Subscriber Retention Intelligence System Using Machine Learning

1 G. Vamsi, 2A Vijaya Krishna

1 Assistant Professor, 2 MCA Final Semester, Master of Computer Applications, Sanketika Vidya Parishad Engineering College, Vishakhapatnam, Andhra Pradesh, India

gvkrishna2002@gmail.com, manikantapaidisetty2@gmail.com

Abstract: The Subscriber Retention Intelligence System Using ML is a web-based application designed to predict customer churn and support data-driven subscriber retention strategies using machine learning techniques. Early identification of customers who are likely to discontinue services enables organizations to implement proactive retention measures and improve customer satisfaction. The system is developed using the Flask framework and provides an interactive interface for business users and decision-makers. It analyzes various customer-related parameters such as demographic information, subscription details, service usage, contract type, payment method, tenure, and billing history. Multiple machine learning algorithms, including Random Forest, Decision Tree, K-Nearest Neighbors, Naive Bayes, and Logistic Regression, are implemented and compared. The model with the highest accuracy is automatically selected for prediction. The system also includes graphical visualizations to evaluate model performance and customer churn trends. It generates churn predictions along with confidence levels to assist organizations in making effective customer retention decisions. Overall, it provides a fast and datadriven approach for subscriber retention analysis, with potential for future enhancements using real-time customer data and advanced predictive analytics.

Keywords: Random Forest, Decision Tree, K-Nearest Neighbors, Naive Bayes, Logistic Regression, Data Mining, Classification Algorithms, Customer Behaviour Analysis.