Yasemin Gultepe et al, International Journal of Computer Science and Mobile Computing, Vol.8 Issue.3, March- 2019, pg. 285-290 © 2019, IJCSMC All Rights Reserved 285 Available Online at www.ijcsmc.com International Journal of Computer Science and Mobile Computing A Monthly Journal of Computer Science and Information Technology ISSN 2320–088X IMPACT FACTOR: 6.017 IJCSMC, Vol. 8, Issue. 3, March 2019, pg.285 – 290 Mining a Marketing Campaigns Data of Bank Yasemin Gultepe 1 ; Wisam Gwad 2 ; Yuosra Aljamel 3 ; Yossf Ahmed 4 ¹ ,2,3,4 Computer Engineering & KASTAMONU University, Turkey 1 yasemingultepe@ogr.kastamonu.edu.tr ; 2 wgwad@ogr.kastamonu.edu.tr 3 usraalhade@gmail.com, 4 yghyth2@gmail.com Abstract— In this paper, we propose a data mining approach to predict the success of telemarketing. We are applying the algorithms for the first time on the dataset. The dataset obtained from UCI, which contain the most common machine learning datasets. The data is related to direct marketing campaigns of a Portuguese banking institution. The marketing campaigns were based on phone calls. Often, more than one contact to the same client was required, in order to access if the product (bank term deposit) would be ('yes') or not ('no') subscribed. The number of the instance is 45212 with 15 input variables and the output variable. Classification is a data mining techniques used to predict group membership for a data instance. we present the comparison of different classification techniques in open source data mining software which consists of a One-R algorithm methods and Naïve-Bayes algorithm The experiment results show are a bout classification sensitivity, specificity, accuracy. The results on bank marketing data discovered that the One-R algorithm is better in classifying the data comparing with the Naïve-Bayes algorithm; where the error rate is lower. Keywords— Bank Marketing, Data mining, One-R Algorithm Naïve-Bayes algorithm. I. INTRODUCTION Data mining (Han, Kamber, & Pei, 2011b) is the process of extracting previously unknown information from a large dataset. Today, data mining is being used by several industries including finance and banking. The bank is marketing department can use data mining to analyse customer datasets and develop statistical profiles of individual customer preference for product and service. In bank direct marketing domain, there are several data mining techniques can be used for classifying marketing service such One-R Algorithm, naive Bayes classifier, classification and association rule mining. Exploratory data analysis on variables will be used, to discover the relation between the variables and the class variable, the relation between two variables according to the class variable, and data mining algorithms ―classification‖ will be used to classify the bank client’s data. The classification goal is to predict if the client will subscribe to a term deposit (variable outcome). Classification is the process of finding a model (or function) that describes and distinguishes data classes or concepts, for the purpose of being able to use the model to predict the class of objects whose class label is unknown. The derived model is based on the analysis of a set of training data (data objects whose class label is known).