IT4609 — Mini Project | St. Joseph's Institute of Technology, Chennai

An intelligent, data-driven platform that provides personalized career recommendations, performs skill gap analysis, and suggests online courses to bridge those gaps — all in one unified system.

• Overview
• Features
• Tech Stack
• Project Structure
• Getting Started
  • Backend Setup
  • Frontend Setup
• API Reference
• Supported Career Domains
• Future Enhancements
• Contributors

Students today struggle to choose the right career path due to rapidly changing industry requirements and the sheer number of options available. CareerIQ solves this by:

1. Collecting the user's current skills and areas of interest.
2. Matching the profile against a curated career dataset using intelligent similarity scoring.
3. Identifying skill gaps — skills required for the top career matches that the user doesn't yet have.
4. Recommending courses from platforms like Coursera, Udemy, and freeCodeCamp to bridge those gaps.

• Python 3.10+
• Flask — REST API framework
• Flask-CORS — Cross-origin support
• Pandas / NumPy — Data processing and recommendation logic
• CSV-based dataset — Career and course data

• React 18 — Component-based UI
• Axios — HTTP client
• CSS (custom) — Inter font, responsive grid layout

intelligence-career-guidance/
├── backend/
│   ├── app.py                   # Flask API (recommendation engine)
│   ├── requirements.txt         # Python dependencies
│   ├── career_dataset.csv       # 20 career profiles with skills & salary data
│   └── skill_courses_dataset.csv# 33 courses mapped to skills & platforms
│
├── frontend/
│   ├── public/
│   │   └── index.html
│   ├── src/
│   │   ├── App.js               # Root component, routing between tabs
│   │   ├── index.js
│   │   ├── components/
│   │   │   ├── Header.js
│   │   │   ├── Footer.js
│   │   │   ├── CareerForm.js    # Skills & interest input form
│   │   │   ├── RecommendationResults.js  # Top-3 results cards
│   │   │   ├── SkillGapCard.js  # Missing skills display
│   │   │   ├── CourseCard.js    # Individual course tile
│   │   │   └── ExploreCareersList.js     # Browse all careers
│   │   └── styles/
│   │       ├── index.css
│   │       └── App.css
│   └── package.json
│
├── docs/
│   └── architecture.md
│
├── .gitignore
├── LICENSE
└── README.md

• Python 3.10+
• Node.js 18+ and npm

# 1. Navigate to the backend folder
cd backend

# 2. Create a virtual environment (recommended)
python -m venv venv

# On Windows:
venv\Scripts\activate
# On macOS/Linux:
source venv/bin/activate

# 3. Install dependencies
pip install -r requirements.txt

# 4. Start the Flask server
python app.py

The backend will start at http://127.0.0.1:5000

# 1. Navigate to the frontend folder (in a new terminal)
cd frontend

# 2. Install npm packages
npm install

# 3. Start the development server
npm start

The React app will open at http://localhost:3000

Note: Make sure the Flask backend is running before starting the frontend, otherwise the API calls will fail.

All endpoints return JSON.

Health check.

Response:

{ "status": "Career Guidance API is running", "version": "1.0.0" }

Get personalized career recommendations.

Request Body:

{
  "skills": "Python Machine Learning SQL",
  "interest": "Data Science"
}

Response:

{
  "user_skills": ["Python", "Machine Learning", "SQL"],
  "user_interest": "Data Science",
  "recommendations": [
    {
      "career": "Data Scientist",
      "domain": "Data Science",
      "description": "...",
      "avg_salary": 95000,
      "job_demand": "Very High",
      "match_percentage": 75.0,
      "skill_gap": ["Statistics", "Data Visualization", "NumPy"],
      "recommended_courses": [ ... ]
    }
  ]
}

Analyse skill gap for a specific career.

Request Body:

{
  "skills": "Python SQL",
  "career": "Data Scientist"
}

List all careers and domains.

List courses filtered by skill (optional query param).

• 🤖 Advanced ML models (Random Forest, Neural Networks) for better matching
• 🗣️ NLP-based resume parsing and analysis
• 🌍 Real-time job market API integration (LinkedIn, Indeed)
• 💬 AI chatbot for conversational career guidance
• 🌐 Multilingual support
• 📱 Mobile application (Flutter / React Native)
• 👤 User accounts and recommendation history (MySQL)
• 📊 Personality assessment integration

| Name | Roll Number |

| Panimaya James R | 312423205158 |

Supervisor: Mrs. P. Saranya M.E (Ph.D), Assistant Professor
Department: Information Technology
Institution: St. Joseph's Institute of Technology, Chennai — 600 119

This project is licensed under the MIT License.