,27,65,/sz8416/eda-baseline-model-using-application,Home Credit Default Risk
1015620,0.73,0,12,/cast42/feature-importance-and-dependence-plot-with-shap,Home Credit Default Risk
1020569,0.772,15,17,/scirpus/basic-jeepy,Home Credit Default Risk
1013173,0.763,13,31,/alena0604/a-little-start-for-a-big-journey,Home Credit Default Risk
998262,0.7440000000000001,0,7,/kosovanolexandr/home-credit-default-risk-competition,Home Credit Default Risk
993158,0.742,4,22,/cafeal/lightgbm-trial-public-0-742,Home Credit Default Risk
12090877,0.0,0,0,/roohisharma/ghouls-goblins-ghosts-ann,"Ghouls, Goblins, and Ghosts... Boo!"
11866085,0.7240000000000001,0,0,/sgoyal4be17/deeplearning-assignment-101703530,"Ghouls, Goblins, and Ghosts... Boo!"
11670872,0.71833,0,0,/and8080/notebook5b67ff7176,"Ghouls, Goblins, and Ghosts... Boo!"
9985851,0.74102,0,0,/junooolee/find-creatures-by-classification-model,"Ghouls, Goblins, and Ghosts... Boo!"
9410401,0.7258899999999999,0,1,/arifintahu/ghouls-goblins-and-ghosts-classification,"Ghouls, Goblins, and Ghosts... Boo!"
5457614,0.7240000000000001,0,0,/abhinavcs13/ghosts-problem-using-multilayer-perceptron-keras,"Ghouls, Goblins, and Ghosts... Boo!"
3263217,0.72022,0,1,/shivamsarawagi/generategouls,"Ghouls, Goblins, and Ghosts... Boo!"
2390978,0.72211,0,0,/dipeshpoudel/ghouls-goblins-and-ghosts-in-random-forest,"Ghouls, Goblins, and Ghosts... Boo!"
1654323,0.73724,0,0,/mofurl/ghouls,"Ghouls, Goblins, and Ghosts... Boo!"
12351983,0.0,0,0,/karthikbhandary2/ghouls-goblins-or-ghosts,"Ghouls, Goblins, and Ghosts... Boo!"
414087,0.2345,0,0,/wangk4/notebook,Porto Seguro’s Safe Driver Prediction
401313,0.2818199999999999,7,30,/aharless/simple-catboost-cv-lb-281,Porto Seguro’s Safe Driver Prediction
10441799,1.88166,0,5,/vibeeshk/earthquake-prediction-submission,LANL Earthquake Prediction
7893633,1.6564900000000002,0,0,/slashie/lanl-review,LANL Earthquake Prediction
3837929,1.881,0,0,/hark99/lanl-earthquake-prediction,LANL Earthquake Prediction
6836920,1.5404799999999998,0,1,/jagannathrk/lanl-earthquake-prediction-xgb,LANL Earthquake Prediction
6704084,4.01773,0,0,/morenovanton/kernel36bdc80f3d,LANL Earthquake Prediction
3063226,1.44164,0,0,/celiendonze/tb-2019-donze-celien,LANL Earthquake Prediction
3704327,1.934,0,1,/eedave86/tuned-dense-dnn,LANL Earthquake Prediction
3622012,1.6880000000000002,0,0,/purehyd/1dxeption-v2-non-cv-lr-0-0001-drop-0-05-l2-0-1,LANL Earthquake Prediction
4029097,2.533,0,0,/ikedak2/lanl-vettejeep-modeling-lgb,LANL Earthquake Prediction
4130396,1.463,0,0,/mg78838/combined,LANL Earthquake Prediction
4180314,2.01459,3,14,/trentb/one-feature-no-ml-gold-medal-range,LANL Earthquake Prediction
4047402,1.506,0,0,/mg78838/xgb-mg-all,LANL Earthquake Prediction
4123892,1.445,0,0,/dobosp/tunning-bayes-search-catmodel,LANL Earthquake Prediction
4142388,1.7760599999999998,11,103,/ilu000/1-private-lb-kernel-lanl-lgbm,LANL Earthquake Prediction
4024370,1.881,0,0,/aaronhma/earthquake-v-2,LANL Earthquake Prediction
3462231,1.6030000000000002,0,2,/wimwim/stacked-raw-lstm,LANL Earthquake Prediction
3674969,1.645,0,3,/scaomath/lanl-earthquake-lightgbm-using-power-spectra,LANL Earthquake Prediction
4027662,1.84387,0,0,/scaomath/lanl-earthquake-rnn-with-multiple-target-values,LANL Earthquake Prediction
2697897,1.97652,2,3,/acauveri/network-fault-prediction,Telstra Network Disruptions
4570856,78.39439,2,34,/speedwagon/ram-dataloader,Generative Dog Images
4575537,34.316759999999995,6,12,/takumiito/commented-imitation-game,Generative Dog Images
5303776,60.08274,0,0,/gsakabe/fork-of-fork-61-02,Generative Dog Images
5290621,224.09003,1,0,/mikeskim/gan-dogs-starter,Generative Dog Images
5281344,72.63253,0,0,/returnofsputnik/fork-of-fork-of-fork-of-fork-of-fork-of-fork-of-ac,Generative Dog Images
5217077,6.977360000000001,0,0,/ms2019/memorizer-cgan-pytorch-version,Generative Dog Images
5020394,61.61377,0,0,/a11rand0m/optimize-threshold,Generative Dog Images
11990786,0.39997,0,0,/vignesh1404/v-xgbt-model,Costa Rican Household Poverty Level Prediction
12232963,0.34524,0,0,/kihaok/costa,Costa Rican Household Poverty Level Prediction
12787490,0.42138,0,1,/fabiolaporte/iesb-graduacao-cia28-costa-rica,Costa Rican Household Poverty Level Prediction
11601503,0.37319,0,2,/luiguip/fork-of-costan-rican-extratreesclassifier,Costa Rican Household Poverty Level Prediction
11556945,0.43972,0,1,/pauloperissin/projeto-final-paulo-perissin,Costa Rican Household Poverty Level Prediction
11374928,0.42693,0,2,/asgvitor/1931133123-trabalho-1-vitor-alves,Costa Rican Household Poverty Level Prediction
10368954,0.40775,0,5,/perene/costa-rican-household-poverty-level-prediction,Costa Rican Household Poverty Level Prediction
6517630,0.3423199999999999,0,1,/ankursxn/costaricanpovertyprediction-eda-and-randomforest,Costa Rican Household Poverty Level Prediction
5966115,0.21721,0,0,/ryukio/pmr3508-2019-80-costa-rican,Costa Rican Household Poverty Level Prediction
5023359,0.4078699999999999,0,0,/alloulir/costa-rican-household-poverty-kernel-1,Costa Rican Household Poverty Level Prediction
5606969,0.33945,0,3,/lucasnseq97/pmr3508-2019-56-knn-classifier-cr-household,Costa Rican Household Poverty Level Prediction
4668312,0.40625,0,0,/dbdb4001/kernel220af5c41e,Costa Rican Household Poverty Level Prediction
4643700,0.4416899999999999,0,0,/gemihaha/costa,Costa Rican Household Poverty Level Prediction
4557357,0.40126,0,0,/kimtaewan/kernel0a06c82478,Costa Rican Household Poverty Level Prediction
4514437,0.39378,0,0,/akumaldo/household-poverty-eda-and-lgb-model,Costa Rican Household Poverty Level Prediction
4151352,0.3786,0,0,/rdewes/iesb-miner-ii-aula-08-parte-2,Costa Rican Household Poverty Level Prediction
3773356,0.19482,0,3,/vsramaraj/costa-rican-modeling-and-tuning,Costa Rican Household Poverty Level Prediction
1655011,0.4029999999999999,0,0,/dnik007/costa-rican-povery-with-voting-classifier,Costa Rican Household Poverty Level Prediction
2877902,0.2239999999999999,0,0,/lihess/pratice2,Costa Rican Household Poverty Level Prediction
2747003,0.42375,0,0,/jackpegler/costaricapoverty-4-0-smote,Costa Rican Household Poverty Level Prediction
10300224,0.9509,1,1,/qinhui1999/post-process-test,Jigsaw Multilingual Toxic Comment Classification
10303505,0.4909,0,0,/kimary/kernel25124b5e4e,Jigsaw Multilingual Toxic Comment Classification
9708389,0.9361,0,0,/lucca9211/multilingual-toxic,Jigsaw Multilingual Toxic Comment Classification
8787271,0.9231,0,1,/miklgr500/jigsaw-tpu-bert-with-multilingual-dataset,Jigsaw Multilingual Toxic Comment Classification
9387724,0.9336,0,3,/alansun17904/wide-and-shallow-cnn,Jigsaw Multilingual Toxic Comment Classification
10043426,0.6599,0,3,/anasofiauzsoy/toxic-comments-with-tf2-roberta,Jigsaw Multilingual Toxic Comment Classification
10132620,0.9472,9,63,/jazivxt/howling-with-wolf-on-l-genpresse,Jigsaw Multilingual Toxic Comment Classification
10100316,0.5779,0,2,/sidharkal/simplernn,Jigsaw Multilingual Toxic Comment Classification
10082126,0.5,0,1,/betweens/submission-for-novice-only,Jigsaw Multilingual Toxic Comment Classification
10085738,0.6997,0,3,/anasofiauzsoy/toxic-comments-tf2-roberta,Jigsaw Multilingual Toxic Comment Classification
10072335,0.6969,0,4,/joydeb28/simplernn-achive-good-accuracy,Jigsaw Multilingual Toxic Comment Classification
10063898,0.6971,0,2,/joydebmondal/simple-and-efficient,Jigsaw Multilingual Toxic Comment Classification
9959511,0.9422,19,126,/riblidezso/train-from-mlm-finetuned-xlm-roberta-large,Jigsaw Multilingual Toxic Comment Classification
9953941,0.9312,0,4,/vgodie/xlm-roberta,Jigsaw Multilingual Toxic Comment Classification
9930852,0.8353,0,1,/ruslanradchenko/pytorch-tpu-starter-training,Jigsaw Multilingual Toxic Comment Classification
9914014,0.7387,2,5,/anasofiauzsoy/toxic-comment-classification-with-tensorflow,Jigsaw Multilingual Toxic Comment Classification
9970794,0.8154,0,0,/yashsudhakardevikar/classifier,Jigsaw Multilingual Toxic Comment Classification
9644987,0.86,1,14,/prateekarma/logistic-regression-with-feature-engineering,Jigsaw Multilingual Toxic Comment Classification
9771573,0.9459,4,31,/sai11fkaneko/data-leak,Jigsaw Multilingual Toxic Comment Classification
86042,2.26508,78,195,/dvasyukova/a-linear-model-on-apps-and-labels,TalkingData Mobile User Demographics
246506,0.3136,29,63,/reynaldo/naive-xgb,Sberbank Russian Housing Market
246022,0.3144699999999999,3,8,/aharless/from-bruno-do-amaral-naive-xgb-v9,Sberbank Russian Housing Market
36618,0.48362,0,0,/eeriee/home-depot,Home Depot Product Search Relevance
35394,0.48505,0,0,/scyllagist/sklearn-random-forest,Home Depot Product Search Relevance
1563743,0.265,0,0,/hilstfelipe/pmr3508-2018-4c69ee14bd,Costa Rican Household Poverty Level Prediction
1563231,0.204,0,0,/rafagv/pmr-3508-d73b94105c,Costa Rican Household Poverty Level Prediction
1561883,0.325,0,0,/gustavof/pmr3508-2018-0f13d01361-t2,Costa Rican Household Poverty Level Prediction
1560398,0.242,0,1,/vinimiquelin/pmr3508-knnclassifier-costa-rica,Costa Rican Household Poverty Level Prediction
1560644,0.25,0,0,/vkiguchi/pmr3508-2018-cef3abfde0,Costa Rican Household Poverty Level Prediction
1559663,0.309,0,0,/rafabl/pmr3508-2018-knn-test-1,Costa Rican Household Poverty Level Prediction
1557823,0.233,0,0,/pmrdisciplina/versao-2-pmr3508-2018-d8c972cdfb,Costa Rican Household Poverty Level Prediction
1555259,0.268,0,0,/rodrigoep/pmr3508-knn-classificador,Costa Rican Household Poverty Level Prediction
1554088,0.385,0,0,/santibermejo/cr-household-poverty-level-with-xgboost,Costa Rican Household Poverty Level Prediction
1496348,0.391,0,3,/danielsousa/lightgbm-rfecv-bayessearchcv,Costa Rican Household Poverty Level Prediction
1541113,0.368,0,0,/nilchat85/costa-rican-household-poverty-level-eda,Costa Rican Household Poverty Level Prediction
1554191,0.3229999999999999,0,0,/luangbbr/kernel5cfe193dc2,Costa Rican Household Poverty Level Prediction
1519858,0.429,0,3,/wangjinghui/just-lightgbm,Costa Rican Household Poverty Level Prediction
1332677,0.3929999999999999,0,0,/jhotor/costa-rican-household-poverty-jt-eda,Costa Rican Household Poverty Level Prediction
1499225,0.285,0,0,/jimenaromeropinto/submission-1-formatted,Costa Rican Household Poverty Level Prediction
1486663,0.4029999999999999,0,1,/victorhz/svm-over-csv,Costa Rican Household Poverty Level Prediction
1462526,0.436,0,2,/boxshapedworld/gradient-boosted-with-sample-weights,Costa Rican Household Poverty Level Prediction
1473679,0.3339999999999999,0,0,/sathvikraju/kernela311bdcd77,Costa Rican Household Poverty Level Prediction
1424335,0.12,0,8,/nikitpatel/keras-deeplearning-classification,Costa Rican Household Poverty Level Prediction
1431643,0.379,0,1,/geodra93/1st-attempt,Costa Rican Household Poverty Level Prediction
1391094,0.3389999999999999,0,1,/davixue/simple-data-cleaning-svm,Costa Rican Household Poverty Level Prediction
1393216,0.44,0,10,/ashishpatel26/lgbm-with-bayesian-approach,Costa Rican Household Poverty Level Prediction
1342212,0.431,0,10,/ashishpatel26/bayesian-approach-automatic-parameter-tuning,Costa Rican Household Poverty Level Prediction
1392666,0.39,2,6,/jeppbautista/eda-feature-selection-xgboost-classifier,Costa Rican Household Poverty Level Prediction
1382442,0.418,4,14,/nikitpatel/hyper-parameter-lgbm-costa-rican,Costa Rican Household Poverty Level Prediction
1385132,0.1939999999999999,0,1,/ruthwikmasina/a-neural-network-in-tensorflow-to-predict,Costa Rican Household Poverty Level Prediction
1366135,0.423,0,11,/ashishpatel26/feature-engineering-lighgbm-with-f1-macro,Costa Rican Household Poverty Level Prediction
4688661,73.18432,0,0,/jobzhf88/generative-dog-images-dcgan-1-commit,Generative Dog Images
4626145,144.10815,0,0,/thanatoz/generating-dogs-with-dcgan,Generative Dog Images
5095747,31.063440000000003,2,32,/tikutiku/gan-dogs-starter-biggan,Generative Dog Images
5123137,67.34186,0,0,/sylvlej/gan-first-model,Generative Dog Images
5272778,15.7853,0,19,/yukia18/sub-rals-biggan-with-auxiliary-classifier,Generative Dog Images
5269291,67.04914000000001,0,1,/blackitten13/best-gan-fork,Generative Dog Images
5083087,119.43325,0,0,/lilanluo/wgan-gp,Generative Dog Images
5251556,72.48938000000003,0,2,/rhodiumbeng/dog-images-dcgan,Generative Dog Images
5289624,65.88056999999999,1,2,/bitthal/dogs-gan-pixel,Generative Dog Images
5308883,30.65492,0,18,/alvaroma/dcgan-lb-30-65,Generative Dog Images
5289387,15.81605,4,30,/yukia18/sub-rals-ac-biggan-with-minibatchstddev,Generative Dog Images
5248588,111.22795,0,1,/chukurimu0903/dcgan-generating-dog-images-with-tensorflow,Generative Dog Images
5147737,5.17518,2,12,/hirune924/public-lb-5-17518-solution-memorizer-cgan,Generative Dog Images
5299687,47.02282,2,7,/timetraveller98/cgan-with-historical-weight-averaging,Generative Dog Images
4621881,36.76123,0,3,/agatan/sagan-resnet-annots,Generative Dog Images
5147948,40.49519,0,7,/leonshangguan/dcgan-data-cleaning-sub-v2,Generative Dog Images
5275275,74.85519000000002,0,0,/purplejester/gan-xx-pgan-attempt,Generative Dog Images
5292817,72.03128000000002,0,1,/matthiaspilz/fork96-0-00015-of-new-doggans-keras-r,Generative Dog Images
5267276,57.51746,0,1,/purplejester/gan-20-dcgan-avg,Generative Dog Images
5215486,78.29074,0,2,/maxgarber/dogs-gan-keras-mg-v3,Generative Dog Images
5253014,163.53781,0,0,/sanjayjalex/dog-generator,Generative Dog Images
4855739,164.5387,0,0,/ibansalankit/doggo-dcgan,Generative Dog Images
5247081,61.79275,0,0,/andreisun/gan-dogs-starter-24-jul-custom-layers,Generative Dog Images
4809492,68.94821999999999,0,0,/vamcochen/kernele9c54ff2a3,Generative Dog Images
5204473,61.02476,1,0,/gsakabe/fork-of-gan-dogs-starter-24-jul-custom-lay-61fc12,Generative Dog Images
5284912,108.57272,0,0,/iiiklw/ragan,Generative Dog Images
5203155,18.72476,0,0,/jackybored/kernel7f5daae71a,Generative Dog Images
4812578,99.0412,0,1,/kpriyanshu256/dogdcgan,Generative Dog Images
11330770,0.0,0,3,/tbourton/resnet50-tl,Google Landmark Recognition 2020
11051205,0.0,0,6,/mekhdigakhramanian/pytorch,Google Landmark Recognition 2020
11052751,0.0,0,7,/mekhdigakhramanian/resnet50-v10-v12,Google Landmark Recognition 2020
10979760,0.0,2,12,/renjithrrkj/land-mark-recognition-with-xception-network,Google Landmark Recognition 2020
13777069,0.502,0,3,/adamabidi/eda-not-overfit,Don't Overfit! II
10944700,0.516,0,0,/darisdzakwanhoesien2/don-t-overfit-ii,Don't Overfit! II
7248164,0.504,0,1,/karnakarthoorpu/robust-to-overfit-using-dnn,Don't Overfit! II
3823514,0.8440000000000001,0,3,/vkt706/don-t-over-fit,Don't Overfit! II
5097319,0.647,0,0,/saoodmohd/don-t-over-fit-starter,Don't Overfit! II
4739694,0.727,0,0,/biswajitbanerjee/trying-to-overfit,Don't Overfit! II
4656469,0.745,0,0,/sakaguti1211/kernel3956095972,Don't Overfit! II
3515290,0.703,0,0,/hirotaka0122/don-t-over-fit,Don't Overfit! II
2887929,0.8440000000000001,0,1,/suhail511/don-t-overfit,Don't Overfit! II
3959954,0.8590000000000001,0,2,/monthepp/don-t-overfit-ii,Don't Overfit! II
2945862,0.7240000000000001,0,0,/julio1397/don-t-overfit,Don't Overfit! II
3815096,0.61,0,0,/gdmacmillan/gp-logistic-regression,Don't Overfit! II
3756772,0.848,0,5,/pradyumnakr/don-t-overfit,Don't Overfit! II
3808231,0.728,0,0,/tattaka/don-t-over-fit-by-nn,Don't Overfit! II
3760007,0.8490000000000001,3,5,/srishilesh/don-t-overfit-solution,Don't Overfit! II
3693408,0.86,14,28,/derekpowll/bayesian-lr-w-cauchy-prior-in-pymc3,Don't Overfit! II
3659239,0.799,0,3,/kumarvivek9097/simple-solution-for-beginner,Don't Overfit! II
3723039,0.519,1,0,/jamesdonconley/pca-and-random-forests,Don't Overfit! II
3673965,0.7759999999999999,0,8,/qy2205/baseline-logistic-binning-feature-selection,Don't Overfit! II
3627722,0.861,0,2,/demonplus/blending-of-the-best-solutions,Don't Overfit! II
3549692,0.8440000000000001,0,0,/sphaso/don-t-overfit-ii,Don't Overfit! II
3544988,0.8009999999999999,0,1,/satyamsanu/don-t-overfit,Don't Overfit! II
3342580,0.846,0,1,/oluwaody/the-parameter-measurement,Don't Overfit! II
3353187,0.8590000000000001,0,6,/shivamsarawagi/overfit,Don't Overfit! II
3407193,0.753,0,1,/benjibb/genetic-algorithm-model-selection,Don't Overfit! II
1814812,6.706,2,7,/ashutosh7/xgboost-with-static-features,PLAsTiCC Astronomical Classification
1765474,2.158,20,116,/kyleboone/naive-benchmark-galactic-vs-extragalactic,PLAsTiCC Astronomical Classification
1762534,32.62,0,9,/onodera/all-class-6,PLAsTiCC Astronomical Classification
3647837,1.641,0,2,/azc2019/seismic-data-eda-and-baseline,LANL Earthquake Prediction
3661439,1.575,0,0,/gdoteof/fastai-tabular-nn,LANL Earthquake Prediction
3543834,1.5219999999999998,0,3,/hsinwenchang/more-dense-layer,LANL Earthquake Prediction
3598082,2.046,0,14,/bigironsphere/a-new-approach-ttf-classifiers-for-fine-tuning,LANL Earthquake Prediction
3554135,1.6880000000000002,0,3,/purehyd/lanl-mobilenet-trial2,LANL Earthquake Prediction
3541235,1.507,7,57,/taqanori/trying-mfcc-mel-frequency-cepstral-coefficients,LANL Earthquake Prediction
3542991,1.533,0,7,/zhanglic/earthquakes-fe-split-the-data-fft,LANL Earthquake Prediction
3558439,1.871,0,1,/pedrormarques/lanl-simple-v2,LANL Earthquake Prediction
3373953,1.523,0,5,/cevangelist/lab-seismic-waves-feature-exploration,LANL Earthquake Prediction
3366865,2.143,0,1,/dumbo666/fork-of-earthquake-prediction-2,LANL Earthquake Prediction
3349017,1.527,0,9,/manyregression/fastai-tabular-nn,LANL Earthquake Prediction
3230381,1.639,1,3,/lavanyadml/lanl-earthquake-prediction,LANL Earthquake Prediction
3058055,1.50147,8,68,/tunguz/lanl-earthquake-with-h2o-automl,LANL Earthquake Prediction
3043584,1.5930000000000002,1,10,/ashay10001/lanl-earthquake-prediction-in-svr,LANL Earthquake Prediction
2973650,2.192,0,7,/simonta/predict-time-of-earthquake-with-only-one-feature,LANL Earthquake Prediction
2970276,1.541,1,11,/harshitholmes/extra-trial,LANL Earthquake Prediction
2950648,1.544,0,6,/kalyankkr/predicting-movement,LANL Earthquake Prediction
12011841,0.26735,0,0,/vnesh123/safe-driver-prediction,Porto Seguro’s Safe Driver Prediction
12280426,0.26194,0,0,/vnesh123/safe-driver-prediction-rfecv2,Porto Seguro’s Safe Driver Prediction
11501505,0.27243,0,0,/rikdifos/lgb-bayesian-optimization,Porto Seguro’s Safe Driver Prediction
8428855,0.2837,1,1,/jagannathrk/safe-driver-prediction-lightgbm,Porto Seguro’s Safe Driver Prediction
8174994,0.25397,0,0,/batofgotham/logistic-regression-custom-ensembel,Porto Seguro’s Safe Driver Prediction
6145986,0.27482,0,2,/cookierhkwk/boaz-new,Porto Seguro’s Safe Driver Prediction
4380969,0.25574,0,2,/filipecld/trabalho-de-aprendizagem-de-m-quina-2019-1,Porto Seguro’s Safe Driver Prediction
3353841,0.27018,0,0,/hajekim/simple-logistic-model-porto,Porto Seguro’s Safe Driver Prediction
752660,0.22266,0,0,/mrlzlz/mysafe-driver,Porto Seguro’s Safe Driver Prediction
14356019,0.73412,0,1,/hectorpatio/cover-type,Forest Cover Type Prediction
14012397,0.7313,0,1,/chromerai/notebook1,Forest Cover Type Prediction
13662209,0.7310399999999999,0,0,/sanskrutighadipatil/notebook2c99a0b46d,Forest Cover Type Prediction
10819177,0.05936,0,0,/roohisharma/forest-cover-type,Forest Cover Type Prediction
12086305,0.72826,0,1,/ayusheeagarwal/randomforestclassifier-prediction,Forest Cover Type Prediction
10760317,0.72035,1,2,/kumaran1992/forest-cover-type-prediction,Forest Cover Type Prediction
9991645,0.73009,0,1,/shivaniengg23/forests-pred-project,Forest Cover Type Prediction
9276773,0.74616,8,9,/leela2299/eda-feature-engineering-classification,Forest Cover Type Prediction
9156797,0.81005,2,8,/nehabhandari1/forest-prediction-final,Forest Cover Type Prediction
8223416,0.70539,0,0,/robbiebeane/forest-cover-02-nnet,Forest Cover Type Prediction
6009532,0.76942,0,4,/cuijamm/forest-cover-type-predictions-score-0-77005,Forest Cover Type Prediction
4011534,0.6723600000000001,0,1,/rajashri/forest-cover,Forest Cover Type Prediction
1988038,0.7468100000000001,0,0,/ivarvb/forest-cover-type,Forest Cover Type Prediction
1515243,0.6704600000000001,0,7,/jacksmengel/home-away-submit-csv,Home Credit Default Risk
1483510,0.77083,1,6,/mithlesh14/home-credit-default-risk-0-7785,Home Credit Default Risk
1477262,0.514,0,2,/anihaldaran/home-cred-default-risk,Home Credit Default Risk
1432477,0.8,31,93,/ishaan45/thank-you,Home Credit Default Risk
1224566,0.79,0,3,/nicolasgourlaouen/soumission-rc,Home Credit Default Risk
1021471,0.748,0,0,/nicolasgourlaouen/previous-models,Home Credit Default Risk
1396051,0.794,2,8,/ashukr/the-model-3,Home Credit Default Risk
1306095,0.738,0,3,/rabbani/home-credit-eda-model,Home Credit Default Risk
1367213,0.5,0,0,/bhetey/home-credit-default-analysis,Home Credit Default Risk
1348434,0.782,0,2,/kumar234/home-credit-risk-soln-78-3,Home Credit Default Risk
1334096,0.763,18,35,/aaraneo/nn-with-convolution-over-prev-app-and-bureau,Home Credit Default Risk
1322040,0.746,0,4,/victorceo/home-credit-kernel-victore,Home Credit Default Risk
120251,0.74669,1,4,/negation/find-the-ghosts-ghouls-and-goblins,"Ghouls, Goblins, and Ghosts... Boo!"
118348,0.73534,1,4,/dssariya/ghouls-goblins-and-ghosts,"Ghouls, Goblins, and Ghosts... Boo!"
115576,0.7258899999999999,2,7,/xingobar/ghost-data-visualization,"Ghouls, Goblins, and Ghosts... Boo!"
115226,0.7088800000000001,0,3,/sudalairajkumar/notebook6d0de153b7,"Ghouls, Goblins, and Ghosts... Boo!"
14647310,4755.345,0,2,/saitodevel01/fisher-lda,Jane Street Market Prediction
14348016,7581.953,1,15,/code1110/janestreet-resnet-with-autoencoder-infer,Jane Street Market Prediction
14450922,5932.382,0,0,/raisamsknopf/raisams-guess-02,Jane Street Market Prediction
14303324,1892.247,29,83,/kwonyoung234/jane-pytorch-lstm-implementation,Jane Street Market Prediction
14071194,6947.139,0,1,/namm2008/ensemble-model-with-xgb-and-simple-nn,Jane Street Market Prediction
14556997,4096.765,0,0,/marouf/notebook8d74163ae7,Jane Street Market Prediction
14304313,3340.1020000000008,0,0,/huzzefakhan/pca-xgboost-classifier,Jane Street Market Prediction
14172427,5715.439,20,51,/taherhaggui/eda-missing-values-tsne-clustering-class-imb,Jane Street Market Prediction
14254764,6005.581999999999,4,28,/wendelfariaslopes/easy-and-simple-xgboost-good-score,Jane Street Market Prediction
14098276,5103.332,0,2,/zwdnet/quant-inverstment,Jane Street Market Prediction
14225057,10638.165,7,70,/code1110/janestreet-nn-xgb-ensemble,Jane Street Market Prediction
14099988,8697.373,0,43,/finlay/encoder-mlp,Jane Street Market Prediction
14088531,94.645,0,2,/walkiriarvieira/first-tradding-robot,Jane Street Market Prediction
89434,0.75216,0,0,/krvperera/simple-xgboost-week-9,Grupo Bimbo Inventory Demand
179366,0.6318699999999999,18,165,/aikinogard/random-forest-starter-with-numerical-features,Two Sigma Connect: Rental Listing Inquiries
11979428,0.01893,4,4,/yutohisamatsu/moa-keras-multilabel-neural-network-v1-2,Mechanisms of Action (MoA) Prediction
11750242,0.0197199999999999,2,6,/nur988/simple-keras-moa,Mechanisms of Action (MoA) Prediction
12132782,0.14459,1,4,/glm233/xgboost,Mechanisms of Action (MoA) Prediction
12110691,0.01974,3,5,/tejasharitsavk/mechanisms-of-action-moa-prediction-v3-2,Mechanisms of Action (MoA) Prediction
12071640,0.01992,0,0,/shashankpulijala/shallow-models,Mechanisms of Action (MoA) Prediction
12152238,0.12164,0,0,/lcxustc/pca-tree-based-model,Mechanisms of Action (MoA) Prediction
12088900,0.02134,1,1,/rosarr/mode-action3,Mechanisms of Action (MoA) Prediction
12036739,0.0185,19,48,/pavelvpster/moa-ensemble,Mechanisms of Action (MoA) Prediction
12074180,0.024,5,20,/ash1706/exploring-moa-eda-pca-xgboost-improved,Mechanisms of Action (MoA) Prediction
12082803,0.1307299999999999,0,0,/alexandrebernat/megamente-ita-gurizada,Mechanisms of Action (MoA) Prediction
12071461,0.02316,2,8,/vithal2311/moa-deep-learning-simple-code,Mechanisms of Action (MoA) Prediction
12042743,0.02058,1,2,/ozoozo/moa-basic-lightgbm,Mechanisms of Action (MoA) Prediction
12074353,0.02001,0,0,/ozoozo/moa-basic-keras-nn-modelling,Mechanisms of Action (MoA) Prediction
11763398,0.01867,37,153,/utkukubilay/pytorch-moa-0-01867,Mechanisms of Action (MoA) Prediction
11647219,0.0198,0,1,/manjjimnav/baseline-experimentation,Mechanisms of Action (MoA) Prediction
11824888,0.01959,2,4,/hamishs/moa-keras-bayesian-optimisation,Mechanisms of Action (MoA) Prediction
11898823,0.01933,0,29,/tolgadincer/moa-tensorflow-fast-convergence,Mechanisms of Action (MoA) Prediction
11906676,0.0199699999999999,0,2,/alekseyeliseev/moa-xgboost-baseline,Mechanisms of Action (MoA) Prediction
11863241,0.26354,0,0,/abhirajkanse/pytorch-fcv1-0,Mechanisms of Action (MoA) Prediction
2262061,4.67618,0,0,/nikhilpandey360/submission-using-inceptionv3,Dog Breed Identification
2175944,0.88708,1,0,/roboanil/transfer-learning-with-resnet-ver-02,Dog Breed Identification
12443711,0.02354,0,0,/ikobzev/moa-version1,Mechanisms of Action (MoA) Prediction
12344067,0.01854,20,130,/rahulsd91/moa-multi-input-resnet-model,Mechanisms of Action (MoA) Prediction
12425263,0.019,0,1,/alexandershpitalnik/first-submission,Mechanisms of Action (MoA) Prediction
12439793,0.02398,0,0,/aredosbyk/notebookb061c42785,Mechanisms of Action (MoA) Prediction
12330141,0.01871,1,2,/compoundv/pytorch-nn-moa-clean,Mechanisms of Action (MoA) Prediction
12068201,0.02229,0,0,/yangam/moa-prediction,Mechanisms of Action (MoA) Prediction
12315911,0.02276,0,0,/nasere/catboost-with-ovr,Mechanisms of Action (MoA) Prediction
12328696,0.02268,0,0,/sergeydor/notebookc256ef4cbe,Mechanisms of Action (MoA) Prediction
12198728,0.0186,30,133,/kushal1506/moa-pytorch-0-01859-rankgauss-pca-nn,Mechanisms of Action (MoA) Prediction
12229797,0.01871,1,37,/felipebihaiek/torch-continued-from-auxiliary-targets-smoothing,Mechanisms of Action (MoA) Prediction
12110559,0.01852,30,142,/ragnar123/moa-dnn-feature-engineering,Mechanisms of Action (MoA) Prediction
11867111,0.02254,0,0,/bclark94/brandon-s-moa-submission-w-keras,Mechanisms of Action (MoA) Prediction
12172426,0.0186199999999999,16,58,/tolgadincer/moa-tensorfow-mx10,Mechanisms of Action (MoA) Prediction
12096049,0.0189699999999999,0,4,/epocxy/multi-labels-self-attention-pytorch-transformer,Mechanisms of Action (MoA) Prediction
12147082,0.01968,0,6,/bootiu/moa-pytorch-lightning-baseline,Mechanisms of Action (MoA) Prediction
12116441,0.01865,35,130,/rahulsd91/moa-label-smoothing,Mechanisms of Action (MoA) Prediction
11629384,0.02115,0,0,/eiichishimizu/moa-prediction,Mechanisms of Action (MoA) Prediction
3347180,0.46805,0,0,/beymehdi/nyc-taxi-compet-bey,New York City Taxi Trip Duration
10427936,0.10339,4,11,/roydatascience/silver-medal-solution-stacking-m5-submissions,M5 Forecasting - Uncertainty
10172713,0.12378,0,0,/iamprateek/finding-uncertainty-sales,M5 Forecasting - Uncertainty
10289380,0.12193,9,34,/ulrich07/do-not-write-off-rnn-cnn,M5 Forecasting - Uncertainty
9146057,0.15905,0,0,/akashsuper2000/point-to-uncertainty-different-ranges,M5 Forecasting - Uncertainty
9991441,0.12565,31,81,/ulrich07/quantile-regression-with-keras,M5 Forecasting - Uncertainty
8817835,0.18269,1,2,/belov38/bykkliller,M5 Forecasting - Uncertainty
8549556,0.86069,11,33,/robertburbidge/lightgbm-poisson-w-scaled-pinball-loss,M5 Forecasting - Uncertainty
8894881,0.25748,0,0,/akashsuper2000/simple-quantiles-of-training-set,M5 Forecasting - Uncertainty
8686321,0.30585,0,0,/akashsuper2000/quantiles-w-custom-loss-func-124a74,M5 Forecasting - Uncertainty
181202,0.63852,0,0,/evanwang1028/2j5n5j6o,Two Sigma Connect: Rental Listing Inquiries
179492,0.78169,0,10,/ayaseeli/getting-started,Two Sigma Connect: Rental Listing Inquiries
179634,0.77099,0,0,/rrqqmm/exploring-data,Two Sigma Connect: Rental Listing Inquiries
181718,0.55092,0,0,/elejke/xgb-starter-in-python,Two Sigma Connect: Rental Listing Inquiries
181486,0.55209,0,0,/cowking/xgb-starter-in-python,Two Sigma Connect: Rental Listing Inquiries
243069,0.53962,0,0,/vignesh2323/fork-of-fork-of-fork-of-fork-of-xgboost-tri-d6f587,Two Sigma Connect: Rental Listing Inquiries
14205611,3787.884,2,4,/bigtobe/notebook6a34ec5fe0,Jane Street Market Prediction
14102531,220.052,3,14,/carlmcbrideellis/jane-street-what-do-you-do-when-you-have-no-gpu,Jane Street Market Prediction
14051950,1979.69,0,1,/egorplyashkov/catboostclassifier-hyper-parameters-optimization,Jane Street Market Prediction
14087117,6876.781999999998,1,1,/tc2w2988/notebook1a19bdc7fe,Jane Street Market Prediction
13807251,3677.986,0,1,/lazyso/janestreet-test,Jane Street Market Prediction
13893162,3941.086,0,1,/kenightlam/jane-street-xgboost,Jane Street Market Prediction
13911877,0.0,3,4,/satorushibata/preprocess-feature-engineering-custom-metrics,Jane Street Market Prediction
13666508,336.327,0,2,/zwdnet/janestreet,Jane Street Market Prediction
13826708,3741.118,22,30,/satorushibata/lightgbm-classifier-pca-logit-on-utility-score,Jane Street Market Prediction
122436,0.7429100000000001,0,1,/gd10396101/fork-of-first-data-exploration-model-validation,"Ghouls, Goblins, and Ghosts... Boo!"
120963,0.72778,3,1,/robscovell/gggaussian,"Ghouls, Goblins, and Ghosts... Boo!"
11637346,0.54146,0,3,/rahulpawade/regression,Mercedes-Benz Greener Manufacturing
11248178,0.54404,0,0,/ninjapiggz/mb-greener-comp-presentation,Mercedes-Benz Greener Manufacturing
7928244,0.50756,0,0,/thallapavanreddy/kaggle-mercedes-dataset,Mercedes-Benz Greener Manufacturing
6889836,0.53832,0,2,/devkhant24/mercedes-car-manufacturing,Mercedes-Benz Greener Manufacturing
4981037,0.55232,0,0,/leesangju92/mercedes-benz,Mercedes-Benz Greener Manufacturing
5542598,0.55545,0,0,/sushmabiswas/lift-the-curse-of-dimensionality-benz,Mercedes-Benz Greener Manufacturing
4455833,-5.09428,0,0,/wakamezake/leaderboardproving,Mercedes-Benz Greener Manufacturing
3388269,0.5525899999999999,0,1,/theophilebu/mercedes-125746,Mercedes-Benz Greener Manufacturing
1248260,0.5603100000000001,11,31,/deadskull7/78th-place-solution-private-lb-0-55282-top-2,Mercedes-Benz Greener Manufacturing
580125,0.4850399999999999,0,0,/priyankas/my-first-attempt-in-data-analysis,Mercedes-Benz Greener Manufacturing
1311450,0.7440000000000001,0,0,/turbineyang/lightgbm-version-5,Home Credit Default Risk
1255405,0.792,32,304,/willkoehrsen/automated-model-tuning,Home Credit Default Risk
1259411,0.755,0,0,/queirozfcom/v1-main-dataset-and-bureau-only,Home Credit Default Risk
1215924,0.772,0,8,/nikitpatel/home-credit-lightgbm,Home Credit Default Risk
1206950,0.6779999999999999,0,1,/johassan/start-here-a-gentle-introduction,Home Credit Default Risk
1073828,0.773,0,0,/nickel/austral-uni-k2-2nd-benchmark,Home Credit Default Risk
1156791,0.782,33,264,/willkoehrsen/introduction-to-feature-selection,Home Credit Default Risk
1174214,0.737,1,5,/mmiraglio/automated-feature-engineering-lightgbm-783,Home Credit Default Risk
1149926,0.741,4,11,/hamzaben/eda-random-forest-lightgbm-0-758,Home Credit Default Risk
1136973,0.763,2,16,/danilz/merge-all-data-base-glm-vs-xgb-explained-0-763,Home Credit Default Risk
1132174,0.7759999999999999,0,0,/debabratakaggle/loan-pred-with-lgbm-no-change-to-cat-cols,Home Credit Default Risk
1095268,0.759,12,37,/davidsalazarv95/fast-ai-pytorch,Home Credit Default Risk
4047385,1.416,4,16,/roydatascience/lanl-lightgbm-and-keras-nn,LANL Earthquake Prediction
4010019,1.585,0,1,/pedrormarques/signal-convolution-v6,LANL Earthquake Prediction
3827161,1.52265,7,53,/wimwim/wavenet-lstm,LANL Earthquake Prediction
3567150,1.789,1,5,/isaranja/lanl-earthquake-simple-conv1d,LANL Earthquake Prediction
3962420,1.4980000000000002,7,28,/scirpus/unoriginal-lstm,LANL Earthquake Prediction
3969902,1.779,0,4,/matsumotoshintaro/so-simple-nn,LANL Earthquake Prediction
3960067,1.536,0,0,/venkatakkinapalli/earthquake,LANL Earthquake Prediction
3862943,1.8,5,21,/matsumotoshintaro/so-simple-lightgbm-lightgbm,LANL Earthquake Prediction
3871945,1.55,1,19,/amignan/baseline-rf-model-reproducing-the-2017-paper,LANL Earthquake Prediction
3709374,1.635,0,0,/anuragshas/lanl-earthquake-prediction,LANL Earthquake Prediction
3827708,1.721,8,24,/robikscube/lanl-earthquake-melspectrogram-images-fastai-nn,LANL Earthquake Prediction
3810478,1.517,0,1,/oguzkoroglu/andrew-features-and-catboost,LANL Earthquake Prediction
3724649,2.115,0,0,/mrgodsay/fe-and-regression,LANL Earthquake Prediction
3647397,1.52,0,4,/fernandoramacciotti/wavelet-features-xgb-bayesianopt,LANL Earthquake Prediction
3448941,1.4880000000000002,2,9,/wjholst/lanl-earthquake-eda-and-ensemble-prediction,LANL Earthquake Prediction
3665805,1.507,0,7,/sandy1112/gabriel-s-kernel-with-updated-params-lb-1-50,LANL Earthquake Prediction
3668221,1.514,0,1,/arpitr07/lanl-earthquake-prediction-xgbregressor,LANL Earthquake Prediction
2598292,0.391,0,0,/varwolf/poor-pretiction-adaboost,Costa Rican Household Poverty Level Prediction
2589945,0.263,0,0,/varwolf/poor-prediction-sk-huigui,Costa Rican Household Poverty Level Prediction
2316248,0.369,0,1,/prabhatkumarsahu/costa-rican-household-poverty-level-prediction,Costa Rican Household Poverty Level Prediction
2211479,0.377,0,2,/supermoooonjy/one-pick-df-n-h,Costa Rican Household Poverty Level Prediction
1974082,0.408,0,0,/dandanwei/fast-ai-dl,Costa Rican Household Poverty Level Prediction
1931693,0.319,0,1,/faraaz54/data-cleaning-eda-bayesian-optimization-gbm,Costa Rican Household Poverty Level Prediction
1853878,0.37,0,0,/anoojnair/various-ensemble-techniques,Costa Rican Household Poverty Level Prediction
1853522,0.36,0,0,/mingtian233/kernelac6abb9f20,Costa Rican Household Poverty Level Prediction
1574966,0.441,0,8,/jaytalati87/lgbm-w-random-split-2,Costa Rican Household Poverty Level Prediction
1697698,0.422,0,2,/justforgags/poverty-analysis-costa-rica,Costa Rican Household Poverty Level Prediction
1540477,0.297,0,0,/monizearabadgi/pmr3508-2018-knn-hhi,Costa Rican Household Poverty Level Prediction
1750576,0.384,0,2,/jigarsutaria/costa-rica-poverty-prediction,Costa Rican Household Poverty Level Prediction
1397889,0.4479999999999999,8,30,/skooch/xgboost,Costa Rican Household Poverty Level Prediction
1682026,0.373,0,2,/zingo3245/costa-rica-poverty-level,Costa Rican Household Poverty Level Prediction
1672928,0.364,0,0,/xinchen201795/ml-for-the-poverty,Costa Rican Household Poverty Level Prediction
1658264,0.417,0,1,/tharug/householdpoverty,Costa Rican Household Poverty Level Prediction
1571138,0.374,0,1,/justforgags/testforfun,Costa Rican Household Poverty Level Prediction
1584948,0.3939999999999999,0,0,/george123321/try-watch-household-as-representative,Costa Rican Household Poverty Level Prediction
1564683,0.1939999999999999,0,0,/kikomaru/pmr3508-2018-v2,Costa Rican Household Poverty Level Prediction
1587610,0.408,0,1,/bparesh/costa-rican-household-poverty-eda-basic-model,Costa Rican Household Poverty Level Prediction
1556798,0.376,0,0,/tanmaylata/costa-rica-submission,Costa Rican Household Poverty Level Prediction
1564396,0.294,0,0,/elkose/pmr3508-2018-58cdb1c0be-atv2,Costa Rican Household Poverty Level Prediction
13199904,0.8677,0,1,/omkar2795/bert-multilingual-toxic,Jigsaw Multilingual Toxic Comment Classification
11782504,0.8632,0,0,/dahouda/toxic-comment-recognition,Jigsaw Multilingual Toxic Comment Classification
11605417,0.9398,0,0,/skloveyyp/jigsaw-xlm,Jigsaw Multilingual Toxic Comment Classification
10072198,0.8738,0,0,/sujeongcha/multilingual-toxic-comment-classification,Jigsaw Multilingual Toxic Comment Classification
11379836,0.9455,0,6,/kalashnimov/xlm-roberta-ensemble,Jigsaw Multilingual Toxic Comment Classification
11159951,0.9378,0,0,/yunxiaoli/xlm-roberta-with-cleaned-data,Jigsaw Multilingual Toxic Comment Classification
11011934,0.9406,0,1,/ceshine/jigsaw-tpu-xlm-roberta,Jigsaw Multilingual Toxic Comment Classification
10900698,0.9557,0,9,/mint101/jmtc-20-lb-9508-mono-lingual-models,Jigsaw Multilingual Toxic Comment Classification
10890861,0.9461,0,0,/mint101/transfer-to-monolingual-mix,Jigsaw Multilingual Toxic Comment Classification
10766733,0.8392,0,1,/michaelzeng71/jigsaw-bert-zero-shot-offenseval-t5-few-shots,Jigsaw Multilingual Toxic Comment Classification
10827001,0.9335,0,0,/michaelzeng71/jigsaw-olid-solid-xlm-roberta-same-tokenizer,Jigsaw Multilingual Toxic Comment Classification
10592739,0.9296,2,9,/bond005/siamese-xlm-roberta-and-bayesian-nn,Jigsaw Multilingual Toxic Comment Classification
10204450,0.9367,0,6,/jesudasdsouza/kernel-x,Jigsaw Multilingual Toxic Comment Classification
10305945,0.9214,0,0,/bond005/siamese-xlm-roberta,Jigsaw Multilingual Toxic Comment Classification
9458368,0.9523,2,12,/xiwuhan/jmtc-fine-tune,Jigsaw Multilingual Toxic Comment Classification
9102027,0.9498,9,24,/roydatascience/introduction-to-rankdata-ensembling,Jigsaw Multilingual Toxic Comment Classification
245169,0.32727,0,2,/akilaw/naive-xgb-lb-0-317,Sberbank Russian Housing Market
245142,0.32185,0,0,/dreaml/data-analysis,Sberbank Russian Housing Market
11475252,0.99375,10,22,/pawan300/digit-recognition,Digit Recognizer
11549076,0.98625,0,0,/tracyporter/recognise-the-digit-easy-sequential,Digit Recognizer
11486725,1.0,0,4,/ahmedmurad1990/leaf-classification,Digit Recognizer
9635229,0.907,0,0,/chriscc/logistic-regression-for-mnist-ganify,Digit Recognizer
11019056,0.98825,4,5,/ozkanozturk/cnn-deeplearning-with-99-05-accuracy,Digit Recognizer
11427431,0.99435,0,2,/laytsw/simple-multiclass-classification-model,Digit Recognizer
11356858,0.90517,1,5,/hayrilatif/digit-recognizer-with-numpy-adam-optimizer,Digit Recognizer
11361694,0.85921,6,16,/gauravduttakiit/digit-recognizer-using-decision-tree,Digit Recognizer
11360913,0.91617,0,12,/gauravduttakiit/digit-recognizer-using-logistic-regression,Digit Recognizer
11332869,0.98803,1,2,/vivekyadav21/digit-recognizer-using-cnn-using-keras,Digit Recognizer
11381668,0.97167,0,0,/xhdhr10000/lenet5,Digit Recognizer
10925075,0.9951,0,15,/shivabhatt/digit-recognizer,Digit Recognizer
11328976,0.97478,0,2,/kshivi99/intro-to-deep-learning-simple-neural-network,Digit Recognizer
10850392,0.3262199999999999,0,4,/dmkravtsov/11-sberbank,Sberbank Russian Housing Market
3379498,0.3141199999999999,0,1,/dromosys/russian-housing-market,Sberbank Russian Housing Market
9146098,0.9459,2,0,/akashsuper2000/tpu-inference-super-fast-xlmroberta,Jigsaw Multilingual Toxic Comment Classification
7641189,0.0409999999999999,0,0,/taka0119/center-resnext50-baseline,Peking University/Baidu - Autonomous Driving
7264830,0.05,15,71,/khoongweihao/autonomous-driving-epoch-on-fire-resnext50,Peking University/Baidu - Autonomous Driving
6891109,0.001,55,114,/diegojohnson/centernet-objects-as-points,Peking University/Baidu - Autonomous Driving
6738886,0.04,93,95,/mobassir/center-resnext50-baseline,Peking University/Baidu - Autonomous Driving
65696,0.51031,0,0,/ymcdull/empty-for-fun,Ultrasound Nerve Segmentation
66629,0.48876,0,0,/ruktech/mad-script-battle-in-pandas,Facebook V: Predicting Check Ins
8913923,0.936,0,2,/ahmedabdelfattah20/ensemble-learning-googlenet-resnet,Plant Pathology 2020 - FGVC7
8863270,0.936,4,19,/sinchubhat/plantpathologyusingmonk,Plant Pathology 2020 - FGVC7
8765099,0.972,2,7,/lextoumbourou/plant-2020-plant-village-transfer-learning-tpu,Plant Pathology 2020 - FGVC7
8707480,0.94,0,1,/bluewizard/plant-pathology-prediction-with-efficientnet,Plant Pathology 2020 - FGVC7
8532080,0.923,0,2,/nickteim/fast-ai-v3-plant-pathology1,Plant Pathology 2020 - FGVC7
8553784,0.939,0,5,/arpytanshu/plant-pathology-2020-resnet-pytorch,Plant Pathology 2020 - FGVC7
8632602,0.963,0,1,/jhayescao/inceptionresnetv2,Plant Pathology 2020 - FGVC7
8474197,0.69,0,1,/khiwila/kernel37b4718519,Plant Pathology 2020 - FGVC7
8569803,0.951,0,10,/davidhiggins/fastai-with-resnet50,Plant Pathology 2020 - FGVC7
8541937,0.937,1,9,/samarthsarin/resnet-pytorch,Plant Pathology 2020 - FGVC7
8471396,0.974,3,6,/ganeshmundra/average-efficientnet,Plant Pathology 2020 - FGVC7
8517442,0.85,0,1,/ryches/sdml-starter,Plant Pathology 2020 - FGVC7
8475971,0.975,1,6,/lkatran/tf-zoo-models-on-tpu,Plant Pathology 2020 - FGVC7
8462927,0.965,0,11,/anyexiezouqu/tpu-5k-fold-efficientnet-b5,Plant Pathology 2020 - FGVC7
8394549,0.957,1,4,/shivam17818/kaggle-new-problem,Plant Pathology 2020 - FGVC7
8425542,0.55,0,0,/chariots17/using-convolutional-neural-network,Plant Pathology 2020 - FGVC7
8327011,0.965,26,121,/pestipeti/plant-pathology-2020-pytorch,Plant Pathology 2020 - FGVC7
8351536,0.951,1,6,/vbookshelf/apple-leaf-health-analyzer-w-web-interface,Plant Pathology 2020 - FGVC7
8345113,0.953,0,3,/anubhav1302/plantdisease-tf-tpu,Plant Pathology 2020 - FGVC7
8336060,0.6940000000000001,0,3,/ajax0564/kernel427156046d,Plant Pathology 2020 - FGVC7
9105705,0.544,0,0,/ooo256/decision-tree2,Plant Pathology 2020 - FGVC7
8912555,0.973,0,0,/akashsuper2000/classification-densenet201-efficientnetb7,Plant Pathology 2020 - FGVC7
382194,0.2445099999999999,1,2,/binilg/simple-linear-regression,Porto Seguro’s Safe Driver Prediction
377816,0.28364,8,23,/jeru666/random-forest-pipeline,Porto Seguro’s Safe Driver Prediction
421527,0.28209,0,0,/danieleewww/new-features-xgb-upsampling-bayesopt,Porto Seguro’s Safe Driver Prediction
380345,0.2593,0,0,/eoakley/porto-seguro-lightgbm,Porto Seguro’s Safe Driver Prediction
11858046,0.7912399999999999,2,4,/emresirma/home-credit-default-risk-project-with-lightgbm,Home Credit Default Risk
11179058,0.75,0,1,/dmkravtsov/2-0-home-credit-xgboost,Home Credit Default Risk
9993358,0.7603300000000001,0,1,/onurcopur/defaultrisk-dreamteam,Home Credit Default Risk
9058158,0.79604,6,15,/berkanacar/home-credit-default-risk-production-level,Home Credit Default Risk
564445,0.2106,0,2,/azakhtyamov/simple-logreg-with-only-3-leaky-features,Statoil/C-CORE Iceberg Classifier Challenge
421258,0.1969,0,0,/green05031/first-image-recognition,Statoil/C-CORE Iceberg Classifier Challenge
514348,0.1357,21,39,/submarineering/submarineering-best-public-score-until-now,Statoil/C-CORE Iceberg Classifier Challenge
498280,0.2117,0,0,/ayanmaity/fork-of-iceberg-recognition,Statoil/C-CORE Iceberg Classifier Challenge
504080,0.1416,2,9,/chechir/explore-stacking-another-hi-lo-and-clip-probs,Statoil/C-CORE Iceberg Classifier Challenge
478711,0.5351,0,0,/praveenjune17/sigmoid-model,Statoil/C-CORE Iceberg Classifier Challenge
404932,0.3321,6,57,/kmader/exploring-the-icebergs-with-skimage-and-keras,Statoil/C-CORE Iceberg Classifier Challenge
13461353,347.1,0,4,/ahmedewida/rock-paper-scissors-xgboost,"Rock, Paper, Scissors"
13404037,818.2,3,15,/ht5brer/not-losing-strategy,"Rock, Paper, Scissors"
12875052,-119.0,0,3,/ngxingyu/lowest-possible-score,"Rock, Paper, Scissors"
4034816,0.8567600000000001,0,0,/simpletonwang/dl-hw1,Invasive Species Monitoring
9391677,0.52881,0,0,/slavik0505/kernel43aae13b62,Two Sigma Connect: Rental Listing Inquiries
7385189,0.55298,0,0,/shrikantds/rental-prediction-with-xgb-and-eda,Two Sigma Connect: Rental Listing Inquiries
1872293,0.61992,0,0,/timsonrisa/two-rental-p-2-random-forest,Two Sigma Connect: Rental Listing Inquiries
415588,1.00331,0,4,/axelderomblay/mlbox-a-fully-automated-package,Two Sigma Connect: Rental Listing Inquiries
241910,0.63612,0,0,/slackerdog/firsttry,Two Sigma Connect: Rental Listing Inquiries
238711,0.53628,0,0,/lujing/cv-statistics-better-parameters,Two Sigma Connect: Rental Listing Inquiries
10549945,0.544,3,20,/katanasoulx/for-beginner-simple-bird-classification,Cornell Birdcall Identification
10524837,0.55,0,5,/radek1/esp-starter-pack-v3-res34-minmax,Cornell Birdcall Identification
10270063,0.54,0,7,/slavaz/mfcc-features-as-numpy-files-for-training,Cornell Birdcall Identification
10233384,0.54,4,12,/thebratattack/eda-audio-processing-and-lstm,Cornell Birdcall Identification
10184179,0.18,14,101,/shonenkov/sample-submission-using-custom-check,Cornell Birdcall Identification
10160384,0.54,3,46,/seriousran/mfcc-feature-extraction-for-sound-classification,Cornell Birdcall Identification
10147263,0.54,11,61,/rohitsingh9990/eda-visualizations-simple-baseline,Cornell Birdcall Identification
943946,0.2374,2,19,/gidutz/text2score-keras-rnn-word-embedding,Avito Demand Prediction Challenge
921923,0.2292,1,18,/bguberfain/naive-lgb-with-text-images,Avito Demand Prediction Challenge
917024,0.2319999999999999,0,17,/dicksonchin93/xgb-with-mean-encode-tfidf-feature-0-232,Avito Demand Prediction Challenge
915886,0.2369,0,6,/jingqliu/fasttext-conv2d-with-tf-on-title-and-description,Avito Demand Prediction Challenge
909390,0.2493,0,7,/axelstram/category-name-mean,Avito Demand Prediction Challenge
902036,0.2356,0,18,/shujian/avito-lightgbm-starter,Avito Demand Prediction Challenge
901430,0.2663,1,12,/hakeem/mean-benchmark,Avito Demand Prediction Challenge
965086,0.2386,0,0,/scirpus/beat-the-benchmark,Avito Demand Prediction Challenge
938008,0.2383,0,0,/deeiip/simplest-xgb-without-description-or-title-0-2377,Avito Demand Prediction Challenge
2099735,0.00026,11,3,/kmader/applying-simple-model-to-the-real-competition,RSNA Pneumonia Detection Challenge
1543183,0.1169999999999999,0,9,/ashishpatel26/cnn-remedies,RSNA Pneumonia Detection Challenge
1584860,0.118,0,0,/prasunmishra/resnet152-transfer-learning-pre-trained-weights,RSNA Pneumonia Detection Challenge
1569318,0.075,27,29,/hmendonca/mask-rcnn-with-submission,RSNA Pneumonia Detection Challenge
1588907,0.0,2,13,/vbookshelf/python-generators-to-reduce-ram-usage-part-2,RSNA Pneumonia Detection Challenge
1589852,0.003,0,2,/returnofsputnik/use-dicom-data-to-correct-your-predictions,RSNA Pneumonia Detection Challenge
1548047,0.114,3,2,/aharless/cnn-segmentation-connected-components,RSNA Pneumonia Detection Challenge
