Online Access: https://career-compass-omega-one.vercel.app/
This project is part of the CSE6242 course at Georgia Tech and involves developing a full-stack data visualization app using React, Node.js, and Python. The app visualizes job market trends and provides predictive analyses of job salaries using various machine learning models.
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The frontend is built with React using Vite for rapid development and Material UI for UI components. Visualization libraries like Recharts and Echarts are utilized to display interactive data graphs.
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The backend is powered by Node.js with Express.js for efficient data handling and routing.
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The predictive model runs on Python, employing techniques like linear regression, neural networks, SVR, and XGBoost to forecast job salaries.
- Node.js 17.9.1
- React 18.2.0
- Python 3.9 or higher
- VITE 4.5.3
- Git
git clone https://github.gatech.edu/CSE6242SPR24/CSE6242_Spr24_T2_Job.git
cd CSE6242_Spr24_T2_Job/Code/react-data-visual-analytics
cd server
npm install
npm start or node server.js
Running successfully on port 3000:
Server is running on http://localhost:3000
The package XGBoost requires the python version 3.9 or higher, such that you should ensure your python version is suitable to run our model successfully.
cd model
python app.py
(use pip install to install packages if necessary)
Running successfully on port 5001:
Running on http://127.0.0.1:5001
cd client
npm install
npm run dev
Running successfully on port 5173:
Local: http://127.0.0.1:5173/
- Vite: Enhances the development experience with fast rebuilds.
- Material UI: Provides a robust set of React components.
- Recharts & Echarts: Used for rendering complex and responsive charts.
- Node.js & Express.js: Facilitate efficient server-side logic and API routing.
- Python: Handles the machine learning model operations.
- client/: Contains the React application's source code and frontend logic.
- server/: Manages backend operations, including data processing and API endpoints.
- model/: Includes the Python machine learning models for salary prediction.
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Start the server and model before initiating the client to ensure all backend services are available.
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For detailed chart interactions, make sure client-side dependencies are correctly installed, especially the specific versions required for map visualizations.
When downloading the map of China, you cannot directly use npm to download the latest version of echarts (version 5.x). Instead, you need to download "echarts": "4.5", otherwise the "react-echarts-map-china": "^0.1.9" package will not be found.
If you encounter any issues or have queries, please reach out via email at xwang3234@gatech.edu or file an issue in the GitHub repository.