Given a transactions table (user ID, transaction type, location, amount), which three ML applications are appropriate?

Unsupervised learning for fraud detection is appropriate because fraud often lacks labeled examples, making anomaly detection techniques (a form of unsupervised learning) suitable for identifying unusual patterns. Clustering is also appropriate as it naturally groups similar transactions, which can reveal underlying categories or behaviors without prior labels. Supervised learning to predict transaction location is suitable if historical transaction data with known locations is available. This allows a model to learn the relationship between transaction features and location. Reinforcement learning is not appropriate as there's no agent interacting with an environment to maximize a reward. Unsupervised learning for location prediction is incorrect because predicting a specific location is a regression or classification task, which typically requires labeled data.