11383096,0.1966099999999999,0,1,/danigarci1/houseprice-predicition-randomforestregressor,House Prices - Advanced Regression Techniques
11350296,0.10897,0,9,/dmkravtsov/3-3-house-price-one-hot-xgbr,House Prices - Advanced Regression Techniques
11355899,0.1669,0,0,/ruandiassantana/advanced-housing,House Prices - Advanced Regression Techniques
11103782,4.18825,0,0,/yutohisamatsu/ridge-lasso-elasticnet-regressions-sutra-practice,House Prices - Advanced Regression Techniques
11190811,0.13047,0,0,/yutohisamatsu/feature-selection-and-elasticnet-sutra-practice,House Prices - Advanced Regression Techniques
10665948,0.14231,0,0,/doaajaber/house-price-adv-gridsearchcv,House Prices - Advanced Regression Techniques
11335160,0.12637,0,0,/aylinndersev/house-price-prediction,House Prices - Advanced Regression Techniques
11304424,0.13768,0,4,/senaduman/house-prices-regression-models,House Prices - Advanced Regression Techniques
11264002,0.1304799999999999,0,0,/yutohisamatsu/fork-of-houseprice-elasticnet-featureengineering,House Prices - Advanced Regression Techniques
11318911,0.12293,0,2,/kevinbtwcodes/xgboost-stacking-blending-hyperopt-lots-of-eda,House Prices - Advanced Regression Techniques
10693532,0.24893,4,16,/basarkayastudent/eda-house-prices,House Prices - Advanced Regression Techniques
11246589,0.13473,1,4,/otonyeamietubodie/house-prediction,House Prices - Advanced Regression Techniques
5411436,0.8333,0,2,/nikhilikhar/fastai-steel-unet-1-submission,Severstal: Steel Defect Detection
5573776,0.37374,0,5,/joaorobson/u-net-notebook,Severstal: Steel Defect Detection
5755467,0.85674,2,12,/barnwellguy/inference-kernel-producing-a-successful-submission,Severstal: Steel Defect Detection
5708530,0.85674,0,0,/joaorobson/dumb-predictions-notebook,Severstal: Steel Defect Detection
5550835,0.83539,14,7,/anubhav1302/steel-masking-unet,Severstal: Steel Defect Detection
5626225,0.87731,0,0,/haixingfengzi/unet-pytorch-inference-kernel,Severstal: Steel Defect Detection
5244400,0.85674,1,5,/nikhilikhar/pytorch-u-net-steel-1-submission,Severstal: Steel Defect Detection
5251141,0.85674,5,47,/rabbitcaptain/keras-drn-pspnet,Severstal: Steel Defect Detection
5057910,0.8222299999999999,7,37,/amanooo/defect-detection-starter-u-net,Severstal: Steel Defect Detection
13594808,5.31935,0,1,/wonhojin/dog-breed-jwh,Dog Breed Identification
12862404,10.60479,0,0,/roohisharma/dog-breed-identification,Dog Breed Identification
11567677,5.17687,0,0,/myuferov/1-version,Dog Breed Identification
11081545,10.69395,3,4,/pradyut23/dog-breed-identifier,Dog Breed Identification
8100228,0.18212,2,18,/phylake1337/0-18-loss-simple-feature-extractors,Dog Breed Identification
7878323,0.136,0,1,/darshan9/condata2019-competition,CareerCon 2019 - Help Navigate Robots
3934402,0.434,0,1,/fk0728/weasel-muse-method-for-classifying-robot-readings,CareerCon 2019 - Help Navigate Robots
3332332,0.6,0,0,/anuragshas/carrercon2019,CareerCon 2019 - Help Navigate Robots
3615264,0.8222,7,23,/purplejester/pytorch-deep-time-series-classification,CareerCon 2019 - Help Navigate Robots
3530510,0.73,0,0,/narendrashu/k-fold-rf-gbm,CareerCon 2019 - Help Navigate Robots
3575094,0.6997,0,1,/ammar111/help-navigate-robots-65th-place-solution,CareerCon 2019 - Help Navigate Robots
3560929,0.63,2,13,/whoiskk/15-solution-private-0-77,CareerCon 2019 - Help Navigate Robots
3538495,0.6,2,9,/pnussbaum/cnn-v03q1,CareerCon 2019 - Help Navigate Robots
3270699,0.7,0,0,/narendrashu/baseline-rf,CareerCon 2019 - Help Navigate Robots
3302359,0.72,0,0,/ludovicoristori/scratching-the-surface,CareerCon 2019 - Help Navigate Robots
3329690,0.69,0,0,/deisler/navigate-robots,CareerCon 2019 - Help Navigate Robots
3510312,0.53,0,0,/ggopalan/spectral-analysis-with-lightgbm-v1,CareerCon 2019 - Help Navigate Robots
3503701,0.67,0,0,/taigokuriyama/k-nn-feature-extraction,CareerCon 2019 - Help Navigate Robots
3426215,0.65,9,4,/prathamsolanki/can-xgboost-help-robots,CareerCon 2019 - Help Navigate Robots
3402873,0.63,0,0,/drrdrem/cnn-lenet5-two-input,CareerCon 2019 - Help Navigate Robots
3402810,0.67,0,0,/askanio/rf-0-67-eulers-viz,CareerCon 2019 - Help Navigate Robots
3359965,0.69,0,0,/anki54/tiptaptoe,CareerCon 2019 - Help Navigate Robots
3379626,0.6,0,3,/ykhatami/robots-in-wilderness,CareerCon 2019 - Help Navigate Robots
3369413,0.71,4,12,/donkeys/my-little-eda-with-random-forest,CareerCon 2019 - Help Navigate Robots
13037483,0.01821,1,6,/kaerunantoka/moa-blending24,Mechanisms of Action (MoA) Prediction
13194125,0.0214199999999999,0,0,/sangonana/moa-light-gbm,Mechanisms of Action (MoA) Prediction
13011319,0.01962,0,1,/fernandonieuwveldt/wide-and-deep-keras-implementation,Mechanisms of Action (MoA) Prediction
12100451,0.01825,0,0,/alturutin/moa-inference,Mechanisms of Action (MoA) Prediction
11531776,0.0183,0,1,/thakurudit/moa-rankgauss-tabnet-resnet-labelsmooth-xgb,Mechanisms of Action (MoA) Prediction
12144817,0.01885,0,0,/mosrihari/moa-v1,Mechanisms of Action (MoA) Prediction
13211686,0.01864,0,0,/chandanpandey/tabnet-pca-stratification-final,Mechanisms of Action (MoA) Prediction
12400093,0.01834,0,1,/ericfreeman/moa-multi-input-resnet-model,Mechanisms of Action (MoA) Prediction
13185129,0.01825,0,3,/wawawasunny/4-models,Mechanisms of Action (MoA) Prediction
12031343,0.01863,0,0,/skinyx/moa-prediction-torch,Mechanisms of Action (MoA) Prediction
12933811,0.02128,0,1,/dravid/moa-dataanalysis,Mechanisms of Action (MoA) Prediction
12765998,0.01844,0,1,/arun016/fork-of-introduction-to-tabnet-kfold-10-patient,Mechanisms of Action (MoA) Prediction
12643979,0.01884,0,0,/aeryss/moa-no-feature-engineering-neural-net,Mechanisms of Action (MoA) Prediction
13034828,0.01925,0,0,/aeryss/fork-of-moa-pca-feature-engineering-keras-neural,Mechanisms of Action (MoA) Prediction
12708254,0.0186099999999999,0,0,/aeryss/tabnet-hyper-fe,Mechanisms of Action (MoA) Prediction
12527580,0.01969,0,0,/deathlyhallows/xg-boost-moa-baseline,Mechanisms of Action (MoA) Prediction
12379568,0.01821,0,0,/crackle/moa-14,Mechanisms of Action (MoA) Prediction
12578148,0.01869,0,0,/alturutin/moa-tabnet,Mechanisms of Action (MoA) Prediction
13041728,0.01856,0,0,/hamishs/moa-elasticnet,Mechanisms of Action (MoA) Prediction
13174581,0.01974,0,0,/javiercarnero/moa-prediction-fastai,Mechanisms of Action (MoA) Prediction
13226636,0.01828,0,0,/jared8920/fork-of-fork-of-inference-blending-pretrain-lbweig,Mechanisms of Action (MoA) Prediction
13205003,0.01857,0,0,/ryutarohashimoto/moa-rep-3-model-training-and-inference,Mechanisms of Action (MoA) Prediction
12959596,0.01994,0,0,/pandaman817/ansenmble-lgbm-and-2pytorch-models,Mechanisms of Action (MoA) Prediction
13144439,0.01855,0,1,/caicaicaicaicai/selfsupervisedtabnet,Mechanisms of Action (MoA) Prediction
13074961,0.69314,0,1,/valeriagab/notebooke6bdaaee89,Mechanisms of Action (MoA) Prediction
13060674,0.01983,0,0,/vinayaknyk/moa-prediction-autoencoders-cv,Mechanisms of Action (MoA) Prediction
12649937,0.01944,1,5,/vinniepalazeti/moa-pred,Mechanisms of Action (MoA) Prediction
12675005,0.01859,0,2,/omniking1999/notebook-v4-0,Mechanisms of Action (MoA) Prediction
13095547,0.02453,0,1,/sg1993/neural-net,Mechanisms of Action (MoA) Prediction
12979155,0.0198099999999999,0,1,/dliend/fastai-tabular-learner-with-cross-validation,Mechanisms of Action (MoA) Prediction
13012112,0.01877,7,18,/giorgosfoukarakis/moa-eda-fastica-dnn-label-smoothing,Mechanisms of Action (MoA) Prediction
13040445,1.1629200000000002,0,11,/imoore/moa-complex-spaghetti-model-for-top-kagglers,Mechanisms of Action (MoA) Prediction
13048385,0.02318,0,0,/lashamaev/baseline6researche,Mechanisms of Action (MoA) Prediction
13050257,0.026,0,0,/swofde/baseline6pseudo,Mechanisms of Action (MoA) Prediction
12865764,0.01841,23,56,/kushal1506/moa-tabnet-inference,Mechanisms of Action (MoA) Prediction
12990275,0.01835,0,8,/mamoru1992/pytorch-transfer-learning-with-k-folds-by-drug-ids,Mechanisms of Action (MoA) Prediction
12997038,0.01937,0,0,/alexandervc/moa50-logreg-blend-new-drugaware-folds,Mechanisms of Action (MoA) Prediction
12928075,0.0232199999999999,0,0,/wickkiey/moa-lightgbm-206-models,Mechanisms of Action (MoA) Prediction
12845972,0.01961,0,0,/dliend/testing-moa-based-on-grid-search-results,Mechanisms of Action (MoA) Prediction
12945346,0.02087,0,0,/namndt/mechanisms-of-action-prediction,Mechanisms of Action (MoA) Prediction
12937089,0.01846,0,3,/caicaicaicaicai/moa-predictions-overfitting-with-tabnet-reduce,Mechanisms of Action (MoA) Prediction
12943076,0.01944,0,2,/iraqai/first-try-random-bigining,Mechanisms of Action (MoA) Prediction
12930060,0.02123,0,5,/sarthakrastogi/deep-learning-models-2,Mechanisms of Action (MoA) Prediction
12588657,0.01829,7,20,/mekhdigakhramanian/moa-blend,Mechanisms of Action (MoA) Prediction
12906163,0.0464399999999999,0,1,/lashamaev/baseline6,Mechanisms of Action (MoA) Prediction
6371985,0.85549,0,0,/kanchannannavare/severstal-steel-defect-detection,Severstal: Steel Defect Detection
6371450,0.91401,4,12,/phunghieu/severstal-24-10-19-private-score-0-90348,Severstal: Steel Defect Detection
6386360,0.9054,0,0,/chesnokov/mlcomp-catalyst-infer-post-proc-fix-0-887-0-888,Severstal: Steel Defect Detection
5809998,0.85605,0,0,/vovaekb90/xception-baseline-for-severstal-transfer-learning,Severstal: Steel Defect Detection
6256670,0.90726,5,13,/samrat77/severstal-mlcomp-catalyst-infer-0-90726,Severstal: Steel Defect Detection
6144241,0.8878,0,1,/takanosuke/pytorch-severstal-steel-defect-detection,Severstal: Steel Defect Detection
4999731,0.85674,0,12,/hamditarek/basic-eda-submission,Severstal: Steel Defect Detection
6215484,0.89051,2,1,/naresh31/severstal-using-fast-ai,Severstal: Steel Defect Detection
5028898,0.85674,0,2,/ksooklall/severstal-steel-batching-inputs-256x256,Severstal: Steel Defect Detection
6057519,0.54999,0,2,/poteman/severstal-2-step-pipeline,Severstal: Steel Defect Detection
5647745,0.8939,3,17,/evgenyshtepin/severstal-fast-ai-256x256-crops-sub-0-89318,Severstal: Steel Defect Detection
5781557,0.8781899999999999,3,31,/watanabe2362/trainandtest,Severstal: Steel Defect Detection
5836384,0.85674,0,0,/md5520/kernel48f4ee46b7,Severstal: Steel Defect Detection
11272061,0.14797,0,1,/shams1/house-price-prediction,House Prices - Advanced Regression Techniques
11250723,0.12849,0,5,/atifrahman/house-price-prediction-using-xgbregressor,House Prices - Advanced Regression Techniques
11239812,0.13607,0,2,/shams1/housing-prices-using-xgboost,House Prices - Advanced Regression Techniques
7386100,9.45426,0,0,/simhashourya/house-prices-advanced-regression-with-comments,House Prices - Advanced Regression Techniques
11196086,0.12005,0,9,/katchupalvarenga/house-prices-top-8-em-portugu-s,House Prices - Advanced Regression Techniques
11056446,0.13851,1,8,/miguelquiceno/house-prices-keras,House Prices - Advanced Regression Techniques
11053348,0.12924,0,0,/sauravprasad/house-price-prediction,House Prices - Advanced Regression Techniques
11169341,0.12585,4,12,/nachiket273/simple-xgboost-with-gridsearch,House Prices - Advanced Regression Techniques
9872186,0.14629,0,0,/lithickaanandavel/kernel6b37bf4468,House Prices - Advanced Regression Techniques
11099056,0.13721,2,14,/an0utlier/house-price-regression,House Prices - Advanced Regression Techniques
11067661,1.02993,0,1,/chintanrabadiya/house,House Prices - Advanced Regression Techniques
1527338,0.0,33,98,/kmader/lung-opacity-classification-transfer-learning,RSNA Pneumonia Detection Challenge
1534510,0.0,0,0,/returnofsputnik/predict-the-mean-box-location,RSNA Pneumonia Detection Challenge
4117131,0.88287,0,4,/dsaichand3/plant-seedling-classification,Plant Seedlings Classification
3566460,0.92191,0,2,/aditya100/plant-seedling-classification,Plant Seedlings Classification
3534953,0.97481,0,1,/anuragshas/plant-seedlings,Plant Seedlings Classification
3204233,0.09319,0,0,/dineshm2/planet-seedling-classification,Plant Seedlings Classification
2763889,0.97607,4,8,/kharbanda/fast-ai-v1,Plant Seedlings Classification
2157602,0.8589399999999999,0,7,/tonyzheng/image-classification-plant-seedlings-using-cnns,Plant Seedlings Classification
1658950,0.77455,0,2,/atrisaxena/keras-plant-seedlings-vgg19-augmentation,Plant Seedlings Classification
750069,0.86146,1,1,/naveenc131/dense-cnn-model,Plant Seedlings Classification
1279710,0.83501,4,2,/zhoulingyan0228/seedling-classification-cnn-w-data-augmnt,Plant Seedlings Classification
1225734,0.6926899999999999,0,11,/meenavyas/plant-seedlings-classification,Plant Seedlings Classification
1020809,0.95843,3,29,/omkarsabnis/seedling-classification-using-cnn-v13-0-95,Plant Seedlings Classification
749577,0.92695,2,3,/xalphahelix/plant-seedlings-classification-cnn,Plant Seedlings Classification
624629,0.45969,2,6,/borenamahlet/seedling-classification-cnn-tensorflow,Plant Seedlings Classification
477878,0.1114599999999999,2,3,/abhishekdogra007/cnn-with-keras-in-progress,Plant Seedlings Classification
3124605,0.10201,0,0,/gianfa/plant-seedlings-classification-fastai-v1,Plant Seedlings Classification
890975,0.6146,0,0,/thanatoz/plant-seedling,Plant Seedlings Classification
11434711,0.545,0,0,/pubgpmec/inference-birdsongwith-no-call,Cornell Birdcall Identification
11207595,0.568,4,15,/ffares/resnest50-fast-oof-ensemble,Cornell Birdcall Identification
11155611,0.568,14,51,/kneroma/resnest50-fast-too-much-birds-could-hurt,Cornell Birdcall Identification
10929175,0.568,0,1,/akashsuper2000/inference-resnest50-fast-with-example-test-audio,Cornell Birdcall Identification
11094153,0.5670000000000001,0,9,/marcogorelli/cln-inference-birdsong-baseline-resnest50-fast,Cornell Birdcall Identification
10908209,0.532,4,5,/meckdahl/zo-submit-help-blind-predictions,Cornell Birdcall Identification
10859109,0.476,1,8,/mineshjethva/making-prediction-with-keras-pre-trained-model,Cornell Birdcall Identification
10685422,0.564,0,2,/dragonzhang/inference-baseline-resnest50-fast-forked4learn,Cornell Birdcall Identification
10617839,0.545,15,39,/radek1/esp-starter-pack-from-training-to-submission,Cornell Birdcall Identification
11062975,0.52766,0,1,/jejutphyunchangwoo/nytaxi-neural-network-0813-3,New York City Taxi Trip Duration
8293414,1.02537,0,1,/cmedelaval/kernel3170c7301b,New York City Taxi Trip Duration
4188708,0.5714,0,0,/yadechi/kernel-yadechi3,New York City Taxi Trip Duration
4173564,0.4491,0,0,/sabderemane/train-taxi,New York City Taxi Trip Duration
4173598,0.89235,0,0,/cyrildev95/cyrilsantos-examtaxi,New York City Taxi Trip Duration
4184798,0.4503899999999999,0,0,/anoune/asy-nyttd-last-version,New York City Taxi Trip Duration
4173642,0.44387,0,0,/tetevemasque/est-ve-evalutation,New York City Taxi Trip Duration
4173651,0.46282,0,0,/zakariae11/evaluation-zakariae,New York City Taxi Trip Duration
4187014,0.46271,0,1,/cheinhtidiane/kernel2999147c42,New York City Taxi Trip Duration
4173788,0.5637300000000001,0,0,/bgwendoline/kernel-gwendoline-billong,New York City Taxi Trip Duration
4173603,0.4456699999999999,0,0,/mangoal34/kernel3482df9bb7,New York City Taxi Trip Duration
4173467,0.44865,0,0,/remiar/kernel3cdfb871bc,New York City Taxi Trip Duration
4173496,0.44956,0,0,/edmoss/kernel61808aed02,New York City Taxi Trip Duration
3579847,0.47229,0,0,/mariamandiaye/nyc-taxi-duration-mariam-n,New York City Taxi Trip Duration
3337222,0.4258699999999999,0,0,/solozabar/fork-of-fork-of-test-xgboost,New York City Taxi Trip Duration
3253511,0.43656,0,0,/hugomez/new-york-city-taxi-trip-duration,New York City Taxi Trip Duration
2862903,0.6344,0,0,/hugowind/hugo-submission,New York City Taxi Trip Duration
2913559,0.46037,0,0,/skeptikode/nyc-taxi-trip-duration-eda-predictive-modeling,New York City Taxi Trip Duration
2877082,0.5851,0,0,/younesayeb/nyc-taxi-trip-duration-ayeb,New York City Taxi Trip Duration
2863342,0.4440199999999999,0,2,/identiq/new-york-city-taxi-trip-duration,New York City Taxi Trip Duration
7589667,0.12,0,0,/khushmanvar/deep-learning-with-bert-tensorflow-2-0,TensorFlow 2.0 Question Answering
13062180,4766.924,46,66,/jatta3399/starter-notebook-eda-1-min-fit-lgbm-xgboost,Jane Street Market Prediction
13077143,0.0,2,8,/carlmcbrideellis/baseline-a-random-walk-down-jane-street,Jane Street Market Prediction
13076803,1126.953,4,3,/shinomoriaoshi/janestreet-catboost-baseline,Jane Street Market Prediction
13068767,3.007,0,3,/shubham9455999082/notebookf5b52b50a5,Jane Street Market Prediction
13272282,3890.939,0,6,/isaienkov/jane-street-market-prediction-keras-nn,Jane Street Market Prediction
9084649,0.967,0,1,/manyregression/private-fastai2-in-few-lines,Plant Pathology 2020 - FGVC7
9442405,0.969,0,0,/anupreetbhatia8/66th-place-solution-using-efficient-net-b5,Plant Pathology 2020 - FGVC7
9674478,0.983,0,4,/meysonua/the-greater-ensembler-0-983,Plant Pathology 2020 - FGVC7
9623501,0.982,3,5,/razasaleemi/the-great-ensembler-0-982,Plant Pathology 2020 - FGVC7
9609008,0.843,0,0,/kapbi4/pytorch-custommodel-224x224noaug-0-843,Plant Pathology 2020 - FGVC7
9504021,0.981,0,12,/redwankarimsony/ensemble-top-kernels-with-entropy,Plant Pathology 2020 - FGVC7
9445550,0.885,0,0,/ichijo/plant-pathology-2020-easy-keras,Plant Pathology 2020 - FGVC7
9323064,0.7290000000000001,0,0,/slipclutch/fork-of-resnet50-tl,Plant Pathology 2020 - FGVC7
9270366,0.928,5,5,/viiids/transfer-learning-with-class-weights,Plant Pathology 2020 - FGVC7
9286510,0.525,0,4,/jimitshah777/from-loading-data-to-submission-in-keras,Plant Pathology 2020 - FGVC7
9228346,0.911,21,13,/chekoduadarsh/ensemble-efficientnet-xception-resnet152,Plant Pathology 2020 - FGVC7
8886479,0.919,0,4,/urayukitaka/image-pre-processing-and-densenet,Plant Pathology 2020 - FGVC7
9238046,0.873,1,0,/abhigyansingh/kernel537814664b,Plant Pathology 2020 - FGVC7
9078242,0.95743,0,5,/lvalue/preprocess,Plant Pathology 2020 - FGVC7
8797528,0.964,0,2,/shubhamai/predicting-plant-disease,Plant Pathology 2020 - FGVC7
8931031,0.8122,0,0,/kumarsuraj9450/jigsawmulti-bertmulti,Jigsaw Multilingual Toxic Comment Classification
8906759,0.8696,0,6,/tanlikesmath/xlm-roberta-inference-pytorch-tpu-xla-1-core,Jigsaw Multilingual Toxic Comment Classification
8863812,0.9122,0,2,/yihdarshieh/jigsaw-tpu-gradient-accumulation,Jigsaw Multilingual Toxic Comment Classification
8888223,0.9465,0,3,/xiwuhan/jg-toxic-training,Jigsaw Multilingual Toxic Comment Classification
8726424,0.8903,6,17,/rftexas/gru-lstm-rnn-101,Jigsaw Multilingual Toxic Comment Classification
8603229,0.9488,123,194,/hamditarek/ensemble,Jigsaw Multilingual Toxic Comment Classification
8633573,0.9242,8,63,/miklgr500/jigsaw-tpu-bert-two-stage-training,Jigsaw Multilingual Toxic Comment Classification
8575499,0.9139,22,127,/abhishek/inference-of-bert-tpu-model-ml-w-validation,Jigsaw Multilingual Toxic Comment Classification
8562393,0.8249,6,55,/abhishek/inference-of-bert-tpu-model-ml,Jigsaw Multilingual Toxic Comment Classification
8552821,0.626,13,33,/youhanlee/bert-pytorch-huggingface-tpu-version-xla,Jigsaw Multilingual Toxic Comment Classification
8557342,0.6502,0,23,/seriousran/bert-starter-under-sampling,Jigsaw Multilingual Toxic Comment Classification
8559769,0.6391,0,4,/theoviel/bert-pytorch-huggingface-with-tpu-multiprocessing,Jigsaw Multilingual Toxic Comment Classification
8552555,0.5,0,2,/grapestone5321/jigsaw-multilingual-toxic-sample-submission,Jigsaw Multilingual Toxic Comment Classification
10873840,0.9306,0,0,/michaelzeng71/fork-of-jigsaw-olid-solid-xlm-roberta,Jigsaw Multilingual Toxic Comment Classification
10252540,0.9477,0,0,/zzy990106/howling-with-wolf-on-l-genpresse,Jigsaw Multilingual Toxic Comment Classification
9532696,0.9463,0,0,/akashsuper2000/ensemble,Jigsaw Multilingual Toxic Comment Classification
11801842,0.99078,2,5,/ayushikaushik/convolutional-neural-network-tensorflow2-0,Digit Recognizer
10965808,0.97928,0,0,/jaishanker/digit-recognition-using-cnn-model,Digit Recognizer
9003270,0.98257,0,0,/sohamsave44/d1g1t-r3c0gn1zer,Digit Recognizer
11722614,1.0,14,41,/nakulsingh1289/score-1-00-in-digit-recognizer,Digit Recognizer
11691016,0.99425,0,1,/canberkarc/0-9955-score-mnist-digit-recognizer-cnn-keras,Digit Recognizer
11722940,0.99157,0,2,/onurakkse/digit-recognizer-with-keras,Digit Recognizer
11698797,0.98128,0,5,/shyamprasath/digit,Digit Recognizer
11664799,0.99289,0,4,/fuinki/pytorch-testcode,Digit Recognizer
11456822,0.99432,0,0,/shubhamkhandale/digit-recognition,Digit Recognizer
11657904,0.97953,0,0,/arnabark/beginner-digit-recognizer,Digit Recognizer
11608944,0.99103,1,4,/iljaavadiev/keras-mnist,Digit Recognizer
10831014,0.0,0,0,/sainiviv/fully-connected-neural-network-using-numpy,Digit Recognizer
11560697,0.95582,3,9,/diksha659/digit-recognizer,Digit Recognizer
11398365,0.9915,3,4,/wongguoxuan/beginner-digit-recognition-with-convnet,Digit Recognizer
11485474,0.99432,4,16,/muhammadshahzadkhan/mnist-digit-recognizer-using-cnn-0-99457-keras,Digit Recognizer
11530975,1.0,0,2,/flacotree/leaf-classification,Digit Recognizer
11484816,0.99346,2,10,/ethanhunt1080/cnn-first-try,Digit Recognizer
11504761,0.99071,0,0,/canozer/digit-recognizer-cnn,Digit Recognizer
14562509,0.898,0,0,/cuimdi/eff-b5-tta-3,Cassava Leaf Disease Classification
14110624,0.836,0,0,/louisheublein/cassava-abstract-notebook,Cassava Leaf Disease Classification
13755017,0.8490000000000001,0,0,/krashennikovalexandr/cassava-resnet50,Cassava Leaf Disease Classification
6202235,0.62668,3,9,/gbatchkala/urss-2019-project-review,Ultrasound Nerve Segmentation
3861973,0.4469,1,12,/micajoumathematics/my-first-semantic-segmentation-keras-u-net,Ultrasound Nerve Segmentation
11731010,2754.36505,0,2,/gllima/forecastze,Walmart Recruiting - Store Sales Forecasting
11488887,5103.70395,0,4,/otaviocv/walmart-recruiting-sales-forecasting-otaviocv,Walmart Recruiting - Store Sales Forecasting
8848729,3831.36934,0,1,/guidolino/envioz-teste,Walmart Recruiting - Store Sales Forecasting
8725242,2684.15209,30,75,/avelinocaio/walmart-store-sales-forecasting,Walmart Recruiting - Store Sales Forecasting
8322473,2835.6990100000007,0,1,/jagannathrk/walmart-recruiting,Walmart Recruiting - Store Sales Forecasting
6438529,3089.50209,1,6,/nitinx/storesales-randomforest-light-gbm-stacking,Walmart Recruiting - Store Sales Forecasting
13769659,6955.066999999999,0,6,/siddheshshelke/sigmoid-nn-optimized-model,Jane Street Market Prediction
13720229,6615.655,0,2,/oneday/mxnet-mlp,Jane Street Market Prediction
13702667,0.0,0,1,/talpinho/logistic-regression-jane-street-market,Jane Street Market Prediction
13674393,1894.424,4,19,/abiolatti/jane-street-market-baseline-logistic-regression,Jane Street Market Prediction
13666857,0.0,0,1,/kiritusan/randomnesssubmit,Jane Street Market Prediction
13633645,5451.516,1,10,/iamsds123/jane-street-ffill-xgboost,Jane Street Market Prediction
13578699,4739.553,2,10,/harshitt21/jane-street-basic-eda-xgb-baseline,Jane Street Market Prediction
13599300,2837.1240000000007,0,2,/tigersay/pzad-jane-1st-try,Jane Street Market Prediction
13367898,3299.0890000000004,0,6,/elvinagammed/feature-engineering-xgboost-with-gpu,Jane Street Market Prediction
13408374,4624.763,11,94,/marketneutral/target-engineering-cv-fast-ai-multi-target,Jane Street Market Prediction
13457406,7736.2469999999985,6,6,/binhlc/jane-street-tensorflow-dense,Jane Street Market Prediction
13477389,6962.585,0,30,/manavtrivedi/mlp-classifier-encoded,Jane Street Market Prediction
13392689,4253.201,0,1,/jsmithperera/cudf-xgb-tssplit,Jane Street Market Prediction
13458658,6876.781999999998,2,4,/ahmedewida/jane-street-nn-model,Jane Street Market Prediction
8344996,0.69982,0,0,/ryohanoi/fork-of-kernel-20200311,Home Credit Default Risk
7612873,0.7388,0,1,/soheilsoroush/home-credit-default-risk-eda-and-ml,Home Credit Default Risk
6839813,0.67877,0,0,/jesseo/level-3-home-credit-a-gentle-introduction,Home Credit Default Risk
6014073,0.7903,0,0,/pendras/simple-features,Home Credit Default Risk
5652967,0.75459,0,3,/sanholee/home-credit-kor-ver,Home Credit Default Risk
5715659,0.62539,1,3,/mehul8055/home-credit,Home Credit Default Risk
4461974,0.77204,0,2,/maria591/lightgbm,Home Credit Default Risk
4565567,0.7472300000000001,0,0,/maria591/log-reg-lda,Home Credit Default Risk
3927803,0.73375,0,0,/harishreddy18/home-credit-default-risk,Home Credit Default Risk
3612124,0.6844100000000001,0,0,/lihongze/home-credit-default-risk,Home Credit Default Risk
3609609,0.5123,0,1,/akumaldo/simple-eda-and-lgb-model,Home Credit Default Risk
3087840,0.71309,1,7,/currypurin/simple-lightgbm,Home Credit Default Risk
37517,0.67305,0,0,/fsharifi/testtest1,Prudential Life Insurance Assessment
36610,0.60233,0,0,/bidhya/keras-nn,Prudential Life Insurance Assessment
35779,0.67305,0,1,/srikiyer/xgb-prud1,Prudential Life Insurance Assessment
31170,0.54881,0,1,/gustavodemari/prudential-classification-mlp-with-keras,Prudential Life Insurance Assessment
30079,0.52683,0,0,/totalrecall/getting-started-0-1,Prudential Life Insurance Assessment
335935,0.42803,2,10,/priyanka13/nyc-using-xgboost-0-41,New York City Taxi Trip Duration
232409,0.6462600000000001,0,0,/hartleyg/notebook702d3a8ffa,Two Sigma Connect: Rental Listing Inquiries
229470,0.7458199999999999,0,0,/budhiraja/testing-for-different-classifiers,Two Sigma Connect: Rental Listing Inquiries
4003682,0.605,0,1,/calemolech/mid-checkpoint-fastai-resnet50,iMet Collection 2019 - FGVC6
3994821,0.626,1,5,/dilapsky/easy-71st-0-626-7fold-fastai-incepresv2-resnet50,iMet Collection 2019 - FGVC6
3964665,0.305,0,1,/tony92151/pytorch-densenet161-resnet18,iMet Collection 2019 - FGVC6
3953046,0.519,0,2,/gskdhiman/public-version-pytorch-resnet50-starter-v2,iMet Collection 2019 - FGVC6
3871791,0.3829999999999999,3,4,/autuanliuyc/imet-densenet-se-resnext101-32x4d,iMet Collection 2019 - FGVC6
3718622,0.515,0,0,/lorenzomnto/imet-separate-culture-tag-models,iMet Collection 2019 - FGVC6
3548660,0.537,2,20,/ttahara/imet2019-chainer-starter-seresnet152-focalloss,iMet Collection 2019 - FGVC6
3474574,0.56,9,34,/ratthachat/imet-resnet50-keras-starter,iMet Collection 2019 - FGVC6
3467362,0.569,0,12,/axel81/imet-fastai-starter-resnet152-focal-loss,iMet Collection 2019 - FGVC6
3426571,0.474,12,46,/hidehisaarai1213/imet-pytorch-starter,iMet Collection 2019 - FGVC6
3422255,0.18366,0,7,/takamichitoda/fine-tuning-vgg16,iMet Collection 2019 - FGVC6
3412219,0.00053,1,11,/bejeweled/fast-and-simple-eda-sample-sub-0-00053,iMet Collection 2019 - FGVC6
3616339,0.491,0,0,/narendrashu/imet-fastai,iMet Collection 2019 - FGVC6
2032247,0.2488,0,0,/nickel/simple-nn,Avito Demand Prediction Challenge
1212814,0.2204,2,3,/leeasy/liyz-itmba7-avito,Avito Demand Prediction Challenge
1098458,0.2255,0,4,/krithi07/fork-of-baseline-model-with-new-features-i,Avito Demand Prediction Challenge
1108814,0.2288,0,0,/bofeee/catboostattempt,Avito Demand Prediction Challenge
1122457,0.2299,0,0,/ashukr/simple-exploration-baseline-notebook-avito-1,Avito Demand Prediction Challenge
1089566,0.2406,0,9,/ashishpatel26/avito-demand-challange-exploration-analysis,Avito Demand Prediction Challenge
1068292,0.2269,3,22,/krithi07/baseline-model-with-new-features,Avito Demand Prediction Challenge
1047870,0.2343,0,0,/mehdib/lightgbm-with-nlp-pred,Avito Demand Prediction Challenge
1042091,0.2324,14,47,/samratp/wordbatch-ridge-fm-frtl-target-encoding-lgbm,Avito Demand Prediction Challenge
1022366,0.2246,17,83,/shanth84/rnn-detailed-explanation-0-2246,Avito Demand Prediction Challenge
1025095,0.2255,0,1,/sukhyun9673/fork-of-lgb-only-crucial-factors,Avito Demand Prediction Challenge
987728,0.2276,12,27,/shanth84/fast-text-rnn-keras-0-2276,Avito Demand Prediction Challenge
978883,0.2231,32,182,/bminixhofer/aggregated-features-lightgbm,Avito Demand Prediction Challenge
982073,0.2295,0,21,/johnfarrell/adp-mixed-nn-rush,Avito Demand Prediction Challenge
969670,0.3019,2,5,/kuniyoshit/simple-lightgbm-vgg16-image-tfidf-text,Avito Demand Prediction Challenge
12037275,0.0,0,1,/tbui001/lung-opacity-classification-densenet,RSNA Pneumonia Detection Challenge
11100518,0.11904,0,5,/sovitrath/rsna-pytorch-hackathon-fasterrcnn-resnet-test,RSNA Pneumonia Detection Challenge
6319247,0.01406,0,1,/adairclo/region-proposal-network,RSNA Pneumonia Detection Challenge
4022440,0.11458,0,0,/mrxuehb/rsna-maskrcnn,RSNA Pneumonia Detection Challenge
13885246,0.85431,0,2,/kittiyaneerungon/loan-default-prediction,Loan Default Prediction - Imperial College London
3337689,0.92,75,268,/jesucristo/1-smart-robots-most-complete-notebook,CareerCon 2019 - Help Navigate Robots
3342350,0.71,15,28,/whoiskk/validation-strategy-randomforest-0-71,CareerCon 2019 - Help Navigate Robots
3342084,0.23,0,5,/donkeys/distribution-hack,CareerCon 2019 - Help Navigate Robots
3331877,0.68,0,3,/chanuran/tsfresh-feature,CareerCon 2019 - Help Navigate Robots
3325904,0.64,1,3,/myoung859/3809-samples-vs-one-inexperienced-boi,CareerCon 2019 - Help Navigate Robots
3296401,0.55,3,12,/nikitpatel/lstm-nn-nn-gru-deep-learning,CareerCon 2019 - Help Navigate Robots
3311492,0.63,2,6,/jackg0/multi-input-deep-learning-model-baseline,CareerCon 2019 - Help Navigate Robots
3284934,0.7219,35,71,/gpreda/robots-need-help,CareerCon 2019 - Help Navigate Robots
3305761,0.66,0,2,/rstogi896/deep-learning-randomforest-lightgbm-k-fold,CareerCon 2019 - Help Navigate Robots
3256284,0.49,0,0,/hermesdt/pytorch-cnns,CareerCon 2019 - Help Navigate Robots
3258277,0.16,1,0,/mehranrafiee/robots-of-future-career,CareerCon 2019 - Help Navigate Robots
3265281,0.7,42,93,/prashantkikani/help-humanity-by-helping-robots,CareerCon 2019 - Help Navigate Robots
3270541,0.7,8,29,/pluceroo/new-features-lgbm-and-simple-rf,CareerCon 2019 - Help Navigate Robots
3268829,0.58,0,8,/dimitreoliveira/deep-learning-helping-navigate-robots,CareerCon 2019 - Help Navigate Robots
3259430,0.57,0,3,/hsinwenchang/more-bidirectional-cudnnlstm-layer,CareerCon 2019 - Help Navigate Robots
3252195,0.6,0,11,/palend/basemodel-rnn,CareerCon 2019 - Help Navigate Robots
3675413,0.6439,0,0,/cyones77/cnn-with-residual-connections-25th-private,CareerCon 2019 - Help Navigate Robots
8643200,0.7703300000000001,0,1,/shubh7/random-forest-minimal,Titanic - Machine Learning from Disaster
13078191,0.7799,0,0,/mistyd/cs-100-data-science,Titanic - Machine Learning from Disaster
12597715,0.7703300000000001,1,4,/fernandoitallo/titanic-itallo,Titanic - Machine Learning from Disaster
13641398,0.7751100000000001,0,0,/sstroa/getting-started-with-titanic,Titanic - Machine Learning from Disaster
13539621,0.7751100000000001,0,6,/gianlucabison/titanic,Titanic - Machine Learning from Disaster
13135141,0.85406,0,0,/piyushsharma003/titanic-piyush,Titanic - Machine Learning from Disaster
13619774,0.7703300000000001,0,1,/japandata509/titanic-sgdclassifier,Titanic - Machine Learning from Disaster
13600712,0.7440100000000001,0,0,/yassinehane/titanic-prediction-challenge-2,Titanic - Machine Learning from Disaster
13310876,0.78947,0,3,/desparzaalba/desparza-titanic,Titanic - Machine Learning from Disaster
6603549,0.8114899999999999,0,1,/ma7555/inceptionresnetv2-83,Dog Breed Identification
4848859,12.22393,0,1,/shubhendumishra/dog-breed-classification-using-vgg16-on-pytorch,Dog Breed Identification
3481806,4.78659,0,0,/mehulgupta2016154/dog-breed-cnn,Dog Breed Identification
3035751,0.91015,0,0,/dynamicarpita/not-just-ml-who-let-the-dogs-out,Dog Breed Identification
3026997,0.4821399999999999,0,1,/liuyd2018/120-dogbreeds-fast-ai-v1-0-x,Dog Breed Identification
13426792,0.77751,0,0,/suryaprakashpathak/getting-started-with-titanic,Titanic - Machine Learning from Disaster
13301273,0.7751100000000001,0,0,/dxkariya/titanic03,Titanic - Machine Learning from Disaster
13422882,0.76315,0,6,/rmznurk/komutanlogar-titanik-eda,Titanic - Machine Learning from Disaster
13285656,0.7751100000000001,2,1,/amareshmaduraiveeran/titanic-ml-disaster-survival-prediction-using-xgb,Titanic - Machine Learning from Disaster
13372512,0.78947,1,4,/roocey/randomforestclassifier-test,Titanic - Machine Learning from Disaster
10549352,0.7751100000000001,0,0,/jtaylor886/getting-started-with-titanic,Titanic - Machine Learning from Disaster
13278672,0.79425,0,1,/oliverlocke/titanic,Titanic - Machine Learning from Disaster
12980839,0.7822899999999999,2,13,/morihosseini/comprehensive-exploratory-data-analysis-of-titanic,Titanic - Machine Learning from Disaster
12490647,0.7799,1,5,/jishapjoseph/getting-started-titanic-machine-learning-models,Titanic - Machine Learning from Disaster
13270938,0.7703300000000001,9,9,/feyzazkefe/titanic-welcome-on-board,Titanic - Machine Learning from Disaster
13189930,0.78708,0,0,/merakit/omer-titanic-2,Titanic - Machine Learning from Disaster
13217204,0.79904,33,103,/hasanburakavci/titanic-eda-and-classification-top-5,Titanic - Machine Learning from Disaster
13233557,0.77272,0,5,/jeongwonkim10516/titanic-machine-learning-from-disaster,Titanic - Machine Learning from Disaster
12857431,0.0192099999999999,0,0,/scleeza/cv6-tfa-labeling,Mechanisms of Action (MoA) Prediction
11782970,0.01956,0,0,/smits31/moa-prediction,Mechanisms of Action (MoA) Prediction
14378587,0.01845,0,0,/lililycai/1dcnn-pytorch-moa,Mechanisms of Action (MoA) Prediction
14295292,0.01906,0,1,/yxohrxn/moa-baseline,Mechanisms of Action (MoA) Prediction
13206099,0.01919,0,0,/franksheng/notebookbyfranksheng2,Mechanisms of Action (MoA) Prediction
14038393,0.04289,0,1,/tokakhaled/linearregression,Mechanisms of Action (MoA) Prediction
11961799,0.0188,0,0,/soerendip/multi-label-fastai-with-pytorch-model,Mechanisms of Action (MoA) Prediction
12755352,0.01841,0,1,/amacpherson88/moa-tabnet-mlp-ensemble-private-lb-0-01628,Mechanisms of Action (MoA) Prediction
13508659,0.01829,0,10,/ttahara/stacking-gcn-drugcv,Mechanisms of Action (MoA) Prediction
13512231,0.01829,0,7,/ttahara/stacking-mlp-drugcv,Mechanisms of Action (MoA) Prediction
13544818,0.03047,0,0,/shreeya3k/ml-project,Mechanisms of Action (MoA) Prediction
13487392,0.69314,0,0,/srigutta/codefile,Mechanisms of Action (MoA) Prediction
13441751,0.01915,0,2,/peterse583/moa-late,Mechanisms of Action (MoA) Prediction
13008673,0.01832,0,0,/nakagawahironori/moa-tabnet-ver7-pca-6,Mechanisms of Action (MoA) Prediction
13316384,0.01808,0,10,/markpeng/final-best-lb-cleaned,Mechanisms of Action (MoA) Prediction
13159562,0.03092,0,0,/masterr/thelayer,Mechanisms of Action (MoA) Prediction
13191403,0.0182,0,2,/kazumitsusakurai/submission-for-moa,Mechanisms of Action (MoA) Prediction
12199392,0.01815,47,148,/kokitanisaka/moa-ensemble,Mechanisms of Action (MoA) Prediction
13143226,0.01821,11,44,/kushal1506/moa-prediction-complete-walkthrough-eda-ensemble,Mechanisms of Action (MoA) Prediction
13025111,0.01803,5,32,/gogo827jz/multi-label-pbestpre-inference-grownet-pl,Mechanisms of Action (MoA) Prediction
13152847,0.0183,1,10,/intwzt/inference-tabnet-1830,Mechanisms of Action (MoA) Prediction
13199645,0.01811,0,2,/eemonn/4th-place-solution-with-pp,Mechanisms of Action (MoA) Prediction
2488067,0.91034,3,11,/kesarianubhav/dog-breed-identification-by-anubhav-kesari,Dog Breed Identification
2293406,0.39931,0,1,/nikhilpandey360/inceptionresnet50-trasnfer-learning,Dog Breed Identification
2253924,4.28437,0,1,/nikhilpandey360/custom-cnn-small,Dog Breed Identification
2226884,5.1594,0,0,/batsy16/assignments-rishi,Dog Breed Identification
2261883,2.70371,0,0,/nikhilpandey360/prediction-using-xception,Dog Breed Identification
1967903,0.33,1,6,/slambert1/fastai-dog-breeds,Dog Breed Identification
13240928,0.90444,1,3,/husthb/severstal-hrnet-1,Severstal: Steel Defect Detection
13241975,0.8523299999999999,0,1,/attischen/notebook20ab0a00c0,Severstal: Steel Defect Detection
11351076,0.91467,1,2,/thevenkat/u-net-model-3,Severstal: Steel Defect Detection
11079128,0.90426,0,6,/uclh09/severstall-competition-single-model-with-2-heads,Severstal: Steel Defect Detection
11002488,0.0,0,0,/mamuadam/kernel271114f32d,Severstal: Steel Defect Detection
5925156,0.8930600000000001,0,0,/genvsdis/heng-s-model,Severstal: Steel Defect Detection
6185878,0.91201,0,0,/genvsdis/kernel5db1659c50,Severstal: Steel Defect Detection
10341698,0.90487,2,9,/uclh09/severstal-mlcomp-catalyst-infer-0-90672,Severstal: Steel Defect Detection
6317285,0.91567,0,5,/carnav0400/individual-models-normal-pytorch-main-600-6b2ed3,Severstal: Steel Defect Detection
7563551,0.88331,6,1,/knightwisdom/fork-of-13012020-sever-submission,Severstal: Steel Defect Detection
6298007,0.90104,0,0,/negi009/fork-of-test-one-image-90-4,Severstal: Steel Defect Detection
6636340,0.26894,0,0,/rsutaria/vgg-unet,Severstal: Steel Defect Detection
13235022,0.76555,3,4,/adityamishra19/titanic-story-by-machine,Titanic - Machine Learning from Disaster
13210587,0.7488,3,11,/angieangie/titanic-coding-project-angie,Titanic - Machine Learning from Disaster
13139682,0.76315,2,2,/orhanari/titanic-v1-2-1,Titanic - Machine Learning from Disaster
13209215,0.77751,0,2,/jonatanrestrepo/titanic-predictions,Titanic - Machine Learning from Disaster
13209006,0.78468,0,1,/stpeteishii/titanic-shufflesplit-xgboost,Titanic - Machine Learning from Disaster
13005751,0.75598,0,0,/nissria/first-project-titanic-prediction,Titanic - Machine Learning from Disaster
12734089,0.7799,2,3,/gabrielhenrique123/titanic-os-abelhudos,Titanic - Machine Learning from Disaster
13116165,0.77272,4,11,/yasnbalcilar/titanic-eda-feature-enginnering-5-clf-model,Titanic - Machine Learning from Disaster
13101856,0.85406,14,38,/blackhurt/my-approach-to-be-in-top-2,Titanic - Machine Learning from Disaster
13113788,0.80861,2,3,/laurent93/titanic-competition,Titanic - Machine Learning from Disaster
13095258,0.74162,0,0,/caseyidzikowski/titanic-1,Titanic - Machine Learning from Disaster
12976590,0.78468,0,0,/endidcs/titanic-pt-br,Titanic - Machine Learning from Disaster
13084367,0.80861,0,1,/ikanurlaf/titanic-classification,Titanic - Machine Learning from Disaster
13127762,0.79186,0,0,/robertperic/notebook8afa9cb736,Titanic - Machine Learning from Disaster
9954828,0.79904,0,0,/yusukearai/rev-titanic-score-80-over-ensemble,Titanic - Machine Learning from Disaster
13514343,0.8157800000000001,17,22,/alexanderossipov/titanic-simple-ensemble-voting-top-3,Titanic - Machine Learning from Disaster
11955348,0.67464,0,0,/nobuakiotani/kaggle-tutorial-02-overview,Titanic - Machine Learning from Disaster
13529399,0.76555,1,3,/babinghosh2020/titanic-eda,Titanic - Machine Learning from Disaster
13484368,0.7822899999999999,18,26,/mdhamani/titanic-getting-better-eda-top-14,Titanic - Machine Learning from Disaster
13493956,0.78468,0,8,/nislam4/titanic-survivor-prediction,Titanic - Machine Learning from Disaster
13509818,0.76076,0,1,/gautamomjain/titanic-survival-prediction-top-33,Titanic - Machine Learning from Disaster
12860499,0.0,1,9,/dominicnyambane/deep-dive-into-titanic,Titanic - Machine Learning from Disaster
13407215,0.79186,1,4,/liyilang/getting-started-with-titanic,Titanic - Machine Learning from Disaster
13473984,0.7751100000000001,0,0,/rodrigomedina/titanic-1-0,Titanic - Machine Learning from Disaster
13460233,0.7751100000000001,0,1,/vishnuramachandran/getting-started-with-titanic,Titanic - Machine Learning from Disaster
13430729,0.78708,2,12,/alexryzhkov/lightautoml-titanic-love,Titanic - Machine Learning from Disaster
8473903,41.84038,0,2,/simonstochholm/web-traffic-forecasting,Web Traffic Time Series Forecasting
8238106,23.70297,0,1,/steveazzolin/masked-lstm-to-predict-rainfall,How Much Did It Rain? II
3704197,23.90269,0,0,/alikhil/howmuchdiditrain-hw,How Much Did It Rain? II
2879773,23.70898,1,18,/ilya16/lstm-models,How Much Did It Rain? II
2897580,23.98215,0,2,/zytfoo/how-much-did-it-rain-kernel,How Much Did It Rain? II
14301014,0.8501200000000001,0,0,/thillainathansarubi/plant-seeds-classifier-by-group-ars,Plant Seedlings Classification
13733693,0.0,6,5,/princejaiswal03/plant-seedling-classification,Plant Seedlings Classification
12273947,0.20843,0,0,/jitendramalik/working-cnn,Plant Seedlings Classification
9841147,0.96599,0,1,/engmahmoudkhaled/plant-seedlings,Plant Seedlings Classification
7876655,0.68765,3,13,/praanj/transfer-learning-vgg-19-resnet-50-with-kfold,Plant Seedlings Classification
6377036,0.96221,0,4,/niteshksingh/plant-seedling-cnn-acc-0-96,Plant Seedlings Classification
6251605,0.95843,0,0,/maxboels/plant-seedling-image-processing-and-classification,Plant Seedlings Classification
4766313,0.93954,0,0,/lemondante/seedling-classification-using-cnn-2,Plant Seedlings Classification
4142271,0.1146,0,0,/arjunrao2000/seedling-classification-cnn-data-augmentation,Plant Seedlings Classification
11813192,0.616,0,29,/taggatle/cornell-birdcall-identification-1st-place-solution,Cornell Birdcall Identification
11752420,0.625,0,1,/yaroshevskiy/inference-birdsong-refactoring-gpu-blend5-1,Cornell Birdcall Identification
11065147,0.625,7,26,/theoviel/inference-theo,Cornell Birdcall Identification
11756223,0.6,1,11,/cpmpml/infer-model-210,Cornell Birdcall Identification
11265565,0.5870000000000001,0,3,/rsinda/inference-sound-event-detection,Cornell Birdcall Identification
11713855,0.595,0,3,/gaborfodor/blend,Cornell Birdcall Identification
10725659,0.589,0,1,/yuvaramsingh/bird-inference-v1,Cornell Birdcall Identification
11726541,0.547,0,0,/blaxkdolphin/birdcall-identification-talnet-predicting,Cornell Birdcall Identification
10896516,0.5479999999999999,2,3,/shikha130vv/multichannel-spectrogram,Cornell Birdcall Identification
11500463,0.545,0,2,/prashantmudgal/basic-resnet50,Cornell Birdcall Identification
11406965,0.544,12,16,/itsuki9180/birdcall-using-tpu-inference,Cornell Birdcall Identification
2820525,0.44236,0,1,/boristchague/nycttc-boris-tchague,New York City Taxi Trip Duration
2835472,0.44165,0,3,/mariaman95/mariama-ndiaye-nytaxi-data-science,New York City Taxi Trip Duration
2828374,0.49172,0,0,/lilmane/ny-taxi-prediction-lounis,New York City Taxi Trip Duration
2814812,0.40302,2,7,/emilaye/predictions-of-nyc-taxi-trip-duration-edaongam,New York City Taxi Trip Duration
2800699,0.39939,0,5,/comems/nytaxis-comems-machine-learning,New York City Taxi Trip Duration
2818880,0.4516,0,0,/bybell/nyc-taxi-trip-duration,New York City Taxi Trip Duration
2818059,0.72343,0,0,/freel21/moreno-antonin-first-attempt,New York City Taxi Trip Duration
2814139,0.48113,0,0,/dilva25/new-york-taxi-trip-duration,New York City Taxi Trip Duration
2785523,0.4178,0,0,/xaviertrs/nyc-taxi-trip-duration-by-xavier-terrasson,New York City Taxi Trip Duration
2802992,0.41684,2,6,/antoinecabon/antoine-cabon-ny-taxi-duration,New York City Taxi Trip Duration
2798237,0.40431,4,4,/david75116/david-leroy-nyc-taxi-prediction,New York City Taxi Trip Duration
2820748,0.47093,0,0,/mmyriam16/fork-of-nyc-taxi-trip-m-myriam,New York City Taxi Trip Duration
1425760,0.40975,3,6,/aiswaryaramachandran/eda-baseline-model-0-40-rmse,New York City Taxi Trip Duration
1027918,0.53664,0,1,/rdcmdev/2016-nyc-taxi-trip-neural-network,New York City Taxi Trip Duration
1031676,0.69071,0,0,/rdcmdev/2016-nyc-taxi-trip-linear-regression,New York City Taxi Trip Duration
1011041,1.12805,0,2,/rishabhgarg1023/trip-time,New York City Taxi Trip Duration
402051,0.8607600000000001,0,1,/theishank/nyc-trip-analysis-version-1-0,New York City Taxi Trip Duration
7662632,0.71,1,3,/prokaj/fork-of-baseline-html-tokens-v5,TensorFlow 2.0 Question Answering
7643681,0.65,5,24,/kashnitsky/solid-bert-joint-baseline-0-65-0-66,TensorFlow 2.0 Question Answering
7168301,0.61,3,7,/vanle73/rank68-the-simplest-idea-to-get-medal,TensorFlow 2.0 Question Answering
7407952,0.61,3,8,/jiweiliu/jb-tf-2-0,TensorFlow 2.0 Question Answering
7637880,0.52,0,0,/ruhong/tensorflow2-question-answering,TensorFlow 2.0 Question Answering
7571842,0.09,0,2,/vaibhavsxn/tf2-0,TensorFlow 2.0 Question Answering
7109231,0.48,0,2,/vaibhavsxn/bert-baseline,TensorFlow 2.0 Question Answering
7200602,0.19,0,3,/aaroha33/tensorflow-question-answering,TensorFlow 2.0 Question Answering
6905672,0.0,1,1,/higepon/how-to-get-public-score-quickly,TensorFlow 2.0 Question Answering
6963276,0.19,2,0,/isikkuntay/bert-and-bidaf,TensorFlow 2.0 Question Answering
6814057,0.15,76,721,/abhinand05/bert-for-humans-tutorial-baseline,TensorFlow 2.0 Question Answering
6472511,0.23,4,14,/rooshroosh/tf2-0-qa-binary-classification-baseline,TensorFlow 2.0 Question Answering
1516372,0.775,0,0,/rvinamra/homecredit-automated-hyperparameter-tuning,Home Credit Default Risk
2189129,0.6704100000000001,0,0,/sonujha090/homecredit,Home Credit Default Risk
2239779,0.76252,0,0,/achyutb6/kerneldd93cdce5a,Home Credit Default Risk
2023615,0.77789,0,2,/nisargpatel/lightgbm-by-bayesian-optimization,Home Credit Default Risk
1941324,0.74532,0,3,/robertknight/home-credit-default-risk-modelling,Home Credit Default Risk
1926174,0.76684,0,0,/boomberung/eda-new-feauters-lightgbm,Home Credit Default Risk
1516673,0.715,0,0,/nocturnaltribe/home-credit-risk,Home Credit Default Risk
1875417,0.73998,1,39,/zikazika/lightgbm-automated-feature-engineering-easy,Home Credit Default Risk
1580153,0.7884399999999999,0,1,/vjgupta/light-gbm-easy-peasy,Home Credit Default Risk
1543852,0.79461,0,7,/rahullalu/hcdr-single-model-private-score-0-79167-catboost,Home Credit Default Risk
1538177,0.68682,0,6,/boehmrya/preprocessing-feature-engineering-models,Home Credit Default Risk
1476446,0.789,0,4,/buntyshah/simple-home-default-credit-lb-score-0-789,Home Credit Default Risk
1531344,0.753,0,3,/shikha130vv/first-kernel,Home Credit Default Risk
113503,0.7258899999999999,4,6,/quadmx08/monsters-first-submission,"Ghouls, Goblins, and Ghosts... Boo!"
