Machine Learning Based Customer Churn Prediction In Banking

Abstract

The number of service providers are being increased very rapidly in every business. In these days, there is no shortage of options for customers in the banking sector when choosing where to put their money. As a result, customer churn and engagement has become one of the top issues for most of the banks. In this paper, a method to predicts the customer churn in a Bank, using machine learning techniques, which is a branch of artificial intelligence is proposed. The research promotes the exploration of the likelihood of churn by analyzing customer behavior. The KNN, SVM, Decision Tree, and Random Forest classifiers are used in this study. Also, some feature selection methods have been done to find the more relevant features and to verify system performance. The experimentation was conducted on the churn modeling dataset from Kaggle. The results are compared to find an appropriate model with higher precision and predictability. As a result, the use of the Random Forest model after oversampling is better compared to other models in terms of accuracy.