I explained the goal of each approach, gave short examples (linear regression vs clustering), and mentioned when each is typically used in real business cases. In supervised learning, the dataset includes both input features and labeled outcomes, so the model can learn to predict future outputs based on known examples. It's mainly used for prediction tasks. In unsupervised learning, there are no predefined labels. The goal is to identify hidden structures, relationships, or patterns within the data, which can later be used for clustering or feature extraction.