An ecommerce company wants to discover natural customer groups in its transaction dataset, but there are no predefined labels. Which type of machine learning technique is MOST appropriate for this task?
Unsupervised learning
Supervised learning
Reinforcement learning
Batch inference
Unsupervised learning algorithms work with data that lack target labels. They look for hidden patterns such as clusters or associations inside the dataset, allowing the company to group customers with similar purchasing behavior without needing churn, value, or other labels in advance. Supervised learning requires labeled examples, reinforcement learning relies on reward signals from an environment, and batch inference is an inference strategy rather than a learning method. Therefore, unsupervised learning is the correct choice for segmenting customers when labels are unavailable.
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