Prediction of the academic performance of slow learners using efficient machine learning algorithm

Maintaining of immense measure of data has always been a great concern. With expansion in awareness towards educational data, the amount of data in the educational institutes is additionally expanded. To deal with increasing growth of data leads to the usage of a new approach of machine learning. Predicting student’s performance before the final examination can help management, faculty, as well as students to make timely decisions and avoid failing of students. In addition to this, the usage of sentimental analysis can gain insight to improve their performance on the student’s next term. We have used various machine learning techniques such as XGboost, K-Nearest Neighbor (K-NN), and Support Vector Machine (SVM) to build predictive models. We have evaluated the performance of these techniques in terms of the performance indicators such as accuracy, precision and recall to determine the better technique that gives accurate results. The evaluation shows that XGBoost is superior in the prediction of poor academic performers than SVM and K-NN with large dataset.