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Hello, my friends.

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All right, here we are at the final,
very exciting step

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of this implementation
where we visualize the results.

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So we're going to clearly visualize
the rules and see for each of them,

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you know, their support, their confidence
and their lift.

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And mostly we're going to see
which are going to be the best deals

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by one product
and get another one for free.

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Will clearly see which food goes
well with some other food

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for these French people
in the south of France.

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Okay, so let's do this.

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We already have the rules here, which were
the output of this primary function.

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So you will see that
it will be very simple to display them.

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Therefore let's select that cell in order
to create a new code cell just below.

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And indeed, in order
to have a first look at these rules, well,

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we simply need to create a new variable
which we're going to call results.

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You know, the results of this, a model
which are nothing else than the rule.

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But see what I'm going to do?

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I'm going to set this result variable
as a list of

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the rules, which will just put these rules
into a list.

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Right? As simple as that.

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We just want to have our rules
into a list.

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So that then when we display the results,
you know, by

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just calling this variable
and executing this cell, indeed

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we will get in the output
all our rules in this list.

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You recognize the square brackets here
and at the end as well.

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All right.

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So all these rules are listed one by one,
you know in different rows.

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All right so there you go.
These are all the rules.

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You know all the rules
following these criteria a minimum support

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of 0.3 percent, a minimum confidence
of 0.2, a minimum lift of three.

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And of course

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two products in the rules, you know,
one on the left and one on the right.

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All right.

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So let's have a look at them
one by one starting with the first one.

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So that's the first rule here.

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And what do we see.

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Well I'm just going to you know
highlight it.

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All right. So that's the first rule.

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All right. So let's see.

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First we see that the two products in
this rule are live cream and chicken.

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But be careful the order is not.

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If customers buy light cream
then they will buy chicken.

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No it's actually the other way around.

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Because indeed if we scroll to the right
well that's important.

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You see, items base
is equal to light cream and then items add

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is equal to chicken.

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So that means that actually the left hand
side of the rule is light cream.

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And the right hand
side of the rule is chicken okay.

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So that means that
if people buy live cream

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then they will have a high chance to buy
chicken.

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And that high chance is actually measured
by the confidence, which is 0.19 here.

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So that means that if customers
buy light cream, then they will have

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29% chance of buying chicken,
which makes sense, right?

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Well, at least in France,
because usually French people love to put

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some nice light cream with lemon
as a sauce for their chicken.

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Right?
It's a common traditional French meal.

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I actually really enjoyed myself.

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All right.

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And then we can also see the lift,
which is very good. 14.

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84 I remind that all our rules here
will have lift larger than three.

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And then where is the support?

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The support is right here.

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The support is 0.0 45,
meaning that the rule containing

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these two products appears in
oh point 45% of the transactions.

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All right then, let's see what we have.

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So the second one is mushroom cream sauce.

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But same the items base is mushroom
cream sauce.

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And the items at is zero,
meaning that if customers buy

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mushroom cream sauce then they will have
a high chance of buying scallop.

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And that high chance is 30%, right, 30%,

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and the lift is 3.793.8

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and the support is 0.05.

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Okay. Good.

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So you see you have all these rules.
They look great.

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But we would actually appreciate
a nice way to have a look at them

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because here you see we have to do
these scrolls to the right and left.

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And it's a bit overwhelming.

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So don't worry I actually found
a good code snippet online.

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I think it was from StackOverflow
or another source like that.

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So we're going to use that code snippet
to actually put the results

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well organized into a pandas dataframe.

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We won't have to re-implement that code
snippet from scratch, because, you know,

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it is very specific to this association
rule learning implementation.

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And therefore
you will just see that once in your life.

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So it's not essential.

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And therefore we will just copy and paste
it here from our original implementation.

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But of course
I will explain to you the code.

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So let's get it first.

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It's actually at the bottom
and it is this one.

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All right.

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We make an inspect function
which will return the rules.

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Meaning these rules will organized
into a pandas dataframe.

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And the cool thing about this
is that we will even be able

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to sort the rules
by a descending metric, right?

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Because if we notice here, well,
they're not sorted.

