In this project we are going to work with COVID19 dataset, published by John Hopkins University, which consist of the data related to cumulative number of confirmed cases, per day, in each Country. Also we have another dataset consist of various life factors, scored by the people living in each country around the globe. Your task is to merge 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.

The Mini-Project on COVID19 Data Analysis using Python is divided into following Section:

Section 1: COVID19 dataset & Libraries Task: Visualize the data for India, China & US countries

Section 2: Finding a good Measure Task: Calculate the maximum ‘Infection Rate’ for each country and store it in a new column named ‘max_infection_rate’ Create a New Data Frame name ‘Corona Data’ with ‘Country/Region’ as an index and ‘max_infection_rate’ as a column

Section 3: World happiness report dataset Task: Create a DataFrame named ‘data’ by merging ‘happiness_report’ with ‘Corona Data’ and find correlation among all variables

Section 4: Visualization using Folium Map Task: Add the Latitude & Longitude information of countries in ‘data’ and Visualize it using Folium world map

Section5: Visualization of results using Seaborn.

Task: Based on the plot above, comment on the Indicators having strong relationship with COVID19 Infection?