Analysis of classification learning algorithms

Hana Rashied Esmaeel


The paper attempts to apply data mining Technique, Five classification algorithms were used to build data they are (ZeroR, SMO, Naive Bayesian, J48 and Random Forest).The analysis implemented using WEKA (3.8.2) Data mining software tool. The information was collected from college of Information Engineering (COIE) In Al Nahrain University within the variety of form using "Referendum" to estimate the teacher performance; it was store in Excel file CSV format then regenerate to ARFF (Attribute Relation File Format). Many criteria like (Time taken to create models, accuracy and average error) was taken to evaluate the algorithms Random forest and , SMO Predicts higher than alternative algorithms ,since  their  accuracy is the highest and have lowest average error compared to others  ,"The teacher clarification and  wanting to be useful  to students " was the strongest attribute. Further removing the bad ranked attributes (10, 11, 12, and 14) that have a lower contact on dataset can increase accuracies of algorithms


Data mining, Weka, Decision tree, classification, teacher evaluation

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