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You know, the lift store at 4.84
then goes down to 3.79

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and then goes back up to 4.7
and here 5.16.

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And remember that
the lift is actually the most relevant

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metric to measure the strength of a rule,
you know.

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So if we want to store the rule, you know,
to get the strongest one, if we want to,

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you know, select a few deals, well,
we would rather do it with the lift.

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So what will be really nice here.

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You know, when putting these results into
a well organized pandas dataframe is that

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then we will be able to sort easily
the rules by the lift or another metric.

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All right. So let's get this function.

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I'm going to copy it here

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and we will paste it inside our
implementation right here.

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There we go.

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Paste.

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And now let me explain to you
how it works.

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So as we can see it is a function
that takes as input the results meaning

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these results, the rules as they are
right now, you know, organized this way.

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And then it will take separately
the left hand

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side of the rule, meaning the product
at the left hand side of the rule,

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then the product at the right hand
side of the rule,

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then the support of all the rules,
then the confidences,

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the lift of these rules,

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and then it will return all the rules
with the left hand sides

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and the right hand side.

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And there are supports, confidences
and lifts inside the list.

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Right. That's
why we have this list function here again.

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All right. So that's what
this inspect function will do.

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And then at the end
we create the final pandas dataframe

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which takes as input
the output of this inspect function.

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And besides we add the column names.

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You know the first column
will be the left hand side of the rule.

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The second column will be the right hand
side of the rule.

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The third column will be the support
for the confidence.

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And finally the lift.

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So we will have a super nice table
with these columns

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and giving all the important informations
for each of the rules.

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Okay, so that will be super practical.

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And now let me explain how you know
we get each of these elements,

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you know the left hand side
and then the right hand side.

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All right.

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So the first thing to notice
is that for loop inside a single

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row indeed we take the full list
of all the rules here.

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You know that's the full list of results.

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And then for each of the rule
in this full list of rules,

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well we're going to access each of
these elements and take them separately.

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And so let's start with left hand side.

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How do we take that left hand side
which is you know right here

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the item space frozen set light cream
basically light cream.

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Well let's see.

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So first we take the single rule.

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You know let's say for example
this one to explain

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how we're going to get these elements.

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So we take these rules.

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And then in this rule
we first access the index to.

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All right.
So let's access it step by step.

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This element here you know up to the comma

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is the element of index zero okay.

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Then this element
is the element of index one.

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This is the second element.

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And then this full element
you know up to the closing

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square brackets
here is actually the element of index two.

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All right.

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So all I've highlighted here
is the element of index two.

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So now in that element of index two
then we're going to access

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the element of index zero
meaning the first element.

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And the first element

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is actually the first element
of that list, you know in square brackets.

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And that is of course all this, you know,
ordered statistic up to be careful,

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you know, up to the closing parenthesis
here, meaning right here.

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So all this is the first element
inside that,

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you know, big element
which I've just highlighted before,

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you know, from ordered statistics
up to actually the end.

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All right.

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So this is the element of index zero.

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And then in this new element of index zero

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we're going to access the first element
you know of index zero again.

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And well you know we have to look inside
this parenthesis and that

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of course this element
which is indeed the left hand side right.

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Because this is what we want to get.

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Left hand side item space.
That's the same.

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And that is light cream.

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And then for the right hand side
that's the same.

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You know we took the same
first element of the next two.

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Then that whole element of index

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zero inside this
first element of the next two.

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And then instead of taking the index zero
here we take the index one,

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which is that element items add frozen
set chicken.

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All right.

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So that element is you know
that element of index two then index zero

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then next one.

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And then that index is to indeed catch
what's inside, you know chicken.

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And same for that index zero here
there was two catch light cream.

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All right.

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So you see we just play with the indexes
to access what we want.

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But really you will only have to do that
once in your life.

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So don't worry too much about this kind
of overwhelming code, even if it's quite

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funny to, you know, follow the indexes
path to find the products.

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But then the good news is that for the
support, it's going to be much simpler.

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Indeed,
we just need to take the full rule.

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You know, the single rule.

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So you know, this whole rule.

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And then very simply we access
the element of index one directly.

