Data analysis and visualization of COVID-19 trends using Python. Includes data cleaning, time-series analysis, and visual insights to explore global and regional pandemic patterns
📘 Overview
This project focuses on analyzing and visualizing COVID-19 data to uncover patterns, trends, and regional variations. Using Python-based data science techniques, it provides deep insights into infection rates, recovery trends, and mortality patterns through clear and interactive visualizations.
⚙️ Tech Stack & Libraries Used
• Python
• Pandas & NumPy for data cleaning and manipulation
• Matplotlib & Seaborn for static visualizations
• Jupyter Notebook for experimentation and analysis
📊 Key Objectives
• Understand the spread and impact of COVID-19 through data analysis
• Identify peak periods, recovery rates, and case fluctuations
• Visualize patterns to support data-driven decision-making
• Showcase data science and analytics workflow on real-world health data
🔍 Data Processing Steps
• Imported and explored COVID-19 datasets using Pandas
• Cleaned missing and inconsistent data for accurate analysis
• Generated derived metrics such as recovery and mortality rates
• Conducted exploratory data analysis (EDA) to identify key patterns
📈 Visualization Highlights
• Line charts showing the daily progression of confirmed, recovered, and death cases
• Bar plots and heatmaps visualizing country-wise comparisons
• Time-series trend analysis to understand pandemic peaks
• Interactive visualizations for deeper regional insights
🧾 Conclusion
• Successfully analyzed and visualized the global impact of COVID-19
• Derived key insights into infection and recovery patterns across regions
• Demonstrated strong analytical and visualization skills using Python
• Showcased ability to handle real-world, time-sensitive datasets effectively
🚀 Future Work
• Build predictive models using ARIMA, Prophet, or LSTM for forecasting future case trends
• Integrate geospatial mapping using Folium or Plotly to visualize country-level impacts
• Create an interactive COVID-19 dashboard using Streamlit or Dash
• Expand dataset with vaccination and demographic data for richer analysis
🏁 Outcome
This project demonstrates an end-to-end data science workflow — from data collection and cleaning to visualization and insight generation. It highlights practical applications of Python in public health data analytics and serves as a valuable example of real-world data storytelling.