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All right.

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So let's do this starting with X.

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So how are we going to create x.

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Basically that simple.

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We have our data sets
you know containing exactly all this

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all these columns.

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And in order to create X
well we simply have to take the three

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first columns of this data set.

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Because, you know,
X will be exactly all these values here.

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You know with the three first columns.

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And so what we're simply going to do
is play with the indexes to collect.

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Indeed the indexes of these three
first columns, basically of the columns

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of all the columns of the data set
except the last one.

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So let's do this.

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Let me show you how to do this.

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First, what you're going to do
is take your data set that exact

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same variable which you created in this
first line of code here okay.

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Data set.

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Then from this data set and I'm adding
a dot here because we were about to use

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a function, you know, one of the attribute
functions of a pandas dataframe.

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And that function is I look.

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And what will it allow us to do.

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Well as you can see I look here
stands for locate indexes.

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And therefore what this function will do
is it will take the indexes

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of the columns
we want to extract from the data set,

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not only the indexes of the columns,
but also the indexes of the rows.

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And actually we have to start here
with the rows.

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We can specify the rows
that we want to get and put into x.

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And of course these are all the rows.

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You know
we want to get all the rows into x.

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We only want to take the first columns
which we want to keep all the rows.

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And the trick to take all the rows,
whatever data set

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you have with whatever
number of rows is to add here a column.

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Why is that?

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Because of column in Python means a range.

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And when we specify a range
without the lower bound and neither

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the upper bounds, that means in Python
that we're taking everything in the range.

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Therefore here all the rows.

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So that's the trick to take all the rows.

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And you will always have to
take all the rows.

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So here
you won't have anything to change then.

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Now we have to specify which columns
want to select with the indexes

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and to separate the rows
that we just took from the columns

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we need here to add a comma.

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And now we can take care of the columns.

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All right.

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So now I'm going to show you a trick
in order to take all the columns

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except the last one.

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Because indeed, as
I told you, most of the data sets

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you will use to train
your machinery models will have first

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the features
you know, in the first columns

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and last the dependent variable vector
in the last column.

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So now we're going to use a trick
so that we can take automatically,

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you know,
regardless of the number of columns

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in your data
set all the columns except the last one,

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because all the columns except the last
one are exactly the matrix of features.

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And the trick to do
that is to add a new range here,

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which this time will be column minus one.

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So what does it mean?

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Well, as we said,
the column here means the range.

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We know
we're taking a range here on the left.

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We have nothing.

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That means that
we're taking the first index.

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You know, the index zero
because indexes in Python start at zero.

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And then you know
we're going up to minus one.

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So what does this minus one mean.

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Well minus one means here
the last column minus

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one in Python
means the index of the last column.

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However.

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And that's a very important principle
in Python which you must absolutely know.

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A range in Python
includes the lower bound.

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Therefore we including here
the lower bound zero to index

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zero, but exclude the upper bound.

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And therefore here we're excluding this
index minus one meaning

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the index of the last column.

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And therefore what this will do is
it will take all the columns

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excluding the last one.

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And that's exactly what we want
for our matrix of features x.

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So voila. There you go.

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Now you just collected the right indexes
to create your matrix of features X.

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And the beauty of this is that 
you won't have anything to change

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when creating the future
matrices of features X

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of your future data sets,
but make sure that your future data

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sets indeed
have the features in the first columns

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and the dependent variable vector
in the last column.

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Okay, perfect.

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So in order to finish this line of code,
we just need to add here dot values.

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And this just means that we're taking
indeed all the values in all the rows

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of this data set, and in all the columns
except the last one of this data set.

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Perfect. So now you're learning a lot.

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Don't worry if this feels a bit
overwhelming at the beginning.

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I promise you that we will use this trick
many, many times,

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so you will just soon be so familiar with
it and master it like a pro.