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So remember and this full single rule.

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Well all this is
the element of index zero.

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And then all
this is elements of index one.

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And that's exactly the support
which is what we want.

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And we do this for each of the rules
in this whole list of rules okay

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then confidences.

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Once again it's a bit more complicated.

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But just like before
first we get the element of index two.

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So let's take it again.

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This is the element of index zero.

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Then this is the element of the next one.

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And then all
this is the element of index two okay.

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Then inside this whole element
of index two well we're going to access

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the element of index zero 
which is let's take it again.

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So we start from here
you know ordered statistic.

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And then we go up to actually the closing

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parenthesis of here
which should be right here.

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Right. Let's check the parenthesis here.

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So that's the first opening one
then the second opening one.

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Closing here.

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Then this is a new opening one,
the closing here.

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And then there you go okay.

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So that's the closing parenthesis
of indeed this element of index two.

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Then index zero.
And then inside this element.

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Now we're going to get access
to the element of index two.

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So that's the element of index zero.

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Then that's the element of index one.

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And then finally
that's the element of index two.

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And that's exactly the confidence
which is what we want to get right now.

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And then same for the lift.

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So same same path.

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First
we get access to the index two then zero.

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So just like we were right now.

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But then instead of taking the index two
like for the confidence

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we take the next one
which is the index three

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and which corresponds to
of course, the lift.

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All right. And that's how we get the lift.

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00:11:59,433 --> 00:12:02,233
So you see the idea just again
with the indexes,

236
00:12:02,233 --> 00:12:07,000
we access the elements inside
this whole complicated list of rules.

237
00:12:07,133 --> 00:12:10,000
But at least
we will get a nice result in the end.

238
00:12:10,000 --> 00:12:13,066
And really you don't have to worry
too much about this code.

239
00:12:13,066 --> 00:12:16,666
It is very unique, very specific,
and you will probably not have to

240
00:12:16,833 --> 00:12:18,600
re-implement it again in your life.

241
00:12:18,600 --> 00:12:20,566
You can just take it,
you know, I just took it

242
00:12:20,566 --> 00:12:24,700
online and indeed it is very practical
for what we're doing right now.

243
00:12:24,700 --> 00:12:27,066
Meaning association rule learning.

244
00:12:27,066 --> 00:12:27,500
Okay.

245
00:12:27,500 --> 00:12:28,333
So good.

246
00:12:28,333 --> 00:12:31,266
Now let's have a look
at the beautiful result.

247
00:12:31,266 --> 00:12:34,700
So we are going to play the self first to,

248
00:12:34,733 --> 00:12:39,100
you know build that inspect function
and then create that new data frame

249
00:12:39,100 --> 00:12:43,733
which I called results in data frame
and which will contain the same rules

250
00:12:43,733 --> 00:12:47,600
here, but better organized
and more beautifully organized, let's say.

251
00:12:47,933 --> 00:12:50,933
And so now
we're going to create a new code cell here

252
00:12:50,966 --> 00:12:55,866
just before this text cell to first
display the results non sorted.

253
00:12:56,300 --> 00:13:00,933
And to do this well we simply need to call
what we created here meaning results

254
00:13:02,000 --> 00:13:03,533
in data frame.

255
00:13:03,533 --> 00:13:04,366
All right.

256
00:13:04,366 --> 00:13:07,466
And then when pressing play
you will see that

257
00:13:07,666 --> 00:13:11,233
we will get a very nice table
containing all the rules

258
00:13:11,233 --> 00:13:13,433
with the different elements
and separate columns.

259
00:13:13,433 --> 00:13:16,833
So that's the left hand side of the rule,
the right hand side of the rule.

260
00:13:17,033 --> 00:13:18,266
And then the support of the rule,

261
00:13:18,266 --> 00:13:20,700
the confidence of the rule
and the lift of the rule.

262
00:13:20,700 --> 00:13:23,466
So let's have a look at the rules
one by one for the first one.

263
00:13:23,466 --> 00:13:26,700
As we saw earlier,
if customers buy light cream,

264
00:13:26,866 --> 00:13:31,166
then they will have actually 29%
chance of buying chicken.

