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Okay, off we go.

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So this data set, it's got a few columns.

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It's got row number customer ID surname.

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So what we're looking at is a data
set of a bank.

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of course it's all fictional.

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It's not a real bank,
but it's very realistic.

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And here we've got a snapshot of

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if you scroll down to the bottom
is going to be 10,000 customers.

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So 10,000 rows in this data set.

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And what the bank

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did is they measured some things
about these customers.

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Right.

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Why do
why did they undergo this whole thing.

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And what's the challenge here.

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Well the bank has been seeing unusual
churn rates.

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So churn is when people leave the company

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and they've seen customers leaving,
at unusually high rates.

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And they want to understand
what the problem is,

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and they want to assess and address
that problem.

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And that's why they've hired you
to look into this data set for them.

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And give them some insights.

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And how did this dataset come to be?

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Well, six months ago, the bank said,
all right, there's a big problem.

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We got to take a sample of our customers.

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By the way, this is a sample.

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That 10,000 is a tiny number for this
bank.

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This bank has millions of customers.

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It operates this
this fictional bank operates in Europe

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in three countries
France, Spain and Germany.

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And they have lots and lots of customers.

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So what they did is
they took this sample of 10,000 customers

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and they measured six months ago
everything they knew

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about them, their customer ID,
their surname, credit score,

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their geography,
their gender, their age, their tenure.

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So how long they've been with the bank,
the balance of the customers at that point

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in time, the number of products
they had at that point in time.

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So a number of products says

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things like how many products do
they have to have a savings account?

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They have a credit card.

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do they have a loan?

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did the customer
have a credit card or not?

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So there's a yes no flag.

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Is the customer
an active member? Another yes.

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No flag?

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Active member can be measured differently
by different organizations.

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It could be whether or not

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the customer logged into their online
banking in the past month,

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whether they did a transaction
in the past two months or some other.

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A measure like that,
and estimated salary.

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So the bank doesn't know
the salary of the customers,

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but based on the other things they know,
they could estimate a salary

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for that customer.
And they also gave you this information.

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So six months ago
they measured all of these things

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and said, all right,
so for these 10,000 randomly selected

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customers, we're going to just, 
watch them.

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So we're just going to wait
six months and six months down the track.

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We're going to check

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who all of those customers left
and who those customers stayed.

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And that's what this column exited
represents here.

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It tells you whether or not the person
left the bank within those six months.

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So this person over here, sometime
within the six months he left the bank.

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And as of a couple of days ago, he's
no longer with the bank.

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This person over here, on the other
hand, is still off the bank.

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So there's a zero here.

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This person left the bank,
this person stayed and so on.

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So if you see a one, that means
the person is no longer for the bank.

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Is there a person still for the bank?

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And your goal is to create
a geographic segmentation model

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to tell the bank which of their customers
are at highest risk of leaving.

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What I wanted to mention today
is that for a lot of customer

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centric organization,
this is going to be valuable.

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So I've done this personally.

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I've done this so many times.

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It is such an a value add to any customer
centric organization.

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So when whenever an organization deals
with customers this is a lot of value add.

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And then the other thing
is that this skill that you're going to

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learn is very transferable.

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It doesn't have to be for a bank.

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It doesn't have to be for churn rates.

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Geo demographic segmentation
models can be applied

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to millions of scenarios.

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So here, for instance, even in a bank,
the same scenario could work.

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should the person get a loan or not?

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Should
the person be approved for credit or not?

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And once again,
you'd have a binary outcome.

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So based on prior experience
you would know, whether or not

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a person is reliable
and you would build a model and say

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which people are

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more likely to be reliable and
which people are more likely to default.

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And that could govern the bank's decision
on whether or not to give loans.

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This could be in terms
of fraudulent transactions,

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not even in a bank,
in a different financial institutions.

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You could, find out which transactions

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are more likely to be fraudulent,
which are less likely.

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there's lots of, scenarios
where you could apply

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a geo demographic segmentation model,

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and it doesn't
even have to be geo demographic.

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So whenever you have a scenario,
when you have a binary outcome

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and you have lots of,
independent variables, you can build a

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proper robust model and that will tell you
which factors influence the outcome.

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So you can apply this knowledge.

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You're going to learn in
any kind of scenario

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where you have a binary outcome
and lots of independent variable.