ChurnGuard: An AI-PowerCustomer Churn Prediction System for Telecom Industry Using Machine Learning

Customer churn prediction is a critical business intelligence challenge in the telecom industry, where retaining existing customers is significantly more cost-effective than acquiring new ones. This paper presents ChurnGuard, an end-to-end machine learning system designed to identify at-risk telecom customers before they cancel their subscriptions. Using the IBM Telco Customer Churn dataset comprising 7,043 records and 21 features, we implement and compare two supervised learning models: Logistic Regression as an interpretable baseline and Random Forest as the primary classifier. The proposed system covers the complete data science lifecycle including data ingestion, exploratory data analysis (EDA), preprocessing, model training, evaluation using appropriate metrics for imbalanced datasets, feature importance extraction, risk tier segmentation, and deployment as an interactive Streamlit web application. The Random Forest classifier achieved a ROC-AUC of 87% and a Recall of 62%, outperforming Logistic Regression across all key metrics. The system segments customers into High, Medium, and Low churn-risk tiers and provides actionable retention recommendations. A business ROI analysis demonstrates a potential net saving of $100,000 per month through model-driven retention campaigns.