Now, let's build a few other models.
That's to feature engineering and modify features of our dataset and build few other models, so deep
learning neural network contains hardcoded data processing, feature extraction and feature engineering.
Those who don't require much future engineering with deep learning models.
So in order to get good accuracy with our regular machine learning models, we need to look at engineering,
so let's modify our feature columns, so.
Let's modify our age column first.
So first, we're creating an empty list with the name.
So here we are creating for loop and opening those values over age.
So for I in range, land of date of.
So we will run Lupa.
To the left to hold it up, Adam.
If your data from of age, I would use that particular rule.
Less than or equal to 45 each dot append your legenda, youth to adult.
So the age forty five, we're appealing that role in age as adult.
And as is what senior, so if the aid is equal to or less than forty five, then it's ordered if not.
Blitzer, Anderson and Anderson.
Non-automotive I them once employed in.
So here we will calculate months employed as months employed, plus years employed.
So using this condition, we are saving that value in one Templer column.
So for this, we are creating an entire list of the name employed.
For I in range of data from.
It's his goal to be a force once employed.
I used that particular rule, plus the four years employed I, which is rule in goodwill, so suppose.
We have this table.
They've got this little.
One, two, three, four, five, six.
So first for I in range, land of the Earth.
So this is the land of the earth for our children to be of.
So here first our I will read this one.
First rule, so it's difficult to deal for months in blind eye, which is months in blood cholesterol.
It just this little.
And we have all feature columns, so in this role, we have one simple column.
Plus, days of years in blood column.
Well.
So these two columns, these two columns plus.
Interruptible.
Plus, an incredible.
So once employed, Alice is under employed.
And so we are doing this for all our roles and we are spending it in blood list.
Little Anderson, a shift in attitude on the undersell.
Here also in personal account, we are doing the same personal account month, less personal account
here in Boothville, and we are bringing that to be list.
Blitzer Anderson.
Nonexplosive for the.
So now let's calculate average of risk score so we have four risk, of course, risk to score three
or four, original score five.
So we are taking advantage of this for risk.
So first, we are creating an empty list with the name average underscore risk, and yet we are creating
a loop for I in range of div.
It is going to be at its core to I, which is first column.
The army arrived in the first column, so when we are hydrating with the first it all, so you're the
first day of a rescue squad, three column, first from the rescue squad for column, first through.
Deal of risk for Fuster.
So we're adding all this and developing it before, so we're taking an average of.
This Aristarchus course prefers to call them and appealing to average risk for.
So when I used to do in this range, then we are doing the same for Rotu and we are bringing that.
As average, it is for the.
So similarly, we are doing it for quality.
Here we have.
Qualities, corridor and qualities good.
So we are.
Taking average of quality and civility and the quality list, let's run these totals.
So now we have created so many feature columns, now let's convert all of those features, modified
features to data from.
So first will convert all the list into data from so ages, pages of data from each.
So we have created a list.
So we are converting that age list into data.
From then we'll complete the converting employed list to employer data from.
And then be a little to be for the average risk to average risk as debt of Adam and Eve to quality data
for from.
And then finally, we're creating the name feature and concatenating all this little of the transition.
So if you have all these features but we don't have names for the columns and here we are trading column
names, feature columns is equal to age and blood being risk and quality.
So these are the column names for our new date of transition.
Let's visualize our new feature data from.
So here we have each peerce quality through each column is category, rather than seeing it for all
other columns are in numbers.
So let's convert over each column, the numbers.
So we're creating a dummy variable and we're taking each column and we are studying it and we want to
visualize this, that we do different.
We will not head until.
So here we have adult.
So the only one we have stored age in numbers.
So now let's remove our age column, which is in categorical format, which isn't saying you're an adult.
Let's remove this age from our future date of rape and add this column.
So first feature the drop age, they're dropping like the column and then feature is going to.
We did the concatenate feature column feature data for them, and then we did offer them endless.
Join this together and visualize the data from the writing feature that lets Handsell.
So here we have convertor.
All of it categorical features into numbers.
Now, let's proceed further.
Let's proceed further.
Now let's update our features and labels after doing feature engineering.
Let's create a variable with the dependent variable.
Registering as a sign.
So this is a string and we are saving this string in the.
And here are creating a well with the name and the eye is called to the columns to.
So, yes, we are storing all the column names.
Of our data from.
Run, Anderson.
I will show you the value of andI.
It's creating little.
So it has all these different names.
So now from this list, we need to remove this sign.
So for that, you need to remove the B so deep we have to sign the string.
So which will remove the underscore to sign the string.
And we also need to remove a.
So we are removing these two columnists from this list.
Now, let's separate our X and Y, so X is according to data from us, and so we are selecting all these
column names.
In our feature column.
In our features did of them and vice custody of BP, which is overdesign, so envie, which is our labels
from we are selecting only one column and an X, we have all columns except our label column.
It's Grandison.
Now, let's concatenate our modified feature columns with our X, so feature these our newly created
data for them with all modified features.
And we realize this, that having extorted.
Israelson.
So here these are the default columns, each basically dual homeowner current address personal.
It is five.
So.
Until this inquiry is last month, these are default columns from your employer to be the quality of
the columns which we have created.
So as we have already modified few columns and created new columns to remove unnecessary columns.
So Exercycle do drop Labor's call to age, once employed, it employed personal account, personal five,
two or three or go to five.
If the court is called to disqualify soldiers to remove all this columnists, let's understand that
let's we knew it.
So here we are, basically all column and.
All the columns are in numbers, but only this particular column is in category, which is in strength.
So we need to convert this column numbers.
So let's create a dummy variable, dummy variable with the name dummy to.
For Piscatella column.
So we did not get the Army's of Piscatella column and it in the middle and in the middle, let's drop
by quickly.
Because you realize this will not hit.
So after dropping by weekly, we have monthly, semi, monthly and weekly.
So now let's remove your name from our exit and add this to.
So first, we are dropping Piscatella column from EXER and we are concatenating our EXER with our newly
created data from the middle.
Which has vandalism and weekly.
And the this will not help, Anderson.
So this is our final data from.
Which is all numerical values.
Now, let's just kill all our values with standards killer.
So first we are importing from Eskil under preprocessing import.