https://github.com/priyadarshinijain/students-score-analysis

Student Scores Data Analysis
https://github.com/priyadarshinijain/students-score-analysis

python visualization

Last synced: 6 days ago
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Student Scores Data Analysis

• Host: GitHub
• URL: https://github.com/priyadarshinijain/students-score-analysis
• Owner: priyadarshinijain
• Created: 2024-09-12T14:41:07.000Z (2 months ago)
• Default Branch: main
• Last Pushed: 2024-09-12T14:55:48.000Z (2 months ago)
• Last Synced: 2024-09-13T03:12:16.796Z (2 months ago)
• Topics: python, visualization
• Language: Python
• Homepage:
• Size: 566 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
#  Students-Score-Analysis

This project analyzes student performance data by gender using Python's data analysis and visualization libraries, including pandas, seaborn, and matplotlib. 

The primary goal is to understand how various factors affects student performance in different subjects (Math, Reading, Writing).

# Project Overview

This project includes the following key steps:

Data Loading:

The dataset is loaded using pandas, and basic exploratory data analysis (EDA) is performed to understand its structure.

Data Cleaning:

Unnecessary columns are removed, and missing values are handled.

Data Visualization:

Various plots are created to analyze the distribution of students based on gender and their average performance in Math, Reading, and Writing subjects.

Statistical Analysis: The dataset is grouped by gender to calculate and visualize the average scores for each subject.

# Features

Gender-wise analysis of student scores.

Data visualization using seaborn and matplotlib.

Grouping of data for statistical insights into gender-based performance differences.

# Libraries Used

pandas: Data manipulation and analysis.

matplotlib: Data visualization.

seaborn: Statistical data visualization.

numpy: Numerical operations.

# Dataset

The dataset used in this analysis is Expanded_data_with_more_features.csv, which contains the following key fields:

Gender: Gender of the students.

MathScore, ReadingScore, WritingScore: Scores in respective subjects.

Parent's education background 

Parent's Marital status 

Weekly study hours

Students having sibilings 

# License

This project is licensed under the MIT License.