• Adjei-Pokuaa Henrietta Department of Computer Science and Informatics, University of Energy and Natural Resources, Ghana
  • Prof Adebayo F. Adekoya Department of Computer Science and Informatics, University of Energy and Natural Resources, Ghana



Learning Management, Demographic Data, Effectively Predict, Random Forest Algorithm


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.


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How to Cite

Adjei-Pokuaa, H., & F. Adekoya, A. . (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.