🦠 COVID-19 Global Data Tracker

# 🦠 COVID-19 Global Data Tracker

A comprehensive Jupyter Notebook project that analyzes global trends in COVID-19 infections, fatalities, and vaccination progress using real-time data from Our World in Data. This project features data loading, cleaning, exploratory analysis, visualizations, and global insights derived from multiple countries across different continents.

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## 🎯 Project Objectives

- To analyze the global progression of COVID-19 cases and deaths.
- To assess the effectiveness of vaccination efforts across different countries.
- To visualize trends using line charts, bar plots, and choropleth maps.
- To identify key metrics like case fatality rate and vaccination rate.
- To uncover insights into the relationship between population, infections, and vaccination coverage.

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## 🛠️ Tools & Libraries Used

| Tool / Library       | Purpose                                      |
|----------------------|----------------------------------------------|
| `pandas`             | Data loading, wrangling, and analysis       |
| `numpy`              | Numerical operations                        |
| `matplotlib`         | Static plotting                             |
| `seaborn`            | Enhanced visualizations and heatmaps        |
| `plotly.express`     | Interactive choropleth maps                 |
| `datetime`           | Date manipulation and formatting            |
| `warnings`           | Ignore non-critical warnings                |

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## 🚀 How to Run the Project

1. **Clone the Repository:**
   ```bash
   git clone https://github.com/toshlewi/python-final-project.git
   cd covid19-global-tracker

2. Install Required Libraries: Ensure you have Python 3.7+ and the following libraries installed:

   pip install pandas numpy matplotlib seaborn plotly

3. Launch Jupyter Notebook:

   jupyter notebook

4. Open covid19_global_tracker.ipynb and run the cells sequentially.

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📊 Key Insights & Reflections

• The pandemic had distinct waves with varying peak timings across countries.
• Case fatality rates have generally declined over time due to improved treatments and vaccination.
• Higher population countries tend to have higher absolute case counts.
• There is a strong correlation between population, total cases, and vaccination progress.
• Vaccination rates vary widely, highlighting global disparities in healthcare access.
• Visualization tools like matplotlib, seaborn, and plotly greatly enhance understanding of global trends.

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📌 Final Note

This project demonstrates how real-world data analysis can be used to track pandemics and support data-driven public health decisions. Future enhancements may include policy analysis, time-series forecasting, and integration with WHO policy datasets.

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✅ Developed with Python, Data Science, and Public Health in mind.