## 5 Discussion  
In the past, most educational research has been focused on and evaluated students' academic performance in specific institutions or regions. Performance was calculated with consideration for various influences including socio-economic and demographic factors as well as students' personal, family, and academic backgrounds. Apart from student academic results, other factors also have a substantial impact on the performance of any educational institution. The present study focused on the importance of other highly influential factors along with student academic results, such as the students per teacher ratio, the number of schools in a region, whether schools were located in rural or urban areas, the availability or lack of classrooms, electrical facilities in schools, availability or lack of furniture for students, open-air classes, computer lab facilities, science labs, and playgrounds in schools. Previous research [44; 45; 46; 47; 48] suggested that data pre-processing (normalisation, discretisation) techniques enhanced classifier performance, as these techniques reduce the biases among features. Furthermore, related studies showed that the min-max normalisation method performed better than other data normalisation methods [49; 50; 51]. It has also been observed in related studies that binning-based data discretisation techniques outperformed other techniques based on their results [52; 53; 54]. This study will help in the identification of underperforming regions based on institutional performance. It will also support governance in performance monitoring, policy formulation, target-setting, evaluation, and reforms to address the issues and challenges of education. In this research, various feature selection methods were combined with machine learning models to obtain efficient results. The fourteen most significant features were used, as selected through feature selection methods. The J48 decision tree model was combined with feature selection methods. The best results were obtained using the relief-F-based feature selection technique, which achieved maximum accuracy of 68.5% with an ROC value of 0.63. The highest accuracy of 68.5% was achieved with the SVM (RBF kernel) model while employing the relief-F based feature selection method. After the SVM model, the random forest was utilised to obtain more effective results. The highest accuracy of 71.3% with an ROC of 0.65 was obtained. After the random forest, the rotation forest was utilised to obtain more effective results. The highest accuracy of 73.2% was obtained. After J48, SVM, random forest, and rotation forest, artificial neural networks were used to achieve more efficient results. It has been observed that this model predicted the best results with an accuracy of 82.9% while utilising the relief-F based feature selection method. The artificial neural network outperformed and yielded the highest accuracy, of 82.9%, among the five classifiers employed in this study. The performance of ANN also proved efficient while evaluating other performance metrics. It was also observed that the target class (medium) results were better than other target classes (low and high). This is because the number of instances in the medium class were significantly higher than in the high and low classes. The performance of machine learning models is better when trained on large datasets. In our study, the performance of machine learning models on medium classes was also high due to the large amount of data as compared to other classes. This study provides additional support for researchers to employ the ANN model and apply it to social science studies. Moreover, this study showed that there is value in including special education-related predictors to improve classification accuracy. The study demonstrated how geographical and demographic variables could all add to the classification accuracy of prediction models. Lastly, the study results offered strong evidence that school facilities are highly predictive for the performance measurement of public schools.