Long before going deep dive into a recession.
Let's have a walk through on what we all have done in all our previous session.
So found data and particularly the analysis people from lots of data, people sitting on data to make
our data ready for analysis as well as for the modeling purpose.
Then we perform lots of techniques of feature encoding.
Then we have a deal with our outliers as well, using our distribution plot and Berthelot approach.
After what we have to do, we have to separate our independent feature as well as dependent feature.
So intersession what we have to do.
We have to apply feature selection on data.
So what exactly is data selection and why is there the need of the selection?
So feature selection is all about when and where you have to find your best feature, which will contribute
most, and that has a good relationship with the target variable.
So that's it.
That's all about my feature selection and why to apply feature selection, simple to select important
features so that you don't have an issue of multiple dimensions, so that you don't have an issue of
multiple column.
That's why you have to perform several techniques of feature selection.
So basically we are going to select important feature using a concept of information.
So for this, I'm going to say from my cycle on module, you have to use a feature selection, some
module.
And from this I'm going to say I have to import my major underscored info on a school closet.
So just execute it.
And now I'm just going to initialize this.
So I'm going to say this.
And here you have to parse what exactly is your independent data and what is the dependent data.
So my independent data and my friend just executed.
It will take a couple of seconds as well.
And it will exactly return you some kind of importance, some kind of priority with respect to your
target variable.
Now, you will see this is exactly the importance of with respect to your target value.
But let's say I have to thirds here.
I have to create a data frame.
And in that data frame, I have met all the columns with respect to its priority, with respect to its
importance.
So far, this is what I'm going to do.
I'm just going to create a data stream over here.
So here I'm going to say Beadie Dot data frame.
And here very first, I have to use this one because it will exactly return me my importance.
And on this index, I have to say I have to just assign my X dot columns.
Let's say I have to store it somewhere else.
I'm going to say this is nothing but my important during and after.
I have to print it as well.
So just execute it.
So all of this stuff gets executed.
Let's say I have to do some monopolises in this data frame.
Let's say I have to rename this column.
So what I'm going to do, I'm going to say I am Bidart columns equals to actually I have to assign my
own column name.
So I'm going to say say this important.
And after eight, let's say I have to sort this data frame, I have to short this data frame on the
basis of its importance.
So what I'm going to do for this, I'm going to say this ampe dot short underscore values and how you
have to short it.
So if you will shift the stab, you will see over here.
Here you have a barometer which is by it means on what column basis you have to short it.
So here I am going to say because to importance what you have defined earlier.
So because the importance and you have to also mention.
Yeah, I just want descending order of importance.
For this you have to set Ascender equals two fold.
So just execute it and you will see this beautiful little frame that gets shorted on the basis of importance.
So you will easily get to know over here.
Yeah, route to column, this three column, this total columns are basically top three column that
will contribute most to my target variable, whereas this Jet Airways business is that column that contribute
least to my data.
So you can simply skip this column if you really want.
After having conversation with your domain expertise, you guys can simply skip this column.
Similarly, you have all the importances with respect to each and every column of data.
That's what I'm trying to show you.
So that's all about the decision.
Hopefully you will love this session very much.
So thank you, guys.
Have a nice day.
Keep learning.
Keep growing.
Keep practicing.