Credit Card Service Churn Prediction by Machine Learning Models ☆

This paper presents a study on the application of basic machine learning models for churn customer classification. Churn prediction is an essential task in customer retention for businesses, and accurate identification of customers who are likely to churn can significantly impact the organization's revenue and customer satisfaction. In this study, we explore the performance of various machine learning models, including K-Nearest Neighbor, Random Forest, Adaboost and a deep learning model which is CNN-1D. We use the BankChurners dataset, then we predict the probability that customers abandoning bank services such as credit card services. We evaluate the models basing on various performance metrics such as accuracy, precision, recall, and F1-score. The result demonstrates the potential of basic machine learning models for churn customer classification and provides insights into the key factors contributing to customer churn.