Bank Churn Prediction Using Random Forest and Logistic Regression

DOI

10.2991/978-94-6463-546-1_2How to use a DOI?

Keywords

Random forest model (RFM); logistic regression model (LRM); bank churn; machine learning (ML)

Abstract

In the banking industry, customer churn is a growing problem. Solving this problem effectively and choosing the appropriate forecasting model is important. To avoid such problems and select an appropriate model, in this paper, random forest and logistic regression models are used to predict bank churn based on a specific data set. Different situations are set to evaluate the models. During prediction, the parameters of the model and variables are changed slightly for comparison. The accuracy, recall, precision, and stability of models are compared. The accuracy of random forest is about 86%, nearly 3 points higher than logistic regression. After removing the least correlative factor, the accuracy of the random forest remained almost unchanged, while logistic regression had a 4-point decline. Fluctuation brought by removing the least correlative variable is smaller in the random forest which means better stability. Though this study has shown a random forest’s better performance, removing the least correlative factor leads to a decline in both models. This is contradicted by the author’s hypothesis. Hence, further study with enough features will be a good way to compare more about these two models in bank churn prediction.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Shangxuan Du
PY  - 2024
DA  - 2024/10/27
TI  - Bank Churn Prediction Using Random Forest and Logistic Regression
BT  - Proceedings of the 2024 2nd International Conference on Finance, Trade and Business Management (FTBM 2024)
PB  - Atlantis Press
SP  - 4
EP  - 10
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6463-546-1_2
DO  - 10.2991/978-94-6463-546-1_2
ID  - Du2024
ER  -