So in all of a previous session, we have done various data processing stage, we have done so the visas
for our data.
So in this session, I'm going to consider this assignment in which the very first statement in which
I have to check what the coalition data and the second one is basically I have to analyze education
status of the customers that is right now applying for a loan in a bank.
So what I'm going to do, guys, very first, I have to just call some functions for the coalition purpose.
So very first, I have to just call a function, which is my card.
And if you are going to execute it, you will see coordination in terms of nomadic life, say, to make
it more interactive, I have to call a heat map on these metrics that you will see or hear just executed
and you will see these beautiful heat map.
So let's say I'm going to customize this heat map.
So for this, I have to just call, set or figure.
And in this I'm going to set my own side.
So for this, I have to see then comma six Vendel and just executed and you will see your heat map gets
zoom in a little bit.
So to show you all the coalition values, basically you have to pass your energy parameter as cool.
So this is exactly the correlation between all the features to form this coalition table.
You will see each and experience which are basically this one, are very highly correlated.
It means it is fine for us to go with age and drop experience to avoid multipolarity because both of
the features are doing the same task.
So it will basically provide me more quality of the issue.
So we have to basically avoid these multi the issue.
So for these guys, I have to just drop any of the feature.
Either we can drop each or either I can drop experience as well for four days.
I'm going to show call a drop on my face.
And here you have to say I'm going to drop my experience column.
And for this you have to set your access parameters.
Let's say after updating, I'm going to store all this stuff in data as well.
So it is showing not not not because I didn't add E here.
And I have just as you get it now, if I'm going to check ahead so you will see you don't have any of
that Putin's column in your data.
So now it's time for control of the statement in which I have to analyze education, the status of the
customers that are going to apply for the loan in the bank.
So let's say for this purpose, I'm going to check.
Yeah.
What are the unique values in my education column?
So you will see the first two entry.
So first of all, basically that goes to undergraduate there two will basically be efforts to graduate
and three will basically refers to advanced or against the professional.
So let's say I'm going to assign all the three values to one, two, three.
So for this, I'm just going to create a function.
Or you can also do the same task using the concept of the study as well.
So let's say I want to define my function in the function.
Name is Mark.
So here, whatever acts it will receive and then I'm going to add up if s over here, so I'm going to
see if that's equal to one, then basically I have to return undergraduate, so I'm going to say I'm
the graduate.
Then I have to add another condition as I live.
And here my second condition is whenever I have to do so.
In such case, basically I have to return graduate.
So I'm going to see graduate.
Then I have the last condition as an s blog and here I'm going to see I have to treat in such case,
basically I have to return advanced or I can say professional, whatever you want.
It's already so professional.
So just execute this function and now you have to apply this function on your education.
So education don't apply and you have to need the muscle function.
So once I will apply this function, it will return me value.
So whatever value it will return me, I want to store to underscore the mark.
So just sign this and just execute it.
And if you are going to call ahead, you will see this column has been added in your data form, which
have different, different models as well.
So now you have to analyze what are the categories of all the persons that are going to apply for the
loan purpose in your bank for this purpose?
I going use Blakley because I want to provide you already, in fact, a very beautiful resource, very
particular with this one as well.
So for this, I'm going to use pie chart because here I have only three categories, or you can use
Barcia as well, but as having less number of categories, pie chart plays a vital role over there.
So I'm going to use pie chart here.
So here very first, if you are going to press shift crosstab, so you will see the very first one is
my data frame and then I have to parse it in the form of values.
So values.
And then here the second one is my labels.
So in values and in labels.
And here you will see I have names.
So these names and these values plays a major role in case of pie chart.
So here and we do see values equals two.
So let's say I'm going to define a new variable over here and here.
I'm going to see you on this score this.
And here I'm basically going to call off group on my data frame.
So I have to group on the basis of this edu or the score.
So you're doing a school model?
Once I get a group by then, basically on the basis of each, I have to perform a function.
So just execute it.
Now is I'm going to print these edu, edu, edu press tab over here.
And this is exactly my idea on a scale this so you will see it will contain all the values advanced
have Dismas.
Frequency graduate has this much frequency, an undergraduate has this much frequency now in value.
You have to assign this edu underscore this now in names, you have to assign the names or I can see
the index of this edu underscore this.
So Edu on this score makes DOT index.
And let's see, in case of assigning some titles, you can assign some titles using title parameter.
So here I'm going to say title equals by chance and let's say this is exactly my figure and if you have
to show, you have to just call our show on your FAQ, just execute it and it will give you a beautiful
pie chart.
So if you are going to hover your mouse, you will see forty one percent, which basically refers to
this undergraduate, is 30 percent basically opposed to advanced or proficient on whatever you have
assigned in here in this function.
And this twenty one, twenty eight percent will basically refers to this graduate degree.
So that's a type of analysis by using fighter or by using Bottcher, it's all up to you.
What kind of ViSalus you are trying to provoke depending upon what statement you have.
So if you have two entries from this visual, you can see we can see that we can have more number of
undergraduate mythically forty one point nine percent then graduates and advanced professionals as 30
percent.
So hopefully you will love this.
So in all our upcoming session, we are going to perform some beautiful analysis as well as I am trying
to do all these stuff in an automated way.
So I hope you will love it.
Thank you.
Have a nice day.
Keep learning.
Keep going.
Keep practicing.