Categorizing university student applicants with neural networks

University admissions offices are flooded every year by student applicants seeking to be enrolled at the university. Depending on the particular university, twenty percent or fewer of these applicants actually become students. During these hard economic times for the academic community, the acquisition and retention of as many suitable applicants as possible is desirable. This paper describes a neural network system, ADMIT, which has been developed to determine the likelihood that a student applicant, if accepted, will actually attend a particular university. The ADMIT neural network enables admissions counselors to spend their time more effectively