DP-CE/dp-resume-screening-ai

AI Resume Screening & Ranking System

Overview

This is an AI-powered resume screening and ranking system built using Streamlit. The application allows recruiters to upload multiple resumes in PDF format and enter a job description. The system then preprocesses the text, extracts keywords, and calculates similarity scores between resumes and the job description to rank candidates accordingly.

Features

• Upload multiple resumes in PDF format.
• Extract and preprocess text from resumes.
• Compute similarity scores between resumes and the job description using NLP techniques.
• Rank resumes based on relevance.
• Display an interactive recruiter dashboard with insights and data visualization.
• Extract and compare keywords between job descriptions and resumes.

Technologies Used

• Python: Core programming language
• Streamlit: Web framework for UI
• PyPDF2: Extract text from PDFs
• spaCy: NLP processing
• Sentence Transformers: Model for semantic similarity
• Matplotlib & Seaborn: Data visualization
• NumPy & Pandas: Data processing and analysis

Installation & Setup

Prerequisites

Ensure you have Python installed (>=3.7). Then install the required dependencies:

pip install streamlit PyPDF2 pandas numpy spacy matplotlib seaborn sentence-transformers

Download the SpaCy Model

python -m spacy download en_core_web_sm

Run the Application

streamlit run app.py

Usage

1. Open the Streamlit UI in your browser.
2. Upload multiple resume PDFs.
3. Enter the job description in the text area.
4. Click the "Rank Resumes" button to process and rank candidates.
5. View ranked resumes, recruiter insights, and keyword analysis.

Future Improvements

• Support for additional file formats (e.g., DOCX, TXT).
• More advanced NLP models for better resume-job matching.
• Integration with applicant tracking systems (ATS).
• Enhanced AI explainability for deeper resume analysis.

License

This project is open-source and available for modification and enhancement.

Author

[Your Name]

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license: apache-2.0