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Hello my friends.

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Welcome to this new practical activity
on our second classification model,

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which is of course the k nearest neighbors
and now we're going to go into point

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three classification
and then k nearest neighbors k and n.

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All right.

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

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And inside this folder
you will indeed find two files.

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Of course the k nearest neighbors
implementation in ipynb format

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which you can open with either
Jupyter Notebook or Google Colab.

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And the same data set social network ads

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containing 400 observations,
where each observations

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you know, each row corresponds
to customers.

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And for each of these customers,
we get the age.

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That's the first feature,
the estimated salary.

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That's the second feature.

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And their purchase decision, whether
or not they bought yes or no, the SUV.

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And that's of course
the dependent variable.

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And our exercise here is to learn
some correlations between these features.

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The age and estimated salary to predict
this dependent variable purchased.

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And the goal of all this is to
of course, predict which customers

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are going to buy the new SUV
so that we can optimize our targeting.

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All right.

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So exactly the same.

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And now we're going to open
this implementation k nearest neighbor

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with either Google Collaboratory
or Jupyter Notebook.

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Choose your favorite.

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And now it is opening the notebook
laying it out in a second.

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So learning it laying it out there we go.

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And that's the notebook.

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And as you noticed,
as you probably noticed, I actually kept

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the logistic regression implementation
to show you, indeed, that

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all the code cells inside
these two implementations are the same.

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You know,
all the code cells are the same, except

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the one where we indeed build
and train the model on the training set.

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Right?

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So up to here, you know, all
this is the same, all this is the same.

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And once we reach that step
where we train the classification

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model on the train set, well that's
where we have this single change.

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And then all the rest is the same.

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Then we predict a new result,
you know, with the same name

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for the classifier
which we called classifier.

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Then we predict test result here
once again we have nothing to change.

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It's all the same right? All the same.

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So if by any chance
you are starting with K

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and then you know
before logistic regression,

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I really encourage you
to do logistic regression first

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because all these cells
are explained in detail.

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And for this in an implementation
we will just re-implement,

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you know, that cell to train the K
and and model on the training set.

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All right. So see.

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So that's why
it is a classification template

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which you can use to build
any other classification models.

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And together we will
of course use this classification template

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to build all our other classification
models, including K and N support

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Vector Machine, kernel, SVM,
Naive Bayes, Decision Tree Classification

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and Random Forest Classification and we
will all build them in maximum efficiency.

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All right. So let's start with K.

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And as you understood the only cell
that we have to change here is this one.

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So actually since we cannot do it here
you know re-implement it here

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because this is in read only mode.

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Well we'll have to create a new file,
you know, a copy of this file

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by clicking here.

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This button, save a copy and drive.

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This will create a copy.

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And inside
which we will be able to re-implement

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that cell to build and train the K,
and then model.

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All right.

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So let's do this.

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We just need to scroll down here.

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All the rest is the same. And there we go.

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So what we're going to do
is just remove that cell and there we go.

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Now we can re-implement that cell.

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So let's create a new code cell.

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And now I would like you to please

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press pause on this video to indeed
try to do it yourself first.

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Because once again,

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I not only want to train you on machine
learning, but also I want to train you

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on how to be independent
and create things on your own.

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You know, create your machine
learning models on your own.

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I already guided you
on how to navigate the scikit

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learn API
to find some information in some tools.

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And now I would like you to do this
same exercise again.

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Please press pause on the video
and navigate the scikit learn API

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to find the class that allows
indeed to build the k and end model,

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and then you know how to train it
on the training set.

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All right, so in two seconds now we're
going to start the solution together.

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Here we go.

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All right.

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So let's suppose you know I'm just like
you I'm not an expert in machine learning.

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And I would like to build and train
a K in and model on the training set.

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So since I actually have no idea of
what is the name of the class

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that does it, well, I'm going to go
of course, to search it online and inside.

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Well, we will do it this way scikit learn.

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Then we're going to go to the first link.

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Right.

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We will end up in the scikit learn
welcome page.

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Then we're going to go remember to API
which contains all the

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modules and functions
and classes of scikit learn.

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And now there we go.

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We are looking for the class
that allows us to build the k and model.

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So let's scroll down a bit.

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And if we have,
you know, too much difficulty to find it,

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well we will do Ctrl or command
F to find it.

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You know, there are many tricks actually.

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You can also directly type in your search
bar K nearest neighbor class

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scikit learn or scikit learn k nearest
neighbor class, something like that.

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But I like to navigate the secondary API
because it is really well made.

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And so here by scrolling down,
do we find it?

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Yes. Here it is. Nearest neighbor.

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So it was actually hard to miss.

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Indeed we had to scroll down a bit,
but that's okay

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because it's really good to get familiar
with the Scikit-Learn library.