CareerQuest: AI-Powered Career Recommendation Platform

Discover your ideal tech career path with personality and aptitude quizzes powered by Machine Learning!

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🚀 Overview

CareerQuest is an interactive web application that helps users find their best-fit career in tech (Software Developer, AI/ML Engineer, Frontend/Backend Developer, etc.) by analyzing their personality traits and aptitude scores using advanced machine learning models (XGBoost). The platform features:

• Engaging Quizzes: Assess your Big Five personality traits and core aptitudes.
• AI-Powered Recommendations: Get personalized career suggestions based on your responses.
• Modern UI: Responsive design with custom styles and illustrations.
• Fast & Scalable Backend: Built with Flask (REST API) and FastAPI (alternative), leveraging scikit-learn and XGBoost.

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🧩 Features

• Personality & Aptitude Quiz (Big Five + Aptitude)
• Real-time career prediction using trained ML models
• Beautiful homepage, quiz, and result pages
• RESTful API for frontend-backend communication
• Easily extensible for new careers or quiz types

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🏗️ Project Structure

backend_xgb/
├── app.py                # Flask backend (main API)
├── main.py               # FastAPI backend (alternative)
├── requirements.txt      # Python dependencies
├── package.json          # Frontend dependencies (React, Vite)
├── static/               # CSS, JS, images
├── templates/            # HTML templates
├── career_aptitude_dataset.csv  # Training data
├── career_dataset.csv           # Additional data
├── xg_boost, transformer, encoder # ML model files (pickled)
└── ...

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⚡ Quickstart

1. Clone the Repository

git clone https://github.com/prajwal2403/backend_xgb.git
cd backend_xgb

2. Install Python Dependencies

pip install -r requirements.txt

3. (Optional) Install Frontend Dependencies

npm install

4. Train & Save ML Models

Note: Ensure xg_boost, transformer, and encoder files are present. If not, train the model using your data and save them as pickles.

5. Run the Flask Backend

python app.py

6. Access the App

Open http://localhost:5000 in your browser.

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🧠 How It Works

1. User takes the quiz (personality + aptitude).
2. Frontend sends answers to /send_array API endpoint.
3. Backend loads ML model (xg_boost), scaler, and encoder.
4. Prediction is made and career suggestion is returned.
5. Result is displayed interactively on the UI.

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🛠️ Tech Stack

• Backend: Flask, FastAPI, scikit-learn, XGBoost, Pandas, Numpy
• Frontend: React, Vite, HTML/CSS/JS
• Deployment: Vercel (see vercel.json)

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📁 Datasets

• career_aptitude_dataset.csv: Personality & aptitude scores mapped to careers
• career_dataset.csv: Additional career mapping data

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🎨 UI Screenshots

🏠 Homepage

📝 Quiz Interface

🎯 Results Page

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🤝 Contributing

Pull requests, issues, and suggestions are welcome! Feel free to fork and improve the project.

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📄 License

This project is licensed under the MIT License.

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👤 Author

• Prajwal2403 (GitHub)

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💡 Inspiration

CareerQuest was built to help students and professionals discover their ideal tech career using data-driven insights and modern web technologies.

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📬 Contact

For questions or collaborations, open an issue or reach out via GitHub.