This project explores global COVID-19 datasets using Python and Pandas, analyzing confirmed cases, deaths, recoveries, weekly trends, and WHO regional breakdowns. Data cleaning involved renaming columns, fixing duplicates, standardizing country names, and ensuring correct numeric types.

Key metrics and trends were computed to identify countries with highest cases, growth rates, and recovery trends. Insights highlighted nations with zero deaths, rapid weekly growth, and regions where recoveries outpaced new infections, providing actionable understanding of the pandemic’s progression.

Tools Used: Python, Pandas, SQL, Jupyter Notebook