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Hello and welcome to this art tutorial.

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So in the previous section,
we implemented the apriori model

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to find some relevant association rules
that could help us find a more

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strategic placement of some products
in a grocery store in the South of France.

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And therefore, thanks to these association
rules, the manager of this store

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is likely to optimize the sales
and therefore increase the revenues.

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And accordingly,
we managed to create some added values

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in this business
thanks to association rule learning.

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And today we are going to implement
a new model of association

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rule learning,
which is called the E-Class model.

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So the first thing I want to say is to
prevent any disappointment you might have.

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The E-Class model is actually very simple
compared to what we did previously,

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because the model is basically
the apriori model,

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simplified
because previously we had two parameters.

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We had the support and the confidence,
and we also had the lift, by the way,

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when we ranked our rules
by their decreasing lifts.

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But here in the E-Class

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we will only have one parameter
which will be the support parameter.

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And therefore when we obtain our rules,
that will not be the rules

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that we obtained earlier.

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Like people who bought this also,
but that but we will just get some

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different sets of products frequently,
but together we will get

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the different sets of products.

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There are the most frequently purchased
together, so that's what we'll get.

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We have to expect this.

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But you know, the exam model can be
very useful if you don't have much time.

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If you want to get some simple results,

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if you don't want to play

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with too many parameters like the support
and the confidence here,

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we won't even have to compute a value
for the support.

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And moreover, we won't even have to

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choose a value for the confidence
because there is no confidence parameter.

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So you'll see

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this is the way of using association
rule learning in the simplest way.

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So let's do it right now.

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You'll see what I mean
at the end of this tutorial.

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So let's start with the very first step.

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Setting
the right folder is working directory.

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So we'll go to our Machine
Learning A-Z folder.

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Part five Association Rule Learning.

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And right now we are in a club.

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So here it is.

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We have the market basket optimization.

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We will work on the same business problem.

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And so we can click
on this more button here.

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And now set as working directory.

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And you know since I told you
the model is a simplified version

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of the apriori model, what we'll do now
is take our apriori model

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and you'll see that
it will almost be the same.

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And we will just need to change one thing.

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So I'm going to select everything
from here

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to here copy.

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And I'm going to paste it in the model.

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All right.

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So here in this section
data preprocessing.

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We don't have anything to change.

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We are just going to import the data
set with the read transaction functions.

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Be careful not with the red dot CSV file.

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Because we still need to have
our sparse matrix.

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That will also be
an input of the function,

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like here for the apriori function.

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So we will select this and execute.

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And of course we get the same number
of duplicates.

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The CSV file hasn't changed.

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We have five duplicates and no duplicates.

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Just five duplicates okay.

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Then of course

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we can use the summary function
to get some info about this data set.

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But of course these are going to be
the same enforce before.

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So we have 7500 transactions

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119 products, and the density is 0.03.

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That means that the proportion of non-zero
values in the matrix is 0.03 3%.

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And then of course we have
the most frequent items.

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Mineral water comes first and eggs.

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And we can actually have a better
look at these

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most frequent items
by selecting this line and execute.

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And of course
we get the same frequency plot.

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So that's exactly like before.

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We don't have anything to change here.

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However, now we enter
the second code section which is to train

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the model on the data set.

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So first we will just replace
apriori here by Eclat.

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Here we go.

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And guess what? It is so simple.

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Here we have the apriori function
to train the apriori model.

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And guess what the function is going to be
to train the model.

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It's going to be a class. So very simple.

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We are almost ready to train it
on our data set.

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But of course
the economical is much more simple.

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It doesn't include the confidence
in the parameters.

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If right now we get to the slide
of the Eclat algorithm from the intuition

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tutorial, well, we can see that
the algorithm has three steps.

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And the first step
is to set a minimum support.

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Remember before, for the apriori
algorithm, the first step

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was to set a minimum support
and a minimum confidence.

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Now we only need to set a minimum support
so we don't need this.

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Actually this parameter
is only for the apriori model.

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So if we keep that we will get an error.

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So I'll just remove this and

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we can leave the minimum support to 0.04.

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But you will see that
it's even not necessary here.

