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Hello and welcome to this art tutorial.

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Now that we get this curve,
and now that we see

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that we have an excellent model
for our problem,

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we are going to validate this model.

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And this is the model

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we were going to choose
to make our final accurate prediction.

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So let's do it.

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I'm actually going to this new code
section that I prepared for you.

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So a first one
to predict the salary associated

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to a 6.5 level
according to a linear regression model.

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And then the second code section
will predict the salary of the 6.5 level

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according to our polynomial regression.

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This one right here with a fourth degree.

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Okay.

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So let's start with the linear regression
prediction.

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We're going to do exactly
the same as in simple linear regression.

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We're going to use the predict function.

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But actually something is going to be
different and will interest many of you.

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This time
we're not going to make some predictions

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on a whole test
set that is on a vector of observations.

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We are going to make a single prediction.

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That is, we're going to make

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a prediction of a single level,
which is going to be the 6.5 level.

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So you're going to see that the syntax

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is going to change the technique to make
this prediction is going to change.

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But it's actually as easy.

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So let's do it.

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We're going to call this prediction
widespread as before.

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So be careful
why pred was before a vector.

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And now it's only going to be
a single prediction value.

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So let's still call it why pred anyway.

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And same as before.

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We're going to take the predict function.

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So let's here take predict. Here we go.

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And now in this predict function
remember we need to specify first

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the regressor
we want to make the predictions with.

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So that's the first argument.

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And then the second argument
is the new data of which

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we want to make the predictions.

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Okay. So let's input the first argument.

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The first argument is our regressor.

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And since we're predicting the result
with the linear regression regressor.

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And since we called this regressor linear,

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then the first argument
we need to input here is Lin rec.

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All right.

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So that's our regressor.

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And now we need to input the new data 
of which we want to make the prediction.

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So this new data is as you understood, a
single element a single observation point.

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And this observation
point is actually the 6.5 level here.

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So what we'll do now is to actually,
you know,

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since this 6.5 level
doesn't exist in our data set,

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we actually need to create a new data
frame containing the 6.5 value.

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We're not going to add
this 6.5 level in our data set.

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We're going to create a new data
set of only one line and only one column.

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That is of only one cell actually.

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And this cell will contain the 6.5 level.

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So let's do this.

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The syntax in order to do
that is very simple.

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We just need to type here
dataframe separated by a dot.

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So frame here we go data frame.

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And now as you can see it
automatically added some parenthesis.

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And in these parenthesis
we need to input level

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equals 6.5.

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And that's it.

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That's actually all you need to do
to make a single prediction

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using the predict function
with a specific regressor.

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That is here the linear regressor.

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Let's check it out.

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Let's select this line and execute okay.

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Why pred correctly generated.

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So as you can see why pred is the values
here, we can already see

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the predicted value of the 6.5
level salary

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and it's actually $330,000,

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so much higher than what
this employee mentioned its salary was.

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So that's actually a good start for us.

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But remember, we don't want to keep
the linear regression model.

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We want to keep the most accurate
regression model.

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That is of course
the polynomial regression model.

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So what we're going to do now
is make this same prediction.

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But this time according to our
polynomial regression model.