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Hello my friends.

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Welcome to this new section on now
Decision Tree Regression.

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So we're about to tackle
a new practical activity where we will

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all learn together how to build
the decision tree regression model.

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You will see that it will be super easy
based on what we did before.

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You know, with all the feature scaling
and inverse transformation of the SVR, now

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we will not have to apply feature scaling
and therefore we will just smash this.

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All right.

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Are you ready?

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Are you ready to start.

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Before we go into this part two regression
folder,

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let's just make sure that everyone here
is on the same page.

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I give you the link to this folder
just before this tutorial.

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So you just have to click the link.

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And then now
we should all be on the same page.

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So we're going to go into part
two regression.

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And then section eight decision tree
regression.

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We are almost at the end of the regression
part.

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Congratulations for the great progress
you've made so far.

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And now we're going to go,

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of course, to Python in order
to fine our files for this section.

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So there are two files here.

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The Python implementation of the decision
tree regression model in Ipynb format,

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which you can open with either
Google Colab or Jupyter Notebook.

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And our same position salaries data set

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containing that same data
of the previous company, showing the

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different position levels from 1 to 10,
corresponding to business analyst to CEO,

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and the corresponding salaries
from 45,000 to $1 million per year.

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All right.

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So this time we're going to train
a decision tree regression model

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to understand the correlations
between these two features.

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However,
I have to say something important here.

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The decision tree regression
model is not really well adapted to,

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you know, these simple data sets,

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you know, with only one feature
and the dependent variable vector.

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You'll see what I mean by that at the end,
you know, on the visualization graphs.

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But having said that,
I would like you not to worry

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because to implement
one of the decision tree regression model

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we were about to build
will still work on any other data sets.

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You know, with several features.

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Here we have one feature, but the code
we were about to make will work

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for data
set having any number of features.

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All right.

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So even if the results
won't be beautiful in the end, well,

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you will still be able to use this
decision tree regression implementation

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on your other data sets,
even if they have hundreds of features.

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But then make sure to add some data
preprocessing tools if needed.

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For example,
if your data set has some categorical data

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or missing data, but
you don't have to apply feature scaling

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for decision tree regression,
and neither for random forest regression,

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which will be our next model,
you know, in the next section.

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All right.

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So that's the important thing
I wanted to say here.

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And now we're going to start
our implementation by double clicking

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this file here which you can either open
with Google Collab

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if you like, Google Collab
like me or Jupyter Notebook.

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All right.

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So choose your favorite.

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And now let's open Google Colaboratory.

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It is opening the notebook and

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here we go.

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That's the whole implementation.

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All right.

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So as usual now we're going to create
a copy of this notebook.

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Because this is in read only mode
which means you can't modify it or recode

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it. So we're going to go to file here
and then click here.

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Save a copy in drive.

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And this will as you can see
create a copy of this notebook

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on which you will be able to recode on it.

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You know, re-implement
this decision tree regression model.

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Perfect.

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So now you know the next step.

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We're going to delete the code cells.

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But since it is the third time we actually
work on this position salaries data set.

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And of course each time
it's the same two first steps of the data

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preprocessing phase importing
the libraries and importing the data sets.

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Well,
this time we won't re-implement this.

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We will just leave them
and not delete them.

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So we will just delete
all the code cells from here.

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You know, from the step training,
the decision tree

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regression model on the whole data set.
All right.

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So let's do this.

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Let's start by deleting this one
because we will re-implement it together

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then this one.

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And now this one.

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All right.

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Perfect.

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Also you can notice that at the end
we will only visualize

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the decision tree
regression results in high resolution.

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Because you will see
and I will show this to you

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that the decision tree regression
results in low resolution.

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You know, without applying the grid
solution will absolutely not make sense.

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And I will explain that
at the end of this section.