This project offers an intuitive web application for nano-entrepreneurs to predict health insurance costs. Leveraging a machine learning model, it estimates expenses based on personal factors, aiding financial planning. Built with Flask and scikit-learn, it provides a user-friendly interface for quick and accurate insurance premium estimation.

Machine Learning project using Logistic Regression to predict insurance claims based on customer data, including preprocessing, model training, and evaluation.

A GitHub repository hosting an insurance prediction model employing Decision Tree, Random Forest, and KNN algorithms, with KNN achieving the highest accuracy score of 87.7% and tested for response on unseen data.

Machine Learning project to predict health insurance premiums using Random Forest. Achieves 66% R² score with 13.23% MAPE on 1M records. Built with Python, scikit-learn, and pandas.

A data-driven solution that helps Assur'Aimant, a French insurer, optimize insurance premium estimation. This project uses a predictive model to determine the cost of a potential client's premium, enabling efficient and accurate pricing for insurance companies

This is a web app where a user can signup to the website first and then login to access the website. Then, he/she can give their age, select his/her gender, bmi, number of children, select whether he/she is a smoker or not, and select his/her region. Gradient Boosting Regressor is used in this project which gives the best accuracy of 89.798.

Predicting health insurance costs using custom Linear Regression (Gradient Descent) vs Scikit-learn. Includes EDA and data preprocessing.

Predict annual medical insurance costs instantly using Machine Learning! This AI system analyzes age, BMI, smoking habits, and demographics to estimate healthcare expenses with 90% accuracy. Built with Linear Regression, featuring 8+ visualizations and comprehensive data analysis.

Life Insurance Claim Prediction using Machine Learning. This project predicts the probability of an insurance claim based on customer and transaction data. Deployed with Render and integrated with a frontend.

Full-stack Machine Learning Web App for predicting yearly medical insurance charges.

Medical Insurance Price Prediction using Machine Learning and Python to estimate healthcare insurance costs based on demographic and health factors.

Predicting medical insurance charges using regression models like Random Forest, KNN, and Linear Regression.

A Python-based machine learning project for predicting individual medical insurance costs using demographic and health data, including data preprocessing, mapping utilities, model training, and evaluation.

🏥 AI-powered Medical Insurance Premium Prediction System using Gradient Boosting Regressor with interactive Streamlit dashboard for accurate premium estimation.

Healthcare insurance charge prediction using XGBoost Regression and Machine Learning.

Prudential Life Insurance Assessment Prediction

A Machine Learning project that predicts medical insurance charges based on demographic and health-related attributes such as age, BMI, smoking status, number of children, and region.

A React-based frontend application that allows users to input personal details and receive real-time insurance premium predictions by interacting with a FastAPI backend API.

This project predicts medical insurance charges using machine learning models after performing data preprocessing, EDA, and feature engineering. It highlights key cost drivers like age and smoking status and uses trained models for accurate predictions. The entire workflow demonstrates an end-to-end approach to regression-based predictive modeling.