Comparative Study Of Statistical Models For Customer Churn Classification

Customer churn prediction plays a vital role in helping businesses retain customers and minimize revenue loss in competitive markets. This study focuses on developing a predictive framework to identify customers who are likely to discontinue a service based on historical data. The dataset used in this project consists of customer demographic, behavioral, and financial attributes, which are preprocessed and transformed through feature engineering techniques to improve model performance. Multiple machine learning classification models are implemented and evaluated to determine their effectiveness in predicting churn. To address the issue of class imbalance, appropriate techniques are applied to ensure fair model training. The models are assessed using key performance metrics such as accuracy, precision, recall, F1-score, and ROC-AUC, providing a comprehensive comparison of their predictive capabilities. The analysis highlights the importance of factors such as customer tenure, service usage patterns, and billing characteristics in influencing churn behavior. The results demonstrate that machine learning models can effectively capture underlying patterns in customer data and provide reliable predictions. This study offers valuable insights into churn prediction and presents a data-driven approach that can support businesses in designing targeted customer retention strategies.