Unsupervised learning models make predictions by being given data that does not contain any correct answers. An unsupervised learning model’s goal is to identify meaningful patterns among the data. In other words, the model has no hints on how to categorize each piece of data, but instead it must infer its own rules.
A commonly used unsupervised learning model employs a technique called clustering. The model finds data points that demarcate natural groupings.