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All right, so we're almost ready to start.

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Now we're going just to upload,
you know, the data

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sets by clicking this little folder here.

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And then right now it is connecting
to runtime to enable file browsing.

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And in a second we should be able
to see the upload button.

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Here we go to upload.

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Indeed the data set.

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And now we are on my desktop.

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That's where I put my machine learning.

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It is a folder,
but make sure to go wherever you saved it.

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And now inside
we're going to go to part two regression

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and then decision tree regression
and then Python.

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And then there you go.

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You select your position series
data set and click open.

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And this will upload the data
set inside the notebook.

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And now we're ready to start.

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Let's just run these two cells here.

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The first one to import the libraries.

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And then the second one
to import the data set.

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All right. Perfect.

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So now we have the data set.

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And of course the matrix of features
X containing only the position levels

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and the dependent variable vector
containing the salaries.

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All right.

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And here quick reminder this model
that we were able to build.

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You will totally be able to implement it
on your data sets.

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And you will only have two things
to change which are first of course

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the name of the data set.

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Here you will put the name
of your data set.

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And then in a matrix of features.

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Well maybe you will want to select
all the columns.

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And here we just excluded the first column
because this contains only the

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positions and strings which are exactly
the same as the levels in this column.

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So of course we didn't want to include it.

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But check your data sets.

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Check if you want to include
all the columns, and mostly check

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if you need to apply some of the tools
of your data preprocessing toolkit,

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which are either,
you know, taking care of missing data

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or encoding categorical data. Right?

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So you would need to check the variables
and see if there are some categorical

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variables and strings.

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If the order matters, like for example,
the size of a clothes,

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well you will apply label encoder.

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And if the order doesn't matter,
like some countries or some states,

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well you will apply column transformer
with one hot encoder.

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All right.

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And then you don't have to apply
feature scaling.

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You can totally split your data
set into the training set and test it

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if you want to evaluate your model
on you observations.

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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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Why is that?

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That's because you know the predictions

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from a decision tree regression or random
forest regression model, or results

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from successive splits of the data,
you know, through the different nodes

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of your tree,

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and therefore there are not some equations
like with the previous models.

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And that's why, of course,
no feature scaling is needed

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to, you know, split
the different values of your feature

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into these different categories,
leading to different predictions.

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We can still do this
with the original scale of your features,

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even if your features
take different ranges of values.

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All right.

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So remember this no feature scaling.

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And then check for the other tools.

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But just for your future data sets
where you would like

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to apply decision tree regression okay.

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Perfect. So we have everything.
We have the data set.

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And now we are ready to build the decision
tree regression

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model on the whole data set. Right.
This time.

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We don't want to split it.

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We want to leverage the maximum data
to understand the correlations

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in this small amount of information.

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So we will train it on the whole data set.

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Then we will predict our final result.

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You know the salary of the position
table number 6.5.

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And then at the end
we will visualize what the regression

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curve of the decision tree regression
model looks like.

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All right.

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So let's do all this
in the next three tutorials

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starting with this step training
the decision tree regression model.

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On the whole data set.

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And until then enjoy machine learning.