A Predictive Model for Students Admission Uncertainty Using Naïve Bayes Classifier and Kernel Density Estimation (KDE)

Abstract

Uncertainty of getting admission into universities / institutions is one of the global challenges in academic environment. The students are having good marks with high credential but not sure about getting their admission into universities / institutions.  In this research study the researcher built a predictive model using Naïve Bayes Classifiers –machine learning algorithm to extract and analyze hidden pattern in students’ academic records and their credentials. The main objective of this research study is to reduce uncertainty for getting admission into universities / institutions on the basis of their previous credentials and some other essentials parameters. This research study presents a joint venture of Naïve Bayes Classification and Kernel Density Estimations (KDE) approach to predict student’s admission into universities or any higher institutions.  The predictive model is built on training dataset of students’ examination score such as GPA, GRE, RANK and some other essential features that offered the admission with a predictor accuracy rate of 72% and has been experimentally verified.  To improve the quality of accuracy of predictive model the researcher used the Shapiro-Walk Normality Test and Gaussian distribution on large datasets. The predictive model helps in reducing the admission uncertainty and enhances the universities decision making capabilities. The significance of this research study is to reduce human intervention for making decisions with respect to students’ admission into universities or any higher academic institutions, and it demonstrates that many universities and higher-level institutions could use this predictive model to improve their admission process without human intervention.