Health Insurance Premium Prediction System Using Machine Learning

Health insurance premium calculation is a critical process in the insurance industry. Traditional premium estimation methods often require extensive manual analysis and may not provide instant results for customers. This research presents a Health Insurance Premium Prediction System that utilizes Machine Learning techniques to estimate annual health insurance premiums based on an individual's demographic and health-related attributes. The proposed system employs Random Forest Regression as the primary prediction model and is implemented as a full-stack web application using Flask, MongoDB, and Scikit-learn. The system allows users to register, provide health information, and obtain premium predictions instantly. Comparative analysis among Linear Regression, Decision Tree Regression, and Random Forest Regression demonstrates that the Random Forest model achieves the highest prediction accuracy with an R² score of 0.8642. The developed platform provides a secure, scalable, and user-friendly solution for insurance premium estimation.