%0 Journal Article
%T Decision Tree and Naïve Bayes Algorithm for Classification and Generation of Actionable Knowledge for Direct Marketing
%A Masud Karim
%A Rashedur M. Rahman
%J Journal of Software Engineering and Applications
%P 196-206
%@ 1945-3124
%D 2013
%I Scientific Research Publishing
%R 10.4236/jsea.2013.64025
%X <p class=\"MsoNormal\">
	<span lang=\"EN-US\">Many companies like credit card, insurance,
bank, retail industry require direct marketing. Data mining can help those institutes
to set marketing goal. Data mining techniques have good prospects in their target
audiences and improve the likelihood of response. In this work we have investigated
two data mining techniques: the Naive Bayes and the C4.5 decision tree algorithms.
The goal of this work is to predict whether a client will subscribe a term deposit.
We also made comparative study of performance of those two algorithms. Publicly
available UCI data is used to train and test the performance of the algorithms.
Besides, we extract actionable knowledge from decision tree that focuses to take
interesting and important decision in business area.</span><span lang=\"EN-US\"><o:p></o:p></span> 
</p>
%K CRM
%K Actionable Knowledge
%K Data Mining
%K C4.5
%K Na&#239
%K ve Bayes
%K ROC
%K Classification
%U http://www.scirp.org/journal/PaperInformation.aspx?PaperID=30463