Student Exam Performance Predictor

A Machine Learning web application that predicts a student's Maths score based on demographic and academic input features.

Live Demo

Deployed on Render:

https://studentperformanceprediction-zpwn.onrender.com/

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Project Overview

This project uses a Machine Learning Regression model to predict student Maths performance using:

• Gender
• Race/Ethnicity
• Parental Level of Education
• Lunch Type
• Test Preparation Course
• Reading Score
• Writing Score

The application is built using:

• Python
• Flask
• Scikit-learn
• CatBoost
• XGBoost
• HTML/CSS

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Features

• Student score prediction
• Interactive web interface
• ML pipeline integration
• Model deployment using Flask
• Hosted on Render
• End-to-end ML workflow

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Tech Stack

Backend

• Python
• Flask

Machine Learning

• Scikit-learn
• CatBoost
• XGBoost
• Pandas
• NumPy

Frontend

• HTML

Deployment

• Render
• Gunicorn

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Project Structure

MLStudentRegression/
│
├── artifacts/
│   ├── model.pkl
│   └── preprocessor.pkl
│
├── notebooks/
│
├── src/
│   ├── components/
│   ├── pipeline/
│   ├── exception.py
│   ├── logger.py
│   └── utils.py
│
├── templates/
│   ├── home.html
│   └── index.html
│
├── app.py
├── requirements.txt
├── setup.py
└── README.md

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Installation

Clone the repository

git clone https://github.com/prerna-m01/StudentPerformancePrediction

Navigate to project folder

cd your-repo-name

Create virtual environment

python -m venv venv

Activate virtual environment

Windows

venv\Scripts\activate

Linux/Mac

source venv/bin/activate

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Install Dependencies

pip install -r requirements.txt

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Run the Application

python app.py

Open in browser:

http://127.0.0.1:5000

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Deployment on Render

Build Command

pip install -r requirements.txt

Start Command

gunicorn app:app

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ML Workflow

1. Data Ingestion
2. Data Preprocessing
3. Model Training
4. Model Evaluation
5. Prediction Pipeline
6. Flask Deployment

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Screenshots

Home Page

Prediction Page

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Author

Prerna Mishra

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