The use of Linear Regression in predicting enrolment has been shown to be beneficial, although it varies with various datasets and attributes; varying weights of the correlation of the attributes can be discarded if they do not impact the prediction. Data collecting had grown since prior investigations, resulting in a more complicated dataset with many varieties. As a result of the data being created by multiple clerks, cleaning and combining proved tough; nonetheless, the fundamental parameters remain intact. Different algorithms were examined but Linear Regression obtained the highest accuracy with a 12.398 percentage for the absolute error and a root mean squared of 26.936 to create a tangible model to anticipate the enrolment of Region IVA CALABARZON in the Philippines. This demonstrates that it was 2.067 percentage points more than the prior research.

— 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