Python-Data-Analysis-COVID19

This project is based on Coursera project course. For full information, please visit this link. In this analysis, I examined the COVID-19 death dataset for 2020. My exploration included several key outputs: Top 5 countries with the highest death rates: This identified the nations most severely impacted by the pandemic in terms of mortality. Average death rate: This provided a general sense of the global fatality rate for COVID-19. Countries with the highest and lowest death rates: This highlighted both the extreme ends of the mortality spectrum. Furthermore, I combined the 2020 death rate data with the 2019 world happiness dataset. This allowed me to conduct a linear regression analysis, investigating the potential relationship between COVID-19 death rates and factors associated with national happiness.

For your information, I used Jupyter Lab program to run this file. Alternatively, you can use Visual Code Studio and install the Jupyter Lab extensions.

Implementation

1. Find the following statistic results:

• Top five countries with the highest death rate:
  

• Average death rate:
  

• The highest death rate:
  

• The lowest death rate:
  

• The death time series:
  

• Death across all countries:
  

2. Conduct a linear regression analysis, investigating the potential relationship between COVID-19 death rates and factors associated with national happiness. In addition, to determine the correlation:
   • If R^2 is close to 1, it suggests a strong correlation.
   • If R^2 is around 0.5, it suggests a moderate correlation.
   • If R^2 is close to 0, it indicates a weak correlation.

• The correlation betwen GDP per capita against the death rate (Weak correlation):
  

• The correlation betwen Social support against the death rate (Weak correlation):
  

• The correlation betwen Healthy Life Expectancy against the death rate (Weak correlation):
  

• The correlation betwen Freedom to make choices against the death rate (Weak correlation):
  

Future Works

• Fix the death across all countries chart as it looks messy on X-axis.
• Build an interface that can allow us to do the computation as required and needed.
• Add testing/normalization test for processing raw data too.