This project is an AI-powered Resume Screening and Candidate Ranking System built using Streamlit and machine learning techniques. It allows recruiters to automatically analyze and rank resumes based on their relevance to a given job description.
- 📌 Job Description Input: Users can enter the job description manually.
- 📂 Resume Upload: Supports uploading multiple PDF resumes.
- 🔍 AI-Powered Analysis:
- TF-IDF-based ranking for relevance.
- Skill matching against predefined technical skills.
- ATS Score Calculation for resume optimization.
- 📊 Visualization & Insights:
- Resume ranking based on AI analysis.
- ATS score distribution via bar charts.
- AI-powered resume improvement suggestions.
- Detailed extracted resume content display.
🚀 Try it out here → AI-Powered Resume Screening
git clone https://github.com/yourusername/ai-resume-screening.git
cd ai-resume-screeningpip install -r requirements.txtstreamlit run app.py- Enter Job Description: Type in the job description in the text area.
- Upload Resumes: Upload multiple PDF resumes.
- Click "Analyze Resumes": The system processes resumes and ranks them.
- View Results: Get ranked resumes, ATS scores, skill matches, and improvement suggestions.
- Python (Streamlit, Pandas, Scikit-learn, PyPDF2, Regex)
- Machine Learning (TF-IDF Vectorization, Cosine Similarity)
- TF-IDF Vectorization: Converts job description and resumes into numerical vectors.
- Cosine Similarity: Measures textual similarity between job description and resumes.
- Skill Matching: Extracts and compares skills from resumes and job descriptions.
- ATS Scoring: Evaluates resume formatting and keyword density.
- ✅ Add more predefined skills for better matching.
- ✅ Improve ATS scoring algorithm.
- ⏳ Integrate NLP for better resume parsing.
- ⏳ Deploy to a cloud service (e.g., AWS, Heroku).
Pull requests are welcome! Feel free to fork the repository and submit improvements.