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All right, let's do this.

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Let's predict the test set results.

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So as I said in the previous tutorial,
we're going to display two vectors.

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First one being the vector
of the real profits in the test set,

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and the second one being the vector of the
predicted profits in the same test set.

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And we're going to compare next
to each other

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to see if our predictions
are close to the real results.

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All right, let's do this.

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Let's start by creating a new code cell.

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So the first thing I asked you to do was
to indeed get that vector of predictions.

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So first, since we want to get a vector,
I'm going to introduce a new variable,

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which I'm going to call as usual y pred.

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So that's going to be the vector
of the predicted profits in the test set.

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And now how do we get our predictions.

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Well you know the solution.

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It's actually exactly
the same as with simple linear regression.

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We first need to take our regressor
object.

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You know our multiple linear
regression model from which

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we're going to call that predict method.

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And of course in this method we have to
input the features of the test set.

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You know exactly these ones
not including the prophet of course.

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These are all the features with first
the encoded variables

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for the state, then the R&D spend
and the administration spend.

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And the marketing spend.

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All right.

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So these are all the features
which we need to input in our predict

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method here
in order to predict the profits.

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So let's do this X test.

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And there you go.

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Now we get our vector of predicted
profits.

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Perfect. And now the next step.

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The thing that I'm going to do

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is I'm going to call numpy,
which has a shortcut NP from which I'm

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going to call one of its function
which is set underscore print options.

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And now in the parentheses
I'm just going to enter

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precision equals to.

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All right.

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And this will display any numerical value
with only two decimals after comma.

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All right.

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So this will be much more beautiful

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to visualize
and mostly much easier to visualize.

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Okay. Good.

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And now final step.

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This is a step where we're going
to display the two vectors

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of the real profits and of the predicted
profits together next to each other.

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And to do this well we're going to use,
you know, a classic trick with numpy.

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Concatenate.

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Concatenate is a function of numpy
that allows to concatenate

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either vertically or horizontally
two vectors or even arrays.

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And so now we're going to use
the concatenate function to concatenate

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vertically are two vectors of the
real profits and the predicted profits.

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All right
so let's do this. Just follow me.

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I will explain everything as I'm coding.

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So first we're simply going to start
with a print.

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You know because we want to print
that concatenation of the two vectors.

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Then here we go.

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We're going to call that concatenate
function by numpy from which there we go.

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We call the concatenate function
perfect parentheses.

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And now be careful
because this is always a bit confusing.

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The concatenate function by numpy
actually expects as a first argument.

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Check the cell here, the couple of arrays
you want to concatenate

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or you know, vectors.

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So actually all the description is here.

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You know A1, a2 is a sequence of arrays
you want to concatenate.

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And that must have the same shape.

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Well that's perfect for us because
of course our vector of predicted profits

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and the vector of real profits
have exactly the same shape, meaning

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they are one dimensional vector
containing the same number of profits.

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Okay, so that's good.

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But these vectors that we want to
concatenate have to be

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in, you know, some parentheses.

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And these are actually the first argument.

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You know, this double of arrays of vectors
is the first argument

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input in parentheses.

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So here I'm going to add new parentheses.

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All right. This is the first argument.

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And what is going to be inside
this parentheses.

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Well of course are two vectors

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the vector of predicted profits
and the vector of real profits.

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All right.

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So let's first add
the vector of predicted profit.

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This is of course what pred.

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And now because we want to display them
vertically

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and not horizontally remember that.

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You know actually I can show this to you
in data preprocessing tools.

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We actually printed Y at some point.

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There we go.

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You know,
remember when we print the dependent

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variable vector, since it is vector
it is displayed horizontally.

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I actually prefer to display,

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you know, our two vectors of predictions
in real profit vertically.

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So now I'm going to add another trick
to put that vertically.

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You know, to convert that
from being horizontal to vertical.

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And the trick to do that is just add
dot reshape.

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Reshape is an attribute function
that allows

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to, you know,
reshape your vectors or arrays.

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And in order to reshape a vector
from being horizontal to vertical,

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well, we just need to add
as input of this function.

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First, the number of elements in y print.

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And that's actually so far
the number of columns

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because you know it is horizontal.

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And to get that number
we can simply use the

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Len function,
which returns the length of a vector.

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And therefore here
I'm going to input y Brett okay.

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So this is the first element
of the reshape function.

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And the second one is just one.

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All right.

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So what does it mean.

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That means that you want to reshape
your y vector into an array.

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Having Len y red rows.

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Meaning you know that the number of rows
will be equal to the number of strips.

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And then just one column.

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All right.

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That's what it means.

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So it's good that you know this
reshape trick.

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Now you know how to reshape your vectors.

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And you will see that
it is actually going to be much nicer.