Machine Learning in Bank Customer Churn Prediction: Improving Accuracy through Feature Engineering

Customer churn is a critical challenge in the banking sector, where retaining existing customers is more cost-effective than acquiring new ones. This study explores the role of feature engineering and model optimization in enhancing machine learning-based churn prediction. Using a dataset of bank customers, various preprocessing techniques of categorical encoding, feature scaling, and feature selection were applied. Model optimization was performed using hyperparameter tuning technique of Randomized Search CV to refine key parameters. Further, the models were trained with only the most influential features of customer complaints, number of products, age, account activity, and credit card ownership, resulting in improved computational efficiency and interpretability while maintaining strong predictive performance. Multiple machine learning models were evaluated, with XGBoost emerging as the most effective due to its ability to handle complex patterns and structured data efficiently. The findings provide actionable insights for banks to implement targeted retention strategies, ultimately reducing churn rates and improving customer engagement.