Open AccessPredictive Analysis of the Enrolment of Elementary Schools Using Regression Algorithms
Author(s) -
Elizalde Lopez Piol,
Luisito Lolong Lacatan,
Jaime P. Pulumbarit
Publication year - 2021
Publication title -
international journal emerging technology and advanced engineering
Language(s) - English
Resource type - Journals
ISSN - 2250-2459
DOI - 10.46338/ijetae1121_21
Subject(s) - decision tree , linear regression , random forest , proper linear model , regression analysis , statistics , division (mathematics) , regression , support vector machine , bayesian multivariate linear regression , mathematics , tree (set theory) , computer science , algorithm , artificial intelligence , mathematical analysis , arithmetic
— By fitting a linear equation to observable values, linear regression determines the relationship between two variables. The Department of Education enrollment data in the Philippines, specifically in the School Division of Batangas, is needed to produce modules. The data collected is from the division office, where data cleaning was applied. Deep Learning, Decision Tree, Random Forest, Gradient Boosted Tree, Support Vector Machine, and Linear Regression were used to perform the prediction, and linear regression performed the best with an absolute value of 14.465 and a relative error of 84.81%. Keywords— Prediction, Information Management, Linear Regression, Cloud Computing, LDM