265
00:13:31,500 --> 00:13:31,933
All right.

266
00:13:31,933 --> 00:13:33,466
And this rule appears

267
00:13:33,466 --> 00:13:37,533
0.4 percent of the time, you know,
and 0.4 percent of the transactions.

268
00:13:37,533 --> 00:13:41,333
And it has left a 4.84,
which is indeed very good.

269
00:13:41,833 --> 00:13:44,833
Then the second rule,
if people buy mushroom cream sauce,

270
00:13:45,000 --> 00:13:47,033
then they have a good chance
of buying scallop

271
00:13:47,033 --> 00:13:52,066
and that good chance is 30%
and it has a lift of 3.79.

272
00:13:52,066 --> 00:13:53,366
Pretty good still.

273
00:13:53,366 --> 00:13:57,700
Then if people buy pasta, then they will
have a good chance of buying a scallop.

274
00:13:57,933 --> 00:14:00,200
And that good chance
is actually pretty good indeed.

275
00:14:00,200 --> 00:14:03,066
If customers buy pasta,
then they will have 30%

276
00:14:03,066 --> 00:14:06,100
chance of buying a scallop
and the lift is 4.7.

277
00:14:06,333 --> 00:14:07,100
Then check this out.

278
00:14:07,100 --> 00:14:09,633
We have a good lived here for from us
the honey.

279
00:14:09,633 --> 00:14:13,300
Yes of course from us non
honey is a delicious French dessert.

280
00:14:13,300 --> 00:14:15,033
I actually really enjoyed that.

281
00:14:15,033 --> 00:14:17,200
Dessert is one of my favorites.

282
00:14:17,200 --> 00:14:20,400
And indeed
when you buy from us as a French person,

283
00:14:20,400 --> 00:14:23,400
you will definitely enjoy
putting some honey on it.

284
00:14:23,400 --> 00:14:25,466
All right, so indeed, that's a great rule.

285
00:14:25,466 --> 00:14:29,000
And I would definitely make this deal,
you know, buy from us below

286
00:14:29,066 --> 00:14:30,833
and get honey for free, right?

287
00:14:30,833 --> 00:14:33,633
Because indeed the lift is 5.6.

288
00:14:33,633 --> 00:14:35,333
So very very good then.

289
00:14:35,333 --> 00:14:36,900
Herb and pepper, ground beef.

290
00:14:36,900 --> 00:14:40,100
Yes of course French people or even,
you know, other people

291
00:14:40,100 --> 00:14:43,633
from other nationalities love to put herb
and pepper on ground beef.

292
00:14:43,633 --> 00:14:45,633
This is actually exquisite.

293
00:14:45,633 --> 00:14:48,966
And indeed we have a good confidence
for that rule and actually, well,

294
00:14:49,000 --> 00:14:50,366
not that high of olives.

295
00:14:50,366 --> 00:14:51,600
Okay then.

296
00:14:51,600 --> 00:14:52,800
Same for tomato sauce.

297
00:14:52,800 --> 00:14:55,800
It's nice to put tomato sauce on ground
beef.

298
00:14:55,966 --> 00:15:00,000
The lift is still close to
for light cream, olive oil.

299
00:15:00,000 --> 00:15:03,000
Well I don't really see the association,
but why not?

300
00:15:03,000 --> 00:15:05,566
I'm not really a good cook,
but maybe there is a recipe

301
00:15:05,566 --> 00:15:08,700
where we can combine olive oil
and like cream.

302
00:15:09,000 --> 00:15:10,066
I'm not so sure about that.

303
00:15:10,066 --> 00:15:12,333
But however, this one is

304
00:15:12,333 --> 00:15:15,900
definitely something I would do
whenever I buy whole wheat pasta.

305
00:15:15,900 --> 00:15:19,200
I also get olive oil and indeed
the lift is pretty high.

306
00:15:19,433 --> 00:15:20,933
And then pasta and shrimps.

307
00:15:20,933 --> 00:15:25,300
Yes, that's a very good specialty of south
of France, especially near the coast.

308
00:15:25,466 --> 00:15:29,166
French people, of course,
love mixing bass and shrimps all right.

