Machine Learning-Based Customer Churn Prediction and Retention System for Indian Teleco...

Objective: To develop and deploy a predictive analytics solution that identifies high-risk customers likely to churn and recommends personalized retention strategies for telecom providers in India.

Customer churn poses a significant challenge for Indian telecom operators, impacting revenue and operational efficiency. With aggressive competition and price wars, retaining customers has become critical, affecting millions of subscribers and business profitability.

The solution utilizes machine learning on telecom customer data to predict churn probability and generates actionable retention recommendations. The approach combines supervised learning models, feature engineering from call/data/SMS records, and integrates a dashboard for real-time monitoring and intervention.

Key deliverables include a deployed web dashboard, Python-based ML models (Random Forest/XGBoost/Neural Networks), integration with real or simulated telecom datasets, automated report generation, and a working prototype that demonstrates prediction accuracy and retention strategy effectiveness before an examiner panel.

This project enables scalable churn management, improving customer loyalty and reducing revenue loss. The solution can be adapted by Indian telecom operators for large-scale deployment, benefiting both industry and society through improved service and data-driven decision-making.