This project analyzes the global impact of COVID-19 by merging multiple datasets to explore the relationships between economic, social, and health indicators and COVID-19 outcomes. Using advanced data visualization and exploratory data analysis (EDA), this study reveals insightful trends about how factors like GDP, life expectancy, and social support influence the pandemic's effects.
- Merged multi-source datasets to create a comprehensive view of COVID-19 impact worldwide.
- Performed Exploratory Data Analysis (EDA) to identify patterns in COVID-19 cases, deaths, and recoveries across countries.
- Visualized correlations between GDP, life expectancy, social support, and COVID-19 outcomes using Matplotlib and Seaborn.
- Generated impactful visualizations including scatter plots and choropleth maps to illustrate geographic and economic patterns.
- Countries with higher happiness scores, strong GDP, and robust social support generally experienced lower COVID-19 death rates.
- A negative correlation between happiness and COVID-19 impact suggests happier nations might be better equipped to handle crises.
- Some exceptions exist, highlighting the need for further analysis into other contributing factors.
- Visualization techniques helped uncover both geographic and socio-economic patterns in COVID-19 data.
- Python (Pandas, NumPy)
- Data Visualization (Matplotlib, Seaborn)
- Data Cleaning & Merging Techniques
- Jupyter Notebook for interactive analysis
- Clone the repository:
git clone https://github.com/SarthakKumarPathak/covid19-analysis.git
- Install dependencies: pip install -r requirements.txt
- Launch the Jupyter Notebook and explore the analysis: jupyter notebook covid19_analysis.ipynb
This project is licensed under the MIT License - see the LICENSE file for details.
Made with ❤️ by Sarthak Kumar Pathak