This project provides an in-depth analysis of global COVID-19 data sourced from Our World in Data. Using Python and data visualization libraries, the project highlights trends in cases, deaths, and vaccination progress across selected countries.
- Load and explore real-world COVID-19 data
- Perform data cleaning and preparation
- Analyze and compare total cases, deaths, and vaccinations
- Create visualizations to represent global trends
- Extract meaningful insights from the data
- Python
- Jupyter Notebook
- pandas
- matplotlib
- seaborn
- plotly.express
- Clone this repository:
git clone https://github.com/HopeFlynn/covid-analysis.git
2. Open the `covid_analysis.ipynb` file in Jupyter Notebook.
3. Ensure the CSV file `owid-covid-data.csv` is in the same folder.
4. Run all cells from top to bottom to see the analysis and visualizations.
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## 💡 Insights & Reflections
* The USA recorded the highest number of total COVID-19 cases globally.
* India showed a major rise in vaccination rates after May 2021.
* Kenya had fewer cases and deaths but also fewer vaccinations.
* Interactive charts and choropleth maps made trends and disparities more visually apparent.
* Working with real data provided hands-on experience with data analysis workflows.
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## 📊 Dataset Source
* **Our World in Data:** [owid-covid-data.csv](https://ourworldindata.org/coronavirus)
