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

2. Create and activate a virtual environment:

uv venv
source .venv/bin/activate  # On Unix/macOS
# or
.venv\Scripts\activate  # On Windows

3. 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

3. 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')

2. Data Initialization Errors

# Check the console output for initialization errors
# Make sure all dependencies are installed
uv pip install -r requirements.txt

3. 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.