Customer churn in telecommunication using ML

Customer churn is a critical issue in the telecommunications industry, where service providers must continuously strive to retain customers. This study explores the application of machine learning (ML) techniques to predict customer churn in a telecommunications setting. By utilizing key features such as service usage (internet service, tech support) and financial details (monthly charges, total charges), this research aims to predict customer churn using three popular ML algorithms: Decision Tree, Random Forest, and XGBoost. These models were trained and evaluated on a dataset containing customer-related attributes, with the target variable being the churn status (yes/no). The results of the analysis show how well these algorithms can identify potential churners, providing valuable insights that can help companies in devising better retention strategies. The study demonstrates the effectiveness of these algorithms in handling large, complex datasets, and offers actionable insights for enhancing customer retention in the telecommunications industry.

Keywords:

Customer Churn, Telecommunications, Machine Learning, Decision Tree, Random Forest, XGBoost, Churn Prediction, Customer Retention, Python, Scikit-learn.