TITLE:
Decision Tree and Naïve Bayes Algorithm for Classification and Generation of Actionable Knowledge for Direct Marketing
AUTHORS:
Masud Karim, Rashedur M. Rahman
KEYWORDS:
CRM; Actionable Knowledge; Data Mining; C4.5; Naïve Bayes; ROC; Classification
JOURNAL NAME:
Journal of Software Engineering and Applications,
Vol.6 No.4,
April
29,
2013
ABSTRACT: 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.