The project titled "Customer Churn Prediction" aims to develop a web application capable of predicting the churn status in the telecom industry, as well as predicting E-commerce order status and healthcare surgery outcomes. By utilizing machine learning algorithms like Random Forest, Stacking Classifier, Voting Classifier, and Decision Tree, the system leverages datasets from E-commerce, healthcare, and telecom sectors to train predictive models. The web application offers a user-friendly interface for customers and healthcare professionals, enabling real-time predictions based on historical data. In the telecom sector, the model predicts whether a customer is likely to churn, while in the E-commerce domain, it forecasts the delivery or cancellation status of orders. Additionally, for healthcare, the system predicts the recovery or improvement status of patients undergoing surgery. This approach helps businesses and healthcare providers optimize customer retention and improve service delivery.

Keywords: Customer Churn Prediction, E-commerce Order Status, Healthcare Surgery Prediction, Telecom Industry, Random Forest, Stacking Classifier, Voting Classifier, Decision Tree, Machine Learning, Web Application.