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Hello, my friends.

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All right.

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Excellent.

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We already did the essentials
with data preprocessing.

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By importing the libraries
and importing the data set.

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And now.
I'd like us to particularly focus.

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On this.

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Feature scaling that we're about to do.

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So I've already taught you feature
scaling in the data preprocessing part.

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You know, part one where indeed
we implemented.

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Together this feature scaling tool at the
very. End.

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You know, feature scaling where we.

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Learned. How to scale with.

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Standardization,
the training set and the test set.

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Separately. Okay. So that's what we did.

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Now we are in a slightly.

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Different situation.

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In the sense that indeed. We.

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Do not have. A split of the data set.

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Between a training set and a test set.

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And that, I remind, is because
we want to leverage the maximum data

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in order to learn the correlations between
the business levels and the salaries.

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So the first thing is we will actually
apply exceptionally features killing.

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On the. Whole matrix of features x.

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Then the second important point
and the second difference with.

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What I taught you before.

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In the same day.

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Reprocessing tools. Implementation.

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Is the fact.

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That in this situation, we only.

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Applied feature
scaling to the features, right?

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We only applied feature scaling.

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To Xtrain and X.

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Which are the features of respectively
the training.

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Set and to test it.

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And we did not. Apply feature scaling to.

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The dependent variable. Vector y.

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But why was that?

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Why didn't we apply. Here feature scaling.

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To the dependent variable. Vector y.

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Well that's because.

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Remember.

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The data
set for our data preprocessing tools.

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Implementation. Was the data set
where the.

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Dependent. Variable was taking values
zero.

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Or one. Right.

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We can.

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Scroll up to have a. Look again.

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Remember these were the purchased.

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Decisions of this retail company.

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And that's the dependent variable.

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Taking the following. Values zero for the.

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Customer didn't buy the product.

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And one the customer bought the product.

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And therefore since it.

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Took value zero. Or one, then we did not.

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Have to apply feature scaling here
because.

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Exactly same as the dummy variables.

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Here, the values.

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Zero and one are already in the same range
as the one resulting from applying.

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Feature scaling onto the features.
All right.

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But now we are in a different situation.

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Indeed.

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This is our new data set position salaries

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where the features are indeed
the levels going.

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From 1 to 10, and the dependent variable.

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Taking values from 45,000 to.

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1 million.

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And so now I have a question for you.

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And after answering this question
you will know what to do in any.

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Feature scaling. Situation.

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So according to you do we have to apply
feature scaling to this.

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Dependent. Variable the salary.

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And the answer is well yes.

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I'm sure you guessed that right.

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Indeed,
we have to apply feature scaling because.

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Same we don't want this.

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Feature, you know,
which takes values much.

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Lower than the.

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Values of the dependent variable
to be neglected by the SVR model.

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And that even if there is not an.

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Explicit.

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Equation, you know, like in multiple
linear regression, this is.

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An explicit equation
because Y is explicitly.

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Resulting from a linear combination
of the.

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Features.

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And for our SVR model,
this is not the case.

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We have an implicit equation.

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But still through this implicit
equation, well,

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if the salary is way
higher than the features.

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And here this is absolutely the case.

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Well accordingly
the feature might be neglected.

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By the model.

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And actually I of course
try to build this SVR model

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without applying feature scaling.

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And you can check.

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That this absolutely doesn't work.

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Indeed, if we don't apply feature
scaling for our SVR.

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Implementation in training.

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On this data set, well,
you will see that the SVR.

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Model will not. Work at all.

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So we have to apply feature
scaling here. On.

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Both the feature where the values.

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Go from 1 to 10,
and the dependent variable

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taking values from 45,000 to 1 million.

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All right.

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And so now you know everything.

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About future scaling.

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You know what to do in any situation.

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You know that.

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You don't apply feature scaling to.

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Some dummy variables
resulting from one hot encoding.

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You know that when a dependent variable
takes binary.

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Values like. Zero and one, you.

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Don't have to apply feature scaling
either, because the values are already.

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In the right range.

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You also know that when the dependent
variable.

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Takes super high values
with respect to the other features,

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then you have to apply feature scaling
to put all the features and.

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The dependent variable in the same range.

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And finally.

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You also know. That
whenever you want to split.

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Your data set.
Into the training set and test it.

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Well, you have to apply feature scaling
after the split.

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All right.

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So now you know. Everything
about feature scaling.

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And by the end of this implementation
you will also know something

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very important
to do. With feature scaling.

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But you know, on the practical. Side
which will be the.

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Inverse transformation.

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Of feature scaling, you know,
when you scale your features or your.

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Dependent variable, at some. Point,
you know, to get the final.

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Prediction and to.

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Visualize the result,
you need to inverse that feature.

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Scaling,
you know, inverse that transformation.

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To go back to the original. Scale.

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And I. Will,
of course, teach you how to do it.

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So that we can not only.

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Get a very relevant.

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Prediction at the end in this.

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Step here, and.

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Also a super nice. Visualization.

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Where.

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Indeed we have the x.

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Axis and the y axis
back in their original scale.

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You know.

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These scales of the levels
going. From 1 to 10 and.

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The scale of the salaries.

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Going from 45,000 to 1.