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Comparison Of Prediction Algorithms For Student Performance Prediction

BROWSE_DETAIL_CREATION_DATE: 10-08-2018

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BROWSE_DETAIL_TYPE: Thesis

BROWSE_DETAIL_SUB_TYPE: Masters

BROWSE_DETAIL_PUBLISH_STATE: Unpublished

BROWSE_DETAIL_FORMAT: PDF Document

BROWSE_DETAIL_LANG: English

BROWSE_DETAIL_SUBJECTS: TECHNOLOGY,

BROWSE_DETAIL_CREATORS: Bah, Amadou (Author),

BROWSE_DETAIL_CONTRIBUTERS: Karakaya, Ziya (Advisor),

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Machine Learning, Student Performance Prediction, Data Preprocessing, Correlation-based Feature Selection


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This thesis investigates the application of six machine learning algorithms to student performance prediction, using datasets made up of only students information available at the Atilim University administrative systems. In addition, these learning algorithms were compared using four measures: Accuracy, Precision, Recall and F-measure. The study also investigates whether the number of courses predicted together is directly or inversely proportional to the performance of the classifiers used. A measure of the effects of data preprocessing as well as Correlation based Feature Selection (CFS) on the learning algorithms was also conducted, respectively. The algorithms used are: Naive Bayes, Logistic Regression, Multilayer Perceptron, SMO (based on Support Vector Machines), IBk (K-Nearest Neighbor) and J48 (C4.5 Decision Tree).Naïve Bayes and IBk proved to be the best among the compared algorithms. The results also show that as the number of courses being predicted together increases, the prediction performance decreases. Data preprocessing and CFS are also found to generally improve the performance of the machine learning algorithms.


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