International Journal of Scientific Research and Engineering Development

Customer churn is one of the major challenges faced by subscription-based businesses such as SaaS, telecom, and streaming platforms. Predicting customer churn helps companies identify users who are likely to stop using their services. This project presents a Customer Churn Prediction Framework using machine learning techniques. The system collects and analyzes customer behavioral and subscription data. Feature engineering methods are used to improve the quality of prediction. Multiple machine learning models are applied to achieve better accuracy and performance. The framework helps businesses take proactive retention actions for high-risk customers. It reduces customer loss and improves customer satisfaction. The proposed model also increases customer lifetime value and business revenue. Overall, the framework provides an efficient and reliable solution for customer retention management.

M.Vasuki, Sudharsanan R,"Customer Churn Prediction Framework for Subscription-Based Services Using Machine Learning" International Journal of Scientific Research and Engineering Development, V9(3): Page(2023-2028) May-June 2026. ISSN: 2581-7175. www.ijsred.com. Published by Scientific and Academic Research Publishing.