Data-Driven Churn Prediction in Telecom: A Comparative Study of Machine Learning Models

Abstract

Customer churn prediction is a critical challenge in the telecommunications sector, as companies strive to retain their customer base amidst increasing competition. Effective churn prediction models can help telecom operators identify customers likely to leave and implement targeted retention strategies. This study explores various machine learning (ML) models, including Logistic Regression, Decision Trees, Random Forest, Support Vector Machines (SVM), and Neural Networks, for predicting telecom customer churn. The dataset used in this study is pre-processed through data cleaning, feature selection, and balancing techniques to enhance model efficiency. Performance metrics such as accuracy, precision, recall, and F1-score are used to evaluate the models. The results indicate that ensemble learning models, particularly Random Forest and Neural Networks, achieve the highest prediction accuracy. The study also highlights the importance of feature selection in improving model performance. Findings from this research can assist telecom providers in formulating data-driven strategies to reduce customer churn and enhance customer satisfaction. Future work will explore deep learning approaches and real-time predictive analytics for improving churn prediction further.