Bank Customer Churn and Extra Benefits Prediction Using Machine Learning Model

Abstract

Bank customer churn, which results in lost revenue and decreased customer loyalty, is a major concern for banks. Retention tactics that work and precise churn prediction are crucial for resolving this problem. In order to predict customer churn and suggest additional benefits, this study presents the Extra Benefit Classifier, a cutting-edge machine learning model that combines the best features of the Random Forest Classifier and Extra Tree Classifier. A dataset with customer data, including demographics, transaction history, and interaction details, is used to train the model. Results from the experiment show how well the Extra Benefit Classifier predicts churn and suggests tailored benefits. The Extra Benefit Classifier performs better than conventional models in terms of efficiency and accuracy. This study offers insights and advances the field of customer churn prediction. This study contributes to the field of customer churn prediction and provides insights for developing proactive customer retention strategies in the banking sector.

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