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Hello and welcome to this art tutorial.

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So in the previous tutorials
we prepared our data set.

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Then we fitted the simple linear
regression model to our training set.

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And now we're going to predict the test
set results.

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Because we trained our model
on the training set.

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And we want to see how it can predict
new observations.

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So to do this we're going to create
our vector of prediction y pred.

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I'm calling it why pred.

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Because this is a vector of predictions.

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That is the vector that will contain
the predicted values of the test set.

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Observations.

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And we are going to use the predict
function.

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So I'm going to write that here.

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So that's the predict
function parentheses.

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And in this predict function
it's really really simple.

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We will just need to input two arguments.

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The first one is our regressor.

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So that's the simple linear regressor.

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And then comma.

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And then the second argument is new data.

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So that's that's the name of the argument.

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But then this new data is the data

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that contains the new observations
that we want to predict the result.

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And so obviously that's the test set.

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And actually that is ready.

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We are ready to build
our vector of predictions.

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I will just remind
what this is going to do.

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Here we have a test set.

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This is the column of the number
of years of experience.

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So what will happen
is that our simple linear regression

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will predict for each of these test set
observations, the salary.

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So it won't be the same salary here
because these are the real salaries,

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the salaries
that actually exist in real life.

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But since we saw that
there is a strong linear

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dependency between the years of experience
and the salary, the predictions

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returned by our simple linear regression
model should be close to the salary.

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Anyway, we're going to see that
in the final step of this simple linear

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regression model, because we will
visualize on the graph all the results.

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But so far,
just to explain you what we are expecting.

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So let's go back to our model
and let's select

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this command or Control
plus enter to execute.

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And here we go.

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The vector of prediction is now created.

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So let's have a look at it.

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We're going to 
type widespread here in the console.

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And here it is.

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These are all the salary predictions
for our ten observations in our test set.

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Okay.

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So for example let's let's have a look
at the first observation.

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The real value,
the real salary of the first,

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employee of the test set
was 46,000 and $205.

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And let's see what our model predicted.

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It predicted $37,000.

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So okay, so that's not too close.

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But then if we look
at the second observation,

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which is the fourth employee,
let's call it the fourth employee.

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His real salary is 43,000 and $525.

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And our model predicted $44,322,
which is this time much closer.

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Okay, so that's it with this tutorial.

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And now the next tutorial
is my favorite part,

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the part where we visually see
what everything that we've been making.

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That is, we will be visualizing on a graph
the training set results

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and the test set results.

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So we will make two graph a first graph
showing the predictions

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of the training set observations,
and the second graph

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showing the predictions
of the test set observations.

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And we will see how the simple linear
regression line approach

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the real observations.

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So I look forward to seeing you
in the next tutorial.

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And until then enjoy machine learning.