Introduction
In the previous chapter, you were introduced to decision trees and their applications in classification. You were also introduced to regression in Chapter 2, An Introduction to Regression. Both regression and classification are part of the supervised learning approach. However, in this chapter, we will be looking at the unsupervised learning approach; we will be dealing with datasets that don't have any labels (outputs). It is up to the machines to tell us what the labels will be based on a set of parameters that we define. In this chapter, we will be performing unsupervised learning by using clustering algorithms.
We will use clustering to analyze data to find certain patterns and create groups. Apart from that, clustering can be used for many purposes:
- Market segmentation detects the best stocks in the market you should be focusing on.
- Customer segmentation detects customer cohorts using their consumption patterns to recommend products better. ...
