Predicting Online Social Network Student Performance Using Enhanced RandomBayesian Algorithm
Keywords:
Data mining, Classification, Random Forest, Feature SelectionAbstract
Online social networking sites, or OSNs, have become immensely popular in recent times. Many websites focus on locating and sharing various kinds of content as well as on making and keeping contacts. Statistical techniques for exploring data in educational settings and analyzing student performance are less effective than educational data mining. The research aims to employ various data mining approaches to investigate the effects of distinct variables on students' performance. This paper presents an Improved Mutual information based Filter Pearson’s Correlation (IMIFPC) feature selection with Enhanced RandomBayesian (ERB) classification method for predicting the student performance. IMIFPC is a feature selection technique that uses combination of Filter method of Pearson’s correlation with Mutual information method. The real-time OSN user dataset were used to test the proposed ERB approach to predict the students performance with classes (Excel and Vivekanandha) based on the data mining methods. The experimental results from the OSN User dataset demonstrate that the suggested approach using ERB classification performance achieved Accuracy of 98.00 and F1-Score of 97.99 percent.
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