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https://github.com/inoles/resumeinsight
AI-powered resume screening tool
https://github.com/inoles/resumeinsight
numpy pandas pandas-python pdfminersix python resume resume-screening spacy spacy-nlp
Last synced: 5 days ago
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AI-powered resume screening tool
- Host: GitHub
- URL: https://github.com/inoles/resumeinsight
- Owner: iNoles
- License: gpl-3.0
- Created: 2025-02-15T19:20:57.000Z (6 days ago)
- Default Branch: main
- Last Pushed: 2025-02-15T20:18:21.000Z (6 days ago)
- Last Synced: 2025-02-15T20:27:24.869Z (6 days ago)
- Topics: numpy, pandas, pandas-python, pdfminersix, python, resume, resume-screening, spacy, spacy-nlp
- Language: Python
- Homepage:
- Size: 24.4 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 3
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# ResumeInsight
ResumeInsight is an AI-powered resume screening tool that analyzes and ranks resumes based on job descriptions. It utilizes NLP techniques to extract relevant information from resumes and compare them to job requirements.
## Features
- Parses resumes in PDF and DOCX formats
- Extracts key information like skills, experience, and education
- Uses NLP to rank candidates based on job descriptions
- Provides insights into resume relevance
## Installation
1. Clone the repository:
```bash
git clone https://github.com/iNoles/ResumeInsight.git
cd ResumeInsight
```
2. Create a virtual environment and activate it:
```bash
python -m venv venv
source venv/bin/activate # On Windows use `venv\Scripts\activate`
```
3. Install dependencies:
```bash
pip install -r requirements.txt
```
## Usage
1. Place resumes in the ```resumes/``` directory.
2. Run the script with a job description:
```bash
python resume_insight.py --job "Software Engineer with Python and ML experience"
```
3. View ranked candidates in the output.
## Dependencies
- python-docx (for parsing DOCX files)
- pdfminer.six (for extracting text from PDFs)
- spacy (for NLP processing)
- numpy & pandas (for data handling)
## Contributing
1. Fork the repository
2. Create a new branch (```feature-branch```)
3. Commit your changes
4. Push to your fork
5. Submit a pull request