Our project.
Let's first import important libraries.
So first, we are importing bonders, Speedy.
That is a machine learning laboratory in Britain that brought data structures of high level and a wide
variety of tools for analysis.
One of the great feature of this library is it has an ability to translate complex operations with data
using just one or two commands, bonders, make sure that the entire process of manipulating data will
be easier next.
Very important number as and B number is considered as one of the most popular machine learning the
ability then Zurfluh and many other Ledbetter's uses numbers internally to perform multiple operations.
No, makes complex mathematical implementations.
Very simple.
Then we are importing matplotlib the by plot as bialy.
Matplotlib Bayport is a platoon leader he used for duty graphics.
Then they're embodying Seabourne as as inUS Seabourne is also a visualisations librarian, Biton, it
is built on top of microglia.
If matplotlib tries to make easy things, easy and hard things possible, Seabourne tries to make well-defined
set of things easy.
Seabourne has many features comparing to microglia.
Then we're importing accuracy's good from and scaling that matrix.
Import Accuracy's.
Could we use accuracy score to check our model's accuracy then import warning's.
Warnings, dot warnings ignored.
So if you have any updated library.
So when we run the cells.
If there is any outdated library, then it will show to updated.
So if you use warnings, dot filter warnings ignored, so it will ignore all the warnings.
So it will ignore only warnings, not errors.
Randall Elbrus shifting to the.
Knowledge, our dataset.
So data citizens, yes, information, so our filing is financial and data.
So in order to lower this year to file, we are using pandas so we don't read underscores ESV and in
brackets, we have final financial data and we are storing this in the.
And to see top photos in our data from.
The writing the DOT had.
So they've got a head function, gives us top five rules and information about top Fayrouz.
Now, later, Anderson, I should point out to Randall.
So here we have Collum names us into Idy age, Batia Deal, which is weekly, biweekly and homeowner
the house are not and their income.
And moms employed in total years employed and their current address, your.
So from how much time they're staying at that address and their personal account details.
So there are so many columns, Hoyer.
So it was Fallone.
And then rescuers.
There are many risks, of course.
And then we have U.S..
That they have signed on.
So this is our level.
So all these are our features and this is our level.
So using all these features, we need to predict this is using.
So in our label, we have one zero, which means sign or not sign.
Let's get the count for signed.
I'm not saying let's proceed for the.
So here in this segment, they're printing total number of signed, which is equal to zero.
So from data from D.F., you are taking column you signed.
So in that column, your condition has equal to equal to one, which means in its own column it will
print count of all rules, which has one.
So here it will bring.
All roads which have zero hits, Anderson and Anderson.
So here we have a total of nine thousand six hundred ten people who have signed, which is equal to
one and eight thousand to 16 and people who have not signed.
Now, let's check the percentage of missing data in each column.
So for that, we are taking our data from seasonal.
Good values and not some.
To give some of our land values.
In two hundred, divided by Lindop over what is needed of them, and we are storing a 10 percent and
missing.
Then are creating another bit of room with the name missing underscore value, underscore veev is going
to be deducted from.
And here we are giving it this very IT person underscore missing this additional key name and person
underscored missing are the values.
So all the values which are important underscore missing we are using here and we are starting this
in missing and got value.
And it could be a sort of visualized, missing, underscored value in the scope of your writing if your.
I said, Anderson.
So we have zero percent missing values, values in all the columns.
OK, let's put aside for the.
Now, let's create another data set with the name dataset to Anderlecht to remove unnecessary columns
from here.
So to remove unnecessary columns right there, that drop and the columns is equal to column names.
So we are removing these columns from our beef and storing it in the to.
And also, let's create a histogram for our columns.
First, go configure our figure site.
So that figure figure is equal to.
Well.
Not subplot for all the columns, and let's give the subtitle as histograms of numerical columns.
And says is colleague Wendy.
So here we are creating a for loop.
For I in range of data to the ship of one.
Do not subplot six rows, three columns.
And that physical reality that G.K.
And if the SEC title deeds are to dot column dot values, I saw this look will run on all column names.
So I hear is the column name.
So it will print that column for that title, so value is equal to impetuses dataset, to the dialogue
all rose.
I column.
So I thought I'd the.
So I call on them.
So this will repeat for all columns, not unique.
So if Valse is greater than or equal 200, then Multiskilled 100, not exploit the Instagram, so build
it up histo data say that I log.
All rules, and I tried to call them, I call them and Benz's call Duval's and color is equal to this
color code, which is blue.
So finally, we are going to call out a rectangle is equal to zero comma zero point zero three comma
one zero point nine five.
Knowledge that Anderson Elbrus shifted from little.
So this is the histogram for all of our plants.
First we have age.
So in this program here, we have value and here we have found so mostly the value is between 20 to
70, which means that the maximum people from 20 to 70 years of age.
And here is the.
So there are a thousand people of today's age, there are more than a thousand people who are forty
five years of age.
Before this all this blood.
It is the value and it is a value.
Just in.
So we more than 750 people have income, more than three thousand, approximately three thousand.
Similarly, for all columns, we have different values.
With different on.
So this is the risk score and this is the amount requested.
So among the more than 2000 people requested one thousand.
Now, let's check the correlation between our futures and levels so generally used to create heightmap,
but today we're going to create a lot of correlation.
Let's proceed further.
So you're you're creating correlation, Bacolod, so we're taking data to that correlation with the
design.
So here we are correlating data to features.
With the U.S. Collomb, so U.S. Collum from the.
Dot, dot, dot.
But so we're creating a plot and figures are used for more than.
And we're creating a title as good relations with design, just our DNA is our column name, and the
font size is equal to 20.
Criticism's true colors is equal to being green, blue and magenta.
So these are the colors, so to check all the parameters and all the values which are used in this,
not just click in between this parenthesis and Shifta type.
So in this vertical bar, practically, sure, all the parameters on the.
Which can be used in this butler.
Monarchs are undersell Elbrus shift and retronasal.
So we have created a backlog.
So these are the column names which are related with.
Or is I'm.
So each and current address here is negatively geared, mostly negatively correlated.
And this one and this one, so among registered.
And the personal account has a more positive correlation with the E signed column.
And the US score is like zero point.
Zero one zero.
Not much got injured and household income was also negatively correlated.
Lettuce is also mostly made routinely, according to.
So from this blog, we can see that you seem to have high negative correlation with variable age and
current address.
I can't address your.
And have positive correlation with the amount requested.