TBMOR Customer Churn Predication using XGBoost Classifier

Abstract

This comprehensive study delves into the efficacy of cutting-edge machine learning models in predicting customer churn, a critical challenge faced by businesses across various sectors including Telecom, Banking, Medical, and Online Retail. By conducting a detailed comparative analysis of several predictive models, the research highlights the outstanding performance of the Proposed XGBoost Classifier. This advanced model consistently outshines its counterparts across a wide array of performance metrics: it achieves an impressive accuracy rate of up to 95%, precision as high as 99%, recall equally remarkable at 99%, F1-score peaking at 98%, and an AUC score of 96%. These figures starkly contrast with the performance metrics of traditional models such as Logistic Regression and Decision Trees, which, while useful for baseline comparisons, exhibit a broad performance spectrum with scores generally oscillating between 70% and 90% across the evaluated metrics. The study's findings reveal a clear superiority of ensemble methods, particularly the XGBoost algorithm, in modeling the complex dynamics and nuanced patterns inherent in customer churn data. This superiority is attributed to XGBoost's robustness in handling diverse data types and its proficiency in capturing intricate interactions within the data, thereby providing a more accurate and nuanced prediction of churn. Moreover, the variability in the performance of traditional models across different datasets underscores the critical importance of model selection and customization according to specific dataset characteristics. It also highlights the necessity of advanced machine learning techniques that can adapt to and efficiently process the unique challenges presented by each dataset.