You work for a manufacturing company that produces retail apparel, such as shoes, dresses, blouses, etc. Your head of manufacturing has asked you to use your data science skills to determine which product, among a list of potential next products, your company should invest its resources to produce. You decide that you need to predict the sales levels of each of the potential next products and select the one with the highest predicted purchase rate. Which type of machine learning approach should you use?

Answer options:

A.You are trying to solve for the greatest number of sales across the potential next products. Therefore, you are solving a multiclass classification problem, and you should use multinomial logistic regression.
B.You are trying to solve for the greatest number of sales across the potential next products. Therefore, you are solving a classification problem, and you should use the random cut forest model.
C.You are trying to solve for the greatest number of sales across the potential next products. Therefore, you are solving a regression problem, and you should use a linear regression model.
D.You are trying to solve for the greatest number of sales across the potential next products. Therefore, you are solving a binary classification problem, and you should use a logistic regression model.

Answer: C
Option A is incorrect. This is not a multiple classification problem that you are trying to solve for more than two outcomes. So, a multinomial logistic regression would be the wrong choice for your machine learning model.
Option B is incorrect. You are trying to solve a numeric result: the number of purchases customers will make for each next potential product. From the Amazon SageMaker developer guide titled How RCF Works: “Amazon SageMaker Random Cut Forest (RCF) is an unsupervised algorithm for detecting anomalous data points within a dataset.”
Option C is correct. You are trying to solve a numeric result: the number of purchases customers will make for each next potential product. This numeric result case calls for the use of a regression model such as the linear regression model.
Option D is incorrect. This is not a classification problem where you’re solving a binary (yes or no) result. So, a logistic regression model would be the wrong choice for your machine learning.
Reference:
Please see this AWS overview of machine learning concepts: https://docs.aws.amazon.com/machine-learning/latest/dg/machine-learning-concepts.html, and the Amazon Machine Learning developer guide titled: Types of ML Models.