An end-to-end Machine Learning web application that predicts whether a student is likely to get placed based on academic and technical performance.
🌐 Live Demo: https://placementai-rjho.onrender.com/
PlacementAI is a Machine Learning web application built using Flask and Scikit-learn. The model predicts a student's placement status using five important features:
- CGPA
- IQ
- Internship Experience
- Number of Projects
- DSA Rating
The application provides:
- ✅ Placement Prediction
- ✅ Prediction Confidence Score
- ✅ Modern Responsive UI
- ✅ Real-time Prediction using Flask API
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https://placementai-rjho.onrender.com/
- Logistic Regression
| Feature | Description |
|---|---|
| CGPA | Student's cumulative grade point average |
| IQ | IQ Score |
| Internship | Internship completed (Yes/No) |
| Projects | Number of projects completed |
| DSA Rating | Data Structures & Algorithms Rating |
- Placed
- Not Placed
Along with the prediction, the model displays a confidence score based on the predicted probability.
- HTML5
- CSS3
- JavaScript
- Flask
- Scikit-learn
- NumPy
- Render
PlacementAI
│
├── app.py
├── model.pkl
├── scaler.pkl
├── requirements.txt
├── Procfile
│
├── static
│ ├── style.css
│ └── script.js
│
├── templates
│ └── index.html
│
├── data
│ └── placement.csv
│
└── notebook
└── placement_prediction.ipynb
- Modern Dark UI
- Responsive Design
- Logistic Regression Model
- Real-time Predictions
- Confidence Score
- Flask REST API
- Deployed on Render
The model was trained on a synthetic campus placement dataset containing academic and technical attributes.
Features used:
- CGPA
- IQ
- Internship
- Projects
- DSA Rating
Target:
- Placement (0 = Not Placed, 1 = Placed)
Kumar Aaditya
GitHub: https://github.com/heliosII