112141,0.49149,0,3,/geekfox/chasing-ghosts-with-keras,"Ghouls, Goblins, and Ghosts... Boo!"
1057511,0.0499,0,0,/asatoonishi/using-sine-matrix,Nomad2018 Predicting Transparent Conductors
627049,0.0742,0,0,/mightmay/nomad,Nomad2018 Predicting Transparent Conductors
504973,0.0565,5,5,/scirpus/bigger-gp,Nomad2018 Predicting Transparent Conductors
501043,0.0685,2,5,/scirpus/teeny-gp,Nomad2018 Predicting Transparent Conductors
13421579,2677.153,2,23,/louise2001/imputing-missing-values,Jane Street Market Prediction
13452204,4347.573,0,1,/mouafekmk/xgboost-risk-based-weighted-predictions-test-v0,Jane Street Market Prediction
13436557,3308.824,0,1,/satorushibata/market-prediction-xgboost-with-gpu-modified,Jane Street Market Prediction
13404830,5314.799,0,5,/manavtrivedi/xgbtfirsttrial,Jane Street Market Prediction
13340502,4758.727,9,103,/gogo827jz/jane-street-super-fast-utility-score-function,Jane Street Market Prediction
13378948,663.944,0,2,/kengjianli/linear-model,Jane Street Market Prediction
13283464,351.624,1,5,/fbykov/rule-3443,Jane Street Market Prediction
13246776,5453.373000000001,44,137,/gogo827jz/jane-street-ffill-xgboost-purgedtimeseriescv,Jane Street Market Prediction
13262745,4725.1,2,10,/code1110/janestreet-lgb-with-optuna-and-treelite,Jane Street Market Prediction
13279132,505.432,0,2,/tchaye59/jmarket-rnn-with-keras-submit,Jane Street Market Prediction
13119588,3505.562,10,21,/yushg123/a-walk-down-jane-street-eda-baseline,Jane Street Market Prediction
13081048,100.226,0,1,/tkmachlearn/modelling-using-xgboost-classifier-with-gpu,Jane Street Market Prediction
2816697,1.609,0,5,/pankajb64/shake-it-up,LANL Earthquake Prediction
2719237,1.51,1,14,/byfone/neat-baseline-code-for-earthquake-prediction,LANL Earthquake Prediction
2727089,1.4480000000000002,34,205,/scirpus/andrews-script-plus-a-genetic-program-model,LANL Earthquake Prediction
2675973,1.581,6,19,/karanjakhar/neural-network-earthquake-time-to-failure,LANL Earthquake Prediction
2638614,1.516,11,83,/jsaguiar/baseline-with-multiple-models,LANL Earthquake Prediction
2621088,1.489,141,656,/artgor/earthquakes-fe-more-features-and-samples,LANL Earthquake Prediction
2615048,1.526,6,33,/andrekos/basic-feature-benchmark-with-quantiles,LANL Earthquake Prediction
2621086,1.528,3,32,/ashishpatel26/updated-mix-model-with-mxtend-gp,LANL Earthquake Prediction
2624886,1.835,2,7,/srsteinkamp/quick-look-at-data-and-just-for-fun-analysis,LANL Earthquake Prediction
4112398,1.582,0,0,/pnussbaum/dwt-earthquake-w-lto-v0301,LANL Earthquake Prediction
3946533,1.658,0,0,/kessido/fork-of-fork-of-lanl-earthquake-prediction-in-dl,LANL Earthquake Prediction
3750179,1.54,0,0,/yjc381818896/fork-of-earthquake-1-54,LANL Earthquake Prediction
13233465,0.95916,0,0,/sarathydw/parthapath,Plant Pathology 2020 - FGVC7
13253304,0.5202100000000001,0,0,/sudharsanvijayraghav/plant,Plant Pathology 2020 - FGVC7
13100231,0.96733,0,0,/anku5hk/tpu-plant-pathology-advance,Plant Pathology 2020 - FGVC7
12703918,0.89413,0,0,/zeeshan99/plant-pathology-2020-submission-file,Plant Pathology 2020 - FGVC7
12988238,0.94858,0,4,/hamonk/plant-pathology-fastai,Plant Pathology 2020 - FGVC7
9669854,0.982,0,1,/redwankarimsony/the-great-ensembler-0-982,Plant Pathology 2020 - FGVC7
11940298,0.97716,0,0,/stardust87/plant-pathology-2020-ensemble,Plant Pathology 2020 - FGVC7
12055543,0.91997,0,7,/harshitlakhani/plant-pathology-pytorch-resnet50-93-15,Plant Pathology 2020 - FGVC7
11848493,0.96014,0,7,/sohelranaccselab/plant-pathology-2020-using-efficient-net-b7,Plant Pathology 2020 - FGVC7
11236288,0.93188,1,1,/nagsdata/plant-pathology-simple-cnn,Plant Pathology 2020 - FGVC7
9096214,0.972,0,3,/salazarslytherin/plant2020,Plant Pathology 2020 - FGVC7
13059579,0.401,0,0,/pksx01/cassava-leaf-disease-classification-fastai,Cassava Leaf Disease Classification
13061614,0.8759999999999999,0,2,/lukachkhetiani/se-resnext-101-efficientnet-b5-ensemble,Cassava Leaf Disease Classification
13057847,0.612,0,3,/ashwinrachha1/efficientnetb4-with-keras,Cassava Leaf Disease Classification
13014695,0.879,1,11,/slm37102/cassava-leaf-disease-classification-fastai,Cassava Leaf Disease Classification
13025119,0.8959999999999999,0,13,/ludovick/baseline-tpu-tf-efficientnet-kfold-gpu-inference,Cassava Leaf Disease Classification
12996419,0.89,5,103,/yasufuminakama/cassava-resnext50-32x4d-starter-inference,Cassava Leaf Disease Classification
13019143,0.893,2,5,/vineeth1999/pytorch-efficientnet-baseline-inference-tta,Cassava Leaf Disease Classification
12994238,0.895,13,37,/reighns/pytorch-ensemble,Cassava Leaf Disease Classification
13012425,0.7040000000000001,2,3,/gokulnath31/cassavaleafdiseaseclassificationmobilenetinference,Cassava Leaf Disease Classification
12985644,0.825,3,5,/lwendo/very-simple-notebook-keras-densenet-gpu,Cassava Leaf Disease Classification
12988162,0.83,0,3,/thedrcat/cassava-fastai-starter,Cassava Leaf Disease Classification
12989171,0.12,0,1,/anubhav1302/plantdisease-efn,Cassava Leaf Disease Classification
13007949,0.614,0,0,/awsaf49/cassava-starter-keras-gpu-efficientnetb0,Cassava Leaf Disease Classification
1362132,0.395,0,1,/aristeia/xgb-and-lgb-simple-analysis,Costa Rican Household Poverty Level Prediction
1358089,0.363,0,1,/wangyije/baseline,Costa Rican Household Poverty Level Prediction
1342144,0.426,0,1,/skooch/feature-engineering-lighgbm-with-f1-macro,Costa Rican Household Poverty Level Prediction
1329456,0.428,7,11,/kuriyaman1002/reduce-features-140-85-keeping-f1-score,Costa Rican Household Poverty Level Prediction
1337847,0.1939999999999999,0,1,/sai3247/simple-program,Costa Rican Household Poverty Level Prediction
1335435,0.346,0,1,/gobert/how-to-reproduce-macro-f1-score-locally,Costa Rican Household Poverty Level Prediction
1315583,0.1939999999999999,0,1,/christinampoid/short-analysis-and-first-attempt-of-modelling,Costa Rican Household Poverty Level Prediction
1327213,0.357,0,1,/hulkbulk/noobskenerl,Costa Rican Household Poverty Level Prediction
1317462,0.336,0,0,/xmj9999/lightgbm-baseline,Costa Rican Household Poverty Level Prediction
1319083,0.424,5,12,/mlisovyi/lighgbm-hyperoptimisation-with-f1-macro,Costa Rican Household Poverty Level Prediction
1319259,0.384,0,3,/rsilveira79/baseline-randomforest-balanced,Costa Rican Household Poverty Level Prediction
1318441,0.381,0,1,/acamara/cleaning-random-forest,Costa Rican Household Poverty Level Prediction
1315369,0.389,0,4,/splgeo/costa-rican-household-poverty-using-keras,Costa Rican Household Poverty Level Prediction
4667488,0.37211,0,0,/castlebin12/catboost-better,Costa Rican Household Poverty Level Prediction
11831084,0.0,0,5,/sohelranaccselab/gendered-pronoun-resolution,Gendered Pronoun Resolution
3603363,0.0,1,4,/gdoteof/kernel55afed7533,Gendered Pronoun Resolution
3548243,0.27757,25,62,/kashnitsky/simple-logistic-regression-bert-0-27-lb,Gendered Pronoun Resolution
3412975,0.48953,3,22,/gdoteof/pytorch-bert-baseline-wd-epochs-cnn-lstm,Gendered Pronoun Resolution
3380326,0.47597,16,65,/chanhu/bert-score-layer-lb-0-475,Gendered Pronoun Resolution
3145960,0.93445,5,2,/isikkuntay/simple-nlp,Gendered Pronoun Resolution
3127294,1.09861,1,3,/abevieiramota/001-avm-exploratory-data-analysis,Gendered Pronoun Resolution
3032869,0.70138,0,35,/keyit92/end2end-coref-resolution-by-attention-rnn,Gendered Pronoun Resolution
3001116,1.0352,0,1,/isikkuntay/fast-n-e-z,Gendered Pronoun Resolution
2942474,0.8645,0,7,/eoveson/ml-model-with-spacy-coref-feature,Gendered Pronoun Resolution
2897818,0.92217,2,4,/yukinkgwa/lightgbm-cv-example-with-train-test,Gendered Pronoun Resolution
2894439,0.0,5,21,/jazivxt/ml-model-example-with-train-test,Gendered Pronoun Resolution
2866225,1.20186,7,10,/ryches/applying-spacy-coreference-but-nothing-goes-right,Gendered Pronoun Resolution
2874738,0.0,0,1,/cloverdharmendra/one-line-code-simple,Gendered Pronoun Resolution
11418486,1177.1932,0,2,/rahulpawade/allstate-claims-severity-xgboost-regression,Allstate Claims Severity
395670,1253.95585,0,2,/kmader/using-lstms-to-read-categories,Allstate Claims Severity
216881,1927.96145,0,1,/kmader/auto-ml-with-teapot,Allstate Claims Severity
11879973,0.18227,1,5,/ayoubsandali/house-prices-model,House Prices - Advanced Regression Techniques
11852784,0.14528,6,13,/vivekprajapati2048/a-complete-advanced-house-price-predictions,House Prices - Advanced Regression Techniques
8153413,0.12538,1,2,/spiiiii/house-pricing-lm,House Prices - Advanced Regression Techniques
11874261,11.33103,0,3,/emnikkhil/advancehouse-price-prediction,House Prices - Advanced Regression Techniques
11776433,0.16655,0,0,/topapa/cross-val,House Prices - Advanced Regression Techniques
11850412,0.2475,0,11,/j0ker00/advanced-regression-baseline,House Prices - Advanced Regression Techniques
11802666,0.11902,0,8,/d0nghe/house-price-6,House Prices - Advanced Regression Techniques
11733708,0.11309,28,54,/orhankaramancode/ensemble-stacked-regressors-top-3-92-acc,House Prices - Advanced Regression Techniques
11815695,0.12675,0,0,/yutohisamatsu/houseprice-ensemble,House Prices - Advanced Regression Techniques
11739451,0.14244,2,8,/rahulpawade/house-prices-advanced-regression-techniques,House Prices - Advanced Regression Techniques
2032541,0.6673399999999999,19,119,/jsaguiar/baseline-with-news,Two Sigma: Using News to Predict Stock Movements
2016744,0.65904,1,3,/vasumani/simple-xgboost-with-only-few-years-data,Two Sigma: Using News to Predict Stock Movements
1992828,0.66972,0,12,/codlife/with-news-10-31,Two Sigma: Using News to Predict Stock Movements
1968537,0.5534100000000001,5,7,/silvernine/lb-0-53-market-news-xgboost-for-beginners,Two Sigma: Using News to Predict Stock Movements
1988211,0.62238,8,32,/eeronan/catboost-vs-xgboost-5x-faster,Two Sigma: Using News to Predict Stock Movements
1986668,0.8816700000000001,33,50,/nareyko/prediction-based-on-test-data,Two Sigma: Using News to Predict Stock Movements
1983007,0.57307,0,1,/zhangyang/same-as-previous-10day-benchmark,Two Sigma: Using News to Predict Stock Movements
1982824,4.621659999999999,24,44,/pennacchio/env-var07,Two Sigma: Using News to Predict Stock Movements
1919721,0.5857,0,1,/s4sarath/1-baseline-tf-neural-network,Two Sigma: Using News to Predict Stock Movements
1890369,0.64935,39,205,/christofhenkel/market-data-nn-baseline,Two Sigma: Using News to Predict Stock Movements
14501571,1.08665,0,0,/kasevgen/tfidf-xgboost-predict-for-medicine-treatment,Personalized Medicine: Redefining Cancer Treatment
1042691,2.09293,0,0,/omkarsabnis/predicting-the-effects-of-genetic-variations-lgb,Personalized Medicine: Redefining Cancer Treatment
11345717,-7.1011,0,4,/gopidurgaprasad/osic-ngboost,OSIC Pulmonary Fibrosis Progression
11450143,-13.1045,0,2,/ajaykumar7778/3d-resnet-with-3d-augmentations,OSIC Pulmonary Fibrosis Progression
11421284,-6.8279,0,9,/jagadish13/multiple-quantile-regression-eda-better-params,OSIC Pulmonary Fibrosis Progression
11333667,-6.8252,0,1,/beamers/pulmonary-my-version,OSIC Pulmonary Fibrosis Progression
11404109,-8.4285,11,2,/nooblearning/submission-4,OSIC Pulmonary Fibrosis Progression
11343859,-6.9372,2,21,/gopidurgaprasad/osic-tabnet,OSIC Pulmonary Fibrosis Progression
11237323,-6.9135,1,19,/doctorkael/osic-incrementally-improved-models,OSIC Pulmonary Fibrosis Progression
11174092,-6.8245,0,7,/akashsuper2000/efficientnets-quantile-regression-inference,OSIC Pulmonary Fibrosis Progression
11285664,-6.9584,0,8,/konumaru/simple-baseline-of-lightgbm-reg,OSIC Pulmonary Fibrosis Progression
11278499,-9.1032,3,10,/srikanthpotukuchi/osic-random-forest-new-height-extracted,OSIC Pulmonary Fibrosis Progression
9408376,0.00635,0,0,/drainvers/ponpare-coupon-benchmark-cosine,Coupon Purchase Prediction
9438674,0.00636,0,0,/ouwyukha/cpp-turi-itemcontent,Coupon Purchase Prediction
12266446,11287542.0,8,12,/piantic/faster-simple-baseline-using-datatable,INGV - Volcanic Eruption Prediction
12247352,11374571.0,2,6,/doanquanvietnamca/lstm-feature-eda,INGV - Volcanic Eruption Prediction
12262217,11281065.0,0,3,/mahmoudvaziri/svm-regression-mean,INGV - Volcanic Eruption Prediction
12941907,5306000.0,0,0,/plasticbob/volcano-stft-data-optimisation,INGV - Volcanic Eruption Prediction
10233956,0.4318899999999999,0,21,/confirm/xfeat-cudf-lightgbm-catboost-wip,BNP Paribas Cardif Claims Management
9602463,0.47407,0,0,/yukikita/ramdomforest-labelencoding,BNP Paribas Cardif Claims Management
691304,0.4569399999999999,0,1,/ozgurb/paribas-v1,BNP Paribas Cardif Claims Management
46198,0.4535899999999999,1,3,/kishoreb4/extratrees,BNP Paribas Cardif Claims Management
45857,0.4535899999999999,0,0,/mujtabaasif/extratrees,BNP Paribas Cardif Claims Management
9339963,0.3064199999999999,0,0,/ouwyukha/imba-turicreate-itemsim-jaccard-pol,Instacart Market Basket Analysis
9339826,0.3064199999999999,0,0,/ouwyukha/imba-turicreate-fr-adagrad,Instacart Market Basket Analysis
9274635,0.3064199999999999,0,0,/ouwyukha/imba-turicreate-rfr-sgd,Instacart Market Basket Analysis
9301054,0.3064199999999999,0,0,/ouwyukha/imba-turicreate-rfr-adagrad,Instacart Market Basket Analysis
5764628,0.34518,0,3,/errolpereira/xgboost-with-minimal-feature-engineering,Instacart Market Basket Analysis
4117454,0.37146,0,2,/bad19008/instacart-ml-3-notebook-v2-dc163d,Instacart Market Basket Analysis
3992696,0.30955,0,0,/kotsina/instacart-ml-2-notebook-42d609,Instacart Market Basket Analysis
3951013,0.331,0,0,/malioglou/instacart-ml-2-notebook,Instacart Market Basket Analysis
3954804,0.37489,0,0,/dimosraptis/instacart-ml-2-notebook-xgboost,Instacart Market Basket Analysis
2390859,0.3725131,0,4,/jacksmengel/lgbm-instacart-using-cross-join,Instacart Market Basket Analysis
2104212,0.3455267,0,1,/rodrigoavalos/final-ia-an-lisis-de-la-cesta-de-mercado,Instacart Market Basket Analysis
379904,0.0644504,0,0,/sraone96/xgboost-lightgbm-nn-ols,Zillow Prize: Zillow’s Home Value Prediction (Zestimate)
374444,0.064895,0,0,/iamprakashom/beginner-xgboost,Zillow Prize: Zillow’s Home Value Prediction (Zestimate)
6598740,0.01408,7,39,/gertjac/regression-approach,NFL Big Data Bowl
6593158,0.01785,5,6,/s13658695/nfl-xgboost-simple,NFL Big Data Bowl
6570302,0.0125,0,13,/go5kuramubon/nfl-mixturedensitynetworks-output-gmm-param-nn,NFL Big Data Bowl
6513499,0.0147,1,6,/sanshengshi/lightgbm-clean-stadiumtype,NFL Big Data Bowl
6474851,0.0146,0,4,/aleksthegreat/tfp-gaussian-distribution-nn,NFL Big Data Bowl
6481933,0.01272,0,6,/antoniorivero/nfl-v2,NFL Big Data Bowl
6432822,0.01399,11,44,/kenmatsu4/nn-outputs-gaussian-distribution-directly,NFL Big Data Bowl
6470905,0.01374,2,1,/scirpus/fork-of-hybrid-gp-and-nn,NFL Big Data Bowl
6464807,0.01578,0,0,/funaki/fork-of-fork-of-fork-of-funaki-nfl-3,NFL Big Data Bowl
6450152,0.0,3,5,/gertjac/just-to-show-why-i-wonder-about-online-learning,NFL Big Data Bowl
6436880,0.01942,0,1,/funaki/fork-of-funaki-nfl-2,NFL Big Data Bowl
6409125,0.01383,3,18,/muhakabartay/update-on-nn-multiple-output-stadium-clean,NFL Big Data Bowl
6401233,0.01369,1,26,/muhakabartay/update-of-hybrid-gp-and-nn,NFL Big Data Bowl
6381023,0.01286,7,47,/ryches/cnn-on-the-playing-field,NFL Big Data Bowl
6388356,0.01384,0,4,/fallen123/nfl-big-data-bowl,NFL Big Data Bowl
6332915,0.0138599999999999,7,72,/mrkmakr/lgbm-multiple-classifier,NFL Big Data Bowl
6385732,0.01328,0,0,/nicodre/nfl-comp-v3,NFL Big Data Bowl
6265806,0.01394,18,157,/sryo188558/cox-proportional-hazard-model,NFL Big Data Bowl
1312489,0.69,17,47,/johnfarrell/breaking-lb-fresh-start-with-lag-selection,Santander Value Prediction Challenge
1296891,1.55,0,9,/ashishpatel26/sentander-value-challanges,Santander Value Prediction Challenge
1276318,0.66,23,120,/ogrellier/feature-scoring-vs-zeros,Santander Value Prediction Challenge
1282561,1.48,0,0,/tomigelo/benchmark-model-with-lgbm-1-48,Santander Value Prediction Challenge
1262279,1.44,1,47,/leighplt/simple-pytorch-with-kaggle-s-gpu,Santander Value Prediction Challenge
1248835,1.42,1,10,/hmendonca/testing-features-with-shapley-values,Santander Value Prediction Challenge
1232154,1.38,34,70,/scirpus/santander-gp-clustering-ii,Santander Value Prediction Challenge
1228517,1.44,12,9,/lightsalsa/ensemble-of-lgbm-and-xgb,Santander Value Prediction Challenge
1224986,1.49,0,2,/lightsalsa/lgbm1,Santander Value Prediction Challenge
1223958,1.44,0,9,/seiya1998/light-gbm-with-ridge-and-basic-aggregates,Santander Value Prediction Challenge
1213719,1.47,5,13,/haimfeld87/randomforest-with-50-features,Santander Value Prediction Challenge
1208894,1.73,0,3,/psbhat/simple-random-forest-kernel-for-a-beginner,Santander Value Prediction Challenge
1170976,1.47,0,1,/tkm2261/simple-lightgbm,Santander Value Prediction Challenge
2063132,0.6920000000000001,10,1,/mko000/help-me-improve,"Quick, Draw! Doodle Recognition Challenge"
2032226,0.8592,2,16,/guntherthepenguin/fastai-resnet18-color-coded-and-focalloss,"Quick, Draw! Doodle Recognition Challenge"
2143069,0.7340000000000001,10,3,/kotarojp/first-step-for-submission-keras-resnet50,"Quick, Draw! Doodle Recognition Challenge"
1998655,0.901,4,24,/jsylas/mobilenet-lb-0-900-forked-from-beluga,"Quick, Draw! Doodle Recognition Challenge"
1899614,0.892,91,299,/gaborfodor/greyscale-mobilenet-lb-0-892,"Quick, Draw! Doodle Recognition Challenge"
1877041,0.7709999999999999,16,45,/gaborfodor/black-white-cnn-lb-0-77,"Quick, Draw! Doodle Recognition Challenge"
1801791,0.449,2,12,/olgabelitskaya/quick-draw-doodle-recognition-2,"Quick, Draw! Doodle Recognition Challenge"
224295,0.48906,0,1,/darrellulm/quora-interactive-eda-test-run,Quora Question Pairs
221491,0.4161899999999999,0,4,/golthitarun/quora-question-pairs,Quora Question Pairs
8142381,0.66152,0,1,/wangqiyuan/ms-melware-with-h2o-automl,Microsoft Malware Prediction
2883404,0.625,0,0,/vonkorff/jvk-ms-malware-fit,Microsoft Malware Prediction
3160792,0.6759999999999999,1,0,/benjibb/self-normalizing-network,Microsoft Malware Prediction
3249160,0.69325,31,90,/cdeotte/private-leaderboard-0-750,Microsoft Malware Prediction
3166750,0.6970000000000001,0,24,/bejeweled/blending-0-697,Microsoft Malware Prediction
2411277,0.623,0,3,/prabanch/microsoft-malware-prediction,Microsoft Malware Prediction
3109383,0.5,8,32,/cdeotte/external-data-malware-0-50,Microsoft Malware Prediction
3039301,0.696,4,19,/ajithvallabai/microsoft-malware-prediction,Microsoft Malware Prediction
2893111,0.69,24,79,/guoday/xdeepfm-baseline,Microsoft Malware Prediction
2967746,0.6970000000000001,5,24,/gpucloud/ensmbl2,Microsoft Malware Prediction
2902953,0.6890000000000001,24,86,/rquintino/2-months-train-1-month-public-1-day-private,Microsoft Malware Prediction
2658561,0.634,6,4,/karanjakhar/malware-detection,Microsoft Malware Prediction
2773440,0.6729999999999999,28,82,/cdeotte/neural-network-malware-0-67,Microsoft Malware Prediction
2587329,0.639,4,5,/itamargr/decision-tree-learning,Microsoft Malware Prediction
28990,0.96144,0,0,/ldw4678/preprocessing,Homesite Quote Conversion
26456,0.96692,0,1,/suriya/trial-script,Homesite Quote Conversion
7176647,0.6578,0,0,/amaity0/bengali-grapheme-first-try,Bengali.AI Handwritten Grapheme Classification
7194113,0.9639,37,144,/iafoss/grapheme-fast-ai-starter-inference,Bengali.AI Handwritten Grapheme Classification
7140788,0.9516,197,473,/kaushal2896/bengali-graphemes-starter-eda-multi-output-cnn,Bengali.AI Handwritten Grapheme Classification
7156101,0.9259,3,29,/hanjoonchoe/grapheme-resnet-18-n-l-inference-lb-0-9259,Bengali.AI Handwritten Grapheme Classification
7140282,0.8566,3,25,/hanjoonchoe/grapheme-resnet-18-n-l-inference-lb-0-8566,Bengali.AI Handwritten Grapheme Classification
7160852,0.0615,0,0,/wakamezake/bengali-publicscore-check,Bengali.AI Handwritten Grapheme Classification
7126213,0.0614,2,5,/p4rallax/bengali-ai-eda-visualization,Bengali.AI Handwritten Grapheme Classification
8845725,0.9529,0,0,/garyongguanjie/resnet18public,Bengali.AI Handwritten Grapheme Classification
8398709,0.9754,0,0,/vagrawal/ensemble-efficientnet-b3-densenet161,Bengali.AI Handwritten Grapheme Classification
8242309,0.9518,0,0,/julienbeaulieu/bengaliai-model-inference-mobilenet-v2,Bengali.AI Handwritten Grapheme Classification
7999579,0.9428,0,0,/anirbansen3027/kernel52940e0a49,Bengali.AI Handwritten Grapheme Classification
7892986,0.9696,0,0,/inhoii/how-to-get-score-of-0-98-with-one-gtx-1080ti,Bengali.AI Handwritten Grapheme Classification
244962,0.13491,1,19,/novikovanastya/submission-novikova,Rossmann Store Sales
244904,0.13948,0,13,/mdementyev/dementyev-maxim,Rossmann Store Sales
244935,0.14941,0,9,/zigawick/sphere-belozerov-hw,Rossmann Store Sales
244890,0.14435,0,4,/horoshenkiy/horoshenkiy-rossman-technosphere,Rossmann Store Sales
242902,0.14939,1,11,/evgenyvasilyev/evgeny-vasilyev,Rossmann Store Sales
242408,0.1524299999999999,3,12,/sirkrabye/efimovvladislav-technosphere,Rossmann Store Sales
243149,0.4327899999999999,0,0,/petertrr/notebook4ffbcedf04,Rossmann Store Sales
11252654,0.15952,0,4,/c7934597/rapids-ensemble-for-trends-neuroimaging,TReNDS Neuroimaging
10527313,0.15941,0,1,/ngo1013/svr-stacking,TReNDS Neuroimaging
10212749,0.1595,0,0,/khanalkiran/neuro-imaging,TReNDS Neuroimaging
10442714,0.15769,0,6,/miykael/trends-data-scaling-and-modeling,TReNDS Neuroimaging
9487101,0.15815,1,26,/gunesevitan/trends-neuroimaging-linear-model-ensemble,TReNDS Neuroimaging
10383557,0.15853,0,6,/prateekagnihotri/35th-place-stacked-ensemble,TReNDS Neuroimaging
9871943,0.1592,0,4,/joatom/trends-tabular-nn-0-159,TReNDS Neuroimaging
10384868,0.15858,0,4,/rohitsingh9990/36th-place-trends-ensemble,TReNDS Neuroimaging
10380296,0.162,0,5,/mahmudds/neuroimaging-analysis-visualization-modeling,TReNDS Neuroimaging
10234957,0.1987,2,8,/hrfhgrthdyrd/baseline-cnn-with-keras-cut-2d-picture,TReNDS Neuroimaging
10077349,0.1764,0,1,/benjfisk/kernel31e58d2846,TReNDS Neuroimaging
10130437,0.1858,0,3,/dhuang718/lasso-fnc-and-loading-as-predictors,TReNDS Neuroimaging
10026489,0.1642,0,2,/dhuang718/huber-loading-df-only,TReNDS Neuroimaging
9348653,0.1605,0,2,/amrabed/trends-chain,TReNDS Neuroimaging
9921918,0.1593,6,10,/rohitsingh9990/trends-ensemble-all-public-kernels,TReNDS Neuroimaging
9723475,0.1611,8,32,/hemavivekanandan/trends-eda-dnn-for-predicting-age,TReNDS Neuroimaging
9704624,0.165,0,1,/yoshimasaiwano/kernel218eb3bdb8,TReNDS Neuroimaging
9709568,0.1601,1,20,/tunguz/h2o-automl-aggregator-and-submission,TReNDS Neuroimaging
9533389,0.1595,26,118,/tunguz/rapids-ensemble-for-trends-neuroimaging,TReNDS Neuroimaging
9557135,0.1639999999999999,1,2,/joatom/trends-exploring-tabular-augmentation,TReNDS Neuroimaging
9505816,0.185,0,4,/soham1024/rapids-sgd-on-trends-neuroimaging,TReNDS Neuroimaging
1187029,1.4808,12,65,/samratp/beginner-guide-to-eda-and-modeling,Santander Value Prediction Challenge
1190671,1.49,1,10,/scirpus/how-low-can-you-go,Santander Value Prediction Challenge
1186475,1.72,0,1,/vasilis73/descriptive-satander,Santander Value Prediction Challenge
1170536,1.47,79,417,/sudalairajkumar/simple-exploration-baseline-santander-value,Santander Value Prediction Challenge
1184579,1.46,0,9,/ashishpatel26/santander-value-prediction-xgb-lightgbm-catboost,Santander Value Prediction Challenge
1182360,1.52,0,21,/mbkinaci/xgboost-with-best-features-feature-importance,Santander Value Prediction Challenge
1178553,1.7,16,18,/madhurisivalenka/eda-pca-and-tsvd,Santander Value Prediction Challenge
1170639,1.47,6,50,/artgor/satander-eda-nn-features-lgb,Santander Value Prediction Challenge
1177688,1.75,6,6,/nandum/my-first-notebook,Santander Value Prediction Challenge
6264138,0.01431,0,10,/hatunina/nfl-simple-pytorch-by-16-feats,NFL Big Data Bowl
6250741,0.0141699999999999,1,8,/donkeys/keras-multi-input,NFL Big Data Bowl
6196730,0.0132,5,61,/kusunokichihiro/description-in-japanese-and-first-model-example,NFL Big Data Bowl
6215617,0.01404,3,22,/davidcairuz/nfl-neural-network-w-softmax,NFL Big Data Bowl
6197377,0.01426,18,104,/kingychiu/keras-nn-starter-crps-early-stopping,NFL Big Data Bowl
6192291,0.01295,11,55,/miklgr500/fork-of-neural-networks-radam-repeatkfold,NFL Big Data Bowl
6195988,0.0133699999999999,6,23,/kashnitsky/nfl-median-baseline-0-01457,NFL Big Data Bowl
6194965,0.0143699999999999,8,19,/jpmiller/simple-distribution,NFL Big Data Bowl
6156819,0.01599,36,189,/hukuda222/nfl-simple-model-using-lightgbm,NFL Big Data Bowl
6159456,0.01393,3,32,/rgoodman/nfl-01393-with-ridge-regres-no-nn-or-boost,NFL Big Data Bowl
6718852,0.01381,0,0,/yuminson/neural-network-with-mae-objective-0-01385,NFL Big Data Bowl
14552282,0.8338899999999999,26,24,/vivekam101/bert-model-lb-83-3-for-text-classification,Natural Language Processing with Disaster Tweets
14467684,0.81121,1,1,/lewisdevereux/fork-of-first-nlp-word-vectors,Natural Language Processing with Disaster Tweets
14376555,0.7759699999999999,0,1,/lewisdevereux/first-nlp,Natural Language Processing with Disaster Tweets
14477169,0.7992600000000001,0,0,/joolousada/nlp-disaster-tweets-tf-idf-linearsvc,Natural Language Processing with Disaster Tweets
10709376,0.75727,0,0,/bhaveshgupta3421/nlp-with-disaster-tweets,Natural Language Processing with Disaster Tweets
11393997,0.72295,0,0,/kazumamorita/lstm-model,Natural Language Processing with Disaster Tweets
14373725,0.7821,0,0,/sefikaefeoglu/nlp-with-fast-ai,Natural Language Processing with Disaster Tweets
14207665,0.82745,0,0,/kishikawanaoki/nlp-with-disaster-tweets,Natural Language Processing with Disaster Tweets
14168707,0.81795,0,0,/rutvik04/disaster-nlp,Natural Language Processing with Disaster Tweets
14135352,0.8063100000000001,1,1,/rohan2002/nlp-for-disaster-tweets-tuned-bert,Natural Language Processing with Disaster Tweets
13808097,0.68924,0,1,/shutupandsquat/my-first-nlp,Natural Language Processing with Disaster Tweets
14004433,0.8317399999999999,14,15,/teyang/transformer-is-all-you-need-for-disaster-tweets,Natural Language Processing with Disaster Tweets
13991627,0.79773,2,5,/jamesmcguigan/nlp-naive-bayes,Natural Language Processing with Disaster Tweets
4515172,0.0909,2,6,/tanreinama/tensorflow-baseline-using-u-net,SIIM-ACR Pneumothorax Segmentation
321613,0.0645562,13,38,/aharless/xgboost-using-4th-quarter-for-validation,Zillow Prize: Zillow’s Home Value Prediction (Zestimate)
14033329,0.76364,0,1,/ar89dsl/genetic-algo-feature-selection-hyperparameters,Santander Customer Satisfaction
12664506,0.8263299999999999,4,5,/carlmcbrideellis/tabnet-simple-binary-classification-example,Santander Customer Satisfaction
12253294,0.82652,0,2,/leechinghoe/customer-satisfaction-xgboost-smote-gridsearch,Santander Customer Satisfaction
10960426,0.55703,0,2,/tecnologiamorpheus/morpheus-santander-v01,Santander Customer Satisfaction
9220611,0.83639,3,5,/saga21/customer-satisfaction-models-explainability,Santander Customer Satisfaction
7202665,0.79428,0,1,/adage14175/santander-customer-satisfaction-notebook2,Santander Customer Satisfaction
6556076,0.71102,2,7,/ruchibahl18/customer-classification,Santander Customer Satisfaction
5742899,0.69458,0,1,/eudmar/customer-satisfaction-level-with-xgboost,Santander Customer Satisfaction
3404190,0.8362700000000001,0,2,/alefsegura/santander-prediction,Santander Customer Satisfaction
2370814,0.5010180000000001,0,1,/mitulshah97/santander,Santander Customer Satisfaction
747287,0.8369690000000001,0,0,/xagor1/santander-part-2,Santander Customer Satisfaction
12029456,-6.8068,9,27,/imoore/osic-ensemble-modelling-2,OSIC Pulmonary Fibrosis Progression
11954584,-6.807,10,28,/imoore/osic-ensemble-modeling-learning,OSIC Pulmonary Fibrosis Progression
11909990,-7.2689,1,2,/zhangyue199/lgb-quantile-regressor-starter,OSIC Pulmonary Fibrosis Progression
11866198,-6.8631,4,12,/alifrahman/osic-pulmonary-fibrosis-progression-submission,OSIC Pulmonary Fibrosis Progression
11862036,-8.7699,0,2,/kamaljain77/osic-pulmonary-fibrosis-progression-v1,OSIC Pulmonary Fibrosis Progression
11822238,-6.8774,1,14,/gilfernandes/lightgbm-simple-with-statsmodels,OSIC Pulmonary Fibrosis Progression
11762400,-6.879,0,0,/lhagiimn/embedding-layers-pytorch-implementation,OSIC Pulmonary Fibrosis Progression
11748935,-6.9567,0,0,/taikiishii/lung-volume,OSIC Pulmonary Fibrosis Progression
11666975,-6.8071,7,53,/mekhdigakhramanian/higher-lb-score-by-tuning-mloss-upgrade-1696e2,OSIC Pulmonary Fibrosis Progression
11703889,-6.9863,0,1,/sawans/basic-linear-regression-on-tabular-data,OSIC Pulmonary Fibrosis Progression
11645395,-6.9027,1,6,/eladwar/conditional-rnn,OSIC Pulmonary Fibrosis Progression
10732270,-6.968,2,3,/makhloufsabir/osic-pulmonary-fibrosis-progression-predict,OSIC Pulmonary Fibrosis Progression
11562705,-6.8085,26,122,/vbmokin/higher-lb-score-by-tuning-mloss-upgrade-visual,OSIC Pulmonary Fibrosis Progression
11586161,-6.8692,0,18,/shangweichen/pytorch-osic-multiple-quantile-regression-starter,OSIC Pulmonary Fibrosis Progression
11512132,-6.8208,0,2,/prateek3g/change-in-dropout-and-mloss-values,OSIC Pulmonary Fibrosis Progression
13525908,0.949,22,137,/yasufuminakama/ranzcr-resnext50-32x4d-starter-inference,RANZCR CLiP - Catheter and Line Position Challenge
13521245,0.957,4,92,/xhlulu/ranzcr-efficientnet-submission,RANZCR CLiP - Catheter and Line Position Challenge
13517383,0.926,3,27,/tanlikesmath/ranzcr-clip-a-simple-eda-and-fastai-starter,RANZCR CLiP - Catheter and Line Position Challenge
13517186,0.511,1,1,/drcapa/catheter-line-position-eda-keras-starter,RANZCR CLiP - Catheter and Line Position Challenge
13532558,0.923,0,0,/mylonsong/ranzcr-resnext50-32x4d-starter-inference-1,RANZCR CLiP - Catheter and Line Position Challenge
2436502,0.80603,5,20,/aditya1702/create-data-pipeline-and-lgbclassifiercv,Two Sigma: Using News to Predict Stock Movements
2501632,0.6281399999999999,0,2,/oriormeir/lgbm-market-news,Two Sigma: Using News to Predict Stock Movements
2434613,-0.17032,0,0,/rongtouchtouch/kernel34f3d09108,Two Sigma: Using News to Predict Stock Movements
2471892,0.6399100000000001,0,1,/oguzkaplan/outlier-1-5-iqr-method-nn-on-market,Two Sigma: Using News to Predict Stock Movements
2463239,0.31505,0,0,/meatik/simple-example-ml,Two Sigma: Using News to Predict Stock Movements
2416182,0.5689,0,0,/timsonrisa/two-stock-news-random-forest-starter,Two Sigma: Using News to Predict Stock Movements
2436031,0.0678,0,1,/dgrachev28/tutorial-timeseriesapproach-18c735,Two Sigma: Using News to Predict Stock Movements
2373592,0.6824100000000001,0,7,/alexjmartinez/minor-parameter-tuning-for-lightgbm-scaling-boost,Two Sigma: Using News to Predict Stock Movements
2413787,0.11424,0,0,/regonn/two-sigma-2018-12-18-optuna-lightgbm-output,Two Sigma: Using News to Predict Stock Movements
2342068,4.4897300000000016,1,14,/raosushant/lstm-model-with-market-and-news-data,Two Sigma: Using News to Predict Stock Movements
2280685,3.83954,0,0,/hzk123/lgbm-v1,Two Sigma: Using News to Predict Stock Movements
2293803,0.5412,1,2,/nimitsolanki/two-sigma-stock-price-prediction,Two Sigma: Using News to Predict Stock Movements
2215832,0.00614,4,18,/orange90/a-benchmark-for-the-prediction,Two Sigma: Using News to Predict Stock Movements
2242341,0.5491199999999999,0,1,/getshitdone95/eda-feature-engineering-and-everything,Two Sigma: Using News to Predict Stock Movements
2245319,0.49904,0,0,/johnlingle/wsw4-a-simple-model-jhl-amaral,Two Sigma: Using News to Predict Stock Movements
6022803,0.152,0,11,/anubhav1302/rsna-simple-notebook,RSNA Intracranial Hemorrhage Detection
5989729,0.33131,4,21,/drcapa/hemorrhage-imagegenerator-resnet50,RSNA Intracranial Hemorrhage Detection
11670247,0.12677,0,0,/mirandora/houseprices-tutorial-code,House Prices - Advanced Regression Techniques
11624377,0.13247,1,3,/yutohisamatsu/houseprice-xgboost-part2,House Prices - Advanced Regression Techniques
11605258,0.14361,0,2,/mohitkarelia/advanced-house-price-prediction,House Prices - Advanced Regression Techniques
11599658,0.15102,0,0,/tracyporter/ames-house-prices-keras,House Prices - Advanced Regression Techniques
11605572,0.13967,0,0,/tracyporter/ames-house-prices-linear-reg-gradient-boostin,House Prices - Advanced Regression Techniques
11589267,0.12733,0,0,/ahmadalsharif/fork-of-ahmad-al-sharif-regression,House Prices - Advanced Regression Techniques
11574737,0.1206,0,4,/sayakpaul/notebook18b90290dd,House Prices - Advanced Regression Techniques
11474955,0.1194799999999999,17,24,/duygut/eda-with-tableau-end-to-end-ml-price-prediction,House Prices - Advanced Regression Techniques
11480280,0.15064,2,12,/carlmcbrideellis/stacking-ensemble-using-the-house-prices-data,House Prices - Advanced Regression Techniques
8926909,0.15258,0,0,/bdokkkk/house,House Prices - Advanced Regression Techniques
11453222,0.16105,5,13,/carlmcbrideellis/ml-ensemble-example-using-house-prices-data,House Prices - Advanced Regression Techniques
9324697,0.1296,0,9,/thomaswoolley/house-price-engineering-predictions,House Prices - Advanced Regression Techniques
128683,1134.38762,0,0,/aakash2121995/notebookccc577aa6b,Allstate Claims Severity
4142529,0.88646,0,0,/lakshmi25npathi/santander-customer-transaction-prediction,Santander Customer Transaction Prediction
3900264,0.84977,0,0,/carlosdl09/red-neuronal-keras-d-lacoste,Santander Customer Transaction Prediction
3400900,0.901,0,0,/izmth1211/lgb-2-leaves-augment-17b3b7,Santander Customer Transaction Prediction
59890,0.829809,0,1,/oxjlisa/customersatisfaction-v1,Santander Customer Satisfaction
56303,0.8314450000000001,0,1,/nooreen/santander-data-exploration,Santander Customer Satisfaction
56221,0.5,0,0,/canran/santander-satisfaction,Santander Customer Satisfaction
7061520,0.0,0,0,/singhaditya5842/with-gbdt-model,Jigsaw Unintended Bias in Toxicity Classification
4529721,0.9273,0,0,/chengjingye/lstm-cnn-3layer2d-bn,Jigsaw Unintended Bias in Toxicity Classification
4361553,0.93699,0,0,/saileshmohanty/lstm-1,Jigsaw Unintended Bias in Toxicity Classification
3982502,0.92326,0,0,/godeye/kernelf66a675087,Jigsaw Unintended Bias in Toxicity Classification
14354540,5203604.0,0,0,/dikuchan/volcanoes-team-cringe,INGV - Volcanic Eruption Prediction
14183020,4406108.0,2,12,/ekhtiar/18th-place-predicting-eruption-full-tutorial,INGV - Volcanic Eruption Prediction
13750200,6277516.0,0,0,/cdisario/eruptions-baseline-pycaret,INGV - Volcanic Eruption Prediction
13732831,7365382.0,2,13,/andtre/optuna-baseline,INGV - Volcanic Eruption Prediction
13506214,5268535.0,0,6,/sergeyzaitsev/eruption-prediction-with-fft-peaks-and-lgbm,INGV - Volcanic Eruption Prediction
13250858,5821067.0,2,13,/kylesnyder/ingv-spectral-density-w-randomforest,INGV - Volcanic Eruption Prediction
12996310,7919685.0,1,18,/amanjain1008/erupting-volcano-all-in-one-different-eda,INGV - Volcanic Eruption Prediction
12792653,5682670.0,0,1,/khangtran97/cs675-khangtran,INGV - Volcanic Eruption Prediction
12849621,5488711.0,0,5,/adaubas/ingv-challenge-fast-feature-engineering,INGV - Volcanic Eruption Prediction
12746605,5272454.0,6,49,/amanooo/ingv-volcanic-basic-solution-stft,INGV - Volcanic Eruption Prediction
12686561,11961662.0,1,3,/plasmaichor/plasmaichor-volcano-eruption-submission,INGV - Volcanic Eruption Prediction
12575143,4989282.0,17,48,/carpediemamigo/ingv-catboost-baseline-tsfresh,INGV - Volcanic Eruption Prediction
12339273,6601566.0,0,6,/carlmcbrideellis/volcanic-feature-importance-using-boruta-shap,INGV - Volcanic Eruption Prediction
12259996,6642172.0,5,17,/soheild91/ingv-nn-xgb-baseline,INGV - Volcanic Eruption Prediction
13109417,0.80324,0,1,/kanthichajuntepa/dsi-206,Natural Language Processing with Disaster Tweets
13088927,0.80784,0,0,/paweeyaphumwanphen/disaster-tweets-ridge-regression,Natural Language Processing with Disaster Tweets
13011974,0.8004899999999999,0,0,/dada07sahassawadee/dsi206-project,Natural Language Processing with Disaster Tweets
13057822,0.77965,0,0,/bamnichaporn/sentiment-twitter,Natural Language Processing with Disaster Tweets
13045856,0.81121,0,2,/martinhaha/fastai-approach,Natural Language Processing with Disaster Tweets
13117480,0.80784,0,0,/natjirachamusri/notebooke1fa31a1e9,Natural Language Processing with Disaster Tweets
13060474,0.79742,0,0,/mollapankuabphimai/tweet,Natural Language Processing with Disaster Tweets
12724253,0.7618699999999999,0,2,/kamleshshimpi78/nlp-disaster-tweets-using-keras,Natural Language Processing with Disaster Tweets
12821181,0.81366,1,9,/rakkaalhazimi/concat-embedding-bilstm-and-maxpooling2d,Natural Language Processing with Disaster Tweets
12591551,0.82531,0,2,/apretrue/pytorch-roberta-for-classify,Natural Language Processing with Disaster Tweets
12825686,0.62611,0,1,/indranilbiswas7/torchtext-glove,Natural Language Processing with Disaster Tweets
10777292,0.80355,0,0,/wburchenal/nlp-disaster-tweet-prediction,Natural Language Processing with Disaster Tweets
12743315,0.79803,0,1,/ashishsingh226/simple-model-for-beginners,Natural Language Processing with Disaster Tweets
12632035,0.78363,0,0,/tracyporter/identify-the-disaster-pipeline-linearsvc,Natural Language Processing with Disaster Tweets
11015414,0.71682,0,0,/mosheziat/the-disasters,Natural Language Processing with Disaster Tweets
10390092,0.79098,0,0,/maximinjoshua/kernel2702608a49,Natural Language Processing with Disaster Tweets
12490525,0.8296,5,19,/ezeanyi/nlp-cleaning-glove-lstm-bert,Natural Language Processing with Disaster Tweets
12486025,0.78976,0,1,/egorpinkovskii/catboost-text-features-with-some-preprocessing,Natural Language Processing with Disaster Tweets
6280206,0.01401,0,1,/eristoddle/fork-of-neural-networks-different-2-comm,NFL Big Data Bowl
6794598,0.0123,0,0,/artemshramko/voronoi-pc-def-pos-nn,NFL Big Data Bowl
6768135,0.01228,0,0,/artemshramko/voronoi-play-control-fe,NFL Big Data Bowl
6184822,0.01835,0,0,/alexanderdbooth/nfl-big-data-bowl-wooooo,NFL Big Data Bowl
6161155,0.01964,0,0,/adibpriatama/pipeline-design-simple,NFL Big Data Bowl
6232013,0.01578,0,0,/arindam235/andy-s-draft,NFL Big Data Bowl
6610566,0.01244,0,0,/jasonrockphelps/best-etc-on-cdf-model,NFL Big Data Bowl
7756862,0.012565,2,25,/nyanpn/pytorch-transformer-public-14th-private-22nd,NFL Big Data Bowl
6751470,0.0168,0,0,/jinangefrank/nfl-lgb-adjust-params-based-on-xingyuan,NFL Big Data Bowl
6739102,0.01256,1,1,/ivakat/mainn,NFL Big Data Bowl
6686186,0.0117299999999999,1,0,/adityakumarsinha/firstkernel,NFL Big Data Bowl
6743207,0.01324,0,0,/hozhen/kernel31436ca839,NFL Big Data Bowl
6687439,0.01255,0,0,/pwendel3/generational-talent,NFL Big Data Bowl
6345538,0.1079599999999999,0,0,/ananthreddy/big-data-bowl,NFL Big Data Bowl
180395,0.5582,0,0,/auygur/xgb-starter-in-python,Two Sigma Connect: Rental Listing Inquiries
179618,0.6282399999999999,0,0,/brainonastick/first-approach,Two Sigma Connect: Rental Listing Inquiries
12436751,1.4714,3,8,/paulrohan2020/tutorial-kernel-on-lightgbm-xgboost-and-catboost,Santander Value Prediction Challenge
11976418,1.47932,0,3,/dskagglemt/santander-value-prediction-challenge-lgb,Santander Value Prediction Challenge
1349508,0.63,0,0,/hunningsmike854/love-is-the-answer-b30fa1,Santander Value Prediction Challenge
5870719,1.7366400000000002,0,0,/daphnetree/ramdom-forest,Santander Value Prediction Challenge
3480348,1.73616,0,0,/vienvien/modelo-keras-simple,Santander Value Prediction Challenge
2630035,1.5921,0,1,/yairh3/feature-selection-techniques-and-lgbm,Santander Value Prediction Challenge
1191301,1.88,0,1,/hsankesara/santander-first-attempt,Santander Value Prediction Challenge
1442724,1.74,0,0,/anweshasinha/kerneld1f4c66934,Santander Value Prediction Challenge
1929748,0.5857,0,0,/boomberung/leak-abuse-v2,Santander Value Prediction Challenge
1408281,1.74,0,2,/mytymohan/santander-value-prediction-challenge-my-solution,Santander Value Prediction Challenge
1482080,1.76,0,4,/yuikitaml/stacking3402,Santander Value Prediction Challenge
1369867,1.77,0,2,/joaquympc/santander-value-prediction-challenge,Santander Value Prediction Challenge
1413024,1.47,0,1,/praxitelisk/santander-value-prediction-challenge-eda-ml,Santander Value Prediction Challenge
1446613,1.83,0,2,/ajaysub110/santander-random-forest-regressor,Santander Value Prediction Challenge
1394887,1.45,2,18,/felipebottega/experimental-neural-network,Santander Value Prediction Challenge
1360060,0.66,0,21,/zeus75/xgboost-features-scoring-with-ligthgbm-model,Santander Value Prediction Challenge
1316568,1.45,0,0,/saloni0101/notebook-santander,Santander Value Prediction Challenge
218582,0.35477,0,4,/sowbarani/quora-question-pairs-eda,Quora Question Pairs
217709,0.55411,0,0,/anshbansal/eda-for-competition,Quora Question Pairs
218785,0.35201,0,0,/frownyface/notebook58e7474975,Quora Question Pairs
218720,0.35372,0,0,/wpncrh/data-analysis-xgboost-starter-0-35460-lb,Quora Question Pairs
215404,0.5541,1,1,/mttzju/001notebook-erin,Quora Question Pairs
213947,0.3532,0,1,/giranntu/data-analysis-xgboost-starter-0-35460-lb,Quora Question Pairs
6928443,0.8732,0,1,/monthepp/quick-draw-doodle-recognition-challenge-mobilenet,"Quick, Draw! Doodle Recognition Challenge"
6451799,0.67324,0,2,/adldotori/advanced-image-classification,"Quick, Draw! Doodle Recognition Challenge"
6475439,0.91948,0,1,/marco0332/mobilenet-with-3-types-image-encoding,"Quick, Draw! Doodle Recognition Challenge"
6467241,0.8174,1,2,/keunyoungjung/kynet-quickdraw,"Quick, Draw! Doodle Recognition Challenge"
6488587,0.74141,0,1,/ee33hhee/kernel41c02d3b47,"Quick, Draw! Doodle Recognition Challenge"
5811659,0.89775,0,0,/laymanbrother/quickdrawpretrained,"Quick, Draw! Doodle Recognition Challenge"
3947743,0.7497199999999999,0,0,/tjghdia/quick-draw-assignment,"Quick, Draw! Doodle Recognition Challenge"
1849760,0.78571,0,5,/monthepp/quick-draw-doodle-recognition-challenge-keras,"Quick, Draw! Doodle Recognition Challenge"
3934852,0.89528,0,0,/mnmjh1215/mobilenet-v1-128x128x3-image,"Quick, Draw! Doodle Recognition Challenge"
3949900,0.78482,0,0,/yjkwon/kernel540295b0bb,"Quick, Draw! Doodle Recognition Challenge"
3895506,0.7549399999999999,0,1,/haley2203/kernel35ed4032ee,"Quick, Draw! Doodle Recognition Challenge"
2198984,0.87,1,0,/qlasty/mobilenet-area-context,"Quick, Draw! Doodle Recognition Challenge"
2596233,0.59,3,7,/rambabusure/malware-prediction-eda-detailed,Microsoft Malware Prediction
2555179,0.612,1,1,/zombiecleansdata/microsoft-malware,Microsoft Malware Prediction
2389354,0.655,11,30,/harmeggels/random-forest-feature-importances,Microsoft Malware Prediction
2465860,0.6970000000000001,6,27,/saurabh502/why-no-blend,Microsoft Malware Prediction
2417449,0.6607,3,16,/tunguz/ms-melware-with-h2o-automl,Microsoft Malware Prediction
2372003,0.685,80,301,/artgor/is-this-malware-eda-fe-and-lgb-updated,Microsoft Malware Prediction
2381185,0.628,8,30,/shaz13/excelsior-microsoft-malware-eda-baseline,Microsoft Malware Prediction
2372388,0.612,3,12,/delayedkarma/lazy-feature-engg-lightgbm,Microsoft Malware Prediction
799455,0.41167,0,0,/mdumair1/lstm-notebook,Quora Question Pairs
10295596,0.12434,1,1,/brandog/rossmann-sales-fastai-and-external-data-sets,Rossmann Store Sales
6518412,0.25756,0,1,/narendra123580/rossman-store-sales-analysis,Rossmann Store Sales
8648443,0.10776,0,0,/offerupedu/offerup-edu-rossmann,Rossmann Store Sales
7859060,0.10776,0,5,/sunlightsedu/sunlightsedu-rossmann,Rossmann Store Sales
7242953,0.6454300000000001,0,0,/santoshkumar1709/rossmann-store-sales,Rossmann Store Sales
6511399,0.51502,0,7,/amulyamanne/rossmann-store-sales-case-study,Rossmann Store Sales
6606006,0.09928,0,0,/zongtseng/rossmann-time-series-prediction-deep-learning,Rossmann Store Sales
6511408,0.31179,1,15,/ashwathbalaji/rossmann-store-sales,Rossmann Store Sales
6511396,0.17052,5,16,/swinalmeshram/rossmann-sales,Rossmann Store Sales
3237641,0.4424,0,1,/omarsayed7/xgboost-rossman-sales,Rossmann Store Sales
2095107,0.10962,1,0,/massyl/rossman-store-sales,Rossmann Store Sales
9169698,0.159,30,199,/aerdem4/rapids-svm-on-trends-neuroimaging,TReNDS Neuroimaging
9221957,0.1639999999999999,0,0,/obeckman/trends-data-exploration,TReNDS Neuroimaging
9143347,0.161,1,4,/jafarib/trends-eda-fe-submission,TReNDS Neuroimaging
9107495,0.1639999999999999,1,11,/mercury01/pca-base-keras-nn-nae-metric,TReNDS Neuroimaging
9106572,0.177,5,14,/kmatsuyama/simple-nn-baseline-using-keras,TReNDS Neuroimaging
9098292,0.1639999999999999,0,6,/zacktack/data-scientist-journey-neuroimaging-lgbm,TReNDS Neuroimaging
9086798,0.187,16,31,/hamditarek/trends-neuroimaging-xgbregressor,TReNDS Neuroimaging
9090221,0.161,3,13,/digvijayyadav/neuroimag-trends-eda-fe-submissions,TReNDS Neuroimaging
5155883,0.8532,84,154,/rishabhiitbhu/unet-with-resnet34-encoder-pytorch,SIIM-ACR Pneumothorax Segmentation
5068577,0.8405,5,2,/wuwei41/unet-plus-plus-with-efficientnet-encoder-256x256,SIIM-ACR Pneumothorax Segmentation
