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Identifying Patterns and Trends in Campus Placement Using Machine Learning

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PlacemenTrack - Campus Placements Analyzer

The PlacemenTrack is a cutting-edge application that revolutionizes the way educational institutions, students, and recruiters approach campus placements. This app harnesses the power of data analysis, machine learning, and web technologies to provide a comprehensive solution for optimizing the placement process.

Docker images:

Backend: https://hub.docker.com/r/partheev8/campus-backend

Frontend: https://hub.docker.com/r/partheev8/campus-frontend

Block Diagram

block-diagram

Flow Chart

flow chart

Folder Structure

campus-placement-analysis/
├─ frontend - React application
├─ backend - Flask application
├─ EDA_Notebooks - Contains All EDA work of this project and their datasets
│  ├─ datasets/
│  │  ├─ Predicted_data.xlsx
│  │  ├─ gdp.xlsx
│  ├─ Campus_Placements_Insights
│  ├─ Salary_Prediction.ipynb
│  ├─ Placement_prediction.ipynb
│  ├─ GDP_VS_Placements_EDA.ipynb
├─ project reports - Project Documents
│  ├─ block-diagram.png
│  ├─ flow-chart.png
│  ├─ project-report.pdf
├─ screenshots - Contains sheetshots of insights, predictions, and analytics.
├─ .gitignore

How to run the project in your system

Clone the repo

Run frontend

  • cd frontend
  • npm install
  • npm run dev

Note: Node runtime must be installed to run the above commands. Create .env.local file and add VITE_BACKEND_URL variable name with endpoint as value.

keep VITE_BACKEND_URL=http://localhost:5000 while running the application locally in development mode.

Run backend

  • cd backend
  • pip install -r requirements.txt
  • flask run

Note: Python must be installed in the system (v3.9+ preferred). Configure env variables in backend/.env file.

Add these enviroment variables - ML_DEPLOYMENT_API_KEY, RESUME_PARSER_API, RECOMMEND_SKILLS_API

You can visit the application at http://localhost:3000 in development mode.

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