That was my friends.
Disillusion and congratulations.
Really, if you got this right, because you had to pay attention to two things.
First, the right format of the 2D array
here for your input and then transforming that input.
You know, those features to get the right skill for your predict method.
Okay. Perfect.
So now we're actually ready to run this.
But we'll actually get a better output, you know, nicer one
if we put all this into a print
you know, print that output of the predict method.
And now let's press play.
And remember let's see if our classifier manages to predict the right outcome.
Meaning the right purchase decision, which according to the test set
which contains the real result is zero, meaning that first customer of age
30 and $87,000 estimated salary doesn't buy that new SUV.
Okay, so let's press play and let's see if it is zero and great, it is zero.
So good job.
Our model did amazing here on this single observation single customer.
Great.
So now
we're going to move on to the next step which will be to predict the test results.
So that I'm sure you know exactly how to do.
Just make sure to figure out if you need to apply feature
scaling or not, and you will get to the right solution.
Also, I would like you to please display you know, the vector of predictions.
You know the vector of predicted purchase decisions for the test set
and the vector of the
real purchase decisions at the same test sit next to each other
and you don't have to figure out how to implement that.
Again, remember I want you to be efficient,
so I encourage you to take that little piece of code
we made in many of the regression models to display this efficiently.
And that's exactly what we'll do together in the next tutorial.
So until then, enjoy machine learning.