Abstract :

Customer Retention Is A Critical Challenge For Modern Banking Institutions, As Maintaining Existing Customers Is More Cost-effective Than Acquiring New Ones And Plays A Key Role In Long-term Profitability. Customer Churn, Which Refers To Customers Leaving A Bank’s Services, Can Lead To Significant Financial Losses If Not Addressed Proactively. Therefore, Predicting Customer Churn Has Become An Essential Task For Banks To Enhance Customer Satisfaction And Implement Effective Retention Strategies. This Project Focuses On Developing A Machine Learning-based System To Predict Customer Churn Using Various Demographic And Financial Attributes Of Bank Customers. The Model Analyzes Features Such As Credit Score, Age, Tenure, Account Balance, Number Of Products, Credit Card Ownership, Active Membership Status, And Estimated Salary To Determine Whether A Customer Is Likely To Leave The Bank. Multiple Machine Learning Algorithms, Including Logistic Regression, Random Forest, And Gradient Boosting, Were Implemented And Evaluated To Identify The Most Effective Model For Churn Prediction. Among These, The Random Forest Classifier Demonstrated Superior Performance Due To Its Ability To Capture Complex Relationships Within The Data And Reduce Overfitting Through Ensemble Learning Techniques.

Published:

06-4-2026

Issue:

Vol. 26 No. 4 (2026)

Page Nos:

1199-1206

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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