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.

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.

   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