4614006,0.8227,21,78,/meaninglesslives/unet-xception-keras-for-pneumothorax-segmentation,SIIM-ACR Pneumothorax Segmentation
4564873,0.7998,22,260,/abhishek/mask-rcnn-with-augmentation-and-multiple-masks,SIIM-ACR Pneumothorax Segmentation
4551485,0.8009999999999999,19,119,/hmendonca/mask-rcnn-and-medical-transfer-learning-siim-acr,SIIM-ACR Pneumothorax Segmentation
4537528,0.7945,32,164,/mnpinto/pneumothorax-fastai-u-net,SIIM-ACR Pneumothorax Segmentation
4553206,0.7923,1,16,/zaharch/sample-submission-on-steroids,SIIM-ACR Pneumothorax Segmentation
4532477,0.7895,0,25,/raddar/better-sample-submission,SIIM-ACR Pneumothorax Segmentation
13963160,0.77536,3,6,/jamesmcguigan/nlp-tf-idf-classifier,Natural Language Processing with Disaster Tweets
13917828,0.79773,0,0,/phuasoongern/prediction-with-nlp-phua-sn,Natural Language Processing with Disaster Tweets
13689703,0.8201,0,0,/teyang/tweet-backup,Natural Language Processing with Disaster Tweets
12819935,0.79313,0,0,/manatsaweewutticharn/dsi206-beautifulgirls,Natural Language Processing with Disaster Tweets
13253158,0.7726,0,0,/aadeshbaral/real-or-fake-disaster-tweets,Natural Language Processing with Disaster Tweets
13405321,0.81029,0,1,/grjasewe/bert-only,Natural Language Processing with Disaster Tweets
13535235,0.7477699999999999,0,0,/charanrajshetty/disaster-tweet-sentiment-analysis-lstm,Natural Language Processing with Disaster Tweets
13493410,0.79405,0,5,/rojinzandi/nlp-project2,Natural Language Processing with Disaster Tweets
12960658,0.8011,0,0,/khaledyoung/disaster-tweets-svc-tf-idf,Natural Language Processing with Disaster Tweets
13429207,0.79987,0,3,/mkudlick/real-or-not-nlp-with-disaster-tweets-notebook,Natural Language Processing with Disaster Tweets
13379441,0.8458399999999999,2,13,/salmanhiro/glove-baseline-bert,Natural Language Processing with Disaster Tweets
13289051,0.77689,2,6,/drsurabhithorat/predicting-disaster-tweets,Natural Language Processing with Disaster Tweets
13189851,0.768,0,1,/nikhilprashar/nikhil-real-or-not,Natural Language Processing with Disaster Tweets
13069315,0.57033,0,7,/raghavjha858/disaster-nlp-word2vec,Natural Language Processing with Disaster Tweets
8616808,0.7729,0,0,/drcapa/real-or-not-nlp-with-disaster-tweets-starter,Natural Language Processing with Disaster Tweets
12963615,0.768,0,1,/anvole/ee258-f20-anvo,Natural Language Processing with Disaster Tweets
2684375,0.0641374,0,1,/bilguun0203/zestimate-catboost,Zillow Prize: Zillow’s Home Value Prediction (Zestimate)
463429,0.0651722,1,4,/umerfarooq807/prediction-model-in-keras,Zillow Prize: Zillow’s Home Value Prediction (Zestimate)
374495,0.064895,0,3,/zusmani/baseline-lb-0-0648950,Zillow Prize: Zillow’s Home Value Prediction (Zestimate)
14541673,0.965,5,35,/ammarali32/resnet200d-inference-single-model-lb-96-5,RANZCR CLiP - Catheter and Line Position Challenge
13627860,0.931,0,1,/jasonkwm/tube-placement,RANZCR CLiP - Catheter and Line Position Challenge
14019702,0.965,52,116,/underwearfitting/resnet200d-public-benchmark-2xtta-lb0-965,RANZCR CLiP - Catheter and Line Position Challenge
14038580,0.5,6,33,/hackspyder/eda-catheter-position,RANZCR CLiP - Catheter and Line Position Challenge
13623632,0.935,0,3,/ptran1203/ranzcr-efficientnetb5-tf2-inference,RANZCR CLiP - Catheter and Line Position Challenge
13872653,0.935,0,2,/user123454321/pytorch-densenet201-starter,RANZCR CLiP - Catheter and Line Position Challenge
13536137,0.623,0,0,/dmikar/ranzcr-keras-2-level-classification,RANZCR CLiP - Catheter and Line Position Challenge
13673322,0.597,0,1,/ssarkar445/catheter-line-starter-code,RANZCR CLiP - Catheter and Line Position Challenge
13083232,-6.9131,0,0,/khadijatulkobra/submission-3,OSIC Pulmonary Fibrosis Progression
11345331,-6.9003,0,1,/larsran/pulmonaryfibrosis-environment,OSIC Pulmonary Fibrosis Progression
11684615,-6.8883,0,2,/pradyut23/pulmonary-fibrosis-eda,OSIC Pulmonary Fibrosis Progression
11952098,-6.9226,0,0,/quandapro/osic-inference,OSIC Pulmonary Fibrosis Progression
12385746,-7.0381,0,0,/mikhailkennerley/notebook7d5092baaa,OSIC Pulmonary Fibrosis Progression
10846730,-6.8359,0,1,/mvnewlife/mnl-osic-pfp-mqr-2,OSIC Pulmonary Fibrosis Progression
11564877,-6.8006,0,6,/snowpea8/osic-eda-efficientnet-quantile-reg,OSIC Pulmonary Fibrosis Progression
11703657,-6.955,0,1,/gunawanmarbun/osic-pulmonary-modelling,OSIC Pulmonary Fibrosis Progression
10737205,-6.8019,3,13,/gunesevitan/osic-pulmonary-fibrosis-progression-3-cv-3-model,OSIC Pulmonary Fibrosis Progression
11803828,-6.9241,4,3,/gautham11/quantile-regression-pytorch-lightning-inference,OSIC Pulmonary Fibrosis Progression
12016776,-6.9742,0,4,/bjoernholzhauer/bayes-decision-theory-using-pystan,OSIC Pulmonary Fibrosis Progression
12146380,-6.9411,1,3,/dcasbol/osic-my-best-solution-6-8348,OSIC Pulmonary Fibrosis Progression
10943697,-6.9304,0,5,/ulrich07/osic-basic-tabular-data-augmentation-nn,OSIC Pulmonary Fibrosis Progression
11765837,-6.852,0,0,/htopper/omqr-refactored-mean-slope,OSIC Pulmonary Fibrosis Progression
11532209,-6.9109,0,0,/balogi85/pulmanolog-test,OSIC Pulmonary Fibrosis Progression
11828556,-6.8609,0,0,/zefirchik/fork-of-test-da-nu-na-57e426,OSIC Pulmonary Fibrosis Progression
12150672,-6.919,0,0,/ilu000/osic-my-normalized-submission-with-explanation,OSIC Pulmonary Fibrosis Progression
138059,0.806082,0,0,/richarlee/satisfaction-richarlee,Santander Customer Satisfaction
3481137,0.91943,0,1,/cevangelist/a-little-more-detailed-eda-and-simple-lstm,Jigsaw Unintended Bias in Toxicity Classification
3647730,0.88494,0,2,/rx74tsns/tf-idf-vectorizer-and-logistic-regression,Jigsaw Unintended Bias in Toxicity Classification
3643285,0.09379,0,0,/saurabhrathor/toxicity-check,Jigsaw Unintended Bias in Toxicity Classification
3611078,0.92765,8,97,/christofhenkel/bert-embeddings-lstm,Jigsaw Unintended Bias in Toxicity Classification
3580777,0.89912,2,10,/dimitreoliveira/toxicity-bias-extensive-eda-and-bi-lstm,Jigsaw Unintended Bias in Toxicity Classification
3615338,0.88245,0,3,/francoisdubois/build-an-embedding-based-on-fasttext,Jigsaw Unintended Bias in Toxicity Classification
3495165,0.92142,16,82,/tarunpaparaju/jigsaw-competition-eda-and-modeling,Jigsaw Unintended Bias in Toxicity Classification
3602545,0.92569,0,3,/maxjeblick/keras-lstm-with-competition-metric-callback,Jigsaw Unintended Bias in Toxicity Classification
3480384,0.8970299999999999,0,12,/takafumif/simple-lgbm,Jigsaw Unintended Bias in Toxicity Classification
3522342,0.9315,86,376,/bminixhofer/simple-lstm-pytorch-version,Jigsaw Unintended Bias in Toxicity Classification
3533248,0.93319,1,22,/kunwar31/simple-lstm-fastai,Jigsaw Unintended Bias in Toxicity Classification
3498799,0.93014,11,61,/abhigupta4981/pytorch-train-with-callbacks,Jigsaw Unintended Bias in Toxicity Classification
3487596,0.90148,3,15,/coolcoder22/simple-logisticregression,Jigsaw Unintended Bias in Toxicity Classification
3488980,0.90887,0,1,/pedrormarques/jigsaw-gru-classes,Jigsaw Unintended Bias in Toxicity Classification
3444639,0.92562,23,217,/christofhenkel/keras-baseline-lstm-attention-5-fold,Jigsaw Unintended Bias in Toxicity Classification
3441287,0.69026,3,7,/masterscrat/lsa-lgbm-baseline-w-system-monitoring,Jigsaw Unintended Bias in Toxicity Classification
3464232,0.64687,0,3,/mohanamurali/bgow-tf-idf-lr-w2v-lgb-bayesopt,Jigsaw Unintended Bias in Toxicity Classification
3428090,0.92429,14,119,/artgor/cnn-in-keras-on-folds,Jigsaw Unintended Bias in Toxicity Classification
3446033,0.90583,0,1,/lsjsj92/toxic-simple-eda-and-modeling-with-lstm,Jigsaw Unintended Bias in Toxicity Classification
3427871,0.92845,2,38,/artgor/comparing-architectures-and-embeddings,Jigsaw Unintended Bias in Toxicity Classification
3433791,0.66562,4,4,/justjun0321/simple-sklearn-models,Jigsaw Unintended Bias in Toxicity Classification
7882517,0.0,0,0,/jtdsouza/bert-inference-jds,Jigsaw Unintended Bias in Toxicity Classification
2147621,0.66151,0,3,/rikitikitavy/priceprediction,Two Sigma: Using News to Predict Stock Movements
2465024,0.6584899999999999,0,0,/danechen/eda-script-67-pct-change-in-market-data-useful,Two Sigma: Using News to Predict Stock Movements
2124707,0.7134199999999999,0,1,/sijinwu/eda-script-67-151076,Two Sigma: Using News to Predict Stock Movements
2248629,2.20514,0,4,/returnofsputnik/fulltimeseriesvalid-lstm-lgbm-goss-dart-v2,Two Sigma: Using News to Predict Stock Movements
2518467,3.01095,0,6,/charleslandau/iterative-approach,Two Sigma: Using News to Predict Stock Movements
2014328,2.6958,0,1,/chrisiew/kerneldd5874fb16,Two Sigma: Using News to Predict Stock Movements
2228195,0.50145,0,0,/jsarda/kernel-gama-2,Two Sigma: Using News to Predict Stock Movements
2507437,0.61564,0,1,/shegerking/analysis,Two Sigma: Using News to Predict Stock Movements
2561839,0.63935,0,1,/felsal/combined-of-market-data-only-new-beginnig,Two Sigma: Using News to Predict Stock Movements
3058518,0.861,0,1,/shawngoldw/santander-neural-network-classification,Santander Customer Transaction Prediction
3057642,0.778,0,0,/pk5097/santander-customer-transaction-prediction,Santander Customer Transaction Prediction
3028198,0.875,0,1,/juminator/nb-rf-and-simple-ensemble-with-both-models,Santander Customer Transaction Prediction
3010325,0.885,7,46,/quanghm/fastai-1-0-tabular-learner-with-ensemble,Santander Customer Transaction Prediction
3002643,0.773,0,3,/neeraj22ny/catboost-with-smote-combination,Santander Customer Transaction Prediction
3009017,0.887,11,28,/danielgrimshaw/sklearn-model-exploration,Santander Customer Transaction Prediction
3046848,0.898,1,0,/seanbb93/pica-sctp-how-it-works,Santander Customer Transaction Prediction
3006923,0.8690000000000001,7,7,/juminator/pca-lr-lda-qda-simple-ensemble,Santander Customer Transaction Prediction
2999460,0.899,2,16,/dromosys/sctp-working-lgb,Santander Customer Transaction Prediction
2999099,0.898,2,5,/kalyankkr/single-model-lgbm,Santander Customer Transaction Prediction
2981985,0.888,0,12,/ortempo/2-naives-bayes,Santander Customer Transaction Prediction
2943889,0.9,55,440,/mjbahmani/santander-ml-explainability,Santander Customer Transaction Prediction
2957231,0.8590000000000001,0,19,/higepon/starter-keras-simple-nn-kfold-cv,Santander Customer Transaction Prediction
2947683,0.89207,4,45,/tunguz/santander-with-h2o-automl,Santander Customer Transaction Prediction
2957016,0.858,2,3,/tarunpaparaju/santander-customer-transaction-dnns,Santander Customer Transaction Prediction
2943240,0.863,13,32,/morenoh149/rosegold-eda-pca-and-lightgbm-baseline,Santander Customer Transaction Prediction
2957725,0.634,0,1,/saurav90/santander-transaction-prediction,Santander Customer Transaction Prediction
2948681,0.897,0,6,/anu0012/quick-start-basic-approach-lb-0-897,Santander Customer Transaction Prediction
11447135,0.16405,0,0,/dullaz/ich-submitter,RSNA Intracranial Hemorrhage Detection
6588747,0.4479999999999999,1,0,/fanconic/ensembling-of-submission-files,RSNA Intracranial Hemorrhage Detection
6396116,0.3289999999999999,0,0,/fanconic/windowing-efficientnet-keras,RSNA Intracranial Hemorrhage Detection
6618083,0.6409999999999999,3,53,/mathormad/5th-place-solution-stacking-pipeline,RSNA Intracranial Hemorrhage Detection
6318049,0.07086,1,3,/saquibhashmi/efficientnetb2-keras-baseline-model,RSNA Intracranial Hemorrhage Detection
6411290,0.105,0,5,/niranjankumarc/rsna-fastai-baseline-model,RSNA Intracranial Hemorrhage Detection
6041997,0.289,32,139,/allunia/rsna-ih-detection-baseline,RSNA Intracranial Hemorrhage Detection
5887490,0.168,1,4,/juanrami/rsna-intracranial-hemorrhage-detection,RSNA Intracranial Hemorrhage Detection
3768761,0.0261899999999999,0,10,/jake126/memory-based-collaborative-filtering,Santander Product Recommendation
1686811,0.0225860999999999,0,0,/hanene1/logistic-regression-012c88,Santander Product Recommendation
1688400,0.0135599,0,0,/hanene1/decision-tree-with-demographic,Santander Product Recommendation
1420346,0.0298957,6,12,/hachemsfar/keras,Santander Product Recommendation
1401369,0.0230789,0,1,/hachemsfar/random-forest,Santander Product Recommendation
1398451,0.0291241,0,1,/hachemsfar/xgboost,Santander Product Recommendation
956881,0.0225860999999999,1,2,/hachemsfar/logistic-regression,Santander Product Recommendation
119435,1479.31801,0,0,/marc000/first-try,Allstate Claims Severity
1828892,0.312,0,0,/tcapelle/4-channel-darknet-sz-128,Human Protein Atlas Image Classification
1943478,0.10727,10,28,/mickeypvx/actually-this-is-my-first-rodeo,Human Protein Atlas Image Classification
1916215,0.327,34,120,/rejpalcz/cnn-128x128x4-keras-from-scratch-lb-0-328,Human Protein Atlas Image Classification
1832768,0.305,14,47,/kwentar/two-branches-xception-lb-0-3,Human Protein Atlas Image Classification
1794080,0.021,17,50,/kmader/transfer-learning-for-human-protein-submission,Human Protein Atlas Image Classification
2531795,0.433,0,0,/nikhilpandey360/pretrained-resnet34-with-rgby-0-460-public-lb,Human Protein Atlas Image Classification
2404516,0.4636,0,0,/regonn/two-sigma-2018-12-17,Two Sigma: Using News to Predict Stock Movements
8965351,0.96809,0,0,/redwankarimsony/kernel-merge-sub-densenet201-efficentnetb7,Flower Classification with TPUs
12300137,0.95411,0,0,/rizkioktafianto/flower-classification-tpu-enet-b7,Flower Classification with TPUs
9053665,0.95365,0,0,/sohamsave44/flower-classifier-using-enet,Flower Classification with TPUs
9056118,0.95078,0,0,/sohamsave44/flower-classifier-using-densenet-and-enet,Flower Classification with TPUs
10934998,0.00582,0,0,/julianbenny/flower-cnn,Flower Classification with TPUs
9095564,0.9507,0,0,/saumyaborwankar/flower,Flower Classification with TPUs
10281968,0.63365,0,3,/rblcoder/me-trying-tpus-a-simple-tf-2-2-notebook,Flower Classification with TPUs
10229857,0.63958,0,2,/fadzlinrafi/using-tpus-data-augmentation-and-efficientnet,Flower Classification with TPUs
8048227,0.96166,0,0,/akashsuper2000/flowers-on-tpu-ii,Flower Classification with TPUs
9003685,0.97982,0,0,/kintumiku/flower-classification-with-tpu,Flower Classification with TPUs
8553822,0.95759,0,0,/taohoang/getting-started-with-100-flowers-on-tpu,Flower Classification with TPUs
9415150,0.96851,0,1,/coreacasa/flower-classification-tpu-inference-v3,Flower Classification with TPUs
9645803,0.96102,0,1,/ibrahimsobh/flower-classification-tpu-0-96-pub,Flower Classification with TPUs
9334150,0.96112,0,0,/mitkadiver/flowers-on-tpu,Flower Classification with TPUs
9362301,0.96617,0,1,/mutantspore/flower-classification-petals-2a,Flower Classification with TPUs
9427217,0.95609,0,4,/superficiallybot/efficient-net-b7,Flower Classification with TPUs
8899017,0.96385,0,0,/qinhui1999/flowertpuwin-densenet201,Flower Classification with TPUs
8225674,0.9645,0,0,/qinhui1999/fork-of-fork-of-tpu-enet-b7-incepentionresnetv2,Flower Classification with TPUs
8317151,0.96488,1,5,/qinhui1999/flower-classification-enet-b7-densenet-aug,Flower Classification with TPUs
9274348,0.9623,1,10,/ilosvigil/ensemble-lamb-gridmask-on-tpu,Flower Classification with TPUs
9181906,0.97777,0,3,/liangqingyuan/tpu-single-model-enet-b7,Flower Classification with TPUs
8626764,0.95815,0,0,/andyden/efficientnetb7-for-flower-recognition,Flower Classification with TPUs
9387185,0.98201,0,0,/rreddington/flower-tpu-new,Flower Classification with TPUs
9316689,0.9709,0,5,/serosh/accurate-efficientnet-no-ensembling,Flower Classification with TPUs
9182959,0.5718300000000001,3,2,/moizhk/beginner-guide-to-classification-step-1,Flower Classification with TPUs
13511455,0.0567299999999999,0,0,/arko007/knn-car-buy,Don't Get Kicked!
10597789,0.05902,0,2,/roohisharma/don-t-get-kicked,Don't Get Kicked!
10520515,0.0567299999999999,0,0,/teramera/kernel782317bb70,Don't Get Kicked!
437940,0.5888,0,0,/ebertolo/basic-naive-bayes-tutorial-pt-br,Spooky Author Identification
424855,0.4717699999999999,0,0,/ayanmaity/fork-of-spooky-2,Spooky Author Identification
7603827,0.511,0,0,/marcogorelli/fork-of-quick-and-dirty-regression,2019 Data Science Bowl
1562890,1.25011,0,6,/nasirislamsujan/predict-future-sales,Predict Future Sales
1267386,1.02736,1,0,/ressaleh/exponentially-weighted-time-series,Predict Future Sales
1220580,0.90684,225,890,/dlarionov/feature-engineering-xgboost,Predict Future Sales
1144222,1.02615,1,16,/ashishpatel26/predicting-sales-with-a-nested-lstm,Predict Future Sales
1071029,1.21174,3,14,/sarvajna/random-forest-with-variable-importance,Predict Future Sales
911438,1.15345,32,127,/plasticgrammer/future-sales-prediction-playground,Predict Future Sales
886319,1.78338,0,1,/chemaplana/the-accountant-predict-sales-keras-1,Predict Future Sales
918857,1.0428,1,17,/kcbighuge/xgboost-with-item-categories-mapped,Predict Future Sales
681729,3.77216,0,0,/sebask/eda-of-1c-data,Predict Future Sales
11132920,1.02147,0,0,/tracyporter/predict-future-sales-1c-using-sequential,Predict Future Sales
10010423,2.71263,0,0,/saptarshineogi/predicting-future-sales-1-0,Predict Future Sales
10810750,-6.8968,5,25,/jameschapman19/pytorch-tabular-qr-histogram,OSIC Pulmonary Fibrosis Progression
10792655,-6.879,0,1,/saitamaisbest/osic-pulmonary-fibrosis-progression,OSIC Pulmonary Fibrosis Progression
10670018,-6.9176,25,119,/miklgr500/linear-decay-based-on-resnet-cnn,OSIC Pulmonary Fibrosis Progression
10660234,-6.847,7,17,/aadhavvignesh/lb-6-847-osic-keras-starter-optimized-nn,OSIC Pulmonary Fibrosis Progression
10661098,-8.2872,0,5,/legend507/eda-for-osic-pulmonary-fibrosis-progression,OSIC Pulmonary Fibrosis Progression
10618661,-6.877000000000002,8,53,/andypenrose/osic-multiple-quantile-regression-starter,OSIC Pulmonary Fibrosis Progression
10650203,-6.8979,0,9,/ulrich07/pointwise-ensemble-with-deterministic-uncertainty,OSIC Pulmonary Fibrosis Progression
10617537,-6.901,1,10,/satnam007/osic-bayesian-ridge-regression-1,OSIC Pulmonary Fibrosis Progression
10550678,-13.104,27,128,/gunesevitan/osic-pulmonary-fibrosis-progression-eda,OSIC Pulmonary Fibrosis Progression
10580132,-6.912999999999999,2,17,/paulorzp/baseline-ridge,OSIC Pulmonary Fibrosis Progression
10554600,-8.229,9,35,/ulrich07/osic-keras-starter-with-custom-metrics,OSIC Pulmonary Fibrosis Progression
715384,0.1257,1,7,/tackytachie/ml-project-kkbox-churn-or-not-challenge,WSDM - KKBox's Churn Prediction Challenge
496673,0.13516,1,3,/infinitewing/lightgbm-using-scala-label-201703,WSDM - KKBox's Churn Prediction Challenge
413553,0.6106199999999999,3,10,/sudhirnl7/simple-logistic-regression-wisdom,WSDM - KKBox's Churn Prediction Challenge
11383714,0.81182,0,1,/pabloamc/draft-nlp-learning,Natural Language Processing with Disaster Tweets
11264027,0.7912899999999999,0,0,/marcuscheong/disaster-prediction-from-tweets,Natural Language Processing with Disaster Tweets
11256523,0.77137,2,12,/kayademirs/nlp-lr-nb-rf-xgboots,Natural Language Processing with Disaster Tweets
11276724,0.80508,0,12,/kaushiksuresh147/basic-nlp-approach-to-get-0-8050,Natural Language Processing with Disaster Tweets
11186130,0.79865,0,1,/haijingli/easy-workflow-management-with-d6tflow,Natural Language Processing with Disaster Tweets
11247383,0.79313,0,6,/jamestaylor46/disaster-tweets-nlp-first-attempt,Natural Language Processing with Disaster Tweets
7999777,0.83726,0,0,/subratasarkar32/real-or-not-diaster-google-bert,Natural Language Processing with Disaster Tweets
11178131,0.7830199999999999,39,108,/naim99/your-first-nlp-competition-submission,Natural Language Processing with Disaster Tweets
11200399,0.81458,1,9,/sachin93/nlp-disaster-prediction-ulmfit,Natural Language Processing with Disaster Tweets
11027505,0.82194,1,5,/georgedittmar/huggingface-tf-2-0-example,Natural Language Processing with Disaster Tweets
11127042,0.80018,0,8,/galvaowesley/basic-nlp-eda-tensorflow-real-or-fake-tweets,Natural Language Processing with Disaster Tweets
11111164,0.7922100000000001,0,0,/ibrahimnofal/nlp-text-classification-using-scikit-learn-python,Natural Language Processing with Disaster Tweets
11560275,0.01923,0,4,/ajaykumar7778/moa-pytorch-nn-starter,Mechanisms of Action (MoA) Prediction
11520899,0.0204,19,103,/nroman/moa-lightgbm-206-models,Mechanisms of Action (MoA) Prediction
11554014,0.03444,0,6,/sihagmnis36/a-baseline-randomforest-classifier,Mechanisms of Action (MoA) Prediction
11520286,0.02278,6,86,/stanleyjzheng/baseline-nn-with-k-folds,Mechanisms of Action (MoA) Prediction
11543810,0.01907,4,13,/ChristianDenich/neural-net-lr-schedulers-model-checkpts,Mechanisms of Action (MoA) Prediction
11556667,0.02001,0,2,/parmarsuraj99/neural-mechanism-for-moa-keras,Mechanisms of Action (MoA) Prediction
11523325,0.69314,18,42,/supreethmanyam/adversarial-moa-private-test-included,Mechanisms of Action (MoA) Prediction
11526830,0.01927,1,10,/tuckerarrants/moa-eda-nn-ensembling,Mechanisms of Action (MoA) Prediction
11523989,0.02048,0,19,/artgor/inference-kernel,Mechanisms of Action (MoA) Prediction
11531403,0.11557,1,4,/soham1024/mechanisms-of-action-moa-rapids-gpu-knn,Mechanisms of Action (MoA) Prediction
11520317,0.693,0,3,/sajikim/moa-first-notebook-eda,Mechanisms of Action (MoA) Prediction
13225507,0.0182699999999999,0,0,/jared8920/inference-blending-pretrained-4-models-25cf1d,Mechanisms of Action (MoA) Prediction
13196412,0.0183599999999999,0,0,/aeryss/moa-pretrained-non-scored-targets-as-meta-features,Mechanisms of Action (MoA) Prediction
13083671,0.0291,1,0,/douglaskgaraujo/xgboost,Mechanisms of Action (MoA) Prediction
13050688,0.06298,0,0,/simonplatonov/baseline6pseudo,Mechanisms of Action (MoA) Prediction
13046917,0.01825,0,0,/akashsuper2000/training-and-inference-for-3-models,Mechanisms of Action (MoA) Prediction
6552923,0.01719,0,0,/peacemaket/nfl-data-bowl,NFL Big Data Bowl
7908208,0.9625,72,78,/ipythonx/keras-grapheme-gridmask-augmix-ensemble,Bengali.AI Handwritten Grapheme Classification
7910342,0.9382,10,5,/yuyuta/submission-error-solved,Bengali.AI Handwritten Grapheme Classification
7840452,0.9696,28,102,/h030162/version1-0-9696,Bengali.AI Handwritten Grapheme Classification
7903230,0.0614,0,0,/antonp23/kernel3324ec5e6d,Bengali.AI Handwritten Grapheme Classification
7855456,0.906,0,1,/seraphwedd18/three-part-model-training-kernel-only-submission,Bengali.AI Handwritten Grapheme Classification
7816444,0.9663,4,17,/tahirmuslimscientist/bangali-grapheme-seresnext-0-9663,Bengali.AI Handwritten Grapheme Classification
7785036,0.9351,0,4,/wardenga/offline-predictions-with-automl-models-for-bengali,Bengali.AI Handwritten Grapheme Classification
7398358,0.8841,6,1,/abbasidaniyal/bengali-handwritting,Bengali.AI Handwritten Grapheme Classification
7573242,0.0,0,0,/linainversez/bengali-fast-ai-inference,Bengali.AI Handwritten Grapheme Classification
7585778,0.7707,5,12,/chekoduadarsh/multi-output-cnn-starter-kit,Bengali.AI Handwritten Grapheme Classification
7512190,0.9362,33,39,/pnussbaum/grapheme-mind-reader-panv12-nogpu,Bengali.AI Handwritten Grapheme Classification
7476428,0.9483,1,4,/khoongweihao/bengali-ai-eda-deep-multi-output-cnn,Bengali.AI Handwritten Grapheme Classification
7456315,0.9338,6,52,/deshwalmahesh/bengali-ai-complete-beginner-tutorial-95-acc,Bengali.AI Handwritten Grapheme Classification
7510289,0.9393,0,0,/pnussbaum/grapheme-mind-reader-panv00,Bengali.AI Handwritten Grapheme Classification
7301456,0.9547,19,54,/shawon10/bangla-graphemes-image-processing-deep-cnn,Bengali.AI Handwritten Grapheme Classification
7361360,0.8061,5,1,/suryaparsa/bengaliai-resnet50,Bengali.AI Handwritten Grapheme Classification
2330518,0.6659999999999999,0,0,/parvathich/lstm-with-pre-processed-inputs,Quora Insincere Questions Classification
2371509,0.65,0,2,/dakshmiglani/65-cudnn-gru,Quora Insincere Questions Classification
2083654,0.604,1,3,/zhangjb/stacking-with-simple-tf-idf-feature,Quora Insincere Questions Classification
2352507,0.6940000000000001,63,208,/bminixhofer/deterministic-neural-networks-using-pytorch,Quora Insincere Questions Classification
2377870,0.629,0,0,/xsakix/cnn-base-classifier-pretrained-avg-att,Quora Insincere Questions Classification
2046437,0.65,0,18,/ashishpatel26/deep-learning-nlp-quora-solutions,Quora Insincere Questions Classification
2358993,0.69,0,3,/hespozel/testing-platform-choose-and-run,Quora Insincere Questions Classification
2320010,0.696,30,128,/hung96ad/magic-numbers-is-all-you-need-0-696-lb,Quora Insincere Questions Classification
2376591,0.599,0,0,/xsakix/cnn-base-classifier-pretrained-att,Quora Insincere Questions Classification
2119109,0.513,0,0,/alexandruuu/first-submission,Quora Insincere Questions Classification
2142967,0.6609999999999999,0,1,/taranhundal/kernel8ebd6016b8,Quora Insincere Questions Classification
2329670,0.581,0,0,/sinkevichanastasiya/nastuhatashit,Quora Insincere Questions Classification
2317779,0.688,2,0,/snanurag/simple-kernel-glove-and-lstm-only-0-688,Quora Insincere Questions Classification
2314310,0.1889999999999999,0,0,/alexfilippov/first-commit,Quora Insincere Questions Classification
2055888,0.636,0,10,/sdelecourt/simple-lstm-that-does-the-job,Quora Insincere Questions Classification
2291587,0.64,2,3,/xsakix/cnn-word2vec,Quora Insincere Questions Classification
2280251,0.659,1,13,/robertke94/pytorch-textcnn,Quora Insincere Questions Classification
2297332,0.662,0,0,/robertke94/pytorch-bi-lstm,Quora Insincere Questions Classification
2282520,0.613,0,3,/diansheng/mimic-tensorflow-tutorial-f1-0-62,Quora Insincere Questions Classification
2298914,0.535,0,0,/wil2210/nlp-with-machine-learning-mnb,Quora Insincere Questions Classification
2266587,0.66,0,3,/mgancita/logit-regression-detailed-building-of-lstm-model,Quora Insincere Questions Classification
2256029,0.632,0,3,/cristianossd/lstm-without-word-embeddings,Quora Insincere Questions Classification
2231477,0.655,0,0,/sumon23/cnn-lstm-lstm-dnn-ensamble,Quora Insincere Questions Classification
2274752,0.6559999999999999,0,0,/xsakix/all-embeddings-4,Quora Insincere Questions Classification
999234,0.2536,3,11,/meaninglesslives/classifier-hdbscan-helixfitting,TrackML Particle Tracking Challenge
389210,0.45787,0,0,/rajeshmanikka/notebook703a148078,Quora Question Pairs
246482,0.41497,0,0,/shivagangachennnu/quora-question-pairs-final,Quora Question Pairs
11390726,0.81035,0,0,/mmotoki/distilled-logistic-regression,Instant Gratification
8130803,0.7600899999999999,0,2,/darwinwin/instant-h2o-automl,Instant Gratification
4021302,0.95338,1,3,/unilageni/instant-gratification,Instant Gratification
4362670,0.97004,0,0,/jade95/pl-lasso-gmm-pca-qda-aaa,Instant Gratification
4308791,0.96949,2,5,/vaibhavmathur96/instant-gratification-playground,Instant Gratification
4622999,0.7728,0,0,/bleach31/kernel8effbf0a64,Instant Gratification
4041854,0.96118,0,0,/gouzmi/pca-knn-svm-lgb-lr-mlp,Instant Gratification
4402019,0.69166,0,0,/neonninja/xgboost-magic,Instant Gratification
4399207,0.97485,1,5,/cpmpml/simple-avg,Instant Gratification
4394358,0.97024,0,0,/jsdae1/ensemble1,Instant Gratification
4424337,0.97436,7,18,/andrilko/top-score-lb0976-with-qda-and-unsprvsd-g-b-mm,Instant Gratification
4388308,0.97476,1,7,/pietromarinelli/15th-private-38th-plublic-solution,Instant Gratification
4409887,0.97452,3,13,/lovedm/private-0-97603-kneighborsregressor,Instant Gratification
4275635,0.97472,4,11,/zaharch/instant-success-gmm,Instant Gratification
4268385,0.97477,5,10,/raghaw/my-gratification-v2-10th-place-on-public-lb,Instant Gratification
4376897,0.9748,0,4,/kainsama/gmm-v0-2-6,Instant Gratification
4380654,0.97431,0,5,/titericz/with-modif-private-0-97584,Instant Gratification
4391058,0.97439,0,1,/yeonmin/private-proving,Instant Gratification
4341160,0.97109,0,0,/suuuuuu/gl-gmm-with-ls-feat,Instant Gratification
4407454,0.96974,2,4,/hhiraguchi/scipy-multivariate-normal-pdf,Instant Gratification
4366726,0.96924,5,45,/ryomiyazaki/semi-supervised-gaussian-mixture-model-with-em,Instant Gratification
4388893,0.96606,0,3,/ny0893/the-relationship-between-train-data-and-test-data,Instant Gratification
4410715,0.97465,0,0,/alexsemenov/simple-gmm,Instant Gratification
4181923,0.96954,4,1,/bloodybananas/knn-qda,Instant Gratification
2241301,0.69,0,6,/nikhilroxtomar/mix-result-of-cnn-and-lstm-lb-0-690,Quora Insincere Questions Classification
2231861,0.653,1,12,/rohandx1996/attention-in-my-class,Quora Insincere Questions Classification
2164615,0.6579999999999999,10,17,/rohandx1996/cnn-lstm-vs-lstm-vs-n-gram-cnn-vs-n-gram-lstm-cnn,Quora Insincere Questions Classification
2217763,0.639,0,0,/xsakix/embeddings-with-word2vec,Quora Insincere Questions Classification
2232189,0.635,0,0,/xsakix/embeddings-with-word2vec-2,Quora Insincere Questions Classification
2234562,0.608,0,0,/xsakix/embeddings-with-word2vec-3,Quora Insincere Questions Classification
2200776,0.6709999999999999,0,1,/mohith7548/my-notebook-2,Quora Insincere Questions Classification
2146175,0.627,0,1,/dmonga/neural-network,Quora Insincere Questions Classification
2192764,0.684,1,7,/nikhilroxtomar/bilstm-with-kfold-and-data-cleaning-lb-0-684,Quora Insincere Questions Classification
2174501,0.691,21,66,/shujian/single-rnn-with-5-folds-snapshot-ensemble,Quora Insincere Questions Classification
2158901,0.6759999999999999,0,0,/ufoo68/mix-of-nn-models-and-embeddings-preprocessing,Quora Insincere Questions Classification
2156898,0.691,29,89,/suicaokhoailang/beating-the-baseline-with-one-weird-trick-0-691,Quora Insincere Questions Classification
2163783,0.67,5,24,/kagsen/k-fold-on-a-single-model-vs-a-model-salad,Quora Insincere Questions Classification
2173019,0.616,0,0,/zz2k17/simple-cnn-gru,Quora Insincere Questions Classification
2142908,0.6829999999999999,0,4,/braquino/fusion-of-blended-models-in-one-need-help,Quora Insincere Questions Classification
2045361,0.6759999999999999,0,0,/arretvice/detecting-caustic-questions-on-quora,Quora Insincere Questions Classification
2159859,0.597,0,0,/benharris247/keras-cnn-w-attention,Quora Insincere Questions Classification
2112505,0.6659999999999999,0,2,/kalyankkr/quora-questions-insincere-words,Quora Insincere Questions Classification
2141547,0.6679999999999999,2,11,/nitinaggarwal008/basic-model,Quora Insincere Questions Classification
2148274,0.631,0,0,/yashbhalgat/2d-cnn-with-processing,Quora Insincere Questions Classification
2115791,0.687,24,174,/shujian/mix-of-nn-models-based-on-meta-embedding,Quora Insincere Questions Classification
2137495,0.624,0,0,/dineshramasamy/simple-model,Quora Insincere Questions Classification
2123966,0.649,5,8,/fareise/lstm-with-capsule,Quora Insincere Questions Classification
2116343,0.643,0,8,/manrunning/advanced-deep-models-with-pretrained-embeddings,Quora Insincere Questions Classification
2119057,0.6779999999999999,0,0,/nikhilroxtomar/combine-embeddings-combine-results,Quora Insincere Questions Classification
8393379,0.9698,1,2,/rohitsingh9990/ensemble-efficientnet-b3-densenet161,Bengali.AI Handwritten Grapheme Classification
8309326,0.9735,4,58,/shonenkov/ghostnet-densenet121-super-easy-inference,Bengali.AI Handwritten Grapheme Classification
8304308,0.9704,0,7,/shivyshiv/ensemble-of-all-weights,Bengali.AI Handwritten Grapheme Classification
8289380,0.9728,4,14,/salazarslytherin/tensorflow-submission-max-0-9728,Bengali.AI Handwritten Grapheme Classification
7994600,0.9692,10,18,/kaushal2896/resnet34-efficientnetb3-ensemble,Bengali.AI Handwritten Grapheme Classification
8266984,0.9703,4,7,/anushakarthik1991/using-keras-efficientnet,Bengali.AI Handwritten Grapheme Classification
8231025,0.9457,0,0,/ddhawan/bengali-char-recog2,Bengali.AI Handwritten Grapheme Classification
8233754,0.9613,16,26,/saife245/handwritten-grapheme-classification-resnet-0-97,Bengali.AI Handwritten Grapheme Classification
8147077,0.9555,0,1,/lakshit77/resnet34-pytorch,Bengali.AI Handwritten Grapheme Classification
8186452,0.0961,3,5,/jasonyikim/data-augmentation-infogan,Bengali.AI Handwritten Grapheme Classification
8143596,0.953,2,11,/gopidurgaprasad/efficietnet3-pytorch-training-inference,Bengali.AI Handwritten Grapheme Classification
8128114,0.9613,20,38,/gopidurgaprasad/resnet34-inference-by-abhishek-thakur,Bengali.AI Handwritten Grapheme Classification
8103650,0.9703,109,158,/rsmits/keras-efficientnet-b3-training-inference,Bengali.AI Handwritten Grapheme Classification
8017677,0.9598,2,3,/amaity0/bengali-grapheme-multioutput-inference,Bengali.AI Handwritten Grapheme Classification
7979644,0.9694,0,1,/aryanshomray/fork-of-kernel33f49d2e20,Bengali.AI Handwritten Grapheme Classification
4299819,0.8014,0,10,/mks2192/stumbleupon-eda-and-model-baseline,StumbleUpon Evergreen Classification Challenge
12968273,0.06935,0,0,/nikolaypirozhkov/moapie,Mechanisms of Action (MoA) Prediction
12909599,0.01831,0,0,/akashsuper2000/simple-deep-learning-model-for-moa-part-2,Mechanisms of Action (MoA) Prediction
12636143,0.0184,0,0,/akashsuper2000/moa-pytorch-rankgauss-pca-nn-upgrade-3d-visual,Mechanisms of Action (MoA) Prediction
12463079,0.69314,0,0,/dariapetrenko/sample-submission,Mechanisms of Action (MoA) Prediction
11533549,0.01937,10,60,/isaienkov/keras-autoencoder-dae-neural-network-starter,Mechanisms of Action (MoA) Prediction
10852971,0.75666,0,0,/jiukeem/real-or-not-real,Natural Language Processing with Disaster Tweets
11060823,0.76647,0,2,/myonin/custom-tfidf-and-logisticregression-with-pytorch,Natural Language Processing with Disaster Tweets
10817978,0.84921,1,9,/sokolheavy/multi-dropout-aug-oof,Natural Language Processing with Disaster Tweets
10984305,0.8133600000000001,1,6,/aditya08/twitter-sentiment-bert-finetuning,Natural Language Processing with Disaster Tweets
10944529,0.84186,1,20,/tuckerarrants/bert-with-huggingface-transformers,Natural Language Processing with Disaster Tweets
10751612,0.80661,4,5,/apresswala52/classifying-disaster-tweets-top-30,Natural Language Processing with Disaster Tweets
10709840,0.83083,1,3,/alexandertesemnikov/bert-pytorch-pretrained-fine-tuning,Natural Language Processing with Disaster Tweets
10807669,0.77137,0,8,/jagadeesh23/disaster-tweet-classifier,Natural Language Processing with Disaster Tweets
10894948,0.79895,0,0,/parkyounghun/kerneld708a284f3,Natural Language Processing with Disaster Tweets
10834226,0.80937,1,7,/socathie/glove-embeddings-sentence-classification,Natural Language Processing with Disaster Tweets
10878074,0.79589,0,0,/hiropi/kernel4fa05fbe4d,Natural Language Processing with Disaster Tweets
10844129,0.79589,0,0,/yumakomoto/fork-of-kernel62555b9134,Natural Language Processing with Disaster Tweets
10744447,0.79037,4,8,/sachinsharma1123/simple-nlp-model-using-svm,Natural Language Processing with Disaster Tweets
10789191,0.78761,0,0,/yumakomoto/kernel62555b9134,Natural Language Processing with Disaster Tweets
11246469,-7.4686,0,0,/nike0good/notebook610db9ae3c,OSIC Pulmonary Fibrosis Progression
11186088,-6.8986,1,11,/abiolatti/train-baseline,OSIC Pulmonary Fibrosis Progression
11171848,-9.3831,5,10,/srikanthpotukuchi/linear-regression-model-with-submission,OSIC Pulmonary Fibrosis Progression
11117034,-6.8322,0,19,/mekhdigakhramanian/forked-osic-multiple-quantile-regression,OSIC Pulmonary Fibrosis Progression
11108552,-7.037000000000001,3,7,/ozoozo/osic-simple-eda-and-basic-lightgbm,OSIC Pulmonary Fibrosis Progression
10871902,-7.2457,0,17,/niksapraljak/osic-using-rapids-ai,OSIC Pulmonary Fibrosis Progression
10931195,-6.9841,4,45,/havinath/eda-observations-visualizations-pytorch,OSIC Pulmonary Fibrosis Progression
10764302,-6.8677,0,12,/satnam007/osic-intended,OSIC Pulmonary Fibrosis Progression
10842704,-6.8322,8,53,/jagadish13/osic-multiple-quantile-regression-eda,OSIC Pulmonary Fibrosis Progression
4528949,0.93711,0,0,/yetman/simple-gru-bert,Jigsaw Unintended Bias in Toxicity Classification
10781102,0.0,0,3,/omarkhald/toxicity-classification-ipynb,Jigsaw Unintended Bias in Toxicity Classification
4351581,0.5513899999999999,0,0,/marinan67/kernel-kerasregressor-tfidf,Jigsaw Unintended Bias in Toxicity Classification
4332030,0.89909,0,0,/yeayates21/bert-v6,Jigsaw Unintended Bias in Toxicity Classification
7321883,0.0,0,1,/bparesh/bilstm-jigsaw,Jigsaw Unintended Bias in Toxicity Classification
6801713,0.0,0,0,/bhuvan93/simple-lstm-pytorch-version,Jigsaw Unintended Bias in Toxicity Classification
3943093,0.89485,0,0,/sukpyohong/lstm1st,Jigsaw Unintended Bias in Toxicity Classification
6677599,0.0,3,4,/saranyashalya/pytorch-bert-sequence-classification,Jigsaw Unintended Bias in Toxicity Classification
3906966,0.93297,0,0,/abimannan/lstm-with-parameters,Jigsaw Unintended Bias in Toxicity Classification
5612390,0.0,0,3,/s8a496b/char-model-test,Jigsaw Unintended Bias in Toxicity Classification
4337171,0.93895,0,0,/mzmey37/bert-usage,Jigsaw Unintended Bias in Toxicity Classification
4032066,0.91254,0,0,/arcticmarmot/using-conv1d-and-gru,Jigsaw Unintended Bias in Toxicity Classification
5471882,0.0,0,4,/nihal24/toxicity-with-simple-single-gru-layer,Jigsaw Unintended Bias in Toxicity Classification
4515781,0.94201,0,0,/splacorn/submission1-inference-4-360-1-220,Jigsaw Unintended Bias in Toxicity Classification
3543397,0.93563,0,5,/shishu1421/simple-lstm-with-identity-parameters-fastai,Jigsaw Unintended Bias in Toxicity Classification
4791572,0.0,0,1,/yosefardhito/pytorch-lstm-end-to-end-walkthrough,Jigsaw Unintended Bias in Toxicity Classification
4061876,0.94258,0,3,/phseidl/pytorch-bert-fast-bucket-inference-blend-individ,Jigsaw Unintended Bias in Toxicity Classification
4527865,0.94713,1,24,/matsuik/fork-of-pred-lstm-gpt2-bu12c45-lu0c1-ftpe18-7c1c82,Jigsaw Unintended Bias in Toxicity Classification
4552496,0.92076,0,4,/christianmarechal/futur-sales-xgboost-lstm,Predict Future Sales
4252360,1.21792,0,0,/vikrantdh/future-sales,Predict Future Sales
4360252,2.83808,2,2,/yzx915/gb-only,Predict Future Sales
4131728,1.14466,0,1,/wardnath/predict-future-sales-analytics-20190603,Predict Future Sales
3835471,0.9065,0,1,/jatinmittal0001/predict-future-sales-part-2,Predict Future Sales
3960239,0.91345,5,6,/emaksone/linear-model-xgboost-and-stacking,Predict Future Sales
3857573,1.42469,0,0,/bauuuu1021/mid-project,Predict Future Sales
3847874,0.91126,0,0,/s104ats/kernelf1941529c6,Predict Future Sales
2830693,1.0368,0,2,/chandraroy/simple-time-series-analytic,Predict Future Sales
2725027,1.02083,1,6,/econdata/predicting-future-sales-with-lstm,Predict Future Sales
1894154,1.06262,0,0,/baoanh/lightgbm-future-sales-predict,Predict Future Sales
2323228,1.09624,19,96,/dimitreoliveira/time-series-forecasting-with-lstm-autoencoders,Predict Future Sales
2130246,1.1383,120,343,/dimitreoliveira/model-stacking-feature-engineering-and-eda,Predict Future Sales
2125033,3.14434,0,0,/uni20151096/cse463-project-2-ksh,Predict Future Sales
1609431,0.90646,4,49,/dhimananubhav/feature-engineering-xgboost,Predict Future Sales
7093572,0.505,0,0,/keremt/fastai-model-part2-regression,2019 Data Science Bowl
442928,0.33157,3,33,/marcospinaci/talking-plots-2-adding-grammar,Spooky Author Identification
437752,0.5888,0,7,/wmariusso/basic-naive-bayes-tutorial-pt-br,Spooky Author Identification
413433,0.35613,28,139,/maheshdadhich/creative-feature-engineering-lb-0-35,Spooky Author Identification
412100,0.5354899999999999,0,1,/tlegrand/happy-halloween,Spooky Author Identification
409433,0.8984200000000001,1,3,/raresbarbantan/one-line-spooky-classifier,Spooky Author Identification
407968,0.47296,2,7,/rwexler/eda-for-spooky-author-identification,Spooky Author Identification
5644971,0.653,27,73,/gogo827jz/resunet-keras-with-some-new-ideas,Understanding Clouds from Satellite Images
5487454,0.466,1,3,/mukul1904/in-the-clouds-eda-and-first-submission,Understanding Clouds from Satellite Images
5379347,0.237,0,2,/rabbitcaptain/kernelb4202f856f,Understanding Clouds from Satellite Images
5374704,0.621,40,52,/dimitreoliveira/understanding-clouds-eda-and-keras-u-net,Understanding Clouds from Satellite Images
5361357,0.594,22,136,/xhlulu/satellite-clouds-u-net-with-resnet-encoder,Understanding Clouds from Satellite Images
1899396,0.62774,2,2,/sunqiankun/day-1,Two Sigma: Using News to Predict Stock Movements
1852667,0.57628,0,2,/returnofsputnik/feature-engineer-to-improve-your-score-v2,Two Sigma: Using News to Predict Stock Movements
1789878,0.0369699999999999,0,0,/ryancaldwell/learn-how-to-use-module,Two Sigma: Using News to Predict Stock Movements
1735862,0.03785,0,13,/ashishpatel26/light-gbm-on-market-data,Two Sigma: Using News to Predict Stock Movements
1776933,0.57272,0,4,/blackboards/use-1w-rows-to-reach-0-5,Two Sigma: Using News to Predict Stock Movements
1737526,0.4682699999999999,41,290,/bguberfain/a-simple-model-using-the-market-and-news-data,Two Sigma: Using News to Predict Stock Movements
1722429,0.0708,141,988,/dster/two-sigma-news-official-getting-started-kernel,Two Sigma: Using News to Predict Stock Movements
1730311,0.17654,3,29,/sjdlloyd/it-s-fake-news-this-is-top-of-the-leaderboard,Two Sigma: Using News to Predict Stock Movements
7263353,69761.84,0,0,/takbull/optimization-preference-cost-mincostflow,Santa's Workshop Tour 2019
2529523,0.305,0,0,/nikhilpandey360/tensorflow-transfer-learning,Human Protein Atlas Image Classification
2275240,0.018,0,0,/sujatak44/dl-xferlearn-finetune-inceptionresnetv2,Human Protein Atlas Image Classification
3229329,0.03691,0,0,/mohanamurali/pretrained-inceptionresnetv2,Human Protein Atlas Image Classification
2605023,0.20829,0,1,/jmourad100/4-channel-v2-with-rare-plus-threshold-and-swa,Human Protein Atlas Image Classification
2423591,0.324,0,2,/nikhilpandey360/rgby-adam-sigmoid-500-10-ep-10-inception,Human Protein Atlas Image Classification
2446659,0.451,0,18,/guntherthepenguin/fastaiv1-weighted-loss-oversampling,Human Protein Atlas Image Classification
2420788,0.4429999999999999,10,9,/wordroid/resnet50-4x256-globalmax-lb-0-443,Human Protein Atlas Image Classification
2350890,0.021,0,2,/gaojunsu/fork-of-ee258-project-2-inception-v4,Human Protein Atlas Image Classification
2324956,0.0069999999999999,0,1,/gaojunsu/ee258-project2-seresnet50,Human Protein Atlas Image Classification
1820142,0.387,8,12,/nikhilroxtomar/pretrained-inceptionresnetv2-lb-0-390,Human Protein Atlas Image Classification
2034579,0.355,5,4,/alexus1000/nasnet-mobile,Human Protein Atlas Image Classification
1802596,0.074,0,1,/rickychwong/protein-nb,Human Protein Atlas Image Classification
9140499,0.96221,0,0,/zungmann/densenet-with-100-flowers-on-tpu,Flower Classification with TPUs
8989212,0.90704,1,9,/akshat4112/104-flower-classification-tpus-efficientnet-b7,Flower Classification with TPUs
9097594,0.96323,1,5,/rhtsingh/tpu-flower-classification,Flower Classification with TPUs
8945721,0.96001,0,0,/chrismartinis/100-what-flower,Flower Classification with TPUs
8713478,0.96172,2,5,/maianz/data-augmentation-naive-ensemble,Flower Classification with TPUs
8387932,0.93838,0,0,/leosuky/flower-classification-with-tpu-efficientnet,Flower Classification with TPUs
8967234,0.00129,0,0,/gauravjagnani/flower-classification-with-tpus,Flower Classification with TPUs
8892632,0.96531,3,34,/ar2017/flower-classification-with-densenet201-enetb7,Flower Classification with TPUs
8905462,0.93924,1,3,/tbourton/transfer-learning-densenet201,Flower Classification with TPUs
8832542,0.95699,0,1,/calebeverett/efficientnetb7-with-transformation,Flower Classification with TPUs
8519592,0.9662,1,14,/luonganhtuan93/ensemble-enet-b7-and-densenet201,Flower Classification with TPUs
8357552,0.24686,0,1,/alexjj/getting-started-with-100-flowers-on-tpu,Flower Classification with TPUs
8128326,0.63768,0,2,/rbsathish27/fork-of-flower-classifier,Flower Classification with TPUs
8186867,0.96112,0,2,/darwinwin/tpu-enet-b7-densenet,Flower Classification with TPUs
8169667,0.38356,1,1,/nickshow11/flower-classification-tpu,Flower Classification with TPUs
7983966,0.9619,4,20,/anyexiezouqu/tpu-enet-b7-xception-densnet201,Flower Classification with TPUs
7938614,0.96029,0,0,/shank885/flower-classification-with-tpu,Flower Classification with TPUs
7964304,0.96487,2,6,/volody/kernel-mashup,Flower Classification with TPUs
11088963,0.99378,1,2,/aranga81/kickoff-with-simple-cnn-tf2,Digit Recognizer
11056782,0.97414,0,0,/aymen311/kernel28920e353a,Digit Recognizer
11046742,0.97485,1,6,/hoangnguyen719/simple-tricks-that-greatly-improve-your-nets,Digit Recognizer
11048711,0.99328,2,9,/viroviro/introduction-to-cnns,Digit Recognizer
11025326,0.99207,1,8,/simo333/digit-recog3-0-kfold-99,Digit Recognizer
11023722,0.98632,0,9,/sachin93/digit-recognizer-fastai,Digit Recognizer
11064537,0.98717,0,0,/yujiyamauchi/kernel2cffdab16b,Digit Recognizer
10987682,0.995,0,1,/lulzseq/tensorflow-mnist-accuracy-99-542,Digit Recognizer
10966393,0.0978899999999999,0,0,/bobsapps/kernel27bfd1f363,Digit Recognizer
