Forecasting red wine quality: A comparative examination of machine learning approaches

Bohui Zhan

doi:10.54254/2755-2721/32/20230184

Research Article

Open access

Published on 31 January 2024

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Zhan,B. (2024). Forecasting red wine quality: A comparative examination of machine learning approaches. Applied and Computational Engineering,32,58-65.

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Forecasting red wine quality: A comparative examination of machine learning approaches

Bohui Zhan *^,1,

¹ Art and Science, University of Roche, Rochester, 14627, United State

* Author to whom correspondence should be addressed.

https://doi.org/10.54254/2755-2721/32/20230184

Abstract

This research explores the forecast of red wine quality utilizing machine learning algorithms, with a particular emphasis on the impact of alcohol content, sulphates, total sulfur dioxide, and citric acid. The original dataset, comprised of Portuguese "Vinho Verde" red wine data from 2009, was bifurcated into binary classes to delineate low-quality (ratings 1-5) and high-quality (ratings 6-10) wines. A heatmap verified the potent correlation between the chosen variables and wine quality, paving the way for their inclusion in our analysis. Four machine learning techniques were employed: Logistic Regression, K-Nearest Neighbors (KNN), Decision Tree, and Naive Bayes. Each technique was trained and assessed through resulting metrics and graphical visualizations, with diverse proportions of data assigned for training and testing. Among these techniques, Logistic Regression achieved an accuracy score of 72.08%, while KNN slightly surpassed it with an accuracy rate of 74%. The Decision Tree technique rendered the peak accuracy of 74.7%, while Naive Bayes underperformed with a score of 60.2%. From a comparative viewpoint, the Decision Tree technique exhibited superior performance, positioning it as a viable instrument for future predictions of wine quality. The capacity to predict wine quality carries significant implications for wine production, marketing, customer satisfaction, and quality control. It enables the identification of factors contributing to high-quality wine, optimization of production processes, refinement of marketing strategies, enhancement of customer service, and potential early identification of substandard wines before reaching consumers, thereby safeguarding the brand reputation of wineries.

Keywords

red wine quality, Logistic Regression, decision tree, Naive Bayes, machine learning

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References

[1]. Feher J, Lenguello G and Lugasi A. The cultural history of wine—Theoretical background to wine therapy. Cent. Eur. J. Med. 2007,2, 379–391.

[2]. Sirén H, Sirén K and Sirén J. Evaluation of organic and inorganic compounds levels of red wines processed from Pinot Noir grapes.Anal. Chem. Res. 2015, 3, 26–36.

[3]. Gupta Y. Selection of important features and predicting wine quality using machine learning techniques. Procedia Computer Science. 2018; 125:305-312.

[4]. Grewal P, Sharma P, Rathee A and Gupta S. COMPARATIVE ANALYSIS OF MACHINE LEARNING MODELS. EPRA International Journal of Research and Development (IJRD). 2022;7(6):62–75.

[5]. Reimann C, Filzmoser P, Hron, K, Kynčlová, P and Garrett, R G. A new method for correlation analysis of compositional (environmental) data – a worked example. Sci. Total Environ. 2017, 607–608, 965-971.

[6]. Al-Ghamdi A S, Using logistic regression to estimate the influence of accident factors on accident severity. Accid. Anal. Prev. 2002, 34(6), 729-741.

[7]. Bisong E. Introduction to Scikit-learn. In: Building Machine Learning and Deep Learning Models on Google Cloud Platform. Apress, Berkeley, CA, 2019.

[8]. Susmaga R. Confusion Matrix Visualization. In: Kłopotek, M.A., Wierzchoń, S.T., Trojanowski, K. (eds) Intelligent Information Processing and Web Mining. Advances in Soft Computing, vol 25. Springer, Berlin, Heidelberg, 2004

[9]. Guo G, Wang H, Bell D, Bi Y and Greer K. KNN Model-Based Approach in Classification. In: Meersman, R., Tari, Z., Schmidt, D.C. (eds) On The Move to Meaningful Internet Systems 2003: CoopIS, DOA, and ODBASE. OTM 2003. Lecture Notes in Computer Science, vol 2888. Springer, Berlin, Heidelberg, 2003.

[10]. Song Y Y, Lu Y. Decision tree methods: applications for classification and prediction. Shanghai Arch Psychiatry, vol 27, no. 2, 2015, pp.130-5.

[11]. Apté C, Weiss S. Data mining with decision trees and decision rules. Future Generation Computer Systems, vol 13, issues 2–3, 1997, pp. 197-210.

[12]. Rish I. An empirical study of the naive Bayes classifier. In IJCAI 2001 workshop on empirical methods in artificial intelligence, vol. 3, no. 22, 2001, pp. 41-46.

Cite this article

Zhan,B. (2024). Forecasting red wine quality: A comparative examination of machine learning approaches. Applied and Computational Engineering,32,58-65.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 2023 International Conference on Machine Learning and Automation

Conference website: https://2023.confmla.org/

ISBN：978-1-83558-289-3(Print) / 978-1-83558-290-9(Online)

Conference date: 18 October 2023

Editor：Mustafa İSTANBULLU

Series: Applied and Computational Engineering

Volume number: Vol.32

ISSN：2755-2721(Print) / 2755-273X(Online)

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