FORECASTING OF SUPERMARKET SALES USING BIG DATA ANALYTICS AND MACHINE LEARNING TECHNIQUES IN BUSINESS SECTOR

In the modern digital age, the conventional approach to business analysis has been altered as a result of advancements in machine learning. As marketplaces evolve, customers and businesses need to properly predict future markets and behaviour to achieve sustainable success. These advanced technologies have revolutionised how organisations carry out data analysis, gain knowledge, and come to effective decisions. One of the recent trends is a popularity of predictive analytics as part of business analytics. Other related abilities that several of the algorithms share include the capacity to analyse vast databases of earlier occurrences in requests to identify patterns and trends, which aid an organisation in generating accurate prognoses relating to future activities. The use of big data analytics methods to enhance retail sales forecasting has gained popularity in the last several years. Using big data analytics, this article compares and contrasts several ML methods for forecasting sales at supermarkets. The 2013 BigMart Sales dataset is utilised in this study, which explores the use of ML algorithms to predict retail sales patterns. A variety of thorough preprocessing techniques were used, such as PCA feature extraction, outlier detection, and handling of missing variables. F1-score, recall, accuracy, and precision metrics were utilised to assess a variety of classification models, including XGBoost, GLM, Decision Tree, and KNN. With an accuracy of 84.7%, the results show that KNN performed best, demonstrating its potency in forecasting sales patterns. Index Terms—Component, Data Analytics, Sales prediction, Machine learning, KNN, Decision tree, XGBoost, Generalized Linear Model, Dimensionality reduction.