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You'll see why at the end.

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However
we might need to add another parameter.

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It's because since, you know,
I mentioned that the algorithm

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is just going to return the sets of items
most frequently bought together.

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Well, it wouldn't be interesting
to have sets of only one item.

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So in order to get some sets

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of at least two items,
we will add another parameter here,

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which we actually encountered earlier
and which is the Midlane parameter.

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And we will set it to two,
because we want to have the different sets

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of at least two items
most frequently purchased together.

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Okay. And now we're ready.

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See how it was? Simple.

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So let's select this
and train the model on our data set.

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Done.

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Okay.

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So now things change a little bit.

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First of course we see that
we just trained the model obviously.

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Then we have the parameter specification
like before.

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But this time
we don't have the confidence parameter.

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We have the support that we set to 0.04.

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That is the minimum support.

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And of course this time
we have the middle and equals to two.

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Remember before
the million parameter was set to one.

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But we didn't have any problem with that
because all our rules contained

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at least two products.

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But when using class we need to set this
Midlane parameter to two,

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because otherwise
we will get some sets of only one item.

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Okay.

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Then we have these other more
advanced informations here.

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And then there is something
that I would like to highlight here.

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It's the number of sets and not rules.

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Remember before we had
you know, let's say we had 845,

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it was written 845 rules
because we had the rules of the forum.

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If people buy live cream,

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then they're likely to buy chicken
with a confidence of 40%.

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That is, with 40% chance.

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And here, since, you know, we don't get
these kind of rules of this form

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and we only get two sets of items, well,
this time we have indeed 845 sets.

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So even if Eclair is considered
as a association

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rule learning model,
well this doesn't return rules.

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This actually returns some sets.

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Okay.
So we're going to have a look at the sets.

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Right now.

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We are ready to move
to the last step of our model.

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Well we can actually jump
back to the slide.

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And as we can see the step two is to take
all the subsets and transactions

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having higher support
than minimum support.

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So that's what the function
excluded itself.

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And then the last step step three is
to sort this subset by decreasing support.

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So this time it's not by decreasing lift
like for Priory

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there is no lift in Aguila.

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So this time we're going to
sort the rules by decreasing

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support.

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All right.

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And we're going to take the ten
first rules with the ten highest support.

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So we are actually ready.

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Well the whole code is actually ready.

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We did it very efficiently.

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Well it was very simple.

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So eventually
let's have a look at the rules.

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And you'll see that it's much more simple
and honestly less

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interesting than apriori.

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But that's what we'll get.

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So I'm going to select this line
and execute.

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And as I just told you,
we simply get the different sets of items

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most frequently purchased together.

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So for example, the set of items
most frequently purchased together

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is mineral water and spaghetti
with the support of 0.059.

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And then it's chocolate,
and then it's chocolate and mineral water,

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then eggs and mineral water and that's,
of course, strongly related to this

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most frequently
purchased products in this store.

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So that's the éclair results,
much less interesting than the apriori.

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But that can be very useful if you're
looking for some very simple information.

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And by the way, since I was telling you,
if we, you know, change the support

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and set it to 0.03 like we did
for the apriori model in the first place,

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well, you'll see that

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we'll get the same ranking here
because as you can see, the supports

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of these ten first sets of items
most frequently purchase together,

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all have supports
higher than 0.04 or 0.03.

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So indeed, if we retrain the model
with the minimum support of 0.03,

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and if we select that again and execute,

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we get the same ranking with mineral water
and spaghetti coming first

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and then frozen vegetables, mineral water

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as the 10th set of products
most frequently purchased together.

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All right.

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So that was the Éclair model.

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Again,
if you want to have a serious analysis

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about how to create some added value
for your business,

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optimize the sales and the revenue,
you should go for apriori.

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But if you are looking
for some very simple informations like

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the sets of products
most frequently purchased together,

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then you can go for it.

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So congratulations!

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Anyway, you now know how to implement
two association rule learning models

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the apriori and Eclat.

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And you know what to use them for.

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So thank you very much
for watching these tutorials

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and I look forward to seeing you
in the next part.

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Reinforcement learning.

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Until then, enjoy machine learning.