Student Performance Analysis and Visualization using Julia

This project involves analyzing and visualizing factors that affect student performance using the StudentPerformanceFactors dataset from Kaggle. The analysis is conducted in Julia, utilizing key packages such as CSV, DataFrames, StatsBase and Plots. The goal is to extract insights from the data and present them through meaningful visualizations.

Project Objectives

The objective of this project is to:

1. Clean and explore the dataset.
2. Perform statistical analysis on student performance.
3. Create visualizations to uncover patterns and relationships.

Dataset

The dataset used in this project is the StudentPerformanceFactors dataset from Kaggle, which includes various factors such as:

• Study time
• Parental education
• Attendance
• Sleep hours
• Extracurricular activities
• Grades

Download the dataset from Kaggle and place it in the root directory of the project.

Requirements

To run this project, you will need:

• Julia 1.x or later
• The following Julia packages:
  • CSV
  • DataFrames
  • StatsBase
  • Plots

Installation

To install the required packages, run the following commands in the Julia REPL:

using Pkg
Pkg.add("CSV")
Pkg.add("DataFrames")
Pkg.add("StatsBase")
Pkg.add("Plots")

Results

1. The correlation data indicates that sleep and exam grades are weakly correlated. Students with sleep less than and greater than 7 hours of sleep showed small negative correlations, suggesting a narrow advantage for students with an average of 7 hours of sleep.
2. The positive slope between Hours Studied and Exam Scores indicates that increasing study hours is associated with higher scores achieved by students, at least moderately.
3. According to the bar plot, there is a strong correlation between parents' educational level and their children's academic scores. Students with parents who have higher levels of education tend to achieve better scores on academic assessments.