Construction Extrapolative Models For Data Mining Schemes

DOI

No DOI

Published In

Volume 2, Issue 2 (2022)

Published Online

March-April 2022

Pages

06-08

Article Type

Research Article

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This paper focused in on building perceptive models for data mining projects and knowledge discovery functionalities. The objectives are data selection and transformation, Generation of a prediction models using classification data mining techniques, ID of different characteristics which affects retention and performance of students. The survey used dataset from the students pursued the BS Computer Engineering program.Decision tree classifiers such as ID3, J48 and Truck were used to build models. Results of the study showed that when the attribute evaluation was conducted using WEKA (Waikato Environment for Knowledge Analysis),the College Entrance Test (CET) got the highest significant regard among the perceived attributes in expecting the retention and execution of students while J48 got the highest accuracy rating when classifying instances. However, further research on factors or attributes that influence retention and performance of students should be explored and to include other programs in the School to deal with the accuracy of the results of collection. Index Terms: Data Mining, ID3, J48, CART, CET, GWA, HSGPA, SCHLR