Predicting academic performance of the student is crucial task as it depends on various factors. To perform such predictions the machine learning and data mining algorithms are useful. This paper presents investigation of application of C5.0, J48, CART, Naïve Bayes (NB), K-Nearest Neighbour (KNN), Random Forest and Support Vector Machine for prediction of students’ performance. Three datasets from school level, college level and e-learning platform with varying input parameters are considered for comparison between C5.0, NB, J48, Multilayer Perceptron (MLP), PART, Random Forest, BayesNet, and Artificial Neural Network (ANN). Paper presents comparative results of C5.0, J48, CART, NB, KNN, Random forest and SVM on changing tuning parameters. The performance of these techniques is tested on three different datasets. Results show that the performances of Random forest and C5.0 are better than J48, CART, NB, KNN, and SVM.