COVID-19 Epidemiology Data Analysis Project

This project focuses on analyzing global COVID-19 epidemiological data using Python. The objective of the project is to explore pandemic trends, clean and process real-world datasets, and extract meaningful insights about infection, recovery, and mortality rates.

The workflow begins with loading raw datasets containing COVID-19 statistics such as confirmed cases, deaths, testing rates, and country-level indicators. The data is then cleaned and transformed to handle missing values and ensure consistency.

After preprocessing, exploratory data analysis (EDA) is performed to identify patterns and relationships between key epidemiological metrics such as infection rates, testing coverage, and recovery ratios. The project also generates multiple visualizations to better understand the spread and impact of COVID-19 across different regions.

Key highlights of the project include:

Data cleaning and preprocessing of epidemiological datasets

Feature engineering to derive new indicators such as mortality and recovery rates

Exploratory data analysis (EDA) to identify global trends

Visualization of infection, testing, and recovery patterns

Statistical summaries to compare countries and regions

This project demonstrates practical skills in data analysis, public health data interpretation, and visualization of epidemiological datasets.