https://github.com/shuddha2021/stellar-candidate-selector

A sophisticated candidate selection algorithm leveraging multi-criteria analysis and machine learning to identify top software engineering candidates. This tool features flexible filtering, score adjustment, and detailed visualizations to streamline the recruitment process.
https://github.com/shuddha2021/stellar-candidate-selector

candidate-selection data-analysis data-visualization machine-learning pandas plotting-in-python python python-data-analysis recruitment scikit-learn

Last synced: about 1 month ago
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A sophisticated candidate selection algorithm leveraging multi-criteria analysis and machine learning to identify top software engineering candidates. This tool features flexible filtering, score adjustment, and detailed visualizations to streamline the recruitment process.

• Host: GitHub
• URL: https://github.com/shuddha2021/stellar-candidate-selector
• Owner: shuddha2021
• Created: 2024-06-03T20:21:49.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2024-06-03T20:30:23.000Z (over 1 year ago)
• Last Synced: 2024-12-26T03:41:58.848Z (10 months ago)
• Topics: candidate-selection, data-analysis, data-visualization, machine-learning, pandas, plotting-in-python, python, python-data-analysis, recruitment, scikit-learn
• Language: Python
• Homepage:
• Size: 4.88 KB
• Stars: 0
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# StellarCandidateSelector

StellarCandidateSelector is a sophisticated candidate selection algorithm that leverages multi-criteria analysis and machine learning to identify top software engineering candidates. This tool features flexible filtering, score adjustment, and detailed visualizations to streamline the recruitment process.

## Features

- **Flexible Filtering**:

  - Configure criteria and weights via JSON.

  - Filter candidates based on minimum experience and required skills.

- **Machine Learning Integration**:

  - Adjust candidate scores using a linear regression model.

- **Visualization**:

  - Bar chart representation of candidate final scores.

- **Detailed Logging**:

  - Comprehensive logging of the selection process.

## Technologies Used

- **Python**: The application is written in Python.

- **Pandas**: Utilized for data manipulation and analysis.

- **scikit-learn**: Used for implementing the machine learning model.

- **Matplotlib**: For visualizing candidate scores.

- 

## Core Logic

- **Flexible Filtering**:

  - Loads selection criteria from a JSON file.

  - Filters candidates based on minimum experience and required skills.

- **Skill Match Score Calculation**:

  - Calculates a skill match score considering required and preferred skills.

- **Machine Learning Score Adjustment**:

  - Uses a linear regression model to adjust candidate scores.

- **Final Score Calculation**:

  - Combines experience, skill match score, and adjusted score with configurable weights.

- **Visualization**:

  - Plots a bar chart of the candidates' final scores for easy comparison.

## Project Structure

The project consists of the following main files:

- `main.py`: Contains the implementation of the candidate selection logic, including filtering, score calculation, and visualization.

- `criteria.json`: Defines the selection criteria and weights for the candidate evaluation process.

- `requirements.txt`: Lists the project's dependencies.

## Getting Started

To get started with this project:

1. Clone the repository.

   ```bash

   git clone https://github.com/yourusername/stellar-candidate-selector.git

 2. Navigate to the project directory.

    cd stellar-candidate-selector

 3. Install the required packages.

    pip install -r requirements.txt

 4. Run the application.

    python main.py

## Why This Project Is Useful

 This project serves as a practical example of implementing a sophisticated candidate selection algorithm using Python. It demonstrates various concepts such as data manipulation with Pandas, machine learning model integration with scikit-learn, and data visualization with Matplotlib in a real-world scenario.

## Contributing

Contributions to this project are welcome. Please fork the repository and create a pull request with your changes.

## License

This project is licensed under the MIT License