Mastering Python for Finance - Second Edition

Unsupervised learning

Unsupervised learning builds a model based on given input data that does not contain labels, but instead is asked to detect patterns in the data. This may involve identifying clusters of observations with similar underlying characteristics. Unsupervised learning aims to make accurate predictions to new, never-before-seen data.

For example, an unsupervised learning model may price illiquid securities by looking for a cluster of securities with similar characteristics. Common unsupervised learning algorithms include k-means clustering, principal component analysis, and autoencoders.