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All right.

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So, as usual, you know,
in order to be as much efficient

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as we can, we're going to do this
with scikit learn, right.

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This data science library
that has all the tools

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and the tool that we're about to use
is a class called standard scaler,

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in which will exactly perform
standardization on

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both your matrix of features
of the training set

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and the matrix of features
of the test set.

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So let's do this.

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Let's start by importing this class,
which we have to take first from.

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Well scikit learn of course as k learn.

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And then from which we're going
to get access to the pre processing

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module perfect which is a module
that contains that standard scalar class.

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All right.

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So then we're ready to import what we want
which is the standard scalar class.

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Perfect. So now we have the class.

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And then the natural next step here
is of course to create

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an object of the class
I'm about to reveal soon.

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That answer to one of the most frequent
questions in the data science community.

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So let's create this object.

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We're going to call it
SC for standard scalar.

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And then well this object will be created
as an instance of the standard

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scalar class.

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So I'm taking it here pasting that
right here adding some parenthesis.

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And good news here.

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We don't have any arguments to input
because what we simply want to do

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is get that mean, get
that standard deviation,

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and then apply this formula
to all the values in the feature.

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And for this
we don't need actually any parameters.

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This will automatically do the job.

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All right. So then next step.

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And now
well now is the time for me to reveal

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the answer to that question,
which is one of the most frequently asked

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questions in the data science community.

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And that question is do we have to apply

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feature scaling, you know, standardization

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to the dummy variables
in the matrix of features?

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This is one of the
most frequently asked questions.

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You will find it everywhere
online as well.

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And once again, actually

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the answer is pretty obvious,
but only after you get the explanation.

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So let me tell you the answer.

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The answer is no.

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The answer is no because simply

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well remember the goal of standardization
or feature scaling in general,

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it is to have all the values
of the features in the same range.

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And since I told you that standardization
actually transforms your features

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so that they take values between more or
less minus three and plus three.

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Well, since here are dummy variables
already, take values

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between minus three and plus three
because they're equal to either 1 or 0.

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Well, there is nothing extra
to be done here with standardization

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and actually standardization
will only make it worse because indeed

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it will still transform these values
between minus three and plus three.

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But then you will totally lose
the interpretation of these variables.

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In other words,

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you will lose the information of which
country corresponds to the observation.

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Now we perfectly know that you know,
remember one,

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zero and zero corresponds to France
because that's how it was encoded.

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And then zero, zero
and one corresponds to Spain.

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But you know, after

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we apply feature scaling,
if we apply it on the dummy variables,

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we will get nonsense numerical values
and we will be absolutely incapable

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to say which tuple of three values here
correspond to which country.

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So we will totally lose interpretation.

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And besides, this won't improve at all
to training performance

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because indeed our dummy
variables are anyway already

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between the same scale range
as your other variables

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you will see online that applying
standardization to your dummy

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variables might still increase
slightly the performance.

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You know the final accuracy of your model.

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But I've experimented many times
and I've never seen,

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you know, a considerable difference
that would justify here to apply feature

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scaling on the dummy variables.

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So really don't do this.

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Only apply feature
scaling to your numerical values.

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Right here we have clearly some variables
taking values in a very different range.

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Right.

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The age goes between 0 and 100
and the salary goes between 0 and 100,000.

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So clearly here
if it's better for the machine

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learning model
we have to apply feature scaling.

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But let's leave these dummy
variables alone

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so that we can keep
the interpretability of the model.

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All right.

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So that was the other
very important questions.

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Now I've covered everything.

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There should not be any confusion
left in data preprocessing.

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I'm really glad that you know this.

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And therefore I encourage you now
to press pause on this video.

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And guess what will be the next step
to apply feature

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scaling to R matrices
a feature extreme and excess.