Prediction of Customer Churn for Abc Multistate Bank Using Machine Learning Algorithms

Customer churn is defined as the tendency of customers to cease doing business with a company in a given period. ABC Multistate Bank faces the challenges to hold clients. The purpose of this study is to apply machine learning algorithms to develop the most effective model for predicting bank customer churn. In this study, six supervised machine learning methods, K-Nearest Neighbors, Support Vector Machine, Naïve Bayes, Decision Tree, Random Forest, and Extreme Gradient Boosting (XGBoost), are applied to the churn prediction model using Bank Customer Data of ABC Multistate Bank obtained from Kaggle. The results showed that XGBoost outperformed the other six classifiers, with an accuracy rate of 84.76%, an F1 score of 56.95%, and a ROC curve graph of 71.64%. The bank may use XGBoost model to accurately identify customers who are at risk of leaving, concentrate their efforts on them, and possibly make a profit. Future research should focus on various machine learning approaches for determining the most accurate models for bank customer churn datasets