You are a machine learning specialist at a music subscription company. Your company needs to understand its customer churn rate better. They plan to leverage this information to spend their marketing budget on attempting to retain customers that are likely to leave the service in the near future. Your task is to group your customer subscribers into categories based on which subscribers may or may not cancel their subscription in the near future (3 months). You have already performed data engineering and have subscriber data that is labeled. Which type of model is best suited to your task?

Answer options:

A.Linear regression
B.Clustering
C.Classification
D.Unsupervised learning

Correct Answer: C
Option A is incorrect. The key to this scenario is that you have labeled data, and you are trying to group your customers into categories. The linear regression algorithm type is better suited to predicting a numeric/continuous value, such as estimating the value of a house.
Option B is incorrect. Clustering is best suited to grouping similar objects when you have unlabeled data. You have labeled your data.
Option C is correct. The classification algorithm type is best suited to grouping similar objects when you have labeled data. This is the case described in the scenario.
Option D is incorrect. The unsupervised learning algorithm type is best suited to clustering, dimension reduction, pattern recognition, and anomaly detection. These types of algorithms are used when you have unlabeled data. You have labeled your data.
References:
Please see the Amazon SageMaker developer guide titled Use Amazon SageMaker Built-in Algorithms (https://docs.aws.amazon.com/sagemaker/latest/dg/algos.html),
The Amazon SageMaker developer guide titled Unsupervised Learning (https://docs.aws.amazon.com/sagemaker/latest/dg/algos.html#algorithms-built-in-unsupervised-learning)