Summary

Data problems where the input data is unrelated to the labeled output are handled using unsupervised learning models. The main objective of such data problems is to understand the data by finding patterns that, in some cases, can be generalized to new instances.

In this context, this chapter covered clustering algorithms, which work by aggregating similar data points into clusters, while separating data points that differ significantly.

Three different clustering algorithms were applied to the dataset and their performance was compared so that we can choose the one that best fits the data. Two different metrics for performance evaluation, the Silhouette Coefficient metric and the Calinski-Harabasz index, were also discussed in light of the inability to represent all of the features in a plot, and thereby graphically evaluate performance of the algorithms. However, it is important to understand that the result from the metric's evaluation is not absolute as some metrics perform better (by default) for some algorithms than for others.

In the next chapter, we will understand the steps involved in solving a data problem using supervised machine learning algorithms and learn how to perform error analysis.