Student Grade Prediction

Overview

The Student Grade Prediction project uses machine learning techniques to predict students' academic performance based on various features such as study hours, attendance, and previous grades. This project provides an end-to-end solution from data preprocessing to model deployment.

Table of Contents

1. Project Description
2. Installation
3. Dependencies
4. Usage
5. Dataset

Project Description

This project predicts student grades using machine learning models, specifically trained on datasets containing information about student behavior and academic history. The objective is to provide an automated system that can forecast grades, helping educators identify students in need of additional support.

Installation

Clone the Repository

First, clone the repository to your local machine:

git clone https://github.com/OrestBahlai/student_grade_prediction.git

Dependencies

Install Dependencies

Navigate into the project directory and install the required dependencies:

cd student_grade_prediction
pip install -r requirements.txt

MacOS:

To ensure proper functionality of XGB Regressor, install libomp using Homebrew:

brew install libomp

Linux (Ubuntu/Debian):

On Linux, make sure to install libomp using the following:

sudo apt-get install libomp-dev

Usage

• Run the Server: Start the server by running the following command in your terminal:

python server/server.py

• Access the Application: Once the server is running, open the index.html file in your browser. You can do this either through the console or directly in your working environment.

Dataset

Dataset - Student Performance Data Set (https://www.kaggle.com/datasets/larsen0966/student-performance-data-set).