COVID-19 Global Data Tracker

A data analysis project that explores the global spread, impact, and response to the COVID-19 pandemic using real-world data.

🌍 Project Overview

This project leverages real COVID-19 datasets to uncover trends, visualize the pandemic's spread, and compare case numbers, death rates, and vaccination efforts across countries such as France, Spain, and Togo.

🎯 Objectives

• Explore and clean COVID-19 global data.
• Visualize trends in total cases, deaths, and vaccinations over time.
• Compare country-specific responses and outcomes.
• Highlight anomalies or interesting patterns using visual analysis.
• Summarize insights in a clear and concise narrative.

🧰 Tools and Libraries Used

• Python
• Jupyter Notebook
• Pandas
• Matplotlib
• Seaborn
• Plotly (for choropleth maps)
• NumPy

🚀 How to Run/View the Project

1. Clone the repository:

   git clone https://github.com/IamSamDdev/python-final-project-assignment.git
   cd final-python-project
   

2.Open the Jupyter Notebook:

jupyter notebook

3.Run the notebook covid_analysis.ipynb cell by cell to view data cleaning, analysis, and visualizations.

Alternatively, view the rendered notebook on GitHub if available.

📊 Key Insights France had the fastest and widest vaccine rollout, followed by Spain.

Togo consistently showed near-zero cases, potentially due to low transmission or underreporting.

France’s early pandemic death rate spiked, possibly due to overwhelmed healthcare or reporting lags.

Choropleth maps reveal high case concentrations in North America and Europe, with sparse data in central Africa and parts of Asia.

Missing data was a significant challenge—especially for early dates and lower-resourced countries.

💭 Reflections Working with real-world data highlighted the importance of data cleaning, careful interpretation of missing values, and the power of visual storytelling in data science.