What is the primary goal of unsupervised learning?

The primary goal of unsupervised learning is to find hidden patterns in unlabelled data. In unsupervised learning, the algorithm is presented with data that does not have any accompanying labels or predefined outputs. The focus is on discovering the underlying structure or distribution in the data, which can involve identifying clusters, associations, or relationships between the data points.

By analyzing the patterns within the data, unsupervised learning can provide insights that were not previously evident, enabling tasks such as market segmentation, anomaly detection, and more. This approach is distinct from supervised learning, which relies on labeled data to predict outcomes. In unsupervised settings, the lack of labels pushes the model to explore the data freely, often leading to novel findings that can inform further analysis.