Python-Visualization-_Project

🦠 COVID-19 Data Visualization Project

📊 Project Overview

This project analyzes COVID-19 data using Python to uncover patterns and trends related to confirmed cases, deaths, and vaccinations.
The goal is to visualize how the virus spread over time and across different regions using various data visualization techniques.

🧰 Tools & Libraries

• Python
• Pandas – for data manipulation and cleaning
• Matplotlib – for creating visualizations
• Seaborn – for advanced data plots
• Jupyter Notebook – for analysis and visualization

📁 Dataset

The dataset contains COVID-19 data, including:

• Country/Region
• Date
• Confirmed Cases
• Deaths
• Recoveries

📈 Key Visualizations

• Trend of confirmed cases over time
• Comparison between countries
• Death vs Recovery rates
• Vaccination progress (if available)

🧠 Insights

Confirmed cases show a very strong positive correlation with both recovered and deaths. This confirms that higher infection numbers directly drove both outcomes. Active COVID-19 cases initially increased sharply, reaching clear peaks during outbreak waves, and then gradually declined as recoveries and control measures took effect.

🎯 Conclusion

"Our Python-based analysis has effectively uncovered critical patterns in the COVID-19 data, demonstrating the power of data science in understanding complex global health crises.

📎 Files

• https://drive.google.com/file/d/1oTxMoH5lx1nqkqD-G7iC1SkymHyaHB8v/view?usp=drive_link` (notebook) https://docs.google.com/presentation/d/1qocw8if80XJUC1lxU-A54BGvOzUtFzd3/edit?slide=id.p1#slide=id.p1(persentation)