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All right, my friends,

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I hope you did amazing with that
new exercise, which was to predict

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the purchase decision of a new customer in
the data set that was the first customer

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who is actually 30 years old and earns

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an estimated salary of $87,000, right.

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If we have a look at our test set,

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you know, the original test
before the scaling, this one,

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this is exactly the customer
you had to input in your predict method

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in order
to predict the purchased decision.

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All right.

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And now in this tutorial
we're going to check two things

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that first, you've got the right
implementation of the predict method

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and mostly
the right input inside the brick method.

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And secondly we will also check
if the prediction is correct.

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And in order to do this
well we're going to have a look at why

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test the purchase decision of that
first customer is exactly this one.

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You know because white test contains

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all the real results here
or the real purchase decision.

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And so we're going to see
if indeed our model was able

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to make a correct prediction
by predicting zero

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for this first customer of age
30 and estimated salary 87 K.

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All right.

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Let's do this.

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Let's proceed to the solution.

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Here it is predicting a new result.

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I will leave the training and there we go.

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So let's create a new code sale.

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And now what was the first step.

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Well of course
the first step was to take our classifier

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because of course the predict method
as any other method

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has to be called from the classifier
object itself.

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So we take our classifier object.

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And then from this object
we're going to call this predict method.

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By the way let me show you this again.

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You have all the predict method

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which are predict log probability
which will return the log.

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You know, the logarithmic function of the
probability that your prediction is one.

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You know that
the purchase decision is one.

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And you also have to predict proba method,
which returns

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of course directly the probability
that the purchase decision is one.

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All right.

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So depending on whether you want
to have directly the final prediction 0

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or 1 or the probability that the dependent
variable is equal to one,

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well you have the choice
between predict and predict proba okay.

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So here we're going to take predict
because we directly want to get the

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prediction whether or not

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that first customer of the test
set purchased yes or no the SUV.

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So here
we're going to add some parenthesis.

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And now now I'm really interested
if you got this right,

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what did you put inside the predict method
in order

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to predict the purchase decision
of that customer who is 30 years old

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and earns an estimated salary of 87,000?

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All right, well, 
first of all, let's remind the essential

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any single observation inside the predict
method

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has to be input
in a double pair of square brackets.

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Why is that?

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That's
because the predict method expects from

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its input a two dimensional array.

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And how do we create a two dimensional
array with only one observation?

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You know, meaning only one row?

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Because what we're about input
is a two dimensional matrix of one

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row and two columns,
where the row corresponding

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to that single customer and columns
correspond in fact to the feature

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is the h in the first column
and salary in the second column.

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So there you go.

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Now I've told you everything
what you have to input here inside

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this second pair of square brackets,
which corresponds to the columns.

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Well, you have to input the two values
of these two features,

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the age and the salary.

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And therefore, since our first customer is
30 years old and earns

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an estimated salary of $87,000,

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well, the two features here
that we had to input in these.

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Therefore, two columns here is first 30

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and then $87,000.

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All right.

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So that's the first thing
you must absolutely do.

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You know that
you must absolutely have the right reflex.

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Okay.

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Input your single observation
within a double

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pair of square brackets
to give the expected

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format of the two dimensional array
to your prediction method.

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All right.

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So that was the first thing.
But then not enough.

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You had to do something else which is

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of course
to scale that single observation.

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Right.

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Because 30 years old here and $87,000

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as the estimated salary
are in the original scale,

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you know, before applying feature scaling
standardization.

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And since our model was actually trained,

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as we can see, on X train and Y train,
which were just previously

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feature scaled, you know,
that's the result of feature scaling.

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Well, as I told you, the Predict method

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can only be applied to observations
where the features

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have the exact same scale
as the one that was used for the training.

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And therefore here
we need to apply the transform method

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in order to give us word break method.

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Not only the right format here,
but also the right skill

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and that's why we have to transform
this single observation

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input in the right format
into that new skill.

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And so what we have to do here
is take our SC object.

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Remember that was the name of the feature
scaling object,

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which we used to apply feature
scaling on both the matrix of features

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of the training set extreme and the matrix
of features of the test set X test.

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So SC and then remember we call

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there
it is just below the transform method.

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The transform method
which has to take as input this

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whole single prediction
input in a 2D array.

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All right. In a second one. And perfect.