https://github.com/asghar-rizvi/eda_student_dataset
This repository contains the results of data analysis and exploratory data analysis (EDA) conducted on the Student_Dataset. The analysis focuses on understanding various factors affecting student grades and visualizing these relationships using Matplotlib and Seaborn.
https://github.com/asghar-rizvi/eda_student_dataset
data-analysis data-analysis-python data-science jupyter-notebook python3
Last synced: 3 months ago
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This repository contains the results of data analysis and exploratory data analysis (EDA) conducted on the Student_Dataset. The analysis focuses on understanding various factors affecting student grades and visualizing these relationships using Matplotlib and Seaborn.
- Host: GitHub
- URL: https://github.com/asghar-rizvi/eda_student_dataset
- Owner: asghar-rizvi
- Created: 2024-07-31T15:53:38.000Z (10 months ago)
- Default Branch: main
- Last Pushed: 2024-07-31T15:58:59.000Z (10 months ago)
- Last Synced: 2024-08-01T20:27:38.044Z (10 months ago)
- Topics: data-analysis, data-analysis-python, data-science, jupyter-notebook, python3
- Language: Jupyter Notebook
- Homepage:
- Size: 263 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: readme.md
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README
# Student Dataset Analysis
## Overview
This repository contains the results of data analysis and exploratory data analysis (EDA) conducted on the Student_Dataset. The analysis focuses on understanding various factors affecting student grades and visualizing these relationships using Matplotlib and Seaborn.
### Dataset
The Student_Dataset includes the following columns:
math_score: Score in mathematics.
history_score: Score in history.
physics_score: Score in physics.
chemistry_score: Score in chemistry.
biology_score: Score in biology.
english_score: Score in English.
geography_score: Score in geography.
grade: Final grade of the student.
gender: Gender of the student.
part_time_job: Whether the student has a part-time job (True or False).
Data Analysis and EDA
#### 1. Data Cleaning
Handling Missing Values: Removed any missing or null values from the dataset.
Column Creation: Added columns for total score and percentage.
Grade Calculation: Added a grade column based on the total score and percentage.
#### 2. Summary Statistics
Computed descriptive statistics to understand the distribution of scores and grades.
#### 3. Visualizations
##### a. Distribution of Grades
Created a bar plot to show the distribution of student grades.
##### b. Effect of Gender on Grades
Used a box plot to visualize the relationship between gender and grades.
##### c. Effect of Part-Time Job on Grades
Analyzed how having a part-time job affects student grades using a box plot.
##### d. Correlation Analysis
Generated a correlation matrix to examine the relationships between different academic scores.
##### e. Impact of Part-Time Job on Grades, Segmented by Gender
Created a grouped bar plot to investigate how part-time employment influences grades, with gender as a hue.