So let's go back to our polynomial regression model.
And here let's select these two columns we are only interested in.
So to do this we will reset the data set.
So I'm taking my data set here.
And again I'm taking my data set then brackets and then this bracket.
Here we will input the indexes of the columns
we want to keep to train our machine learning model.
So let's see what these indexes are.
Okay.
So we have three columns indexes and are started one.
So this column has index one.
This column has index two and this column has next three.
So since we want to keep these last two columns
then we want to keep the indexes two and three.
Okay. So let's input that.
to input this we
can simply input two column three.
Let's check it out.
Let's select this line and execute.
And as you can see now our data set if we look at it
only has these two columns level and salary.
So as a reminder
level is the independent variable and salary is the dependent variable.
So we will use the correlations
between the levels and the salaries to train our nonlinear machine
learning polynomial regression model to predict some new salaries,
such as the salary associated to a 6.5 position level.
Okay, so let's go back to the polynomial regression model.
Our data set is well important.
Now it's well prepared.
We have everything we need.
And now let's move on to the next step.
So the next step is to split the data set into the training set and the test set.
And actually this time and normally this time we will not do that.
And the reason is that we only have ten observations here.
So this is a very small data set.
And by the way I chose this data set because we're still shaping
our intuition of machine learning.
We're still learning the basics of machine learning.
And therefore I choose the simple data set because we have two dimensions here.
So we will be able to plot the form of the polynomial regression model itself,
as well as the future nonlinear regression models.
Okay, so let's go back to our model.
And as I just said,
we will need to split the data set into the training set and the test set.
So what I'm going to do now is I'm going to put all this section in comments.
So let's do this.
Press command or control plus shift plus C.
And that puts all your selected lines in comments.
So no training set and no test set will be created.
Okay.
And the last step now is feature scaling.
And the good news is that we won't need feature scaling either.
And the reason is simple.
It's that because a polynomial regression model is actually
a multiple linear regression model with polynomial terms.
You know, instead of having different features
like features, that represents something very different,
we are taking a first feature which is actually the position levels from 1 to 10.
And as the other independent variables that will be in our multiple
in our regression model, we will take the squares
and other exponents of these levels. Here.
Okay.
So actually that's done. That was actually very simple.
No need for training set or test set.
No need for feature scaling.
Just some little changes in the data set here with the indexes.
And we're all fine.
We're ready to build our first nonlinear regression model.
That is the polynomial regression model.
And that's what we will be doing in the next tutorial.
So I look forward to creating this bluffing detector with you.
And until then enjoy machine learning.