Paper Title
Building and Implementation of Naive Bayes Classification Model: A Domain of Educational Data Mining

Data mining is a technique uses to identify and extract the hidden knowledge from the dataset. It has vast range of applications such as hospital, weather forecast, business industries, and others. Now a days, Educational Data Mining (EDM) is one of the common applications use for analyzing the data related to students, course, and faculty performance. This paper discusses the EDM using Naïve Bayes classification algorithm. The purpose of this research is to identify the relative pattern in the collected dataset from educational database to predict the performance of the students. Therefore, a conceptual framework presented to understand the main theme of this research and execution of the experiment. The framework implementation applied using Rapid Miner tool, whereas the model presented in this research indicates the preparation of training and testing dataset to predict the student’s potential performance. The result elaborates the positive execution of the model, which is being proved by the “performance” operator provided in rapid miner tool. The accuracy of model measured as 96.43%, that highlights the encouraging impact of the EDM model presented in this study. The research can be useful for the college/university instructors to predict the performance of their students in ongoing and future semesters. Keywords - Educational Data Mining; Classification; Naïve Bayes Algorithm; Prediction.