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Prediction of Wine Quality Using Machine Learning Algorithms

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Document pages: 12 pages

Abstract: As a subfield of Artificial Intelligence (AI), Machine Learning (ML) aimsto understand the structure of the data and fit it into models, which later canbe used in unseen data to achieve the desired task. ML has been widely used invarious sectors such as in Businesses, Medicine, Astrophysics, and many otherscientific problems. Inspired by the success of ML in different sectors, here,we use it to predict the wine quality based on the various parameters. Amongvarious ML models, we compare the performance of Ridge Regression (RR), SupportVector Machine (SVM), Gradient Boosting Regressor (GBR), and multi-layer ArtificialNeural Network (ANN) to predict the wine quality. Multiple parameters thatdetermine the wine quality are analyzed. Our analysis shows that GBR surpasses allother models’ performance with MSE, R, and MAPE of 0.3741, 0.6057, and 0.0873respectively. This work demonstrates, howstatistical analysis can be used to identify the components that mainly controlthe wine quality prior to the production. This will help wine manufacturer tocontrol the quality prior to the wine production.

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