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Hello my friends.

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Welcome to this new practical activity.

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On this time,
the Naive Bayes classification model,

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we already implemented logistic regression
k nearest neighbors, SVM and kernel SVM.

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And so far the best accuracy
we've got with these four models

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is oh point 93, which we got with both

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K-nearest neighbors and kernel SVM.

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And now we're about to implement
a new classification model,

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the Naive Bayes
one, a very well known and widely used.

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So you definitely want
to have it in a toolkit.

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And so the big question is will we beat

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that best accuracy of 0.03.

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And now let's do this.

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Let's start
the implementation of Naive Bayes.

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So we're going to go into this folder

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and we're going to start with Python
as usual.

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And inside this folder
you will find two files

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the Naive Bayes
implementation in the Ipynb format

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and the same social network as data set,
which I will quickly re-explain.

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So this is a dataset
containing 400 customers.

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Each row here represents the customer,
and for each customer

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we get the two features age
and estimated salary.

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With which we are going to predict
the dependent variable purchased,

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which tells if yes or no, the customer
but the SUV.

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So zero means that indeed the customer
didn't buy the SUV,

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and one means that the customer
but the SUV.

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All right, so we're going to train
a new classification model to understand

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the correlations

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between these two features age and salary,
and that dependent variable purchased

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in order to predict the customers
that will buy the SUV.

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And once we get these predictions,
we will target these customers

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on social networks with some beautiful
ad of this beautiful car.

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All right, so that's the same story.

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And now we're going
to start our implementation.

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And I can't wait to see
if we're going to beat the accuracy.

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And I can also to show you
the visualization results at the end.

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All right.

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So right now
it is loading and laying out the notebook.

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And here it is.

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Here is a Naive Bayes implementation

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still resulting
from the same classification template

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which we made in the first section
of this part three.

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When implementing logistic regression.

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So basically all the cells here
are the same

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as in this logistic regression
implementation or classification template.

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The only cell that will change will be,
you know, the one where we build

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and train the classification model,
meaning this one.

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That's the only cell that we will

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re-implement ourselves
because all the rest is the same.

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But this notebook is in read only mode.

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And therefore, in order to re-implement
that cell, we need to create

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a copy of this notebook by clicking
save a Copy in Drive.

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And as you can see,
this will create a copy

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in which we will be able to re-implement
that cell.

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All right.

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So once again laying out the notebook
and there we go.

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Here is our copy on which
we are authorized to modify anything.

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And especially that cell
we want to re implement.

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So I'm scrolling down here to find it.

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Here it is training the naive base model
on the training set.

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So let's immediately

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remove that cell because I want you
to re-implement it from scratch.

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As if we had no idea
on how to implement this.

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All right, let's create a new code cell.

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And now your turn.

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I want you to train your machine learning
independence by figuring out by yourself

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how to indeed build and train
that naive base model on the training set.

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So of course, to do this,
you have several options.

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You can directly type
in the search bar of Google or Bing.

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Well, Naive Bayes scikit learn class okay.

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Or you can navigate the cycling API
which is right here.

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In order to find that class which
we need to build our Naive Bayes model.

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And my personal recommendation
is to try it with the second option,

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because indeed, this
will get you familiar with this API.

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And the more you get familiar
with it, the better.

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Okay, so please press pause.

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Now try to find this class

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and then try to build this naive base
model in training on the training set.

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And now in two seconds
we will implement the solution together.

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All right let's do this.

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Let's build
this naive base model with scikit learn.

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So the API is huge.

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And this is organized
in alphabetical order.

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And so the first thing we'll try
is to find, you know, a module called

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maybe Naive Bayes. Right.

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Just the name of the model.

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So we will scroll down down to N,
you know the letter N

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and we'll see if we get Naive Bayes
somewhere or something close to it.

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Let's see.

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Linear model meaningful learning metrics
we're getting close.

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And Gaussian mixture models
model selection multiclass.

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Well there is a lot of n okay
perfect naive Bayes.

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So let's click this Naive Bayes.

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All right.

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And so the model indeed
is naive underscore Bayes.

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And among these models.

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Well according to which one
are we going to take here.

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That's a good question actually.

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Well in fact the classic one and the one
that you learned in the intuition

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lecture is of course, this one,
the Gaussian, naive Bayes, Gaussian and B.

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And that's exactly what we'll use
to implement our Naive Bayes model.

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All right.

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So the name of the class is Gaussian B.

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And this time

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the good news is
that we won't have to worry

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too much about the parameters because
there are actually only two parameters.

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And so here very simply

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we will just call this class
without inputting any parameters.

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So that's super easy.

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Let's copy this and let's
go back to our implementation to copy one.

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Let's paste that here.

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Let's remove this little thing here.

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And now you know how to adapt this.

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We need to start with from,

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you know, the Naive Bayes
module of the scikit learn library.

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And then here you add import.

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There we go that Gaussian and B class.