So in the previous version, we have done some of the very interactive visas we have done of our various
processing steps in our data, the move to features, because both of the features doesn't make sense
at all for our analysis.
So in this session, I'm going to consider this one assignment in which I have all these problems statement.
The very first one is I had with life distribution of data in which I'm going to consider some of the
particular columns.
So let's say for this edition, I'm going to consider PARNAS Library for the retention purposes as well.
But very first, we have already check.
Yeah, it has some kind of a skewness in data.
So let's say I'm going to check ready for what type of skewness in my each and every feature.
So for this I have to just call this a skew function on my frame so it will return me Skewness in terms
of positive in done some some negative values.
So in case of income, you will see it has highest skewness in all these five features.
You will see this shows positive skewness.
It means it contains high positive outliers in data, whereas in case of age and in case of experience,
you will see it contained very less number of low outliers in data that basically a type of conclusion
you can drawn from this by just calling this a skew function on your data.
So very first, let's say I'm going to check what are my data types of each and every column you will
see all are of basically Intisar or of fraud.
So the very first one is I have to visualize distributional data.
So for this, I have to just call my hest function, just call his let's say I'm also going to set my
own site, my own window side.
So for this, you have to just set your custom parameter, which is fixie.
And in this case, you have to set, let's say, a window of 20 call model.
So just executed and you will see our distribution or I can see a histogram of each and every feature
available in your data.
So if you want to size of the colors so that you will make your histogram a little bit interactive so
you can play with your color parameter as well.
So if you want to inference from this histogram, you will see it and experience are to extend.
You will see equally distributed whereas income and you will see this C C average, which is my credit
card average, is due to the left, you see, whereas income and coverage expanding are just is skewed
to the right.
So it means it contains some high positive outliers in their entries or I can see in their features.
So we have some more undergraduate than graduate from this education.
You will see this one basically refers to undergraduate this to basically the first to graduate.
And these three basically refers to some advanced and professional or I can see those persons that have
done masters or those who have done some of the higher studies.
So in case you will see in this online feature, you'll see this one basically refers to somewhere more
than 50 to 60 percent of customers have enabled online banking.
And given this that type of instance, you can drawn from your data.
So let's say I'm going to visualize our distribution of basically my experience feature.
So for this, I'm basically going to use this block and just execute it.
So this thing you haven't imposed as a..
So what you guys can do and is going to import my Seiple library.
So Seabourne as soon as I can.
I don't have imported earlier.
Yeah.
This was an issue.
So as an as so if you are again going to execute it now, you will see your beautiful distribution of
your experience.
So you will see it has contained some negative values.
That's our main goal.
That's how we have to deal with this feature so that it is available for a better analysis purposes.
So let's say I'm going to check what exactly is the NE.
This experience, so this got me and you will see it mean is somehow close to 20 so that whatever negative
values and experience, I'm just going to store it in a separate Degerfeldt So for this I'm going to
put a conditioner's deal of experience less than zero.
And this is exactly my filter.
Then I have to pass this filter in my face so that I will get my filter.
So let's say the name is let's say negative on those called E x b so this is exactly my grapheme.
So let's say I'm going to check ahead on my this data so you will see it contains all the negative values
of experience.
So for this, if I'm going to check here, what is our distribution of aid in this negative, the so-called
XP?
For this, I have to just call this part, which is my distribution plot.
So just execute it and you will see this is basically a distribution of each in case of having negative
experience.
So you will see over here, most of them are those customers that are aged between 20 to 30 that are
basically having negative experience.
If you will think logically how it is possible a person can have negative experience.
So that's atter in your data and that's how dottiness in your data and how you have to deal with those
data.
You have to pre-process those data.
So let's say I'm going to check what exactly the mean right now of this negative one is called experience
of the basically of, oh, let's say I'm going to check me out of experience.
So of this experience, not me.
So now you will see how much fluctuation we have in this mean, because right now Vimy has somewhere
close to negative.
And this is exactly the mean, right.
Not positive.
So you will see how much fluctuation in our data we have.
Let's say I'm going to check here what are my total number of entries in this negative on a school experience.
So you will see and results until six twenty four.
So now you can visualize.
Yeah, among five thousand entries I have somewhere around six, twenty four entries that has negative
experience next year.
I have to print this stuff so I'm going to say I have to just print this.
So here I'm going to see there are a number of records, so there are a number of records which has
basically negative values for experience and say I'm going to say approx.
And here again, I'm going to add a placeholder and once I will add my placeholder, I will fill the
values of placeholders using by my format function.
So dot of format.
And here basically very first, I have to add the size of this negative experience here.
I'm going to say I have this much number of negative experience.
So here I have to see this.
So this one placeholder gets replaced by this one and this one second placeholder will get replaced
by whatever I'm going to write over here.
So here I'm going to see negative of experience, dark page divided by D.F., Dark Side, whatever will
be the side of my data frame.
And then I have to convert it in the form of percentage by basically multiplying it by one hundred.
So I'm going to see into one grade and you have to just add over here, you prentice's and here one
more parenthesis because you have to compute all the stuff in your parenthesis.
So just execute it and you will see you have six twenty four required which has negative values for
experience approx, one point zero four percent.
You can add overhead here and design as well.
So you will see one point zero four percent of data, which is raw right now.
So anyhow, you have to deal with that.
So very first, whatever I have contained in my div, I'm just going to copy in my data.
So for this I have to call this copy.
Just execute it.
And if you.
We are going to call ahead on your data.
So this is exactly what your day was.
So now you will see whatever negative value in your experience has.
I'm just going to use on them by their function to change the negative values to mean value derived
from data within the same age group.
So for this, I have to just call a simple function as no by got.
And here I have to see data of experience.
You can test that as well.
So wherever I have experience as negative, it means wherever I have experience as negative, just fill
it with the meaning of the experience.
Just fill it with the meaning of the experience.
And whenever I don't have this negative experience it as what actually it is so forte's.
I have to see that experience, then I have to update it as well.
So data of X be regions of experience.
So now just as you did and if you are going to call ahead on your data, let's say not had, let's say
I'm going to check whether I have any negative value in my Netafim or not.
So for this, I have to add this filter and now I have to just pass this filter in my data.
So you will see it will return me a blank because you don't have any energy as negative right now.
So that's all about decisions in the upcoming session.
We are basically going to be analyzing this education feature, personal loan feature, as well as some
more add ons visuals.
And after that, I'm going to automate all these stuff using some basic code, using some functions
in Python.
So that's all about hope.
So you love it.
So thank you, guys.
Have a nice day.
Keep learning, keep growing and keep practicing.