Muthimaeenah/COVID-19-Data-Analysis-and-Visualization

Introduce the goal of the analysis, e.g., "In this notebook, we analyze COVID-19 data for countries like the USA, India, and Kenya, exploring case trends, vaccination rates, and more. # COVID-19 Data Analysis and Visualization This project analyzes global COVID-19 data with a focus on selected countries: the United States, India, and Kenya. It explores case trends, deaths, and vaccinations through data visualization and analysis. ## 🎯 Objectives - Load and clean COVID-19 data from Our World in Data - Analyze key metrics such as total cases, total deaths, and daily new cases - Visualize trends using line plots and choropleth maps - Derive insights about the pandemic's impact on different countries ## 🧰 Tools and Libraries Used - **Python** - **Pandas** – for data manipulation - **Matplotlib** – for visualizations - **Plotly Express** – for interactive maps - **Jupyter Notebook** – for documentation and analysis ## 🚀 How to Run/View the Project 1. Download or clone the repository. 2. Ensure you have Jupyter Notebook installed (`pip install notebook`). 3. Open the `COVID19_Data_Analysis.ipynb` file using Jupyter Notebook. 4. Make sure the dataset file `owid-covid-data.csv` is in the same directory. 5. Run each cell sequentially to view the results and visualizations. ## 💡 Insights & Reflections - **USA** reported the highest total cases and deaths. - **India** experienced the steepest spikes during variant waves. - **Kenya** had relatively fewer cases but also lower vaccination rates. - **Death rates** appeared to decline with increased vaccine rollout. - Data completeness varies across countries, affecting interpretation.