Authors :
Samira Kabir Nabade; Anas Shehu
Volume/Issue :
Volume 8 - 2023, Issue 9 - September
Google Scholar :
https://bit.ly/3TmGbDi
Scribd :
https://tinyurl.com/5ejfs3bb
DOI :
https://doi.org/10.5281/zenodo.8334695
Abstract :
Machine learning is a subfield of Artificial
Intelligence (AI) that equips computers to learn from
records to make inferences about the future. It has been
employed in different fields such as medicine,
agriculture, finance, and education to make explanatory
data analyses and projections. Machine learning models
have been used in making projections and planning. In
this project, we have built a machine learning model to
predict students’ enrollment with the view to having
better planning in the area of teaching and non-teaching
staff recruitment, lecture halls and laboratories
development, and student hostel construction. We have
used support vector machine (SVM). SVM achieved a
root mean square error (RMSE) and coefficient of
determination (R2) of 0.61 and 0.54.
Machine learning is a subfield of Artificial
Intelligence (AI) that equips computers to learn from
records to make inferences about the future. It has been
employed in different fields such as medicine,
agriculture, finance, and education to make explanatory
data analyses and projections. Machine learning models
have been used in making projections and planning. In
this project, we have built a machine learning model to
predict students’ enrollment with the view to having
better planning in the area of teaching and non-teaching
staff recruitment, lecture halls and laboratories
development, and student hostel construction. We have
used support vector machine (SVM). SVM achieved a
root mean square error (RMSE) and coefficient of
determination (R2) of 0.61 and 0.54.
