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All right.

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Good.
So now I'm going to give you the solution.

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So first of all we're going to 
fit or scaler.

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You know our standardization tool
on the training set.

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So I'm taking the training set
first X train.

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All right.

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And then since I've just explained
that we want

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apply feature
scaling on the dummy variables.

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Well then that means that we will fit
our standard scaler object

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only on these two columns here
containing the ages and the salaries.

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And therefore here I'm going to take
only the two columns here

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for the age and the salary.
And then of course all the rows.

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And remember the trick
to take all the rows

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we just need to add a column,
which means we are taking the range

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from the lower bound to the upper bound,
meaning everything.

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And then to take the columns well.

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Be careful
this is what we have to look at.

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Now this because indeed
we want to take this column and this one.

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And so now the question is
what are the indexes of these columns.

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Well remember that indexes in Python
start from zero.

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So this has index zero.

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Then the second column has an x
one and x two.

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And this one has index with each
column has index three.

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And the salary column has index four.

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But since I want to make this template
as much generic as we can,

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and since when you one hot encode
your categorical variables,

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they always automatically
go as the first column.

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Well we're going to do something
even better.

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We're going to specify the indexes
we want here by three,

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which is the index of the h column.

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And then a simple column. Right.

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Because this will take the range
from the column of the next three,

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which is the edge
up to all the other columns.

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You know, there is not a minus one here.

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So this will take all the remaining
columns from the H, meaning the age

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and the salary. Basically,
this will take these two columns.

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And if you have a larger
matrix of features with numerical values

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in your feature as well,
this will just take all the columns.

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All right. So that's a little trick.

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More elegant let's say.

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And so now we are
of course going to use our object

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which we called as C from which.

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Well we're going to use that fit method
that will indeed

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for each feature of X train compute
the mean of the feature

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meaning the mean of the age,
and then the mean of the salary,

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and then compute the standard deviation
of the feature, the age and the salary.

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And that's exactly what the fit
method will do.

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It will only compute the mean and
the standard deviation of all the values.

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And then you have the transform method
that will indeed apply this formula by,

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you know, transforming each of the values
here of each feature

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into this value
resulting from this formula.

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All right.

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So it's important to understand the
difference between fit and transform fit.

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We'll just get the mean and standard
deviation of each of your features.

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And transform will apply this formula
to indeed transform

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your values
so that they can all be in the same scale.

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All right.

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And now the good news
is that one of the methods

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of the standard scalar
class is actually fit transform,

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which of course will proceed
to the two tools at the same time, meaning

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it will fit your matrix of features
to get the mean and standard deviation.

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And then right after that, transform

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all the values of the features
to turn them into this formula.

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All right.

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So let's call this method
right away to make it efficient.

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You know fit underscore transform form
then some parentheses.

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And now obviously you know what
to input inside this fit transform method.

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Well that's of course
exactly the same as Xtrain here

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because indeed
we will only apply feature scaling

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to our numerical columns here
containing non-integer values.

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Right. Non dummy variables value.