mohamedawnallah / covid19-dataanalysis-python

how to preprocess and merge datasets to calculate needed measures and prepare them for an Analysis. In this project, we are going to work 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, we have another dataset consist of various life factors, scored by the people living in each country around the globe. We are going 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.

COVID19 Data Analysis Python

how to preprocess and merge datasets to calculate needed measures and prepare them for analysis. In this project, we are going to work with the COVID19 dataset, published by Johns Hopkins University, which consists of data related to the cumulative number of confirmed cases, per day, in each Country. Also, we have another dataset consisting of various life factors, scored by the people living in each country around the globe. We are going 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...

Correlations:

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Correlation Matrix

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GDP per capita vs max infection rate Correlation

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Healthy Life Expectancy vs max infection rate Correlation

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Freedom to make life choices vs max infection rate Correlation

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_ Conclusions: _

• As we can see from the above analysis that happiness factors are positive, weakly correlated with the maximum infection rate of covid19, and that is really interesting :)
• But it might come to our mind 🤔 that is not 100 percent right conclusion as may be covid19 test in developed countries is not good compared the one in developing countries you are right so next project we will do nearly same steps but with covid19 deaths instead of covid19 infections data set SEE YOU SOON ☺️