https://github.com/oldhero5/talent_track
TalentTrack is an open‐source recruitment analytics web application built with Flask and Python. It leverages advanced machine learning techniques—such as Product Quantization (PQ) for candidate ranking and SHAP for model interpretability—to help HR teams and recruitment professionals identify high-quality candidates efficiently.
https://github.com/oldhero5/talent_track
active-learning analytics candidate-ranking data-visualization faiss flask hrtech machine-learning open-source python recruitment shap talent-analytics
Last synced: 2 months ago
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TalentTrack is an open‐source recruitment analytics web application built with Flask and Python. It leverages advanced machine learning techniques—such as Product Quantization (PQ) for candidate ranking and SHAP for model interpretability—to help HR teams and recruitment professionals identify high-quality candidates efficiently.
- Host: GitHub
- URL: https://github.com/oldhero5/talent_track
- Owner: oldhero5
- Created: 2025-02-03T11:33:52.000Z (4 months ago)
- Default Branch: main
- Last Pushed: 2025-02-03T11:37:19.000Z (4 months ago)
- Last Synced: 2025-02-03T12:33:31.211Z (4 months ago)
- Topics: active-learning, analytics, candidate-ranking, data-visualization, faiss, flask, hrtech, machine-learning, open-source, python, recruitment, shap, talent-analytics
- Language: Jupyter Notebook
- Homepage:
- Size: 0 Bytes
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# 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:
```bash
git clone https://github.com/oldhero5/talent_track.git
cd talent_track
```
2. Create and activate a virtual environment:
```bash
uv venv
source .venv/bin/activate # On Unix/macOS
# or
.venv\Scripts\activate # On Windows
```
3. Install dependencies:
```bash
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:
```bash
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
```bash
# 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
```bash
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
```bash
# 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
```bash
# Check the console output for initialization errors
# Make sure all dependencies are installed
uv pip install -r requirements.txt
```
3. Visualization Errors
```bash
# 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.