TY  - JOUR
TT  - Using Data Mining Techniques for Detecting the Important Features of the Bank  Direct Marketing Data
AU  - Parlar, Tuba
AU  - Acaravcı, Songül Kakilli
PY  - 2017
DA  - June
JF  - International Journal of Economics and Financial Issues
JO  - IJEFI
PB  - İlhan ÖZTÜRK
WT  - DergiPark
SN  - 2146-4138
SP  - 692
EP  - 696
VL  - 7
IS  - 2
KW  - Bank marketing
KW  - feature selection
KW  - machine learning methods
KW  - data mining
KW  - chi-square
KW  - information gain
N2  - Collection of customer information is seen necessary for development of the marketing strategies. Developing technologies are used very effectively in bank marketing campaigns as in many field of life. Customer data is stored electronically and the size of this data is so immense that to analyse it manually with a team of human analysts is impossible. In this paper, data mining techniques are used to interpret and define the important features to increase the campaign’s effectiveness, i.e. if the client subscribes the term deposit. The bank marketing dataset from the University of California at Irvine Machine Learning Repository has been used for the proposed paper. We consider two feature selection methods namely Information Gain and Chi-square methods to select the important features. The methods are compared using a supervised machine learning algorithm of Naive Bayes. The experimental results show that reduced set of features improves the classification performance.
UR  - https://dergipark.org.tr/en/pub/ijefi/article/354551
L1  - https://dergipark.org.tr/en/download/article-file/365990
ER  -