Harlette continue with our project, and this video will do as Kelly, so in this project, will you
make it hard to get on the scale that are in that range from zero and one because computer only understand
zero and one.
So how do the formula work?
Let's try some formula for that.
It's very easy.
So.
Scale.
Equal X minus.
Mean.
So that total.
We'll be revived by.
X marks.
Minor, I mean.
And does it do to perform features, scowling, we can use the prepossessing package available in the
second library so that I could learn library is a free software machine learning library for a python
programming language.
So it's free chose BRIAREOS classification Chryson and clustering algorithms, including the Support
Whitemarsh, SVM, Random Forest Radio and boosting games and dibby dibby debates scan and is SDI to
incorporate with the python numerical and scientific libraries, nimbies and Skype.
So remember to import our library that is not present in the initial distribution of Python.
You must be in.
Come on, come on in here.
We don't need to install anything because everything is installed in a Google CoLab, so we just Yoda,
Escalon, not prepossessing Buckeye's Provisor or Common Utility Functions and Transformers Kos's to
modify the features available in the representation that bessus our need.
So we need to import our baksh is very easy so that.
Creation and not from České Lord, not pray.
Processing.
Import.
Mean.
Max.
And.
Skalla.
So to scale features between people and minimum and maximum value, in our case between zero and one,
so that the maximum absolute value of a scale to the units, I mean, my scale of function.
So let's start by defining the scale object.
So that really scared the object.
Scale are equal men.
Mark Skalla.
And now you're to have a confirmation of what we are doing.
We need to bring the perimeter that we will use for the next resizing.
So bring.
Skalla.
Dafydd.
Dadar.
And then run the sale and we got Muscala equal feature featurette from Zero and one.
So the fit method can boost the minimum and maximum that is to be used for the later Skellig.
So that riot.
They can put the minimum.
And maximum.
That is to be you for later scaling.
So.
Now, what we were doing, we knew the scale of we just so let ground near Cazale and the variable where
you data scale.
Equal Skyler Dafydd.
Underscored transform.
Dadar.
And a freight train for my third fit to the data and then transform it so that run this El.
So an umpire right now is retired and advisable to report and always in the starting from Vandersloot
offramps or at least for comparison purposes.
So, like.
What does your data scale equa?
PD or data frame.
And then data scale and then column.
Equa.
Behesht names.
And to verify that that information work area, we due to some basic statistics that we had already
calculated so.
That being so Samari.
Equa.
Detat scale.
Distri.
And then summary, Igor.
Samori.
Address, post.
And then we need to bring.
Summary.
And we got our summary, so as you say that.
Would that driver with a preference for our resolve, every variable is included in the range between
zero and one, so as you say, is from zero and one.
Now, all villages have values zero and one we will now move on to visually visual analysis.
For example, what we can do is block the variable.
So what is referred to as Whiskas charge is a graphic representation that is used to describe the distribution
of assemble by symbol dispersion and boxes and indexes so I can be presented either horizontally or
vertically by making a rectangle.
Partition divided by two segments, so the rectangle box is delineated by the first quarter.
Twenty five quanti the third quartile, 70 75 quanti and divided by the media and 50.
Fifty by the S.I, so.
The median is 50 percent high, so let's have some tax for that.
The first one is lower, riska.
And then a 75.
By S.I.
The next one is Maryann, with a 50 per cent high.
The next one, A.
Seven DFI.
By Senti.
And the next one will be.
Upper.
Whisker, and that is the end of this video.
I hope you enjoy it and I will see you in the next video.