CUSTOMER CHURN PREDICTION ANALYSIS

Customer churn is a significant concern in the telecom industry as retaining existing customers is more cost-effective than acquiring new ones. Predicting customer churn is crucial for telecom companies to improve retention strategies. This study focuses on developing a churn prediction model using machine learning techniques to help telecom operators identify customers likely to churn. The model utilizes a large dataset containing customer information such as demographics, services, and payment methods. The model was built and tested in a Spark environment. Three machine learning algorithms were implemented: Decision Tree, Random Forest, and XGBoost. The goal is to assist telecom operators in predicting and managing customer churn, ultimately improving customer retention and business performance.