Volume 2 - Issue 2, March - April 2026

The Customer Churn Prediction in Telecom Industry aims to predict whether a telecom customer will stay, join, or churn using machine learning techniques. Data analysis and model training were performed using Jupyter Notebook, while Django was used to build a web interface for churn visualization and prediction. Various algorithms such as Random Forest, Logistic Regression, and MLP Neural Network were evaluated, with Random Forest achieving the highest accuracy of 99%. The system includes data preprocessing, visualization of customer behavior, and an interactive web-based prediction tool, enabling telecom providers to take timely action for customer retention.

Singireddy Keerthi, Matta Anugna, Shaik Jansaida, Sangala Rishi,"Customer Churn Prediction in Telecom Industry" International Journal of Advanced Multidisciplinary Research and Educational Development, V2(2): Page(1236-1239) Mar-Apr 2026. ISSN: 3107-6513. www.ijamred.com. Published by Scientific and Academic Research Publishing.