Unsupervised learning

Conversely, in unsupervised learning, the typical use case is when figuring out the input and output labels proves to be difficult. Using the election scenario, when you are unsure of what features are really going to provide data points for the model to determine a voter's vote, unsupervised learning could provide value and insight. The benefit of this approach is that the algorithm of your choice determines what features drive your labeling. For instance, using a clustering algorithm such as k-means, you could submit all of the voter data points to the model. The algorithm would then be able to group voter data into clusters and predict unseen data. We will deep dive into unsupervised learning with clustering in Chapter 5, Clustering Model.