Unsupervised Learning refers to a machine learning paradigm that discovers hidden patterns, structures, and relationships in data without labeled examples or target outputs, enabling algorithms to identify clusters, reduce dimensionality, detect anomalies, and uncover latent representations from raw data alone. This approach mimics human pattern recognition abilities by finding meaningful organization in complex datasets where