Efficient Wine Quality Prediction and Classification Using LightGBM Model

Abstract

This study develops an efficient machine learning model using the Light Gradient Boosting Machine (LightGBM) algorithm to predict and classify wine quality based on physicochemical properties. The dataset used in this research consists of multiple chemical attributes, including alcohol content, acidity levels, sulphates, and phenolic compounds, which collectively influence wine quality. The preprocessing stage involved data cleaning, outlier treatment, feature scaling, and handling class imbalance using the Synthetic Minority Oversampling Technique (SMOTE). Feature selection was conducted using mutual information and recursive feature elimination to identify the most influential predictors. The optimized LightGBM model achieved superior performance with 100% accuracy, precision, recall, and F1-score across all quality classes, outperforming traditional algorithms such as Random Forest, SVM, and Logistic Regression. Feature importance analysis revealed that Proline, Flavanoids, and Magnesium were the most significant attributes contributing to wine classification. These findings demonstrate that LightGBM is a robust and scalable solution for wine quality prediction, offering an efficient, data-driven alternative to traditional sensory evaluations. The proposed model can enhance quality control processes in the wine industry by providing accurate and interpretable insights into the chemical determinants of wine quality.