COVID19-Data-Analysis-Using-Python

In this project, I had done how to preprocess and merge datasets to calculate needed measures and prepare them for an Analysis. In this project, I had worked with the COVID19 dataset, published by John Hopkins University, which consists of the data related to the cumulative number of confirmed cases, per day, in each Country. Also, have another dataset consist of various life factors, scored by the people living in each country around the globe. I had merged these two datasets to see if there is any relationship between the spread of the virus in a country and how happy people are, living in that country.

Project Structure

The hands on project on COVID19 Data Analysis using Python is divided into following tasks:

Task 1: Introduction

Understand the purpose of the project, the datasets that will be used, and the question we will answer with our analysis.

Task 2: Importing COVID19 dataset

Import COVID19 dataset and prepare it for the analysis by dropping columns and aggregating rows.

Task 3: Finding a good Measure

Decide on and calculate a good measure for our analysis.

Task 4: Importing World happiness report dataset

Import World happiness report dataset, dropping useless columns and Merge it with COVID19 dataset to find correlations among our data.

Task 5: Visualizing the results

Visualize our results using Seaborn.