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

Acknowledgements

• COVID-19 Dataset.
• Happiness Dataset.

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