Unsupervised Learning: Machine Learning Approach Finds Patterns in Unlabeled Data

Unsupervised learning is a machine learning approach where algorithms analyze data without predefined labels or known outcomes. This method allows models to independently discover hidden patterns, structures, or groupings within datasets. It is particularly useful for exploring large, unlabeled datasets where underlying relationships are unknown. Common applications include customer segmentation, fraud detection, and market research. The approach contrasts with supervised learning, which trains models using labeled data to predict specific outcomes.