Machine Learning based Prediction of Customer Churning in Banking Sector

Abstract

The term “customer churn” refers to the process of losing customers over time in the commercial and financial worlds. Businesses are more prepared to take preventative steps against customer turnover when they are aware of which of their customers are most likely to defect. The bank will gain by knowing which customers are most likely to switch banks in the near future both practically and theoretically. This article offers a technique for identifying which banking clients are most likely to move banks by using algorithms created for machine learning. This article shows how, given sufficient customer data such as age, location, gender, credit card information, balance, etc., machine learning models such as Logistic Regression (LR), Naive Bayes' (NB)can accurately predict which customers are most likely to leave the bank in the future. Additionally, this article illustrates how machine learning models like Logistic Regression (LR), Naive Bayes (NB), can accurately predict what customers are most likely toFinally, this research analysisshows that NB is better than LR.