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.
- 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.
- 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
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
- Node.js
- Python 3.x
- MongoDB
- RabbitMQ
- Docker (optional, for RabbitMQ)
git clone https://github.com/Theory903/CarrerQuest.git
cd CarrerQuest
Navigate to the Webapp
folder:
cd Webapp
npm install
Navigate to the ML
folder and install the Python dependencies:
cd ../ML
pip install -r requirements.txt
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
).
cd ML
python scripts/worker.py
cd Webapp/server
npm start
cd Webapp/client
npm run dev
- Frontend User Interaction: Users take quizzes, explore career paths, and interact with the platform.
- Backend: Node.js manages API requests, stores data in MongoDB, and sends tasks to RabbitMQ.
- ML Models: Python-based machine learning models process user data (e.g., quiz results) and return personalized career suggestions.
- Message Queue: RabbitMQ facilitates task management between the Node.js backend and Python services, ensuring asynchronous, non-blocking operations.
We welcome contributions to improve CareerQuest. Please read the CONTRIBUTING.md for detailed guidelines.
This project is licensed under the Apache-2.0 License. See the LICENSE file for more information.