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Prediction in research fulfills the desires of humanity to detect the future and know what fate holds. Technologically, prediction is a data mining approach that finds extensive use in the field of business (Al Sonosy, Rady, Badr, & Hashem, 2017; Megahed, Yin, & Nezhad, 2016), health (Alshurafa et al., 2017; Basu & Roy, 2017), education (Amornsinlaphachai, 2016; Kumar, Chowdary, Venkatramaphanikumar, & Kishore, 2016), and many other fields. For organizations, prediction helped management in analyzing data needed for decision-making and ultimately improves performance and gaining competitive advantage over others (Chiu, C., & Shu, 2017; Sugiarto, V. C., Sarno, R., & Sunaryono, 2016). Organizations like department stores, small business enterprises, multi-corporations, churches, hospitals, banks and even schools are making use of the capabilities of prediction mechanism in improving their performance and incomes (Padilla, W. R., Jesús, G. H., & Molina, 2018; Pai, P. F., & Liu, 2018). As the educational systems of the country is improving, schools are embracing the advantages of technology to improve their services to the stakeholders and helped them in decision-making activities (Hasbun, T., Araya, A., & Villalon, 2016). Further, data mining techniques economically offer more customized education, improved system efficiency, and reduce the education process expenses for universities (Devasia, T., Vinushree, T. P., & Hegde, 2016).Thus, predictive analysis is essential for the school administrators in order to help them in their day to day undertakings. Several algorithms are available for organizations in their decision-making undertakings. The predictive algorithms like Naïve Bayes Classification (Aneja & Lal, 2015; Mori, 2016; Walia, Kalra, & Mehrotra, 2016), Support Vector Machine (SVM) (Patel, 2017; H. Zhang, Zhao, Yong, Zhang, & Ji, ABSTRACT

A Novel Approach in Determining the Reasons of Student Attrition based on Enhanced Genetic Algorithm with Cross-Average Crossover Operator