ChurNet Deep Learning Enhanced Customer Churn Prediction

Authors: Ishwar Rajedra Bhalerao, Pradhuman Dilip Gunjal, Dipak Kailas Kadlag, Snehal Mohan Patel

Country: India

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Abstract: The Churn Prediction System is designed to help companies identify which customers are likely to stop using their services in the near future. This project uses Machine Learning and Deep Learning techniques to analyze customer behavior and predict churn patterns. The system allows an Admin to register, log in, and upload multiple company datasets that are created for this purpose. Once the datasets are uploaded, the system processes them using predictive models to determine which customers might leave or not reorder next month. The results are displayed on an interactive dashboard with visual insights through bar charts, pie charts, and growth graphs, helping companies easily understand
their churn trends. The system also provides a download option to export customer details (in Excel format) for those predicted to leave. Additionally, it generates feedback and improvement suggestions to help companies understand the reasons behind customer loss. Finally, as a customer retention strategy, the system automatically sends personalized email notifications (using a student’s mail ID) to at-risk customers offering special discounts — for example, a 25 offer — encouraging them to stay with the company. This project aims to help organizations reduce customer churn, improve loyalty, and enhance overall business growth through intelligent data-driven insights.

Keywords: Customer Churn Prediction, Machine Learning, Deep Learning, Data Analytics, Customer Retention Predictive Modeling.

Paper Id: 233049

Published On: 2026-04-13

Published In: Volume 14, Issue 2, March-April 2026

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