Evaluation of Teaching Capability of Teachers Based on Two-Class Support Vector Machine

Globally, teaching of Machine Learning (ML) is significant as well as relevant in schools. In spite of that, current dissertation as well as enhanced research in a developing area, teachers' commencements of the ML for the teachers remain under-researched. In this research, the supervised ML approach of Two-class Support Vector Machine (SVM) for the evaluation of the Teacher's capability. In this approach, various categories were appeared from determination of semi-structured meetings such as support the student for technical understanding, focused on professional development engineering, contextualizing the teaching resources as well as progress areas sustainability. The teachers must be included in the co-designing procedure of the resources examining the contextual factors as well as crucially, the course to combine the ML into conventional education. Concerning teachers in an innovation procedure will support contextualize ML and offering to actual influence as well as social changes. The experimental results shows that the proposed two-class SVM approach achieves the superior accuracy result of 98.5% and minimum errors such as Mean Absolute Error (MAE) of 0.363, Mean Squared Error (MSE) of 0.346, Root Mean Square Error (RMSE) of 0.573, and R-Squared (R2) of 0.763 respectively.