MLS-C01 Question 137
Single answerA retail company wants to analyze customer purchasing behavior to improve their product recommendations. They have historical data about customer purchases, product details, and customer demographics. Which type of machine learning approach is most suitable for this use case?
- A
Classification
- B
Regression
- C
Recommendation
- D
Clustering
Show answer and explanation
Correct answer: C
Explanation
The goal of the use case is to provide personalized product recommendations to customers. Recommendation systems are explicitly designed for this purpose, leveraging algorithms like collaborative filtering or content-based filtering to suggest products or services based on user behavior and preferences. Other approaches such as classification, regression, and clustering do not directly address the requirements of this scenario.
- A. Incorrect.
Classification is used to predict categorical outcomes, such as determining whether a transaction is fraudulent or not. While useful in certain scenarios, it is not the best fit for recommending products.
- B. Incorrect.
Regression is used to predict continuous numerical values, such as forecasting sales revenue or stock prices. It is not applicable for generating personalized product recommendations.
- C. Correct.
Recommendation systems are specifically designed to suggest products, services, or content to users based on their historical behavior, preferences, and other data. This is the best approach for the given use case.
- D. Incorrect.
Clustering groups similar data points into clusters without the need for labeled data. While clustering can provide insights into customer segments, it is not directly used for generating personalized product recommendations.