The College Acceptance dataset contains observations at the individual
student level. Each observation consists of a GMAT score, GPA, work
experience metric, Age, and categorical indicator of the student's admittance
status. The categorical variable is called "admitted" and indicates whether
the applicant was accepted (2), placed on a waiting list (1) or not admitted
(0).

For TripleBlind examples, this dataset is split in the '0_build_fake_data.py
' Python script. Each dataset is then placed on an independent Access Point
in the '1_position_data_on_accesspoint.py' script. Placing each dataset on
independent Access Points demonstrates a situation in which an organization
needs to use data from two separate universities to train a model without
seeing the data.

Ultimately this outputs the datasets:
 * "EXAMPLE - NYU Student Admissions Data"
 * "EXAMPLE - UMN Student Admissions Data"

This dataset is used by the following examples:
Random_Forest (both NYU and UMN data)
Outlier_Detection (UMN data only)
