Medical Insurance Prediction

Abstract

This project develops a Tkinter-based predictive system for estimating medical insurance premiums and assessing claim probabilities using machine learning models. By processing user inputs such as age, BMI, annual income, smoking status, gender, alcohol consumption, and pre- existing conditions, the system leverages pre-trained regression and classification models, coupled with data scaling, to deliver accurate and personalized predictions. Historical prediction data is stored in an SQLite database, enabling users to review past results and track their insurance planning. The system is designed to provide actionable insights for policyholders and insurance providers, facilitating informed decision-making.

To enhance user experience and security, the system incorporates a robust login and registration module with CAPTCHA verification to ensure secure access to personalized services. An admin dashboard enables administrators to manage user accounts and insurance policies, including adding or removing policies and users. Additional features include a BMI calculator, which helps users understand a key health metric influencing insurance costs, and a contact form for support and inquiries.