Journal of Intelligent Computing Bilal Mehboob, Rao Muzamal Liaqat, Nazar Abbas 8 2 2017 http://www.dline.info/jic/fulltext/v8n2/jicv8n2_2.pdf Today we are surrounding with large data related to student performance (class participation, attendance, pre student history, quiz result, subject dependency, student CGPA till to final semester). In this paper we will evaluate the reason of student failure basis on the previous data, predict the risk of failure for next course so that students may be mentally prepare for offered course as well dependency level of the course. In engineering it is common practice if a student doesn’t knows about the basic course he/she can’t perform well in advance courses of same scopes. In this paper we will back trace the failure cause with the help of decision tree. This work wills also help out to estimate the risk in early phase, which can help the teachers to design an effective planning for the students who are at risk. We have used the six algorithms for prediction and risk analysis ID3 gives the best results as compared to other. In this paper we have used the data set of CEME, NUST. Our dataset consists of 450 records extracted from five degrees (DE_29, DE_30, DE_31, DE_32, and DE_33).