Educational data mining (EDM) is an emerging interdisciplinary research area concerned with analyzing and studying data from academic databases to better understand the students and the educational settings. In most of the Asian countries, it is a challenging task to perform EDM due to the diverse characteristics of the educational data. In this study, we have performed students’ educational performance prediction, pattern analysis and proposed a generalized framework to perform rigorous educational analytics. To validate our proposed framework, we have also conducted extensive experiments on a real-world dataset that has been prepared by the transcript data of the students from the Marketing department of a renowned university in Bangladesh. We have applied six state-of-the-art classification algorithms on our dataset for the prediction task where the Random Forest model outperforms the other models with accuracy 94.1%. For pattern analysis, a tree diagram has been generated from the Decision Tree model.