Answer :
To extract the first two principal components for each data point, you can use principal component analysis (PCA) which is a statistical technique for dimensionality reduction.
PCA works by transforming data from a high-dimensional space into a lower-dimensional space, while preserving as much of the variance as possible.
To draw a scatter plot of two-dimensional representations of the countries using their two principal components, you will need to first fit a PCA model on the data, then use the fitted model to transform the data into two principal components. You can then plot the two principal components in a scatter plot.
When plotting the two principal components, you can mark the countries on the plot by hand. The scatter plot will show how the countries are related to each other in terms of the two principal components.
A pattern you might observe in the scatter plot is that countries that are similar in terms of their characteristics (such as economic development, population, culture, etc.) tend to be clustered together in the plot. For example, countries in Europe may be grouped together, while countries in Africa may be grouped separately. This suggests that the two principal components are able to capture some of the similarities and differences between countries.
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