Predicting Scholarship Grants Using Data Mining Techniques

Allemar Jhone P Delima

doi:10.18178/ijmlc.2019.9.4.834

Abstract

Predicting Scholarship Grants Using Data Mining Techniques

Highlights

Every university has its own methods to realize scholarship assessment
Step 3: Objects clustering denoted as OCL0: Based on the result generated by K-Means function in R where each indexed scholarship grants was assigned based on the minimum distance, Academic Scholarship, Athletic Scholarship, Barangay Scholarship, Choir, CSSGP, Dance Troupe, ESGP-PA, LGU Basilisa, LGU Claver, PGMC, StuFAPS, Taganito Mining Corporation, Tulong Dunong 01 were assigned to group 1
Predicting grantees for each scholarship grants can be beneficial to the sponsoring agents since it will give them insight as to the number of their future grantees

Summary

INTRODUCTION

Scholarships are established by schools as a motivation for students that have outstanding and exceptional achievements It is a form of incentive and benefit to inspire and embolden students and improve schools’ learning principles [1]. One of the most popular data mining techniques is clustering It represents an unsupervised learning method whose objective is to divide the data set so that the distance among the clusters should be minimal, whereas the inter-cluster distance should be maximal. This will provide a predicted increase and decrease of the grantees in the five years This is to monitor and to identify which scholarship grant will exhibit an increase in number of grantees within targeted time as a preparation for the budget allocation by the sponsoring agents

RELATED LITERATURE

OPERATIONAL FRAMEWORK

Clustering

Data Forecasting

Forecasting

CONCLUSION