Student Performance Prediction Web Application

This repository contains a web application that predicts student performance based on various features like gender, parental level of education, lunch type, and test preparation course. The project follows a modular architecture, utilizing machine learning techniques to predict students' math scores, and is developed using Python, Flask, and multiple regression models.

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Table of Contents

• Project Overview
• Project Architecture
• Folder Structure
• How to Use
• Modeling Process
• Web Application Screenshots
• Technologies Used
• Contributing
• License

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Project Overview

The goal of this project is to predict a student's math score using various demographic and academic features. We employed several machine learning models, evaluated them, and deployed the best-performing model using Flask for real-time predictions. The application allows users to input custom data and receive predicted math scores based on trained models.

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Project Architecture

• Data Ingestion: Collecting and splitting the dataset into training and testing sets.
• Data Transformation: Handling missing values, scaling numerical features, and encoding categorical features using pipelines.
• Model Training: Training multiple machine learning models (Random Forest, Decision Tree, Gradient Boosting, XGBoost, etc.) using GridSearchCV to find the best parameters.
• Prediction Pipeline: Serving predictions via a web interface where users input features, and the system returns the predicted math score.

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Folder Structure

repo/
│
├── artifacts/                 # Contains generated artifacts like models and preprocessing objects
├── catboost_info/             # CatBoost specific info
├── logs/                      # Logging information
├── notebook/                  # Jupyter notebooks for EDA and model training
├── src/                       # Main source code
│   ├── components/            # Components for data ingestion, transformation, and model training
│   │   ├── __init__.py        # Initialization script
│   │   ├── data_ingestion.py  # Script for data ingestion
│   │   ├── data_transformation.py # Script for data transformation
│   │   ├── model_trainer.py   # Script for training models
│   ├── pipeline/              # Pipeline for predictions
│   │   ├── __init__.py        # Initialization script
│   │   ├── predict_pipeline.py # Prediction pipeline script
│   ├── exception.py           # Custom exception handling
│   ├── logger.py              # Logging configuration
│   ├── utils.py               # Utility functions like saving/loading models, model evaluation
├── static/                    # Static files for the web application (CSS, JS)
├── templates/                 # HTML templates for the web application
├── app.py                     # Flask application entry point
├── setup.py                   # Setup file for the project
├── Readme.md                  # README file
├── .gitignore                 # Files to ignore in Git
└── requirements.txt           # Required libraries for the project

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How to Use

Prerequisites

• Python 3.7 or higher
• pip (Python package installer)

Installation

1. Clone the repository:

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

2. Install the required dependencies:

   pip install -r requirements.txt

3. Start the Flask web application:

   python app.py

4. Open your browser and go to http://127.0.0.1:8001/ to access the web interface.

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Modeling Process

Data Ingestion

The data_ingestion.py script reads the dataset and splits it into training and testing sets. The raw data is saved to the artifacts/ folder for reference.

Data Transformation

The data_transformation.py script applies transformations like imputing missing values, scaling numerical columns, and one-hot encoding categorical variables. The transformations are applied via a scikit-learn Pipeline for seamless integration with model training.

Model Training

We train several models, including:

• Random Forest
• Decision Tree
• Gradient Boosting
• XGBoost
• CatBoost
• AdaBoost

The model_trainer.py script uses GridSearchCV to find the best parameters for each model. The best model is saved to the artifacts/ folder as model.pkl.

Prediction Pipeline

The predict_pipeline.py script loads the trained model and preprocessing object, applies transformations to the input features, and returns the predicted math score.

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Web Application Screenshots

Home Page

Prediction Page

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Technologies Used

• Languages: Python
• Web Framework: Flask
• Machine Learning: scikit-learn, XGBoost, CatBoost
• Data Processing: pandas, numpy
• Model Tuning: GridSearchCV
• Model Evaluation: r2_score

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Contributing

Contributions are welcome! Please follow these steps:

1. Fork the repository.
2. Create a new branch (git checkout -b feature-branch).
3. Commit your changes (git commit -m 'Add some feature').
4. Push to the branch (git push origin feature-branch).
5. Open a Pull Request.

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License

This project is licensed under the MIT License.