Admission Prediction for Freshmen Students Using Low-cost Data Analytics Tool

This research paper tackles the admission prediction of enrollment for the tertiary level using the Naïve Bayes classification technique. Using data mining, the Naïve Bayes algorithm is simulated using Data Analytics Tool (DAT) and Weka data mining to provide the classifier model in evaluating if the given college freshmen applicant will continue or discontinue to be admitted to college. In line with the method, the researcher used gender, senior high school average grade, senior high school type, senior high school strand, and college entrance exam result. The training data set used for this machine learning model comprises 750 records from the different freshmen applications. As a result of the classifier model testing in Weka, the correct instances captured from the original 750 records is 86.27%, which is significantly higher and makes the classifier model reliable. In addition, a confusion matrix is performed and computed with an accuracy of 86.27%, precision of 83.13%, recall of 95.34%, and F1 score of 88.82%. Thus, the model test result is favorable to the reliability of the classifier model using Naïve Bayes technique.