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So in all of a previous session, we have done various data processing stage, we have done so the visas

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for our data.

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So in this session, I'm going to consider this assignment in which the very first statement in which

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I have to check what the coalition data and the second one is basically I have to analyze education

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status of the customers that is right now applying for a loan in a bank.

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So what I'm going to do, guys, very first, I have to just call some functions for the coalition purpose.

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So very first, I have to just call a function, which is my card.

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And if you are going to execute it, you will see coordination in terms of nomadic life, say, to make

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it more interactive, I have to call a heat map on these metrics that you will see or hear just executed

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and you will see these beautiful heat map.

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So let's say I'm going to customize this heat map.

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So for this, I have to just call, set or figure.

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And in this I'm going to set my own side.

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So for this, I have to see then comma six Vendel and just executed and you will see your heat map gets

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zoom in a little bit.

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So to show you all the coalition values, basically you have to pass your energy parameter as cool.

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So this is exactly the correlation between all the features to form this coalition table.

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You will see each and experience which are basically this one, are very highly correlated.

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It means it is fine for us to go with age and drop experience to avoid multipolarity because both of

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the features are doing the same task.

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So it will basically provide me more quality of the issue.

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So we have to basically avoid these multi the issue.

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So for these guys, I have to just drop any of the feature.

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Either we can drop each or either I can drop experience as well for four days.

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I'm going to show call a drop on my face.

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And here you have to say I'm going to drop my experience column.

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And for this you have to set your access parameters.

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Let's say after updating, I'm going to store all this stuff in data as well.

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So it is showing not not not because I didn't add E here.

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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

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that Putin's column in your data.

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So now it's time for control of the statement in which I have to analyze education, the status of the

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customers that are going to apply for the loan in the bank.

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So let's say for this purpose, I'm going to check.

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Yeah.

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What are the unique values in my education column?

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So you will see the first two entry.

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So first of all, basically that goes to undergraduate there two will basically be efforts to graduate

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and three will basically refers to advanced or against the professional.

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So let's say I'm going to assign all the three values to one, two, three.

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So for this, I'm just going to create a function.

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Or you can also do the same task using the concept of the study as well.

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So let's say I want to define my function in the function.

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Name is Mark.

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So here, whatever acts it will receive and then I'm going to add up if s over here, so I'm going to

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see if that's equal to one, then basically I have to return undergraduate, so I'm going to say I'm

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the graduate.

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Then I have to add another condition as I live.

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And here my second condition is whenever I have to do so.

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In such case, basically I have to return graduate.

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So I'm going to see graduate.

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Then I have the last condition as an s blog and here I'm going to see I have to treat in such case,

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basically I have to return advanced or I can say professional, whatever you want.

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It's already so professional.

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So just execute this function and now you have to apply this function on your education.

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So education don't apply and you have to need the muscle function.

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So once I will apply this function, it will return me value.

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So whatever value it will return me, I want to store to underscore the mark.

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So just sign this and just execute it.

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And if you are going to call ahead, you will see this column has been added in your data form, which

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have different, different models as well.

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So now you have to analyze what are the categories of all the persons that are going to apply for the

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loan purpose in your bank for this purpose?

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I going use Blakley because I want to provide you already, in fact, a very beautiful resource, very

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particular with this one as well.

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So for this, I'm going to use pie chart because here I have only three categories, or you can use

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Barcia as well, but as having less number of categories, pie chart plays a vital role over there.

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So I'm going to use pie chart here.

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So here very first, if you are going to press shift crosstab, so you will see the very first one is

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my data frame and then I have to parse it in the form of values.

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So values.

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And then here the second one is my labels.

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So in values and in labels.

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And here you will see I have names.

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So these names and these values plays a major role in case of pie chart.

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So here and we do see values equals two.

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So let's say I'm going to define a new variable over here and here.

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I'm going to see you on this score this.

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And here I'm basically going to call off group on my data frame.

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So I have to group on the basis of this edu or the score.

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So you're doing a school model?

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Once I get a group by then, basically on the basis of each, I have to perform a function.

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So just execute it.

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Now is I'm going to print these edu, edu, edu press tab over here.

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And this is exactly my idea on a scale this so you will see it will contain all the values advanced

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have Dismas.

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Frequency graduate has this much frequency, an undergraduate has this much frequency now in value.

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You have to assign this edu underscore this now in names, you have to assign the names or I can see

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the index of this edu underscore this.

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So Edu on this score makes DOT index.

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And let's see, in case of assigning some titles, you can assign some titles using title parameter.

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So here I'm going to say title equals by chance and let's say this is exactly my figure and if you have

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to show, you have to just call our show on your FAQ, just execute it and it will give you a beautiful

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pie chart.

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So if you are going to hover your mouse, you will see forty one percent, which basically refers to

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this undergraduate, is 30 percent basically opposed to advanced or proficient on whatever you have

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assigned in here in this function.

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And this twenty one, twenty eight percent will basically refers to this graduate degree.

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So that's a type of analysis by using fighter or by using Bottcher, it's all up to you.

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What kind of ViSalus you are trying to provoke depending upon what statement you have.

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So if you have two entries from this visual, you can see we can see that we can have more number of

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undergraduate mythically forty one point nine percent then graduates and advanced professionals as 30

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percent.

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So hopefully you will love this.

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So in all our upcoming session, we are going to perform some beautiful analysis as well as I am trying

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to do all these stuff in an automated way.

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So I hope you will love it.

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Thank you.

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Have a nice day.

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Keep learning.

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Keep going.

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Keep practicing.