Credit card customers churn prediction by nine classifiers

Abstract

Recently, losing credit card customers has been particularly serious. Using the found data set from kaggle website, this paper wants to help the bank manager by predicting for them to identify the customers who are likely to leave, so they can approach them in advance to offer them better services and sway their decisions. Nine classifiers are used to carry out model training and evaluation and finally develop credit card customers churn prediction. AdaBoost, XGBoost, Naive Bayes, Decision Tree, Random Forest, K-Nearest Neighbors, Support Vector Classifier, and Logistic Regression are the nine classifiers. The result shows that the credit card customer churn model can be predicted by all machine learning models. Among them, the XGBoost model performs exceptionally well, with a training accuracy of 100%, a test accuracy of 97%, and the highest F1 score of 92%. So it can be concluded that this model can be applied to relevant datasets for prediction in order to assist banks in better retaining their existing customers.