The Use of Classification Algorithm for Forecasting the Academic Performance of Students of Biological Sciences, University of Africa, Toru-Orua

In recent years, the application of Data Mining has grown exponentially, spurred by its ability to allows us discover new, interesting and useful knowledge about data in almost every facet of discipline. Its application in education is also gaining a lot of attention across the globe. In this research, a data mining technique known as classification algorithm (Decision Tree) was used to forecast students’ academic performance. The methodology adopted in this work is the CrossIndustry Standard Process for Data Mining (CRISP-DM) which is a cyclic approach that includes six principal phases. CRISP-DM was preferred over other approaches because it is a well-established and generally accepted data mining methodology. The data set used in this experiment is the student academic data of Biological Sciences, University of Africa, Toru-Orua (UAT), Bayelsa State, Nigeria.From our findings, the performance of the students was predicted with very high accuracy of 95.24% using WEKA Datamining tool.