A web application that predicts the admission chances of Indian students applying to foreign universities using a Linear Learner ML model. Built with Bootstrap 5, Lottie animations, and optionally integrated with a Flask backend.
- Predict admission chances using GRE, TOEFL, University Rating, SOP+LOR, GPA, Research Experience.
- Fully responsive Bootstrap 5 design with mobile-friendly stacked form fields.
- Lottie animations change dynamically based on prediction results.
- Predict button disables after result is shown to avoid duplicate submissions.
- Modal form resets automatically when closed.
The dataset contains parameters important for Masters program admissions:
| Feature | Description |
|---|---|
| GRE | Score out of 340 |
| TOEFL | Score out of 120 |
| University Rating | 1 to 5 |
| SOP + LOR | Strength rating from 1 to 5 |
| GPA | Undergraduate GPA (0-10) |
| Research | 0 = No, 1 = Yes |
| Chance of Admit | Target variable (0 to 1) |
Dataset link: [https://www.kaggle.com/mohansacharya/graduate-admissions]
- Click Test Model on the navbar or home page to open the modal.
- Enter the required fields and click Predict Admission.
- See the prediction result with dynamic animation.
- Front-end: HTML, Bootstrap 5, JavaScript, Lottie animations
- Back-end (optional): Python Flask for connecting a real ML model
- ML Model: Linear Learner
- Clone the repository:
git clone https://github.com/satishf889/admission-predictor.git
cd admission-predictor- (Optional) Set up Flask backend:
python -m venv venv
source venv/bin/activate # Linux/macOS
venv\Scripts\activate # Windows
pip install -r requirements.txt
python Server/app.py- Open
index.htmlin your browser (works fully without Flask for demo).
- This project is for demo and learning purposes only.
- Predictions do not guarantee admission.
Satish Fulwani Email: satish.fulwani63@gmail.com