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Hello and welcome to this tutorial.

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In this one we're going to learn
how to import the data set.

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Okay so here I am on R.

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And as for Python
we need to set the working directory.

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So to do this we have to go to files here.

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Here I'm on my desktop.

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So let's go to our folder data
pre-processing part one.

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Here it is section two.

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And this is the right folder
that contains the data set data dot CSV.

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So this is the folder that you want
to choose to set as working directory.

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And now to set this working directory
on all you need to do one more thing.

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You need to click on this more button here
and click on set as Working directory.

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And here it is.

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Now we know that this is the right working
directory.

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And we are ready to
start importing the data set.

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Okay.

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So to do this
in R what we need to do is very simple.

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We will just need one line of code.

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So as in Python
we're going to call it data set.

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So that's the variable
that will be the data set itself.

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And now to import it
data set is very easy.

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You need to type read dot csv.

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And then in parenthesis you just type
the name of your data set in quotes.

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So here you have to type data dot CSV.

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And that's it. That's all you need to do.

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So now we're going to select
this line of code and execute.

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Okay.

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So let's have a look at our data set.

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To do this you just need to click here
on the data set.

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And it just displays here.

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Okay so we have our four columns
country age

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salary purchased and our ten observations.

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And what's interesting to see here
is that unlike

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Python indexes don't start at zero
but at one.

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So that's why the first observation
started one here and added ten.

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So that's the second distinction you have
to understand between Python and R.

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Of course
you don't have to program on both.

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You can choose the one you prefer,

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but if you want to program on both,
as I usually do.

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It's good to have this distinction
in mind.

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Okay, so we have our data set.

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It's all fine.

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And as I just said here,
we don't have to make the distinction

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between a matrix of features
and a dependent variable vector.

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And this will make perfect sense
for you as we go along with this.

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Part one Data pre-processing.

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You'll perfectly understand
why in the next tutorials.

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Okay,
so that's it for, importing the data

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set step of the data pre-processing phase.

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So that's it for this tutorial.

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I look forward to seeing you
in the next one,

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which will be to learn
how to take care of missing data,

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because sometimes your data set
will contain missing data.

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And you have to take care of this.

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And that's what
we're going to learn in the next tutorial.

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So I look forward to seeing you there.

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And until then, enjoy machine learning.