A Novel Data Handling Technique for Wine Quality Analysis using ML Techniques

Abstract

In this era, wine is a regularly redeemed beverage, and industries are seeing increased sales due to product quality certification. This research aims to identify key wine characteristics that contribute to significant outcomes through the application of machine learning classification techniques, specifically Random Forest (RF), Decision Tree (DT) and Multi-Layer Perceptron (MLP), using white and red wine datasets sourced from the UCI Machine Learning repository. This research aims to develop a multiclass classification model using machine learning (ML) to accurately assess the quality of a balanced wine dataset comprising both white and red wines. The dataset is balanced by random oversampling to avoid biases in ML techniques for the majority class obtained by the imbalanced multiclass dataset (IMD). Furthermore, we apply a Yeo-Jhonson transformation (YJT) to the datasets to reduce skewness. We validated the ML algorithm's result using a 10-fold cross-validation approach and found that RF yielded the highest overall accuracy of 93.14%, within a range of 75% to 94%. We have observed that the proposed approach for balanced white wine dataset accuracy is 93.14% using RF, 90.83% using DT, and 75.49% using MLP. Similarly, for the balanced red wine dataset, accuracy is 89.36% using RF, 85.36% using DT, and 78.00% using MLP. The proposed approach improves accuracy by RF 23%, DT 30%, and MLP 21% for the white wine dataset. Similarly, accuracy by RF remained the same, DT 10%, and MLP 22% is improved in the red wine dataset. Additionally, the proposed approach's RF, DT, and MLP yield mean squared error (MSE) values of 0.080, 0.151, and 0.443 for the white wine dataset and 0.143, 0.221, and 0.396 for the red wine dataset. We also observed that the RF accuracy for the proposed technique is the highest among all specified classifiers for white and red wine datasets, respectively.