📝 Resume Ranking System

A sophisticated web-based application that automatically ranks resumes based on company-specific preferences using advanced NLP techniques, machine learning, and modern web technologies.

🚀 Key Features

• Intelligent Resume Parsing: Extracts 9 key metrics including GPA, skills, projects, and experience using NLP
• Weighted Scoring Algorithm: Implements a sophisticated scoring system (35% skills, 25% education, 25% projects, 15% experience)
• Real-time Ranking: Automatically scores and ranks resumes against multiple companies simultaneously
• Pattern Recognition: Advanced text analysis for project detection and skill extraction
• Responsive Dashboard: Interactive UI for viewing candidate rankings and company-wise performance metrics

💻 Tech Stack

Backend

• Node.js + Express.js
• Python (NLP, NLTK, spaCy, PyTorch)
• MongoDB
• RESTful APIs

Frontend

• React.js
• Tailwind CSS
• Modern UI/UX Design

👥 Team Contributions

Aditya Onam (@AdityaOnam)

Role: Model Designer

• Led the development of the weighted scoring algorithm
• Implemented the core ranking system architecture
• Data Collection
• Generate the data for model training

Aditya Gupta (@code-epic-adi)

Role: Data Engineer

• Implemented resume data extraction and preprocessing
• Developed the PDF text extraction system
• Implemented data validation and cleaning processes
• Generate the data for model training

Varada Patel

Role: NLP Engineer

• Developed the skill extraction system
• Implemented text similarity analysis
• Enhanced keyword extraction using TF-IDF
• Optimized NLP processing for better accuracy

Kushal Kesherwani (@Krishal23)

Role: Deployment Engineer

• Developed the React.js frontend
• Implemented the real-time ranking dashboard
• Created responsive UI components
• Integrated NLP components with the Node.js backend
• Integrated MongoDB for efficient data storage

Sarthak Kushwah

Role: Fairness & Explainability Engineer

• Implemented scoring transparency
• Developed ranking validation system
• Ensured fair evaluation across different resume formats
• Implemented error handling

🛠️ Setup Instructions

1. Clone the Repository

git clone https://github.com/Krishal23/resume-ranking-system.git
cd resume-ranking-system

2. MongoDB Setup

• Ensure MongoDB is installed and running on your system
• Create a .env file in the server root directory:

MONGO_URI=mongodb://localhost:27017/resume_ranking

3. Backend Setup

cd server
pip install -r requirements.txt
npm install
npm start

4. Frontend Setup

cd client
npm install
npm run dev

📊 Project Structure

resume-ranking-system/
├── client/                 # React frontend
├── server/                 # Node.js + Express backend
│   ├── python/            # Python scripts for resume parsing
│   ├── controllers/       # API controllers
│   ├── models/           # Database models
│   ├── routes/           # API routes
│   └── services/         # Business logic
└── README.md

✨ Contributing

Contributions, issues, and feature requests are welcome! Feel free to open an issue or submit a pull request.