MLS-C01 Question 134
Single answerA data scientist at an e-commerce company wants to segment customers into groups based on their purchasing behavior to design targeted marketing strategies. The dataset includes features such as purchase frequency, total spend, and product categories purchased. Which type of machine learning approach is most appropriate for this task?
- A
Supervised Learning
- B
Unsupervised Learning
- C
Reinforcement Learning
- D
Semi-Supervised Learning
Show answer and explanation
Correct answer: B
Explanation
Unsupervised Learning is the best choice for this scenario because the goal is to identify patterns or clusters in the data without predefined labels. Clustering algorithms like K-Means or hierarchical clustering can be used to group customers based on their purchasing behavior. Supervised Learning, Semi-Supervised Learning, and Reinforcement Learning are not appropriate as they either require labeled data or focus on entirely different problem domains.
- A. Incorrect.
Supervised Learning requires labeled data, where the target variable (such as a class or value) is known. In this scenario, there is no labeled data indicating predefined customer groups.
- B. Correct.
Unsupervised Learning is the correct approach as it works with unlabeled data to discover hidden patterns or groupings, such as clustering customers based on their purchasing behavior.
- C. Incorrect.
Reinforcement Learning is not suitable for this scenario as it is typically used for sequential decision-making problems, such as training agents to perform tasks, rather than analyzing static datasets.
- D. Incorrect.
Semi-Supervised Learning requires a mix of labeled and unlabeled data, which is not mentioned in the scenario. Additionally, the task does not involve predicting labels for the unlabeled data.