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Educational Data Mining (EDM) is one of the crucial application areas of data mining which helps in predicting educational dropout and hence provides timely help to students. In Indian context, predicting educational dropouts is a major problem. By implementing EDM, we can predict the learning habits of the student. At present EDM has not been introduced at higher education level. Due to this we cannot recognize the genuine problems of students during their education. The objective of this analysis is to find the existing gaps in predicting educational dropout and find the missing attributes if any, which my further contribute for better prediction. After that we try to find the best attributes and DM techniques which are frequently used for dropout prediction. Based on the combination of missing attribute and best attribute of student data thus far, a new algorithm can be tested which may overcome the shortcomings of previous work done.

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Data Mining is a field in which hidden information is extracted from a large database by using some algorithms implementation. These algorithms are further divided into some categories like classification, clustering, association rule mining etc according to information we want to extract. Data mining is a field which is widely spread over different areas like telecommunication, marketing, operation, hospitals, hotel industry, education etc. Predicting the academic’s performance and progress of the students has revealed the attention of the young researchers. To facilitate the task of building an academic prediction model, historical student academic dataset is used. In this paper, the contributions are exhibited in two different folds. In the first fold, the main aim is to build the prediction model by different families of the Machine Learning Techniques on the selected dataset for consideration. In the second fold, implementations of different ensemble meta-based model are presented by combining with different classification algorithms of Machine Learning Techniques. Different ensemble meta-based model taken into consideration for implementation are Bagging, AdaBoostM1, RandomSubSpace. The implementation results demonstrate that the ensemble meta-based technique (AdaBoostM1) gained a superior accuracy performance with MultilayerPerceptron Machine Learning technique reaching up to 80.33%.

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