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Every year thousands of students get admitted into different universities in Bangladesh. Among them, a large number of students complete their graduation with low scoring results which affect their careers. By predicting their grades before the final examination, they can take essential measures to ameliorate their grades. This article has proposed different machine learning approaches for predicting the grade of a student in a course, in the context of the private universities of Bangladesh. Using different features that affect the result of a student, seven different classifiers have been trained, namely: Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Logistic Regression, Decision Tree, AdaBoost, Multilayer Perceptron (MLP), and Extra Tree Classifier for classifying the students' final grades into four quality classes: Excellent, Good, Poor, and Fail. Afterwards, the outputs of the base classifiers have been aggregated using the weighted voting approach to attain better results. And here this study has achieved an accuracy of 81.73%, where the weighted voting classifier outperforms the base classifiers. Kabra and Bichkar [3] collected data from the entry form, filled by the students in an engineering college during the time of admission. Using J48 algorithm, they predicted the final grades of the first year students'. When they classified the results into three categories they gained an accuracy of 60.46% and in the case of classifying the results into two categories they gained an accuracy of 69.94%. Kapur et al. [5] used various classification algorithms to classify the performance of the students into three categories: high, medium, and low. Their dataset included 480 entries with 16 attributes. Among these classifiers Random Forest showed the highest accuracy of 76.67%.

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