Predictive Modeling for Customer Churn

Abstract

Customer churn is a major concern for the banking industry, where retaining existing customers is often more profitable than acquiring new ones. With increasing competition from digital banks and fintech startups, it has become vital for traditional banks to proactively identify customers who are likely to leave. This project focuses on developing a predictive modeling system to analyze and forecast customer churn using real-world banking data. By understanding the key indicators of churn, banks can implement targeted retention strategies and improve customer satisfaction. To build an accurate and robust churn prediction system, over seven machine learning models were implemented and evaluated. These include Logistic Regression, Decision Tree, Random Forest, Gradient Boosting, XGBoost, Support Vector Machine (SVM), and K-Nearest Neighbors (KNN). Each model was trained and tested using cross-validation and evaluated based on key performance metrics such as accuracy, precision, recall, F1-score, and ROC-AUC. Among these models, ensemble techniques like Random Forest and XGBoost showed superior performance, indicating their strength in capturing complex patterns in customer behaviour.