"Leveraging Machine Learning and Data Mining for Effective Customer Churn Prediction in Telecom"

Abstract

Since keeping current customers is far more cost-effective than finding new ones, research on customer churn prediction has become crucial in the telecom sector. This study creates a prediction model for detecting clients who are at risk by utilizing data mining and machine learning approaches. The study makes use of the IBM Telco Customer Churn dataset, which includes important characteristics including account duration, billing trends, service subscriptions, and customer demographics. Customers were categorized according to the likelihood of churn using a variety of machine learning models, such as Decision Trees, Random Forest, and XGBoost. According to the results, Random Forest and XGBoost perform better than the other models, obtaining more generalization and prediction accuracy. Key predictors of churn include contract type, availability of tech assistance, and payment method, according to feature significance study. The report goes on to address the commercial ramifications of predictive analytics, offering telecom operators practical advice on how to boost consumer engagement, tailor offerings, and employ targeted retention techniques. The results highlight how AI-driven analytics may reduce attrition, boost customer happiness, and increase overall company profitability.