Data Science Project README

Student Performance Prediction using Machine Learning

Student Performance Prediction using Machine Learning

Table of Contents

1. Introduction
2. Installation
3. Data
4. Documentation
5. Contributing
6. License

Introduction

Project Overview

The goal here is to harness the power of machine learning to understand and forecast how different factors influence students' academic outcomes. By diving into various aspects of students' lives and their educational environment, we aim to build a model that can predict their final grades with accuracy.

Purpose

We’re tackling a pressing question: What really impacts student performance? By analyzing data on everything from family background to study habits, we’re looking to uncover insights that could help educators and schools provide better support to students. Our hope is that these insights will lead to improved strategies for enhancing academic success.

Installation

No installation needed.

Data

Source

Cortez,Paulo. (2014). Student Performance. UCI Machine Learning Repository. https://doi.org/10.24432/C5TG7T.

Description

This data approach student achievement in secondary education of two Portuguese schools. The data attributes include student grades, demographic, social and school related features and it was collected by using school reports and questionnaires. Two datasets are provided regarding the performance in two distinct subjects: Mathematics (mat) and Portuguese language (por). In [Cortez and Silva, 2008], the two datasets were modeled under binary/five-level classification and regression tasks.

Documentation

For more detailed documentation, please refer to the documentaion.

Contributing

At this time, this project is not open to contributions. If you have any feedback or suggestions, feel free to reach out, but please note that pull requests or other contributions will not be accepted.

License

This project is licensed under the MIT License. For the full text, see the LICENSE file.