Customer Churn Prediction Using Machine Learning

Abstract

Customer churn is a common problem for businesses that depend on keeping customers for a long period of time. This includes industries such as telecommunications, banking, insurance, subscription services, and online platforms. Customer churn happens when an existing customer stops using a company's products or services. Losing customers can reduce revenue and can also increase the cost of attracting new ones. For this reason, being able to identify customers who may leave can help businesses take action before the customer actually churns. Machine learning provides a useful way to approach churn prediction because it can analyze large amounts of customer data and identify patterns linked with customer departure. This research compares four machine learning algorithms for predicting customer churn: Logistic Regression, Decision Tree, Random Forest, and XGBoost. The proposed process includes data collection, preprocessing, feature selection, model training, and evaluation. Accuracy, precision, recall, F1-score, and ROC-AUC are considered when comparing the models. The analysis focuses on the strengths and limitations of each algorithm and highlights the importance of customer-related features such as contract type, service usage, payment method, tenure, and monthly charges. Logistic Regression provides a simple and understandable baseline, while Decision Tree can identify nonlinear relationships. Random Forest improves stability by combining multiple trees, and XGBoost can learn more complex patterns through gradient boosting. Overall, the study shows that machine learning can help businesses identify customers who are more likely to leave and support the development of targeted customer-retention strategies.