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TalentTrack

TalentTrack is a recruitment analysis tool that uses Product Quantization (PQ) for candidate similarity matching and provides visualizations for talent pool analysis, model monitoring, and recruitment pipeline tracking.

Features

  • Talent Pool Visualization with TSNE clustering
  • Candidate similarity scoring using Product Quantization
  • Model explanability with SHAP values
  • Geographic distribution analysis
  • Model drift detection
  • Recruitment pipeline monitoring
  • Interactive data tables and filtering

Installation

  1. Clone the repository:
git clone https://github.com/oldhero5/talent_track.git
cd talent_track
  1. Create and activate a virtual environment:
uv venv
source .venv/bin/activate  # On Unix/macOS
# or
.venv\Scripts\activate  # On Windows
  1. Install dependencies:
uv pip install -r requirements.txt

Project Structure

talent_track/
├── talent_track/
│   ├── __init__.py
│   ├── app.py
│   ├── functions.py
│   ├── static/
│   │   ├── css/
│   │   │   └── style.css
│   │   └── js/
│   │       ├── talent_pool.js
│   │       ├── model_analysis.js
│   │       └── model_monitoring.js
│   └── templates/
│       ├── index.html
│       ├── talent_pool.html
│       ├── model_analysis.html
│       └── model_monitoring.html
├── pyproject.toml
└── requirements.txt

Running the Application

  1. Make sure you're in the project root directory and your virtual environment is activated

  2. Run the Flask application:

python -m talent_track.app
  1. Open your browser and navigate to:
http://localhost:5000

Features in Detail

Talent Pool Visualization

  • Interactive scatter plot showing employee and candidate clustering
  • Filter by status and experience level
  • Hover for detailed information
  • Click for candidate details

Model Analysis

  • Candidate similarity scoring
  • SHAP value explanations
  • Feature importance visualization
  • Individual candidate analysis

Model Monitoring

  • Data drift detection
  • Confusion matrix
  • ROC curve analysis
  • Performance metrics tracking

Development

Prerequisites

  • Python 3.8+
  • uv package manager
  • Node.js (for DataTables)

Setup Development Environment

# Clone repository
git clone https://github.com/yourusername/talent_track.git
cd talent_track

# Create virtual environment
uv venv
source .venv/bin/activate

# Install dependencies
uv pip install -r requirements.txt

Running Tests

pytest tests/

Contributing

  1. Fork the repository
  2. Create your feature branch (git checkout -b feature/AmazingFeature)
  3. Commit your changes (git commit -m 'Add some AmazingFeature')
  4. Push to the branch (git push origin feature/AmazingFeature)
  5. Open a Pull Request

License

This project is licensed under the MIT License - see the LICENSE file for details

Acknowledgments

  • FAISS library by Facebook Research
  • SHAP (SHapley Additive exPlanations)
  • Flask web framework
  • DataTables for interactive tables
  • Plotly for interactive visualizations

Troubleshooting

Common Issues

  1. Template Not Found
# Make sure your template folder path is correct in app.py
app = Flask(__name__,
            template_folder='/path/to/your/templates',
            static_folder='static')
  1. Data Initialization Errors
# Check the console output for initialization errors
# Make sure all dependencies are installed
uv pip install -r requirements.txt
  1. Visualization Errors
# If visualizations aren't showing:
# - Check browser console for JavaScript errors
# - Verify that static files are being served correctly
# - Check that matplotlib backend is set to 'Agg'

Support

For support, please open an issue in the GitHub repository or contact the maintainers.