In this project I have preprocessed and merged datasets to calculate needed measures and prepare them for an Analysis. For the analysis, I have 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, I have used another dataset consisting of various life factors, scored by the people living in each country around the globe. I have 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.

I have done the following tasks in the project:

-Importing COVID19 dataset and preparing it for the analysis by dropping columns and aggregating rows.

-Deciding on and calculating a good measure for our analysis.

-Merging two datasets and finding correlations among our data.

-Visualizing the analysis results using Seaborn.

As a conclusion it is found that Developed Countries are more prone to the infection and have most confirmed cases which might be due to lack of proper testing in undeveloped countries.