10939062,0.99375,1,7,/socathie/multiple-model-ensemble,Digit Recognizer
10934618,0.96589,0,4,/maxmar/random-forest-to-recognize-image,Digit Recognizer
10671992,0.98407,0,1,/kamlesh1102/digit-recognizer-using-tensorflow,Digit Recognizer
10874331,0.97675,0,0,/joaossmacedo/mnist-basic-nn,Digit Recognizer
10923554,0.98707,0,0,/maimahdi/digitrecognizer,Digit Recognizer
10932587,0.99378,0,0,/datasciencebio/data-science-e-200731,Digit Recognizer
9797336,0.8690000000000001,0,1,/barteksadlej123/keras-efficientnetb2,SIIM-ISIC Melanoma Classification
9762300,0.8190000000000001,3,5,/elbanan/clinical-imaging-features-pipeline-poc-w-radtorch,SIIM-ISIC Melanoma Classification
9746103,0.682,0,7,/pragyanbo/exploring-the-patients-data-and-starter-model,SIIM-ISIC Melanoma Classification
9747746,0.838,0,2,/yeayates21/siim-keras-efficientnetb0-starter-tfrec-tpu,SIIM-ISIC Melanoma Classification
11114084,0.938,0,0,/yeon1234/train-cv,SIIM-ISIC Melanoma Classification
10998897,0.7489,0,0,/priteshshrivastava/tabular-data-model,SIIM-ISIC Melanoma Classification
10774888,0.9461,0,0,/chiraggodaw/efn-with-cv,SIIM-ISIC Melanoma Classification
10338009,0.904,0,0,/hubinb/siim-isic-hh,SIIM-ISIC Melanoma Classification
7612806,0.5660000000000001,0,2,/a763337092/3models-add-feat-clip-time,2019 Data Science Bowl
7394053,0.524,0,0,/shubhampandey27/regclass,2019 Data Science Bowl
7451734,0.525,0,1,/manishkumarnaik999/manishfirst,2019 Data Science Bowl
2957433,0.3923,0,0,/zameji/predict,Google Cloud & NCAA® ML Competition 2019-Women's
3111971,0.44745,0,3,/hsinwenchang/lgbm-parameter-tuning,Google Cloud & NCAA® ML Competition 2019-Women's
3231074,0.10964,0,3,/lavanyadml/google-cloud-ncaa-ml-competition-2019-women-s,Google Cloud & NCAA® ML Competition 2019-Women's
3030999,0.12235,1,14,/duketemon/random-forest,Google Cloud & NCAA® ML Competition 2019-Women's
2959852,0.4706399999999999,1,7,/addisonhoward/basic-starter-kernel-ncaa-women-s-dataset-2019,Google Cloud & NCAA® ML Competition 2019-Women's
9350686,0.3064199999999999,0,0,/ouwyukha/imba-turicreate-itemsim-cosine,Instacart Market Basket Analysis
9339753,0.30644,0,0,/ouwyukha/imba-turicreate-fr-adagrad-pol,Instacart Market Basket Analysis
9339582,0.0930899999999999,0,0,/ouwyukha/imba-surprise-normalpredictor,Instacart Market Basket Analysis
9339566,0.0930899999999999,0,0,/ouwyukha/imba-surprise-coclustering,Instacart Market Basket Analysis
9301428,0.06552,0,0,/ouwyukha/imba-surprise-svdpp,Instacart Market Basket Analysis
4060896,0.37674,0,0,/dimosraptis/instacart-ml-xgboost-last5,Instacart Market Basket Analysis
10563982,-7.022,1,5,/ragnar123/osic-simple-lgbm-rf-baseline,OSIC Pulmonary Fibrosis Progression
10558551,-6.959,0,10,/mohitkaushik12/eda-and-model,OSIC Pulmonary Fibrosis Progression
12123049,-6.8071,0,0,/akashsuper2000/osic-ensemble-modelling-2,OSIC Pulmonary Fibrosis Progression
11999237,-6.8076,0,0,/akashsuper2000/osic-ensemble-modeling-learning,OSIC Pulmonary Fibrosis Progression
44352,0.83642,0,3,/cast42/xgboost-after-removal-columns-std-0,Santander Customer Satisfaction
3990164,0.8938299999999999,0,3,/kishore6157/jigsaw-toxicity-classification,Jigsaw Unintended Bias in Toxicity Classification
3963668,0.93699,17,94,/cristinasierra/pretext-lstm-tuning-v3,Jigsaw Unintended Bias in Toxicity Classification
3953497,0.91988,0,1,/andaoba1/preproccesing-and-cudnnlstm,Jigsaw Unintended Bias in Toxicity Classification
3945621,0.93371,0,0,/afmartinezt/miia-submit-1,Jigsaw Unintended Bias in Toxicity Classification
3460617,0.92575,0,0,/shakespere/keras-baseline-lstm-att-5-fold-bn-dp-2embedding-l,Jigsaw Unintended Bias in Toxicity Classification
3965696,0.9222,0,0,/rangaritab/kernelb79e9290dc,Jigsaw Unintended Bias in Toxicity Classification
3958747,0.93073,0,0,/hvjaime/kernel2d3a6ba71d,Jigsaw Unintended Bias in Toxicity Classification
3890344,0.93628,20,294,/christofhenkel/how-to-preprocessing-for-glove-part2-usage,Jigsaw Unintended Bias in Toxicity Classification
3899849,0.8780600000000001,0,0,/rangaritab/p3-team12-v3,Jigsaw Unintended Bias in Toxicity Classification
3915533,0.64667,0,1,/andaoba1/ensayo-p3,Jigsaw Unintended Bias in Toxicity Classification
3720682,0.92892,19,36,/kenkrige/bert-inference-for-upload,Jigsaw Unintended Bias in Toxicity Classification
3796056,0.88859,0,0,/hydrilla/kerneld016f7f293,Jigsaw Unintended Bias in Toxicity Classification
3786637,0.8620899999999999,0,0,/albahnsen/p3-samplesolution,Jigsaw Unintended Bias in Toxicity Classification
3727902,0.74341,0,0,/ivansv/text-mining-toxicity,Jigsaw Unintended Bias in Toxicity Classification
3769297,0.75142,0,0,/sklasfeld/predict-toxicity-v1,Jigsaw Unintended Bias in Toxicity Classification
3738973,0.89359,0,1,/gilads/dnn1-gilad,Jigsaw Unintended Bias in Toxicity Classification
3651165,0.89586,0,2,/maxl28618/toxic-comments-baseline-model,Jigsaw Unintended Bias in Toxicity Classification
3671641,0.8960100000000001,0,0,/cibi075/kernal-cb-t,Jigsaw Unintended Bias in Toxicity Classification
11836764,0.5623600000000001,0,4,/rog007/nlp-disaster-tweets-using-tf-idf,Natural Language Processing with Disaster Tweets
11869856,0.4505,0,1,/kkhandekar/disaster-tweet-nlp-bi-gru,Natural Language Processing with Disaster Tweets
10968045,0.78087,0,0,/jaishanker/tweet-classification-using-cnn,Natural Language Processing with Disaster Tweets
11693519,0.7900699999999999,0,1,/jaipaldeora/naive-bayes-real-or-fake-disaster,Natural Language Processing with Disaster Tweets
11679459,0.8155,1,5,/marktsvirko/spacy-simple-mlp-81-in-5-min,Natural Language Processing with Disaster Tweets
11663707,0.8011,0,0,/hajimetch/nlp-getting-started,Natural Language Processing with Disaster Tweets
11402561,0.77781,0,1,/svenjaf/mixed-input-model,Natural Language Processing with Disaster Tweets
11542344,0.8335799999999999,1,4,/bercarucostin/nlp-disaster-tweets,Natural Language Processing with Disaster Tweets
11482738,0.81765,0,4,/officialshivanandroy/xlnet-out-of-the-box-performance,Natural Language Processing with Disaster Tweets
11583455,0.78087,0,2,/ekzemplaro/real-or-not-sep07,Natural Language Processing with Disaster Tweets
11572776,0.7934399999999999,1,6,/chiragsidhdhapura/basic-eda-tf-idf-and-logistic-regression,Natural Language Processing with Disaster Tweets
11554408,0.79742,0,7,/musama619/conventional-nlp-steps,Natural Language Processing with Disaster Tweets
11603268,0.8124399999999999,0,0,/ikechan/bert-with-pytorch-examples,Natural Language Processing with Disaster Tweets
11486803,1.0,2,9,/ahmedmurad1990/nlp-disaster-tweets-with-electra-base,Natural Language Processing with Disaster Tweets
9171532,0.64174,0,0,/anshagrawal93/twitter-prediction,Natural Language Processing with Disaster Tweets
11286674,0.80232,0,1,/jeeperscreepers/real-or-not-disaster-tweets,Natural Language Processing with Disaster Tweets
112245,1.23807,0,1,/towhid1/lineardiscriminantanalysis,Leaf Classification
109294,0.07947,10,7,/towhid1/rforest-with-calibration-score-0-07948,Leaf Classification
108964,0.0198099999999999,0,6,/yhyu13/leaf-classification-with-sklearn-mlp-lbfgs,Leaf Classification
234216,0.35372,0,0,/badoun/data-analysis-xgboost-starter-0-35460-lb-0e9cbe,Quora Question Pairs
230710,0.35372,0,0,/smartbao/data-analysis-xgboost-starter-0-35460-lb,Quora Question Pairs
3976835,0.52503,3,11,/snakayama/lightgbm-using-optuna-optuna-lightgbm,Instant Gratification
3951536,0.87798,20,109,/speedwagon/neural-network-baseline,Instant Gratification
3963665,0.80206,4,10,/baomengjiao/512-nn,Instant Gratification
3970517,0.7945399999999999,0,3,/mhviraf/ig-data-augmentation,Instant Gratification
3941479,0.94209,10,121,/tunguz/instant-eda,Instant Gratification
3938480,0.95708,5,32,/jazivxt/cognitive-conditioning,Instant Gratification
3939413,0.83078,4,25,/artgor/pytorch-model,Instant Gratification
3940358,0.80374,1,15,/atikur/instant-gratification-fastai-starter,Instant Gratification
3937833,0.52818,0,7,/baomengjiao/nn-with-reducelr,Instant Gratification
3960805,0.80585,0,0,/marcocarnini/instant-gratification-fastai-starter,Instant Gratification
4542782,0.97412,0,0,/ryomiyazaki/semi-supervised-multi-clustered-gmm-with-em,Instant Gratification
4401715,0.97484,0,0,/tks0123456789/sc-gm,Instant Gratification
6272466,0.36041,0,3,/qqgeogor/starter-with-more-features-and-complex-model,Quora Question Pairs
4304662,0.48575,0,5,/simpletonwang/dl-hw3,Quora Question Pairs
4363389,0.43241,0,0,/dummydevacc/character-level-cnn-classification-with-dilations,Quora Question Pairs
1400615,6.01888,0,3,/ananthreddy/only-tf-idf-vectors,Quora Question Pairs
535236,0.49958,0,1,/prashant10/simplexgb,Quora Question Pairs
4318891,0.9666,3,9,/tayorm/auto-ml-tpot-qda-pseudo-labelling,Instant Gratification
4322699,0.95209,0,3,/nikhileshp12/using-lr-for-instant-gratification,Instant Gratification
4286010,0.96571,10,23,/code1110/best-parameter-s-for-qda,Instant Gratification
4176713,0.97008,4,41,/raghaw/flip-y,Instant Gratification
4233448,0.70804,0,8,/basu369victor/lets-solve-instant-gratification-with-knn,Instant Gratification
4219161,0.52205,2,7,/matsumotoshintaro/simple-model-for-beginner,Instant Gratification
4199247,0.96949,13,80,/christofhenkel/lets-implement-qda-by-ourself,Instant Gratification
4205310,0.93116,2,8,/scaomath/insta-knn-numpy-from-scratch,Instant Gratification
4085149,0.8433700000000001,0,1,/aritrase/gratification-kstratifiedfold-pytorchmodel,Instant Gratification
4129881,0.96762,0,2,/juminator/qda-baseline2-nusvc-baseline-qda-nusvc-blends,Instant Gratification
4116852,0.96668,0,0,/laymanbrother/qda-nusvc-ensamble,Instant Gratification
4142798,0.96588,1,2,/umemiyaumeume/k-means,Instant Gratification
4143845,0.52265,0,0,/jesicajones/instant-gratification-kernel-ankita,Instant Gratification
4135364,0.96595,0,3,/barrozo/multifold-qda,Instant Gratification
4129322,0.96588,0,1,/juminator/qda-baseline,Instant Gratification
4080108,0.9659,33,217,/speedwagon/quadratic-discriminant-analysis,Instant Gratification
4066850,0.96073,0,1,/bhavikbb/instant-gratification,Instant Gratification
4039320,0.96964,0,22,/plasticgrammer/instant-gratification-playground,Instant Gratification
4037098,0.96087,0,9,/jazivxt/pca-nusvc-2,Instant Gratification
4026039,0.96584,0,16,/dimitreoliveira/ensembling-and-evaluating-magic-models,Instant Gratification
4007753,0.95653,1,31,/jazivxt/code-golf-cognitive-illusion-ruler-check,Instant Gratification
3967470,0.92895,46,280,/cdeotte/support-vector-machine-0-925,Instant Gratification
3997273,0.84939,0,3,/marcocarnini/experimenting-with-a-feature-and-many-classifiers,Instant Gratification
3982945,0.92377,3,7,/qiaoshiji/512-knn-with-variance-feature-selection-copy,Instant Gratification
7810764,0.9683,0,0,/ibraheemmoosa/fork-of-bangla-handwritten-grapheme-inference,Jigsaw Unintended Bias in Toxicity Classification
13749257,0.9579,0,0,/jamesccc/bengali-fusion,Jigsaw Unintended Bias in Toxicity Classification
13748116,0.9533,0,0,/jia072/bengali-ai,Jigsaw Unintended Bias in Toxicity Classification
8224012,0.9671,0,0,/brodzik/bengali-ai-inference,Bengali.AI Handwritten Grapheme Classification
10922720,0.9482,0,3,/sinfini8/bengali-ai-grapheme-classification,Bengali.AI Handwritten Grapheme Classification
7594742,0.9522,0,0,/davlanigan/bengali-characters-simplenet,Bengali.AI Handwritten Grapheme Classification
9231749,0.9763,0,0,/garyongguanjie/seresnext-50-inference,Bengali.AI Handwritten Grapheme Classification
8016726,0.9432,0,0,/blaxkdolphin/simplecnn-resnet34-multihead-classifer,Bengali.AI Handwritten Grapheme Classification
8648569,0.9593,0,0,/leonisviridis/bengali-submission-2-0,Bengali.AI Handwritten Grapheme Classification
8492241,0.9284,0,0,/ajinkyaindulkar/sub-mlip-model-noaug,Bengali.AI Handwritten Grapheme Classification
12374283,0.8391,0,1,/paulpidou/nlp-with-disaster-tweets-naive-bayes-vs-bert,Natural Language Processing with Disaster Tweets
12353377,0.78915,1,2,/samawel97/disaster-tweet-classification-with-l-regression,Natural Language Processing with Disaster Tweets
12289929,0.8332799999999999,0,2,/hak999/tweetdistilbert,Natural Language Processing with Disaster Tweets
12279403,0.7900699999999999,2,4,/alicanolgen/predict-disaster-tweets-using-naive-bayes,Natural Language Processing with Disaster Tweets
12244401,0.78577,0,1,/shuditkumar/nlp-with-disaster-tweets,Natural Language Processing with Disaster Tweets
12233709,0.8004899999999999,0,2,/vaishnavkapil/nlp-disaster-real-or-not,Natural Language Processing with Disaster Tweets
12207047,0.8403299999999999,0,0,/sapthrishi007/tweet-finetune-bert,Natural Language Processing with Disaster Tweets
12109687,0.8329700000000001,2,8,/deepakat002/bert-as-classifier-disaster-tweets,Natural Language Processing with Disaster Tweets
12111666,0.7885300000000001,0,1,/vaishnavikhilari/nlp-with-disaster-tweets,Natural Language Processing with Disaster Tweets
12081387,0.81795,0,1,/cristianfat/looking-for-disasters-on-twitter,Natural Language Processing with Disaster Tweets
12026152,0.7912899999999999,0,0,/makhalil/notebook12233,Natural Language Processing with Disaster Tweets
12016286,0.7900699999999999,0,0,/shehabhosny/solution-1,Natural Language Processing with Disaster Tweets
11951608,0.83205,0,21,/naim99/text-classificatio-ktrain-keras-bert,Natural Language Processing with Disaster Tweets
11921164,0.8351200000000001,0,4,/pankhilprajapati/bert-disaster-classifier,Natural Language Processing with Disaster Tweets
11911886,0.71713,0,1,/proribone/adkkhn-a-doc2vec,Natural Language Processing with Disaster Tweets
11885260,0.79865,0,3,/dipankarsrirag/competition-tweets-with-ensembling,Natural Language Processing with Disaster Tweets
7563191,0.7992600000000001,0,0,/spiiiii/real-or-not-svm,Natural Language Processing with Disaster Tweets
51300,0.838335,0,1,/towhid1/checking,Santander Customer Satisfaction
50073,0.839109,0,0,/zihexu/lb-0-84-for-starters,Santander Customer Satisfaction
47644,0.834765,0,0,/omarelgabry/santander-correlation-predictions,Santander Customer Satisfaction
46763,0.839089,0,0,/srodriguex/lb-0-84-for-starters,Santander Customer Satisfaction
46409,0.817351,0,0,/bakiev/lb-0-84-for-starters,Santander Customer Satisfaction
44791,0.61095,3,2,/rishikksh20/basic-data-analysis-and-prediction,Santander Customer Satisfaction
12911093,0.5179,0,0,/teramera/notebookf31f32c43a,Sentiment Analysis on Movie Reviews
11354257,0.59323,0,0,/najiaboo/notebookf251803aab,Sentiment Analysis on Movie Reviews
2067772,0.49241,0,0,/mahesharvinds/rotten-tomatoes-movie-review-sentimental-analysis,Sentiment Analysis on Movie Reviews
9612396,0.6942,4,5,/carmensandiego/keras-bert-tfhub-scores-0-695,Sentiment Analysis on Movie Reviews
7002226,0.59672,0,0,/shrinidhin/rottentomatoes-kernel36a56b9b62,Sentiment Analysis on Movie Reviews
4794020,0.6503399999999999,0,3,/nikkisharma536/movie-review-sentiment-deep-learning-fastai,Sentiment Analysis on Movie Reviews
3995628,0.61574,0,3,/harishreddy18/sentiment-analysis-on-movie-review,Sentiment Analysis on Movie Reviews
3468269,0.57726,0,1,/julienicula/sa-movie-reviews,Sentiment Analysis on Movie Reviews
2380733,0.64615,13,75,/chiranjeevbit/movie-review-prediction,Sentiment Analysis on Movie Reviews
4770480,0.58358,0,0,/apoorvm/sentiment-analysis-movie,Sentiment Analysis on Movie Reviews
4503550,0.94057,1,2,/snakayama/first-bert-model-yeahhhh,Jigsaw Unintended Bias in Toxicity Classification
3608905,0.92438,0,0,/adarsh415/jigsaw-classification-problem,Jigsaw Unintended Bias in Toxicity Classification
4424501,0.93449,3,5,/codeastar/load-keras-rnn-model,Jigsaw Unintended Bias in Toxicity Classification
4136242,0.92363,0,0,/ruhong/jigsaw-unintended-bias-bert-pytorch,Jigsaw Unintended Bias in Toxicity Classification
4297249,0.8907,2,3,/jasonjensen/tensorflow-bert,Jigsaw Unintended Bias in Toxicity Classification
4143497,0.91059,0,0,/carrot6869/lstm-preprocess,Jigsaw Unintended Bias in Toxicity Classification
3463675,0.75659,0,0,/prabanch/jigsaw-unintended-bias-in-toxicity-classification,Jigsaw Unintended Bias in Toxicity Classification
4286506,0.92772,0,0,/nikhilroxtomar/bi-gru-lstm-cnn-fasttext,Jigsaw Unintended Bias in Toxicity Classification
4201811,0.94063,30,68,/chriscc/jigsaw-starter-blend,Jigsaw Unintended Bias in Toxicity Classification
4210159,0.93585,4,7,/hatunina/lstm-with-normal-features-base-on-dieter,Jigsaw Unintended Bias in Toxicity Classification
4154746,0.93845,9,85,/chechir/bert-lstm-rank-blender,Jigsaw Unintended Bias in Toxicity Classification
3781385,0.92493,1,5,/sklasfeld/simple-lstm-annotated-v2,Jigsaw Unintended Bias in Toxicity Classification
3460027,0.92363,11,6,/plasticgrammer/jigsaw-toxicity-classification-playground,Jigsaw Unintended Bias in Toxicity Classification
4061895,0.93376,0,0,/sanjayroberts1/lstm-toxic-comments,Jigsaw Unintended Bias in Toxicity Classification
4080142,0.8838600000000001,0,1,/ruhong/jigsaw-unintended-bias-baseline-cnn,Jigsaw Unintended Bias in Toxicity Classification
4043458,0.92312,0,0,/surbhibhardwaj/kernelb3c83b7be2,Jigsaw Unintended Bias in Toxicity Classification
13848885,1811249.63511,2,5,/ruiyap/detailed-eda-data-processing-model-building,Restaurant Revenue Prediction
12034947,1747785.15592,0,0,/takaomikadokura7069/ds-stdy-restaurant-redge,Restaurant Revenue Prediction
11615449,1750512.98341,0,0,/namiki1984/ds-stdy-restaurant-namiki,Restaurant Revenue Prediction
11611498,2360298.47972,0,1,/rahulpawade/restaurant-revenue-prediction,Restaurant Revenue Prediction
11088275,1819562.88753,0,3,/toshihikok/restaurantrevenueprediction-notebook,Restaurant Revenue Prediction
10522332,2043613.61072,0,1,/taiyaki014/kernel7ada661c64,Restaurant Revenue Prediction
9371122,1771329.7511900002,0,0,/kengofujii/restaurant-revenue-prediction,Restaurant Revenue Prediction
8274379,1934440.51257,0,3,/funxexcel/resturant-revenue-prediction-with-basic-pipeline,Restaurant Revenue Prediction
6470191,1825148.31493,0,1,/thduik/kernel69e5e8b263,Restaurant Revenue Prediction
5832465,2072340.75377,0,1,/abeerabuzayed/restaurant-revenue-prediction-v4,Restaurant Revenue Prediction
5678705,1769240.658,5,29,/ahayek84/restaurant-revenue-predict,Restaurant Revenue Prediction
2094493,1696289.57718,1,9,/jquesadar/restaurant-revenue-1st-place-solution,Restaurant Revenue Prediction
11252037,0.99846,49,46,/jedrzejdudzicz/mnist-dataset-100-top-6,Digit Recognizer
11273039,0.99621,3,9,/wahyusetianto/cnn-keras-cv-0-996-tpu,Digit Recognizer
11280163,0.98346,0,4,/lokeshduvvuru/implementation-of-lenet,Digit Recognizer
11255647,0.99285,2,7,/akshitrai/digit-recognizer,Digit Recognizer
11248517,0.97507,0,2,/lokeshduvvuru/mnist-neural-network-hyperparameters,Digit Recognizer
11234442,0.98328,1,11,/prashanthprince/98-2-accuracy-digit-recognizer-using-simple-cnn,Digit Recognizer
11214198,0.93621,1,2,/joshuabriones/cnn-for-digit-recognition,Digit Recognizer
11055119,0.99364,0,0,/yeanwei/learn-pytorch-with-mnist,Digit Recognizer
11181155,0.99275,0,2,/mikhailg0/digit-recognizer-solution-keras,Digit Recognizer
11181921,0.974,0,1,/rahulpawade/digit-recognizer-xgboost,Digit Recognizer
11152468,0.99139,2,19,/sshikamaru/beginner-cnn-99-2-accuracy,Digit Recognizer
11123532,0.99246,0,1,/kunalgupta3001/kernel787d5c3c2a,Digit Recognizer
11102074,0.999,0,10,/mohamedtayser/cnn-with-tpu-for-mnist,Digit Recognizer
10874400,0.99471,0,0,/raghavendrarao/beginner-s-guide-to-solve-mnist-99-4-top-20,Digit Recognizer
11643997,0.38611,6,11,/awesomehidingspot/my-first-kaggle-nlp-notebook,Spooky Author Identification
10884502,0.4274199999999999,0,1,/odedgolden/spooky-authors-analysis,Spooky Author Identification
8086207,0.5885199999999999,0,0,/manishachakraborty/spooky,Spooky Author Identification
6608270,0.4649899999999999,2,3,/guidosalimbeni/recurrent-neural-network-on-text,Spooky Author Identification
6516958,0.4992,1,5,/guidosalimbeni/multinomial-naive-bayes,Spooky Author Identification
4161370,0.4686899999999999,0,1,/ma7555/simple-and-short-classification-naive-bayes,Spooky Author Identification
2556218,0.62712,0,0,/ratnesh88/find-authors-using-nlp,Spooky Author Identification
477039,0.48695,0,0,/suyue715/spooky-eda,Spooky Author Identification
11839406,0.6027,0,0,/logos78/clouds-segmentation,Understanding Clouds from Satellite Images
6685432,0.67532,1,16,/gogo827jz/blending-submissions-using-np-logical-or,Understanding Clouds from Satellite Images
6607118,0.5971,0,2,/kenanajk/classifying-clouds-u-net-with-resnet-34-encoder,Understanding Clouds from Satellite Images
6431955,0.638,0,0,/avinashrai/effnet-unet,Understanding Clouds from Satellite Images
6382640,0.3939999999999999,0,3,/parmarsuraj99/fastai-unet,Understanding Clouds from Satellite Images
5578437,0.584,1,9,/jrw2200/satellite-cloud-image-segmentation-with-fast-ai,Understanding Clouds from Satellite Images
5902642,0.655,17,108,/ratthachat/cloud-convexhull-polygon-postprocessing-no-gpu,Understanding Clouds from Satellite Images
5589313,0.647,4,21,/hung96ad/fine-turning-model-with-adabound,Understanding Clouds from Satellite Images
5738776,0.6582,20,80,/mobassir/inceptionresnetv2-for-cloud-classifier,Understanding Clouds from Satellite Images
7983928,0.93394,0,5,/shrutimechlearn/check-out-tpu,Flower Classification with TPUs
7988563,0.34914,8,7,/atamazian/100-flowers-on-tpu-with-nasnetlarge,Flower Classification with TPUs
7977234,0.95532,0,7,/phunghieu/flowers-with-tpu-ensembling-models,Flower Classification with TPUs
7983264,0.92907,0,3,/phunghieu/flowers-with-tpu-inception-focalloss,Flower Classification with TPUs
7977332,0.93971,1,3,/phunghieu/flowers-with-tpu-inceptionresnet-focalloss,Flower Classification with TPUs
7960855,0.96523,0,6,/msheriey/104-flowers-direct-submission,Flower Classification with TPUs
7931274,0.96523,9,106,/wrrosa/tpu-enet-b7-densenet,Flower Classification with TPUs
7951425,0.94911,1,6,/iamprateek/flower-classification-with-tpu,Flower Classification with TPUs
7920473,0.95914,28,141,/xhlulu/flowers-tpu-concise-efficientnet-b7,Flower Classification with TPUs
7927308,0.94681,0,7,/gogo827jz/deeper-efficientnet-b7,Flower Classification with TPUs
7921899,0.0004,0,7,/grapestone5321/flower-classification-sample-submission,Flower Classification with TPUs
13476999,0.95367,0,0,/farrahtaheri/basket-of-flowers-ensemble,Flower Classification with TPUs
12502543,0.95698,0,0,/teramera/notebookc2601cc966,Flower Classification with TPUs
8472011,0.95466,0,0,/akashsuper2000/flower-classification-ensemble,Flower Classification with TPUs
8259836,0.74811,0,0,/wangqiyuan/super-minimalistic-starter,Flower Classification with TPUs
8068492,0.94421,0,0,/qinhui1999/5-kfold-efficentne-e7-p5,Flower Classification with TPUs
7565960,72666.22,0,3,/szabo7zoltan/simple-diy-graph-algorithms-give-less-than-73000,Santa's Workshop Tour 2019
7548781,69332.66,0,8,/wrrosa/mipcl-c-stochastic-or-tools-mip-69332-in-3-5h,Santa's Workshop Tour 2019
7022143,70926.27,0,3,/maxvanhaastrecht/santapulp,Santa's Workshop Tour 2019
7389628,69805.7,2,16,/raghaw/mip-optimization-preference-cost,Santa's Workshop Tour 2019
7335818,72567.43,6,10,/kwabenantim/heuristic-ensemble,Santa's Workshop Tour 2019
7263782,69827.7,1,17,/khoongweihao/santa-requires-more-time-to-find-bad-kids,Santa's Workshop Tour 2019
7226557,78065.49,22,57,/golubev/manual-to-improve-submissions,Santa's Workshop Tour 2019
7160080,69761.84,17,143,/golubev/c-stochastic-product-search-65ns,Santa's Workshop Tour 2019
7062211,787941.99,5,3,/antekirt/faster-genetic-algorithm,Santa's Workshop Tour 2019
6864894,75597.74,8,24,/slobo777/pytorch-giant-gradient-bandit,Santa's Workshop Tour 2019
7003372,325203.63,3,4,/brobear1995/santa-workshop-a-beginner-s-approach,Santa's Workshop Tour 2019
6828380,672254.02,0,2,/jagannathrk/santa-s-2019-cost-function,Santa's Workshop Tour 2019
6920756,76273.55,2,10,/linshokaku/santa-s-2019-gpu-accelerated-cost-function-12-s,Santa's Workshop Tour 2019
6834170,72107.38,20,70,/xhlulu/santa-s-2019-stochastic-product-search,Santa's Workshop Tour 2019
6834604,1.6432311995470503e+39,8,16,/ichabuddaeta/how-to-be-in-last-place-and-still-be-happy,Santa's Workshop Tour 2019
6815963,72046.79,22,91,/jazivxt/using-a-baseline,Santa's Workshop Tour 2019
6800691,96934.58,3,21,/dan3dewey/santa-s-simple-scheduler,Santa's Workshop Tour 2019
13658702,0.1294799999999999,0,0,/p77091122/the-game-of-life-cnn,Conway's Reverse Game of Life 2020
13658443,0.14915,0,0,/p77091122/the-game-of-life-cnn-delta-1,Conway's Reverse Game of Life 2020
13205850,0.14448,2,5,/ebouteillon/perfect-solve-of-puzzles-using-a-sat-solver,Conway's Reverse Game of Life 2020
12869178,0.14689,1,1,/jamesmcguigan/game-of-life-random-forest,Conway's Reverse Game of Life 2020
12793594,0.08431,1,6,/mehrankazeminia/game-of-life-soliset-201,Conway's Reverse Game of Life 2020
12275798,0.14559,0,7,/lakitha/game-of-life-code-keras-cnn-accuracy-84,Conway's Reverse Game of Life 2020
12167902,0.14689,0,0,/akashsuper2000/reverse-game-of-life-with-cnns,Conway's Reverse Game of Life 2020
11546266,0.12908,0,1,/nahumsa/reversing-using-neural-networks-keras,Conway's Reverse Game of Life 2020
11632795,0.12452,0,16,/maxjeblick/crgl2020-iterative-cnn-approach-with-postproces,Conway's Reverse Game of Life 2020
11614110,0.13242,3,10,/rohitiscute/cnn-conway-s-reverse-game-of-life-2020,Conway's Reverse Game of Life 2020
11544310,0.1381599999999999,0,7,/shahraizanwar/bidirectional-lstm-approach,Conway's Reverse Game of Life 2020
11495736,0.13346,3,28,/ulrich07/quick-neighborhood-fe-mlp-keras,Conway's Reverse Game of Life 2020
11517507,0.17146,0,2,/shams1/sample-submission,Conway's Reverse Game of Life 2020
11488698,0.14689,0,1,/eladwar/conways,Conway's Reverse Game of Life 2020
12143715,0.11109,0,0,/akashsuper2000/crgl-probability-extension-true-target-problem,Conway's Reverse Game of Life 2020
13123044,83856.0,0,1,/janbecker000/drones-v8,Hash Code Archive - Drone Delivery
11957855,114164.0,6,25,/egrehbbt/greedy-solution-post-processing,Hash Code Archive - Drone Delivery
14565148,0.295,10,44,/its7171/mmdetection-for-segmentation-inference,Human Protein Atlas - Single Cell Classification
14635335,0.84426,9,13,/ccollado7/tps-feb-2021-eda-pycaret-basic-models,Tabular Playground Series - Feb 2021
14668613,0.8423299999999999,38,62,/hamzaghanmi/lgbm-hyperparameter-tuning-using-optuna,Tabular Playground Series - Feb 2021
14648671,0.8439,0,2,/radofanantenana/tps-feb-2021-xgb-lgbm,Tabular Playground Series - Feb 2021
14504109,0.86365,8,25,/inversion/get-started-feb-tabular-playground-competition,Tabular Playground Series - Feb 2021
14610524,0.8428200000000001,2,8,/rmiperrier/tps-feb-bagged-lgbm,Tabular Playground Series - Feb 2021
14616506,0.8438200000000001,1,2,/oldwine357/tabular-series-xgb-feb,Tabular Playground Series - Feb 2021
14610287,0.84303,0,1,/rmiperrier/tps-feb-easy-lgb-with-pycaret,Tabular Playground Series - Feb 2021
14641017,0.84495,0,0,/kingabzpro/tps-feb-label-encoder-lgbm,Tabular Playground Series - Feb 2021
14642344,0.8424299999999999,1,10,/gpreda/february-solution-stratified-kfolds,Tabular Playground Series - Feb 2021
14617194,0.8426600000000001,0,0,/shogosuzuki/optuna-lightgbm-ordinalencoder,Tabular Playground Series - Feb 2021
11453599,39.97285,24,61,/dimitreoliveira/improving-cyclegan-monet-paintings,I’m Something of a Painter Myself
10748379,0.83054,0,0,/mankomyk/efficientnetb7-model-30-epochs,Petals to the Metal - Flower Classification on TPU
10546151,0.91848,0,3,/anjanatiha/a-simple-flower-classification-with-tpus,Petals to the Metal - Flower Classification on TPU
10509774,0.51339,0,6,/salmaneunus/petals-to-the-metals-competition-vgg16-tuning,Petals to the Metal - Flower Classification on TPU
10311935,0.8667799999999999,1,3,/fatimasalman/competitions-about-100-types-of-flowers-on-tpu,Petals to the Metal - Flower Classification on TPU
10232699,0.95201,0,3,/fadzlinrafi/simple-efficientnetb7,Petals to the Metal - Flower Classification on TPU
10217393,0.8094899999999999,4,4,/sarques/tpu-starter-basic-model-training-and-prediction,Petals to the Metal - Flower Classification on TPU
14156994,0.8775299999999999,0,0,/startover205/fastai-2-tpu-getting-started-xla-extension,Petals to the Metal - Flower Classification on TPU
14141754,0.8662299999999999,0,0,/akataev96/start-with-pre-train-4a9cdd,Petals to the Metal - Flower Classification on TPU
13987233,0.95886,0,0,/denismetelev/start-with-ensemble,Petals to the Metal - Flower Classification on TPU
13558595,0.95094,0,0,/vladscherbakov/start-with-ensemble,Petals to the Metal - Flower Classification on TPU
13366635,0.94603,0,0,/nurkasimov/start-with-ensemble,Petals to the Metal - Flower Classification on TPU
13237164,0.91322,0,0,/stefankovacs/exercise-create-your-first-submission,Petals to the Metal - Flower Classification on TPU
13170796,0.93277,0,0,/adebelyi/start-with-pre-train,Petals to the Metal - Flower Classification on TPU
12027367,0.2496199999999999,0,0,/safacanmetin/exercise-create-your-first-submission,Petals to the Metal - Flower Classification on TPU
10763894,0.25497,0,0,/saitejasmopuri/exercise-create-your-first-submission,Petals to the Metal - Flower Classification on TPU
10974778,0.88565,0,11,/tpothjuan/basic-eda-xnli-mnli-xlm-bert,"Contradictory, My Dear Watson"
11037852,0.8350299999999999,16,38,/tuckerarrants/watson-kfold-xlm-r-translation-augmentation,"Contradictory, My Dear Watson"
11056462,0.64157,0,4,/dimasmunoz/a-simple-bert-approach,"Contradictory, My Dear Watson"
11038328,0.7930699999999999,0,10,/shaitender/my-dear-watson-with-xlm-roberta-large,"Contradictory, My Dear Watson"
11042447,0.56053,0,2,/kkhandekar/contradiction-detection-distilbert,"Contradictory, My Dear Watson"
11002152,0.34436,0,12,/aceconhielo/getting-into-the-problem-baseline-model,"Contradictory, My Dear Watson"
11008485,0.7944100000000001,0,1,/leoisleo1/fork-from-contradiction-xlm-kfold-tpu,"Contradictory, My Dear Watson"
10994839,0.63272,0,5,/doanquanvietnamca/trainning-mbert-tpu-pytorch,"Contradictory, My Dear Watson"
10987392,0.35264,1,8,/shubham9455999082/simple-notebook,"Contradictory, My Dear Watson"
10966859,0.80288,6,21,/rhtsingh/tpu-training-pytorch-nlp-xlmroberta,"Contradictory, My Dear Watson"
10962965,0.34706,0,6,/vbookshelf/the-game-is-afoot-baseline,"Contradictory, My Dear Watson"
14499159,0.64003,0,0,/vijayalakshmibhagat/vb-contradict,"Contradictory, My Dear Watson"
12536084,0.8313299999999999,0,0,/safonenkomax/start-with-pre-train,Petals to the Metal - Flower Classification on TPU
12363766,0.94181,1,1,/ya10ya10/flower-class,Petals to the Metal - Flower Classification on TPU
12260560,0.47455,0,0,/sneky369/start-w-o-pre-train,Petals to the Metal - Flower Classification on TPU
12255631,0.5715100000000001,0,1,/lkatran/start-w-o-pre-train,Petals to the Metal - Flower Classification on TPU
10563986,0.82132,0,0,/forlucas27/petal-first-test,Petals to the Metal - Flower Classification on TPU
12056946,0.9124,0,0,/alexinicab/petals,Petals to the Metal - Flower Classification on TPU
11979825,0.6400600000000001,0,2,/qrsforever/pytorch-lightning-tpu-getting-started-on-vgg,Petals to the Metal - Flower Classification on TPU
11688366,0.85672,0,0,/krishnasandeep09/exercise-create-your-first-submission,Petals to the Metal - Flower Classification on TPU
11757999,0.94056,2,4,/ljh415/using-tpu,Petals to the Metal - Flower Classification on TPU
11704220,0.95127,0,5,/shamiulislamshifat/petal-to-metal-flower-predction-on-tpu-with-97,Petals to the Metal - Flower Classification on TPU
11587289,0.902,0,0,/atulmishra1596/first-tpu-project,Petals to the Metal - Flower Classification on TPU
11525493,0.71172,1,2,/mikeberhane9/mike-s-forth-submission,Petals to the Metal - Flower Classification on TPU
11374700,0.98035,0,1,/ahmedmurad1990/fc-ensemble-external-data-2,Petals to the Metal - Flower Classification on TPU
11412915,0.93509,0,11,/hongym7/ensemble-efficientnetb3-b4-b5-augmetation,Petals to the Metal - Flower Classification on TPU
10560297,0.92981,0,0,/luckuenemann/flower-classification-on-tpu,Petals to the Metal - Flower Classification on TPU
11367077,0.97275,0,0,/andyden/flowers-with-keras,Petals to the Metal - Flower Classification on TPU
11237035,0.24525,0,5,/hassan11196/exercise-create-your-first-submission,Petals to the Metal - Flower Classification on TPU
11191255,0.84449,0,2,/ris320/inference-densenet201-with-tensorflow,Petals to the Metal - Flower Classification on TPU
11184095,0.92,0,6,/fuyixing/flower-classification-with-keras-tuner-and-kpl,Petals to the Metal - Flower Classification on TPU
11138277,0.73723,0,2,/srikanthpotukuchi/max-epoch,Petals to the Metal - Flower Classification on TPU
10465415,0.87539,0,1,/shirishsharma/flowers-and-efficientnet,Petals to the Metal - Flower Classification on TPU
11043394,0.19569,2,5,/ddmasterdon/flower-classification,Petals to the Metal - Flower Classification on TPU
10961414,0.97004,1,15,/chankhavu/a-beginner-s-tpu-kernel-single-model-0-97,Petals to the Metal - Flower Classification on TPU
10901030,0.80691,0,3,/athews/transfer-learning-cv-tpu-101,Petals to the Metal - Flower Classification on TPU
10796611,0.17436,0,1,/sanyamjain1999/using-resnet,Petals to the Metal - Flower Classification on TPU
10464090,0.97685,0,1,/leoisleo1/fork-of-flower-classification-with-tpus,Petals to the Metal - Flower Classification on TPU
10762955,0.96623,4,27,/tuckerarrants/kfold-efficientnet-augmentation-s,Petals to the Metal - Flower Classification on TPU
10762168,0.8376899999999999,0,4,/mankomyk/xception-model-with-dropout-30-epochs,Petals to the Metal - Flower Classification on TPU
14395673,0.77189,6,8,/jswxhd/nli-beginner-eda-bert-baseline,"Contradictory, My Dear Watson"
14249652,0.65351,2,5,/katharinamenz/tutorial-notebook-edited-by-katharinamenz,"Contradictory, My Dear Watson"
13243693,0.66313,0,0,/fabienravet/bert-multilingual,"Contradictory, My Dear Watson"
12885026,0.74744,0,0,/cweic220591/inference-model-ensemble,"Contradictory, My Dear Watson"
13452482,0.94629,0,6,/rahulbana/contradictory-my-dear-watson-using-xlni-robert2,"Contradictory, My Dear Watson"
13337770,0.65312,1,15,/daotan/watson-using-bert,"Contradictory, My Dear Watson"
12580829,0.63156,1,2,/cweic220591/pytorch-lightning-multilingualbert-baseline,"Contradictory, My Dear Watson"
12098613,0.65563,0,2,/geekycoder0/contradictory-watson,"Contradictory, My Dear Watson"
11845161,0.6363800000000001,0,0,/nur988/dear-watson,"Contradictory, My Dear Watson"
11457206,0.65563,0,0,/itsbitan/contradictary,"Contradictory, My Dear Watson"
11729237,0.37613,0,4,/zaynhaider/detecting-contradiction-and-entailment,"Contradictory, My Dear Watson"
11605397,0.64927,3,14,/krsna540/holmes-deductions,"Contradictory, My Dear Watson"
11337591,0.8571700000000001,0,3,/izarortin/more-data-and-data-aug-nli-by-keras,"Contradictory, My Dear Watson"
11351244,0.85293,0,2,/aiswaryaramachandran/mistake-to-theorize-before-one-has-data-watson,"Contradictory, My Dear Watson"
11392634,0.923,3,10,/irinagia/xlm-roberta-large-xnli,"Contradictory, My Dear Watson"
11279241,0.7360899999999999,0,4,/fstcap/contradictory-tpu-bert,"Contradictory, My Dear Watson"
11332546,0.64927,0,0,/mickaeljuillet/nlp-analysis-and-prediction,"Contradictory, My Dear Watson"
11099472,0.7794,2,5,/marcellosusanto/input-configuration-benchmark-using-distilbert,"Contradictory, My Dear Watson"
11205177,0.6469600000000001,1,4,/maya01/my-dear-watson,"Contradictory, My Dear Watson"
11229967,0.6485,1,0,/barracoda/pytorch-bert-multilingual,"Contradictory, My Dear Watson"
11202878,0.6487,0,0,/ht5brer/watson-bert-baseline,"Contradictory, My Dear Watson"
11164467,0.8386899999999999,0,14,/rhtsingh/tpu-inference-pytorch-nlp-xlmroberta,"Contradictory, My Dear Watson"
11122350,0.43022,5,30,/pradeepmuniasamy/contradictory-my-dear-watson-everything-you-need,"Contradictory, My Dear Watson"
11132972,0.65986,1,5,/rodsaldanha/contradictory-my-dear-watson-competition,"Contradictory, My Dear Watson"
11096512,0.76727,1,4,/dimasmunoz/clean-english-data-roberta,"Contradictory, My Dear Watson"
14539228,0.20404,5,8,/tasneemabdulrahim/a-simple-petals-tf-2-2-notebook,Petals to the Metal - Flower Classification on TPU
10997373,0.95898,2,2,/ibrahimsherify/bit-transfer-learning-in-tf,Petals to the Metal - Flower Classification on TPU
13862467,0.94518,0,0,/huukhue/flower-classification-with-tpus-dataming,Petals to the Metal - Flower Classification on TPU
14245025,0.8939,0,1,/iamadhee/petals-to-the-metal-tpu,Petals to the Metal - Flower Classification on TPU
14136147,0.5473399999999999,1,2,/kenshinakamura/petals-to-the-metal-002,Petals to the Metal - Flower Classification on TPU
13920182,0.91591,0,1,/elmervega/petal-to-metal-v2-0,Petals to the Metal - Flower Classification on TPU
13981647,0.11873,0,2,/muneshwarmca1uoa/metal2petal,Petals to the Metal - Flower Classification on TPU
13991705,0.88851,0,0,/davidcanorosillo/a-simple-petals-tf-2-2-notebook,Petals to the Metal - Flower Classification on TPU
13965051,0.87565,0,0,/drs251/petals-to-the-metal-first-try-with-xception,Petals to the Metal - Flower Classification on TPU
13884075,0.0168,0,0,/drcapa/flower-classification-tpu-tutorial,Petals to the Metal - Flower Classification on TPU
12725617,0.97883,0,1,/dmitrynokhrin/xception-aug-additional-data,Petals to the Metal - Flower Classification on TPU
12496441,0.73449,0,0,/shayantaherian/petals-to-the-metal-keras-mobilenet,Petals to the Metal - Flower Classification on TPU
13708786,0.94584,4,6,/xuanzhihuang/flower-classification-densenet-201,Petals to the Metal - Flower Classification on TPU
12628524,0.7146899999999999,0,0,/tomhonzk/petals,Petals to the Metal - Flower Classification on TPU
13467734,0.93908,0,0,/medvedevlev/start-with-ensemble-v2,Petals to the Metal - Flower Classification on TPU
13407206,0.81716,1,2,/vladscherbakov/notebook42adc8ec97,Petals to the Metal - Flower Classification on TPU
13357247,0.90654,0,0,/matveevayulia/fork-of-start-with-pre-train-255147,Petals to the Metal - Flower Classification on TPU
13081559,0.9519,0,0,/yuriromamov/start-with-ensemble-v2,Petals to the Metal - Flower Classification on TPU
12896376,0.81795,0,3,/kwjcndocjn/flower-petals,Petals to the Metal - Flower Classification on TPU
12937204,0.94621,0,0,/sagnikmazumder37/petals-to-the-metals-tpu,Petals to the Metal - Flower Classification on TPU
12919327,0.92311,0,0,/smirnyaginandr/start-with-pre-train-508d78,Petals to the Metal - Flower Classification on TPU
12690809,0.96449,0,1,/lkatran/start-with-ensemble-v2,Petals to the Metal - Flower Classification on TPU
12641759,0.95491,0,2,/ameya98/flower-tpu-finetuning,Petals to the Metal - Flower Classification on TPU
12647163,0.91436,0,0,/matveevayulia/start-with-pre-train,Petals to the Metal - Flower Classification on TPU
12535127,0.4957399999999999,0,0,/medvedevlev/start-w-o-pre-train,Petals to the Metal - Flower Classification on TPU
12202360,0.94284,0,0,/rakkaalhazimi/flowertpu-merged-pretrained,Petals to the Metal - Flower Classification on TPU
12550447,0.29288,0,0,/julessharova/start-w-o-pre-train,Petals to the Metal - Flower Classification on TPU
14588144,13.168,10,57,/devinanzelmo/wifi-features-lightgbm-starter,Indoor Location & Navigation
14608342,73.491,0,5,/titericz/targetencode-wifi-1,Indoor Location & Navigation
4769556,0.75598,2,1,/biswajitbanerjee/titanic-with-pytorch,Titanic - Machine Learning from Disaster
8994009,0.98857,2,5,/alfarias/mnist-with-pytorch-catalyst-amp-nvidia-apex,Digit Recognizer
4072605,0.99342,0,3,/abyaadrafid/fastai-vision-versus-tabular-learner,Digit Recognizer
4346121,0.97871,0,0,/mouri11/mnist-digit-recognition-using-deep-learning,Digit Recognizer
5646106,0.98771,0,0,/ashishohri/digit-recognition-cnn-pytorch,Digit Recognizer
6935137,0.75598,1,3,/juliuszziomek/titanic-neuron-network-on-numpy-only-no-keras,Titanic - Machine Learning from Disaster
9420897,0.1235,0,3,/matanwinstok/top-15-ml-fast-ai-tensorflow,House Prices - Advanced Regression Techniques
10660222,0.99496,1,3,/victoriya99/ensemble-of-6-cnn-acc-0-997,Digit Recognizer
5725290,0.1143,0,5,/thisisjisu/predicting-house-prices-using-tensorflow,House Prices - Advanced Regression Techniques
5713277,0.99057,0,1,/rashmiek99/digit-recognition-cnn,Digit Recognizer
5841534,0.98871,0,0,/schateau/hands-on-kaggle-s-mnist-dataset-99-07,Digit Recognizer
9647467,0.991,0,2,/austinpowers/mnist-fastai-0-99500-accuracy-on-kaggle,Digit Recognizer
6144784,0.78468,0,0,/arunjithm/titanic-mlpclassifer-and-kerasclassifier,Titanic - Machine Learning from Disaster
7255521,0.97257,2,1,/shivamanhar/tensorflow-start,Digit Recognizer
10626666,0.99032,0,4,/sahil2398/mnist-digits-using-cnn,Digit Recognizer
2027969,0.982,0,1,/alukashenkov/nnnn-a-naive-neutral-network-introduction,Digit Recognizer
2004105,0.99742,2,3,/acaciopassos/digits-cnn-ensemble,Digit Recognizer
7203006,0.80508,19,22,/amoghjrules/nlp-intro-detect-realness-of-a-tweet-tensorflow,Natural Language Processing with Disaster Tweets
1273841,0.98414,0,1,/perlinwarp/introduction-to-solving-mnist-using-a-cnn,Digit Recognizer
1235800,0.99528,2,10,/mridul02/digit-recognizer-cnn-keras,Digit Recognizer
1317183,0.99671,0,0,/kanishkapsingh/mnist-data-cnn-lenet-from-scratch,Digit Recognizer
967291,0.4334199999999999,12,16,/dimitreoliveira/house-prices-deep-learning-aproach-tensorflow,House Prices - Advanced Regression Techniques
1943209,0.994,1,4,/dreamhome/digit-recognizer,Digit Recognizer
11217180,0.80143,1,14,/carefree0910/titanic-neural-networks-made-easy-with-cflearn,Titanic - Machine Learning from Disaster
2742944,0.99742,8,12,/josh24990/recognising-digits-with-keras-top-8-score,Digit Recognizer
972273,0.78947,0,5,/dimitreoliveira/titanic-deep-learning-tensorflow-core-api,Titanic - Machine Learning from Disaster
4079040,0.78947,0,0,/cher1998/titanic-predict,Titanic - Machine Learning from Disaster
1525013,0.98814,0,15,/karanjakhar/digit-recognizer,Digit Recognizer
2185409,0.619,0,1,/shrivastava/grus-with-embeddings,Quora Insincere Questions Classification
1897004,0.7751100000000001,2,7,/amneves/titanic-keras-dnn,Titanic - Machine Learning from Disaster
3519574,0.99614,0,1,/artem239/mnist-cnn-simple,Digit Recognizer
11193217,2.03918,1,12,/obione26/facial-keypoints-detection-keras-albumentations,Facial Keypoints Detection
1204028,0.99285,17,57,/amarjeet007/visualize-cnn-with-keras,Digit Recognizer
1567801,0.99571,5,33,/puneetgrover/training-your-own-cnn-using-pytorch,Digit Recognizer
7467910,0.98257,1,6,/hirotaka0122/triplet-loss-with-pytorch,Digit Recognizer
7636093,0.83144,51,234,/ratan123/in-depth-guide-to-google-s-bert,Natural Language Processing with Disaster Tweets
7963147,0.77536,0,0,/thakursc1/attending-to-disaster,Natural Language Processing with Disaster Tweets
9627776,0.1001399999999999,4,5,/digvijayyadav/cnn-digit-recognizer,Digit Recognizer
2923820,0.76555,4,8,/bgmello/eda-nn-with-repeatedkfold,Titanic - Machine Learning from Disaster
9989086,0.99542,0,1,/sidagar/digit-recognizer-using-a-cnn,Digit Recognizer
10686186,0.98989,0,4,/sahil2398/mnist-digits-using-adv-model,Digit Recognizer
2734582,0.992,0,0,/andriyantohalim/digits-kernel-v1-2,Digit Recognizer
2716867,0.99557,0,2,/rosiejh/digit-recognizer-with-cnn,Digit Recognizer
1608214,0.99571,0,4,/brayanarrietaalfaro/digit-recognizer-with-keras,Digit Recognizer
4549263,0.997,5,11,/masfour/99-7-accuracy-top-10-digit-classifier-tutorial,Digit Recognizer
2530157,0.99557,0,2,/alokevil/simple-digit-recognition-with-accuracy-99-557,Digit Recognizer
2463236,0.971,0,6,/krunal3kapadiya/mnist-digit-recognition-with-tensoflow,Digit Recognizer
547340,0.97657,0,0,/kaustubholpadkar/digit-recognizer-dnn,Digit Recognizer
1041036,0.99428,1,3,/russbeuker/cnn-with-fast-loading-and-bad-image-removal,Digit Recognizer
2173042,0.99142,0,1,/sohaibanwaar1203/beginners-deep-learning-cnn,Digit Recognizer
7017384,0.7751100000000001,1,4,/jcardenzana/titanic-pytorch,Titanic - Machine Learning from Disaster
1761266,0.99614,0,2,/hengulkakaty/hand-written-digit-recognition-and-error-analysis,Digit Recognizer
1788618,0.99442,0,12,/nhlr21/deep-keras-cnn-tutorial,Digit Recognizer
11616439,0.78947,5,21,/isaienkov/keras-neural-network-architecture-optimization,Titanic - Machine Learning from Disaster
9213852,0.99107,1,4,/adeephande/mnist-kerascnn,Digit Recognizer
6933496,0.81052,2,10,/isaienkov/keras-nn-with-embeddings-for-cat-features-fe,House Prices - Advanced Regression Techniques
714742,0.78468,13,50,/damienpark/artificial-neural-network-using-keras,Titanic - Machine Learning from Disaster
7201466,0.79742,0,2,/orion99/disaster-detection-from-tweets-using-lstm,Natural Language Processing with Disaster Tweets
7369322,0.76003,0,1,/kishor1210/nlp-hands-on-project-part-1-eda-and-preprocessing,Natural Language Processing with Disaster Tweets
