Data-Oriented Classification of Red Wine Quality Using Machine Learning

Abstract

This study examines the use of supervised machine learning to classify thequality level of red wine based on measurable physicochemical properties. The analysis isconducted using the winequality-red.csv dataset, which contains laboratory-basedmeasurements 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 winequality and to compare the classification results produced by different machine learningmodels. The research procedure involves initial data inspection, feature preparation,exploratory analysis, model training using Logistic Regression and Random Forest, andperformance assessment through accuracy, precision, recall, and F1-score indicators. Theresults show that the Random Forest classifier yields more consistent and reliableclassification outcomes than Logistic Regression. These findings suggest that machinelearning techniques can support objective quality evaluation processes in the food andbeverage industry.