https://github.com/yash22222/literacy-exploration-analysis

Delve into India's literacy landscape through data analysis. Uncover regional disparities, high/low literacy states & gender imbalances.
https://github.com/yash22222/literacy-exploration-analysis

csv data-analysis data-visualization government-data india literacy literacy-analysis states

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Delve into India's literacy landscape through data analysis. Uncover regional disparities, high/low literacy states & gender imbalances.

• Host: GitHub
• URL: https://github.com/yash22222/literacy-exploration-analysis
• Owner: Yash22222
• Created: 2024-03-31T17:50:47.000Z (10 months ago)
• Default Branch: main
• Last Pushed: 2024-03-31T18:35:24.000Z (10 months ago)
• Last Synced: 2024-11-09T23:22:09.407Z (3 months ago)
• Topics: csv, data-analysis, data-visualization, government-data, india, literacy, literacy-analysis, states
• Language: Jupyter Notebook
• Homepage: https://yashashokshirsath.netlify.app/
• Size: 8.51 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Literacy-Exploration-Analysis

Delve into India's literacy landscape through data analysis. Uncover regional disparities, high/low literacy states & gender imbalances.

Explore India's literacy landscape through data analysis.

## Overview

This repository contains data analysis scripts and visualizations that delve into various aspects of literacy rates across different states and union territories in India. The analysis aims to uncover regional disparities, identify states with high and low literacy rates, and examine gender imbalances in literacy.

## Contents

- `DATASETS/`: Folder containing the dataset used for the analysis.

- `ANALYSIS_SCRIPT/`: Folder containing Python scripts for data analysis.

- `DOCUMENTATION/`: Folder containing visualizations generated from the analysis.

- `README.md`: This README file provides an overview of the repository.

## How to Use

1. Clone the repository to your local machine.

2. Navigate to the `ANALYSIS_SCRIPT/` directory.

3. Run the Python scripts to perform data analysis and generate visualizations.

4. Explore the visualizations in the `DOCUMENTATION/` folder.

5. Feel free to customize the analysis scripts and visualizations to suit your needs.

## Contributing

Contributions are welcome! If you have suggestions for improvements or would like to add new analyses, please open an issue or submit a pull request.

## License

This project is licensed under the MIT License - see the LICENSE.md file for details.