Building Unsupervised Models with K-Means Clustering
So far, we have learned about building machine learning (ML) models where data is supplied with labels. In this chapter, we will learn about building ML models on a dataset without any labels by using the K-means clustering algorithm. Unlike supervised models, where predictions are made at the observation level, K-means clustering groups observations into clusters where they share a commonality – for example, similar demographics or reading habits.
This chapter will provide detailed examples of business problems that can be solved with these modeling techniques. By the end of this chapter, you will be in a position to identify a business problem that an unsupervised modeling technique can be applied to. You will also learn how to build, train, and evaluate K-means model performance.
In this chapter, we will cover the following main topics:
- Grouping data through cluster analysis
- Creating a K-means ML model...
