Slow Learner Prediction Using Multi-Variate Naïve Bayes Classification Algorithm
Keywords:Classification, Clustering, Confusion Matrix, Multi-Variate, Naïve Bayes, Supervised Machine Learning, Unsupervised Machine learning, WEKA Tool
Machine Learning is a field of computer science that learns from data by studying algorithms and their constructions. In machine learning, for specific inputs, algorithms help to make predictions. Classification is a supervised learning approach, which maps a data item into predefined classes. For predicting slow learners in an institute, a modified Naïve Bayes algorithm implemented. The implementation is carried sing Python. It takes into account a combination of likewise multi-valued attributes. A dataset of the 60 students of BE (Information Technology) Third Semester for the subject of Digital Electronics of University Institute of Engineering and Technology (UIET), Panjab University (PU), Chandigarh, India is taken to carry out the simulations. The analysis is done by choosing most significant forty-eight attributes. The experimental results have shown that the modified Naïve Bayes model has outperformed the Naïve Bayes Classifier in accuracy but requires significant improvement in the terms of elapsed time. By using Modified Naïve Bayes approach, the accuracy is found out to be 71.66% whereas it is calculated 66.66% using existing Naïve Bayes model. Further, a comparison is drawn by using WEKA tool. Here, an accuracy of Naïve Bayes is obtained as 58.33 %.
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