🏥 Health Insurance Cost Predictor

A machine learning-powered web application that predicts health insurance premiums based on individual characteristics and health factors.

📋 Overview

This project uses machine learning algorithms to predict health insurance costs based on various demographic, lifestyle, and health-related factors. The application provides an intuitive interface for users to input their details and receive instant premium predictions.

🎯 Features

• Interactive UI: Clean, organized 4x3 grid layout with dark theme
• Real-time Predictions: Instant insurance cost estimates via ML model
• Comprehensive Input Fields: 12 different health and demographic parameters
• User-friendly Controls: Mix of number inputs and dropdown selectors
• Responsive Design: Built with Streamlit for seamless user experience

🚀 Demo

Visit the live application: Premium Health Insurance Cost Predictor

📊 Dataset & Features

Input Features

• Age: 18-100 years
• Number of Dependants: 0-20 dependents
• Income in Lakhs: Annual income (0-200 lakhs)
• Genetical Risk: Risk score 0-5 based on genetic factors
• Insurance Plan: Bronze, Silver, or Gold tier
• Employment Status: Salaried, Self-Employed, Freelancer
• Gender: Male or Female
• Marital Status: Married or Unmarried
• BMI Category: Normal, Overweight, Underweight, Obesity
• Smoking Status: No Smoking, Regular, Occasional
• Region: Northeast, Northwest, Southeast, Southwest
• Medical History:
  • No Disease
  • Diabetes
  • High blood pressure
  • Diabetes & High blood pressure
  • Thyroid
  • Heart disease
  • High blood pressure & Heart disease
  • Diabetes & Thyroid
  • Diabetes & Heart disease

🛠️ Technology Stack

• Frontend: Streamlit
• Backend: Python
• ML Libraries:
  • Scikit-learn (Model Training & Prediction)
  • Pandas (Data Manipulation)
  • NumPy (Numerical Computing)
• Visualization: Matplotlib, Seaborn (for EDA)
• Model Persistence: Pickle/Joblib

📁 Project Structure

healthcare-premium-prediction/
│
├── artifacts/                      # Trained models and preprocessors
│   ├── model_young.joblib         # ML model for age <= 25
│   ├── model_rest.joblib          # ML model for age > 25
│   ├── scaler_young.joblib        # Scaler for young age group
│   └── scaler_rest.joblib         # Scaler for older age group
│
├── main.py                         # Main Streamlit application
├── prediction_helper.py            # Prediction utility functions
├── requirements.txt                # Python dependencies
├── README.md                       # Project documentation
├── LICENSE                         # Apache-2.0 License
└── .gitignore                      # Git ignore rules

🔧 Installation & Setup

Prerequisites

• Python 3.10 or higher
• pip package manager

Local Setup

1. Clone the repository

git clone https://github.com/thanusree2630/healthcare-premium-prediction.git
cd healthcare-premium-prediction

2. Create a virtual environment

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install dependencies

pip install -r requirements.txt

4. Run the application

streamlit run main.py

5. Access the app Open your browser and navigate to http://localhost:8501

📦 Requirements

streamlit>=1.28.0
pandas>=2.0.0
numpy>=1.24.0
scikit-learn>=1.3.0
matplotlib>=3.7.0
seaborn>=0.12.0
joblib>=1.3.0

🧪 Model Development Process

1. Exploratory Data Analysis (EDA)

• Data distribution analysis
• Correlation studies
• Outlier detection
• Missing value handling
• Feature importance analysis

2. Feature Engineering

• Categorical encoding (One-Hot/Label Encoding)
• Numerical feature scaling
• Feature interaction creation
• Dimensionality reduction (if applicable)

3. Model Training

• Algorithm selection (Linear Regression, Random Forest, XGBoost, etc.)
• Cross-validation
• Hyperparameter tuning
• Model evaluation metrics (RMSE, MAE, R²)
• Model persistence

4. Model Performance Metrics

• R² Score
• RMSE
• MAE

📈 Usage

1. Open the application in your browser

2. Fill in all required fields in the 4x3 grid layout:

   Row 1: Age, Number of Dependants, Income in Lakhs

   Row 2: Genetical Risk, Insurance Plan, Employment Status

   Row 3: Gender, Marital Status, BMI Category

   Row 4: Smoking Status, Region, Medical History

3. Click the "Predict" button

4. View your predicted insurance cost displayed as a success message

🤝 Contributing

Contributions are welcome! Please follow these steps:

1. Fork the repository
2. Create a new branch (git checkout -b feature/improvement)
3. Make your changes
4. Commit your changes (git commit -am 'Add new feature')
5. Push to the branch (git push origin feature/improvement)
6. Create a Pull Request

📝 License

This project is licensed under the Apache-2.0 License - see the LICENSE file for details.

👤 Author

Thanusree

• GitHub: @thanusree2630
• Repository: healthcare-premium-prediction

📞 Support

If you encounter any issues or have questions, please:

• Open an issue on GitHub
• Contact me via email

🔮 Future Enhancements

• Add more ML algorithms comparison
• Implement SHAP values for model interpretability
• Add data visualization dashboard
• Include policy recommendation system
• Multi-language support
• Mobile-responsive design improvements
• API endpoint for programmatic access

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