https://github.com/codewithmayank-py/covid19-data-analysis-using-python

COVID-19 and Happiness Analysis
https://github.com/codewithmayank-py/covid19-data-analysis-using-python

data-analysis data-analysis-python data-visualization dataset jupyter-notebooks numpy pandas python3 seaborn

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COVID-19 and Happiness Analysis

• Host: GitHub
• URL: https://github.com/codewithmayank-py/covid19-data-analysis-using-python
• Owner: CodeWithMayank-Py
• Created: 2024-04-18T10:22:52.000Z (9 months ago)
• Default Branch: main
• Last Pushed: 2024-04-18T11:48:55.000Z (9 months ago)
• Last Synced: 2024-11-17T05:28:17.426Z (2 months ago)
• Topics: data-analysis, data-analysis-python, data-visualization, dataset, jupyter-notebooks, numpy, pandas, python3, seaborn
• Language: Jupyter Notebook
• Homepage:
• Size: 296 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# COVID-19 and Happiness Analysis

## Overview

This repository contains the Jupyter Notebook file for analyzing the COVID-19 dataset in conjunction with the happiness dataset. The analysis aims to explore the relationship between the COVID-19 pandemic and happiness levels across different regions.

## Dataset

- **COVID-19 Dataset**: The COVID-19 dataset contains information about the number of confirmed cases, deaths, and recoveries across various countries and regions.

- **Happiness Dataset**: The happiness dataset includes metrics related to happiness levels, such as GDP per capita, social support, life expectancy, freedom to make life choices, generosity, and perceptions of corruption.

## Analysis

The Jupyter Notebook file (`COVID19_Happiness_Analysis.ipynb`) provides detailed analysis and visualization of the datasets. Some of the key aspects covered in the analysis include:

- Exploratory Data Analysis (EDA) of COVID-19 and happiness datasets.

- Correlation analysis between COVID-19 statistics and happiness metrics.

- Visualization of trends and patterns using matplotlib and seaborn libraries.

## How to Use

To replicate or explore the analysis:

1. Clone this repository to your local machine.

2. Ensure you have Jupyter Notebook installed.

3. Open `COVID19_Happiness_Analysis.ipynb` using Jupyter Notebook.

4. Follow the step-by-step instructions in the notebook to run the analysis.

## Dependencies

- Python 3.9

- Jupyter Notebook

- Pandas

- NumPy

- Matplotlib

- Seaborn

## Contributors

- [Mayank Paliwal](https://github.com/CodeWithMayank-Py)

## Acknowledgements

- COVID-19 [Dataset](https://github.com/CodeWithMayank-Py/Covid19-Data-Analysis-Using-Python/blob/main/datasets/covid19_Confirmed_dataset.csv).

- Happiness [Dataset](https://github.com/CodeWithMayank-Py/Covid19-Data-Analysis-Using-Python/blob/main/datasets/worldwide_happiness_report.csv).

Feel free to contribute to this project by opening issues or pull requests.