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All right, my friends, let's do this.

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Let's proceed to the next tool in our data

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preprocessing toolkit,
which is about encoding categorical data.

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So first
let me explain why we have to do this.

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Let's open the data set again.

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And as we can see this data set contains
one column

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with categories,
you know France, Spain or Germany.

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First you might guess that it will be
difficult for machine learning model

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to compute some correlations
between these columns.

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You know, the features and the outcome,
which is the dependent variable.

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And therefore of course
we will have to turn these strings,

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you know, these categories into numbers.

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So one idea would be to encode France

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into zero, Spain
into one and Germany into two.

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However, if we do this, our future machine
learning model could understand

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that because France is zero,
Spain is one and Germany's two.

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There is a numerical order
between these three countries,

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and mostly
it could interpret that this order

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matters, whereas
of course it is absolutely not the case.

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Right?

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There is not a relationship order

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between these three countries
France, Germany and Spain.

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So we want to avoid the model
to have such an interpretation,

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because that could cause
some misinterpreted correlations

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between the features and the outcome,
which we want to predict.

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Therefore,
we can actually do much better than just

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encode these three countries
into zero, one, and two.

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And this thing that we can do better
is actually one hot encoding.

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And one hot encoding consists
of turning this

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country column into three columns
y three columns.

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Because there are actually three

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different classes in this country column,
you know, three different categories.

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If there were, for example, five countries
here, we would turn this column

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into five columns.

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And one hot encoding consists of creating

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binary vectors for each of the countries.

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Let me explain this right away.

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So very simply, France would, for example,
have the vector 100,

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Spain would have the vector 010

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and Germany would have the vector 001,
so that then

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there is not a numerical order
between the three countries,

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because instead of having zero,
one and two,

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we would only have zeros and ones
and therefore three new columns.

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I'm going to show you, of course,
what we're going to create.

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We're basically going to replace this
country column by three new columns

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containing the zeros
and ones encoding each of the countries.

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That is called one hot encoding.

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And that is a very useful and popular
method to use

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when pre-processing your data
sets containing categorical variables.

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So that's the first thing
we'll do here for this country column.

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And then remember that
there is also this purchased columns

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that has labels, you know,
non-numerical values with yes nos.

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And we will actually have to replace them
by zeros and ones.

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And that's totally fine
for the dependent variable.

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As long as it is a binary outcome,
it is super fine.

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It will actually not compromise
the future accuracy of the model.

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If you just replace no and yes
by zero and one.

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Okay, so I will teach you
how to do these two things.

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And first let's start by one hot
encoding the country column here.

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And there we go.

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Let's create a new code
cell for this new step.

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And coding the independent variable.

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All right.

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So to
do this we're going to use two classes.

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The first one is the column
transformer class

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from the compose module of once again
the scikit learn library.

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And the second class
is the one hot encoding class

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from the preprocessing module
of the same scikit learn library.

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So first let's import these two classes.

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So we have to take them from scikit learn

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from which we're going to call first
the compose module.

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There we go
from which we're going to import

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that class we're interested
in which is as Google Collab.

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Guess it's perfectly gone transform it.

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And then from scikit learn, once again

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we're going to get access
to the pre-processing module perfect,

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from which we're going to import
the one hot encoder class.

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And now we're going to mix
these two classes in order to do this.

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One hot encoding on the country column.