The main objective of any educational organization is to provide quality education and improve the overall performance of an institution by looking at individual performances. One way to analyze learners' performances is to identify the areas of weakness and guide their students to a better future. Although data mining has been successful in many areas, its use in student performance analysis is still relatively new, ie the knowledge is hidden in educational data set and it is extracted using data mining techniques. This paper discusses about a learning model for predicting student performance using classification techniques. Also the paper shows the comparative performance analysis of J48, Naïve Bayesian classifier and Random forest algorithm.