KingsleyTechie/COVID-19-Global-Data-Tracker-Project

COVID-19 Global Trends Analysis: Python EDA project analyzing cases, deaths & vaccinations across countries using real-time Our World in Data. Features visualizations, country comparisons & Kenya-focused insights. #DataScience #PublicHealth COVID-19 Global Trends Analysis A Python-based exploratory data analysis of worldwide COVID-19 patterns, vaccination progress, and country-level comparisons using real-time data from Our World in Data. Objectives Analyze global COVID-19 cases, deaths, and vaccination trends over time Compare pandemic metrics across different countries and regions Visualize vaccination rollout effectiveness and impact Generate actionable insights from pandemic data patterns Provide Kenya-specific analysis alongside international comparisons Tools & Libraries Python 3 with pandas, numpy for data manipulation Matplotlib & Seaborn for data visualization Requests for automated data fetching from Our World in Data Jupyter Notebook for interactive analysis and documentation Plotly (optional) for advanced visualizations How to Run # Clone repository and install dependencies pip install pandas matplotlib seaborn requests numpy # Run the Jupyter notebook jupyter notebook covid-19-analysis.ipynb # The notebook automatically fetches latest COVID-19 data during execution Key Insights Vaccination rates strongly correlate with reduced death rates across countries Countries showed distinct pandemic response patterns and outcomes Time-series analysis reveals clear pandemic waves and recovery periods Kenya's vaccination progress compared favorably with similar nations Data-driven approaches are crucial for effective public health responses Note: Project uses real-time data fetching, results will reflect current pandemic statistics