Wine Quality Detection Using Machine Learning: A Comparative Analysis of Classification Algorithms

The present work describes a fully developed machine-learning based approach for the prediction of wine quality from its physicochemical parameters. Utilizing the UCI Wine Quality Dataset containing 6,497 observations of Portuguese “Vinho Verde” wine, we apply and contrast various classification techniques such as Random Forest, Support Vector Machine (SVM), XGBoost, K-Nearest Neighbours, and Gradient Boosting. The study indicates that Random Forest is able to provide the best accuracy of 89.2% in classifying wine quality, and the most important determinants were alcohol content and volatile acidity. By using extensive feature analysis and model building, we propose the possible relationships between chemical composition and the quality of wine, which may help develop quality measurement devices for wine manufacturing. These results extend the existing library of knowledge based on machine learning and other computational methods in winemaking and suggest that there is a large potential for machine learning to effectively supplement traditional means of wine assessment.