A privacy-preserving AI platform for lung cancer risk prediction.
- Python 3.8+
- pip
- virtual environment (recommended)
# Create virtual environment python -m venv venv source venv/bin/activate # On Windows, use `venv\Scripts\activate` # Install dependencies pip install -r requirements.txt # Install the package pip install -e . # Run tests pytest tests/ Configuration Copy config.yaml.example to config.yaml and modify as needed. Running the Platform bashCopy# Run web server python main.py server # Train model python main.py train --data training_data.csv Comprehensive Implementation Notes: 1. The `config.yaml` provides a flexible, environment-aware configuration 2. `data_loader.py` offers robust, configurable data preprocessing 3. Dependency management with `requirements.txt` and `setup.py` 4. Included a detailed `README.md` for easy setup and usage#