COVID-19 Data Analysis

This repository contains Python code to analyze and visualize COVID-19 data using Pandas, Numpy, Matplotlib and Seaborn.

The main dataset analyzed is a CSV file containing various metrics like confirmed cases, deaths, hospital beds, GDP, population demographics etc. for different countries and regions over time.

Key Tasks

The Jupyter notebook performs the following key tasks:

• Importing libraries and loading the CSV data
• Exploratory data analysis on metrics like cases, deaths, hospital beds etc.
• Visualizing trends by location using plots like histograms, heatmaps, bar charts
• Fitting an exponential model to cumulative cases over time
• Calculating statistics like death rate and F1 score
• Analyzing metrics by location and over time periods

Libraries Used

• Pandas - For data manipulation and analysis
• Numpy - For numerical processing
• Matplotlib - For basic visualizations
• Seaborn - For advanced visualizations

Usage

The notebook contains code, visualizations and explanations for the data analysis. To use:

• Clone the repository
• Install the required libraries
• Run the Jupyter notebook
• The notebook is self-contained and can be run end-to-end to reproduce the analysis. Comments are included extensively to explain the purpose and workings of each section.

Author

Ghufran Hyder

Let me know if you would like any sections expanded or additional details included in the README.