Customer Churn Prediction for Subscription-Based Businesses

Customer churn is a critical challenge for subscription-based businesses, as it directly impacts revenue, profitability, and long-term customer loyalty. Because retaining existing customers is more cost-effective than acquiring new ones, accurate churn prediction is essential for sustainable growth. This work presents a machine learning based framework for predicting and analyzing customer churn, coupled with an interactive Streamlit web application that supports real time decision making. Using historical customer data that includes demographic attributes, usage behavior, transaction history, and engagement patterns, the system applies extensive data preprocessing and feature engineering to construct a modeling-ready dataset. Multiple models Logistic Regression, Random Forest, and XGBoost are trained and evaluated using the Scikit-Learn framework. Model performance is assessed with metrics such as accuracy, precision, recall, F1-score, and ROC-AUC to identify the most effective predictor of churn. The top performing models are serialized and deployed within a Streamlit interface that accepts individual customer inputs or batch data files to generate immediate churn predictions and summaries. Overall, this project demonstrates how machine learning can transform raw customer data into actionable business intelligence and provides a scalable approach to proactive customer retention management.