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And now let's add

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the following component
which is our regression line.

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So here we add a little plus
and then enter.

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So to plot the observation points
we used geom point.

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And now very intuitively
to plot the regression line

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we're going to use geom line.

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All right. So pretty easy to remember.

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And then in this line
same as for the points

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we're going to take the x coordinate and
the y coordinate of our regression line.

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So let's see first we need to take
the function esthetic as parentheses.

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And that's where we put the x and the y.

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So in your opinion
what is going to be the x.

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Well the x is going to be the years
experience of the training set

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because we want to have the predictions

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of the salaries
for the observations in the training set.

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So in the x axis
we need to have the number of years

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of experience
of the training set observations.

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So here I'm just going to copy this.

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There and

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paste it here.

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But now what is going to be the Y.

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It's not going to be training set salary

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obviously because we don't want
to take the real salaries now.

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We want to take the predicted salaries.

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So in order to take the predicted salaries

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we will very simply use again
this predict function.

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We cannot use again y pred here
because why print

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is the predicted salaries
of the test set observations.

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But here we want to get the training set
predicted salaries.

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So we will copy this predict

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to take the predictions

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of the training, set salaries
and therefore in new data.

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Here we need to replace test
set by training set.

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And now it's good.

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Now this geom line function here

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will plot all the predicted salaries
of the training set.

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Observations.

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And so for each employee with

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the same number of years of experience,
because here we have the same x,

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00:02:06,033 --> 00:02:09,600
we're going to have both
the real salaries, the salaries

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they actually have in the company
and the salaries predicted

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by our simple linear
regression model for the same employees.

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Okay, so it's ready. The line is ready.

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00:02:19,366 --> 00:02:22,366
But as for the observation points,
we will just add a color.

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And then that means that we need to put
a comma here and press enter.

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00:02:27,200 --> 00:02:30,200
And that's where we put the second
argument which is going to be the color.

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And this time we will pick

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a blue color.

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So again that's just my taste.

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You're welcome to use some other colors
if you prefer.

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00:02:40,366 --> 00:02:42,933
Okay. So now our plot is almost ready.

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We have all the observation
points of the training set.

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We have the regression line.

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00:02:47,766 --> 00:02:49,433
And so now we could plot the graph now.

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But if we want to have a nice graph
that we want to present to people

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like managers or clients
or even your friends, it's always

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00:02:56,566 --> 00:03:00,100
better to add a little title
and some labels on the axis.

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00:03:00,333 --> 00:03:03,633
So let's do this. Plus here and Gigi.

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00:03:03,666 --> 00:03:07,200
So this time it's not GM, it's Gigi
because it's not a geometrical thing.

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00:03:07,200 --> 00:03:09,933
Gigi. Title parenthesis quotes.

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00:03:09,933 --> 00:03:12,933
Because we have to put the title
of our plot in quotes.

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And here we're going to give
the following title salary

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vs experience.

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00:03:21,966 --> 00:03:23,400
And we are going to specify

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that it's the training set.

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Because then we will do

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the same for the test set
and close the parenthesis.

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And that's it. So perfect.
That's our title.

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00:03:35,133 --> 00:03:38,633
And now let's just add
a name of the x axis.

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So to do this
we use the x lab as in parentheses.

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We will say that the name of our x axis
is going to be years

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of experience

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okay.

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And then plus
and then the same for our y axis.

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And obviously it's going to be y lab.

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And he will choose salary
very simply okay.

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And our graph is ready to be plotted.

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So I'm going to select all this.

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And now press Command and Control.

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Press enter to execute.

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00:04:12,666 --> 00:04:14,966
And here is our beautiful graph.

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So that's the training set results
with the real training set observations

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00:04:19,366 --> 00:04:23,366
with the real salaries and red
and the regression line in blue.

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00:04:23,666 --> 00:04:26,666
So let's zoom this graph and let's
give an interpretation.

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00:04:28,133 --> 00:04:28,433
All right.

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00:04:28,433 --> 00:04:30,900
So this is our training set results.

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00:04:30,900 --> 00:04:32,466
So we can clearly make the distinction

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00:04:32,466 --> 00:04:35,466
between the real observations
which are the red points.

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00:04:35,700 --> 00:04:39,133
So these are the real salaries
of the employees in the company.

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00:04:39,800 --> 00:04:42,466
And this blue straight line here

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00:04:42,466 --> 00:04:45,200
is our linear regression model.

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00:04:45,200 --> 00:04:48,200
So that's the prediction
that means that for example

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if we take this employee here 
well we can see that

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00:04:51,533 --> 00:04:55,433
his number of years of experience is a
little more than five years of experience.

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00:04:55,733 --> 00:05:01,066
His real salary is about $65,000.

96
00:05:01,600 --> 00:05:05,233
But our model predicted
for this particular employee,

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00:05:05,566 --> 00:05:10,100
this result here, that means he predicted
that this employee has a salary

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00:05:10,366 --> 00:05:13,800
of a little less than $75,000.

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00:05:14,633 --> 00:05:14,966
Okay.

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00:05:14,966 --> 00:05:17,600
So that's a real important distinction
to understand.

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00:05:17,600 --> 00:05:21,133
This is the real value, and
this is the predicted value of the model.

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00:05:21,133 --> 00:05:24,200
And to get the predicted salary
you need to project this

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00:05:24,200 --> 00:05:27,566
point of the line
here onto the salary axis,

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00:05:28,033 --> 00:05:32,833
which give us a predicted salary
of a little less than $75,000.