Microsoft Azure AI Fundamentals (AI-900) Practice Exam

Which type of machine learning is best suited for grouping individuals based on similar purchasing habits?

1. Classification

2. Regression

3. Clustering

4. Reinforcement

The correct answer is: Clustering

Clustering is the most suitable type of machine learning for grouping individuals based on similar purchasing habits because it is specifically designed to identify patterns and group similar data points together. This technique does not rely on pre-labeled data; instead, it discovers the inherent structures or groupings in unlabelled data. In the context of purchasing habits, clustering algorithms can analyze various features, such as frequency of purchases, types of products bought, and spending patterns, to form distinct groups of customers who exhibit similar behaviors. This can help businesses target marketing strategies, enhance customer experiences, and personalize recommendations based on the identified clusters. The other types of machine learning are focused on different objectives. Classification is used for predicting categorical outcomes based on input features, regression is aimed at predicting continuous numerical values, and reinforcement learning involves training an agent to make decisions through trial and error to maximize a reward. None of these methods are aimed at discovering groupings in data, making clustering the optimal choice for the given scenario.