Three data mining models to predict bank telemarketing. Issue 5 (April 2019)

• Record Type: Journal Article
• Authors: Jin, Wei; He, Yingying
• Is Part Of: IOP conference series. Volume 490:Issue 5(2019)
• Publication Date: 2019-04
• Subjects: Materials science -- Periodicals; 620.1105
• DOI: 10.1088/1757-899X/490/6/062075
• Languages: English
• ISSNs: 1757-8981

Abstract

Abstract: The bank telemarketing dataset was collected by a Portuguese retail bank when selling a bank long-term deposits product. In order to predict the success of bank telemarketing, an more accurate approach was proposed. Firstly, the dataset was analyzed and processed. The features value of the dataset could be divided into two kinds: continuous variable and categorical variable. The two kinds of variable had been processed with different methods. The continuous variables were transformed to discrete features and then were normalized. The categorical variables were coded with two different methods according to whether they were belong to the ordered categories. The missing value of features had to been processed with two different methods depending on the examples number of the missing value. Secondly, the three data mining models which were support vector machine, neural network and decision trees were adopted to predict the result of the processed bank telemarketing dataset. The classification and regression trees algorithm was adopted in the decision trees model. The BFGS algorithm had been used when the neural network model was programming. The simple sequential minimal optimization (SMO) algorithm was adopted in the support vector machine model. All the three models were executed using python 2.7 and conducted in a Windows7 PC, with an Intel Cerelon G1630 2.80 GHz processor. The results of three models were analyzed and compared. The decision trees presented the best results and cost the least time, and its accuracy, precision, recall value and AUC area of the were all equal to 1.0. It showed that decision trees could predict 100% correct rate. The accuracy, recall value and AUC area of the neural network were better than those obtained by the support vector machine (improvement of 1.5 pp, 2.7 pp and 5 pp respectively), but the neural network cost almost 4.4 times more time than the support vector machine. It showed that not only choosing the data mining models but also processing datasets in advance played a key role in obtaining better prediction results when datasets needed classifying.