Supervised learning involves training a model using labeled data, where each data point has a known outcome. The model learns the relationship between input variables and the desired output (prediction). Unsupervised learning deals with unlabeled data, where the outcome is unknown. The model identifies hidden patterns or structures within the data itself, useful for tasks like customer segmentation or anomaly detection.
