In this chapter, you learned about how the k-means algorithm works, in order to cluster unlabeled data points into clusters or groups. You then learned how to implement the same using scikit-learn, and we expanded upon the feature engineering aspect of the implementation.
Having learned how to visualize clusters using hierarchical clustering and t-SNE, you then learned how to map a multi-dimensional dataset into a two-dimensional space. Finally, you learned how to convert an unsupervised machine learning problem into a supervised learning one, using decision trees.
In the next (and final) chapter, you will learn how to formally evaluate the performance of all of the machine learning algorithms that you have built so far!
