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https://github.com/akshint0407/ai-resume-ranking-system

Automate resume screening with this AI-powered system, leveraging NLP to match candidates with job descriptions efficiently. Streamlit app for easy resume upload and ranking.
https://github.com/akshint0407/ai-resume-ranking-system

npl python streamlit-webapp

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Automate resume screening with this AI-powered system, leveraging NLP to match candidates with job descriptions efficiently. Streamlit app for easy resume upload and ranking.

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# AI Resume Ranking System

[![Streamlit App](https://img.shields.io/badge/Streamlit-App-blue)](https://ai-resume-ranking-system-jhukfgy3rn33cjnbxuyfsg.streamlit.app/)

## Overview

This project is an AI-powered resume screening and ranking system designed to automate the initial stages of the recruitment process. It leverages natural language processing (NLP) and machine learning techniques to efficiently match candidates with job descriptions.

## Features

- **Resume Screening**: Automatically extracts key skills from resumes and compares them to job requirements.
- **Ranking System**: Ranks candidates based on their suitability for the job.
- **User-Friendly Interface**: Streamlit app provides an intuitive interface for uploading resumes and job descriptions.

## Screenshots

![App_Screenshot](https://github.com/Akshint0407/AI-Resume-Ranking-System/blob/main/App_Screenshot.png)
![Resume_Upload](https://github.com/Akshint0407/AI-Resume-Ranking-System/blob/main/Resume_Upload.png)

![Ranked_Results](https://github.com/Akshint0407/AI-Resume-Ranking-System/blob/main/Ranking_Results.png)

## Requirements

See [requirements.txt](requirements.txt) for dependencies.

## Installation

1. Clone this repository.
2. Install dependencies using `pip install -r requirements.txt`.
3. Run the app with `streamlit run resume_app.py`.

## Usage

1. Upload resumes in PDF format.
2. Enter a job description.
3. The system will rank resumes based on their relevance to the job.

## Future Work

- **Continuous Learning**: Implement feedback loops to improve model accuracy over time.
- **Bias Mitigation**: Develop strategies to reduce bias in candidate evaluations.

## Contributing

Contributions are welcome! Please submit pull requests with detailed explanations of changes.

## License

[Apache 2.0 License](LICENSE) - see LICENSE for details