Advances in Economics, Management and Political Sciences

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Proceedings of the 2022 International Conference on Financial Technology and Business Analysis (ICFTBA 2022), Part 2

Series Vol. 6 , 27 April 2023


Open Access | Article

Development and Comparison of Regression Models Predicting Wine Quality using Excel - From a Profit Maximization Perspective

Wanling Xie * 1 , Shining Hu 2 , Jincheng Ji 3 , Qianqi Chen 4
1 Economics and Management School, Wuhan University, Wuhan, 430071, China
2 Ningbo University of Technology, Ningbo, 315211, China
3 College of Liberal Arts, University of Minnesota twin cities, Minneapolis,55455, United States
4 School of Science, University of Rensselaer Polytechnic Institute, Troy, 12180, US

* Author to whom correspondence should be addressed.

Advances in Economics, Management and Political Sciences, Vol. 6, 33-42
Published 27 April 2023. © 2023 The Author(s). Published by EWA Publishing
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Citation Wanling Xie, Shining Hu, Jincheng Ji, Qianqi Chen. Development and Comparison of Regression Models Predicting Wine Quality using Excel - From a Profit Maximization Perspective. AEMPS (2023) Vol. 6: 33-42. DOI: 10.54254/2754-1169/6/20220140.

Abstract

Wine reviewers and the general public usually determine the quality of the wine. There is a great deal of subjec-tivity in such reviews and differences in the reviewers' preferences, making it difficult for the winery to obtain favorable information. This paper will use Excel to build a related model of chemical substances in wine and wine quality. Both linear and logistic regression is used in this article to predict wine quality. In addition, differ-ent from past literature that indicates the quality of red wine based on chemical substances, this paper creatively constructs a profit model based on predicting wine quality. It thus helps wine sellers to make model selections. All these can help producers understand how to make good wine and get higher profits.

Keywords

linear regression, logistic regression, wine quality, profit maximization

References

1. Trei, Lisa, and Lisa Trei. Price Changes Way People Experience Wine, Study Finds. Stanford University, January 16, 2008. https://news.stanford.edu/news/2008/january16/wine-011608.html.

2. Horowitz I, Lockshin L. What Price Quality? An Investigation into the Prediction of Wine-quality Ratings. Journal of Wine Research. 2002; 13: 7-29

3. Antonio, Capurso. The Six Attributes of Quality in Wine. Wine And Other Stories. 24 Jan. 2020

4. Kwak, Young-Sik, Yoon-Jung Nam, Jae-Won Hong. Effect of Online Collective Intelligence in Wine Industry: Focus on Correlation between Wine Quality Ratings and on-Premise Prices

5. Badole, Mayur. Wine Quality Prediction Using Machine Learning: Predicting Wine Quality. Analytics Vidhya, 25 July 2022

6. Yogesh Gupta. Selection of important features and predicting wine quality using machine learning tech-niques. Procedia Computer Science. 2018; 125: 305-312

7. Niggl, Dennis. Predict Red Wine Quality. Kaggle

8. Tingwei, Zhou. Red Wine Quality Prediction through Active Learning. Journal of Physics: Conference Series 1966, no. 1 (2021): 012021.

9. Shaw, B., Suman, A.K., Chakraborty, B. (2020). Wine Quality Analysis Using Machine Learning. In: Mandal, J., Bhattacharya, D. (eds) Emerging Technology in Modelling and Graphics. Advances in Intelligent Systems and Computing, vol 937

10. Kothawade, Rohan Dilip. Wine Quality Prediction Model Using Machine Learning Techniques, n.d.

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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Volume Title
Proceedings of the 2022 International Conference on Financial Technology and Business Analysis (ICFTBA 2022), Part 2
ISBN (Print)
978-1-915371-23-2
ISBN (Online)
978-1-915371-24-9
Published Date
27 April 2023
Series
Advances in Economics, Management and Political Sciences
ISSN (Print)
2754-1169
ISSN (Online)
2754-1177
DOI
10.54254/2754-1169/6/20220140
Copyright
© 2023 The Author(s)
Open Access
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

Copyright © 2023 EWA Publishing. Unless Otherwise Stated