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AI-900: Choosing the Right ML Approach for Segmenting Customers

Learn when clustering algorithms are the best approach for dividing customers into groups with similar behaviors vs. classification and regression models.

Question

Which type of machine learning should you use to identify groups of people who have similar purchasing habits?

A. classification
B. regression
C. clustering

Answer

C. clustering

Explanation

Clustering algorithms are designed to find clusters or segments within data based on similarities. By using clustering, you can identify groups of customers who exhibit comparable purchasing patterns, interests, demographics, etc. This makes clustering the right choice over classification and regression techniques for customer segmentation.

Reference

Microsoft Learn > .NET > ML.NET guide > Resources > Machine learning tasks in ML.NET

Microsoft Azure AI Fundamentals AI-900 certification exam practice question and answer (Q&A) dump with detail explanation and reference available free, helpful to pass the Microsoft Azure AI Fundamentals AI-900 exam and earn Microsoft Azure AI Fundamentals AI-900 certification.

Microsoft Azure AI Fundamentals AI-900 certification exam practice question and answer (Q&A) dump

Alex Lim is a certified IT Technical Support Architect with over 15 years of experience in designing, implementing, and troubleshooting complex IT systems and networks. He has worked for leading IT companies, such as Microsoft, IBM, and Cisco, providing technical support and solutions to clients across various industries and sectors. Alex has a bachelor’s degree in computer science from the National University of Singapore and a master’s degree in information security from the Massachusetts Institute of Technology. He is also the author of several best-selling books on IT technical support, such as The IT Technical Support Handbook and Troubleshooting IT Systems and Networks. Alex lives in Bandar, Johore, Malaysia with his wife and two chilrdren. You can reach him at [email protected] or follow him on Website | Twitter | Facebook

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