Compulsory Task $2$

Follow these steps:

This task handles the normalisation and standardisation of variables. For more information about normalisation and standardisation, see here. Continue to compulsory task $2$ in data$\_$preprocessing.ipynb, in which you do the following:

$1.$ For the following examples, decide whether normalisation or standardisation makes more sense:

a$.$ You want to build a linear regression model to predict someone's grades, given how much time they have spent on various activities during a typical school week. You notice that your measurements for how much time students spend studying aren't normally distributed: some students spend almost no time studying, while others study for four or more hours daily. Should you normalise or standardise this variable?

b$.$ You're still working with your student$$s grades, but you also want to include information on how students perform on several fitness tests. You have information on how many jumping jacks and push$-$ups each student can complete in a minute. However, you notice that

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students perform far more jumping jacks than push$-$ups: the average for the former is $40,$ and for the latter, only $10.$ Should you normalise or standardise this variable?

$2.$ Visualise the $"$EG$.$ELC.ACCS.ZS$"$ column from the countries dataset using a histogram. Then, scale the column using the appropriate scaling method $($normalisation or standardisation$).$ Finally, visualise the original and scaled data alongside each other. Note EG$.$ELC.ACCS.ZS is the percentage of the population with access to electricity.