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Okay, my friends, let's do this.

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Are you ready to implement the code
to visualize your results?

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Here we go. Let's do it together.

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So we're going to do it efficiently.

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We're going to start from

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our polynomial regression implementation
the visualization code at the end.

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So we're going to copy it.

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Then we're going to create a new code
cell here and paste it.

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Okay.

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And now the exercise is to figure out
what we have

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to change here
in order to adapt to our SVR model.

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So let's do this. First
let's make the obvious change.

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Let's replace here
polynomial regression by SVR.

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And then let's see row by row
what we have to change.

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Okay.

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So for the first row remember that x and y

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are the scaled values of the inputs
and the outputs.

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So if we want to have a nice plot
with the original values

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of the position levels and the salaries,
well the first thing we have to do

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is to reverse the scale of the inputs x
and y to put them in their original scale.

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Okay.

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And to do so well,
we're going to take our scaler objects

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X, NSC, y and applied inverse transform
method on each of them.

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Okay.
So let's do this. Let's start with x.

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We take our c x scaler object
from which we apply

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the inverse underscore transform
method onto x.

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Okay. Perfect.

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Now these are the same for y.

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Let's take our c y scaler

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object from which we call again to inverse

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transform form method onto y.

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Excellent.

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And we're done with the first row.

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Now second row.

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Let's take care separately of the input x
and the prediction.

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So for x once again we have to apply
the reverse transformation

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to get the original square.

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So there we go again
we call our c underscore x square object

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from which we apply again
the inverse transform method.

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There we go. Apply to x. Okay.

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And now this is where
things get interesting.

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We're going to take care of
the predictions now of the inputs and x.

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So of course we have to remove
this whole line rect to predict fully

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rect fit transform x
and replace it by something.

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And now the question is
what do we have to replace it with.

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Well it is going to be something similar
to the prediction we made here.

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Only here
we made it for a single observation.

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And so we need to adapt this to X. Right.

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Because now we're making the predictions
of the inputs in the x.

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Okay.

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So what we're going to do
is take this whole prediction here.

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We're going to replace

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that whole line rect to predict rect
fit transform x by the prediction.

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We just copied.

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And now let's see
how we have to adapt this.

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So let's start
from inside the predict method.

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Of course we want to replace 6.5 by x.

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But here
we had to apply x transform on 6.5.

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Because 6.5 is the original square.

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And the predict method
has to be applied on the scaled values.

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But now since we apply the break
method on x and since x is already scaled

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well, we must not reapply the transform
method on x,

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and therefore what we need to replace here
is this whole six transform 6.5

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by indeed x because x is already scaled.

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So there we go.

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And now I think that's it.
I think we're done.

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That was pretty efficient.

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But you know
we're never too safe from human error.

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So we're going to double
check this by executing right away.

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Could see up here.

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So there we go.

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Let's click run cell here.

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And let's see what happens.
And there we go.

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We did it all correctly.

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This is indeed the curve of the SVM model.

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And as we can see the predictions in blue
go pretty close to real outcomes in

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red except for that last one
which would make it an outlier.

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But that's how it is.