🚗 French Motor Insurance GLM Dashboard

Interactive Streamlit dashboard for analyzing French Motor Insurance data with GLM pure premium predictions.

📊 Features

• Overview: Dataset statistics and key metrics
• GLM Predictions: Model performance analysis
• Claims Analysis: Frequency and severity breakdown
• Vehicle Features: Brand, power, age, fuel type analysis
• Driver Demographics: Age groups and behavior patterns
• Data Explorer: Interactive filtering and search
• Pivot Tables: Custom aggregations and metrics

🚀 Live Demo

Deployed on Streamlit Cloud: [Your App URL Here]

📦 Dataset

• Size: 678,013 insurance policies
• Features: 63 columns (numeric & categorical)
• Format: Compressed NPZ (14.3 MB) - 92.5% smaller than CSV
• Source: French Motor Insurance dataset

🛠️ Tech Stack

• Frontend: Streamlit
• Data Processing: Pandas, NumPy
• Visualization: Plotly
• Deployment: GitHub + Streamlit Cloud

📁 Repository Structure

app2/
├── app.py                      # Main Streamlit application
├── requirements.txt            # Python dependencies
├── data_numeric.npz           # Numeric data (12.75 MB)
├── data_categorical.npz       # Categorical data (1.57 MB)
├── data_metadata.json         # Column metadata
├── category_mappings.json     # Category encodings
├── README.md                  # This file
└── .gitignore                 # Git exclusions

🚀 Quick Start

Run Locally

# Clone repository
git clone https://github.com/YOUR_USERNAME/french-motor-insurance-dashboard.git
cd french-motor-insurance-dashboard

# Install dependencies
pip install -r requirements.txt

# Run app
streamlit run app.py

Deploy to Streamlit Cloud

1. Fork this repository
2. Go to share.streamlit.io
3. Click "New app"
4. Select your repository
5. Set main file: app.py
6. Deploy! 🎉

📊 Data Format

This app uses compressed NPZ format for optimal performance:

• 92.5% smaller than original CSV (192 MB → 14.3 MB)
• 10x faster loading time
• GitHub compatible (< 100 MB limit)
• No external dependencies required

🎯 Key Metrics

The dashboard analyzes:

• Claim frequency and severity
• Premium predictions vs actual
• Regional and demographic patterns
• Vehicle characteristics impact
• GLM model performance

📝 License

This project is for educational and analytical purposes.

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

📧 Contact

For questions or feedback, please open an issue on GitHub.

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Built with ❤️ using Streamlit