Customer churn prediction using machine learning

Telecom churn prediction plays a vital role in helping companies retain their customers. Churn refers to the situation in which a customer discontinues their telecom services or subscription. Accurately predicting churn enables companies to take proactive actions by identifying customers who are likely to leave and offering targeted retention strategies. This study provides an overview of telecom churn prediction using machine learning techniques. The problem involves analyzing historical customer data such as demographic details, usage behavior, billing information, and service history to determine the likelihood of churn. Machine learning models are trained to identify patterns and relationships within this data and generate predictions for new customers. The process includes data preprocessing, feature engineering, selection of suitable machine learning algorithms, and performance evaluation using appropriate metrics. Models such as Decision Tree, Random Forest, and XGBoost are commonly used for this purpose. The best-performing model can then be deployed in a real-world environment to support decision-making. By applying this approach, telecom companies can effectively reduce churn rates, enhance customer retention, and improve overall customer satisfaction.