Bank Customer Churn Based on Different Models, Oversampling, and Encoding Methods
DOI:
https://doi.org/10.54691/bcpbm.v26i.2030Keywords:
Bank Churn Prediction; Machine Learning; Logistic Regression; Gradient boosting; Neural Network.Abstract
Customer churn prediction (CCP) is one of the cornerstones of Customer Relationship Management (CRM), in which one seeks to forecast whether or not a customer will quit the organization. Currently, plenty of algorithmic focuses on CCP. To fill the gap in the current study, this paper builds different models to predict bank user churn based on data from Kaggle. Specifically, we investigate the difference between models with and without oversampling, as well as discuss the difference between models under different coding methods. According to the results, ‘smote’ does not necessarily improve the performance accuracy, one hot encoding is more effective than target encoding. Finally, after all aspects of comparison, the logistic regression model is more reliable in the future analysis of customer churn of commercial banks. These results offer a guideline for future bank customer churn prediction.
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