Analog the Performance between Three Classifiers on Bank Marketing Data
Gan Fui Yee1, Suliadi Firdaus Sufahani2, Mustafa Mamat3, Mohamad Afendee Mohamed4, Puspa Liza Ghazali5
1Gan Fui Yee, Department of Mathematics and Statistic, Faculty of Applied Sciences and Technology, Universiti Tun Hussein Onn Malaysia, Pagoh Campus, Pagoh, Johor, Malaysia.
2Suliadi Firdaus Sufahani, Department of Mathematics and Statistic, Faculty of Applied Sciences and Technology, Universiti Tun Hussein Onn Malaysia, Pagoh Campus, Pagoh, Johor, Malaysia.
3Mustafa Mamat, Faculty of Informatics and Computing, Universiti Sultan Zainal Abidin, Kampung Besut, Kuala Terengganu, Terengganu, Malaysia.
4Mohamad Afendee Mohamed, Faculty of Informatics and Computing, Universiti Sultan Zainal Abidin, Kampung Besut, Kuala Terengganu, Terengganu, Malaysia.
5Puspa Liza Ghazali, Faculty of Economics and Management Sciences, Universiti Sultan Zainal Abidin, Kampung Gong Badak, Kuala Terengganu, Terengganu, Malaysia.
Manuscript received on 18 July 2019 | Revised Manuscript received on 03 August 2019 | Manuscript Published on 10 August 2019 | PP: 382-386 | Volume-8 Issue-2S3 July 2019 | Retrieval Number: B10660782S319/2019©BEIESP | DOI: 10.35940/ijrte.B1066.0782S319
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: There are several different classification methods can be used to do the classification which can classified the data into specified groups or classes. This paper presents a comparison of performance between three classifiers which include Naïve Bayes, Decision Tree and Neural Network on Bank Marketing dataset. This study focus on which classifier will have the better performance based on some performance measure in two different datasets. The result shows that machine learning classifier was not able compare to Naïve Bayes and Decision Tree classifier. Based on the results, the huge dataset obtained the more information which can be predict accurately and identify the performance of the classifier correctly.
Keywords: Artificial Neural Network Decision Tree, Marketing Data Classification, Naïve Bayes.
Scope of the Article: Data Analytics