7911374,0.80447,8,47,/parulpandey/getting-started-with-nlp-feature-vectors,Natural Language Processing with Disaster Tweets
7375382,0.75574,22,54,/kushbhatnagar/disaster-tweets-eda-nlp-classifier-models,Natural Language Processing with Disaster Tweets
11744778,0.77751,0,2,/batprem/titanticcompetition,Titanic - Machine Learning from Disaster
11296321,0.79425,15,37,/rifkyahmadsaputra/titanic-survivor-prediction-top-7,Titanic - Machine Learning from Disaster
11108310,0.77751,2,15,/turhancankargin/titanic-basic-eda,Titanic - Machine Learning from Disaster
11030556,0.15108,0,5,/imshakil/practice-model-selection-data-viz-cleaning,House Prices - Advanced Regression Techniques
10843677,0.7799,0,3,/rashasalim/titanic-pytorch-linear-models,Titanic - Machine Learning from Disaster
8809075,0.74162,3,13,/franckepeixoto/titanic-disaster-basic-eda,Titanic - Machine Learning from Disaster
8378917,0.76555,0,2,/nibukdk93/titanic-disaster-prediction-challenge,Titanic - Machine Learning from Disaster
7284089,0.79528,0,14,/anandkenta/disaster-tweets-eda-tfidf-weightw2v-xgb,Natural Language Processing with Disaster Tweets
7006399,0.81818,2,7,/saga21/titanic-comp-extensive-workflow-xgb-top-3,Titanic - Machine Learning from Disaster
6946049,0.12532,0,11,/saga21/house-prices-in-depth-feature-engineering-xgb,House Prices - Advanced Regression Techniques
6244686,0.80382,1,2,/manimannu/titanic-for-beginners,Titanic - Machine Learning from Disaster
5923489,0.78947,2,9,/mjpmorse/data-exploration-and-modeling-for-titanic,Titanic - Machine Learning from Disaster
5766212,0.7799,2,5,/vishnupy/titanic-preds-sklearn-and-ensemble-with-fe-and-eda,Titanic - Machine Learning from Disaster
5763943,0.15492,2,6,/ashishbarvaliya/house-price-modeling-lightgbm,House Prices - Advanced Regression Techniques
5707809,0.80382,1,2,/adridri/get-top-10-with-simple-models,Titanic - Machine Learning from Disaster
5421572,0.78947,2,5,/noelmat/eda-data-cleaning-and-tsne-on-titanic,Titanic - Machine Learning from Disaster
4775880,0.11874,2,6,/akshay1296/house-price-prediction-with-eda-visualization-tpot,House Prices - Advanced Regression Techniques
4063566,0.79904,0,0,/werty12121/titanic-eda-basic-stacknet,Titanic - Machine Learning from Disaster
3869742,0.12103,2,5,/need4data/linear-model-for-house-price-pridiction,House Prices - Advanced Regression Techniques
3846140,0.80382,3,11,/ramezashendy/let-s-all-vote-ensemble-learning-0-803,Titanic - Machine Learning from Disaster
2902446,0.98613,1,1,/nishitjain/predicting-house-prices-using-gradient-boosting,House Prices - Advanced Regression Techniques
2615952,0.78468,0,1,/rohit0009/rohit-chouhan,Titanic - Machine Learning from Disaster
2324043,0.81339,1,0,/nikkisharma536/titanic,Titanic - Machine Learning from Disaster
1987958,0.75598,0,1,/adhishk1/titanic-dataset-predictions,Titanic - Machine Learning from Disaster
1967720,0.11729,10,10,/lucasgiutavares/easy-modelling-with-lasso-gbm-regression-top-19,House Prices - Advanced Regression Techniques
1727523,0.78468,20,32,/sidjhanji/learn-solving-kaggle-problems-with-titanic,Titanic - Machine Learning from Disaster
1630640,0.7751100000000001,0,0,/kanakroy/titanic-prediction-using-random-forests,Titanic - Machine Learning from Disaster
1496791,0.78468,1,3,/yugagrawal95/titanic-disaster-analysis-for-beginner,Titanic - Machine Learning from Disaster
1368889,0.12893,5,19,/ajangir45/detailed-tutorial-for-beginners,House Prices - Advanced Regression Techniques
1168613,0.7799,6,13,/ariadneadler/xgboost-gridsearchcv-stratified-k-fold-top-5,Titanic - Machine Learning from Disaster
1030886,0.75598,4,4,/geoffreygeo/first-kernal-pytanic-using-various-algorithms,Titanic - Machine Learning from Disaster
884233,0.16782,0,0,/ratnesh88/house-price-prediction-and-eda,House Prices - Advanced Regression Techniques
552715,0.7703300000000001,0,10,/vikasbz/titanic-survivors-prediction,Titanic - Machine Learning from Disaster
382666,0.11749,2,3,/aguerrad/simple-aproach-to-get-to-top-15-0-11749,House Prices - Advanced Regression Techniques
326144,0.74162,0,1,/joislobo88/predict-survive-1-0,Titanic - Machine Learning from Disaster
9106511,0.79904,8,22,/kuldeepdhadhwal/complete-titanic-survival-analysis-79-9-accuracy,Titanic - Machine Learning from Disaster
4841852,0.7511899999999999,0,4,/jaykarma/titanic-eda-logistic-reg-and-decision-tree,Titanic - Machine Learning from Disaster
5214844,0.78947,17,33,/kabure/titanic-baseline-eda-pipes,Titanic - Machine Learning from Disaster
10907265,0.7511899999999999,0,0,/coderumba/titanic-machine-learning-from-disaster,Titanic - Machine Learning from Disaster
9931863,0.7703300000000001,2,5,/itsmeprasanna/titanic-survival-prediction,Titanic - Machine Learning from Disaster
6118299,0.78947,2,2,/adldotori/titanic-copy-eda-to-prediction-dietanic,Titanic - Machine Learning from Disaster
7783211,0.14444,0,0,/cvarun/simple-analysis-of-house-prices-competition,House Prices - Advanced Regression Techniques
12627505,0.73205,0,0,/gsaurabh98/titanic-passenger-survival,Titanic - Machine Learning from Disaster
3476653,0.81818,4,6,/heuingttiang/approximate-process-for-data-analysis-simple-ver,Titanic - Machine Learning from Disaster
10864172,0.75598,10,21,/ozdemirh/data-analysis-and-xgboost-modeling,Titanic - Machine Learning from Disaster
10213092,0.15394,0,5,/kanikakhattar/house-price-eda-ridge-lasso-random-forest,House Prices - Advanced Regression Techniques
1602712,0.78468,3,5,/ephemerai/titanic-a-beginner-s-playground-ongoing,Titanic - Machine Learning from Disaster
546554,0.78947,0,3,/aldemuro/improve-algorithm-performance-0-80-to-0-83,Titanic - Machine Learning from Disaster
5240503,0.7703300000000001,6,6,/apratik46/choose-best-classification-algo-tuning-the-model,Titanic - Machine Learning from Disaster
494926,0.79904,14,50,/astandrik/journey-from-statistics-eda-to-prediction,Titanic - Machine Learning from Disaster
6700852,0.7751100000000001,0,2,/hollydail/titanic-exploration-of-features,Titanic - Machine Learning from Disaster
1388734,0.79425,0,0,/varian97/titanic-survival-analysis,Titanic - Machine Learning from Disaster
11378744,0.7822899999999999,0,5,/anurodhmohapatra/preprocessing-sklearn-pipeline-titanic-top-8,Titanic - Machine Learning from Disaster
7354938,0.82286,1,5,/sriramanathan/simple-exploratory-notebook,Natural Language Processing with Disaster Tweets
7217728,0.79742,2,5,/pratik2901/nlp-using-nltk-textblob,Natural Language Processing with Disaster Tweets
10934296,9.45917,8,22,/darkknight98/how-a-data-scientist-buys-a-house-a-tutorial,House Prices - Advanced Regression Techniques
346654,0.7799,0,4,/rnmehta5/titanic-predictions-practice-sklearn,Titanic - Machine Learning from Disaster
11750082,0.77272,0,1,/rahulpawade/titanic-dataset,Titanic - Machine Learning from Disaster
10132121,0.11742,4,7,/kaushalk/house-price-prediction-top-4,House Prices - Advanced Regression Techniques
11188318,0.77751,0,1,/fercampos19/titanic-classification-example,Titanic - Machine Learning from Disaster
11696024,0.80861,11,24,/zacharywyman/titanic-top-4-beginner-tutorial,Titanic - Machine Learning from Disaster
10566591,0.7799,0,2,/ertugrulreis/titanic-ertugrul-eda,Titanic - Machine Learning from Disaster
11184790,0.8062199999999999,4,9,/hramos93/titanic-random-forest-setup-hyperparameters,Titanic - Machine Learning from Disaster
5902397,0.7799,27,74,/vbmokin/three-lines-of-code-for-titanic-top-25,Titanic - Machine Learning from Disaster
8305247,0.8378700000000001,7,34,/dmitri9149/transformer-svm-semantically-identical-tweets,Natural Language Processing with Disaster Tweets
2038144,0.78947,37,73,/skiplik/my-titanic-try,Titanic - Machine Learning from Disaster
2817480,0.78468,19,66,/anisayari/your-1st-data-science-project-simple-explanation,Titanic - Machine Learning from Disaster
8013300,1.0,1,21,/aaroha33/disaster-tweets-evaluation-with-nlp,Natural Language Processing with Disaster Tweets
2249781,0.11855,0,7,/nikkisharma536/house-prediction-dealing-with-outlier,House Prices - Advanced Regression Techniques
12018490,0.75358,6,13,/gauravduttakiit/predict-the-survival-using-xgboost,Titanic - Machine Learning from Disaster
13607454,0.923,15,53,/vishalsiram50/95-accuracy-with-resnet50,RANZCR CLiP - Catheter and Line Position Challenge
11531970,0.79425,2,5,/awanda/titanic-predictions,Titanic - Machine Learning from Disaster
6821059,0.1210099999999999,0,3,/rbizoi/eda-feature-selection-outliers-multiple-models,House Prices - Advanced Regression Techniques
6959663,0.10647,3,19,/catqaq/house-prices-top-3-0-10647-stacking,House Prices - Advanced Regression Techniques
6821394,0.82296,9,47,/vbmokin/merging-fe-prediction-xgb-lgb-logr-linr,Titanic - Machine Learning from Disaster
7982339,0.3844599999999999,22,91,/artgor/march-madness-2020-ncaam-eda-and-baseline,Google Cloud & NCAA® ML Competition 2020-NCAAM
4754840,0.79425,1,16,/iluvmahheart/simple-titanic-using-nn,Titanic - Machine Learning from Disaster
11536833,0.8109999999999999,50,100,/kushal1506/titanic-81-1-leader-board-score-guaranteed,Titanic - Machine Learning from Disaster
9371922,0.13078,9,13,/vishumudgal/zero-to-hero-1,House Prices - Advanced Regression Techniques
9067680,0.78468,1,4,/nidaguler/dataiteam-titanic,Titanic - Machine Learning from Disaster
3842677,0.7177,0,0,/austinpowers/titanic-edawith-bamboolib-and-embeddings,Titanic - Machine Learning from Disaster
10593587,0.76555,0,2,/asemokby/eda-randomforest-precision-recall,Titanic - Machine Learning from Disaster
10642990,0.7368399999999999,6,14,/revanth666/titanic-prediction,Titanic - Machine Learning from Disaster
11447755,0.75837,0,1,/bibeknp/titanic-disaster-eda,Titanic - Machine Learning from Disaster
8989402,0.79904,0,1,/akm132000/titanic-datset-analysis,Titanic - Machine Learning from Disaster
6587175,0.7799,2,5,/rbizoi/pr-dire-les-survivants-du-titanic,Titanic - Machine Learning from Disaster
10734364,0.0,0,2,/nikhilmishra21/titanic,Titanic - Machine Learning from Disaster
1780981,0.78947,1,0,/sharma7292/titanic-survival-prediction,Titanic - Machine Learning from Disaster
11108910,0.79186,0,0,/benyaminghahremani/basic-approaches-to-solve-titanic-problem,Titanic - Machine Learning from Disaster
4025645,0.7799,2,8,/zzaibis/titanic-kernel-that-damn-iceberg,Titanic - Machine Learning from Disaster
11626598,0.78468,9,30,/zacharywyman/titanic-top-10-beginner-tutorial,Titanic - Machine Learning from Disaster
9424226,0.1228,23,68,/prestonfan/stacked-analysis-predicting-prices,House Prices - Advanced Regression Techniques
9019716,0.11678,2,3,/dash25/housing-price-stacking-method,House Prices - Advanced Regression Techniques
9432300,0.76555,2,11,/manishkc06/learn-basics-of-data-science-using-titanic-dataset,Titanic - Machine Learning from Disaster
8229302,0.2399599999999999,5,38,/jagdmir/house-price-prediction-eda-modelling,House Prices - Advanced Regression Techniques
2551951,0.76555,1,7,/nephalem98/a-beginner-approach-to-titanic-survival-prediction,Titanic - Machine Learning from Disaster
525838,0.80861,0,6,/konohayui/titanic-data-visualization-and-models,Titanic - Machine Learning from Disaster
3614931,0.7799,0,9,/adrianoavelar/titanic-evolutionary-algorithm-python,Titanic - Machine Learning from Disaster
12193009,0.78708,9,12,/mohandgamal/beginner-guide-titanc-top-9,Titanic - Machine Learning from Disaster
554028,0.78947,0,15,/samratp/titanic-with-stacking-voting-pseudo-labeling,Titanic - Machine Learning from Disaster
7985375,0.14907,19,77,/ratan123/march-madness-2020-ncaam-simple-lightgbm-on-kfold,Google Cloud & NCAA® ML Competition 2020-NCAAM
5363632,6.57638,0,1,/jrw2200/ames-housing,House Prices - Advanced Regression Techniques
13529294,0.718,0,0,/minatighosh2020/cnn-mnist-digit-classification,Digit Recognizer
12324218,0.7703300000000001,3,6,/krishnakalyan3/titanic-fast-ai-2-0-tabular-minimal-example,Titanic - Machine Learning from Disaster
10914282,0.79665,14,18,/ligtfeather/random-forest-with-grid-search,Titanic - Machine Learning from Disaster
10876864,0.99403,6,23,/darkknight98/mnist-digits-dataset-a-simple-cnn-99,Digit Recognizer
10851833,0.99175,0,4,/socathie/mnist-w-resnet-and-data-augmentation,Digit Recognizer
10783452,0.99121,0,0,/brenootsuka/cnn-for-digit-classification,Digit Recognizer
10345580,0.99589,13,24,/sanchitvj/deep-learning-cnn,Digit Recognizer
10319402,0.99103,3,3,/alexanderkalen/kalen-landi-p1,Digit Recognizer
10172245,0.9876,12,29,/kumarselvakumaran/cnn-a-newbie-friendly-guide-with-visual-knowhow,Digit Recognizer
10080742,0.77751,2,18,/shubhamksingh/titanic-the-ride,Titanic - Machine Learning from Disaster
9912251,0.985,0,57,/mauriciofigueiredo/cnn-simples-com-keras-para-iniciantes,Digit Recognizer
9687925,0.987,0,1,/chenrz925/kernel74198bc53c,Digit Recognizer
9405686,0.99242,0,0,/abhaydayalmathur/mnist-cnn,Digit Recognizer
9084675,0.48689,0,1,/tunguz/mnist-with-rapids-svr,Digit Recognizer
9043414,0.99453,0,0,/aidiary/mnist-by-keras-part-2,Digit Recognizer
8792487,1.18298,0,0,/patrickssfuchs/covid-week3-dl,COVID19 Global Forecasting (Week 3)
8726512,0.98928,0,6,/trolukovich/glorot-vs-he-weight-initialization-experiment,Digit Recognizer
8677885,1.64475,0,3,/neilde/covid-19-eda-lstm,COVID19 Global Forecasting (Week 2)
8601275,0.994,0,0,/jullang/digit-recognition-via-cnn-using-batchnorm-dataaug,Digit Recognizer
12667960,0.7751100000000001,0,0,/andreasettimo/titanic-predict-survived-people,Titanic - Machine Learning from Disaster
11528271,0.14689,0,2,/praveen5108/sample-submission,Conway's Reverse Game of Life 2020
10865517,0.98778,0,1,/avulapatiniranjan/number-detection,Digit Recognizer
10855808,0.99721,0,16,/jvdahemad/mnist-with-keras-99-72-acc-top-7,Digit Recognizer
10793146,0.97332,0,7,/cdefreitas/mnist-digit-recognition,Digit Recognizer
10755803,0.98814,0,3,/anwesande/convolution-nn-and-pooling-starter-0-98-acc,Digit Recognizer
10722778,0.9821,0,3,/abhisingh1/digit-recognizer-cnn,Digit Recognizer
10325003,0.999,1,2,/wanghaoyuu/kaggle-digit-recognizer-keras-cnn-100-accuracy,Digit Recognizer
10174131,0.99585,0,1,/prathadongre/mnist-learning,Digit Recognizer
9799147,0.98514,0,0,/dkrocks/introduction-to-cnn-keras-0-997-top-6,Digit Recognizer
9795411,0.5556,2,8,/priyanath/pricing-house-prediction,House Prices - Advanced Regression Techniques
9275992,0.99342,0,0,/aligh474/cnn-best-practice-2,Digit Recognizer
9196687,0.975,0,0,/destefanim/mnist,Digit Recognizer
9057287,0.13359,19,21,/avnika22/lgbm-predicting-house-prices,House Prices - Advanced Regression Techniques
9056488,0.99185,1,1,/ktasome/mnist-classification-with-cnn,Digit Recognizer
8807295,0.99328,0,0,/jatannvyas/introduction-to-cnn-keras-0-997-top-6,Digit Recognizer
8800787,0.05245,0,0,/lomen0857/covid19-forecasting,COVID19 Global Forecasting (Week 3)
8651233,0.78947,0,0,/arnabdas8901/simple-fully-connected-model-for-prediction,Titanic - Machine Learning from Disaster
11481021,0.79665,5,15,/yashsarjekar/yash-sarjekar-titanic-survival,Titanic - Machine Learning from Disaster
10984426,0.1241099999999999,2,16,/ligtfeather/ensemble-learning-with-eda,House Prices - Advanced Regression Techniques
10569409,0.79425,2,7,/kvdatadragon/0-79425-an-ingenious-logistic-regression,Titanic - Machine Learning from Disaster
9408710,0.1458799999999999,0,2,/sachinssingh/house-prices-prediction,House Prices - Advanced Regression Techniques
8609050,0.32343,0,4,/esotericazzo/covid-19-global-forecasting-model-hyperparameter,COVID19 Global Forecasting (Week 2)
8241551,0.35572,1,3,/jtrotman/blend-ncaaw-with-2020-vision,Google Cloud & NCAA® ML Competition 2020-NCAAW
8079088,0.67729,0,2,/sagaramu/nlp-disaster-adding-features-and-visualization,Natural Language Processing with Disaster Tweets
7726190,0.11553,3,5,/buin6319/house-price-regression-meta-ensemble-top-10,House Prices - Advanced Regression Techniques
7212881,0.84216,74,394,/vbmokin/nlp-eda-bag-of-words-tf-idf-glove-bert,Natural Language Processing with Disaster Tweets
6135017,0.82775,0,0,/zongtseng/titanic-using-extratrees-0-82775,Titanic - Machine Learning from Disaster
5862378,0.14652,6,28,/faressayah/predicting-house-prices-xgboost-regressor,House Prices - Advanced Regression Techniques
5787184,0.78468,5,9,/rohan9889/titanic-kernel-with-xgboost,Titanic - Machine Learning from Disaster
4215533,0.78468,9,23,/iluvmahheart/simple-beginner-titanic-survival-prediction,Titanic - Machine Learning from Disaster
4089372,0.78468,2,5,/wesleywatanabe/titanic-challenge-1th-kernel,Titanic - Machine Learning from Disaster
3817393,0.12292,4,14,/sarthakbatra/housing-prices-tutorial,House Prices - Advanced Regression Techniques
11936282,0.67224,0,6,/alaapdhall/most-comprehensive-guide-to-a-ds-framework,Titanic - Machine Learning from Disaster
11321331,0.80382,6,14,/giorgosfoukarakis/titanic-from-eda-to-the-power-of-ensembles-top4,Titanic - Machine Learning from Disaster
11271490,0.7799,8,10,/akshitrai/ml-in-everything-titanic-disaster-survival,Titanic - Machine Learning from Disaster
10991161,0.79665,185,470,/pedrodematos/titanic-a-complete-approach-to-top-rankings,Titanic - Machine Learning from Disaster
10912154,0.7751100000000001,2,20,/aditi81k/titanic-problem,Titanic - Machine Learning from Disaster
10895453,0.14904,4,6,/saket019/score-0-149-detailed-eda-and-random-forest,House Prices - Advanced Regression Techniques
9325312,0.77272,0,4,/mrhippo/titanic-prediction-and-analysis-with-dsh,Titanic - Machine Learning from Disaster
7958107,0.8004899999999999,0,1,/vikassingh1996/simple-model-feat-nlp-disaster-tweets-lb-0-80572,Natural Language Processing with Disaster Tweets
7470053,0.79425,7,8,/gauthampughazh/titanic-survival-prediction-pandas-plotly-keras,Titanic - Machine Learning from Disaster
6903649,0.79904,0,0,/funnyuk/titanic-total,Titanic - Machine Learning from Disaster
5874735,0.79904,0,3,/aroonkp/titanic-simplest-solution-top-12,Titanic - Machine Learning from Disaster
5617385,0.11819,1,14,/eiosifov/top-20-with-data-cleaning-only-elasticnet,House Prices - Advanced Regression Techniques
5342420,0.82296,2,1,/naeemkh/titanic-utility-functions-prediction-model,Titanic - Machine Learning from Disaster
4030676,0.76555,0,0,/naresh8530/survived-or-died,Titanic - Machine Learning from Disaster
2806215,0.81818,0,1,/dompresutto/titanic-0-818-with-massive-plot-analysis,Titanic - Machine Learning from Disaster
2785972,0.76555,0,2,/arvindcletus/xgboost-model-for-the-titanic-dataset,Titanic - Machine Learning from Disaster
2176366,0.1303,0,0,/nikkisharma536/house-price-prediction,House Prices - Advanced Regression Techniques
2071534,0.76555,5,12,/shadabhussain/titanic,Titanic - Machine Learning from Disaster
1933249,0.80861,4,8,/xdms85/titanic-data-machine-learning,Titanic - Machine Learning from Disaster
1444369,0.1185099999999999,2,12,/lucabasa/an-agile-approach-get-incrementally-better,House Prices - Advanced Regression Techniques
1248013,0.121,3,17,/felgueira/top-20-interpretable-solution-using-lasso,House Prices - Advanced Regression Techniques
1075652,0.7177,8,4,/xezxey/my-heart-will-go-on-titanic-disaster-gridsearchcv,Titanic - Machine Learning from Disaster
11490870,0.75598,0,0,/shivamkc/titanic-dataset-expt-with-xgboost-default-params,Titanic - Machine Learning from Disaster
11230616,0.76555,1,39,/subbuvolvosekar/ml-classification-algorithms-titanic,Titanic - Machine Learning from Disaster
11159572,0.622,0,4,/gauravduttakiit/predict-the-survival-using-svm,Titanic - Machine Learning from Disaster
10872841,0.78468,9,7,/minnieliang/titanic-xgboost-model-0-78468,Titanic - Machine Learning from Disaster
6583594,0.79904,3,5,/loyashoib/beginners-notebook-to-achieve-80-accuracy,Titanic - Machine Learning from Disaster
6358703,0.7751100000000001,1,6,/apopovici/eda-with-seaborn-for-titanic-competition,Titanic - Machine Learning from Disaster
5847018,0.2497199999999999,4,7,/jsvishnuj/data-cleaning-and-k-nearest-neighbors-algorithm,House Prices - Advanced Regression Techniques
5812649,0.79425,15,15,/mdiqbalbajmi/titanic-survival-prediction-beginner,Titanic - Machine Learning from Disaster
5561643,0.81818,3,4,/himaoka/titanic-simple-random-forest,Titanic - Machine Learning from Disaster
4475106,0.79425,0,0,/jolyannebn/titanic-premier-kernel,Titanic - Machine Learning from Disaster
4211690,0.81339,44,145,/kpacocha/top-5-titanic-machine-learning-from-disaster,Titanic - Machine Learning from Disaster
3911986,0.10965,4,43,/alfredmaboa/advanced-regression-techniques-regularization,House Prices - Advanced Regression Techniques
3437098,0.80382,5,4,/y363499859/titanic-solution-top-11,Titanic - Machine Learning from Disaster
2914213,0.1372,14,26,/zenstat/simple-linear-regression-example,House Prices - Advanced Regression Techniques
2747959,0.12497,3,7,/rgoodman/house-prices-from-scratch-ridge-and-lasso-viz,House Prices - Advanced Regression Techniques
2029003,0.13449,65,168,/frtgnn/beginner-s-stop-xgb-lgbm-blend,House Prices - Advanced Regression Techniques
1779373,0.78468,0,8,/dochad/titanic-beginner-coding-kernel,Titanic - Machine Learning from Disaster
1517297,0.1215099999999999,0,4,/taolearnstolearn/clean-and-stream-lined-data-preprocessing,House Prices - Advanced Regression Techniques
1420521,0.78468,0,4,/vjgupta/titanic-simple-model-beginners,Titanic - Machine Learning from Disaster
1051704,0.76076,0,2,/marklvl/titanic-logistic-regression,Titanic - Machine Learning from Disaster
1041334,0.78947,2,5,/tushar786/titanic-how-i-scored-above-80-vis-feature-engg,Titanic - Machine Learning from Disaster
717047,0.13873,2,8,/azzion/amazing-performances-with-lasso-rf-and-xgboost,House Prices - Advanced Regression Techniques
650367,0.1160799999999999,0,5,/rickychwong/a-beginner-s-kernel,House Prices - Advanced Regression Techniques
11913501,0.76794,1,7,/namanmanchanda/titanic-eda,Titanic - Machine Learning from Disaster
11163238,0.7751100000000001,2,4,/gauravduttakiit/predict-the-survival-using-gbm,Titanic - Machine Learning from Disaster
10830300,0.97739,2,8,/prakashjiban/keras-cnn-vs-simple-neural-network-98-acc,Digit Recognizer
10702608,0.13523,0,3,/hayouniachref/house-pricing-model,House Prices - Advanced Regression Techniques
10671850,0.78708,4,19,/zmey56/titanic-2020-ml-and-keras,Titanic - Machine Learning from Disaster
10628170,0.622,2,14,/kamalisekar/titanic-survival-prediction,Titanic - Machine Learning from Disaster
10587083,0.14798,8,26,/srivignesh/data-preprocessing-for-house-price-prediction,House Prices - Advanced Regression Techniques
10414000,0.7751100000000001,20,22,/abisheksudarshan/titanic-ml-from-disaster-eda-dv-ensembling,Titanic - Machine Learning from Disaster
10306526,0.79425,0,15,/nehaprabhavalkar/titanic-machine-learning-from-disaster,Titanic - Machine Learning from Disaster
10033513,0.1287,8,4,/niteshchaurasiya/simple-exploratory-data-analysis-ensemble-modeling,House Prices - Advanced Regression Techniques
9576677,0.79904,4,17,/siraznaorem/survival-skills-by-grylls-titanic-beg-helper,Titanic - Machine Learning from Disaster
8993843,0.79904,0,1,/alfarias/fastanic-fastai-pandas-profiling-h2o-automl,Titanic - Machine Learning from Disaster
8141262,0.1156099999999999,3,2,/bernhardklinger/simple-lasso-model-top-10-with-no-stacking,House Prices - Advanced Regression Techniques
7940761,0.7703300000000001,0,3,/sagaramu/simple-model-building,Titanic - Machine Learning from Disaster
7767077,0.7751100000000001,0,1,/kaggleurroad/titanic-tutorial,Titanic - Machine Learning from Disaster
7612202,0.1181099999999999,3,13,/hrshtporwal5/houseprice-prediction,House Prices - Advanced Regression Techniques
7095839,0.79904,0,0,/harsh1347/titnic-starter,Titanic - Machine Learning from Disaster
6814966,0.12608,0,15,/servietsky/house-prices-voting-stacking-and-belnding,House Prices - Advanced Regression Techniques
6084768,0.79904,96,164,/reighns/titanic-a-complete-beginner-s-guide,Titanic - Machine Learning from Disaster
5238586,0.78468,1,2,/mahendrabishnoi2/titanic-eda-modelling,Titanic - Machine Learning from Disaster
4765830,0.7703300000000001,0,1,/gsingh17/titanic-kernel-beginner-exploration,Titanic - Machine Learning from Disaster
3976211,0.16238,1,5,/imdevskp/house-price-prediction-using-regression,House Prices - Advanced Regression Techniques
3767578,0.1170799999999999,15,44,/gunesevitan/house-prices-advanced-stacking-tutorial,House Prices - Advanced Regression Techniques
14440134,0.497,0,0,/aicentral/house-price-prediction-using-automl,House Prices - Advanced Regression Techniques
13034699,0.76794,0,0,/youssefassouli/titanic-predictions,Titanic - Machine Learning from Disaster
11863318,0.6985600000000001,1,5,/ajaypalsinghlo/titanic,Titanic - Machine Learning from Disaster
11841301,0.7822899999999999,0,1,/mabalogun/my-titanic-story-svm-with-feature-importance,Titanic - Machine Learning from Disaster
340141,0.72727,4,6,/harshithswamy/first-kernel-basic-eda-classification,Titanic - Machine Learning from Disaster
269323,0.99557,0,2,/olgabelitskaya/digit-recognition-models-2,Digit Recognizer
196775,0.7368399999999999,0,1,/anupkumargupta/a-journey-through-titanic,Titanic - Machine Learning from Disaster
11158947,0.73444,2,14,/gauravduttakiit/predict-the-survival-using-decision-tree,Titanic - Machine Learning from Disaster
10293535,0.78468,0,6,/amanjakhetiya/titanic-survival-prediction-with-eda,Titanic - Machine Learning from Disaster
10182143,0.12452,1,3,/hepraph/detailed-data-cleaning-eda-stacking,House Prices - Advanced Regression Techniques
9605458,0.76076,0,2,/donkeys/explaining-models-with-lime-et-al,House Prices - Advanced Regression Techniques
9432577,0.12618,1,0,/pandaalbert/house-price,House Prices - Advanced Regression Techniques
8781854,0.03516,0,0,/technikimobo/analysis-data-plotting-and-prediction,COVID19 Global Forecasting (Week 3)
8737793,1.0,34,52,/brendan45774/titanic-how-i-become-the-top-1,Titanic - Machine Learning from Disaster
8687786,0.589,4,15,/moradnejad/start-from-here-complete-eda-sota,Tweet Sentiment Extraction
8120309,0.8948799999999999,7,16,/ianmoone0617/flower-gpu-fastai,Flower Classification with TPUs
7973969,0.76794,1,10,/brendan45774/titanic-data-solution,Titanic - Machine Learning from Disaster
7692195,0.99014,12,12,/g3rnosh/keras-cnn-digit-recognizer-99,Digit Recognizer
7593070,0.13189,4,12,/vbmokin/universal-eda-fe-prediction,House Prices - Advanced Regression Techniques
5221425,0.7751100000000001,5,7,/durgaprasad64/titanic-predictions-with-different-ml-models,Titanic - Machine Learning from Disaster
5191699,0.7799,4,14,/felipesanchezgarzon/eda-feature-engineering-comparison-ml-accuracy,Titanic - Machine Learning from Disaster
5051181,0.79904,1,8,/nkaps98/titanic-eda-predictions,Titanic - Machine Learning from Disaster
5032272,0.15325,0,2,/thrillanalysis/fastai-implemantation-on-house-price-predication,House Prices - Advanced Regression Techniques
4433878,0.99642,0,3,/kagglemlearner/digit-recognition-cnn-in-keras-0-996-updated,Digit Recognizer
4026676,0.76076,0,2,/fernandoapires/beginner-competition-kernel,Titanic - Machine Learning from Disaster
10728972,0.7799,4,6,/yohannwattiez/a-simple-guide-towards-ensembling-87-accuracy,Titanic - Machine Learning from Disaster
305133,0.79904,41,55,/samratp/beginner-tutorial-using-votingclassifier-82-27,Titanic - Machine Learning from Disaster
1470201,0.79904,0,21,/monthepp/titanic-machine-learning-from-disaster,Titanic - Machine Learning from Disaster
11775306,0.7751100000000001,1,12,/marcelopesse/titanic-survival-machine-learning-with-sklearn,Titanic - Machine Learning from Disaster
9792293,0.13451,1,2,/hjaimes/house-prices-project,House Prices - Advanced Regression Techniques
232566,0.78947,178,487,/headsortails/pytanic,Titanic - Machine Learning from Disaster
9460385,0.14647,2,2,/yatharthmahesh/eda-with-feature-engineering,House Prices - Advanced Regression Techniques
5345902,0.78468,0,0,/txustice/titanic-survival-with-fastai,Titanic - Machine Learning from Disaster
8607724,0.21524,19,23,/ranjithks/few-lines-of-code-without-data-leak,COVID19 Global Forecasting (Week 2)
3101767,0.79425,1,6,/junseokchoen/my-first-submission-beginner-unfamiliar-english,Titanic - Machine Learning from Disaster
2899552,0.80861,1,5,/filco306/titanic-top-7-rf-xgb-gp-feature-engineering,Titanic - Machine Learning from Disaster
1500027,0.76076,0,1,/rdmisal/applying-all-classifier-on-titanic-dataset,Titanic - Machine Learning from Disaster
1256598,0.7799,20,37,/moghazy/simple-mlp-with-feature-engineering-and-eda,Titanic - Machine Learning from Disaster
7416031,0.80382,22,31,/llleedh/titanic-for-beginner,Titanic - Machine Learning from Disaster
987817,0.78947,0,0,/caiqueborges/first-try-titanic-tutorial-practice,Titanic - Machine Learning from Disaster
11170018,0.76315,0,5,/manavd22/titanic-lr-wordcloud-81-56,Titanic - Machine Learning from Disaster
3668939,0.159,4,15,/priteshshrivastava/house-prices-model-explainability-starter,House Prices - Advanced Regression Techniques
11821496,0.78947,10,13,/sametevik/titanic,Titanic - Machine Learning from Disaster
8634857,0.7511899999999999,0,3,/smile26/titanic-distaster-prediction,Titanic - Machine Learning from Disaster
855815,0.80382,30,159,/ydalat/titanic-a-step-by-step-intro-to-machine-learning,Titanic - Machine Learning from Disaster
8086299,0.80382,16,44,/darshanjain29/titanic-survival-from-top-70-to-top-7-on-lb,Titanic - Machine Learning from Disaster
10982918,0.8062199999999999,47,63,/shreyasvedpathak/titanic-survival-prediction-top-4,Titanic - Machine Learning from Disaster
5279516,0.15333,1,4,/shyambhu/housing-data,House Prices - Advanced Regression Techniques
10839756,0.80382,2,9,/vbmokin/titanic-shortest-fastest-nb-with-score-0-803,Titanic - Machine Learning from Disaster
2841716,0.80382,5,6,/sigmaset/hyperparameter-tuning-ensemble-methods,Titanic - Machine Learning from Disaster
11986702,0.76315,0,1,/ayoubsandali/titanic-challenge-neural-networks,Titanic - Machine Learning from Disaster
11111447,0.9019,0,5,/ianmoone0617/jigsaw-multilingual-classification-fastai,Jigsaw Multilingual Toxic Comment Classification
11084035,0.8562,0,3,/amirahmad786/jig-saw-toxic-classifier,Jigsaw Multilingual Toxic Comment Classification
10775483,0.7751100000000001,2,10,/alisharifi2000/titanic-survive,Titanic - Machine Learning from Disaster
10697594,2.17485,0,2,/prateek146/facial-keypoints-detection-model-explained,Facial Keypoints Detection
10620083,0.99025,0,3,/kasevgen/mnist,Digit Recognizer
10539678,0.99353,1,3,/susantotm/digit-recognizer,Digit Recognizer
10371197,0.13195,5,9,/kmtk49/houseprice-using-optuna-lightgbm-tuner,House Prices - Advanced Regression Techniques
10367301,0.99525,0,15,/redwankarimsony/mnist-cnn-callbacks-visualizations,Digit Recognizer
10227771,0.99132,0,1,/marcosrozic/project-1-boada-de-andrade-cnn,Digit Recognizer
10224226,0.9945,0,2,/manuelprieto/proyectopytorch,Digit Recognizer
10221740,0.98589,0,1,/somilrastogi/digit-recognizer,Digit Recognizer
10111511,0.7140000000000001,0,0,/dreamcodes/tweet-label-smoothing,Tweet Sentiment Extraction
9935319,0.99392,2,13,/alisharifi2000/mnist-cnn,Digit Recognizer
9903678,0.95957,1,1,/adhirajbhagawati/keras-basics-and-digit-recognizer,Digit Recognizer
9894364,0.98957,0,1,/mohdaltaf/kernel743843147a,Digit Recognizer
9864770,0.98571,0,0,/abhimanyusethi/digits-recognizer,Digit Recognizer
9712805,0.99475,1,6,/aishwaryapalit/digit-recognizer-using-cnn-top-17,Digit Recognizer
9537829,0.99542,0,0,/simonand/a-simple-notebook-with-99-5,Digit Recognizer
9284592,0.99257,2,1,/ardrey0823/mnist-digit-recognizer-99-25,Digit Recognizer
8980528,0.96975,0,1,/ajsssss/mnist-digit-recognizer-built-with-tf-2-0,Digit Recognizer
8814505,0.96028,0,0,/drozdyuk/digit-recognizer,Digit Recognizer
8811204,0.03902,0,2,/seaofstars/kernel16e9ef2435,COVID19 Global Forecasting (Week 3)
8670912,0.68861,0,0,/fmobrj1975/first-model-inference,COVID19 Global Forecasting (Week 2)
14565180,0.80386,0,1,/firebee/glove-lstm,Natural Language Processing with Disaster Tweets
10602752,0.7822899999999999,0,7,/ricardoprins/has-jack-died-in-vain-an-intro-to-ml,Titanic - Machine Learning from Disaster
439033,0.80861,35,188,/nicapotato/titanic-voting-pipeline-stack-and-guide,Titanic - Machine Learning from Disaster
419180,0.96985,0,1,/databang/mnist-pytorch-simple-non-cnn-example,Digit Recognizer
14633477,0.98875,0,0,/hiabhi/digit-recognition-with-tensorflow,Digit Recognizer
12492191,0.81339,26,78,/korfanakis/titanic-a-beginner-friendly-approach-to-top-3,Titanic - Machine Learning from Disaster
11416608,1.0,57,179,/imoore/titanic-the-only-notebook-you-need-to-see,Titanic - Machine Learning from Disaster
7172287,0.99428,0,2,/dmitriykorzik/digit-recognizer,Digit Recognizer
8387775,1.0,2,6,/zhuangliu1939/bert-abstraction,Abstraction and Reasoning Challenge
3786540,0.76555,1,0,/rolandkopka/first-try,Titanic - Machine Learning from Disaster
3934186,0.99414,3,8,/tonysun94/pytorch-1-0-1-on-mnist-acc-99-8,Digit Recognizer
3144008,0.99914,15,40,/darkside92/simple-best-digit-recognizer-with-cnn-top-5,Digit Recognizer
8275051,0.81703,0,9,/grantgasser/eda-naive-bayes-bert-glove-fasttext-nn,Natural Language Processing with Disaster Tweets
8307467,0.78468,0,0,/dmitrijstrizna/kerasstandardnnmodel,Titanic - Machine Learning from Disaster
7755486,0.79374,0,0,/deeplearn1/naive-bayes-learning,Natural Language Processing with Disaster Tweets
3344044,11.91415,0,0,/yaningzhong/yaning,Facial Keypoints Detection
3766655,0.98357,0,0,/kavyajeet/using-le-net,Digit Recognizer
1671180,0.99157,4,17,/metetik/digit-recognization-with-different-alghorithms,Digit Recognizer
4904037,0.98985,0,2,/voidspiral/cnn-lenet-vgg16-architecture-using-pytorch,Digit Recognizer
4121326,0.99971,0,1,/makvihas/mnist,Digit Recognizer
3995551,0.99257,4,16,/rblcoder/digits-cnn,Digit Recognizer
4835495,0.79904,0,1,/littlefairy/titanic-survial-prediction-gradientboosting,Titanic - Machine Learning from Disaster
4703307,0.987,0,3,/tauseef6462/cnn-lenet-architecture-using-pytorch,Digit Recognizer
5012508,0.98728,0,1,/imoisharma/digit-recognizer-using-keras-implementation,Digit Recognizer
3696423,0.997,0,1,/asauve/mnist-cnn-da-batchnorm-maxpool-kfold10,Digit Recognizer
5241311,0.98914,1,2,/abdeljalil/handwritten-digits-classification-with-keras-cnn,Digit Recognizer
5381346,0.995,1,1,/muerbingsha/mnist-vgg19,Digit Recognizer
4904329,0.97014,0,2,/muerbingsha/mnist-siamese-keras,Digit Recognizer
4896060,0.98657,1,1,/philquinn/cnn-compitiation-mnist,Digit Recognizer
5406432,0.99228,0,1,/diskandar69/beginners-guide-to-mnist-with-fast-ai,Digit Recognizer
4700222,0.99571,0,1,/ercjul/digit-recognizer-by-cnn,Digit Recognizer
7985923,0.99714,0,1,/phylake1337/minst-classification,Digit Recognizer
7903646,0.99528,1,3,/anjanatiha/mnist-classification,Digit Recognizer
3523023,0.91757,0,1,/takepkaggle/my-mnist-prediction,Digit Recognizer
6544283,0.12811,0,3,/whiskeybear/base-line-xgb-randomsearch,House Prices - Advanced Regression Techniques
6618570,0.99471,0,0,/geemunz/simple-cnn-implementation-using-tensorflow,Digit Recognizer
3366679,0.79904,0,3,/rushabhwadkar/titanic-suvival-prediction,Titanic - Machine Learning from Disaster
5157294,0.98742,0,0,/prasannathandhul/mnist-cnn-pytorch,Digit Recognizer
3209101,0.81339,1,11,/axeloh/titanic-disaster-predicting-survivors,Titanic - Machine Learning from Disaster
7915941,0.80447,1,2,/nicapotato/lstm-disaster-text-numeric-embeddings,Natural Language Processing with Disaster Tweets
5929295,0.99671,0,4,/nandor65/c3c3c5c5c7c7c4,Digit Recognizer
4309193,0.97057,0,3,/leekaggle123/mnist-numpy-from-scratch,Digit Recognizer
6175802,0.78468,0,0,/tarimitsu/upura-kaggle-tutorial-01-first-submission,Titanic - Machine Learning from Disaster
6040079,0.99928,3,4,/chandanshinde/digit-recognizer-keras,Digit Recognizer
4382324,0.99557,0,0,/neonninja/cnn2d-train-on-entire-dataset,Digit Recognizer
6930836,0.98514,0,0,/jimimao/minist-recognizer,Digit Recognizer
6445816,0.993,0,4,/atlantistin/digit-recognizer,Digit Recognizer
6487956,0.61342,1,2,/cybercat/recognizing-doodles-with-computer-vision,Homesite Quote Conversion
3807275,0.98357,0,0,/nsvr57/digit,Digit Recognizer
6194235,0.71291,0,0,/salamancakev/proyecto-1-computaci-n-emergente,Titanic - Machine Learning from Disaster
4423675,0.75598,1,4,/gabrielsze/titanic-kernel,Titanic - Machine Learning from Disaster
4440946,0.78947,0,0,/jsvishnu/eda-data-visualization-and-submission,Titanic - Machine Learning from Disaster
4245852,0.99,0,1,/sheik0/cnn-using-keras,Digit Recognizer
1475427,0.99185,6,19,/vijaykris/mnist-classification-using-fast-ai-v2,Digit Recognizer
4695419,0.99628,1,0,/irfanarif39/mnist-classifier-with-cnn-keras,Digit Recognizer
7287517,0.99742,0,6,/trungha/pytorch-improved-lenet5-augmentation-ensemble,Digit Recognizer
5242311,0.99614,6,10,/ankur1401/digit-recognizer-with-cnn-using-keras,Digit Recognizer
7297375,0.99442,0,4,/ahmedsmara/digits-recognition-using-conv-neural-network,Digit Recognizer
5997183,0.994,0,3,/marcossantanauff/basic-digits-recognizer-with-fastai,Digit Recognizer
2657871,0.99028,2,3,/aditya100/digit-recognizer,Digit Recognizer
4160324,0.78947,1,3,/mike201905/titanic-prediction,Titanic - Machine Learning from Disaster
1357232,0.1136099999999999,5,28,/hemingwei/top-2-from-laurenstc-on-house-price-prediction,House Prices - Advanced Regression Techniques
3791798,0.98771,1,2,/mondaysu/digit-recognizer-tensorflow-cnn,Digit Recognizer
2899616,0.1087099999999999,0,0,/vigneshsubramanians/digit-classifier,Digit Recognizer
4341090,0.994,0,3,/nitron/mnist-keras,Digit Recognizer
4323931,0.99357,0,1,/zhou77711/mnist-fastai,Digit Recognizer
4303471,0.99642,0,0,/richardeascanio/convolutional-neural-networks-p2,Digit Recognizer
4255750,0.99528,0,2,/careforyourcandy/proyecto-2,Digit Recognizer
3666179,0.73205,2,0,/leeyuri/subinium-tutorial-titanic-beginner,Titanic - Machine Learning from Disaster
7331649,0.99657,4,6,/ttminh27/digit-recognizer,Digit Recognizer
7212100,0.81152,0,1,/prashantkh19/disaster-tweets,Natural Language Processing with Disaster Tweets
3488437,0.99757,0,5,/sohailrajabi/mnist,Digit Recognizer
4843207,0.977,0,0,/harshith246/keras-mnist,Digit Recognizer
4724556,0.70813,0,1,/mohitmaithani/titanic-survival-prediction,Titanic - Machine Learning from Disaster
5381558,0.76076,0,2,/fmijsters/kernel7ea52ad325,Titanic - Machine Learning from Disaster
7254174,0.83052,1,8,/sarmat/catalyst-simple-distillbert,Natural Language Processing with Disaster Tweets
7201405,0.79497,0,0,/vivek61/tweet-classifier,Natural Language Processing with Disaster Tweets
8035242,0.996,0,0,/ysanojpn/digit-recognizer-convnet-dataaug-lrreduction,Digit Recognizer
4661160,0.98742,0,0,/shobhit1thor/kernel39c4f69d0f,Digit Recognizer
5276725,58.3374,0,1,/ppleskov/gan-dogs-starter-24-jul-custom-layers,empty
8302420,0.9867,0,0,/iloveyyp/handwritten-grapheme-classification-resnet-0-97,Jigsaw Unintended Bias in Toxicity Classification
5624086,0.78468,0,0,/nishiokande/titanic-keras,Titanic - Machine Learning from Disaster
7619185,0.72727,1,1,/kaggleurroad/simple-models-for-beginners-with-eda,Titanic - Machine Learning from Disaster
4639822,0.98942,4,0,/brittosabu/simple-cnn,Digit Recognizer
5709470,0.99514,0,1,/gregorypierce/mnist-learning-notebook,Digit Recognizer
4220305,2.4553,0,2,/jiahongqian/fkd-mxnet,Facial Keypoints Detection
4595311,0.1194099999999999,0,1,/vincenzothaulero/house-pricing-fds-vft,House Prices - Advanced Regression Techniques
2597806,0.79425,0,2,/vladlee/titanic-survival-predictions-regularization,Titanic - Machine Learning from Disaster
5344545,0.74162,6,11,/adibakm/titanic-solution,Titanic - Machine Learning from Disaster
4653035,0.78468,0,3,/fujiinot/first-book,Titanic - Machine Learning from Disaster
10681756,0.17984,0,0,/mksaad/house-price-adv-reg-keras,House Prices - Advanced Regression Techniques
10441605,0.76794,9,28,/db102291/titanic-competition-ensemble-learning,Titanic - Machine Learning from Disaster
10042796,0.7751100000000001,12,16,/christianlillelund/titanic-using-gridsearchcv-10-classifiers,Titanic - Machine Learning from Disaster
9935940,0.12905,7,23,/iamsvp/feature-selection-and-catboost-for-housing-prices,House Prices - Advanced Regression Techniques
8367797,0.991,0,0,/ajisamudra/cnn-on-mnist-dataset,Digit Recognizer
8138071,0.79313,2,10,/saga21/disaster-tweets-comp-introduction-to-nlp,Natural Language Processing with Disaster Tweets
3867128,0.99685,3,1,/tanchris/digit-recognizer-comparing-knn-mlp-and-keras,Digit Recognizer
1621448,0.14885,0,3,/prince1992/kaggle-mnist,Digit Recognizer
1397095,0.99028,0,0,/harshaneigapula/dnn-inspired-from-inceptionv3-model,Digit Recognizer
1264167,0.998,35,98,/moghazy/guide-to-cnns-with-data-augmentation-keras,Digit Recognizer
927375,0.7751100000000001,0,2,/renangomes/solu-o-simplificada-utilizando-mlp-pt-br,Titanic - Machine Learning from Disaster
917118,0.99942,15,71,/endlesslethe/siwei-digit-recognizer-top20,Digit Recognizer
738361,0.78947,2,4,/anezka/titanic-problem,Titanic - Machine Learning from Disaster
449205,0.984,17,97,/juiyangchang/cnn-with-pytorch-0-995-accuracy,Digit Recognizer
8973937,0.12587,0,0,/mattbast/feature-engineering-house-prices,House Prices - Advanced Regression Techniques
8861839,0.79665,0,4,/brunovpm/tuning-hyperparameter-acc-0-78-top-15,Titanic - Machine Learning from Disaster
3611356,0.78468,0,6,/sureshrv/titanic-dataset,Titanic - Machine Learning from Disaster
6527636,0.82296,11,34,/nicodesh/xgboost-with-5-features-0-82296-step-by-step,Titanic - Machine Learning from Disaster
5679255,0.99228,3,15,/ashishbarvaliya/mnist-using-simple-cnn-0-99,Digit Recognizer
6483444,0.79425,1,6,/lantian773030/ensembling-stacking-in-python-0-79425,Titanic - Machine Learning from Disaster
512552,0.79904,10,18,/raoulma/titanic-survival-class-79-90-test-acc,Titanic - Machine Learning from Disaster
1902660,0.79425,6,14,/mtourond/splitting-pclass-and-tuning-models,Titanic - Machine Learning from Disaster
1694754,0.80382,1,16,/easter3163/very-simple-analysis-for-titanic,Titanic - Machine Learning from Disaster
2219537,0.79904,2,1,/southman/pca-vs-mean-encoding,Titanic - Machine Learning from Disaster
5863702,0.96914,15,35,/carlolepelaars/97-on-mnist-with-a-single-decision-tree-t-sne,Digit Recognizer
5768259,0.72727,9,24,/pradeepmuniasamy/beginner-s-guide-to-predict-titanic-survival,Titanic - Machine Learning from Disaster
4181162,0.11769,99,703,/lavanyashukla01/how-i-made-top-0-3-on-a-kaggle-competition,House Prices - Advanced Regression Techniques
9162720,0.78947,1,6,/abhaydhiman/let-s-crack-the-titanic-s-problem,Titanic - Machine Learning from Disaster
10289744,0.80861,7,9,/kagglepankaj/titanic-survival-prediction-xgboost-accuracy-80,Titanic - Machine Learning from Disaster
3303238,0.80382,1,5,/rvmzes/titanic-with-model-ensembling,Titanic - Machine Learning from Disaster
3614282,0.76555,1,1,/fireball684/titanic-visualisation,Titanic - Machine Learning from Disaster
1071823,0.11992,0,3,/anribras/fining-feature-engineering-with-lasso-model,House Prices - Advanced Regression Techniques
1057097,0.81339,0,0,/phul65/titanic-notebook-0-81339,Titanic - Machine Learning from Disaster
1996627,0.13274,0,2,/sudhirnl7/house-price-analysis-ridge-regression,House Prices - Advanced Regression Techniques
12606964,0.78468,0,0,/ratton233/notebook0777cb0135,Titanic - Machine Learning from Disaster
8233083,2.15047,2,7,/phylake1337/2-15-loss-simple-split-trick,Facial Keypoints Detection
7552216,0.84186,5,8,/xwalker/bert-classify,Natural Language Processing with Disaster Tweets
7219310,0.8213299999999999,1,4,/souren/real-or-fake-nlp-with-disaster-tweets,Natural Language Processing with Disaster Tweets
3861782,0.78468,1,1,/luffyluffyluffy/fast-ai-titanic,Titanic - Machine Learning from Disaster
7813882,0.7511899999999999,0,0,/aizardar/predicting-survival-titanic-disaster,Titanic - Machine Learning from Disaster
4361324,0.7703300000000001,0,0,/vladlee/titanic-survival-predictions-lgbm-gridsearchcv,Titanic - Machine Learning from Disaster
4298189,0.99142,0,3,/earama/proyecto2-cnn-mnist-arama-d-lacoste-de-castro,Digit Recognizer
4320589,0.99242,1,4,/mike201905/digit-recognizer-by-cnn,Digit Recognizer
4380907,0.9999,2,9,/snjumaheshwari/fastai,Aerial Cactus Identification
4948527,0.7751100000000001,0,0,/penguinwang96825/xgboost-newbie-for-titanic-dataset,Titanic - Machine Learning from Disaster
3813964,0.98842,0,0,/frmatias/digitcognizer,Digit Recognizer
4867792,0.7799,4,3,/akaditya149/beginner-code-for-survival-analysis-titanic,Titanic - Machine Learning from Disaster
3508271,0.124,0,2,/hrush777/densenet121-transfer-learning,iWildCam 2019 - FGVC6
4104695,1.452,0,16,/iloveyyp/catboost-based-method,"Ghouls, Goblins, and Ghosts... Boo!"
