Customer Churn Prediction Using Machine Learning Techniques

Authors: Ashok Kumar Verma, Krishna, Amar Kumar Yadav, Ayush Chaurasiya, Sanjeev Kumar Pathak

Abstract: Customer churn is one of the major challenges faced by organizations, especially in competitive industries such as telecommunications, banking, and e-commerce. Predicting customer churn helps companies take proactive steps to retain valuable customers. This research focuses on predicting customer churn using machine learning techniques including Logistic Regression, Decision Tree, Random Forest, and XGBoost. The model is trained and evaluated using publicly available datasets. Experimental results show that ensemble-based approaches like Random Forest and XGBoost outperform traditional algorithms, achieving higher accuracy and better recall rates.