All right, let's keep the suspense.
And now, whenever you're ready, let's create a copy of this file.
Because this one is in read only mode.
And therefore we want to create a copy in order
to re-implement it from scratch together.
And make sure to focus.
Because, you know, this will be quite more advanced than before.
So let's put that right here.
And there we go.
Now let's remove the cells.
And let's try not to look at the final result.
Right.
You just put your eyes around here.
You don't look at the results.
Because let's try to keep the surprise of the predicted salary up to the end,
up to the final execution of the code.
All right, so let's remove all this and this as well.
This is well, only the code cells. Right.
Please keep all the text cells so that we can keep
the well highlighted structure of this implementation.
All right.
And now really make sure to not look at the result.
You know the output of the code cell because that's where you will see
you know, the final results.
All right.
I managed to do it.
I did not look at it. Even if of course I know the result.
But what I mean is that it was totally possible not to look at it.
All right, so that's the whole structure.
And now we're ready to start this implementation.
And so there you go.
I suggest that we really tackle in a flash light.
I love seeing this I know but
we will tackle in one second that data preprocessing phase.
Except that part because that part is actually not that direct.
You know, there are going to be some things to explain.
All right. So let's do this.
Let's start by importing the libraries.
And of course to do this we're going to use our data preprocessing template.
I hope you have it prepared. So there you go.
Let's first get this code
to import the libraries in a new code cell base here.
Then we're going to import the data set.
And actually to do this we'll get our polynomial regression implementation.
Because you know this is the exact same data set.
And we don't have to explain that again because you perfectly know and understand.
Now how this works. Right.
And well, that's the thing I wanted to do in one second.
Now we're going to quickly upload the data set in order
to, you know, execute the cell after this one, of course.
So now it's connecting to a runtime to enable file browsing.
And in a second we should be able to see the upload button.
Perfect. So upload.
And then as usual we're going to go into our machine learning is that folder
I like to put it on my desktop but find it wherever you put it on your computer.
Then forward to regression then support vector regression and Python.
And there you go.
That's the data set open and it will be uploaded inside the notebook.
Perfect.
All right then we're going to execute these two cells.
And this one as well.
All right. And perfect.
Now we have the data set.
And now we're going to stop here for this tutorial.
And we will tackle this next step feature scaling in the next one.
And make sure to be prepared and ready to tackle this
because we'll have a few things to do.
It's not difficult, but just make sure to be focused for that next tutorial,
because I'm going to explain a new situation
where you have to apply feature scaling in a certain way.
So I'll see you in the next material.
And until then, enjoy machine learning.