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That was my friends.

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Disillusion and congratulations.

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Really, if you got this right, because
you had to pay attention to two things.

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First, the right format of the 2D array

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here for your input
and then transforming that input.

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You know, those features to get the right
skill for your predict method.

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Okay. Perfect.

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So now we're actually ready to run this.

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But we'll actually get a better output,
you know, nicer one

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if we put all this into a print

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you know,
print that output of the predict method.

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And now let's press play.

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And remember let's see if our classifier
manages to predict the right outcome.

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Meaning the right purchase decision,
which according to the test set

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which contains the real result is zero,
meaning that first customer of age

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30 and $87,000
estimated salary doesn't buy that new SUV.

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Okay, so let's press play and let's
see if it is zero and great, it is zero.

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So good job.

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Our model did amazing here on this
single observation single customer.

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Great.

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So now

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we're going to move on to the next step
which will be to predict the test results.

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So that I'm sure you know
exactly how to do.

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Just make sure to figure out
if you need to apply feature

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scaling or not,
and you will get to the right solution.

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Also, I would like you to please display
you know, the vector of predictions.

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You know the vector of predicted
purchase decisions for the test set

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and the vector of the

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real purchase decisions at the same test
sit next to each other

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and you don't have to figure out
how to implement that.

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Again, remember
I want you to be efficient,

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so I encourage you
to take that little piece of code

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we made in many of the regression models
to display this efficiently.

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And that's exactly what we'll do together
in the next tutorial.

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So until then, enjoy machine learning.