Food Churn Prediction System Using Machine Learning

Developed an end-to-end machine learning solution to predict customer churn in the food delivery industry. The system analyzes customer behavior, order history, ratings, and engagement patterns to identify users who are likely to stop using the service. Built with React.js, FastAPI, PostgreSQL, and XGBoost, the platform provides real-time predictions, customer risk segmentation, and interactive analytics dashboards. The solution helps businesses make data-driven decisions, improve customer retention, reduce revenue loss, and optimize marketing strategies through actionable insights and predictive analytics. This project demonstrates expertise in machine learning, backend development, data visualization, and full-stack application deployment.