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So we're

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going to open our ipynb file or Python
file.

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Let's open this right now.
And there we go.

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So I remind that
this notebook is in read only mode.

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And since we, as usual,
want to re-implement this from scratch,

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we're going to go to file here to create
a copy by saving a copy in drive.

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And as you see it is creating a copy

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in which we will be able to re-implement
the whole model from scratch.

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And there we go.

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All right.

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So now as usual,
we're going to delete all the code cells

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so that we can re-implement
this from scratch.

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Make sure to only delete the code cells
and not the text cells.

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So we can see the structure.

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And lastly this one perfect.

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All right.

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So this is the whole structure
of this implementation.

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Let's have a look.

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So we're going to start by importing
the libraries then importing the data set.

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Then as I told you
we're going to skip this step

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where we split the data

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set into the training set and the test

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set simply because we want to leverage the
maximum data in order to make our future

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prediction of the salary for the position
level between 6 and 7.

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So we skip this step
and then we move on directly to first.

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Actually the training of the linear
regression model on the whole data set.

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And that's for the simple reason
that we want to compare the two

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models, linear regression
and polynomial regression.

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Because I want to show you
that indeed, a polynomial regression

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model will be much more adapted
to this data set.

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So in the end, we will actually,
as you can see, predict the salary of the

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position level between 6 and 5 with linear
regression and polynomial regression.

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And you will see that we will get a much
better result with polynomial regression.

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Okay.

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So after training the linear
regression model on the whole data set,

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we will train the polynomial regression
model on the whole data set as well.

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Then we will visualize the linear
regression

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results on the whole data set again.

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And then the polynomial regression result.

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And finally
we will make our last two predictions.

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Predicting a new result
with linear regression

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and predicting a new result
with polynomial regression.

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And at the same time you will learn
how to make a single prediction.

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You know, because what we've done so far,
you know, when making predictions,

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was to input the whole test set features,
but you actually still don't know

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exactly how to predict the result
of a single observation.

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And that's exactly in this
practical activity that you will learn it.

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Okay.
So that will be a good new skill to know.

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And I will of course ask you to try to do
it on your own before we do it together.

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And so there you go.

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That's the whole structure.

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And now we're ready to start.

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And we're going to tackle this semi data
preprocessing phase.

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And Fleshlight
starting by importing the libraries

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and thanks to our data
preprocessing template.

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So I'm copying this here pasting that
right here in the new code cell.

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Then we're going to import the data
set using still our data

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preprocessing template.

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Because indeed
we will have only one thing to change

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which is of course
the name of our data set.

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No, actually my mistake.

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We will have two things to change.

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And I will explain why right away.

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So first let's change that obvious thing,
which is the name of the data set.

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So let's see the name of the data
set again position salaries.

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All right.

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So let's do this I'm going to replace
data here by position salaries. Oh

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great.

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And now
the second thing I wanted to change

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is of course the matrix of features X.

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Because remember that this automatically
takes all the columns except the last one.

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But actually here
you know these two columns are redundant.

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It's like we already did

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the label encoding, you know, to encode
each of these positions

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into numerical integers
going from 1 to 10.

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But I wanted to leave that column
because it's pretty nice to see the names

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of the different positions,
you know, from business analyst to CEO.

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So we actually don't
want to include this column.

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And therefore

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we will start to get the column
from the second index, which is index one.

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Right.
Because indexes in Python start from zero.

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So there you go.

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We're only going to take that column.

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And to do so well
we're going to replace that

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empty lower bound
which means the first index by index one

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so that it will indeed
take all the columns from the second one,

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you know, of index one up to the last one,
except the last one, and therefore

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it will take all the columns from this one
except this one, and therefore

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just this one. See, just a simple trick.

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All right. So now we're all good.

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This will still take the last column,
the dependent variable vector

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containing all the salaries.

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So we're all good.

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And we can move on now to the next step
which will be to train

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the linear regression model
on the whole data set.

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Great.

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So would you like to execute this now?

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Yes. Maybe. Let's do it now
so that it's done.

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So let's click this folder here
to be able to upload the file.

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Right now it is connecting to a runtime
to enable file browsing.

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And in a second.

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Yet there we go.

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We should be able to upload the data set.

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All right.

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So we're going to go to our machine
learning.

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00:05:03,700 --> 00:05:06,000
It is that folder
I always put it on my desktop.

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Then part two regression
then section six polynomial regression.

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Then Python.

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And there you go.

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Let's get and upload our position salaries
data set.

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That's all good.

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Now we have it.

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And now we can run these first two cells.

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First importing the libraries

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okay done.

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And now importing the data
set and done as well.

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And now we can move on to the next step
training the linear regression model

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on the whole data set.

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So try to do it on your own
because you now know how to implement

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the linear regression model.

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So I trust you will smash this.
There is no trap.

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And this will be a good way
to practice again.

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So as soon as you're ready

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well let's move on together
to the next tutorial to build that

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first linear regression model together
and then the polynomial regression model.

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And until then, enjoy machine learning.