Unsupervised learning is a type of machine learning that focuses on understanding relationships
within data without the need for labeled outcomes. Unlike supervised learning, which requires
labeled data to train models to make predictions or classifications, unsupervised learning works with
unlabeled data and aims to discover hidden patterns, groupings, or structures within the data.
Common applications of unsupervised learning include clustering, where the algorithm groups data
points into clusters based on similarities, and association, where it identifies relationships between
variables in the dataset. Since unsupervised learning does not predict outcomes but rather uncovers
inherent structures, it is ideal for exploratory data analysis and discovering previously unknown
patterns in data .
