ResumeIQ

ResumeIQ is an advanced Resume Intelligence Platform that leverages Machine Learning to automatically categorize resumes, extract technical skills, and semantically match candidates to job descriptions.

Built with a high-performance Python FastAPI backend and a clean, dependency-free vanilla HTML/JS frontend.

Features

• Resume Classification: Automatically categorizes resumes into 6 distinct industry verticals (AI/ML, Software Development, Data Science, Cloud/DevOps, Cybersecurity, Data Engineering) using a TF-IDF and Logistic Regression model.
• Skill Extraction: Parses and extracts core technical skills from unstructured resume text.
• Semantic Job Matching: Compares candidate resumes against job descriptions using cosine similarity to generate an alignment score.
• Real-time Analytics Dashboard: Features an interactive Confusion Matrix, live usage tracking, and classification metrics.
• Clean Export: Dedicated print stylesheets for exporting analyzed data seamlessly to PDF.
• Responsive UI: A highly polished, modern user interface built from the ground up without heavy frontend frameworks.

Tech Stack

• Backend: Python 3.11, FastAPI, Uvicorn
• Machine Learning: Scikit-Learn, Pandas, NumPy
• Frontend: HTML5, CSS3, Vanilla JavaScript
• Deployment: Docker, AWS EC2

Repository Structure

.
├── backend/
│   └── app/
│       └── main.py          # FastAPI application & API endpoints
├── data/                    # Generated synthetic training data
├── frontend/                # Static HTML, CSS, and JS files
├── models/                  # Saved ML model binaries (Pickle files)
├── scripts/
│   ├── gen_data.py          # Generates synthetic data for the ML model
│   └── gen_pages.py         # Utility script for generating HTML templates
├── Dockerfile               # Production container configuration
└── requirements.txt         # Python dependencies

Running Locally

1. Clone the repository:

   git clone https://github.com/Mrvikas06/ResumeIQ.git
   cd ResumeIQ

2. Install dependencies:

   pip install -r requirements.txt

3. Generate training data and train the model:

   python scripts/gen_data.py
   python -c "import sys; sys.path.insert(0, 'backend'); from models.pipeline import train_classifier; train_classifier()"

4. Start the development server:

   uvicorn backend.app.main:app --reload

5. Access the application: Open your browser and navigate to http://127.0.0.1:8000.

Production Deployment (AWS EC2)

ResumeIQ is fully containerized and ready for production deployment on AWS EC2 or any Docker-compatible hosting environment.

1. SSH into your EC2 instance.
2. Clone the repository.
3. Build the Docker image (this step automatically trains the ML model):

   docker build -t resumeiq .

4. Run the container on port 80:

   docker run -d -p 80:8000 resumeiq

The platform will now be accessible via your EC2 instance's public IP address or configured domain name.

License

MIT License