Theory903/CarrerQuest

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 recommendation.

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.