A Study and Analysis of Machine Learning Techniques in Predicting Wine Quality
Mohit Gupta1, Vanmathi C2
1Mohit Gupta*, School of Information Technology and Engineering Vellore Institute of Technology, Vellore (Tamil Nadu), India.
2Vanmathi C, School of Information Technology and Engineering Vellore Institute of Technology, Vellore (Tamil Nadu), India.
Manuscript received on May 12, 2021. | Revised Manuscript received on May 31, 2021. | Manuscript published on May 30, 2021. | PP: 314-319 | Volume-10 Issue-1, May 2021. | Retrieval Number: 100.1/ijrte.A58540510121 | DOI: 10.35940/ijrte.A5854.0510121
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: 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.
Keywords: Machine Learning, KNN, Random Forest, SVM, J48, Wine Quality.