AI-based Customer Churn Prediction System using XGBoost for Early Retention and Revenue Optimization

Abstract

The study aims to develop an AI-based customer churn prediction system using the XGBoost algorithm to improve prediction accuracy and enable early identification of customers who are likely to leave a service. A total of 2000 customer records were used for the analysis. Two categories were considered for comparison; Group 1 employed conventional machine learning models such as Decision Tree and Logistic Regression, while Group 2 implemented the XG Boost algorithm on the same dataset to capture complex customer behavior patterns. Data preprocessing steps including data cleaning, encoding, feature scaling, and class imbalance handling were applied to both groups before model training and testing. Performance evaluation was carried out using accuracy, precision, recall, and F1-score metrics. The XGBoost model achieved superior results with an accuracy of 93.2%, precision of 92.6%, recall of 94.1%, and F1-score of 93.3 when compared to traditional machine learning methods. The results demonstrate that the proposed XGBoost-based system is highly effective in identifying high-risk churn customers at an early stage. Hence, the proposed model provides better predictive performance and supports improved decision-making for customer retention strategies and business growth.