A university wants to develop a targeted recruitment strategy to increase new student enrollment. A data scientist gathers information about the academic performance history of students. The data…

Amazon SageMaker Ground Truth is used for data labeling, which is a necessary step to create the 'enrolled' or 'not enrolled' ground truth labels required for any supervised learning task aimed at predicting enrollment.

Forecasting algorithms predict future values in a time series, which is not applicable to predicting a categorical outcome like student enrollment.

Regression algorithms predict continuous numerical values, whereas predicting whether a student is 'likely to enroll' is a binary categorical outcome.

While a classification algorithm would be used for the final prediction of enrollment, the question asks for a combination of steps to 'build student profiles' and 'predict', and classification alone doesn't directly address profile building without prior labels or unsupervised grouping.

The built-in Amazon SageMaker k-means algorithm is an unsupervised clustering technique that can group students into distinct profiles or segments based on their academic performance, enabling the university to build student profiles for targeted recruitment.