## 1 Introduction  
Data mining (DM) is the procedure of understanding data through cleaning raw data, discovering patterns, producing models, and testing the models. It comprises of several fields such as statistics, machine learning (ML), and database systems. Education DM (EDM) is an emergent field with an arising strategy to investigate the various types of data which are obtained from an education background [1]. It is an interdisciplinary area which inspects data mining (DM), man-fabricated consciousness, and measurable demonstrating with the data produced using an academic organization [2]. EDM uses a calculation method for taking care of elucidating academic data considering a definitive point of examining academic enquiries. To make a nation stand out among different nations across the globe, education frameworks should encounter an essential advancement by re-planning their design. The concealed data and examples from various data sources can be extracted by adjusting the strategies for DM. For summing up the outcomes of students with their qualifications, they investigate the abuse of DM in the academic fields. Crude data can be altogether moved through DM models. The data achieved from an education organization go through examination of various DM strategies [3]. The strategy identifies the conditions wherein students can strive to have a positive impact [4]. Student performance prediction (SPP) has different definitions according to troublesome perspectives; however, the measured assessment assumes a significant part in current education establishments. SPP seems effective in aiding all partners in the education interaction. For students, SPP can assist them with picking reasonable courses or activities and make their arrangements for academic durations [5]. For educators, SPP can assist with changing learning material and presenting programs compatible with the students' capacity, and help identify struggling students. For education chiefs, SPP can assist with checking the education program and enhancing the course framework. Generally, partners in the education advancement have intentions to further develop the education outcome. Moreover, the data-driven SPP study provides a goal of reference for the education framework. Weka, a compelling DM technique was utilized to produce the outcome [6]. The increment of educational information from distinct sources has resulted in desperation for the EDM research [7]. This can help to further objectives and characterize specific goals of education and highlight subset determinations by disposing of the component that is repetitive/is not important. The set of components chosen should follow the Occam Razor rule to provide the best outcome in light of the goal [8]. The data size to be dealt with has expanded in the past five years; hence, the choice is turning into a necessity before any sort of arrangement happens. It is not quite the same as the element extraction strategy because the determination method does not change the first portrayal of the data [9]. The least complex method includes choice, where how much quality in an examination is diminished by choosing just the main view of the circumstances such as a more elevated level of exercises [10]. Since the high dimensionality raises computational costs, it is essential to define a way to reduce the number of considered features. Feature selection (FS) allows reducing a high dimensionality problem and selecting a suitable number of features. This study designed a Feature Subset Selection with optimal machine learning for the Educational Data Mining (FSSML-EDM) model. The proposed FSSML-EDM model involves the Chicken Swarm Optimization-based Feature Selection (CSO-FS) technique for electing feature subsets. Next, the extreme learning machine (ELM) classifier was employed for the classification of educational data. Finally, the Artificial Hummingbird (AHB) algorithm was utilized for adjusting the parameters involved in the ELM model.