Towards Predicting the Quality of Red Wine Using Novel Machine Learning Methods for Classification, Data Visualization and Analysis

Abstract

There is a growing concern among consumers and the wine industry regarding the quality of wine. Traditionally, wine experts determined its quality through tasting, which was time-consuming. Therefore, there is a need to predict wine quality based on specific key features to streamline these tasks. Technological developments like machine learning (ML) approaches have replaced human assessments with computational methods. However, some of these methods have faced criticism due to their low accuracy and lack of interpretability for humans. In this paper, a stacking ensemble method is introduced and demonstrates superior predictive performance when compared to other classification techniques like Logistic Regression (LR), Decision Trees (DT), Gradient Boosting (GB), Adaptive Boosting (AdaBoost), and Random Forest (RF). This evaluation is based on classification metrics such as accuracy, precision, recall, and F1-Score, all under the same conditions. Additionally, outlier detection algorithms were employed to identify exceptional or subpar wines, though their results did not match the accuracy of classification approaches. Lastly, a feature analysis study was conducted to assess the significance of each feature in the model's performance.

Received: 3 November 2023 | Revised: 21 February 2024 | Accepted: 28 April 2024

Conflicts of Interest

The authors declare that they have no conflicts of interest in this work.

Data Availability Statement

The dataset utilized to support the findings of this study originates from research conducted by Cortez et al. [10] and is accessible for download from the UCI machine learning repository at: https://archive.ics.uci.edu/ml/datasets/wine+quality.  The technical details, code, and data can be accessed by visiting the link:
https://github.com/jovialniyo93/red_wine_quality_prediction.