Now, let us discuss about the third project that you need to create, which is the unsupervised cloning
project.
In this particular project, you will be finding out different clusters of data.
So the data which we have is that the market segmentation data.
So in this particular case, you can use any kind of clustering algorithms.
You can use game means you can use the rescan or you can use hierarchical clustering.
In this, the problem statement is that interacting with strangers on social networking estimates has
become a rite of passage for teenagers around the world, and many millions of teenage customers use
this site and have attracted attention for marketers struggling to find an edge in an increasingly competitive
market.
So they are trying to find out what is of interest for these students.
I'm finding out segments of students in what in the market and what they would be like.
So for this, we have this particular data set where we have the graduation year of the student, the
gender of the student age, the number of friends they have on Encinas and their likings like boyfriends,
basketball, football, soccer, so softball, volleyball and so on.
So there are a lot of interests which these students have.
And based on these, these people are trying to segment these students into different market customer
segments.
So this is what you have to implement.
Now, the first thing which you can do is you can find out the number of clusters by implementing gaming's
or clustering or DV Skåne, or you can apply Bazzani and visualize this data on two components using
DME and find out how many clusters are formed.
And based on the number of clusters that you get from this Disney, you can actually decide on the number
of clusters or you can simply use the elbow method in case of game means I'm used to seeing.
Now, the choice of number of clusters completely depends on you.
So you need to find out the best clusters form and you will be using the solid score for that.
So you can use good index and find out how good the clusters are formed and compare different models
comparing different implementations and.
Create the project, so I hope you would have learned a lot from this unsupervised learning sessions
and you will be able to create this project very easily as the implementations for unsupervised learning
are very simple and straightforward.
So thank you.
And you can use these datasets on all the data sets which have been provided in different ways and use
them for either classification regression or unsupervised learning and practice a lot.
And once you have been practicing using these datasets, there are a lot of data sets present online
in GANDIL or any other data universities.
So all of these data sets are present online.
So I hope you will try those on practice a lot using these.
So as an ending note, I would like to say that this entire course was curated for you so that you can
learn machine learning from the very beginning to the very end.
You don't have to get back again to go through these documents again and again.
I want you to learn all of this.
I'm known for Fullwood and learning about new topics instead of going back again.
That is why I have explained everything here from the deep mathematics behind all the algorithms to
the statistics.
So I hope you have gained a lot from this particular obsession and this.
Thank you.
I have a great time learning.
Thank you.