Customer Churn Prediction Using Machine Learning

Customer attrition poses a major issue for the telecommunications sector, resulting in revenue decline and higher expenses for gaining new customers. Predicting churn accurately enables telecom companies to take proactive measures to retain customers. This study leverages specifically Random Forest, Decision Tree, and XGBoost, to develop a robust churn prediction model. These algorithms analyze customer behavioral data, including call duration, internet usage, billing history, and complaints, to identify potential churners. Decision Tree provides an interpretable model, Random Forest improves predictive accuracy through ensemble learning, and XGBoost enhances performance with gradient boosting and optimized handling of imbalanced datasets. The proposed model assists telecom companies in classifying customers based on their churn risk, enabling the implementation of targeted retention approaches like tailored discounts and rewards programs. By integrating advanced machine learning techniques, telecom service providers can enhance customer retention, minimize the churn rates, and improves the business sustainability. The study highlights the importance of data-driven decision-making in the telecom sector, demonstrating how predictive analytics can optimize customer relationship management and drive profitability. Key Words: Predicting telecom customer churn, machine learning techniques, Random Forest algorithm, Decision Tree method, XGBoost model, retention of customers, analytics for forecasting, gradient boosting techniques, telecom sector.