In this chapter, we explored a new class of model that learns structures from unlabeled data -- unsupervised learning. We worked through the required input data and feature extraction, and saw how to use the output of one model (a recommendation model in our example) as the input to another model (our k-means clustering model). Finally, we evaluated the performance of the clustering model, using both manual interpretation of the cluster assignments and using mathematical performance metrics.
In the next chapter, we will cover another type of unsupervised learning used to reduce our data down to its most important features or components -- dimensionality reduction models.