Health Insurance Cost Predictor Using Machine Learning

Health Insurance Cost Predictor

Welcome to the ultimate health insurance premium prediction system – built with cutting-edge ML models and a smart dual-model strategy that delivers up to 99.7% R² accuracy. Whether you’re a developer looking to integrate AI into your insurance product or a data scientist curious about ML deployment – this is the project for you.

📋 Project Details

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🎯 About the Project

This project started as a standard regression task but evolved into a dual-model powerhouse after deep error analysis revealed age as a major factor in prediction variance.

💡 Core Strategy

• 👶 Young Model (Age ≤ 25) → Linear Regression
• 🧓 Older Model (Age > 25) → XGBoost Regressor
• Genetical Risk Factor: Game-changing feature that boosted overall prediction reliability.

📈 Key Results

Young Users (Age ≤ 25)

• Model: Linear Regression + Genetical Risk
• Accuracy (R²): ~0.99 Train/Test

Older Users (Age > 25)

• Model: XGBoost + Genetical Risk
• Accuracy (R²): ~0.997 Train/Test/CV

🧠 Tech Stack

• Frontend: Streamlit
• ML Models: Linear Regression, XGBoost
• Tuning: GridSearchCV, RandomizedSearchCV
• Libraries: NumPy, pandas, scikit-learn, XGBoost
• Visualization: Seaborn, Matplotlib

📦 Available Features

• ML model training & prediction scripts
• Advanced feature engineering (Genetical Risk)
• Pre-trained model notebooks for both age groups
• Ready-to-use Streamlit web app
• Residual & accuracy visualizations
• Test datasets included (.xlsx)
• 📄 SourceOfWork.pdf: Full methodology & implementation breakdown