Hybrid Neural Network With K-Means For Forecasting Response Candidate In Direct Marketing

The larger Bank‘s electronic data customer provides difficulty a marketing campaign. An efficient marketing campaign is needed to promote a product and services. The predictive data mining techniques use to help a marketing analyst provide more value to their customers by the right offer because of...

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Main Author:	Ramadhan, Rakhmat Sani
Format:	Thesis
Language:	English English
Published:	2014
Subjects:	Q Science (General) QA76 Computer software
Online Access:	http://eprints.utem.edu.my/id/eprint/16255/1/Hybrid%20Neural%20Network%20With%20K-Means%20For%20Forecasting%20Response%20Candidate%20In%20Direct%20Marketing%2024%20Pages.pdf http://eprints.utem.edu.my/id/eprint/16255/2/Hybrid%20Neural%20Network%20With%20K-Means%20For%20Forecasting%20Response%20Candidate%20In%20Direct%20Marketing.pdf
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id	my-utem-ep.16255
record_format	uketd_dc
spelling	my-utem-ep.162552022-02-21T12:23:07Z Hybrid Neural Network With K-Means For Forecasting Response Candidate In Direct Marketing 2014 Ramadhan, Rakhmat Sani Q Science (General) QA76 Computer software The larger Bank‘s electronic data customer provides difficulty a marketing campaign. An efficient marketing campaign is needed to promote a product and services. The predictive data mining techniques use to help a marketing analyst provide more value to their customers by the right offer because of decreasing in responses to a direct marketing campaign. Distribution of customer data record in marketing response data are often found issue of imbalanced dataset. This study proposed hybrid Neural Network (NN) methods in data mining to support direct marketing analysis and forecast. Backpropagation NN is supervised learning methods that analyze data and recognize to solve many problems in the real world by building a model that is trained to perform well in some non-linear problems. K-means algorithm grouping process by minimizing the distance between the data and designed can handle very large dataset also continuous and categorical variable for handling imbalanced dataset. This research concerns on binary classification which is classified into two classes. Those classes are yes and no. The data was collected from the Machine Learning Repository Dataset in the University of California Irvine (UCI).This experiment compares hybrid K-Means + NN with basic NN. The result shows the improvement of accuracy from 91.53% to 91.59%, recall 22.15% to 27.7% and F-Measure 44.23% but not to precision from 61.69% to 60.75%. UTeM 2014 Thesis http://eprints.utem.edu.my/id/eprint/16255/ http://eprints.utem.edu.my/id/eprint/16255/1/Hybrid%20Neural%20Network%20With%20K-Means%20For%20Forecasting%20Response%20Candidate%20In%20Direct%20Marketing%2024%20Pages.pdf text en public http://eprints.utem.edu.my/id/eprint/16255/2/Hybrid%20Neural%20Network%20With%20K-Means%20For%20Forecasting%20Response%20Candidate%20In%20Direct%20Marketing.pdf text en validuser http://library.utem.edu.my:8000/elmu/index.jsp?module=webopac-d&action=fullDisplayRetriever.jsp&szMaterialNo=0000091008 mphil masters Universiti Teknikal Malaysia Melaka Faculty of Information and Communication Technology
institution	Universiti Teknikal Malaysia Melaka
collection	UTeM Repository
language	English English
topic	Q Science (General) QA76 Computer software
spellingShingle	Q Science (General) QA76 Computer software Ramadhan, Rakhmat Sani Hybrid Neural Network With K-Means For Forecasting Response Candidate In Direct Marketing
description	The larger Bank‘s electronic data customer provides difficulty a marketing campaign. An efficient marketing campaign is needed to promote a product and services. The predictive data mining techniques use to help a marketing analyst provide more value to their customers by the right offer because of decreasing in responses to a direct marketing campaign. Distribution of customer data record in marketing response data are often found issue of imbalanced dataset. This study proposed hybrid Neural Network (NN) methods in data mining to support direct marketing analysis and forecast. Backpropagation NN is supervised learning methods that analyze data and recognize to solve many problems in the real world by building a model that is trained to perform well in some non-linear problems. K-means algorithm grouping process by minimizing the distance between the data and designed can handle very large dataset also continuous and categorical variable for handling imbalanced dataset. This research concerns on binary classification which is classified into two classes. Those classes are yes and no. The data was collected from the Machine Learning Repository Dataset in the University of California Irvine (UCI).This experiment compares hybrid K-Means + NN with basic NN. The result shows the improvement of accuracy from 91.53% to 91.59%, recall 22.15% to 27.7% and F-Measure 44.23% but not to precision from 61.69% to 60.75%.
format	Thesis
qualification_name	Master of Philosophy (M.Phil.)
qualification_level	Master's degree
author	Ramadhan, Rakhmat Sani
author_facet	Ramadhan, Rakhmat Sani
author_sort	Ramadhan, Rakhmat Sani
title	Hybrid Neural Network With K-Means For Forecasting Response Candidate In Direct Marketing
title_short	Hybrid Neural Network With K-Means For Forecasting Response Candidate In Direct Marketing
title_full	Hybrid Neural Network With K-Means For Forecasting Response Candidate In Direct Marketing
title_fullStr	Hybrid Neural Network With K-Means For Forecasting Response Candidate In Direct Marketing
title_full_unstemmed	Hybrid Neural Network With K-Means For Forecasting Response Candidate In Direct Marketing
title_sort	hybrid neural network with k-means for forecasting response candidate in direct marketing
granting_institution	Universiti Teknikal Malaysia Melaka
granting_department	Faculty of Information and Communication Technology
publishDate	2014
url	http://eprints.utem.edu.my/id/eprint/16255/1/Hybrid%20Neural%20Network%20With%20K-Means%20For%20Forecasting%20Response%20Candidate%20In%20Direct%20Marketing%2024%20Pages.pdf http://eprints.utem.edu.my/id/eprint/16255/2/Hybrid%20Neural%20Network%20With%20K-Means%20For%20Forecasting%20Response%20Candidate%20In%20Direct%20Marketing.pdf
_version_	1747833889652473856

Hybrid Neural Network With K-Means For Forecasting Response Candidate In Direct Marketing

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