Case Study ISSN (Online): 2454-1907 International Journal of Engineering Technologies and Management Research February 2022 9(2), 64-81 How to cite this article (APA): Henrietta, A. P., and Adekoya, A. F. (2022). Predictive Analytics of Academic Performance of Senior High School (Shs) Students: A Case Study of Sunyani Shs. International Journal of Engineering Technologies and Management Research, 9(2), 64-81. doi: 10.29121/ijetmr.v9.i2.2022.1088 64 PREDICTIVE ANALYTICS OF ACADEMIC PERFORMANCE OF SENIOR HIGH SCHOOL (SHS) STUDENTS: A CASE STUDY OF SUNYANI SHS Adjei-Pokuaa Henrietta 1 , Adebayo F. Adekoya 2 1, 2 Department of Computer Science and Informatics, University of Energy and Natural Resources, Ghana Received 03 January 2022 Accepted 01 February 2022 Published 21 February 2022 Corresponding Author Adjei-Pokuaa Henrietta, herttydonkor@gmail.com DOI 10.29121/ijetmr.v9.i2.2022.1088 Funding: This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. Copyright: © 2022 The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. ABSTRACT Due to the availability and increasing adoption of technology in learning management systems, online admission systems, school management systems, and educational databases have expanded in recent years. Motivation/Background: Literature shows that these data contain vital and relevant information that could be used to monitor and advise students’ so that their performance could be enhanced. In this study, the random forest algorithm is proposed to identify and examine the factors that influence students’ performance in WASSCE. Also, predict the future performance of students in WASSCE. Method: A total of one thousand five hundred and twenty students’ data were selected from Sunyani SHS. The results revealed that demographic data (age and gender) do not influence the performance of students in their final WASSCE. Results: However, an accuracy of 89.4% with error metrics (RMSE) 0.001639 and MAPE error of 0.001321 revealed that the proposed model could effectively predict the performance of students in the WASSCE. Keywords: Learning Management, Demographic Data, Effectively Predict, Random Forest Algorithm 1. INTRODUCTION The size of the educational database keeps proliferating every three years and these databases contain useful information that can be effectively employed to improve the academic performance of students Yadav and Pal (2012). There is a growing focus on institutional data mining by administrators, educational planners, and managers due to the exponential growth of educational data. (Anuradha & Velmurugan, 2015). Data mining techniques have been applied in educational systems to increase the understanding of the process of learning by concentrating on key issues such as identifying, mining, and evaluating parameters and variables which are associated with the learning progress of students Yadav and Pal (2012). Students’ academic performance is a critical factor in every educational setting, especially in senior high learning institutes, because educational institutions are rated based on the academic excellence achieved by their students and academic staff Mohamed et al. (2016). Forecasting the academic performance of students in the real world has many challenges, but this has been made easy with the growth in information and communication technologies granting access to a large amount of information that could facilitate the critical decision-making process. Knowledge Discovery in