https://github.com/abhay-rudatala/resume-analyzer
Intelligent Resume Analysis System using Machine Learning and NLP. Features TF-IDF + Naive Bayes/SVM classification (90-95% accuracy), SpaCy NER for information extraction, and interactive Streamlit web app with custom UI. Built with Python, Scikit-learn, and deployed on Streamlit Cloud.
https://github.com/abhay-rudatala/resume-analyzer
classification machine-learning named-entity-recognition nlp portfolio-project python resume-analysis scikit-learn spacy streamlit
Last synced: 27 days ago
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Intelligent Resume Analysis System using Machine Learning and NLP. Features TF-IDF + Naive Bayes/SVM classification (90-95% accuracy), SpaCy NER for information extraction, and interactive Streamlit web app with custom UI. Built with Python, Scikit-learn, and deployed on Streamlit Cloud.
- Host: GitHub
- URL: https://github.com/abhay-rudatala/resume-analyzer
- Owner: Abhay-Rudatala
- Created: 2025-09-28T12:34:33.000Z (about 1 month ago)
- Default Branch: main
- Last Pushed: 2025-09-28T13:51:52.000Z (about 1 month ago)
- Last Synced: 2025-09-28T14:41:43.518Z (about 1 month ago)
- Topics: classification, machine-learning, named-entity-recognition, nlp, portfolio-project, python, resume-analysis, scikit-learn, spacy, streamlit
- Language: Python
- Homepage:
- Size: 620 KB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
RESUME-ANALYZER
Transform Resumes Into Talent Insights Instantly
Built with the tools and technologies:
---
## 🚀 Live Demo
Try the live application: [[Click Here](https://arskye-resume-analyzer.streamlit.app/)]
## ✨ Features
### 🤖 **ML-Powered Analysis**
- **TF-IDF + Naive Bayes** (85-90% accuracy)
- **TF-IDF + SVM** (90-95% accuracy)
- **SpaCy Named Entity Recognition** (85-90% accuracy)
### 📊 **Comprehensive Insights**
- ✅ Job category prediction (15+ categories)
- ✅ Skills extraction and analysis
- ✅ Resume quality scoring (1-10 scale)
- ✅ Personalized improvement recommendations
- ✅ Contact information extraction
- ✅ Interactive visualizations
### 🎯 **Job Categories Supported**
Software Engineer • Data Scientist • Product Manager • Marketing Manager • Sales Representative • HR Manager • Financial Analyst • Designer • Business Analyst • Project Manager • DevOps Engineer • Quality Assurance • Content Writer • Customer Success • Operations Manager
## 🏁 Quick Start
### 1️⃣ **Clone & Install**
```bash
git clone https://github.com/Abhay-Rudatala/resume-analyzer.git
cd resume-analyzer
pip install -r requirements.txt
python -m spacy download en_core_web_sm
```
### 2️⃣ **Train Models**
```bash
python ml_models.py
```
### 3️⃣ **Test System**
```bash
python test_system.py
```
### 4️⃣ **Launch App**
```bash
streamlit run app.py
```
🌐 Open your browser to `http://localhost:8501`
## 📁 Project Structure
```
nlp-resume-analyzer/
├── 🎨 app.py # Main Streamlit application
├── 📄 resume_parser.py # PDF/DOCX text extraction
├── 🤖 ml_models.py # ML models (Naive Bayes, SVM)
├── 🔍 ner_extractor.py # Named Entity Recognition
├── 🛠️ utils.py # Utility functions
├── 📦 requirements.txt # Python dependencies
├── 📊 resume_dataset.csv # Training dataset (3000+ samples)
├── 📁 models/ # Trained model files (generated)
└── 📖 README.md # This file
```
## 🔧 Technical Stack
**Backend:** Python 3.11, scikit-learn, SpaCy, NLTK
**Frontend:** Streamlit with custom CSS
**ML Models:** TF-IDF, Naive Bayes, SVM, NER
**File Processing:** PyPDF2, python-docx, pdfplumber
**Visualization:** Plotly, matplotlib, seaborn
**Data:** 3,000+ resume samples, 15 job categories
## 📈 Model Performance
| Model | Accuracy | Use Case |
|-------|----------|----------|
| **Naive Bayes** | 85-90% | Fast categorization |
| **SVM** | 90-95% | High-accuracy classification |
| **SpaCy NER** | 85-90% | Information extraction |
## 🎯 How It Works
1. **📤 Upload** your resume (PDF/DOCX)
2. **🔍 Analysis** using traditional ML models
3. **📊 Results** with job predictions and insights
4. **💡 Recommendations** for resume improvement
## 🐛 Troubleshooting
### Common Issues
**SpaCy model not found:**
```bash
python -m spacy download en_core_web_sm
```
**Models not trained:**
```bash
python ml_models.py
```
**Import errors:**
```bash
pip install -r requirements.txt
```
## 🤝 Contributing
Contributions are welcome! Please feel free to submit a Pull Request.
1. Fork the repository
2. Create your feature branch (`git checkout -b feature/AmazingFeature`)
3. Commit your changes (`git commit -m 'Add some AmazingFeature'`)
4. Push to the branch (`git push origin feature/AmazingFeature`)
5. Open a Pull Request
## 🌟 Show Your Support
If this project helped you, please ⭐ star this repository!
---
**Built with ❤️ using traditional ML approaches that deliver reliable, interpretable results.**
---
*Ready to analyze your resume? Let's get started! 🚀*