Career Quest 🚀

A comprehensive career guidance platform that combines AI-powered course recommendations, personalized learning paths, and interview preparation tools to help students and professionals navigate their career journey.

✨ Features

• AI-Powered Course Recommendations: Get personalized course suggestions based on your interests and career goals
• Interactive Interview Practice: Practice with AI-generated interview questions and receive detailed feedback
• Course Discovery: Explore and search through a vast database of online courses
• Personalized Learning Roadmaps: Get tailored learning paths for your chosen career
• Speech Analysis: Upload audio responses and get AI-powered feedback on your interview performance
• Modern Web Interface: Clean, responsive UI built with React and TypeScript

🛠️ Tech Stack

Frontend

• React 18 with TypeScript
• Vite for fast development and building
• Tailwind CSS for styling
• React Router for navigation
• Axios for API calls
• Lucide React for icons

Backend

• Flask (Python web framework)
• Scikit-learn for machine learning models
• Pandas for data manipulation
• Google Speech Recognition for audio processing
• Gemini AI API for question generation and feedback
• TF-IDF Vectorization for course recommendations

Machine Learning

• Decision Tree Classifier
• Random Forest Classifier
• Naive Bayes Classifier
• Content-based Filtering for course recommendations

🚀 Getting Started

Prerequisites

• Node.js (v16 or higher)
• Python (v3.8 or higher)
• Git

Installation

1. Clone the repository

   git clone https://github.com/AryanMittal11/Career_Quest.git
   cd Career_Quest

2. Backend Setup

   cd backend
   
   # Install Python dependencies
   pip install -r requirements.txt
   
   # Create environment file
   cp .env.example .env
   # Edit .env and add your GEMINI_API_KEY

3. Frontend Setup

   cd ../frontend
   
   # Install Node.js dependencies
   npm install

Environment Variables

Create a .env file in the backend directory with the following variables:

GEMINI_API_KEY=your_gemini_api_key_here
FLASK_ENV=development
FLASK_DEBUG=True

To get a Gemini API key:

1. Visit Google AI Studio
2. Create a new API key
3. Copy and paste it in your .env file

Running the Application

1. Start the Backend Server

   cd backend
   python app.py

   The backend will run on http://localhost:5000

2. Start the Frontend Development Server

   cd frontend
   npm run dev

   The frontend will run on http://localhost:5173

📁 Project Structure

Career_Quest/
├── frontend/                 # React TypeScript frontend
│   ├── src/
│   │   ├── components/      # Reusable UI components
│   │   ├── pages/          # Page components
│   │   └── ...
│   ├── package.json
│   └── ...
├── backend/                 # Flask Python backend
│   ├── templates/          # HTML templates
│   ├── static/            # CSS and JS files
│   ├── uploads/           # Audio file uploads
│   ├── app.py             # Main Flask application
│   ├── course.py          # Course-related utilities
│   ├── requirements.txt   # Python dependencies
│   └── *.csv             # Training data files
└── README.md

🔧 API Endpoints

Course Recommendation

• GET / - Main course recommendation interface
• POST / - Submit interests and get course recommendations
• GET /api/courses?degree=<degree_name> - Get courses for specific degree

Interview Practice

• GET /generate-questions - Interview question generation interface
• POST /api/get-questions - Generate AI-powered interview questions
• POST /api/submit-audio - Submit audio response for feedback

Course Discovery

• GET /coursera - Course search interface
• POST /predict - Search and get course recommendations

📊 Machine Learning Models

The application uses multiple ML algorithms for course recommendation:

1. Decision Tree Classifier - Fast and interpretable
2. Random Forest Classifier - Improved accuracy through ensemble learning
3. Naive Bayes Classifier - Probabilistic approach
4. Content-based Filtering - TF-IDF vectorization for course similarity

🎯 Usage Examples

Getting Course Recommendations

1. Select your interests from the provided list
2. Choose a machine learning algorithm
3. Get personalized course recommendations with accuracy metrics

Interview Practice

1. Enter your target job domain
2. Get AI-generated interview questions
3. Record your audio response
4. Receive detailed feedback and improvement suggestions

Course Discovery

1. Search for courses using keywords
2. Get similar course recommendations
3. Explore course details and links

🤝 Contributing

We welcome contributions! Please follow these steps:

1. Fork the repository
2. Create a feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

📝 License

This project is licensed under the MIT License - see the LICENSE file for details.

👥 Team

• AryanMittal11 - Project Lead & Developer

🙏 Acknowledgments

• Google Gemini AI for powering the interview questions and feedback
• Scikit-learn for machine learning capabilities
• The open-source community for amazing tools and libraries

📞 Support

If you have any questions or need help, please:

1. Check the Issues page or contact Collaborators vidhan365@gmail.com , nikhilshaw575@gmail.com
2. Create a new issue if your question isn't already answered
3. Contact the maintainers

---

Made with ❤️ for helping people find their perfect career path!