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Hello and welcome to this art tutorial.

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In the previous tutorials we implemented
our logistic regression model on Python.

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And this time we're going to do it on R.

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So the first thing that we need to do
is to set a working directory.

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Right now I'm on my desktop.

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So let's go to the Machine
Learning A-Z folder.

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Part three classification section
logistic regression.

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And here we are on the right folder.

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So that's the folder to set as working
directory.

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Let's make sure we have
the social network.

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CSV file. All good.

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So I'm going to click on this more button
here to set this folder

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as working directory.

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And here we go.

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Everything is fine.

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We're ready to start making the model.

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So the first step as usual
is to preprocess the data.

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And to do this
of course we're going to use our data

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pre-processing template
that we made in part one.

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So I'm going to select all this

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copy.

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And then I'm going to go back to my
logistic regression file to paste it here.

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All right.

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And now we just need to change
a few things to preprocess our data.

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That's of course
the name of the data set here

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which is social

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network ads.

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All right.

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And then we need to change
another few more things.

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But first let's select this line
to see what our data set looks like.

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So come in and control this.

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Enter to execute.

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Here we go. The data set is well imported.

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Let's click on that.

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And here's the data set.

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So as a quick reminder this data set

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contains
informations of users of a social network.

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These informations are the user ID the
gender, the age and the estimated salary.

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And the social network
has several business clients.

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And these business clients

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put ads on the social network
for marketing campaigns purposes.

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And one of their business clients
is a car company who has just launched

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its brand new luxury SUV
at a ridiculous price.

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So this car company put ads of their new
SUV products on the social network.

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And then the social network
gathered some informations about

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which users responded positively

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to the ad by buying the product

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and those who responded negatively
by not buying the product.

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So that's what the last column is about.

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The last column tells for each user
if the user but the car

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and then it's its one or didn't
buy the car and then it's a zero.

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All right.

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So that's the business problem itself.

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And now our mission
is to make a logistic regression model

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that will try to understand
the correlations between information

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such as the age and the salary,
and the decision of the user to buy.

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Yes or no, the SUV.

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All right.

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So let's
go back to our logistic regression model.

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And let's see what we need to change next.

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So this line is to select the variables
we want to train our model with.

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So as I just said
we're going to train our model

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with only the age and the salary.

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So that means that we want to predict
if the user is going to buy the SUV

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based on only the age and the salary.

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So here we will need to select
the indexes of the columns

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we want to take for our model.

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So I'm going to remove that as comment.

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And let's look at the indexes okay.

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So indexes in are sorted one.

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So that's 1234 and five.

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So we only want to take the indexes
three four and five.

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So let's do this.

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We're going to choose from 3 to 5.

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All right.

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So now let's select this and execute.

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All right.

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And now if we go back to our data sets

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you can see that
we only have our three columns of interest

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which are the age
the salary and purchased.

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Okay.

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Now next step.

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Next step is to split the data sets
into the training set and the test set.

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And here what we only need to
change is the split ratio.

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Or maybe not.
But we have 400 observations.

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I think a good split
would be to have 300 observations

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in the training set,
and 100 observations in the test set.

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And to do this
we need to take oh point 75.

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That is 75% going to the training set.

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That is 300 observations.

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Okay.

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So let's select this and command
or Control plus enter to execute.

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Here we go.

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Now let's look at our training
set and our test set.

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All right.

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So our training set
as you can see has 300 observations.

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And the test set has 100
observations listed here.

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Perfect.

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Now let's
go back to our logistic regression

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and take care of the next step
which is the feature scaling.

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So for classification
it's better to do feature scaling.

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So we're going to do it.

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We're going to remove those comments here
by pressing

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command or control plus shift plus C.

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All right.

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And now let's check
that we have the right indexes.

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Here we have indexes two and three.

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Here the dependent variable
is categorical.

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So we will only scale this.

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And that's index one and two.

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So let's
go back to our logistic regression model.

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And so here we need to choose 1 to 2.

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So let's do it for the four

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two. One two

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and one two.

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All right let's select this command
and control plus enter to execute.

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And here we go.

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Now let's have a look at our training
sets.

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Yep. The age
and the salary are perfectly scaled.

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And same for the test set.

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Perfect. Perfectly scaled.

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All right, so we are done with the data
pre-processing phase.

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Now our data set is well pre-processed.

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So that's the end of this tutorial.

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I can't wait to build this
logistic regression model

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on our data set that is now
prepared in the next tutorials.

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Until then, enjoy machine learning.