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The outcomes of the study reveal that decision tree (C5) classifier outperformed the logistic regression (LR) after feature extraction at ISCED-2 level schools and Random Tree classifier predicted more accurately gender of the teacher as compare to the Bayesian Network at level-1 schools.FreshPorts - textproc/weka: Data Mining Software in JavaĪs an Amazon Associate I earn from qualifying purchases. The MissingValue filter of Weka 3.8.1 tool handled well 55641 in ISCED-2 level and 19415 at the ISCED-1 level and normalization is also applied as well. Out of total 158 attributes, self-reduction and auto classifier stabilized only 134 attributes for the Bayesian Network (BN) and Random Tree (RT) at level-1 and 134 attributes for logistic regression and 41 attributes for Decision Tree (C5) at level-2. To predict the gender of teachers based on their answers, the authors applied 4 supervised machine learning algorithms filtering out of 12 classifiers using auto classifiers on ISCED-1 and ISCED-2 level of schools. The survey was conducted by ESSIE at various levels of schools ISCED (International Standard Classification of Education). An experiential study is conducted to solve binary classification problem on big dataset of European Survey of Schools: ICT in Education (known as ESSIE) using IBM modeler version 18.1.

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