Educational data classification using selective Naïve Bayes for quota categorization

Education data classification is a growing interest in the research of data mining. Correctly identifying the education data into particular category is still presenting challenge because of large and vast amount of features in the dataset. In regards to the existing classifying approaches, Naïve Bayes is potentially good at serving as a classification model due to its simplicity and accuracy. Naive Bayes is one of the most efficient and effective algorithms for data mining. The aim of this paper is to highlight the performance of employing Naïve Bayes in education data classification. The data extracted could be used to Find Meaningful Pattern for the students on the real time problem scenario application to be monitored at college level. Also the model can be used for the future planning of student selection criteria at college level.