AI-900 Question 98
Single answerA retail company wants to segment its customers into groups based on their purchasing behavior to create targeted marketing campaigns. Which of the following scenarios is most appropriate for using a clustering machine learning algorithm?
- A
Grouping customers based on their purchase history and spending patterns to identify distinct customer segments.
- B
Predicting the likelihood that a customer will purchase a specific product based on their previous purchases.
- C
Detecting fraudulent transactions by analyzing anomalies in customer behavior data.
- D
Classifying emails as spam or not spam based on their content.
Show answer and explanation
Correct answer: A
Explanation
Clustering is a type of unsupervised machine learning used to group data points into clusters based on their similarities. In this scenario, the goal is to segment customers into groups based on their purchasing behavior, which is a perfect use case for clustering algorithms. Clustering does not rely on predefined labels, making it ideal for exploratory data analysis and segmentation tasks like this.
- A. Correct.
This is the correct answer because clustering is commonly used to group data points (such as customers) with similar characteristics (like purchasing behavior) into segments without requiring predefined labels.
- B. Incorrect.
This is incorrect because predicting the likelihood of a purchase is a supervised learning task, specifically a classification or regression problem, not clustering.
- C. Incorrect.
This is incorrect because detecting anomalies often involves anomaly detection techniques, which are different from clustering. Clustering is not specifically designed for identifying fraud.
- D. Incorrect.
This is incorrect because classifying emails as spam or not spam is a supervised learning classification problem that requires labeled data, not clustering.