Hello, all so welcome to this data analytics project for finance.
So in this series, basically, I'm going to consider data of bank personal loan and here I'm going
to perform some kind of analysis so that I can fetch some insights and some valuable information from
this huge chunk of data.
So this exactly is my data set on which I'm going to perform some sort of analysis over here.
So very first, you have to just go across to the various basic steps of analysis.
Very first, you have to read your data, then you have to go through with the people sitting stage
of data where you are going to deal with the missing values and various other features as well.
So I'm just going to open my own book, and this is exactly my very first.
I have to import my some of the libraries for manipulation, for numerical computation, for visualizations,
as well as for interactive results as well.
So what I'm going to do is I'm just going to import my all the libraries over here so that if I have
to import my panel, so I'm going to say I'm not as big and is basically all of us.
So the second one is basically my name.
So I'm going to import my name by and creating an alias as ENPI.
And the third one is basically my basic library for vision purpose, which is my matplotlib and so on.
For interactive graphs, you can import your seabourne for production.
Even with the additions, you can import your blakley as well.
So I'm just going to execute it very first.
I have to read my data.
So you will see this exactly is my actual data set.
So I'm just going to copy this entire part because my data is somewhere available over here.
So now I have to use a function known as Read an Excel.
And in this basically very flat, I have to pass my entire.
But then you can pass that over here to get to your dataset.
So this is basically my actual data.
And now you have to pass your what exactly the sheet name that you want to read.
So if you are going to open this data, so this is exactly the data.
And in the very first state, we have a description and in the second sheet we have this entire data.
So here we have to basically parse the index of that sheet.
So my index is basically one because zero indexes goals for description and the first one is goals for
data as well.
So it will basically return me.
So I'm going to store it in detail and let's say I'm going to call ahead on my frame.
So it will basically return me data, have some number of rules and some column and to check what are
the rules and columns in your data, you will see somewhere around five thousand rows and columns.
And to check whether I have any null values, indeed or not, you can call it is null, dark, some
to get all your some of the any values in your data.
So now you will see over here you don't have any of the missing values in your data, but you will see.
Yeah, Aidy.
And the zip code doesn't make sense for the analysis purposes.
So I can simply remove these features or I can simply drop both of the features for this.
I'm going to say the drop.
And in this list I have to mention my board, the features, the very first one is my ID and the next
one is basically my zip code.
So you can either copy from here or you can set as manually.
And now you have to set your X's parameter because I had to remove this as entire column.
Then if you want to factor in this parameter, which is responsible for a bit in your data frame.
So now you can set it as well to just execute it.
If you are going to check.
What are the different columns right now available in my data.
So you will see I don't have any idea.
And the zip code features right now available in my data.
Let's say for Advanced ViSalus, I'm going to import my plotless library.
So import floridly Dot Xpress to step and I'm going to create a elías as B.
So now my property is that important in my cell.
So let's open the assignment and this is exactly the assignment, Larry first was in my data preparation
in which I have to go through with a very basic staff checking the missing values.
I reviewed all the features that are you know, that doesn't make sense at all.
And the second one is basically I have to use five points, some concept to get a description about
your data.
So what exactly is five point summary?
The very first one is in five point summary is my minimum value, which stands for the zero percentile
data.
And the second one is basically my 20 percent later and the third one is my 50 percent on data, which
is termed as median.
The third one is basically my seventy five percent, which is termed as two three Esbjorn.
And the last one is of course my maximum value, or you can say as one hundred percent on data so you
can achieve all this concept by just calling box brought on your data.
So for this, I'm going to use floridly in such scenarios for interactive results.
So from plotless, you have to call a function known as Bonks, which gives me a beautiful box plot.
So if you are going to pass a step, the very first one parameter is data frame and then you have to
pass your data on X and Y axis depending upon what exactly you want.
So the very first one is my data.
So my data frame is basically the X and then on X axis I have to pass various data or you can pass it
on Y axis as well.
So let's say I am going to set my y axis data, so I'm going to use five point summary for, let's say
all these features just compute from here and just let's say based on here.
So these are exactly my let's say I'm going to remove this coverage, which is responsible for credit
card average.
And now I have five features over here and I'm going to visualize this.
So let's say this is my finger.
I'm going to store it in seven and I have to just call a show on this finger to visualize my beautiful
or interactive box plot.
So it will take a couple of seconds to execute.
So this is exactly my beautiful box.
So if you are going to hover your mouse, so now you will see for income, I have minimum value eight,
which is this one and two one, which is my basically twenty five on data is basically thirty nine and
median is sixty four, which is basically my 58 percent data and two three which is my seventy five
percent and it stands for two three as ninety eight.
And this is exactly my maximum value, which is my two to four.
So these all are my five point summary for my data, similarly for a similarly for experience, similarly
for family and similarly in case of education as well.
So that's a brief idea how you can visualize your visual, how you can conclude your visas and what
type of visas you guys can basically perform on your data.
So if you have to conclude this visa, you can see this five point summary suggests that experience
has some negative values.
So we have to fix this because experience can't have negative values.
So we can see the minimum max me and the standard deviation for all the other features as well.
And income you will see income has too much noise and it is slightly skewed, right?
It means it is high positive outliers in income.
And if you are going to conclude this age and experience, so you will see age and experience are some
already equally distributed.
So that's a type of conclusion you can draw on.
You can in sense, from this box plot.
So that's all of our discussions in the upcoming session.
We are going to deal with some of the analysis as well as this experience, because it contains right
now some negative values.
So hopefully you will love this session.
Thank you.
Have a nice day.
Keep learning, keep going and keep motivating.