A Comparison of Machine Learning Models for the Quality of Red Wine

The objective of this study is to enhance the process of wine quality assessment by developing a machinelearning model to predict the quality of red wine based on its physicochemical properties. With the use of a dataset that includes characteristics like alcohol content, pH, acidity, and sulfur dioxide levels, the project uses sophisticated classification algorithms including Random Forest, XG Boost, and Cat Boost. Grid Search CV and Recursive Feature Elimination (RFE) will be applied to feature selection in order to improve the performance of the model. The effectiveness of the model will be evaluated using visual aids like confusion matrices and ROC curves in addition to metrics like accuracy, precision, recall, and F1 score. This strategy seeks to provide an accurate, economical, and effective quality control solution for wine production by automating the prediction process. This will lessen the need for conventional testing techniques and guarantee higher consistency in the quality of the final product.