🏆 AI-powered Resume Screening and Ranking System

An AI-driven resume screening and ranking system that matches resumes with job descriptions using Natural Language Processing (NLP). This tool extracts text from PDF resumes, stores them in a database, and ranks them based on similarity to job descriptions.

🚀 Features

• 📂 Resume Upload: Upload multiple PDF resumes.
• 💄 Database Integration: Stores resumes in an SQLite database.
• 🖍 Text Extraction: Extracts resume text using pdfplumber.
• 🔍 AI Ranking: Uses TF-IDF and Cosine Similarity to rank resumes based on job descriptions.
• 📊 Top Match Selection: Highlights the best-matching resumes.
• 📅 Download Option: Allows users to download ranked resumes.
• 🗑 Database Cleanup: Removes entries of missing files.

🛠 Tech Stack

• Frontend: Streamlit
• Backend: Python (sqlite3, pdfplumber)
• AI/ML: Scikit-learn (TfidfVectorizer, cosine_similarity)

📞 Installation

1. Clone the Repository

   git clone https://github.com/Dharmik0712/AI-powered-Resume-Screening-and-Ranking-System.git
   cd AI-powered-Resume-Screening-and-Ranking-System

2. Install Dependencies

   pip install -r requirements.txt

3. Run the Application

   streamlit run app.py

📝 Usage

1. Upload Resumes

   • Click on "Upload PDF Resumes" and select multiple resumes.
   • Resumes are stored in the database and Resume Dataset folder.

2. Enter Job Description

   • Paste a job description into the input box.

3. Find Best Matches

   • Click "🔍 Find Best Matches" to rank resumes.
   • The system displays the Top 3 Matching Resumes with download options.

4. Database Cleanup

   • Click "🗑 Clean Missing Resumes from Database" to remove invalid entries.

🌐 Deployed Link

Click here to access the deployed app

📂 Project Structure

📁 AI-powered-Resume-Screening-and-Ranking-System
️️️️️️️️️️️️️️️️️️️️️️️️
📁 database/          # SQLite database files
📁 Resume Dataset/    # Uploaded resumes
📄 app.py            # Main Streamlit app
📄 requirements.txt   # Dependencies
📄 README.md          # Documentation

🤖 How It Works

1. Extracts text from PDF resumes using pdfplumber.
2. Stores resumes in an SQLite database with job roles.
3. Uses TF-IDF Vectorization to convert text into numerical vectors.
4. Calculates similarity scores with cosine similarity.
5. Ranks resumes based on the highest similarity scores.

⚡ Future Improvements

• ✅ Add support for image-based PDF OCR.
• ✅ Enhance ranking with deep learning (BERT, GPT).
• ✅ Add multi-user authentication.

🏆 Contributing

Want to improve this project? Contributions are welcome! Feel free to fork and submit a pull request.

🐝 License

This project is licensed under the MIT License.

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💡 Developed by Dharmik0712
🚀 AI meets Resume Screening!