Student-Score-Prediction-Model

An end-to-end machine learning project to predict student scores based on input features, deployable as a web application.

Table of Contents

• About the Project
• Features
• Repository Structure
• Setup & Installation

About the Project

This project aims to predict student scores using historical data and features such as study hours, past performance, and other relevant factors. It walks through the entire workflow: data exploration, model training, artifact creation, and deployment as a web app.
It is structured as a modular, reproducible workflow suitable for extension.

Features

• Data exploration and preprocessing via Jupyter notebook
• Model training and artifact generation
• Web application interface (app.py) for making predictions
• Clear project structure for maintainability
• Requirements and setup files for easy environment creation

Repository Structure

Key modules and their roles

• src/: Contains the business logic — data loading, preprocessing, feature engineering, model training and prediction.
• app.py: Launches the web server, accepts user input, passes it through the trained model, returns predicted score.
• artifacts/: Stores the trained model file(s), preprocessor objects (scalers, encoders), logs and metadata.
• Notebook/: Interactive exploration, EDA, modeling experiments, visualizations.

Setup & Installation

├── artifacts/ ← Generated model artifacts (trained model, pre-processors, logs)
├── catboost_info/ ← (If using CatBoost) internal model info directory
├── Notebook/ ← Jupyter notebooks for exploration/modeling
|   └── data/
|       └── stud.csv
├── src/ ← Source code modules
│   ├── components
|   |   ├── __init__.py
|   |   ├── data_ingestion.py
|   |   ├── data_transformer.py
|   |   └── model_trainer.py
|   ├── pipeline
|   |   ├── __init__.py
|   |   └── predict_pipeline.py
|   ├── __init__.py
|   ├── exception.py
|   ├── logger.py
|   └── utils.py
├── templates
├── app.py ← Main Flask (or similar) web application entry-point
├── setup.py ← Setup file to install project as package
├── requirements.txt ← Python dependencies file
├── .gitignore ← Git ignore patterns
└── README.md 

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Prerequisites

• Python 3.10 or above
• Git

Installation steps

Clone the repository

git clone https://github.com/AgrimGupta04/Student-Score-Prediction-Model.git cd Student-Score-Prediction-Model

(Optional) Create a virtual environment

python -m venv venv source venv/bin/activate # On Linux/macOS venv\Scripts\activate # On Windows

Install dependencies

pip install -r requirements.txt

(Optional) Install package for local import

pip install -e .