5154050,0.99271,0,0,/hitluca/kernelf80f7c4a3c,Digit Recognizer
7204493,0.99,0,1,/louisdelloye/mnist-cnn,Digit Recognizer
3907275,0.1228799999999999,0,0,/mrflamboyyandt/housing-predict-team-10,House Prices - Advanced Regression Techniques
6437968,0.99528,0,1,/liweicai/resnet-18-data-augmentation-with-torchvision,Digit Recognizer
5254161,0.99471,0,2,/emillarsson/cnn-with-keras-and-tensorflow-2-0,Digit Recognizer
5551289,0.99385,0,1,/cgurkan/mnist-with-pytorch-cnn,Digit Recognizer
3586379,0.12671,0,0,/doganv/digit-recognizer-challenge,Digit Recognizer
5270589,0.79425,0,1,/tenmayato1/kernaltitanic,Titanic - Machine Learning from Disaster
5425360,0.99185,0,0,/epdrumond/simple-cnn,Digit Recognizer
4281397,0.99742,0,2,/cristianjacob1/kernelbe0a498694,Digit Recognizer
3709470,0.99385,5,10,/omershect/cnn-digit-recognizer,Digit Recognizer
7817598,0.83052,2,8,/xhlulu/disaster-nlp-distilbert-in-tf,Natural Language Processing with Disaster Tweets
4572711,0.99728,0,2,/dsandeep97/digit-recognizer-using-deep-neural-networks,Digit Recognizer
6028026,0.81339,5,10,/claudiohfg/titanic-ensemble-with-sklearn-0-81339,Titanic - Machine Learning from Disaster
5068818,0.7703300000000001,0,0,/koichimaeda/kernel3d24511411,Titanic - Machine Learning from Disaster
5214998,0.98757,0,2,/onkarank/pytorch-mnist-cnn-classification,Digit Recognizer
3438898,0.7511899999999999,0,0,/ashery/predict-survival-on-the-titanic,Titanic - Machine Learning from Disaster
5764477,0.98942,2,3,/tiandaye/pytorch-style-for-cv,Digit Recognizer
6195214,0.7751100000000001,0,0,/torabshaikh/titanic,Titanic - Machine Learning from Disaster
6177646,0.7751100000000001,1,2,/shahmeeralamtab/titanic-decision-tree-vs-neural-network,Titanic - Machine Learning from Disaster
9244113,0.78468,0,0,/rungta/titanic-problem,Titanic - Machine Learning from Disaster
7623258,0.8053899999999999,0,0,/martinobernasconi/simple-ridge-0-81,Natural Language Processing with Disaster Tweets
9472532,0.78468,0,0,/cmds2k/titanic-svc-logisticregression-randomforest,Titanic - Machine Learning from Disaster
9480770,0.75598,1,1,/prayassinghchauhan/titanic-a-beginner-s-step-by-step-guide,Titanic - Machine Learning from Disaster
6447111,0.7703300000000001,10,17,/tanveerdey93/titanic-with-random-forest-classifier,Titanic - Machine Learning from Disaster
11669299,0.7822899999999999,14,24,/namylase/titanic-eda-full-pipeline-ensemble,Titanic - Machine Learning from Disaster
2934623,0.98728,0,2,/toodef/cnn-training-with-less-code,Digit Recognizer
10580077,0.77272,4,8,/vet516lec/titanic-lgbm-xgboost-parameter-tuning,Titanic - Machine Learning from Disaster
3424389,0.988,2,6,/akshat9412165881/cnn-mnist,Digit Recognizer
7811596,0.78947,0,2,/hironobukawaguchi/kaggle-book-ch01-01-titanic,Titanic - Machine Learning from Disaster
916831,0.78468,1,6,/amitkumarjaiswal/beginner-s-tutorial-to-titanic-using-scikit-learn,Titanic - Machine Learning from Disaster
1035434,0.79425,2,15,/uysimty/learn-titanic-survival,Titanic - Machine Learning from Disaster
758301,0.943,0,1,/damienbeneschi/mnist-eda-preprocessing-classifiers,Digit Recognizer
1082150,0.098,0,1,/nmaheshw/mnist-dataset-practice,Digit Recognizer
2002877,0.98614,0,1,/tonteki4/mnist-with-keras-intuitive-way,Digit Recognizer
11563000,0.78468,1,14,/mrod17/logistic-regression-classifier-no-sklearn,Titanic - Machine Learning from Disaster
613260,0.97714,2,2,/ipg2014xxx/neural-network-using-tensorflow,Digit Recognizer
3546338,0.78468,0,6,/akumaldo/titanic-classification-stackingcvclassifier-keras,Titanic - Machine Learning from Disaster
2543155,0.76555,0,0,/akashsinha3008/titanic-survivor-prediction,Titanic - Machine Learning from Disaster
1843787,0.98457,0,0,/sfilios/mnist-trials,Digit Recognizer
1341875,0.99685,13,42,/anebzt/mnist-with-cnn-in-keras-detailed-explanation,Digit Recognizer
11906884,0.76794,4,11,/mrod17/decision-tree-without-ml-libraries,Titanic - Machine Learning from Disaster
97040,0.977,0,0,/alaaawad/mnist-convnet,Digit Recognizer
1248219,0.78947,0,5,/szaitseff/titanic-quick-model-first-then-data-manipulation,Titanic - Machine Learning from Disaster
3401239,0.148,15,46,/xhlulu/densenet-transfer-learning-iwildcam-2019,iWildCam 2019 - FGVC6
9829311,0.7124,0,5,/franckepeixoto/cifar-10-recognition-in-images-to-the-point,CIFAR-10 - Object Recognition in Images
627439,0.78468,15,14,/pliptor/name-only-study-with-interactive-3d-plot,Titanic - Machine Learning from Disaster
3244926,0.81339,51,75,/elcaiseri/simple-models-for-beginners-with-eda,Titanic - Machine Learning from Disaster
8405050,0.98271,4,9,/gauthampughazh/digit-recognition-using-knn,Digit Recognizer
4012939,0.85985,3,3,/ma7555/fisher-s-linear-discriminant-from-scratch-85-98,Digit Recognizer
8280871,0.99485,2,1,/duketemon/digit-recognizer-using-the-fast-ai-library,Digit Recognizer
4598229,0.99214,0,1,/chayannaskar26/stratified-k-fold-cnn,Digit Recognizer
5809830,0.79425,0,0,/redawashere/titanic-survival-predictions,Titanic - Machine Learning from Disaster
5985558,0.98757,0,1,/manas0991/basic-cnn-with-keras,Digit Recognizer
11956551,0.79186,10,7,/shashinkumarsachan/titanic-survival-prediction-using-catboost,Titanic - Machine Learning from Disaster
9758646,0.76076,0,1,/nehajthakur/titanic-survival-rate,Titanic - Machine Learning from Disaster
3552618,0.97285,1,2,/akumaldo/mnist-classification-study-keras-nn,Digit Recognizer
1769363,0.74641,24,45,/harunshimanto/learning-basic-ml-by-titanic-survival-prediction,Titanic - Machine Learning from Disaster
6887223,0.99142,2,8,/ceocampo/digit-recognition-using-the-mnist-dataset,Digit Recognizer
9289220,0.7751100000000001,0,0,/eternalgenin/titanic-data-prediction-adaboost-v-gboost-v-xgb,Titanic - Machine Learning from Disaster
5464596,0.7751100000000001,0,2,/depmountaineer/titanic-logistic-classification,Titanic - Machine Learning from Disaster
3350291,0.79425,0,1,/joshjanjua/titanic-comp,Titanic - Machine Learning from Disaster
3437613,0.80861,2,5,/sagarprasad/titanic-survival-prediction-using-neural-network,Titanic - Machine Learning from Disaster
749572,0.72248,0,0,/uguess/titanic-ml-from-disaster,Titanic - Machine Learning from Disaster
1530416,0.76555,0,2,/vashishtarora/beginner-trying-for-titanic,Titanic - Machine Learning from Disaster
3495669,0.97771,0,4,/thirumani/mnist-svm,Digit Recognizer
392885,0.7368399999999999,0,3,/urvishp80/titanic-disaster-with-rendomforestclassifier,Titanic - Machine Learning from Disaster
713938,0.78468,0,0,/liopic/refining-new-features-using-adaboostclassifier,Titanic - Machine Learning from Disaster
6572865,0.91387,8,24,/guesejustin/91-genetic-algorithms-explained-using-geap,Titanic - Machine Learning from Disaster
7482102,0.992,4,4,/jcardenzana/mnist-pytorch-convolutional-neural-nets,Digit Recognizer
6397499,0.80382,1,33,/vbmokin/titanic-autofeat-automatic-fe,Titanic - Machine Learning from Disaster
8922240,0.79425,32,39,/avnika22/titanic-machine-learning-from-disaster,Titanic - Machine Learning from Disaster
11391723,0.78708,2,12,/gerlandore/titanic-investigation-and-prediction,Titanic - Machine Learning from Disaster
6004276,0.8325299999999999,16,102,/vbmokin/titanic-top-score-one-line-of-the-prediction,Titanic - Machine Learning from Disaster
7984457,0.79865,0,0,/benfraser/simple-bag-of-words-classical-ml-solution,Natural Language Processing with Disaster Tweets
4975355,0.74641,0,6,/jaylaksh94/titanic-survivals-using-log-knn-grid-search,Titanic - Machine Learning from Disaster
11281869,0.78468,0,8,/firefortysix/continuing-with-titanic-shotgun-approach,Titanic - Machine Learning from Disaster
1183331,0.78947,4,37,/imdevskp/titanic-survival-prediction,Titanic - Machine Learning from Disaster
10240880,0.78708,0,3,/hs1214lee/a-simple-tutorial-for-beginners-3-3,Titanic - Machine Learning from Disaster
2108881,0.96485,0,0,/gauravsinghania/digit-recognizer,Digit Recognizer
10100177,0.7751100000000001,3,9,/mohtashimnawaz/simplesttitanic-test-accuracy-85,Titanic - Machine Learning from Disaster
3339990,0.76076,1,4,/subham121singh/titanic-predicting-dead-or-alive,Titanic - Machine Learning from Disaster
6550646,0.79425,1,1,/ghghdfd/my-first-kernel-titanic,Titanic - Machine Learning from Disaster
987347,0.98471,0,1,/nvsabhilash/keras-vgg-like-architecture-on-mnist-0-98471,Digit Recognizer
1170486,0.76555,0,0,/soloman0/data-science-understandings-with-titanic-solution,Titanic - Machine Learning from Disaster
3399907,0.125,1,18,/xhlulu/cnn-baseline-iwildcam-2019,iWildCam 2019 - FGVC6
10658730,0.7440100000000001,0,6,/aryankhatana/titanic-using-ann-keras,Titanic - Machine Learning from Disaster
538579,0.97142,0,2,/dineshsonachalam/introduction-to-keras-with-tensorflow-0-97142,Digit Recognizer
9169383,0.76555,2,3,/nguyncaoduy/titanic-fastai-tabular-learner,Titanic - Machine Learning from Disaster
11387833,0.7799,2,12,/shyam21/using-stacking-classifier-titanic,Titanic - Machine Learning from Disaster
7205564,0.7824,0,0,/ramanalytics1/kernel791d450e48,Natural Language Processing with Disaster Tweets
8817054,0.71029,0,0,/junsukim6361/final-submission,COVID19 Global Forecasting (Week 3)
8425447,0.61722,0,0,/dots9999/titanicsurvive,Titanic - Machine Learning from Disaster
4069579,0.1265099999999999,1,1,/sihlemtolo/team-12-edsa-johannesburg,House Prices - Advanced Regression Techniques
8196867,0.78915,0,0,/santhilata/simple-run,Natural Language Processing with Disaster Tweets
7666645,0.99042,0,0,/zero101010/classifica-o-mnist-numeros-keras,Digit Recognizer
8464594,0.7992600000000001,2,1,/xucheng19991201/disaster-tweets-basic-naive-bayes-spacy,Natural Language Processing with Disaster Tweets
4080726,0.11749,0,0,/riaanswanepoel/edsa-jhb-t20-kernel,House Prices - Advanced Regression Techniques
8083355,0.74839,0,1,/nakatahello/speech-tagging-analysis-approach,Natural Language Processing with Disaster Tweets
8154730,0.8152,0,3,/terminate9298/nlp-with-disaster,Natural Language Processing with Disaster Tweets
6620250,0.79904,0,0,/luluhou/my-firstcode,Titanic - Machine Learning from Disaster
4052485,0.7703300000000001,0,0,/smilingchandra/titanic-contest-check,Titanic - Machine Learning from Disaster
12316450,0.78468,2,1,/mvsharma98/titanic-dataset-model-comparison,Titanic - Machine Learning from Disaster
8526086,0.7751100000000001,0,0,/bikashhalder/geting-started-with-titanic,Titanic - Machine Learning from Disaster
3798444,0.1449099999999999,0,0,/ruhong/house-prices-advanced-regression-techniques-lasso,House Prices - Advanced Regression Techniques
8640194,0.21522,8,7,/khoongweihao/covid-19-week-2-xgboost-lightgbm,COVID19 Global Forecasting (Week 2)
8246925,0.70334,0,0,/pacomeperez/titanic-competition,Titanic - Machine Learning from Disaster
12381316,0.78468,0,0,/afalcigno1/cs-100-data-science,Titanic - Machine Learning from Disaster
8382829,0.6985600000000001,0,0,/nicolassitorus/kernel11304e4d22,Titanic - Machine Learning from Disaster
8491082,0.78947,0,0,/anuj55f/kernel707b4ae37d,Titanic - Machine Learning from Disaster
9038904,0.99403,0,0,/aidiary/mnist-by-keras,Digit Recognizer
3846404,0.27046,0,0,/umutarslan/kernelb2e3fc0773,House Prices - Advanced Regression Techniques
7340959,0.80447,5,10,/kamalpangeni/disaster-tweets-with-lr-rf-dnn,Natural Language Processing with Disaster Tweets
11321810,0.75358,0,2,/jonathanpaserman/titanic-prediction,Titanic - Machine Learning from Disaster
9129874,0.7511899999999999,0,0,/eternalgenin/titanic-data-prediction-using-decision-tree,Titanic - Machine Learning from Disaster
11401717,0.80861,0,6,/dmkravtsov/4-2-titanic-nn-keras,Titanic - Machine Learning from Disaster
3960902,0.99342,0,0,/prabanch/digit-recognition-using-cnn,Digit Recognizer
8564292,0.7751100000000001,0,0,/rjones26/titanic-model,Titanic - Machine Learning from Disaster
8797635,0.10945,0,1,/nxpnsv/logistic-xgb-hybrid,COVID19 Global Forecasting (Week 3)
8629350,1.86461,0,0,/algonell/covid-19-series-poly-fit-w2,COVID19 Global Forecasting (Week 2)
4225358,0.99671,0,1,/arturojose19/proyecto-2-mnist,Digit Recognizer
4042698,0.7751100000000001,0,4,/patilneha09/titanic-dataset-solution-using-random-forest,Titanic - Machine Learning from Disaster
8785407,4.9916,0,0,/arkadipghosh/india-corona,COVID19 Global Forecasting (Week 3)
8822407,2.90954,0,1,/ramondiaz/covid19-logistic-curve-fitting,COVID19 Global Forecasting (Week 3)
9038583,0.78947,0,0,/mujinkeikakupro/kernel-titanic,Titanic - Machine Learning from Disaster
9138175,0.15065,11,9,/abhijithchandradas/xgboost-vs-linear-regression-vs-svm,House Prices - Advanced Regression Techniques
7764331,0.78947,0,0,/pumpkin/titanic-data-exploration,Titanic - Machine Learning from Disaster
4282471,0.14752,0,2,/parthshxh/basic-random-forrest,House Prices - Advanced Regression Techniques
4261946,0.7703300000000001,0,2,/leonardots/infering-missing-data-with-ensembles-rf-xgb,Titanic - Machine Learning from Disaster
7830957,0.78947,0,2,/purpleyupi/titanic-getting-started,Titanic - Machine Learning from Disaster
8383954,0.97885,0,0,/chaitu2595/digit-recognition-using-cnn,Digit Recognizer
8706295,0.13858,1,1,/petersorensen360/kernel6984e17e21,COVID19 Global Forecasting (Week 2)
8826121,0.1902599999999999,0,0,/stecasasso/cv19-w3-bt-sub2-blend,COVID19 Global Forecasting (Week 3)
8315903,0.28961,2,0,/mrboupp/first-submission-using-lightgbm,Google Cloud & NCAA® ML Competition 2020-NCAAW
8444417,0.7751100000000001,0,0,/shreepy/titanic-starter,Titanic - Machine Learning from Disaster
7163587,0.7799,0,5,/rsesha/autoviml-autonlp-on-titanic-demo-kernel,Titanic - Machine Learning from Disaster
8986716,0.12247,0,1,/hamzarabi3/lasso-as-a-base-model,House Prices - Advanced Regression Techniques
8496749,0.79904,7,8,/seriouskang/kaggle-tutorial-titanic,Titanic - Machine Learning from Disaster
8632562,0.8708100000000001,2,3,/ovsienkobohdan/titanic-survival-prediction-gp,Titanic - Machine Learning from Disaster
8767777,0.18753,0,0,/shashwats89/kernele68bfd26ad,COVID19 Global Forecasting (Week 3)
12110717,0.79186,0,3,/jirakst/titanic,Titanic - Machine Learning from Disaster
8592598,0.68223,0,0,/ekzemplaro/approximation-baseline,COVID19 Global Forecasting (Week 2)
8495744,0.77995,0,9,/vaishnavibv/disaster-tweet-classification-logistic-regression,Natural Language Processing with Disaster Tweets
8166969,0.78057,0,0,/ishitakapur/nlp-real-or-not,Natural Language Processing with Disaster Tweets
12594588,0.78708,0,0,/anastasiiaititova/notebook80375a7ae1,Titanic - Machine Learning from Disaster
8608005,0.08319,7,45,/binhlc/sars-cov-2-exponential-model-week-2,COVID19 Global Forecasting (Week 2)
3903251,0.97785,0,1,/wsm1992/tensorflow-mnist,Digit Recognizer
12454832,0.79665,0,0,/nonborghini/light-gbm,Titanic - Machine Learning from Disaster
3858353,9.45398,0,0,/haktaneb/kernel6561f516ec,House Prices - Advanced Regression Techniques
3752012,0.79425,0,0,/nchuweihuang/titanic,Titanic - Machine Learning from Disaster
3758552,0.15627,2,2,/keshavramaiah/house-pricing-using-neural-nets,House Prices - Advanced Regression Techniques
8500728,0.7751100000000001,0,1,/sraj5162/kernel3b3343fe38,Titanic - Machine Learning from Disaster
9291152,0.16288,0,0,/bahadrkelez/kernel63a99c7653,House Prices - Advanced Regression Techniques
12328398,0.77751,0,0,/kamilcieciura/cs-100-data-science,Titanic - Machine Learning from Disaster
7115916,0.7703300000000001,0,0,/szett27/titanic-dataset,Titanic - Machine Learning from Disaster
11924655,0.76076,0,0,/crypto1234/decision-tree-without-ml-libraries,Titanic - Machine Learning from Disaster
7899549,0.7751100000000001,1,3,/ravichouhan/titanic-basic-solution-with-logistic-regression,Titanic - Machine Learning from Disaster
4566454,0.99014,0,0,/liux3021/digit-recognizer,Digit Recognizer
11880675,0.77272,0,0,/mmaeda1968/notebookf947b5672c,Titanic - Machine Learning from Disaster
7514413,0.7799,0,0,/mmanohar10/kernel12638fa613,Titanic - Machine Learning from Disaster
8805462,0.03142,8,8,/abhijithchandradas/global-forcast-covid19-logisticregression,COVID19 Global Forecasting (Week 3)
8827784,2.40407,0,0,/saurabh7/forecast,COVID19 Global Forecasting (Week 3)
7544802,0.81642,3,12,/akazuko/nlp-disaster-tweets-1,Natural Language Processing with Disaster Tweets
4037441,0.79904,6,20,/lostrens/titanic-with-simple-scikit-learn-top-8,Titanic - Machine Learning from Disaster
6808382,0.98657,0,2,/sarachilson/keras-cnn-resnet,Digit Recognizer
3871836,0.93342,0,1,/panpluto/ann-with-pre-defined-convolutional-filters-numpy,Digit Recognizer
4213846,0.78947,1,1,/mpourreza/titanic-supervised-learning-using-random-forest,Titanic - Machine Learning from Disaster
8730717,0.25521,3,8,/mahmudds/covid19-global-forecasting-week-3,COVID19 Global Forecasting (Week 3)
8727142,0.66627,0,0,/akioonodera/covid19-week3-using-regression-analysis,COVID19 Global Forecasting (Week 3)
661903,0.7703300000000001,0,0,/willianw/simplest-solution-0-77-lb,Titanic - Machine Learning from Disaster
6291449,0.12555,0,0,/sarthakpawar/basic-feature-engineering-and-lgbmr,House Prices - Advanced Regression Techniques
8841412,0.7751100000000001,0,0,/vaishalisharmaece/titanic-basic-solution-with-logistic-regression,Titanic - Machine Learning from Disaster
7813312,0.76555,1,3,/koreadataboy/kaggle-titanic-tutorial-eda,Titanic - Machine Learning from Disaster
8505936,0.99271,0,2,/eniacc/step-by-step-using-resnet,Digit Recognizer
8351309,0.99028,0,0,/luiguip/cnn-digit-recognizer-99,Digit Recognizer
8329060,0.98371,0,0,/ajisamudra/solving-mnist-using-cnn-mlp-and-stacking,Digit Recognizer
4065780,0.98428,0,0,/jay2jaykp/my-first-cnn-on-minst,Digit Recognizer
8609686,0.74162,1,1,/epikjjh/titanic,Titanic - Machine Learning from Disaster
8405727,0.99128,0,3,/eniacc/step-by-step-promote-score-cn,Digit Recognizer
3914760,0.91975,0,0,/testprd/kernel50fd3a6f11,Jigsaw Unintended Bias in Toxicity Classification
7590743,0.12669,0,3,/issactai/linear-regression-models-on-ames-housing-dataset,House Prices - Advanced Regression Techniques
8401552,0.82194,0,2,/zhuangliu1939/disaster-nlp-deep-universal-sentence-encoder,Natural Language Processing with Disaster Tweets
8401871,0.78947,0,0,/bensonruanau/titanic-tensorflow-2-0,Titanic - Machine Learning from Disaster
12401585,0.7751100000000001,0,1,/mayanklad/getting-started-with-titanic,Titanic - Machine Learning from Disaster
9000824,0.7751100000000001,0,0,/puddnheadwilson/titanic-model-submission,Titanic - Machine Learning from Disaster
8687991,0.6411399999999999,0,1,/gotutiyan/titanic-tutorial-pytorch-japanese,Titanic - Machine Learning from Disaster
9019171,0.66028,0,0,/rikideguchi/second-trial,Titanic - Machine Learning from Disaster
3084778,0.6970000000000001,23,82,/vanshjatana/microsoft-malware-prediction,TReNDS Neuroimaging
12442241,0.76555,0,0,/arafrahman/cs-100-data-science,Titanic - Machine Learning from Disaster
8767000,0.17272,0,0,/rajatk9962/polyreg,COVID19 Global Forecasting (Week 3)
11081670,0.7751100000000001,0,1,/louisbox/ml-classifier-on-the-titanic-dataset,Titanic - Machine Learning from Disaster
8948761,0.14714,0,0,/nicolasmalloy/housing-predictions,House Prices - Advanced Regression Techniques
8919519,0.76555,0,0,/justinflanagan/fork-of-learning-kaggle-with-titanic-competition,Titanic - Machine Learning from Disaster
9634195,0.80861,1,7,/grantcooper/titanic-survival-predications,Titanic - Machine Learning from Disaster
7899505,0.7751100000000001,0,0,/jindalnishant/titanic-basic-solution-with-logistic-regression,Titanic - Machine Learning from Disaster
7899839,0.7775,0,0,/atripathi3675/applying-basic-bert-model,Natural Language Processing with Disaster Tweets
4348849,0.13663,0,0,/andrelourenco/kerneldecfdb6698,House Prices - Advanced Regression Techniques
7357275,0.78947,1,2,/chun1182/fork-of-titanic-train,Titanic - Machine Learning from Disaster
7902392,0.84216,5,12,/rftexas/text-only-bert-keras,Natural Language Processing with Disaster Tweets
4373850,0.09978,2,3,/helmehelmuto/keras-cnn,Galaxy Zoo - The Galaxy Challenge
7700108,0.6555,0,0,/macespinoza/titanic-base,Titanic - Machine Learning from Disaster
8970352,0.13485,0,3,/robbiebeane/house-prices-v01,House Prices - Advanced Regression Techniques
8689100,0.06853,0,0,/shantanu1118/g-f-week2,COVID19 Global Forecasting (Week 2)
8591010,0.54293,0,0,/kinkpunk/my-forecasting-covid19-week-2,COVID19 Global Forecasting (Week 2)
8710244,0.2290599999999999,0,0,/vishwajeetjadeja/covid19-global-forecast,COVID19 Global Forecasting (Week 2)
8658083,1.60116,0,1,/kaleedfox/covid19-competition-week-2,COVID19 Global Forecasting (Week 2)
8625834,3.03324,0,13,/rayhuang/covid-19-global-forecast-randomforest,COVID19 Global Forecasting (Week 2)
8733262,0.04022,0,0,/takiyu/arima-week3,COVID19 Global Forecasting (Week 3)
8756908,1.84371,0,2,/silverstorm/forecasting-using-xgboost-gradient-booster,COVID19 Global Forecasting (Week 3)
7874343,0.7751100000000001,0,0,/jugglingsnakeboarder/myfirsttitanicsolution,Titanic - Machine Learning from Disaster
7859506,0.67637,0,0,/mvsfnig/beginner-bc-nlp-disaster-tweets,Natural Language Processing with Disaster Tweets
12218313,0.7751100000000001,0,0,/kimmunse/notebook0d255a9db2,Titanic - Machine Learning from Disaster
3678034,0.98928,2,3,/samarthsarin/simple-cnn-using-keras-with-4-different-models,Digit Recognizer
1283544,0.98728,0,2,/bobby33/digit-recognizer-with-a-convolution-neural-network,Digit Recognizer
12200065,0.79186,6,5,/josephsha/titanic-prediction-with-neural-network,Titanic - Machine Learning from Disaster
2100096,0.977,0,11,/soumya044/introductions-to-anns-using-pytorch,Digit Recognizer
5275560,0.99296,0,14,/vitorgamalemos/recognizing-digits-using-cnn,Digit Recognizer
2908627,0.992,15,46,/dejavu23/mnist-sklearn-and-keras,Digit Recognizer
7499128,0.78087,17,31,/vadbeg/pytorch-lstm-with-disaster-tweets,Natural Language Processing with Disaster Tweets
7758216,0.13982,0,1,/raseidi/house-prices-in-a-few-steps,House Prices - Advanced Regression Techniques
7921018,0.80861,1,17,/bhargavpurohit/titanic-top-6-using-only-random-forest,Titanic - Machine Learning from Disaster
12865899,0.7751100000000001,0,1,/akoraingdkb/dkb-titanic,Titanic - Machine Learning from Disaster
7718150,0.7751100000000001,0,4,/sishihara/python-kaggle-start-book-ch02-07,Titanic - Machine Learning from Disaster
8779763,0.63875,0,0,/dinamuktubayeva/titanic-competition,Titanic - Machine Learning from Disaster
7680187,1.62225,0,3,/nahumsa/housing-prices-using-tensorflow-2-0,House Prices - Advanced Regression Techniques
3157846,0.19674,0,0,/bhatb27/ols-regression-with-principal-component-analysis,House Prices - Advanced Regression Techniques
6651401,0.13884,0,7,/hoangnguyen719/null-imputation,House Prices - Advanced Regression Techniques
7275198,0.1191599999999999,3,3,/daniaragaki/using-h2o-automl-to-predict-housing-prices,House Prices - Advanced Regression Techniques
5572767,0.6555,0,5,/anoopshihoorkar/titanic-problem-beginner,Titanic - Machine Learning from Disaster
1741469,0.79904,29,76,/vaibhavsagar/titanic-by-a-beginner,Titanic - Machine Learning from Disaster
1884410,0.81818,0,1,/salh786/ml1-predicting-survivor-on-the-titanic-v6-3,Titanic - Machine Learning from Disaster
7443707,0.79425,0,1,/alexander85/titanic-voting-and-tuned-classifiers,Titanic - Machine Learning from Disaster
5383305,0.1343099999999999,0,3,/tacyonlord/housingprices-using-stuff-i-learnt-on-kaggle-learn,House Prices - Advanced Regression Techniques
4177791,0.98128,1,3,/minc33/k-nearest-neighbours-with-data-augmentation,Digit Recognizer
6240204,0.99142,1,4,/vikassingh1996/tensorflow-2-0-create-and-train-ann-cnn,Digit Recognizer
392977,0.94942,0,3,/del=fced68902ab808df/digit-recognizer-an-optimized-use-of-the-svm,Digit Recognizer
1716875,0.99528,0,0,/rajkkapadia/mnist-resnet-for-beginner,Digit Recognizer
9073831,0.7751100000000001,0,0,/yukini/practice,Titanic - Machine Learning from Disaster
3408388,0.1145099999999999,4,9,/sandeepkumar121995/blending-of-6-models-top-10,House Prices - Advanced Regression Techniques
3034028,0.94428,0,1,/ksashok/very-basic-xgboost,Digit Recognizer
4781241,0.7751100000000001,0,3,/iamsdt/titanic-pytorch-iamsdt,Titanic - Machine Learning from Disaster
1437768,0.98985,0,1,/sanikamal/digit-recognizer-using-cnn,Digit Recognizer
987537,0.1482,0,1,/prince1992/my-first-machine-learning-model,House Prices - Advanced Regression Techniques
1820451,0.15892,1,2,/nikkisharma536/salesprice-predictions,House Prices - Advanced Regression Techniques
915615,0.75598,0,6,/eitarox/start-from-titanic,Titanic - Machine Learning from Disaster
612365,0.13457,9,33,/anand0427/regression-algorithms-for-beginners,House Prices - Advanced Regression Techniques
2110015,0.11972,3,6,/shivatharun/house-price-prediction-using-advanced-regression,House Prices - Advanced Regression Techniques
9232351,0.1328599999999999,0,3,/dnreddy/catboost-regression-house-pricese-predection,House Prices - Advanced Regression Techniques
9253526,0.7703300000000001,3,5,/manuelkraus89/titanic-first-notebook,Titanic - Machine Learning from Disaster
11251951,0.76315,4,8,/rahulpawade/titanic-machine-learning-from-disaster,Titanic - Machine Learning from Disaster
9305251,0.77751,4,6,/rishabhgarg1023/titanic-basic-model,Titanic - Machine Learning from Disaster
3261881,0.78468,1,2,/uchidamasatoshi/lightgbm-to-predict-titanic-data,Titanic - Machine Learning from Disaster
6601049,0.80861,2,2,/vitorgaboardi/titanic-with-only-one-feature-0-8086-python,Titanic - Machine Learning from Disaster
7094339,0.79425,4,11,/daniel68/titanic-a-simple-and-hopefully-clear-approach,Titanic - Machine Learning from Disaster
12408003,0.73205,0,0,/hackonion/titanic-whit-decisiontreeclassifier,Titanic - Machine Learning from Disaster
4167329,0.79425,0,0,/circlejerkhug/titanic-survivor-prediction,Titanic - Machine Learning from Disaster
7526598,0.992,0,1,/sanyamdhawan99/mnist-notebook,Digit Recognizer
2862113,0.99328,1,5,/duketemon/introduction-to-deep-learning-for-beginners,Digit Recognizer
4826342,0.7799,3,7,/yannberthelot/pystacknet-working-implementation,Titanic - Machine Learning from Disaster
2337795,0.78947,2,6,/arty233/encoding-names-and-using-popular-models-in-titanic,Titanic - Machine Learning from Disaster
2664372,0.99914,4,14,/genesis16/digit-recogniser-using-cnn-in-keras-top-3,Digit Recognizer
1850069,0.99557,0,1,/singhsatwinder/minst-digit-data-set-using-cnn-in-keras,Digit Recognizer
5905300,0.97214,5,13,/amarpandey/d-neural-network-for-mnist-handwritten-digits,Digit Recognizer
521087,0.12258,24,34,/vhrique/simple-house-price-prediction-stacking,House Prices - Advanced Regression Techniques
892447,0.76076,0,0,/benjaminwang/titanic-expore-logistic-regression,Titanic - Machine Learning from Disaster
360707,0.7799,0,1,/shrishtiwahal/notebooka631f0ccac,Titanic - Machine Learning from Disaster
1554552,0.1387,0,2,/depmountaineer/eda-and-basic-model-for-housing-prices-improved,House Prices - Advanced Regression Techniques
1541610,0.80861,0,4,/dr1t10/titanicnet-with-pytorch,Titanic - Machine Learning from Disaster
1673188,0.79904,3,4,/mikelkn/titanic-using-voting-ensembling-17th-percentile,Titanic - Machine Learning from Disaster
414929,0.79425,3,3,/natsuki1996/titanic-problem,Titanic - Machine Learning from Disaster
1507161,0.97214,0,1,/varian97/keras-basic-pipeline,Digit Recognizer
2017286,0.72727,0,0,/ankhaatk/titanic-visialize,Titanic - Machine Learning from Disaster
1947995,0.74641,3,10,/sishihara/hypothesis-and-visualization-for-titanic-in-kaggle,Titanic - Machine Learning from Disaster
10860943,0.7751100000000001,1,4,/mileslucey/titanic-logistic-reg-decision-tree-random-forest,Titanic - Machine Learning from Disaster
10008635,0.893,2,5,/mushaya/digit-recog,Digit Recognizer
10268392,0.7751100000000001,0,9,/scorlibpl/my-first-titanic-project,Titanic - Machine Learning from Disaster
9599546,0.13326,14,11,/abhaydhiman/predicting-house-price,House Prices - Advanced Regression Techniques
8331101,0.79405,0,1,/nickteim/simpel-disaster-tweets-nlp,Natural Language Processing with Disaster Tweets
5450342,0.79425,0,0,/abhinavcs13/titanic-ml-model-with-svm-rbf-kernel,Titanic - Machine Learning from Disaster
5381437,0.98271,1,2,/need4data/using-tensorflow-to-implement-lenet-5-modern,Digit Recognizer
2233279,0.6650699999999999,0,1,/bidyutchanda/titanic-dataset,Titanic - Machine Learning from Disaster
1835134,0.98185,0,0,/rachel115/digit-recognizer-using-cnn,Digit Recognizer
940249,0.7751100000000001,0,1,/renangomes/simplified-solution-for-beginners-using-mpl,Titanic - Machine Learning from Disaster
1365585,0.78468,0,0,/chhuang0123/titanic-scikit-learn-lr-rfr-and-nn,Titanic - Machine Learning from Disaster
1471976,0.73205,0,0,/deepanshkhurana/titanic-solution-attempt-1,Titanic - Machine Learning from Disaster
465257,0.79904,0,1,/honeygupta/titanic-survival-predictor,Titanic - Machine Learning from Disaster
1304565,0.1346,0,1,/sharmaharsh/house-price-kernel-17-7,House Prices - Advanced Regression Techniques
96093,0.1209599999999999,310,1515,/apapiu/regularized-linear-models,House Prices - Advanced Regression Techniques
24037,0.74162,409,1349,/omarelgabry/a-journey-through-titanic,Titanic - Machine Learning from Disaster
29004,0.978,198,636,/kakauandme/tensorflow-deep-nn,Digit Recognizer
2348903,0.10583,113,572,/masumrumi/a-detailed-regression-guide-with-house-pricing,House Prices - Advanced Regression Techniques
7115255,0.78179,59,427,/philculliton/nlp-getting-started-tutorial,Natural Language Processing with Disaster Tweets
420670,0.96585,46,179,/kmader/capsulenet-on-mnist,Digit Recognizer
3140481,0.15115,39,219,/fatmakursun/house-price-some-of-regression-models,House Prices - Advanced Regression Techniques
5278946,0.13441,34,163,/allunia/house-prices-tutorial-with-catboost,House Prices - Advanced Regression Techniques
510158,0.78947,48,152,/dejavu23/titanic-eda-to-ml-beginner,Titanic - Machine Learning from Disaster
7115689,0.80753,72,145,/yufengg/automl-getting-started-notebook,Natural Language Processing with Disaster Tweets
3027202,0.80861,51,126,/pavlofesenko/simplest-top-10-titanic-0-80861,Titanic - Machine Learning from Disaster
1512086,0.11544,50,126,/vjgupta/reach-top-10-with-simple-model-on-housing-prices,House Prices - Advanced Regression Techniques
149463,0.97128,32,120,/statinstilettos/neural-network-approach,Digit Recognizer
528605,0.76555,18,105,/sgus1318/titanic-analysis-learning-to-swim-with-python,Titanic - Machine Learning from Disaster
166160,0.1363599999999999,12,81,/miguelangelnieto/pca-and-regression,House Prices - Advanced Regression Techniques
22311,0.76076,9,48,/michielkalkman/kaggle-titanic-001,Titanic - Machine Learning from Disaster
7199272,115384.85,3,32,/golubev/baseline,Santa 2019: Revenge of the Accountants
158943,0.79425,0,15,/zhenqiliu/titanic-survival-python-solution,Titanic - Machine Learning from Disaster
10544630,0.69935,0,24,/amarkumar2/real-or-not-nlp-with-disaster-tweets-solution,Natural Language Processing with Disaster Tweets
9717060,0.13497,11,32,/servietsky/eazy-way-house-price-pycaret,House Prices - Advanced Regression Techniques
152654,0.0,3,11,/robertknight/titanic-mechanic-machine-learning,Titanic - Machine Learning from Disaster
10883006,0.76555,0,13,/datawarriors/easy-beginner-titanic-solution,Titanic - Machine Learning from Disaster
10498020,0.7703300000000001,1,11,/mksaad/fork-of-titanic-competition-xgboosting,Titanic - Machine Learning from Disaster
10537650,0.7751100000000001,0,8,/mksaad/titanic-eda-prediction,Titanic - Machine Learning from Disaster
9847072,0.76555,7,10,/munmun2004/titanic-for-begginers,Titanic - Machine Learning from Disaster
6658024,0.0,1,6,/sudharsan1297/titanic-model-building,Titanic - Machine Learning from Disaster
9256088,0.78468,7,13,/kshivi99/titanic-beginner-s-giude-to-classification,Titanic - Machine Learning from Disaster
265699,0.0,0,5,/nghedberg/an-interactive-data-science-tutorial,Titanic - Machine Learning from Disaster
9740683,0.78947,7,6,/tunguz/titanic-with-rapids-ds-on-gpus,Titanic - Machine Learning from Disaster
9128240,0.98439,1,4,/nitbrok/short-and-simple-mnist-digit-recognizer,Digit Recognizer
10491254,0.77751,0,6,/carlmcbrideellis/ensemble-methods-majority-voting-example,Titanic - Machine Learning from Disaster
9810035,0.7822899999999999,4,9,/agirlcoding/tiatanic-first-ml-competition-ever,Titanic - Machine Learning from Disaster
10074359,0.73205,0,4,/fadzlinrafi/tutorial-simple-gradient-booster-classifier,Titanic - Machine Learning from Disaster
8329613,0.001,2,4,/grapestone5321/iwildcam-2020-sample-submission,iWildCam 2020 - FGVC7
9614995,0.7751100000000001,5,7,/harshmangal11/titanic-myfirst1,Titanic - Machine Learning from Disaster
8816112,0.03092,1,8,/mrmorj/covid-19-sarima,COVID19 Global Forecasting (Week 3)
9650560,0.1431,0,4,/aadityasinghal/predicting-house-prices-using-advanced-regression,House Prices - Advanced Regression Techniques
9610927,0.14055,2,4,/neerunaveenjakhar/simple-regression-modelling,House Prices - Advanced Regression Techniques
9872437,0.78468,3,4,/rubencoppens/my-first-competition-titanic,Titanic - Machine Learning from Disaster
10592911,0.67703,1,5,/maimahdi/titanicdataset,Titanic - Machine Learning from Disaster
10556383,0.78468,37,32,/amandeepsingh12/1-titanic-solution,Titanic - Machine Learning from Disaster
9442797,0.79904,2,5,/georgeyacu/titanic-tree-model-comparison,Titanic - Machine Learning from Disaster
7885354,0.0,6,3,/savitaiitr/eda-and-model-building-for-titanic-dataset,Titanic - Machine Learning from Disaster
9928244,0.98828,0,4,/kayadagli/introduction-to-convolutional-neural-network-cnn,Digit Recognizer
10536577,0.78708,3,7,/ahmedalghaliz/titanic-competition,Titanic - Machine Learning from Disaster
8757985,0.08217,11,17,/mrmorj/covid-19-eda-xgboost,COVID19 Global Forecasting (Week 3)
10901275,0.7703300000000001,0,7,/alexandersmetanin/titanic-data,Titanic - Machine Learning from Disaster
10004253,0.78468,5,7,/nidhaypancholi/titanic-data-visualizations-and-accuracy-0-784,Titanic - Machine Learning from Disaster
9823945,0.1502,0,2,/rohitsanam/house-price-prediction-using-ml,House Prices - Advanced Regression Techniques
9312353,0.7703300000000001,14,8,/dmitry256/titanic-notebook-79-904-accuracy-lightgbm,Titanic - Machine Learning from Disaster
10474639,0.7751100000000001,0,2,/aycancal/getting-started-with-titanic,Titanic - Machine Learning from Disaster
8816440,0.49103,0,3,/lisphilar/combination-of-fitting-and-math-model-week3,COVID19 Global Forecasting (Week 3)
8818522,0.05377,1,7,/kazanova/gbr-169v3-v2,COVID19 Global Forecasting (Week 3)
9988085,0.66985,0,2,/hidekimotohashi/titanic-data-science,Titanic - Machine Learning from Disaster
4938940,110850.89867,0,2,/zharfan104/predict-with-linear-models,I-RICH ML COMPETITION
9972445,0.77751,2,5,/rizbaltazar/first-titanic-submission-v2,Titanic - Machine Learning from Disaster
8776923,2.8530900000000003,2,3,/anbu3003/covid-19-week-3,COVID19 Global Forecasting (Week 3)
9397557,0.76076,5,4,/bhanurdramohapatra/titanic-prediction,Titanic - Machine Learning from Disaster
10596895,0.79904,0,2,/meurice/gradientboostingregressor-baseline,Titanic - Machine Learning from Disaster
8873494,0.76555,0,2,/uday44/titanic-survival-prediction-0-765,Titanic - Machine Learning from Disaster
9837515,0.7799,0,2,/sukruaras/titanic-logistic-regression,Titanic - Machine Learning from Disaster
9218118,0.1318099999999999,6,3,/daniel68/california-house-prices-a-beginner-s-guide,House Prices - Advanced Regression Techniques
247008,0.0,1,2,/visignibraem/an-interactive-data-science-tutorial,Titanic - Machine Learning from Disaster
9161914,0.7751100000000001,4,4,/jr1989/challenge-titanic,Titanic - Machine Learning from Disaster
8953349,0.7799,2,11,/carlmcbrideellis/beginners-random-forest-classifier-script,Titanic - Machine Learning from Disaster
10578169,0.21746,0,1,/italomarcelo/house-data-prices-italo-costa,House Prices - Advanced Regression Techniques
9100265,1.0,0,2,/moradnejad/distweets-perfect-score-for-evaluation-purposes,Natural Language Processing with Disaster Tweets
9089198,0.0,1,3,/jafarib/for-best-score-use-this-notebook-up-vote,House Prices - Advanced Regression Techniques
9259547,0.79486,1,1,/blackitten13/texts-classification-baseline,Text classification
9670929,0.7703300000000001,0,1,/arya172/titanic-pytorch,Titanic - Machine Learning from Disaster
9998423,0.7751100000000001,0,1,/rraghav5600/titanic-fresh,Titanic - Machine Learning from Disaster
10027318,0.76555,0,2,/psvkushal/titanic-notebook,Titanic - Machine Learning from Disaster
8860770,0.66722,0,2,/kirichenko17roman/the-best-solution,COVID-19 diagnostic
9162783,0.7751100000000001,1,1,/sahuankit/titanic-basic-solution-with-logistic-regression,Titanic - Machine Learning from Disaster
8813290,0.05574,2,2,/binhlc/sars-cov-2-arima-model-week-3,COVID19 Global Forecasting (Week 3)
9154869,0.98382,0,2,/barindersingh/digit-recognizer-with-100-accuracy-with-keras,Digit Recognizer
9390605,0.75358,0,4,/franciscogc/titanic-notebook-tensorflow,Titanic - Machine Learning from Disaster
8693806,1.84383,0,2,/liangxuatgroningen/the-government-reaction-plays-a-role,COVID19 Global Forecasting (Week 2)
8822623,0.74659,0,1,/jlgleason/covid19-forecasts-using-probabilistic-rnns,COVID19 Global Forecasting (Week 3)
8825769,0.14807,0,2,/alicuye/bayesian-model,COVID19 Global Forecasting (Week 3)
8826898,0.6956,0,1,/tunguz/covid-19-week-3-blend-2,COVID19 Global Forecasting (Week 3)
8824165,0.52754,0,3,/osciiart/covid-19-lightgbm-with-weather-2,COVID19 Global Forecasting (Week 3)
8711165,1.11583,1,10,/vbmokin/covid19-week-2-sir-model-ridge-regr,COVID19 Global Forecasting (Week 2)
8921603,0.7751100000000001,1,2,/lkatran/titanicforlearn,Titanic - Machine Learning from Disaster
9426150,0.1877,0,1,/vijayravinath/how-to-upload-a-competition,House Prices - Advanced Regression Techniques
9431896,0.66028,0,1,/miroslavrevaj/ui1-titanic,Titanic - Machine Learning from Disaster
9426972,0.93742,2,2,/layahaasini/digit-recognizer,Digit Recognizer
8668446,0.93689,1,2,/moradnejad/covid-week-2-no-leak,COVID19 Global Forecasting (Week 2)
9859245,0.98028,0,1,/ayushmankumar7/tensorflow-keras-convnet,Digit Recognizer
9046378,0.79904,4,7,/ezeanyi/exploring-titanic-survival,Titanic - Machine Learning from Disaster
10547134,0.14914,0,3,/abdalazez/house-price-ml-prediction,House Prices - Advanced Regression Techniques
10532248,0.7751100000000001,0,1,/mmukeshreddy/kernel541bb12a1c,Titanic - Machine Learning from Disaster
9338984,0.15306,0,1,/arshtematida/endterm-2020,House Prices - Advanced Regression Techniques
596702,0.0,0,1,/roniii/simple-effective-comprehensive-solution-titanic,Titanic - Machine Learning from Disaster
9288293,0.79425,0,1,/timheller/titanic-by-random-forest-classification,Titanic - Machine Learning from Disaster
215124,0.0,0,1,/seanhgorman/commit-trial,Titanic - Machine Learning from Disaster
8684703,0.09008,0,4,/ravirajsinh45/covid19-forecasting-using-rnn,COVID19 Global Forecasting (Week 2)
10522253,0.1408599999999999,0,2,/pranshunema/housingprices,House Prices - Advanced Regression Techniques
9276540,0.7751100000000001,1,3,/rahul96rajan/getting-started-with-titanic-on-kaggle,Titanic - Machine Learning from Disaster
9463384,3.59516,0,0,/niibruce/kernele16aa95755,COVID19 Global Forecasting (Week 2)
10552740,0.76555,0,1,/zakariatafesh/titanic-predection,Titanic - Machine Learning from Disaster
9726962,0.6555,0,0,/yusukemigitera/tf-first,Titanic - Machine Learning from Disaster
9283112,0.91625,0,0,/prakhar21god/starter-notebook,KNIT_HACKS
9737324,0.979,0,1,/wojciechmigda/pytsetlini-classifier-1-bit,Digit Recognizer
8812735,0.16102,0,0,/lawrencechen98/covid-19-forecast-and-analysis-week-3,COVID19 Global Forecasting (Week 3)
8800248,0.1668099999999999,1,2,/timriggins/week-3,COVID19 Global Forecasting (Week 3)
8800680,0.07752,0,2,/sarthakpawar/region-wise-arima-for-forecasting,COVID19 Global Forecasting (Week 3)
8800064,1.8493,1,1,/fraserew/covid-week-3-lstm,COVID19 Global Forecasting (Week 3)
10531374,0.7799,0,0,/sameersingh150618/kernel171fdfcc2a,Titanic - Machine Learning from Disaster
10539962,0.77272,0,1,/ahmedmurad1990/titatinc,Titanic - Machine Learning from Disaster
9053045,0.7751100000000001,0,0,/martinma666/titanic-random-forest,Titanic - Machine Learning from Disaster
10034012,0.80382,0,0,/yusukearai/titanic-ensemble-practice-0-80,Titanic - Machine Learning from Disaster
9493488,0.1293,0,1,/leonhackl96/house-prices-basic-machine-learning,House Prices - Advanced Regression Techniques
10548860,0.13245,0,0,/shunnosukemori/kernel65c0c20b7a,House Prices - Advanced Regression Techniques
9491971,0.66028,1,0,/kashishlohiya5/titanic-submission,Titanic - Machine Learning from Disaster
9476039,0.1472,1,0,/anuj007a/kernel6a26c5c167,House Prices - Advanced Regression Techniques
9521630,0.1170099999999999,0,1,/mingweichina/0-11701-top-10-57-with-only-one-entry,House Prices - Advanced Regression Techniques
9504278,0.7511899999999999,0,0,/pallavipannu/titanic-survival,Titanic - Machine Learning from Disaster
9379602,0.34869,0,0,/zongyanj/kernel4912eb4dbe,COVID19 Global Forecasting (Week 2)
8984578,0.79425,0,0,/abhishekrath1995/abhishekr-grad-boost,Titanic - Machine Learning from Disaster
9627583,0.99257,1,0,/naokinnn/minist-original,Digit Recognizer
9626890,0.79425,0,0,/jonas2312/xgboost,Titanic - Machine Learning from Disaster
8857299,0.11184,0,0,/moradnejad/covid-w4,COVID19 Global Forecasting (Week 4)
8869455,0.1701099999999999,0,0,/rayizumi/kernel63b6fe8272,House Prices - Advanced Regression Techniques
9311418,1930.91123,0,0,/noahgrinspoon/t7-playstation-network,ASN10e Final Submission - Detect COML Faces
9310616,892.2679800000002,0,0,/faefewagfygfjayw/t2-idk-what-name-hmm,ASN10e Final Submission - Detect COML Faces
8886338,0.7799,2,7,/carlmcbrideellis/xgboost-classification-minimalist-python-script,Titanic - Machine Learning from Disaster
9311614,0.7822899999999999,0,0,/ranamahmud/titanic-machine-learning-from-disaster-xgboost,Titanic - Machine Learning from Disaster
10625267,0.13084,0,0,/kotaseki/kernel7c2d12b73d,House Prices - Advanced Regression Techniques
8897345,0.77272,2,1,/akashkash/more-advanced-ml,Titanic - Machine Learning from Disaster
10580968,0.17663,0,0,/shanmugarajar/housing-price-eda-regression-2nd-competition,House Prices - Advanced Regression Techniques
10579498,0.14802,0,0,/mohgsam/house-price-comp,House Prices - Advanced Regression Techniques
10587241,0.75598,0,0,/hiro147/kernel6c63041d4b,Titanic - Machine Learning from Disaster
10588489,0.77541,0,0,/g4team/kernel7ae4bfddb0,House Prices - Advanced Regression Techniques
10582791,0.7814800000000001,0,0,/matiasloiseau/nlp-getting-started-tutorial,House Prices - Advanced Regression Techniques
10065516,0.79425,0,1,/meryllmercadier/kerneldc72922422,Titanic - Machine Learning from Disaster
10593726,0.76794,0,0,/losspost/titanic-with-deeplearning,Titanic - Machine Learning from Disaster
10590399,0.13179,0,0,/nakaih/kernel4289d34299,House Prices - Advanced Regression Techniques
9261857,0.79904,0,0,/wrestlingsurfer/titanic-challenge-straightforward-approach,Titanic - Machine Learning from Disaster
9241550,0.15479,1,12,/carlmcbrideellis/recursive-feature-elimination-rfe-example,House Prices - Advanced Regression Techniques
9806941,0.7751100000000001,0,0,/nisargjain/titanic-nisarg,Titanic - Machine Learning from Disaster
9824273,0.76555,0,1,/volodymyrholomb/titanic-imputing-and-classification-pipes,Titanic - Machine Learning from Disaster
9827274,0.7751100000000001,0,0,/fotone/hello-kaggle-with-tensorflow-2,Titanic - Machine Learning from Disaster
9829292,0.73205,0,0,/kartikey24/titanic,Titanic - Machine Learning from Disaster
8789050,0.88954,1,1,/franlopezguzman/covid19-3-minimalist-polynomial-regressor,COVID19 Global Forecasting (Week 3)
8789450,1.72135,0,0,/abdumaa/submission-template-time-series,Data Series Summarization Project (v3)
8791113,0.78468,0,0,/somitragupta/ti-ta-nick,Titanic - Machine Learning from Disaster
8942775,0.78468,0,0,/mariedebi/md-clean-titanic-notebook-ml,Titanic - Machine Learning from Disaster
4037134,0.0,0,0,/geer1997/red-neural-titanic,Titanic - Machine Learning from Disaster
217314,0.0,0,0,/xavierekkel/an-interactive-data-science-tutorial,Titanic - Machine Learning from Disaster
10482804,0.98578,0,0,/eisukeyamamoto/kernel289b126d77,Digit Recognizer
210578,0.0,0,0,/bhavana54/an-interactive-data-science-tutorial,Titanic - Machine Learning from Disaster
204923,0.0,0,0,/atmaks/titanic-predictions,Titanic - Machine Learning from Disaster
220005,0.0,0,0,/geow812/titanic-with-logistic-regression,Titanic - Machine Learning from Disaster
238167,0.0,0,0,/citybuster/an-interactive-data-science-tutorial-3f563b,Titanic - Machine Learning from Disaster
236835,0.0,0,0,/adicohen/an-interactive-data-science-tutorial,Titanic - Machine Learning from Disaster
229293,0.0,0,0,/dhdsjy/an-interactive-data-science-tutorial-dac666,Titanic - Machine Learning from Disaster
204743,0.0,0,0,/gini76/an-interactive-data-science-tutorial,Titanic - Machine Learning from Disaster
171072,0.0,0,0,/deepburner/an-interactive-data-science-tutorial,Titanic - Machine Learning from Disaster
168631,0.0,0,0,/kajayst/an-interactive-data-science-tutorial,Titanic - Machine Learning from Disaster
168630,0.0,0,0,/hansie/an-interactive-data-science-tutorial,Titanic - Machine Learning from Disaster
94344,0.0,0,0,/vodaza36/titanic-competition-my-first-steps,Titanic - Machine Learning from Disaster
69060,0.0,0,0,/bolids/test1,Titanic - Machine Learning from Disaster
200910,0.0,0,0,/shreyd/an-interactive-data-science-tutorial-4175c6,Titanic - Machine Learning from Disaster
174263,0.0,0,0,/anuragw/learning-data-science-titanic-disaster,Titanic - Machine Learning from Disaster
172913,0.0,0,0,/deepanshugarg/an-interactive-data-science-tutorial,Titanic - Machine Learning from Disaster
264096,0.0,0,0,/cchaow/an-interactive-data-science-tutorial,Titanic - Machine Learning from Disaster
349481,0.0,0,0,/kivaschenko/titanic-1,Titanic - Machine Learning from Disaster
720905,0.0,0,0,/zihaozhang9/simple-linear,Titanic - Machine Learning from Disaster
260555,0.0,0,0,/thomasschied/an-interactive-data-science-tutorial,Titanic - Machine Learning from Disaster
692537,0.0,0,0,/zhangrui19950516/an-interactive-data-science-tutorial-c9d176,Titanic - Machine Learning from Disaster
641482,0.0,0,0,/telio18/an-interactive-data-science-tutorial,Titanic - Machine Learning from Disaster
6417932,0.0,0,0,/thibaultbezpalko/titanic-comparison,Titanic - Machine Learning from Disaster
9894663,0.78947,0,0,/mollywiener/titanic-4,Titanic - Machine Learning from Disaster
983924,0.06457,0,0,/mustafaali96/keras-mnist,Digit Recognizer
6926526,0.00953,0,0,/abakumoviv/kernel473a0695d1,DL for exploration geophysics
9340470,0.7751100000000001,0,0,/benjonasherbertz/kernel61b6f99d2c,Titanic - Machine Learning from Disaster
9347064,0.79425,0,0,/rebekahstagg/spring-2020-mat-4113-contract,Titanic - Machine Learning from Disaster
9346323,0.17242,0,0,/mukhamedali1337/endterm-bas,House Prices - Advanced Regression Techniques
9333182,0.26649,0,0,/kossaibek/pricepred,House Prices - Advanced Regression Techniques
9337826,0.68181,0,0,/syedsaifalialvi/titanic-data,Titanic - Machine Learning from Disaster
8611163,0.08461,0,0,/auenwald/covid19-forcast-w2-fitting-a-logistic-curve,COVID19 Global Forecasting (Week 2)
9156312,0.7751100000000001,0,0,/debdinnyabanerjee/titanic-dataset,Titanic - Machine Learning from Disaster
9394153,0.98442,0,0,/anubhabrick/mnist-sol,Digit Recognizer
9148749,0.8373200000000001,0,3,/annabelled/titanic-kaggle,Titanic - Machine Learning from Disaster
9390006,0.96514,0,0,/markmeyers/digit-recog-rf-and-pca,Digit Recognizer
8858498,0.7703300000000001,0,0,/shmuell/titanic-svm,Titanic - Machine Learning from Disaster
9424124,0.4,0,0,/kanameseto/kernel50591b065d,House Prices - Advanced Regression Techniques
8687249,1.0581,0,0,/osciiart/smoking-kills,COVID19 Global Forecasting (Week 2)
8688395,3.34685,0,2,/robertmarsland/covid-19-spreads-as-power-of-elapsed-time,COVID19 Global Forecasting (Week 2)
8698867,0.7751100000000001,0,0,/dh33rajmohan/tutorial-titanic,Titanic - Machine Learning from Disaster
8680067,0.34075,0,0,/sixteenpython/covid-19-transmission-rates-and-factors-w2,COVID19 Global Forecasting (Week 2)
8827099,0.03516,0,0,/surajitcba2021/using-xgboost,COVID19 Global Forecasting (Week 3)
9163964,0.76555,0,2,/barindersingh/titanic-survival-using-ml-algos,Titanic - Machine Learning from Disaster
8826072,1.42292,1,1,/pietromarinelli/very-basic-moving-avg-approach,COVID19 Global Forecasting (Week 3)
8823699,0.0405,0,0,/debanga/fork-of-covid19-eda-week3,COVID19 Global Forecasting (Week 3)
8823807,0.06747,0,0,/andrekos/sub2-sars-cov-2-es-w3-2,COVID19 Global Forecasting (Week 3)
8823910,0.05716,0,0,/andrekos/covid-19-w3-simple-baseline-heuristic,COVID19 Global Forecasting (Week 3)
8823607,0.20109,0,0,/prashant268/kernelcc68fe8231,COVID19 Global Forecasting (Week 3)
8825365,0.05716,0,0,/mystery/covid-19-w3-a-few-charts-and-a-simple-b-dcd3c5,COVID19 Global Forecasting (Week 3)
8825429,3.23481,0,0,/juliochilela/kernel602cda1618,COVID19 Global Forecasting (Week 3)
8825655,0.80896,0,0,/peteralaoui/3-regimes-logistic-exponential-and-linear-w3,COVID19 Global Forecasting (Week 3)
8711619,0.32576,0,1,/vishalvjoseph/covid-19-updated,COVID19 Global Forecasting (Week 3)
8710362,1.39835,0,1,/franlopezguzman/covid19-2-minimal-polynomial-regressor,COVID19 Global Forecasting (Week 2)
10933282,0.17232,0,7,/sidagar/house-prices-using-linear-regression,House Prices - Advanced Regression Techniques
8818932,0.07581,1,0,/appian/covid19-week3-2,COVID19 Global Forecasting (Week 3)
9093776,0.13273,0,1,/urayukitaka/tsne-validation-ensemble-prediction-sale-price,House Prices - Advanced Regression Techniques
8953828,0.148,5,14,/carlmcbrideellis/random-forest-regression-minimalist-script,House Prices - Advanced Regression Techniques
8754100,1.27866,0,1,/amitbhansali/covid-global-forecast-linear-regression,COVID19 Global Forecasting (Week 3)
10874270,0.70598,0,1,/masaykonno/kernel11934301f2,Homework for Students
9131852,9.71719,0,0,/deborareis/how-to-not-do-regression-today-i-am-lazy,House Prices - Advanced Regression Techniques
10900744,0.97992,0,0,/sakata0141/kernel56137a6b73,Digit Recognizer
10911527,0.7751100000000001,0,0,/tracyporter/titanic-competition-seventh-try,Titanic - Machine Learning from Disaster
10882513,0.77272,0,3,/hs1592/adkkhn-krj-titanic,Titanic - Machine Learning from Disaster
15274418,0.50784,0,0,/asttan/tan-astuty-acada-module6,Bike Sharing Demand
15039251,2.47439,2,5,/complexsum/facial-keypoint-detection,Facial Keypoints Detection
8310191,2.74364,0,0,/safamediouni/keypt-detect-0,Facial Keypoints Detection
3331229,2.65053,1,4,/negi009/facial-keypoint-detection,Facial Keypoints Detection
3199857,3.61521,0,0,/antigravityimport/facial-keypoints-detection-fastai-1-0-46,Facial Keypoints Detection
2764538,4.28238,0,1,/dreams0104/first-kaggle-challenge-study-lsm,Facial Keypoints Detection
2213549,6.757560000000002,0,0,/swagmh/cnn-studying,Facial Keypoints Detection
1889675,4.51952,0,1,/glarrea/resnet-facedetection,Facial Keypoints Detection
201234,0.75189,0,7,/ksayantani/exploratory-analysis-on-data,Two Sigma Connect: Rental Listing Inquiries
8339090,0.48523,0,0,/jeansvy/2020-march-madness-ncaaw-feature-engineering,Google Cloud & NCAA® ML Competition 2020-NCAAW
8332826,0.3608,0,2,/miklgr500/keras-nn-ncaaw,Google Cloud & NCAA® ML Competition 2020-NCAAW
8082740,0.16657,4,15,/khoongweihao/ncaaw2020-lightgbm-k-fold-on-fire-viz,Google Cloud & NCAA® ML Competition 2020-NCAAW
8079138,0.20404,0,13,/hamditarek/ncaaw20-eda-and-nn-lgb-catb-starter-7c65f8,Google Cloud & NCAA® ML Competition 2020-NCAAW
8016864,0.19793,10,15,/code1110/ncaaw20-eda-and-nn-lgb-catb-starter,Google Cloud & NCAA® ML Competition 2020-NCAAW
8009069,0.26295,12,19,/chariots17/using-xgboost-lgb-to-predict,Google Cloud & NCAA® ML Competition 2020-NCAAW
7980891,0.44527,2,18,/hiromoon166/2020-women-s-starter-kernel,Google Cloud & NCAA® ML Competition 2020-NCAAW
11086348,0.64241,0,0,/oleksiibernatskyi/m5-5kernel,M5 Forecasting - Accuracy
15854427,0.44272,0,0,/dhawalsoni/sentiment-analysis,Sentiment Analysis on Movie Reviews
15016279,2810.2296300000007,0,2,/alinegoulart/walmart-store-sales-forecasting,Walmart Recruiting - Store Sales Forecasting
212943,1.2972,0,0,/sheikirfanbasha/twosigma-categoraltonumeric-features,Two Sigma Connect: Rental Listing Inquiries
14813036,0.88538,0,0,/danurahul/stumble-upon-challenge-precision-recall,StumbleUpon Evergreen Classification Challenge
8327652,0.28035,0,0,/fujine/2020-03-10-ncaam,Google Cloud & NCAA® ML Competition 2020-NCAAM
8165598,0.0867899999999999,0,0,/kamalnaithani/ncaa-model-no-leaks-simple-random-forest-model,Google Cloud & NCAA® ML Competition 2020-NCAAM
8355729,0.55006,1,1,/omarrodriguez/seed-only-model,Google Cloud & NCAA® ML Competition 2020-NCAAM
8292317,0.54324,0,6,/adarshsng/march-madness-2020-ncaam-simple-dnn-with-sk-fold,Google Cloud & NCAA® ML Competition 2020-NCAAM
8132612,0.61137,1,1,/scirpus/not-another-genetic-program,Google Cloud & NCAA® ML Competition 2020-NCAAM
8100893,0.5636399999999999,0,11,/takaishikawa/no-ml-modeling-ncaam2020,Google Cloud & NCAA® ML Competition 2020-NCAAM
8067852,0.30627,1,2,/jarnel/clipping-spline-experiment-on-test-predictions,Google Cloud & NCAA® ML Competition 2020-NCAAM
8009038,0.47433,1,12,/miklgr500/keras-nn,Google Cloud & NCAA® ML Competition 2020-NCAAM
7993081,0.32227,4,5,/code1110/ncaam20-eda-and-nn-lgb-catb-starter,Google Cloud & NCAA® ML Competition 2020-NCAAM
4570315,0.163,1,1,/qaqwsw123/fastai-starter-iwildcam-2019-ad561b,iWildCam 2019 - FGVC6
246962,0.5599109999999999,0,1,/midouuu/data-mining-mmn,March Machine Learning Mania 2016
14108148,0.0567299999999999,0,0,/subhamsagarpaira/knn-don-t-get-kicked,Don't Get Kicked!
