Student Performance Analysis - Multiple Linear Regression

This repository focuses on analyzing student performance using Multiple Linear Regression models. The project involves using several essential Python libraries such as NumPy, Pandas, Seaborn, Plotly, Matplotlib, and Scikit-learn for data processing, visualization, and model building.

Introduction

This project aims to predict student performance based on various factors like study time, previous test scores, and parental education level, using Multiple Linear Regression. The dataset is analyzed through data exploration, visualizations, and model building.

Dataset

The dataset used for this project is available on Kaggle. You can access it from the following links:

• Dataset: Student Performance Multiple Linear Regression
• Notebook: Student Performance Analysis - Multiple Linear Regression

Libraries Used

Here are the main libraries used in this project:

{numpy , pandas , seaborn , plotly.express , matplotlib.pyplot , sklearn.linear_model , LinearRegression}