Going from unsupervised to supervised learning

The eventual goal of unsupervised learning is to take a dataset with no labels and assign labels to each row of the dataset, so that we can run a supervised learning algorithm through it. This allows us to create predictions that make use of the labels.

In this section, you will learn how to convert the labels generated by the unsupervised machine learning algorithm into a decision tree that makes use of those labels.

Creating a labeled dataset

The first step is to convert the labels generated by an unsupervised machine learning algorithm, such as the k-means algorithm, and append it to the dataset. We can do this by using the following code:

#Reading in the dataset

df = pd...