309
00:15:29,166 --> 00:15:31,633
And the lift is 4.5 so very good.

310
00:15:31,633 --> 00:15:34,733
So you see,
these are much better to observe.

311
00:15:35,000 --> 00:15:39,200
But then we still don't have them salted
which here is fine because you know

312
00:15:39,233 --> 00:15:40,433
we don't have that many rules.

313
00:15:40,433 --> 00:15:44,033
But in other situations
you can end up with many more rules

314
00:15:44,033 --> 00:15:46,200
and therefore you would like to sort them,

315
00:15:46,200 --> 00:15:49,933
for example by descending lift, because
the lift is a very relevant measure.

316
00:15:50,266 --> 00:15:52,566
So I'm going to show you right now
how to do this.

317
00:15:52,566 --> 00:15:55,166
It's very, very simple thanks to blenders.

318
00:15:55,166 --> 00:15:56,766
So let's create a new cocktail.

319
00:15:56,766 --> 00:15:58,900
And here we're going to take again.

320
00:15:58,900 --> 00:16:02,733
Well our new beautiful data
frame results in data frame.

321
00:16:03,033 --> 00:16:06,600
And now we're going to use
a pre-built function by the library

322
00:16:06,600 --> 00:16:09,900
to indeed sort
the results of this data frame

323
00:16:09,900 --> 00:16:13,733
by a specific column, which we are
of course going to specify here.

324
00:16:13,866 --> 00:16:14,166
All right.

325
00:16:14,166 --> 00:16:15,100
So let's do this.

326
00:16:15,100 --> 00:16:16,866
To do this we need to add a dot here

327
00:16:16,866 --> 00:16:20,800
because we're going to call a specific
method of the data frame class.

328
00:16:20,800 --> 00:16:22,900
You know because this results in data

329
00:16:22,900 --> 00:16:26,800
frame is actually an object of the data
frame class by pandas.

330
00:16:27,033 --> 00:16:30,133
And that method is called and largest.

331
00:16:30,800 --> 00:16:32,400
There we go this one.

332
00:16:32,400 --> 00:16:35,633
And inside
we just have to enter three arguments.

333
00:16:35,766 --> 00:16:36,633
As we can see here.

334
00:16:36,633 --> 00:16:39,500
You know you have all the info
thanks to Google Collab.

335
00:16:39,500 --> 00:16:43,866
So the three parameters are first n which
is the number of rows we want to return.

336
00:16:44,000 --> 00:16:47,400
So I'll just put that as you know,
if I was doing a code template

337
00:16:47,400 --> 00:16:52,600
I'll just put n equals ten to you know
in general return the ten best rules.

338
00:16:52,600 --> 00:16:56,400
So n equals ten then keep
we will keep the default values.

339
00:16:56,400 --> 00:16:58,000
You know that's in case
we have duplicates.

340
00:16:58,000 --> 00:17:02,266
But that's fine for us
I actually check them and then columns.

341
00:17:02,266 --> 00:17:02,533
All right.

342
00:17:02,533 --> 00:17:05,366
So that's the other parameter
I want to input here.

343
00:17:05,366 --> 00:17:06,300
And that's of course

344
00:17:06,300 --> 00:17:10,000
you know to specify by which column
you want to have your results sorted.

345
00:17:10,333 --> 00:17:10,600
All right.

346
00:17:10,600 --> 00:17:13,600
So columns equals and

347
00:17:13,600 --> 00:17:17,000
well you know the most relevant metric
is perhaps to lift.

348
00:17:17,000 --> 00:17:19,566
So we're going to sort these rules
by the lift.

349
00:17:19,566 --> 00:17:22,500
And to do this
well we simply need to specify

350
00:17:22,500 --> 00:17:26,500
the lift value in quotes
for that columns parameter.

351
00:17:26,633 --> 00:17:29,633
So in quotes I'm going to enter here lift.

352
00:17:30,100 --> 00:17:30,800
All right.

353
00:17:30,800 --> 00:17:31,566
And that's it.

354
00:17:31,566 --> 00:17:36,033
This is simply what you have to do
in order to sort your rules by the column.

