Amin, A. (2020, June), Predicting Retention Rates from Students’ Behavior  Paper presented at 2020 ASEE Virtual Annual Conference Content Access, Virtual On line . 10.18260/1-2--35073

\bibitem{asee_peer_35073}
  Amin, A. (2020, June), \emph{Predicting Retention Rates from Students’ Behavior}
  Paper presented at 2020 ASEE Virtual Annual Conference Content Access, Virtual On line .
  10.18260/1-2--35073

Awatif Amin.  "Predicting Retention Rates from Students’ Behavior".  2020 ASEE Virtual Annual Conference Content Access, Virtual On line , 2020, June.  ASEE Conferences, 2020.  https://peer.asee.org/35073 Internet.  13 Dec, 2024

\bibitem{asee_peer_35073}
  Awatif Amin.
  "Predicting Retention Rates from Students’ Behavior".
  \emph{2020 ASEE Virtual Annual Conference Content Access, Virtual On line , 2020, June}.
  ASEE Conferences, 2020.
  https://peer.asee.org/35073 Internet.  13 Dec, 2024

@INPROCEEDINGS{asee_peer_35073
author = "Awatif Amin"
title = "Predicting Retention Rates from Students’ Behavior"
booktitle = "2020 ASEE Virtual Annual Conference Content Access"
year = "2020"
month = "June"
address = "Virtual On line "
publisher = "ASEE Conferences"
note = {https://peer.asee.org/35073}
number = {10.18260/1-2--35073}
}

TY  - CPAPER
AB  - Machine learning and Data mining are supported by the same establishments in two different ways. Machine learning basically learns from present data and delivers the necessary basis for a machine to learn. Data mining uses existing data and finds emerging patterns that help the decision-making processes. Data mining is typically used as an information supplier for machine learning to draw information that recognizes the patterns and determines from these patterns how to adapt behavior for future occurrences. It is obvious to see that there is an overlap between data mining and machine learning as the two have the same goal which is to learn from huge data for analytic resolutions. Machine learning and data mining can be considered knowledge science that concentrates on formulating algorithms that learn from the data and make predictions. Machine learning algorithms include supervised and unsupervised learning classifications. This paper deliberates the use of algorithms to analyze data from educational institutions to help them present more detailed methods to improve the efficiency of recruitment, enrollment, and hence retention. 


AU  - Awatif Amin
CY  - Virtual On line 
DA  - 2020/06/22
PB  - ASEE Conferences
TI  - Predicting Retention Rates from Students’ Behavior
UR  - https://peer.asee.org/35073
DO  - 10.18260/1-2--35073
ER  -