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Hello my friends, and welcome to the final
section of part three classification,

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where we're going to answer together
a very important question.

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You know, one of the most frequently asked
question in the data science community,

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which is which classification model
should I select?

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You know, should I choose for my data set?

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And the goal of this tutorial
is to show you how with any data set,

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you know,
regardless of the number of features

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you have in the data set, well,
I will show you how to select quickly

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and efficiently
the best classification model.

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All right.

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So that's why here
we're back into our machine learning.

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It is that model selection folder.

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You know which is a separate folder
compared to the whole machine.

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Learning is a folder with all the codes
and data sets to figure out

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how are we going to select
the best classification model.

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All right.

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So here we are in the classification
folder of our model selection big folder.

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And as you can recognize in this folder
we have all the classification models

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that we implemented together all along
this part three you have all of them.

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However I slightly modified them.

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But the only thing I did, you know
with respect to what we did before,

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is that I removed all the prints,
you know, to alleviate

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or lighten the implementation
so that we can see more clearly.

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And also, of course, you know, at the end,
I removed the two cells

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where we visualize the training set
and test result.

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Right.

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Because remember this visualizations only
work when you have two features and here,

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as you can see, I took a classic data
set with many features.

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You can see all of them here.

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So these are all the features.

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And this is the dependent variable.

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But you can see this
data set as a generic data set

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containing many features
all with numerical values.

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Right.

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We won't do any kind of specific data
preprocessing

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and indeed a binary dependent variable
taking values 2 or 4.

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All right.

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So since we have

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the data set in front of us,
well let me explain what this is about.

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Even if you know it doesn't really matter,
because the goal of this tutorial

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is just to explain
how to deploy efficiently

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all your classification models and quickly
figure out what is the best one

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on any data set,
regardless of the number of features.

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But let me still explain
what this is about.

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So this is a classic data set
which belongs to the UCI

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Machine Learning Repository
and which is about breast cancer.

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So in this data set,
each row corresponds to a patient,

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you know, different patients here.

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And for each of these patients we gathered
well first assemble code number

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the clump thickness
the uniformity of cell size

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the uniformity of cell shape,
the marginal adhesion,

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the single epithelial cell,
the Bernoulli,

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the blood chromatin, the normal nuclei
and the mitosis.

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Okay.

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And all these variables are the features,
you know, from sample code number,

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even if that's not really the feature 
up to mitosis.

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And with all these features,
we are predicting the class which details

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for each patient if the tumor is benign,
in which case class takes the value of two

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or malignant,
in which case class takes a value for.

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All right.
So that's what the data set is about.

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You can find it on the UCI
ML repository by the name breast cancer.

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And you can take the original version.

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But really don't worry
about all these features

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because, you know, most of us
don't understand what they mean.

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You know, we're not doctors here,
but we are data scientists.

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And even if we don't understand
the domain knowledge here of oncology,

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you know, cancer medicine, well,
that's still fine,

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because we can still build
classification models to understand

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the correlations
between all these features here

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and the dependent variable class,
which we want to predict, telling

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if the tumor of each of these patients
is benign or malignant.

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All right.

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And so we're going to use this data
set to deploy

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all our classification models
in a flashlight.

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You know in a matter of seconds.

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And after just a few clicks
we will be able to figure out

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what is the best classification model
for this data set.

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All right. Great.

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So let's do this. Let's close this.

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And now now what we're going to do
in order to start the demo

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is because you know,
this is a Google Drive folder to which

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all of you have access and therefore
you can't modify it, obviously.

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And so what you have to do
in order to modify these cells, you know,

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because we will have to enter

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the name of the data set
because these are all code templates.

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In order to modify these cells
you need to create a copy.

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So that's the first thing we'll do here.

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Let's do this quickly.

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You know you just need to do right click
and then make a copy for each of them.

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All right

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then Colonel
SVM make a copy logistic regression.

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So you see it's pretty fast.
Sorry about that.

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But at least it only takes a few seconds.

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And then you'll get all your copies
in case you know you want to modify them.

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But I recommend to.

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All right.

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Then your copies would go naturally
to your main drive or,

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you know, in the Colab notebooks
folder here.

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They just went into my drive, so all good.

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Now we're going to open them all.

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So starting with the last one,
random forest.

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All right then we're going to open

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the decision tree classification open.

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All right.

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You can open it with Jupyter Notebook
also if you want.

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Right.

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Then we're going to open Naive Bayes.

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All right
then we're going to open kernel SVM.

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Perfect.

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Then we're going to open SVM.

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Where is it right here.

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Support vector machine open. Then

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we're
going to open the K-nearest neighbors.

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All right.

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And finally we're going to open
logistic regression

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I went from the last to the first
because as you can see this is the way

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now we have all the files
in the correct order.