journal-of-information-and-optimization-sciences Journal of Information and Optimization Sciences 2169-0103 0252-2667 Taru Publications 10.47974/JIOS-1599 Telecom customer churn prediction model : Analysis of machine learning techniques for churn prediction and factor identification in telecom sector Pareek Anshul Department of Electronics and Communication Engineering, Maharaja Surajmal Institute of Technology, New Delhi, India Poonam Department of Electronics and Communication Engineering, Maharaja Surajmal Institute of Technology, New Delhi, India Arora Shaifali Madan Department of Electronics and Communication Engineering, Maharaja Surajmal Institute of Technology, New Delhi, India Gupta Nidhi Department of Electrical and Electronics Engineering, Maharaja Surajmal Institute of Technology, New Delhi, India 45 2 613 630 30 03 2024

This research project leverages exploratory data analysis (EDA) on a telecom company’s customer data to predict user churn. Utilizing Python and its libraries, including Pandas, NumPy, Matplotlib, and Scikit-Learn, it identifies key parameters crucial for accurate predictions. The results and predictions are visualized using the Flask framework and Power BI analytics tool. In the highly competitive telecommunications sector, customer churn poses a significant challenge, with an annual churn rate of 15-25%. This study addresses the escalating phenomenon of consumers freely switching between providers, leading to financial losses for businesses. By employing machine learning models, it aims to forecast potential churners, enabling companies to focus targeted retention efforts and mitigate losses. The tool’s efficacy lies in its ability to analyze patterns among churned users, offering a solution to a pressing issue in the telecom industry. Beyond its immediate application, this tool can be extended to other industries, providing valuable insights for customer retention strategies.

 Churn prediction Retention Telecom CRM Machine learning Imbalance data Upsampling Customer churn Random forest model Decision tree model EDA access open retracted no