AI-Based Resume–Job Matching System

An AI-powered web application that matches student resumes with job descriptions using Machine Learning (TF-IDF + Cosine Similarity) and provides skill gap analysis.

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🚀 Project Overview

This system:

• Accepts resume text input
• Compares it with a dataset of job descriptions
• Ranks jobs based on similarity score
• Displays matched and missing skills
• Built using React (Frontend) + FastAPI (Backend)

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🏗 Tech Stack

Frontend:

• React
• JavaScript
• Fetch API

Backend:

• FastAPI
• Python
• Pandas
• Scikit-learn (TF-IDF + Cosine Similarity)

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📂 Project Structure

ai-job-matching-system/
│
├── backend/
│   ├── app/
│   ├── jobs_dataset/
│   ├── requirements.txt
│
├── frontend/
│
└── README.md

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🖥 Backend Setup & Run Instructions

1️⃣ Navigate to Backend Folder

cd backend

2️⃣ Create Virtual Environment

python -m venv venv

3️⃣ Activate Virtual Environment

Windows:

venv\Scripts\activate

Mac/Linux:

source venv/bin/activate

4️⃣ Install Required Dependencies

pip install -r requirements.txt

If requirements.txt is not generated:

pip install fastapi uvicorn pandas scikit-learn openai python-dotenv

5️⃣ Run Backend Server

uvicorn app.main:app --reload

Backend will start at:

[http://127.0.0.1:8000](http://127.0.0.1:8000)

Swagger API Docs:

[http://127.0.0.1:8000/docs](http://127.0.0.1:8000/docs)

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🌐 Frontend Setup & Run Instructions

1️⃣ Navigate to Frontend Folder

cd frontend

2️⃣ Install Node Modules

npm install

3️⃣ Start React App

npm start

Frontend will start at:

[http://localhost:3000](http://localhost:3000)

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🔄 How the System Works

1. User enters resume text in frontend.
2. Frontend sends POST request to backend /match endpoint.
3. Backend:
   • Loads job dataset
   • Preprocesses text
   • Applies TF-IDF vectorization
   • Calculates cosine similarity
   • Performs skill gap analysis
4. Backend returns ranked jobs.
5. Frontend displays results with match percentage.

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📊 Dataset

The system uses a structured CSV dataset located at:

backend/jobs_dataset/jobs.csv

Dataset contains:

• job_id
• job_title
• company
• skills_required
• job_description

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🛠 Development Notes

• Backend must be running before using frontend.
• Ensure CORS middleware is enabled in FastAPI.
• Keep only one .git folder at root level (avoid nested repositories).

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🎓 Academic Purpose

This project demonstrates:

• Natural Language Processing
• Vector Space Model
• TF-IDF
• Cosine Similarity
• Skill Gap Analysis
• Full Stack Integration

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👨‍💻 Author

Developed as a B.Tech Major Project. By Lakshmi Prasanna Rapaka