🏥 Medical Insurance Cost Predictor

Web application to predict medical insurance costs using Machine Learning, deployed on Google Cloud Run.

🔗 Live Demo: insurance-predictor-562289298058.us-central1.run.app

✨ Features

• ML-based medical insurance cost prediction
• Interactive web interface with Streamlit
• Gradient Boosting model with 90% accuracy (R²)
• Deployed on Google Cloud Run

🛠️ Tech Stack

📁 Project Structure

├── app.py                 # Streamlit application
├── train.py               # Training script
├── requirements.txt       # Python dependencies
├── Dockerfile             # Container for Cloud Run
├── Dockerfile.training    # Container for training
├── .env                   # Environment variables (don't push to git)
├── data/
│   └── insurance.csv      # Dataset
└── model/
    ├── model.joblib       # Trained model
    └── feature_names.joblib

🚀 Local Installation

# 1. Clone repository
git clone https://github.com/your-username/ai-insurance-cost-predictor.git
cd ai-insurance-cost-predictor

# 2. Create virtual environment
python -m venv venv
venv\Scripts\activate  # Windows
# source venv/bin/activate  # Linux/Mac

# 3. Install dependencies
pip install -r requirements.txt

# 4. Download dataset from Kaggle
# https://www.kaggle.com/datasets/mirichoi0218/insurance
# Save as data/insurance.csv

# 5. Train model (optional, already included)
python train.py --data-path=data/insurance.csv --model-dir=model

# 6. Run application
streamlit run app.py

App will be available at: http://localhost:8501

☁️ Deploy to Google Cloud Run

Requirements

• Google Cloud account with billing enabled
• gcloud CLI installed and configured

Steps

# 1. Set project
gcloud config set project YOUR-PROJECT-ID

# 2. Enable APIs
gcloud services enable cloudbuild.googleapis.com run.googleapis.com storage.googleapis.com containerregistry.googleapis.com

# 3. Build image in the cloud
gcloud builds submit --tag gcr.io/YOUR-PROJECT-ID/insurance-app .

# 4. Deploy to Cloud Run
gcloud run deploy insurance-predictor --image gcr.io/YOUR-PROJECT-ID/insurance-app --platform managed --region us-central1 --allow-unauthenticated --memory 1Gi --port 8080

📊 ML Model

Performance

Feature Importance

1. 🚬 Smoker (~70%)
2. ⚖️ BMI (~15%)
3. 📅 Age (~10%)
4. 📍 Other (~5%)

📋 Input Variables

💰 Estimated GCP Costs

📂 Dataset

Medical Cost Personal Dataset from Kaggle:
https://www.kaggle.com/datasets/mirichoi0218/insurance

👤 Author

Adrian Zambrana

📄 License

MIT License