CareerQuest

CareerQuest is an innovative, AI-driven web application that helps students navigate career choices by analyzing their skills, interests, and personality traits. Leveraging machine learning algorithms, interactive tools, and personalized recommendations, CareerQuest provides an all-in-one career guidance platform.

The project is part of the Smart India Hackathon 2024 under the FeedMind team.

Features

• Career Assessment: Interactive quizzes and mini-games to evaluate users' skills and interests.
• Personalized Career Suggestions: AI-powered recommendations based on individual traits.
• Mentor Matching: Match students with mentors for personalized guidance.
• Resource Hub: Access a wealth of resources for continuous learning.
• Career Exploration Tools: Visually rich and interactive tools for exploring career paths.
• Social Collaboration: Engage with peers and mentors to foster community learning.

Tech Stack

• Frontend: React.js, Next.js, TailwindCSS
• Backend: Node.js, Express.js, MongoDB
• Machine Learning: Python, Scikit-learn, TensorFlow
• Message Queue: RabbitMQ for task orchestration between Node.js and Python ML models

Project Structure

CareerQuest/
│── docs/
│   ├── ML_documentation.md
│   ├── Git_guide.md
│   ├── RabbitMQ.md
│   ├── UI.md
│   ├── Usage_Instruction.md
│
├── ML/
│   ├── models/
│   ├── data/
│   ├── notebooks/
│   ├── scripts/
│   ├── worker.py
│   ├── preprocessing.py
│   ├── prediction.py
│   └── requirements.txt
│
├── Webapp/
│   ├── src/
│   │   ├── app/
│   │   │   ├── fonts/
│   │   │   ├── mentorships/
│   │   │   ├── students/
│   │   │   ├── favicon.ico
│   │   │   ├── globals.css
│   │   │   ├── layout.tsx
│   │   │   └── page.tsx
│   │   └── components/
│   │       ├── Dashboard/
│   │       │   ├── AcademicPerformanceLineChart.tsx
│   │       │   ├── AcademicPerformanceStackedBarChart.tsx
│   │       │   ├── CareerInterestRadar.tsx
│   │       │   ├── CareerTree.tsx
│   │       │   ├── GoalProgressTracker.tsx
│   │       │   ├── ParticipationDonutChart.tsx
│   │       │   ├── PersonalityRadarChart.tsx
│   │       │   ├── ReflectionTimeline.tsx
│   │       │   ├── SkillMatrix.tsx
│   │       │   └── StrengthsWeaknessesBarChart.tsx
│   │       ├── BadgeDisplay.tsx
│   │       ├── CareerTree.tsx
│   │       ├── CTAButton.tsx
│   │       ├── Footer.tsx
│   │       ├── HeroSection.tsx
│   │       ├── Layout.tsx
│   │       ├── Leaderboard.tsx
│   │       ├── MentorCard.tsx
│   │       ├── Navbar.tsx
│   │       ├── QuizCard.tsx
│   │       └── ResourceCard.tsx
│   ├── public/
│   └── server/
│       ├── controllers/
│       ├── models/
│       ├── routes/
│       ├── utils/
│       └── server.js
│
├── .gitignore
├── LICENSE
├── README.md
└── CONTRIBUTING.md

Installation

Prerequisites

• Node.js
• Python 3.x
• MongoDB
• RabbitMQ
• Docker (optional, for RabbitMQ)

1. Clone the Repository

git clone https://github.com/Theory903/CarrerQuest.git
cd CarrerQuest

2. Install Backend and Frontend Dependencies

Navigate to the Webapp folder:

cd Webapp
npm install

3. Install Python Dependencies

Navigate to the ML folder and install the Python dependencies:

cd ../ML
pip install -r requirements.txt

4. Running RabbitMQ

You can either install RabbitMQ manually or use Docker:

docker run -d --hostname rabbitmq --name rabbitmq -p 5672:5672 -p 15672:15672 rabbitmq:3-management

Access RabbitMQ at http://localhost:15672 (default username/password: guest/guest).

5. Running the Project

Start RabbitMQ Worker (Python ML)

cd ML
python scripts/worker.py

Start Backend (Node.js)

cd Webapp/server
npm start

Start Frontend (React)

cd Webapp/client
npm run dev

How It Works

1. Frontend User Interaction: Users take quizzes, explore career paths, and interact with the platform.
2. Backend: Node.js manages API requests, stores data in MongoDB, and sends tasks to RabbitMQ.
3. ML Models: Python-based machine learning models process user data (e.g., quiz results) and return personalized career suggestions.
4. Message Queue: RabbitMQ facilitates task management between the Node.js backend and Python services, ensuring asynchronous, non-blocking operations.

Contribution Guidelines

We welcome contributions to improve CareerQuest. Please read the CONTRIBUTING.md for detailed guidelines.

License

This project is licensed under the Apache-2.0 License. See the LICENSE file for more information.