# Improving the Accuracy of Predicting Bank Depositor's Behavior Using a Decision Tree
## 1 Introduction  
Data mining is key to extracting insightful knowledge about a problem to which large sets of information exist. It offers a methodological prescriptive approach to deal with the information explosion in current industries so as to obtain valuable information [1]. Data mining can be used to support the decision-making process in order to achieve goals defined by companies; one of the most common data mining tasks adopted in marketing is classification [2]. Data-driven decision-making methods have an important role in responding to the harsh business environment [3]. Specifically, customer targeting consists of identifying prospective buyers of a product (or subscribers of a service) offered within the context of a marketing campaign [4]. By incorporating classification models (i.e., classifiers) into decision support systems, decision makers can determine the most beneficial data mining models in order to improve the business [5]. The large number of available datasets in companies makes data mining a useful approach for finding the information that managers may use to make better decisions. Additionally, knowledge discovery in databases processes (KDD) can help to obtain better models of data [6]. KDD and data mining are not the same. KDD points to the whole process of knowing the valuable data information. Data mining points to identifying new models by focusing on models that obtain valuable information from the database [6]. Nowadays, banks invest a lot in marketing, and related research has emerged to become mainstream in academic literature. Some methods for automatic classifiers with non-statistical classification were investigated in [7], such as the alternating decision tree (AD Tree), random forest, best-first decision tree (BF Tree), C4.5 (J48), RBF (radial basis function), MLP (multilayer perceptron) and SVM (support vector machine); the studyachieved an accuracy of 90.20% using the J48 in a process that took 10 s. Ref. [8] compared four data mining models: J48 graft, radial basis function network, SVM, and logical analysis of data (LAD tree). The best result was obtained using the J48, with an accuracy rate of 76.52%, and the SVM (an advanced classification algorithm) in classification and regression processes. Ref. [9] achieved the highest sensitivity, specificity and accuracy of 86.95% in a bank dataset. Ref. [10] focused on feature selection to reduce the extra features from bank marketing datasets. Ref. [11] achieved an accuracy of 91.19%. After removing extra features and with just 3 features out of 20, the same authors were able to increase the accuracy for predicting the bank long-term deposit subscription within telemarketing campaigns. Ref. [5] proposed an approach to predict the telemarketing success in the bank industry for selling long-term deposits. Altogether, four data mining models were compared: neural networks (NN), logistic regression (LR), decision trees (DT) and support vector machines (SVM); the best results were obtained by the NN. Ref. [12] stated that by anticipating the bank's potential customers who wanted to open long-term deposits reduced marketing costs by saving wasted campaign resources. Ref. [13] used different trained neural networks to reduce the possible risk of unpleasant noise. Other recent studies tended to also use black-box models such as NN and SVM, such as [3]. This paper focuses on data mining classification and the performance of the most important decision tree algorithm called J48. To evaluate the result, models were compared on accuracy, sensitivity and specificity in the bank marketing dataset [11]. The aim of this study is to predict the successful selling of long-term deposits to customers by using bank marketing. Additionally, we adopted resampling to deal with the imbalanced challenge of such a dataset. Details of the bank dataset, preprocessing, feature selection, data mining techniques of the paper are provided in Section 2. Section 3 includes the evaluation metrics for the trained and tested models.