Hello, all in all of a previous session, we have analyzed as much as we can.
We have understand our data and in the previous session, we have applied a concept of what exactly
the correlation and using correlation we have out, what are my topmost features that are going to contribute
more to my machine learning model.
After that, we have used in some basic human knowledge using some basic sense of humor.
I just exclude some features.
And after what we have to do in this session, this is exactly the very first statement for this particular
session.
We have to extract some derived features from data.
So let me show you what exactly the did feature.
So let me show you what exactly the data frame on which you have to find this feature.
So in this data, I have to just pass my cat and a features and let's say this is exactly my data on
a score.
Get data from just executed.
Now, if I am going to call my had all the data on the score, cat not had to get a preview.
How my data looks like you will see over here.
These are all the features on which I have to perform.
Lots of preprocessing.
You will see these are exactly the status and escorted from this feature.
You have to extract your desired features, so you have to extract your you have to extract money.
You have to extract because whenever you are going to build a machine learning model, that model will
now understand what exactly this indeed.
So you have to do in machine learning model.
Yeah, this is my ear.
This is my money.
This is my date.
So these are exactly all your desired features and you have to fetch from this data.
So very first, what I have to do.
Let me check what exactly the data of each and every feature.
So I'm going to say it, cat dot data detector here, just like the cell and you will figure out this
status date, find us by default, assign it as objected.
But we know it supports some data from it.
It means we have to convert this into some data format for this.
I'm just going to say just call this to escort data.
And here I'm going to say data and go get which is exactly this one.
And on this, I have to exit this reservation and status and it's coded.
And now what I have to do, I have to simply update this feature as well.
So I'm going to say Reser version underscore is status and a code named after doing all this stuff.
What I have to do, I have to just agree to this set.
He will see you have some warnings.
Just don't worry at all.
These are just on account of some weather issues, some liability issues.
Some of you might have some issues.
There are lots of issues.
So if you want to ignore or if you want to get rid of this warnings, you can import your learning module
as well.
So I would say import my warnings.
And from this warning, what I have to do, I have to import something known as filter warning.
Just best stab at in this filter warning.
What I have to say, I have to say wherever warning will occur, just ignore this.
That's what this blocks of code will do if I'm going to execute it.
Now, you don't have any warning now.
Your main task is you have to fetch your money and day from this data.
Get off this, this, this.
So to fetch your ear, you have to just collar d dot your over it now to fetch your munt you have to
just call this D dot over it now to fetch your day you have to just call Dot.
That's it.
And let's say whatever it will return me, I have to store it somewhere else.
So I'm going to say data on this go get and where you have to store it.
Yeah I have to basically store it in a new feature so I have to define it.
So using this data to get out of here, you can simply define that feature, that input.
And after it, I have to define a new feature as data underscore, get all select statement.
I'm going to say it is my month after doing all this stuff.
I have to again access this data or get over there and hear what I have to do.
I have to say this feature name or column name is nothing like to do.
So just execute all this stuff.
And again, if I'm going to access this data, underscore or get around this, if I'm going to call
lot or do now, you will see you have three columns added the very first to the year, your month,
your day, and now you will see over here.
This feature, which is exactly this generation is in a school cadet, makes no sense at all because
whatever you want to fetch from this, you have already fetch.
So now you can simply drop this.
I'm going to say to underscore cat dot drop here you have a function.
And what I have to do if I drop this feature, that's it.
Then I have to say to my actors, it goes to one because I had to drop in vertical way.
Then I have to update my little frame as well.
So I'm going to stay in place to just execute.
All this stuff gets executed.
Let's say I have to insert a new column in this data underscore CAT.
Its name is cancellation because I didn't get this exactly that data frame where we have all the categorical
features and we all lose this data.
Let's go.
Let me show you a thing which is exactly DataDot, these types.
So if I'm going to call this Daibes, you will see a feature, which is exactly this is cancer, which
this bond does assign.
It has in sixty four, but we all lose.
It is exactly an object because the values are exactly in the form of zeros and ones.
So zero basically means your bookings are not going to cancel and one means your booking are going to
cancel.
It means it's objective to type column.
So you can definitely consider this feature in this data set.
So I'm going to say data underscore that.
And here I could just define a new column like, say, cancellation.
Actually, this is exactly my column and now I'm going to say data on a out of here.
I have to say data of is underscore cancel all data of IZAN score, cancel it now.
Just execute all this stuff.
And if again I'm going to call my head over there now, you will figure out here you have a new feature
which is exactly cancellation.
So that's all about the session.
In the upcoming session, we are going to perform our feature encoding techniques on our data because
these are exactly my string data.
You will see that at this resort hotel, no deposit.
These all these features are exactly matching data and machine learning.
Now, understand, this is string guitar, so you have to tell machine learning.
It means you have to convert these string data into some Intisar format using some feature encoding
techniques in the upcoming session.
We are going to deal with all these techniques.
So I hope you will love this session very much.
Thank you.
Have a nice day.
Keep learning.
Keep doing.
Keep practicing.