Predicting Telecom Customer Churn Utilising Machine Learning Models

In the present highly competitive telecom environment, loss of customers is a significant problem, jeopardising profitability and sustainable development. This paper investigates a data-driven methodology for churn prediction through a comparative examination of four machine learning models; FT-Transformer, TabNet, Random Forest, and XGBoost, utilising a real-world telecom dataset. The Telco Customer Churn dataset from Kaggle, initially supplied by International Business Machines (IBM) for analytical applications, is employed in this study. Adopting a comprehensive pipeline that included rigorous preprocessing, stratified cross-validation, and performance assessment through several measures, FT-Transformer has been identified as the most efficient model, with an accuracy of 84.49% and an AUC score of 0.9242. TabNet demonstrated competitive performance, surpassing conventional ensemble techniques like Random Forest and XGBoost. The findings indicate that deep learning models, especially transformer-based architectures, excel at modeling intricate patterns in customer behaviour, providing telecom companies with a robust mechanism to implement proactive retention measures.