A bank wants to segment its customers based on their credit card usage behavior. Which learning method and algorithm would be most appropriate for this task?
- Supervised Learning with Decision Trees
- Unsupervised Learning with K-Means Clustering
- Reinforcement Learning with Q-Learning
- Semi-Supervised Learning with Support Vector Machines
Unsupervised Learning with K-Means Clustering is suitable for customer segmentation as it groups customers based on similarities in credit card usage behavior without predefined labels. Supervised learning requires labeled data, reinforcement learning is used for sequential decision-making, and semi-supervised learning combines labeled and unlabeled data.
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