Hello everyone and welcome to the K means clustering project overview lecture and this lecture will
get a brief overview of the K means clustering project we're going to go ahead and help the notebook
now.
All right here I am at the notebook as a quick note.
You can find this note put under the machine learning sections folder under the K means clustering folder
for this project.
We're going to attempt to use Kamins clustering to cluster universities into two groups.
Now we'll actually know that these universities in the data set are already into labeled groups private
universities and public universities.
But we're going to see if k means clustering and actually using an unsupervised method figure out the
two distinctions between the universities.
Again it's very important to know that we actually have labels for this data set.
But we will not use them for the Kamins clustering algorithm.
The algorithm will work with any labels since it's an unsupervised learning algorithm.
That means we are using the Kamins algorithm under normal circumstances.
It's because you don't have those labels.
In this case we have the advantage of the labels so we can try to get an idea of how well the algorithm
performed.
But you don't usually have this for Kamins.
So keep that in mind as towards the end of this project we have a classification report and confusion
matrix but that doesn't actually make sense for a real world setting because in the real world setting
for using an unsupervised learning algorithm you're not going to have labels to actually conduct a classification
report or a confusion matrix.
We just have the luxury of that information to get an idea of how well Kamins clustering can actually
work.
Now in the data we're going to use a data frame with 777 observations with quite a few variables then
you can see the list of 18 variables here.
The target variable is private which is just a factor of yes or no indicating whether or not it's a
private or public university.
And then a bunch of features that universities such as how much it costs how many Ph.D. faculties they
have etc..
And again this is the label which d k means clustering algorithm we'll never actually see.
Then you'll go ahead and just go along.
Import get the data explorative it they'll make some visualizations in the exploratory data analysis
section using seaborne or whatever visualization libraries you prefer and then you'll go ahead and continue
on.
So you get the Kamins cluster creation section of the project and then you'll have this evaluation.
And again there's no perfect way to evaluate clustering.
If you don't have the labels since this is just an exercise we will have the labels.
So we'll take advantage of this.
But keep in mind in the real world you don't usually have that luxury.
OK.
Hope you enjoy the project.
It's pretty interesting.
And I will see at the next lecture we will begin to go over the solutions project.
Thanks everyone and I'll see at the next lecture