AI-900 Question 92
Single answerYou are working on a machine learning solution for a retail company to analyze customer data. The company wants to predict whether a customer will respond to a marketing campaign by purchasing a product. Which of the following scenarios is most suitable for using a classification model?
- A
Predicting whether a customer will purchase (Yes or No) in response to a campaign.
- B
Predicting the total revenue generated by a customer in the next month.
- C
Grouping customers into segments based on purchasing behavior.
- D
Predicting the number of products a customer will purchase in one transaction.
Show answer and explanation
Correct answer: A
Explanation
Classification models are used to predict categories or classes, such as Yes/No or True/False. In this scenario, predicting whether a customer will respond to a marketing campaign by purchasing a product is a binary classification task since the outcome is categorical (Yes or No). Other scenarios such as predicting revenue or grouping customers involve regression or clustering, which are not classification tasks.
- A. Correct.
This is the correct option because the goal is to classify customers into two categories: whether they will purchase (Yes) or not (No). This is a binary classification problem.
- B. Incorrect.
This is incorrect because predicting total revenue is a regression problem, which involves predicting a continuous numeric value, not a category.
- C. Incorrect.
This is incorrect because grouping customers into segments is a clustering problem, which is an unsupervised learning task, not a classification task.
- D. Incorrect.
This is incorrect because predicting the number of products purchased is also a regression problem, as it involves predicting a continuous numeric value.