355
00:17:36,033 --> 00:17:36,466
You want.

356
00:17:36,466 --> 00:17:36,966
Here.

357
00:17:36,966 --> 00:17:39,966
Lift if you want to order them
by confidence which is also great.

358
00:17:40,233 --> 00:17:43,166
You would just specify here in quotes
confidence.

359
00:17:43,166 --> 00:17:44,100
All right. So let's try.

360
00:17:44,100 --> 00:17:47,966
Let's play the cell and there we go.

361
00:17:47,966 --> 00:17:52,000
Now we have indeed
the rules sorted by the lift.

362
00:17:52,000 --> 00:17:55,100
This is the highest lift 5.16 down

363
00:17:55,100 --> 00:17:58,100
to the lowest lift 3.11.

364
00:17:58,633 --> 00:17:59,300
And so let's see.

365
00:17:59,300 --> 00:18:00,500
Well as I told you,

366
00:18:00,500 --> 00:18:04,066
you know that's a very popular association
especially for French people.

367
00:18:04,066 --> 00:18:06,633
They love to put honey in from each below.

368
00:18:06,633 --> 00:18:10,333
Each time you go to a French restaurant,
you will see in the dessert menu, well,

369
00:18:10,333 --> 00:18:11,333
from us with honey.

370
00:18:11,333 --> 00:18:14,200
And even if you don't see it,
you can always ask the restaurants.

371
00:18:14,200 --> 00:18:15,966
They always have honey, not too far.

372
00:18:15,966 --> 00:18:19,033
So that's definitely
a very strong rule here.

373
00:18:19,200 --> 00:18:23,500
And you know, if I was the owner of the
shop, I would definitely make that too.

374
00:18:23,500 --> 00:18:26,900
You buy from us and get honey for free
then same.

375
00:18:26,900 --> 00:18:29,900
This is the second strongest rule
light cream chicken.

376
00:18:30,066 --> 00:18:31,733
So another great deal would be

377
00:18:31,733 --> 00:18:34,466
if you buy light cream,
you will get chicken for free.

378
00:18:34,466 --> 00:18:36,200
And that's actually a very good deal.

379
00:18:36,200 --> 00:18:41,000
But you know, as I told you, the owner
can put some smart prices that you know

380
00:18:41,000 --> 00:18:44,000
would amount to a good price for,
you know, buying the two.

381
00:18:44,100 --> 00:18:44,400
Right.

382
00:18:44,400 --> 00:18:45,533
But that's another story here.

383
00:18:45,533 --> 00:18:48,766
We're doing association rule
not pricing strategy.

384
00:18:49,066 --> 00:18:51,066
So there we go.
Let's move on to the next rule.

385
00:18:51,066 --> 00:18:55,800
If people buy pasta then they will
enjoy buying scallop as well.

386
00:18:55,800 --> 00:18:59,833
So a third great deal would be buy pasta
and get scallop for free.

387
00:19:00,266 --> 00:19:02,233
And then same with pasta and shrimp.

388
00:19:02,233 --> 00:19:05,533
Of course if you bypass
so you can get shrimps for free,

389
00:19:05,900 --> 00:19:09,400
then if you buy whole wheat pasta
you can get olive oil for free.

390
00:19:09,400 --> 00:19:12,866
You know these deals
will be very attractive to the customers

391
00:19:12,866 --> 00:19:17,000
because indeed customers
buy these association of products.

392
00:19:17,000 --> 00:19:18,266
Okay. All right.

393
00:19:18,266 --> 00:19:19,066
So there you go.

394
00:19:19,066 --> 00:19:22,200
These rules, you know
for most of them actually make sense.

395
00:19:22,200 --> 00:19:24,600
And there you go
my friends. Congratulations.

396
00:19:24,600 --> 00:19:29,433
You built your first association rule
learning model to identify clear rules

397
00:19:29,433 --> 00:19:33,400
that will create some added value
to a retail business.

398
00:19:33,400 --> 00:19:37,666
So your job is done and now you can enjoy
the beautiful days of South of France

399
00:19:37,833 --> 00:19:41,300
after completing successfully
this new data science mission.