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Okay.

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Actually, this is all we had to change
in this regression template.

3
00:00:05,166 --> 00:00:08,100
And now we are ready
to execute the sections one by one

4
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to create our model
and find out about the final results

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and the final verdict,
whether it's truth or bluff.

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So let's do it.

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I'm going to import the dataset first.

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All right here we go.

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Well import it. We can have a look.

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So these are our data set with the levels
as the independent variable

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and the salaries
as the dependent variable.

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Okay great.

13
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And now we need to split the dataset
into the training set and the test set.

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No need for feature scaling

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because we're using the very popular
E10 71 library that includes it.

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And now let's create our model.

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So I'm going to select
this whole section here.

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Press Command and Control Plus
and do the cute done regress or created.

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00:00:48,900 --> 00:00:49,800
Great.

20
00:00:49,800 --> 00:00:52,666
And now let's predict the new results
which I'd like to start

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00:00:52,666 --> 00:00:56,500
by visualizing the SVR results
first or predicting the new result.

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00:00:56,500 --> 00:01:00,833
Well, let's actually predict the result
because this is the most exciting step.

23
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And let's keep the best for the end.

24
00:01:02,833 --> 00:01:04,533
But this step is actually very exciting.

25
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Also, because

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we are getting the final prediction,
we are getting the final predicted salary.

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So you know it says you want
if you're doing actually this code

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by yourself before me,
that's actually a very good exercise.

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So I really encourage you to do this.

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Choose whatever you want to execute first.

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It's ready anyway.

32
00:01:21,300 --> 00:01:23,300
You don't need to change anything
more now.

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00:01:23,300 --> 00:01:26,100
So I'm going to execute that right now.

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And actually we obtain a predicted salary

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of $177,000.

36
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Great.

37
00:01:33,100 --> 00:01:36,600
So remember this employee who we are
negotiating the future salary right

38
00:01:36,600 --> 00:01:39,733
now, said that its previous
salary was 160 K.

39
00:01:40,033 --> 00:01:45,166
And our model predicted
that its previous salary was 177 K.

40
00:01:45,500 --> 00:01:49,200
So first of all, that's actually close
to what this employee said.

41
00:01:49,200 --> 00:01:52,133
And besides,
it's on the good side of the negotiation.

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So that's actually pretty good.

43
00:01:53,600 --> 00:01:57,333
And we can be satisfied with this result
and our model.

44
00:01:57,333 --> 00:02:01,866
But to be really satisfied let's see
what's happening with the graphic result.

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00:02:01,866 --> 00:02:04,866
So I'm going to execute this section

46
00:02:04,866 --> 00:02:07,733
and let's have a look at the SVR model.

47
00:02:09,600 --> 00:02:10,933
And here it is.

48
00:02:10,933 --> 00:02:14,233
I'm going to zoom on it and better okay.

49
00:02:14,700 --> 00:02:18,900
So first of all this model fits
very well most of the data points.

50
00:02:19,266 --> 00:02:22,266
As a reminder the real observation
points are the red points here.

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00:02:22,500 --> 00:02:26,966
And all the points in this blue curve here
which is the SVR model itself

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00:02:27,233 --> 00:02:28,466
are the prediction points.

53
00:02:28,466 --> 00:02:32,100
So for example, if we're taking this
red observation point here,

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00:02:32,366 --> 00:02:35,366
well the prediction is perfect
because the prediction point

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00:02:35,366 --> 00:02:38,333
is the projection of this red points
on the blue curve.

56
00:02:38,333 --> 00:02:41,000
So it's actually the red point itself
because the red point

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is on the blue curve.

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00:02:42,466 --> 00:02:43,866
So that makes a perfect prediction.

59
00:02:43,866 --> 00:02:45,133
But here for example

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if we take a less accurate prediction
but still a very good one,

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00:02:48,200 --> 00:02:50,100
then we take the real observation
point here.

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00:02:50,100 --> 00:02:53,100
The red point
we projected on the blue curve.

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And that's the difference
between the real salary

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00:02:57,000 --> 00:02:58,866
and the predicted salary, which is here.

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If we projected back on the y axis,
which contains the salaries.

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00:03:02,700 --> 00:03:07,100
But you can see that for all of these
points from this one to actually this one,

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the blue curve is actually getting
very close to the real observation

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points, the red points.

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And so the predictions
are very close to real results.

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But that's for all these points
of the data set except for this one here.

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This one is left alone
which by the way corresponds to the CEO.

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So I'm sorry for the CEO,

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00:03:26,733 --> 00:03:31,500
but the reason for what is happening here
is that this is actually an outlier.

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Not to call the CEO, not liar.

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00:03:33,266 --> 00:03:36,266
But this is an outlier
because as you can see,

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this point is actually far from
the other points in terms of salaries.

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00:03:41,100 --> 00:03:45,500
The CEO has a much higher salary
than the previous positions.

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So for the SVR model, that's an outlier.

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00:03:47,766 --> 00:03:49,800
And therefore he actually didn't
consider it.

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00:03:49,800 --> 00:03:53,100
It's like it excluded this point
in the model by not looking at it

81
00:03:53,400 --> 00:03:56,733
and making its predictions
on these points here.

82
00:03:57,266 --> 00:04:00,466
So that's specifically due to the SVR
algorithm itself.

83
00:04:00,466 --> 00:04:02,466
But there are a lot of parameters.

84
00:04:02,466 --> 00:04:05,533
And you can actually play
with the parameters to change

85
00:04:05,533 --> 00:04:08,833
the way the SVR model perceives outliers.

86
00:04:09,000 --> 00:04:10,033
So for example,

87
00:04:10,033 --> 00:04:13,900
these parameters are the penalty
parameters, the regularization parameters.

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00:04:14,233 --> 00:04:16,766
It's most of the time
well described in the description.

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00:04:16,766 --> 00:04:20,300
And of course the E 1071 library includes
such techniques.

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00:04:21,000 --> 00:04:25,533
But we're not going to do it here in this
tutorial because we actually don't need

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00:04:25,533 --> 00:04:29,800
to get a good prediction of the CEO salary
and remember what we actually needed.

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What is a good prediction

93
00:04:30,633 --> 00:04:33,633
of the previous salary of this employee
we are negotiating with?

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00:04:33,833 --> 00:04:36,300
And this employee has a level 6.5.

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00:04:36,300 --> 00:04:39,533
And around this point we can see that
the SVR model fits very well

96
00:04:39,533 --> 00:04:40,633
to our data set.

97
00:04:40,633 --> 00:04:45,366
And we actually get a prediction of 177
K quite close to the real

98
00:04:45,366 --> 00:04:49,533
or the mentioned salary of this employee,
which is 160 K.

99
00:04:49,900 --> 00:04:51,100
So that's actually pretty good.

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00:04:51,100 --> 00:04:54,233
And therefore the verdict of truth

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or bluff
according to our SVR model is rather true.