🦠 COVID-19 Data Analysis with Python

A complete Exploratory Data Analysis (EDA) project on the global COVID-19 dataset using Python, Pandas, NumPy, Matplotlib, and Seaborn.

This project demonstrates the complete data analysis workflow, from data cleaning and preprocessing to visualization and extracting meaningful insights.

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📌 Project Overview

The objective of this project is to analyze the global COVID-19 dataset and identify trends, compare countries and WHO regions, and visualize important statistics through professional charts.

The project includes:

• Data Cleaning
• Exploratory Data Analysis (EDA)
• Statistical Summary
• Correlation Analysis
• Data Visualization
• Key Findings and Conclusion

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🎯 Objectives

• Clean and preprocess COVID-19 data
• Analyze confirmed, death, recovered, and active cases
• Compare countries with the highest COVID-19 impact
• Analyze WHO regions
• Understand relationships between numerical variables
• Visualize distributions and detect outliers

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🛠️ Technologies Used

• Python
• Pandas
• NumPy
• Matplotlib
• Seaborn
• Jupyter Notebook

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📂 Project Structure

covid19-analytics/
│
├── datasets/
│   ├── raw/
│   └── processed/
│
├── notebooks/
│   ├── 01_data_cleaning.ipynb
│   └── 02_exploratory_data_analysis.ipynb
│
├── images/
│   ├── top10_confirmed_cases.png
│   ├── top10_deaths.png
│   ├── top10_recovered.png
│   ├── who_region_confirmed.png
│   ├── correlation_heatmap.png
│   ├── histogram_confirmed.png
│   ├── boxplot_confirmed.png
│   └── pairplot.png
│
├── README.md
├── requirements.txt
└── .gitignore

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📷 Sample Visualizations

Top 10 Confirmed Cases

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Correlation Heatmap

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Top 10 Deaths

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Top 10 Recovered Cases

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🔍 Key Findings

• The United States recorded the highest number of confirmed cases, deaths, and recoveries.
• The Americas reported the highest total confirmed COVID-19 cases among WHO regions.
• Most countries had relatively low confirmed case counts, while a few countries contributed a large share of global cases.
• Strong positive correlations exist between confirmed cases, deaths, and recoveries.
• Confirmed cases distribution is highly right-skewed with several significant outliers.

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🚀 Installation

Clone the repository:

git clone https://github.com/BishalBh900/covid19-data-analysis-python.git

Navigate to the project folder:

cd covid19-data-analysis-python

Install required libraries:

pip install -r requirements.txt

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▶️ How to Run

1. Open the project in VS Code or Jupyter Notebook.
2. Navigate to the notebooks/ folder.
3. Run:

01_data_cleaning.ipynb

4. Run:

02_exploratory_data_analysis.ipynb

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📈 Skills Demonstrated

• Data Cleaning
• Data Wrangling
• Exploratory Data Analysis (EDA)
• Statistical Analysis
• Data Visualization
• Correlation Analysis
• Python Programming
• Analytical Thinking

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📚 Libraries Used

• pandas
• numpy
• matplotlib
• seaborn

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👨‍💻 Author

Bishal Bhandari

• 🎓 BSc CSIT Student
• 🐍 Python Developer
• 📊 Data Analytics Enthusiast

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⭐ If you found this project useful, consider giving it a star on GitHub!