14577717,0.0567299999999999,0,0,/anu332/don-t-get-kicked-anurag,Don't Get Kicked!
244995,0.2488,1,0,/jekrus/rossmann,Rossmann Store Sales
15162029,0.65935,0,0,/hongshitan/nustarcat,COVID19 Global Forecasting (Week 4)
8676289,0.16983,0,0,/pararols/sars-cov-2-exponential-model-week-2,COVID19 Global Forecasting (Week 2)
8669813,0.1541,0,1,/gabrielmilan/covid-19-forecasting-with-lstm,COVID19 Global Forecasting (Week 2)
8662334,0.16983,0,0,/akashsuper2000/sars-cov-2-exponential-model-week-2,COVID19 Global Forecasting (Week 2)
8651073,1.49291,0,0,/nsaleille/global-randomforestclassifier-baseline,COVID19 Global Forecasting (Week 2)
8646220,0.42731,0,0,/mystery/covid-19-week-2-xgboost-lightgbm,COVID19 Global Forecasting (Week 2)
8631422,0.24544,0,0,/priteshshrivastava/sars-cov-2-exponential-model-week-2,COVID19 Global Forecasting (Week 2)
8600338,0.22288,0,0,/autokad/wg-feature,COVID19 Global Forecasting (Week 2)
8591720,0.8638399999999999,0,1,/mehdi16/covid-19-week-2,COVID19 Global Forecasting (Week 2)
8623707,1.78611,0,4,/vaibhavsxn/covid-global-forecast-sir-model-ml-regressions,COVID19 Global Forecasting (Week 2)
8604640,1.48794,3,9,/sulianova/covid-19-forecasting-with-random-forest,COVID19 Global Forecasting (Week 2)
8594269,0.47518,0,3,/yatinece/exp-new-week,COVID19 Global Forecasting (Week 2)
8655144,1.1974200000000002,0,0,/lnmkey68/kernel4af8e7b577,COVID19 Global Forecasting (Week 2)
8631678,1.08376,0,0,/ritwikganguly/kernel7762fcff21,COVID19 Global Forecasting (Week 2)
8612816,1.6851900000000002,0,0,/deeshantk/kernel2036adfb74,COVID19 Global Forecasting (Week 2)
8712261,2.32415,0,0,/saurabhbhogale/fork-of-kernel3178b29c7e,COVID19 Global Forecasting (Week 2)
8710762,0.22669,0,0,/stecasasso/blend,COVID19 Global Forecasting (Week 2)
8710321,0.93689,0,0,/janosk21/covid-19-basic-model-not-leaky,COVID19 Global Forecasting (Week 2)
8707265,1.98769,0,0,/williamsabodunrin/kernel67c2de8f36,COVID19 Global Forecasting (Week 2)
8706438,0.08319,0,0,/yaroshevskiy/sars-cov-2-exponential-model-week-2,COVID19 Global Forecasting (Week 2)
8693229,0.93707,2,8,/giginghn/covid19-analysis-eda-seir-model-predictions,COVID19 Global Forecasting (Week 2)
8682077,1.09113,0,0,/sudhamshsuraj/kernel5655112544,COVID19 Global Forecasting (Week 2)
13719402,1.83122,0,0,/prakashvm/notebook6dce06d71f,COVID19 Global Forecasting (Week 2)
9443260,0.74631,0,0,/clairvoyanto/id5059-final,COVID19 Global Forecasting (Week 2)
8614692,1.7527,0,7,/dferhadi/covid19-population-feature-engineering,COVID19 Global Forecasting (Week 2)
8660964,1.77527,3,12,/manasmohapatra1998/covid19-week2-forecasting-using-random-forest,COVID19 Global Forecasting (Week 2)
8621636,1.72678,0,1,/sulianova/times-series-double-exponential-smoothing,COVID19 Global Forecasting (Week 2)
8622677,0.30399,0,1,/liuzhangzhen/arima-influenza-baselines,COVID19 Global Forecasting (Week 2)
8706656,0.0832,0,1,/rejpalcz/sars-cov-2-exponential-model-week-2,COVID19 Global Forecasting (Week 2)
8656520,0.16489,0,1,/skeller/lgbt-and-arima-with-influenza-baselines,COVID19 Global Forecasting (Week 2)
8672759,0.39711,0,0,/kashish2801/covid-19-week-2,COVID19 Global Forecasting (Week 2)
8602964,0.17481,0,0,/ivince20x4/covid19-week2-prediction,COVID19 Global Forecasting (Week 2)
8662755,0.2069,0,0,/kamalnaithani/covid-19-forecast,COVID19 Global Forecasting (Week 2)
8690364,1.2194,0,0,/lollcat/logistic,COVID19 Global Forecasting (Week 2)
8655431,1.77527,1,11,/manasmohapatra1998/kernel67e3892b6d,COVID19 Global Forecasting (Week 2)
8701162,0.28661,0,0,/ajaysamp/covid-19-xgboost,COVID19 Global Forecasting (Week 2)
8607463,0.2867099999999999,0,1,/deshmane/kernel1220a6465e,COVID19 Global Forecasting (Week 2)
8642672,0.06853,0,0,/pilipeykogrisha/covid-2019-xgboost-lgb-ply,COVID19 Global Forecasting (Week 2)
8679622,0.25597,0,0,/timriggins/kernel33fd888537,COVID19 Global Forecasting (Week 2)
8664585,0.59213,0,0,/divyacnambiar/covid19-forecasting-sarima,COVID19 Global Forecasting (Week 2)
8649708,0.53928,0,1,/irfanazeem/covid19-eda-interactive-plots-xgboost,COVID19 Global Forecasting (Week 2)
8627707,3.49834,2,1,/begehr/scaling-and-translating-hubei-s-curve,COVID19 Global Forecasting (Week 2)
8703344,1.17963,0,0,/nikolayanikolaev/copy-covid-19-global-forecasting-2,COVID19 Global Forecasting (Week 2)
8697423,0.06853,0,0,/letili0417/covid-19-week-2-xgboost,COVID19 Global Forecasting (Week 2)
8711765,0.06168,7,33,/aerdem4/covid19-w2-final-v2,COVID19 Global Forecasting (Week 2)
8671619,3.7381,0,0,/abhia1999/kernel4d158f4665,COVID19 Global Forecasting (Week 2)
8711196,1.19767,0,0,/oksanasem/covid19-eda-interactive-plots-xgboost,COVID19 Global Forecasting (Week 2)
8621138,0.22308,0,0,/amitgairola/kernel216638044e,COVID19 Global Forecasting (Week 2)
8711498,0.06853,0,0,/georbuz/4ver-arima-2ver-xgboost-newbie,COVID19 Global Forecasting (Week 2)
8627774,0.1969999999999999,0,0,/skorsun/sars-cov-2-exponential-model-week-2,COVID19 Global Forecasting (Week 2)
8698656,0.0822,0,0,/rohitmidha23/covid2-attempt2,COVID19 Global Forecasting (Week 2)
8710880,0.77485,0,3,/pietromarinelli/new-pp-double-model-public-private,COVID19 Global Forecasting (Week 2)
8710237,0.08813,1,15,/dott1718/cv19w2-2-sub,COVID19 Global Forecasting (Week 2)
8651962,0.31987,0,0,/ilialar/logcurve,COVID19 Global Forecasting (Week 2)
8703218,0.05474,0,6,/kazanova/gr1621-v2,COVID19 Global Forecasting (Week 2)
8627089,0.06766,0,0,/yustasalex/covid19-gf-week2,COVID19 Global Forecasting (Week 2)
8707288,2.69542,0,3,/meetdoshi996/global-covid19-forecast,COVID19 Global Forecasting (Week 2)
8604802,2.02279,1,4,/danevans/covid-19-week-2-global,COVID19 Global Forecasting (Week 2)
8632600,1.32252,0,5,/baratheonr6/week-2-eda-and-non-leaky-modelling,COVID19 Global Forecasting (Week 2)
9515763,0.395,0,1,/gb00000/wildcam-nn,iWildCam 2020 - FGVC7
8525897,0.435,3,10,/qinhui1999/iwildcam-2020-efficientnetb7-tpu-starter-v1,iWildCam 2020 - FGVC7
8408189,0.425,2,62,/ateplyuk/iwildcam2020-pytorch-start,iWildCam 2020 - FGVC7
14986100,0.73037,0,0,/xevator/tutorial,Forest Cover Type Prediction
5262083,0.1069099999999999,0,7,/ptyshevs/cnn-for-reversing-game-of-life,Conway's Reverse Game of Life
200431,1.1833,0,2,/brown471/cats-dogs-brown,Dogs vs. Cats Redux: Kernels Edition
201866,0.5514399999999999,0,0,/nmud19/nik-xgb-starter,Two Sigma Connect: Rental Listing Inquiries
2183564,0.4398899999999999,0,10,/roydatascience/bike-share-demand-model-version-1-0,Bike Sharing Demand
15688808,0.47206,0,0,/shimheungwoon/bike-sharing-demand,Bike Sharing Demand
14817647,52.07722,0,0,/shanan93/facial-keypoints-detection,Facial Keypoints Detection
7630243,2.03554,23,50,/balraj98/data-augmentation-for-facial-keypoint-detection,Facial Keypoints Detection
4819931,3.08313,0,0,/zhudongxiao/1st-try-based-on-cnn,Facial Keypoints Detection
3595552,2.19286,0,10,/utkarsh4430/facial-keypoints-detection-basic-keras-model,Facial Keypoints Detection
3278086,2.32171,2,21,/aparajit0511/facial-keypoint-detection-udacity,Facial Keypoints Detection
2701708,4.11725,0,1,/div1996p/facial-key-detection,Facial Keypoints Detection
2617252,4.13406,0,2,/ruchibahl18/facial-keypoint-detection,Facial Keypoints Detection
2493358,3.61818,0,4,/mirodil/facial-keypoints-detection,Facial Keypoints Detection
1888718,4.41109,0,0,/glarrea/face-detection,Facial Keypoints Detection
202889,0.90125,0,0,/om1042/two-sigma-connect-rental-listing-inquiries,Two Sigma Connect: Rental Listing Inquiries
8249926,0.09545,0,0,/kamalnaithani/ncaa-w-no-leaks-simple-random-forest-model,Google Cloud & NCAA® ML Competition 2020-NCAAW
8303877,0.4292899999999999,0,2,/miklgr500/bayesian-neural-network-in-keras-ncaaw,Google Cloud & NCAA® ML Competition 2020-NCAAW
8192805,0.20489,0,5,/darwinwin/ncaaw20-eda-and-nn-lgb-catb-starter-7c65f8,Google Cloud & NCAA® ML Competition 2020-NCAAW
8191645,0.2220599999999999,0,3,/darwinwin/ncaaw20-eda-and-nn-lgb-catb-starter,Google Cloud & NCAA® ML Competition 2020-NCAAW
8087017,0.45292,0,9,/takaishikawa/no-ml-modeling-ncaaw2020,Google Cloud & NCAA® ML Competition 2020-NCAAW
8084358,0.48157,7,11,/code1110/ncaaw20-finally-no-leak-starter-with-lgb,Google Cloud & NCAA® ML Competition 2020-NCAAW
8094832,0.61052,0,1,/johnt666/no-leaks-what-do-you-think,Google Cloud & NCAA® ML Competition 2020-NCAAW
15129169,0.64463,0,1,/pasryd/sentiment-analysis-tokenization-and-embeddings,Sentiment Analysis on Movie Reviews
14731079,0.5920000000000001,0,1,/semenedel/sentiment-analysis-rnn,Sentiment Analysis on Movie Reviews
241722,0.93594,0,0,/kirsteny/model-lr,Two Sigma Connect: Rental Listing Inquiries
241333,1.7152,0,0,/kirsteny/model-nb,Two Sigma Connect: Rental Listing Inquiries
15002109,3011.86332,0,2,/williana/walmart-store-sales-forecasting,Walmart Recruiting - Store Sales Forecasting
217728,0.59868,0,1,/geekfox/a-simple-keras-nn,Two Sigma Connect: Rental Listing Inquiries
213630,0.63639,0,0,/tukichen/exploratory-analysis-of-rental-interest-data,Two Sigma Connect: Rental Listing Inquiries
8132680,0.0,0,5,/revanthrex/2020-ncaa-eda-score-0-000,Google Cloud & NCAA® ML Competition 2020-NCAAM
8313389,0.44581,0,0,/fujine/2020-03-09-ncaam,Google Cloud & NCAA® ML Competition 2020-NCAAM
8046734,0.56552,0,1,/marginalreturns/ncaam20-model-selection-and-testing,Google Cloud & NCAA® ML Competition 2020-NCAAM
8356503,0.53676,0,2,/ben519/professor-data-cleaning-and-modeling,Google Cloud & NCAA® ML Competition 2020-NCAAM
8385569,0.49346,0,0,/byfone/ncaa-m-lightgbm,Google Cloud & NCAA® ML Competition 2020-NCAAM
8210334,0.51968,0,1,/tnmasui/ncaam-2020-lgb-w-fe-on-three-datasets,Google Cloud & NCAA® ML Competition 2020-NCAAM
8187632,0.43304,6,7,/santohide/my-first-gbdt,Google Cloud & NCAA® ML Competition 2020-NCAAM
8034043,0.47433,4,21,/latimerb/2020-model-comparison-no-leak-submission,Google Cloud & NCAA® ML Competition 2020-NCAAM
7990667,0.03966,27,53,/khoongweihao/ncaam2020-2021-xgboost-lightgbm-k-fold,Google Cloud & NCAA® ML Competition 2020-NCAAM
7990996,0.35928,6,13,/nxrprime/march-madness-2020-ncaam-simple-lightgbm-on-kfold,Google Cloud & NCAA® ML Competition 2020-NCAAM
4109956,0.109,0,1,/s3chwartz/iwildcam-2019,iWildCam 2019 - FGVC6
3810836,0.122,0,2,/autuanliuyc/iwildcam-2019-densenet-se-resnet152,iWildCam 2019 - FGVC6
3480019,0.128,0,5,/twhitehurst3/keras-transfer-learning-iwildcam-2019,iWildCam 2019 - FGVC6
3393059,0.094,4,52,/ateplyuk/iwildcam2019-keras-efficientnet,iWildCam 2019 - FGVC6
3391563,0.141,25,64,/gpreda/iwildcam-2019-eda-and-prediction,iWildCam 2019 - FGVC6
2122158,0.84795,0,12,/roydatascience/airbnb-new-user-booking-random-forest,Airbnb New User Bookings
15627352,0.0567299999999999,0,0,/mohdmaazkhan/dontgetkicked,Don't Get Kicked!
14737277,0.0567299999999999,0,0,/nishita17/dgk-n,Don't Get Kicked!
95831,0.8697239999999999,0,0,/darshanms1991/msd-notebook-1,Predicting Red Hat Business Value
15495195,0.62347,0,1,/virus138/dr-fundus,Jigsaw Multilingual Toxic Comment Classification
8876222,0.35675,0,0,/kashish2801/covid-19-prediction-using-prophet-by-facebook,COVID19 Global Forecasting (Week 4)
8936212,0.03589,0,0,/haplophyrne/week-4-ensemble,COVID19 Global Forecasting (Week 4)
9299601,4.2394,1,3,/sureshmecad/covid-19-global-forecast-week4,COVID19 Global Forecasting (Week 4)
9264856,1.70453,0,1,/vladlee/covid-19-week-4-xgb,COVID19 Global Forecasting (Week 4)
8679954,1.09183,0,0,/sudhamshsuraj/kernel196d7fff6c,COVID19 Global Forecasting (Week 2)
8673524,1.42142,0,0,/nsaleille/06-global-gradientboostingregressor-enriched,COVID19 Global Forecasting (Week 2)
8670523,1.48996,0,0,/nsaleille/global-randomforestclassifier-enriched-finetuned,COVID19 Global Forecasting (Week 2)
8652580,0.1976099999999999,0,0,/lakshpri/covid19-global-forecasting-week2-forecast,COVID19 Global Forecasting (Week 2)
8649358,0.06444,0,0,/hugoluo/sars-cov-2-exponential-model-week-2,COVID19 Global Forecasting (Week 2)
8639446,1.29277,0,0,/pdevine/covid-global-forecast-sir-model-ml-regressions,COVID19 Global Forecasting (Week 2)
8632709,1.33761,0,0,/mehdi16/covid-19-w2-lgbm-based-on-non-cumulative-data,COVID19 Global Forecasting (Week 2)
8621648,0.07764,0,0,/lomen0857/covid19-fatalities,COVID19 Global Forecasting (Week 2)
8616331,0.27736,0,0,/ilu000/arima-influenza-baselines,COVID19 Global Forecasting (Week 2)
8604291,0.74631,0,0,/bernhardklinger/kernel20c01d5e21,COVID19 Global Forecasting (Week 2)
8595051,0.27286,0,0,/ayushijain29/covid19-dataset,COVID19 Global Forecasting (Week 2)
8603749,0.30399,8,14,/skeller/arima-influenza-baselines,COVID19 Global Forecasting (Week 2)
8614841,2.1652400000000003,1,1,/anbu3003/covid-week-2-forecast,COVID19 Global Forecasting (Week 2)
8598938,0.2549,0,0,/gopiyedla/covid-will-be-defeated,COVID19 Global Forecasting (Week 2)
8649987,1.83207,0,0,/deeshantk/kernel6645fb0c31,COVID19 Global Forecasting (Week 2)
8590324,1.81691,0,0,/benhamner/no-new-cases-baseline-covid-19-week-2-global,COVID19 Global Forecasting (Week 2)
8599628,1.43825,0,0,/iamabhi1/kernel26e4ca93e5,COVID19 Global Forecasting (Week 2)
9443679,2.07525,0,0,/eleanorwright/covid-w2-preds,COVID19 Global Forecasting (Week 2)
9292017,0.43267,0,0,/leticijadubickait/make-output,COVID19 Global Forecasting (Week 2)
8712402,0.06392,0,0,/luweijia2heather/fork-of-fork-of-heathernocov-07ba0a,COVID19 Global Forecasting (Week 2)
8712052,1.22662,0,0,/mystery/seir-hcd-model,COVID19 Global Forecasting (Week 2)
8710564,0.63438,0,0,/sasrdw/gbt1u,COVID19 Global Forecasting (Week 2)
8710235,0.09008,0,0,/shantanu1118/global-forecasting-covid19,COVID19 Global Forecasting (Week 2)
8708348,0.06355,0,0,/debanga/covid-19-week2-eda,COVID19 Global Forecasting (Week 2)
8707202,0.6047100000000001,0,0,/sasrdw/gbt1n,COVID19 Global Forecasting (Week 2)
8704243,0.09008,0,0,/syedhamzahussain/covid19-forecasting-using-rnn,COVID19 Global Forecasting (Week 2)
8694402,0.06672,1,0,/kowjan1/xgboost-previous-days-and-special-days,COVID19 Global Forecasting (Week 2)
8692272,0.2069,0,0,/georbuz/sars-cov-2-exponential-model-week-2,COVID19 Global Forecasting (Week 2)
14729321,0.7729199999999999,0,0,/arko007/logistic-regression-food-cuisine,What's Cooking?
8704428,0.06236,0,2,/cpmpml/lr-010,COVID19 Global Forecasting (Week 2)
8711261,0.06601,0,0,/aerdem4/covid19-w2-final,COVID19 Global Forecasting (Week 2)
8664386,1.98248,2,13,/manasmohapatra1998/covid19-week2-forecasting-using-xgboost,COVID19 Global Forecasting (Week 2)
8655034,0.06444,0,0,/girish01/time-series-model,COVID19 Global Forecasting (Week 2)
8709922,3.25798,0,0,/chrisholmes1/covid-19-week-2-notebook,COVID19 Global Forecasting (Week 2)
8645388,3.25798,0,3,/sureshmecad/covid19-global-forecasting-week-2,COVID19 Global Forecasting (Week 2)
8674790,1.44757,0,1,/begehr/leocorona,COVID19 Global Forecasting (Week 2)
8600014,0.73364,0,0,/hockmood/covid19-april-2020-forecast-basic-lstm-network,COVID19 Global Forecasting (Week 2)
8699995,0.17112,0,1,/nxpnsv/covid19-week2,COVID19 Global Forecasting (Week 2)
8642898,0.21391,0,1,/skeller/sars-cov-2-exponential-model-week-2,COVID19 Global Forecasting (Week 2)
8688087,0.66695,0,0,/worldkeeping/week2-global-one-layer,COVID19 Global Forecasting (Week 2)
8652250,0.31826,0,0,/shamsvahid2/covid-week2-xgboost,COVID19 Global Forecasting (Week 2)
8698955,0.23519,0,0,/shamsvahid2/covid-week2-version2,COVID19 Global Forecasting (Week 2)
8687219,1.48486,0,0,/jmtopple/jesscovid19kernel,COVID19 Global Forecasting (Week 2)
8707897,0.06411,0,1,/xscripter/kernel58a2ca5f7f,COVID19 Global Forecasting (Week 2)
8646306,0.06829,0,2,/dipesh17/xgbregressor,COVID19 Global Forecasting (Week 2)
8689890,1.0931,0,0,/syedhamzahussain/covid19-week2-mix,COVID19 Global Forecasting (Week 2)
8592275,0.88134,0,0,/deepakks1995/kernel503d33eb18,COVID19 Global Forecasting (Week 2)
8650640,1.39126,0,0,/yuzusan/initial-draft,COVID19 Global Forecasting (Week 2)
8681076,1.09312,4,5,/blackmantis/covid19-week2-dt-model,COVID19 Global Forecasting (Week 2)
8668309,0.60633,1,2,/diamondsnake/covid-19-logistic-curve-fitting-week-2,COVID19 Global Forecasting (Week 2)
8711253,0.12708,0,0,/luweijia2heather/fork-of-heathernocov-07ba0a,COVID19 Global Forecasting (Week 2)
8672216,1.59648,0,0,/keerthivikram/covid-19-week-2,COVID19 Global Forecasting (Week 2)
8711666,0.08319,0,0,/andrekos/sars-cov-2-exponential-model-week-2-b3299e,COVID19 Global Forecasting (Week 2)
8653827,1.77527,3,6,/janmejoy/covid19-week2-forecasting-using-random-forest,COVID19 Global Forecasting (Week 2)
8646871,1.46245,1,2,/peranto/simple-xgb-lgb-random-forest,COVID19 Global Forecasting (Week 2)
8622138,1.50697,13,31,/khotijahs1/covid19-forecasting-randomforest,COVID19 Global Forecasting (Week 2)
8647348,0.52777,0,0,/artem99/covid-19,COVID19 Global Forecasting (Week 2)
8695797,0.1885099999999999,0,0,/ericfreeman/kernel436b72c212,COVID19 Global Forecasting (Week 2)
8617620,2.9592400000000003,0,0,/mariikoma/kernel33c2db1f7c,COVID19 Global Forecasting (Week 2)
8675865,2.58182,0,0,/jasonbenner/fork-of-1d-cnn-redo,COVID19 Global Forecasting (Week 2)
8687678,0.5069899999999999,0,0,/sadeka007/wk2-covid-19-global-forecasting,COVID19 Global Forecasting (Week 2)
8708573,0.10312,0,6,/robikscube/rob-s-covid19-week-2-submission-less-aggressive,COVID19 Global Forecasting (Week 2)
8710514,0.09349,1,8,/philippsinger/cv19w2-pl4-sub,COVID19 Global Forecasting (Week 2)
8653599,0.1048099999999999,0,2,/robikscube/rob-s-covid19-week-2-submission,COVID19 Global Forecasting (Week 2)
8715437,0.3305,0,0,/vishrutikaria/vishruti-karia,COVID19 Global Forecasting (Week 2)
8601137,0.11137,0,0,/zhengli0817/lroger-covid19-global-forecasting-week-2-lgbm,COVID19 Global Forecasting (Week 2)
8704050,0.0619299999999999,0,0,/aerdem4/covid-w2-nn-submission,COVID19 Global Forecasting (Week 2)
8680658,0.62579,0,0,/sakethaux/covid-19-week-2-predictions,COVID19 Global Forecasting (Week 2)
8665810,0.58303,2,4,/lisphilar/combination-of-fitting-and-math-model-week2,COVID19 Global Forecasting (Week 2)
8605304,0.07127,0,1,/js942106361/covid-19-world-week2-modified-sir,COVID19 Global Forecasting (Week 2)
8345137,0.205,2,13,/seriousran/simple-starter-iwildcam-2020,iWildCam 2020 - FGVC7
15668039,0.67308,0,2,/bobsideshow/pytorch-smp-resnet34-unet,Ultrasound Nerve Segmentation
528256,0.9337958119,2,20,/glenslade/baseline-python-ortools-algo-0-933795,Santa Gift Matching Challenge
515984,0.9264754309,14,18,/dongxu027/path-to-improve-score-hungarian-slowness,Santa Gift Matching Challenge
2799544,0.14249,1,5,/ruchibahl18/starting-of-an-end-game,Conway's Reverse Game of Life
14616817,0.10654,0,0,/slothfulwave612/pytorch-dogs-and-cats-classifier,Dogs vs. Cats Redux: Kernels Edition
238924,1.08794,1,3,/rameshkumarak/predict-bike-sharing-demand-using-boosting-pca,Bike Sharing Demand
14136133,2.75278,1,1,/ritvik1909/facial-keypoint-detection-pyradox,Facial Keypoints Detection
6134593,2.909,2,18,/liudmyla/easy-keras-facial-keypoint-detection,Facial Keypoints Detection
5094451,4.13239,0,1,/jiaofenx/facial-keypoints-detection,Facial Keypoints Detection
2693082,3.38472,0,1,/jatinmittal0001/facial-feature-recog,Facial Keypoints Detection
2102499,4.0855,0,0,/aktaruzzaman/facial-keypoint-aman,Facial Keypoints Detection
241414,0.63559,0,0,/kirsteny/model-rf,Two Sigma Connect: Rental Listing Inquiries
8385417,0.4257,0,0,/byfone/ncaa-w-linear-regression,Google Cloud & NCAA® ML Competition 2020-NCAAW
8212330,0.44515,0,2,/tnmasui/ncaaw-2020-lgb-w-fe-on-three-datasets,Google Cloud & NCAA® ML Competition 2020-NCAAW
8192807,0.17343,1,5,/darwinwin/ncaaw2020-lightgbm-k-fold-on-fire-viz,Google Cloud & NCAA® ML Competition 2020-NCAAW
8012595,0.43906,5,9,/immvab/nn-starter-tensorflow,Google Cloud & NCAA® ML Competition 2020-NCAAW
11401313,1710649.72075,5,24,/ayushikaushik/eda-regression-analysis,Restaurant Revenue Prediction
14837215,0.53969,0,0,/finlay/two-sigma-eda-and-tree-models,Two Sigma Connect: Rental Listing Inquiries
242755,0.53955,0,0,/vignesh2323/fork-of-fork-of-fork-of-xgboost-trialrun-2-9f8a50,Two Sigma Connect: Rental Listing Inquiries
213069,0.7168100000000001,0,0,/sheikirfanbasha/renthop-categoraltonumeric-features,Two Sigma Connect: Rental Listing Inquiries
13799686,0.8809999999999999,0,0,/phsaikiran/cifar-10,CIFAR-10 - Object Recognition in Images
15836413,0.6987300000000001,0,0,/oussamaouardini/notebooka9ff694d7d,StumbleUpon Evergreen Classification Challenge
15009012,0.87692,2,4,/adishah3103/stumbleupon-challenge-using-bert,StumbleUpon Evergreen Classification Challenge
8342651,0.26961,0,0,/fujine/2020-03-11-ncaam,Google Cloud & NCAA® ML Competition 2020-NCAAM
8376594,0.1701099999999999,0,0,/fujine/2020-03-13-ncaam,Google Cloud & NCAA® ML Competition 2020-NCAAM
8340156,0.69314,0,0,/grapestone5321/ml-competition-2020-ncaam-sample-submission,Google Cloud & NCAA® ML Competition 2020-NCAAM
8303223,0.5955199999999999,3,2,/miklgr500/bayesian-neural-network-in-keras-ncaam,Google Cloud & NCAA® ML Competition 2020-NCAAM
8210356,0.51876,0,1,/bsherman10/ncaa-march-madness-2020,Google Cloud & NCAA® ML Competition 2020-NCAAM
8071879,0.59323,0,5,/tovvelie/simple-seed-based-model,Google Cloud & NCAA® ML Competition 2020-NCAAM
8085813,0.55398,2,9,/code1110/ncaam20-finally-no-leak-starter,Google Cloud & NCAA® ML Competition 2020-NCAAM
7979510,0.54999,2,35,/hiromoon166/2020-basic-starter-kernel,Google Cloud & NCAA® ML Competition 2020-NCAAM
7983412,0.52281,2,5,/tenffe/autogluon-for-basic-data-process,Google Cloud & NCAA® ML Competition 2020-NCAAM
3950537,0.108,0,1,/sofyafenicheva/kernele92a7946e4,iWildCam 2019 - FGVC6
3471173,0.123,4,18,/tanlikesmath/fastai-starter-iwildcam-2019,iWildCam 2019 - FGVC6
3528612,0.115,6,62,/ateplyuk/iwildcam2019-pytorch-starter,iWildCam 2019 - FGVC6
236101,0.65498,0,0,/blairhudson/rental-interest-classifier-w-latlong-and-features,Two Sigma Connect: Rental Listing Inquiries
232325,2.81296,0,0,/colingrodecoeur/first-try,Two Sigma Connect: Rental Listing Inquiries
66907,0.5,0,1,/ymcdull/frank-first-test-1,Painter by Numbers
15190279,0.0567299999999999,0,1,/xevator/notebookb73bc79c5c,Don't Get Kicked!
15173213,0.0567299999999999,0,0,/mgen2020/carbuy,Don't Get Kicked!
3352071,0.8710100000000001,0,2,/leirahua/classify-bird-songs-using-spectrogram,Multi-label Bird Species Classification - NIPS 2013
180920,0.55209,0,0,/budhiraja/xgb-starter-in-python,Two Sigma Connect: Rental Listing Inquiries
180696,0.72049,0,0,/fangxiaofang/numerical-categorical-variable-preprocessing,Two Sigma Connect: Rental Listing Inquiries
241252,0.1706599999999999,0,0,/ploshkin/testlr,Rossmann Store Sales
15113533,0.32702,0,0,/huhaolei/milestone1-eda-modelling-forecasting,COVID19 Global Forecasting (Week 4)
3334951,0.86675,0,4,/sdoliver/bayesian-optimized-lgbm,Influencers in Social Networks
8679612,0.07693,0,0,/sayan341/latent-component-model-covid-19,COVID19 Global Forecasting (Week 2)
8672391,0.10459,0,4,/enric1296/covid-the-power-of-representations-rnn,COVID19 Global Forecasting (Week 2)
8669924,0.16983,0,0,/neilde/sars-cov-2-exponential-model-week-2,COVID19 Global Forecasting (Week 2)
8662353,0.21524,0,0,/akashsuper2000/arima-influenza-baselines-newbie,COVID19 Global Forecasting (Week 2)
8647704,1.42662,0,1,/monnniil/covid-19-week-2-forecasting-monil-shah,COVID19 Global Forecasting (Week 2)
8638047,0.06342,0,0,/vikraantpy/kernel54f00e992a,COVID19 Global Forecasting (Week 2)
8631541,0.24309,0,0,/chrischow/week-2-submissions,COVID19 Global Forecasting (Week 2)
8609505,1.26134,0,0,/marsel171/covid-19-week2,COVID19 Global Forecasting (Week 2)
8603592,0.8094,0,0,/asimjalis/covid19-global-forecasting-week-2,COVID19 Global Forecasting (Week 2)
8604232,0.8894200000000001,0,5,/skeller/sprawling-cov-fastai,COVID19 Global Forecasting (Week 2)
8591112,0.69657,0,9,/sergeyverbitskiy/approximation-baseline,COVID19 Global Forecasting (Week 2)
8591207,0.92768,5,5,/akioonodera/covid19-week2-using-regression-analysis,COVID19 Global Forecasting (Week 2)
8633827,0.28336,0,2,/varalakshmia/kernel60368d2f6f,COVID19 Global Forecasting (Week 2)
9267102,2.0434400000000004,0,2,/pk50st/last-version,COVID19 Global Forecasting (Week 2)
8712388,0.6068899999999999,0,0,/jeploretizo/kernelc8991c13e9,COVID19 Global Forecasting (Week 2)
8710323,0.11755,0,0,/jaehooncha/submitweek2,COVID19 Global Forecasting (Week 2)
8707827,0.08521,0,0,/sayan341/latent-component-model-v2-covid-19,COVID19 Global Forecasting (Week 2)
8706666,0.06853,0,0,/pradeepmuniasamy/4ver-arima-2ver-xgboost-newbie,COVID19 Global Forecasting (Week 2)
8702205,2.01571,0,0,/williamsabodunrin/kernel1c65daac22,COVID19 Global Forecasting (Week 2)
8701171,1.05564,0,0,/janosk21/covid-19-forecasting-lstm-v1,COVID19 Global Forecasting (Week 2)
8698428,0.06853,0,0,/ebbygorg/4ver-arima-2ver-xgboost-newbie,COVID19 Global Forecasting (Week 2)
8692241,0.10178,0,0,/nboukraa/exponential-smoothing-damped-multiplicative-trend,COVID19 Global Forecasting (Week 2)
15401237,0.7729199999999999,0,1,/xevator/notebook6aa40e94bb,What's Cooking?
8704017,0.0824299999999999,1,0,/kazanova/gbr-train,COVID19 Global Forecasting (Week 2)
8711477,0.0825,0,10,/muhakabartay/covid-19-a-few-charts-and-a-simple-baseline-f03715,COVID19 Global Forecasting (Week 2)
8642329,0.8421799999999999,2,11,/dferhadi/logistic-curve-fit-parameter-tuning,COVID19 Global Forecasting (Week 2)
8708374,0.06749,0,1,/vtaquet/week-2-logistic-regression,COVID19 Global Forecasting (Week 2)
8699007,3.95086,0,0,/divyacn/covid-19-using-random-forest,COVID19 Global Forecasting (Week 2)
8632619,0.06853,2,3,/hakimnasaoui/covid19-global-forecasting-week-2,COVID19 Global Forecasting (Week 2)
8698757,0.08685,0,0,/liangpang/prediction-week2,COVID19 Global Forecasting (Week 2)
8712497,0.0825,0,1,/skeller/covid-19-a-few-charts-and-a-simple-baseline,COVID19 Global Forecasting (Week 2)
8653915,0.32238,0,1,/nilanshk/covid-19-w2-try2,COVID19 Global Forecasting (Week 2)
8635514,0.30399,0,1,/skeller/arima-influenza-baselines-9cc709,COVID19 Global Forecasting (Week 2)
8632526,0.06853,0,0,/yilmazalp/covid19-2,COVID19 Global Forecasting (Week 2)
8635963,0.33698,0,0,/shashwats89/covid-19-week-2-compare,COVID19 Global Forecasting (Week 2)
8637223,0.28336,0,0,/xscripter/kernel28bfc1c377,COVID19 Global Forecasting (Week 2)
8706608,0.97381,0,0,/lilylong/kernel139c05055c,COVID19 Global Forecasting (Week 2)
26251,0.10659,0,0,/shivaniginde/kaggle-model2,What's Cooking?
8703255,0.28808,0,0,/timriggins/fork-of-kernel33fd888537,COVID19 Global Forecasting (Week 2)
8669469,2.03037,0,0,/ravishankariyer/covid-19-xgboost,COVID19 Global Forecasting (Week 2)
8655742,1.69741,0,0,/apoorvm/rf-covid-19-2,COVID19 Global Forecasting (Week 2)
8667757,0.96665,0,0,/mactom1980/kernel1377906b6d,COVID19 Global Forecasting (Week 2)
8684601,1.14785,0,0,/stecasasso/seir-hcd-model,COVID19 Global Forecasting (Week 2)
8611480,0.25834,0,0,/manasgargv6/kernel3fe8c58d93,COVID19 Global Forecasting (Week 2)
8602975,1.7333,0,0,/dimaquick/kernel303a0b031a,COVID19 Global Forecasting (Week 2)
8704542,1.18002,0,0,/singh16ananya/kernel785c407068,COVID19 Global Forecasting (Week 2)
8671813,0.20443,0,0,/joljol/covid19-lstm-embedding-for-latent-info-on-country,COVID19 Global Forecasting (Week 2)
8704159,0.13876,0,0,/petersorensen360/kernel40c24c2269,COVID19 Global Forecasting (Week 2)
8668073,2.41614,0,0,/mehdi16/covid-19-week-2alternative,COVID19 Global Forecasting (Week 2)
8645396,0.2290599999999999,0,1,/mauddib/covid-19-logistic-curve-fitting-and-submission,COVID19 Global Forecasting (Week 2)
8633884,2.45667,0,2,/durgaprasadk10/covid-forecast-dp,COVID19 Global Forecasting (Week 2)
8631106,0.30399,0,0,/priteshshrivastava/arima-influenza-baselines,COVID19 Global Forecasting (Week 2)
8639335,0.39134,0,0,/hideshoyama/kernel31f4259a38,COVID19 Global Forecasting (Week 2)
8685291,0.17924,0,11,/uvinetz/week-2-submission,COVID19 Global Forecasting (Week 2)
8688066,0.32576,0,0,/atamazian/covid-19-week-2-xgboost-lightgbm,COVID19 Global Forecasting (Week 2)
8601098,0.06853,0,0,/mohitesh07/kernel402aed056d,COVID19 Global Forecasting (Week 2)
8708421,0.4961899999999999,0,0,/chvbs2000/curve-fitting-ac06c3,COVID19 Global Forecasting (Week 2)
8711688,1.16746,0,0,/therealroman/kernel5a0e8d9b45,COVID19 Global Forecasting (Week 2)
8627814,0.08621,0,0,/razasaleemi/covid19-global-forecasting-week2-v3,COVID19 Global Forecasting (Week 2)
8700898,0.78162,0,2,/pietromarinelli/double-model-public-private,COVID19 Global Forecasting (Week 2)
8672851,0.45188,0,1,/amezet/covid-19-global-forecast-seir-visualize-math,COVID19 Global Forecasting (Week 2)
8640496,0.58307,0,0,/myh0307/covid-sarima,COVID19 Global Forecasting (Week 2)
8642498,2.67473,0,5,/itsbitan/covid-19-analysis,COVID19 Global Forecasting (Week 2)
8611214,3.03242,1,24,/rohanrao/covid-19-w2-lgb-mad,COVID19 Global Forecasting (Week 2)
8595725,0.78723,0,11,/osciiart/covid-19-lightgbm-no-leak,COVID19 Global Forecasting (Week 2)
9112402,0.325,0,1,/taohoang/iwildcam-2020-efficientnetb7-tpu-starter-v1,iWildCam 2020 - FGVC7
8404396,0.356,0,6,/nayuts/iwildcam-2020-efficientnetb2,iWildCam 2020 - FGVC7
15128456,0.07528,0,3,/skydevour/cats-vs-dogs,Dogs vs. Cats Redux: Kernels Edition
15668527,0.7287600000000001,0,0,/mohdmaazkhan/f-cover,Forest Cover Type Prediction
14881993,0.7307100000000001,0,0,/diyadodwad/tutorial,Forest Cover Type Prediction
15675072,0.6734600000000001,0,0,/maximkovito/nerve-segmentation-unet,Ultrasound Nerve Segmentation
15717600,0.65958,0,0,/maxfarafonov/notebook-lab-3,Ultrasound Nerve Segmentation
1990791,0.9349606576,2,3,/naturebalance/starter-approach-on-gift-matching-case,Santa Gift Matching Challenge
504569,0.745513777,0,11,/lemonkoala/greedy-v2,Santa Gift Matching Challenge