Feature analysis and model comparison of logistic regression and decision tree for customer churn prediction

Ziqi Wen

doi:10.54254/2755-2721/20/20231073

Research Article

Open access

Published on 23 October 2023

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Wen,Z. (2023). Feature analysis and model comparison of logistic regression and decision tree for customer churn prediction. Applied and Computational Engineering,20,55-61.

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Feature analysis and model comparison of logistic regression and decision tree for customer churn prediction

Ziqi Wen *^,1,

¹ Southwestern University of Finance and Economics

* Author to whom correspondence should be addressed.

https://doi.org/10.54254/2755-2721/20/20231073

Abstract

Customer churn has long been a concern for companies because it not only reduces the company's profit in the short term, but is also extremely detrimental to the company's growth in the long term. This paper focuses on the analysis of customer churn in banks by using two machine learning methods, namely logistic regression and decision tree, to predict the churn rate of customers and analyze the decision tree results based on the premise that decision trees are more accurate in prediction and do not have a large prediction bias for a certain group as logistic regression does. The results show that age, estimated salary and the number of products are important factors when predicting and customer groups with some specific characteristics will show a higher departure rate. To address this situation, this paper recommends that bankers continuously optimize their business systems and focus on user groups with high churn rates.

Keywords

machine learning, decision tree, logistic regression, customer churn prediction

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References

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Cite this article

Wen,Z. (2023). Feature analysis and model comparison of logistic regression and decision tree for customer churn prediction. Applied and Computational Engineering,20,55-61.

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 5th International Conference on Computing and Data Science

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

ISBN：978-1-83558-031-8(Print) / 978-1-83558-032-5(Online)

Conference date: 14 July 2023

Editor：Roman Bauer, Marwan Omar, Alan Wang

Series: Applied and Computational Engineering

Volume number: Vol.20

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

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