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Title |
Prediction of Academic Performance of College Students with Bipolar Disorder using different Deep learning and Machine learning algorithms |
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Author |
S. Peerbasha, M. Mohamed Surputheen |
| Citation |
Vol. 21 No. 7 pp. 350-358 |
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Abstract |
In modern years, the performance of the students is analysed with lot of difficulties, which is a very important problem in all the academic institutions. The main idea of this paper is to analyze and evaluate the academic performance of the college students with bipolar disorder by applying data mining classification algorithms using Jupiter Notebook, python tool. This tool has been generally used as a decision-making tool in terms of academic performance of the students. The various classifiers could be logistic regression, random forest classifier gini, random forest classifier entropy, decision tree classifier, K-Neighbours classifier, Ada Boost classifier, Extra Tree Classifier, GaussianNB, BernoulliNB are used. The results of such classification model deals with 13 measures like Accuracy, Precision, Recall, F1 Measure, Sensitivity, Specificity, R Squared, Mean Absolute Error, Mean Squared Error, Root Mean Squared Error, TPR, TNR, FPR and FNR. Therefore, conclusion could be reached that the Decision Tree Classifier is better than that of different algorithms. |
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Keywords |
Logistic Regression, Random Forest classifier, Decision tree classifier, K-Neighbours classifier, Ada Boost classifier, Extra Tree classifier, GaussianNB. |
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