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Journal article Open Access

A Study and Analysis of Machine Learning Techniques in Predicting Wine Quality

Mohit Gupta; Vanmathi C

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<oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
  <dc:contributor>Blue Eyes Intelligence Engineering and Sciences Publication(BEIESP)</dc:contributor>
  <dc:creator>Mohit Gupta</dc:creator>
  <dc:creator>Vanmathi C</dc:creator>
  <dc:description>In today’s trend consumers are very much concern about the quality of the product in turn, Industries are all working on various methodologies to ensure the high quality in their products. Most of consumers judge the quality of the product based on the certification obtained for the product. In Earlier days, the quality is measured and validated only through human experts. Nowadays most of the validation tasks are automated through software and this ease the burden of human experts by assisting with them in predicting the quality of the product and that leads to greater a reduction of time spent. Wine consumption has increased rapidly over the last few decades, not only for recreational purposes but also due of its inherent health benefits especially to human heart. This chapter demonstrates the usage of various machine learning techniques in predicting the quality of wine and results are validated through various quantitative metrics. Moreover the contribution of various independent variables facilitating the final outcome is precisely portrayed.</dc:description>
  <dc:source>International Journal of Recent Technology and Engineering (IJRTE) 10(1) 314-321</dc:source>
  <dc:subject>Machine Learning, KNN, Random Forest, SVM, J48, Wine Quality.</dc:subject>
  <dc:subject>Retrieval Number</dc:subject>
  <dc:title>A Study and Analysis of Machine Learning  Techniques in Predicting Wine Quality</dc:title>
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