🦠 COVID-19 Data Analysis with SQL

📊 Project Overview This project contains an in-depth SQL-based analysis of COVID-19 data, focusing on key metrics such as infection rates, vaccination trends, and mortality statistics. The project demonstrates SQL skills in data cleaning, transformation, and visualization through detailed queries.

🔍 Features and Insights Data Cleaning: Standardized date formats, handled missing values, and normalized data types. SQL Queries: Analyzed COVID-19 cases by region and date. Calculated vaccination rates and population coverage. Identified mortality rates and case fatality ratios. Rolling averages for better trend visualization. Key Metrics: Infection and recovery trends. Vaccination progress by region. Death rates and case distribution.

💾 Dataset Description The dataset contains the following fields: Date: The reporting date of the data. Region/Location: Country or region names. Population: Total population in each region. New Cases/Deaths: Daily reported cases and deaths. Vaccination Data: New and total vaccinations administered.

🚀 Future Enhancements Adding advanced visualizations using Power BI or Tableau. Performing predictive analysis for future COVID-19 trends. Optimizing SQL queries for performance.