Summary

You are now ready to perform cluster analysis with the k-means algorithm on your own dataset. This type of analysis is very popular in the industry for segmenting customer profiles as well as detecting suspicious transactions or anomalies.

We learned about a lot of different concepts, such as centroids and squared Euclidean distance. We went through the main k-means hyperparameters: init (initialization method), n_init (number of initialization runs), n_clusters (number of clusters), and random_state (specified seed). We also discussed the importance of choosing the optimal number of clusters, initializing centroids properly, and standardizing data. You have learned how to use the following Python packages: pandas, altair, sklearn, and KMeans.

In this chapter, we only looked at k-means, but it is not the only clustering algorithm. There are quite a lot of algorithms that use different approaches, such as hierarchical clustering, principal component analysis, and the Gaussian...