About

Customer churn is one of the biggest challenges faced by subscription-based and service-oriented businesses. Acquiring new customers is significantly more expensive than retaining existing ones, making early identification of churn risk a critical business need.

This project focuses on building a Customer Churn Prediction System that uses historical customer data to identify users who are likely to discontinue using a product or service. The system analyzes behavioral, transactional, and demographic features such as usage frequency, subscription duration, payment patterns, and customer support interactions to predict churn probability.

A machine learning model is trained to classify customers as “churn” or “non-churn”, enabling businesses to proactively take retention actions like targeted offers, discounts, or personalized engagement before customers leave.

The solution is validated using standard machine learning metrics such as accuracy, precision, recall, and ROC-AUC, ensuring reliable predictive performance. Beyond technical validation, the system demonstrates real business value through Business KPIs like reduced churn rate, improved customer retention, and potential revenue savings.

Finally, the trained model is deployed on Zerve as a production-ready API, allowing real-time churn predictions for new or existing customers. This enables seamless integration with business workflows and supports data-driven decision-making at scale.