The Impact of Customer Demographics on Churn Rates in the Telecommunications Industry.

Abstract

In this thesis, I aim to develop a machine learning model for predicting customer churn in the telecom industry, using data from major telecom operators in India, including Airtel, Reliance Jio, Vodafone, and BSNL. The dataset contains 243,553 customer records with demographic, usage, and geographic features, along with a binary variable indicating whether the customer has churned. The goal of this project is to accurately predict customer churn, providing telecom companies with valuable insights to retain at-risk customers and optimize marketing efforts.I explore several machines learning models, including Logistic Regression, Random Forest, and Gradient Boosting. After preprocessing the data, addressing missing values, encoding categorical variables, and handling class imbalance using SMOTE, I evaluate each model’s performance using accuracy, ROC-AUC score, and classification metrics such as precision, recall, and F1-score. Among the models tested, Gradient Boosting outperforms others, achieving a high accuracy of 95.2% and a robust ROC-AUC score of 0.9251. This model shows a balanced trade-off between precision and recall, especially for the minority churn class. The findings demonstrate that Gradient Boosting is a highly effective tool for churn prediction in the telecom sector, capable of providing actionable insights for customer retention strategies. The results also highlight the importance of feature engineering and data preprocessing in improving model performance.This research offers a solid foundation for applying machine learning to real-world business problems, particularly in customer retention within the telecom industry.