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All right, my friends.

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Are you ready for the final steps
of this implementation?

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First, we're going to visualize
the training set results.

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And second,
we will visualize the test set results.

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So let's do this.

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Let's start by creating a new code cell.

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And there we go.

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Now we just want to plot on a nice graphic
the real salaries

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compared to the predicted salaries.

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So basically we're going to have a 2D plot

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with the x axis
being the numbers of years of experience,

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you know, from 1 to 10
and the y axis being the salaries,

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you know, in the range of salaries
given in this data set.

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And so we will plot in red points
the real salaries

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and in the blue straight line,
the predicted salaries.

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Okay.

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And we will do that both

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for the predictions in the training set
and the predictions in a test set.

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Are you ready?

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Let's do this.

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We're going to first call matplotlib
as a reminder.

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It has the shortcut plt.

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Actually what we're going to call

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is exactly the pie plot module
from the matplotlib library.

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And this is
what is having the PLT shortcut.

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So there we go.

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Let's start by calling Pyplot plt.

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And then we're going to call

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a specific function from this module
which is called scatter.

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And scatter will allow us
to put the red points

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in a corresponding
to the real salaries in the 2D plot.

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Okay, so naturally what we have to input
here are the coordinates

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of these red points
representing the real salaries

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and these coordinates are nothing else
than X train and Y train.

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Right.

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Because remember the x axis will be
the numbers of years of experience.

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That's what is contained in x train.

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And the y axis will be the salaries.

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And that is what is contained in y train.

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So here and scatter here.

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We just have to input the coordinates

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first X train
for the numbers of years of experience.

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There you go.

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And then Y train for the salaries okay.

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And then we

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can add another argument which is color

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and which will allow us
to choose color of these points.

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And as we said, we're going to choose red.

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Perfect.

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Then next step
is to plot the regression line.

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So remember from the intuition lectures,
the regression line is the line

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of the predictions coming as close
as possible to the real results,

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you know, to the real salaries
and therefore the predictions.

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You know, the points corresponding
to the predicted salaries will follow

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a straight line, right,
as in a linear function.

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And therefore here
we're not going to use the scatter method.

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We're going to use the plot method

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because that's what we use
to plot the curve of a function.

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And since our function is linear,
it will actually be a straight line.

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All right.

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So there we go.

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Let's call first the PLT module pyplot.

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Then from this module
we're going to call the plot function.

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There you go.

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And in this function will same
as in the scatter function

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we have to input the coordinates
of the points or line nodes

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corresponding to the predictions.
So let's do this according to you.

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What is the first coordinate
of these predicted salaries.

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Well that's of course X train Y is.

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That is because right now we're
visualizing the result of the training set

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okay.

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So these corresponds

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to the numbers of years of experience
of the employees in the training set.

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That's the X coordinates okay.

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Now we're going to input
the y coordinates.

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And according to you which ones are these.

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Well it's something
we haven't created yet.

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You know we created actually the y print

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variable containing the predicted
salaries of the test set.

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But we haven't created a vector
containing the predicted salaries

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of the training set.
And that's totally fine.

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What we'll actually enter here
as the y coordinate of this plot

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we're about to make,

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are going to be exactly the predicted
salaries of the training set.

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And to get them I'm going to copy this
and paste it right here.

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But instead of inputting here X test

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I'm going to input actually X train
because calling the predict method

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on X train,
meaning on the numbers of years

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of experience of the employees
in the training set will get me

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exactly the predicted
salaries of the training set.

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All right,
just as before with predict X test.

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So xtrain. Yes.

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And then just as before,
we're going to choose a color

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which this time will be blue, right.

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So that we can clearly see the difference

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between the real salaries
and the predicted salaries.

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Okay.
So this will plot the regression line.

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Now next step what can we do.

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So here we're going to add a title.

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And to do this
we have to call the title function.

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And inside in the parentheses
we just have to input in quotes.

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Well the title we want for our graph.

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And so let's say salary

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versus experience okay.

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You can choose any other title
if you prefer another one.

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So salary versus experience.

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And then we're going to specify

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the training set because indeed
then we'll do the same for the test set.

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Now we're just going to add a label
to the x axis and to the y axis.

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And for this
we have to use the x label function

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into which we have to input well the label
we want to display on the x axis.

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And we're going to choose and quote
of course years of experience.

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All right.

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Good.

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Now we're going to copy this because
we're going to do the same for the y axis.

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And the name of the function for
this is y label.

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And for the y label.

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Well we will choose to display salary
okay.

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And finally in order to show the graphic
well we just had to finish here

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with PLT dot show,
you know the show function.

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And this will display
the graphic in the output.