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

Research Article
Open access

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    
  • *corresponding author wanling.xie@foxmail.com
Published on 27 April 2023 | https://doi.org/10.54254/2754-1169/6/20220140
AEMPS Vol.6
ISSN (Print): 2754-1177
ISSN (Online): 2754-1169
ISBN (Print): 978-1-915371-23-2
ISBN (Online): 978-1-915371-24-9

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

Xie,W.;Hu,S.;Ji,J.;Chen,Q. (2023). Development and Comparison of Regression Models Predicting Wine Quality using Excel - From a Profit Maximization Perspective. Advances in Economics, Management and Political Sciences,6,33-42.
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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.

Cite this article

Xie,W.;Hu,S.;Ji,J.;Chen,Q. (2023). Development and Comparison of Regression Models Predicting Wine Quality using Excel - From a Profit Maximization Perspective. Advances in Economics, Management and Political Sciences,6,33-42.

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

ISBN：978-1-915371-23-2(Print) / 978-1-915371-24-9(Online)
Editor：Javier Cifuentes-Faura, Canh Thien Dang
Conference website: http://www.icftba.org
Conference date: 16 December 2022
Series: Advances in Economics, Management and Political Sciences
Volume number: Vol.6
ISSN：2754-1169(Print) / 2754-1177(Online)

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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.

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