Abstract
The recognition of enrollment of primary, secondary and tertiary education level has become an important part of thinking about education system. Pattern recognition becomes an important part as the whole study consists of statistics. In the present study, different classifiers of pattern recognition have been used, in which we have discovered great outcome by using BayesNet classifier, NaiveBayes classifier, NaiveBayesUpdateable, and lastly lazyIBk, that is, 99.2003, 96.407, 96.407 and 100%, respectively. When we apply NaiveBayesMultinomialText class the outcome is poor in contrast with other classifiers, that is, 13.3403%. On the off chance if we apply classifier and same sort of information in the future, we could get great outcomes by the use of above classifiers. Only Naive BayesMultinomialText classifier will be considered as exceptional.
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Jaiswal, P. et al. (2021). Identification of Educationally Backward Countries in Primary, Secondary and Tertiary Level Students by Using Different Classification Techniques. In: Rathore, V.S., Dey, N., Piuri, V., Babo, R., Polkowski, Z., Tavares, J.M.R.S. (eds) Rising Threats in Expert Applications and Solutions. Advances in Intelligent Systems and Computing, vol 1187. Springer, Singapore. https://doi.org/10.1007/978-981-15-6014-9_91
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DOI: https://doi.org/10.1007/978-981-15-6014-9_91
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