🎓 Student Performance Prediction Form

A simple Python web application that collects student data through a form, predicts the student’s grade category using a trained machine learning model, and displays the predicted result.

✨ Features:

• 📝 Collects student details via a user-friendly web form
• 🤖 Uses a machine learning model to predict student grade category
• 📊 Displays the predicted grade class dynamically
• ✅ Validates input data before prediction
• 🔧 Easily extendable for different datasets or prediction targets

🛠️ Technologies Used:

• 🐍 Python – Backend logic and ML model integration
• 🌐 Flask – Lightweight web framework for building the form and displaying results
• 📚 scikit-learn – For building/training the prediction model (can be pre-trained)
• 🎨 HTML/CSS – Frontend form interface

🚀 Installation & Usage:

1. Clone the repository:

   git clone https://github.com/yourusername/student-performance-prediction.git
   cd student-performance-prediction

2. Install required packages:

   pip install flask scikit-learn pandas numpy

3. Place your trained ML model file (model.pkl) in the project directory (or update the script to use your model).

4. Run the Flask app:

   python app.py

5. Open your browser and go to:

   http://127.0.0.1:5000/

6. Fill out the form with student data and submit to see the predicted grade class.

⚙️ How It Works:

• The app presents a form to enter student info such as gender, parental education, tutoring status, etc.
• Upon submission, the backend script preprocesses the data and feeds it to the ML model.
• The model outputs a predicted grade class (1, 2, 3, or 4).
• The prediction is shown on the result page dynamically.

📂 Project Structure:

📂student-performance-prediction/
├── app.py            # Flask app and prediction logic
├── model.pkl         # Pre-trained ML model file (pickle)
├── templates/
│   ├── index.html    # HTML form page
│   └── result.html   # Prediction result page
└── README.md         # Project documentation