Hi it's Navjyot here and in today's lecture
we are talking on market basket analysis
it's a data science tutorial series
market basket analysis is an important
topic and finding out different
Association rules and these are used and
different businesses especially sales
and marketing so before we study what is
market basket analysis now let us
understand why is it important and why
we studying or discussing this in order
to understand why market basket analysis
is important we need to understand what
is the objective of doing this thing so
basically this improves the
effectiveness of the marketing and also
it improves the sales tactics because
with customer data which is available at
the hand we can easily find out
different patterns and we can find out
the Association rules through this and
MBA which is the short form of Market
Basket analysis is a technique based
upon this theory where we group the
certain number of items and we are
trying to find out the more likelihood
of the group of items happening it will
also answer certain questions which is
you know which are the products which we
are really interested in so if I'm a
reporting analyst or a data analyst was
working for a sales or a marketing
agency it is really important that I
understand what is I'm supposed to find
out from my data so there are some you
know customers which are really
interested in my products there are
certain products which are really
important in us which are the products
which are getting sold very well what is
the combination of them there are
certain combination of the products
which I use in terms of you know sales
marketing or some schemes or one of the
hidden patterns which I'm getting
through my transaction data so this is
something which is available through
market basket analysis and this as I
said is to be performed by mode of
reporting analysts or somebody who's
doing it to find out the interestingness
of the patterns so now I understand what
is market basket analysis market basket
analysis is a technique or an algorithm
which is part of the data mining
algorithms and it's used to find out the
Association rules from the given data
and when I say data mostly it is used in
transactional data our day-to-day data's
which are huge in numbers and there are
some patterns which are not available through
the open eye so basically it is you know
something which we perform to get
patterns which helps us discuss
interestingness in their data and there
are two concepts which are part of it the
mathematical concepts of support and
confidence support and confidence are
similar to mathematics which we are
studying in the high school so support
is probability of A union B and
confidence is given that we are having
that support of a product given the
other product so if we were at to
understand support confidence in a
simpler term it would be that if there
is a product which we are selling let's
say mobile phone so given a particular
mobile phone and a list of mobile phones
how many chances are there a person
coming to a store but buy that mobile
phone and confidence is the person
buying that particular mobile phone how
many of them would actually buy the
accessories also let's understand that
with a simpler example as you can see
here there is a transaction table which
is given there are five records in there
which has a day-to-day products from a shop
or a supermarket and we are trying to
find out the customer coming to a shop
and buying a milk and also buying a
bread so first of all we have to find
out the support for a person coming to a
shop and finding that if he or she is
buying the milk so if you can see
properly in here it's having in the
first transaction milk second is milk
and then we can see the milk in the
fourth transaction so basically there
are three occurrence out of the five
table data so that comes out as in 3 by
5 into 100 that gives you the 60 percent
support now the confidence is a
parameter that given the person buying a
milk how many likelihood there is that a
person will also buy a bread so again
going back to the table then we'll of
only how to three through three
transactions which is having the milk
and out of that if you can see one and
four are having the bread so the
confidence is two by three which is
around 66% so this is how you actually
find out support and confidence so given
that milk is to be bought you can also
associate the bread with that you can
come up with the scheme so that is
discounts there you can put them club them
together in the rack and this is how our
people have been using this this all
started with a server which was
performed in the Walmart study wherein
they find out through the patterns of
the transaction data that has likelihood
that person buying a diaper is also by
beer so there is no logic associated
with that but there was a lot of
likelihood found through the records and
then the association rule was saying
that you can actually plant them to
provide some discounts so this is the
basic introduction of MBA there are also
terms such as lift which is coming into
the account or other Association rule
mining algorithms and it's learn more to
discuss on that so we'll see that in the
next lecture thank you so much for your
time I hope you understand the topic if
there are anything which you have as
inquiries please feel free to comment in
the below section thank you so much
