Unsupervised Learning

Unsupervised learning algorithms analyze and cluster input data without any labeled responses. This method is useful for tasks such as clustering, anomaly detection, and dimensionality reduction. It helps to uncover relationships in the data, revealing insights that can inform further analysis or decision-making processes. Common techniques include k-means clustering and hierarchical clustering.