Data-Oriented Classification of Red Wine Quality Using Machine Learning

Abstract

This study examines the use of supervised machine learning to classify the quality level of red wine based on measurable physicochemical properties. The analysis is conducted using the winequality-red.csv dataset, which contains laboratory-based measurements such as acidity components, alcohol percentage, and sulfur dioxide levels. The primary goal of this research is to explore the contribution of these attributes to wine quality and to compare the classification results produced by different machine learning models. The research procedure involves initial data inspection, feature preparation, exploratory analysis, model training using Logistic Regression and Random Forest, and performance assessment through accuracy, precision, recall, and F1-score indicators. The results show that the Random Forest classifier yields more consistent and reliable classification outcomes than Logistic Regression. These findings suggest that machine learning techniques can support objective quality evaluation processes in the food and beverage industry.