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Hello and welcome to this art tutorial.

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This tutorial is the fun tutorial

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because we did our job
by finding the optimal number of clusters.

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Thanks to the dendrogram,

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and by fitting the hierarchical clustering
algorithm to our data x.

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And now.

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So time we will be visualizing
our clusters using the class plot method.

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However,
we will not rewrite the whole code because

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as you will see, we made the exact
same code structure as we did for K-means.

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So now what I'm going to do
is I'm going to open our K-means code,

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and I will show you that
because we have the same code

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structure between K-means
and hierarchical clustering,

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we just need to replace
one tiny little thing to plot

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our five clusters
obtained by hierarchical clustering.

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So let's go to files here.

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Then let's click on these two dots
here to go back to our part

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three clustering folder
to find our K-means clustering folder.

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And in this K-means folder let's open
the K-means r r file.

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And now let's compare
our two codes for hierarchical clustering

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and K-means clustering.

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So in step one,
we both did exactly the same thing.

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We imported our small data set.

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Then in step two we looked
for the optimal number of clusters

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using two different methods
the elbow method for K-means

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and the dendrogram
for hierarchical clustering.

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Then in step three,
we fitted our algorithm to our data X

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and we created our vector of clusters
y k means in K-means.

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And you see in hierarchical clustering.

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And now if we look

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at the last step visualizing the clusters
in the K-means algorithm, we can see that

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the only thing in our code that is related
to K-means is a y k K-means vector.

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Here.

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And we have the same vector of clusters
in our hierarchical clustering code.

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It's y h c.

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So basically the only thing

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that we have to do to plot
our cluster is in hierarchical clustering

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is to take our class plot function here
with the same parameters.

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Copy it

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and paste it in our hierarchical
clustering visualizing the clusters.

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And then the only thing that we have to do
is to replace

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y k means by y axis.

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And now it's ready.

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Let's select our code section here.

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Execute it.

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And voila that's our hierarchical
clustering results.

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That's the end of this tutorial.

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But the fun continues
in the next tutorial,

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where we'll be analyzing our results
and explain what those clusters are.

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I will also explain how you can reuse
this code for any other business problem.

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And this will conclude our our tutorials.

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So I look forward to seeing you
in the next tutorial.

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And until then, enjoy machine learning.