Telecom Customer Churn Prediction and Retention System Using Machine Learning

Abstract

Customer churn prediction is an important analytics problem in the telecom sector because losing an existing subscriber can reduce recurring revenue and increase the cost of customer acquisition. This paper presents a machine learning-based framework for identifying customers who are likely to discontinue telecom services. The workflow covers data cleaning, duplicate removal, missing-value treatment, categorical encoding, numerical scaling, class-imbalance handling, feature selection, model training, and evaluation on an unseen test set. Five classifiers are considered: Logistic Regression, K-Nearest Neighbor, Support Vector Machine, Random Forest, and gradient boosting using an XGBoost-style model. Special attention is given to telecom variables such as contract type, tenure, monthly charges, technical support, payment method, internet service, online security, and billing behavior. Rather than relying only on accuracy, the framework evaluates precision, recall, F1-score, and confusion matrices so that false alarms and missed churners can be interpreted from a business perspective. The study is structured as an original methodology and implementation framework using a publicly available telecom churn dataset. Numerical results, values, and feature rankings are intentionally left for completion after the actual experiment is executed, avoiding unsupported performance claims. The framework provides a practical basis for early intervention and customer-retention decision support while allowing future integration of explainable artificial intelligence and time-dependent customer behavior.