Data mining and machine learning play an important role in both research estimation and learning. The present study is conducted to identify the gender of student according to their answers given in survey related to information and communication technology (ICT) in European schools. The student dataset which consists of a total number of 156 attributes and 50478 instances are tested to identify student gender. To develop the ensemble predictive model after comparing prediction accuracy achieved by various supervised machine learning classifiers such as Support Vector Machine (SVM), Random Forest (RF), Naïve Bayes (NB), Artificial Neural network (ANN) and J48 tree with various k-fold cross-validation. The K-nearest neighbor (IbK or KNN) is also trained with data-set with varying value of k at 8-fold cross-validation. The dichotomous variable is gender and 131 predictors belong to ICT in education are taken into consideration after applying feature reduction methods. Findings of the study reveal that the maximum prediction is gained by SVM (76%) at each fold as compared to others. The total number (23535) of correct females are identified by RF at 6-fold and correct perdition of males is 14678 which is achieved by SVM at 2-fold. The authors also found lowest accuracy for prediction is achieved by NB classifier at each fold. Finally, the ensemble predictive model is presented by joining the best classifier such as SVM at 2-fold, ANN at 2-fold and RF at 6-fold to accurate identification of student gender over data-set. The ensemble confusion matrix also concludes the maximum prediction of the female student as compared to male student towards their response given to survey.
Verma et al. (Thu,) studied this question.