Automated Resume Ranking System 📝💻

🚀 Features

• Extracts key details from resumes, including names, emails, skills, and work experience.

• Utilizes machine learning and natural language processing to rank resumes based on their relevance to job descriptions.

• SpaCy extracts key information from resumes while scikit-learn ranks them based on relevance to job descriptions.

• Supports bulk uploads of multiple PDF resumes for simultaneous analysis.

• Alerts users to fill out required fields (Job Description, Resume Upload) and ensure valid PDF uploads.

• Allows users to input job descriptions to tailor the ranking process based on specific requirements.

• Calculates similarity scores between resumes and job descriptions, providing rank insights into candidate fit.

• Enables users to download the ranked resume score results in CSV format for further review or record-keeping.

• Features a toggle for dark mode, enhancing user experience during late-night usage.

• Users can give their feedback and we use that for our future enhancements and they can also download their feedback which was stored in a csv file.

🔧 Setup and Usage

1. Clone the repository:

   https://github.com/Aryan_Rajesh_Python/Automated_Resume_Ranking_System.git

2. Install dependencies (Install the latest libraries instead of the specific ones mentioned in the txt file):

   pip install -r requirements.txt

3. Run the Flask app:

   python app.py

📝 Contributing

Contributions are welcome! Feel free to open an issue or submit a pull request.

💡 Inspiration

This project was inspired by the desire to create an interactive tool for HR professionals to easily analyze job applicants' resumes.

📫 Contact

Have questions or feedback? Feel free to reach out via [email protected].