TY - JOUR
AB - A Machine Learning framework for predicting enrollment is proposed. The framework consists of Amazon Web Services SageMaker together with standard Python tools for data analytics, including Pandas, NumPy, MatPlotLib, and Scikit-Learn. The tools
    are deployed with Jupyter Notebooks running on AWS SageMaker. Based on three years of enrollment history, a model is built to compute—individually or in batch mode—probabilities of enrollments for given applicants. These probabilities can then be
    used during the admissions period to target undecided students. The audience for this paper is both SEM practitioners and technical practitioners in the area of data analytics. Through reading this paper, enrollment management professionals will
    be able to understand what goes into the preparation of a Machine Learning model to help with predicting admission rates. Technical experts, on the other hand, will gain a blueprint for what is required from them.

AU - Hung Dang, Ginger Reyes Reilly, Katherine Soltys, and Michael Soltys
CY - Washington, DC
DA - 
EP - 
IS - 2
J1 - Strategic Enrollment Management Quarterly
J2 - SEMQ
JA - SEM Quarterly
JF - Strategic Enrollment Management Quarterly
JO - Strategic Enrollment Management Quarterly
L1 - 
LA - EN
SP - 
T1 - Enrollment Predictions with Machine Learning
UR - enrollment-predictions-with-machine-learning
VL - 1
Y1 - 2021/7/21
Y2 - 2024/4/20
ER -