🎓 College Admission Predictor

Predict a student's probability of getting admission to a graduate program using Machine Learning and an interactive Streamlit web application.

🚀 Features

• Predict admission chances based on academic profile
• Interactive Streamlit Web Interface
• Linear Regression Model
• Data Analysis using Pandas & NumPy
• Model Training with Scikit-Learn
• Visualization using Matplotlib
• Jupyter Notebook for experimentation

---

📂 Project Structure

College-Admission-Predictor/
│
├── app.py
├── admission_predict.csv
├── Admission prediction.ipynb
├── requirements.txt
└── README.md

---

📊 Dataset Features

The model uses the following inputs:

• GRE Score
• TOEFL Score
• University Rating
• SOP Strength
• LOR Strength
• CGPA
• Research Experience

Output:

• Chance of Admission (%)

---

🛠️ Technologies Used

• Python
• Pandas
• NumPy
• Scikit-Learn
• Matplotlib
• Streamlit
• Jupyter Notebook

---

⚙️ Installation

Clone the Repository

git clone https://github.com/YOUR_USERNAME/College-Admission-Predictor.git
cd College-Admission-Predictor

Create Virtual Environment

python -m venv venv

Activate Virtual Environment

Windows

venv\Scripts\activate

Linux/macOS

source venv/bin/activate

Install Dependencies

pip install -r requirements.txt

---

▶️ Run the Streamlit Application

streamlit run app.py

or

python -m streamlit run app.py

The application will open in your browser:

http://localhost:8501

---

📓 Run Jupyter Notebook

jupyter notebook

Open:

Admission prediction.ipynb

and run all cells.

---

🎯 Sample Prediction

Input:

GRE Score         : 330
TOEFL Score       : 115
University Rating : 5
SOP               : 4.5
LOR               : 4.5
CGPA              : 9.5
Research          : Yes

Output:

Chance of Admission: 92.8%

---

📈 Model Performance

• Algorithm: Linear Regression
• Cross Validation Accuracy: ~81%
• R² Score: ~0.82

---

🤝 Contributing

Contributions, issues and feature requests are welcome.

1. Fork the repository
2. Create your feature branch
3. Commit your changes
4. Push to the branch
5. Open a Pull Request

---

⭐ Support

If you found this project useful, consider giving it a star on GitHub.

---

Developed by Vedant Ved 🚀