CUSTOMER CHURN PREDICTION AND CATEGORIZATION A MACHINE LEARNING APPROACH TO ANALYSE CUSTOMER BEHAVIOR AND DECISION MAKING IN THE TELECOMMUNICATIONS INDUSTRY

Customer churn remains a critical challenge for subscription- based businesses, particularly in the telecommunications indus- try, where retaining customers is significantly more cost-effective than acquiring new ones. This study leverages machine learn- ing to develop a robust churn prediction framework and identify key behavioral drivers of churn. Using the Telco customer churn dataset, we employ an ensemble Voting Classifier composed of Logistic Regression, Random Forest, XGBoost, and CatBoost models.