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.