International Journal on Science and Technology

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Analyzing the Effectiveness of Ensemble and Classical Machine Learning Techniques in Wine Quality Prediction

Author(s) Dr. Mukesh Rani, Dr. Sunil Kumar
Country India
Abstract Wine quality prediction is of high interest to food chemists owing to its significance for quality assurance and consumer satisfaction. Conventional tasting techniques used to assess wine quality today are unsophisticated, expensive and time-consuming with requiring semi-skilled personnel for expert sensory analysis. Therefore, the present study aims to provide a comparative study of ensemble and classical machine learning techniques for the prediction of wine quality from 12 physicochemical properties derived from wine samples. The study includes classical machine learning models such as Logistic Regression, Decision Tree, K-Nearest Neighbors, and Support Vector Machine, along with bagging-based ensemble models including Random Forest and Extra Trees, and boosting-based ensemble models such as XGBoost, LightGBM, and CatBoost. In this paper, we have also used some preprocessing techniques such as KNN imputation, power transformation, feature standardization and SMOTE to evaluate these nine machine learning algorithms. The experimental results reveal that the Random Forest classifier performed best, with 82.73% accuracy and an ROC-AUC score of 0.8869. In this paper, we have also made efforts to measure the computational performance of these learning techniques, such as memory usage, CPU usage, execution time and time complexity. The results demonstrate that ensemble methods effectively predict wine quality and support automated quality assessment in the wine industry.
Keywords Artificial Intelligence, Wine Quality Prediction, Machine Learning, Logistic Regression, Decision Tree, K-Nearest Neighbors, and Support Vector Machine, Random Forest, Extra Trees, XGBoost, LightGBM, CatBoost etc.
Field Chemistry
Published In Volume 17, Issue 2, April-June 2026
Published On 2026-06-07
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