Original subtitles

Instructor: Hey, let's continue the problem

from the last lecture.

As you can see, we had one other piece of information

that we did not use, language.

In order to make use of it,

we must first encode it in some way.

The simplest way to do that is by using numbers.

I'll create a new variable called data_mapped

equal to data.copy.

Next, I'll map the languages using the usual method.

Data_mapped language equals data_mapped language.map.

And I'll set English to zero,

French to one, and German to two.

Note that this is not the optimal way to encode them

but it will work for now.

Here's the result, cool.

Next, let's choose the features

that we want to use for clustering.

Did you know that we can use a single feature?

Well, we certainly can.

Let x be equal to data_mapped.iloc:,3:4.

I am basically slicing all rows, but only the last column.

What we are left with is this.

Now we can perform clustering.

I have the same code ready, so I'll just use it.

We are running k means clustering

with three clusters.

Run, run, run, run and we are done.

The plot is unequivocal.

The three clusters are USA, Canada, UK and Australia

in the first one, France in the second

and Germany in the third.

That's precisely what we expected, right?

English, French and German.

Great.

By the way, we are still using the longitude

and latitude as axis of the plot.

Unlike regression, when doing clustering

you can plot the data as you wish.

The cluster information is contained

in the cluster column in the data frame

and is the color of the points on the plot.

Can we use both numerical

and categorical data in clustering?

Sure, Let's go back to our input data, x,

and take the last three series instead of just one.

Run, run, run, run.

Okay, this time the three clusters turned out

to be based simply on geographical location

instead of language and location.

Hmm, what if we use two clusters?

We've seen that solution too, haven't we?

We will have to work on figuring out

what's going on in the following lesson.

Thanks for watching.