# Improving the Accuracy of Predicting Bank Depositor's Behavior Using a Decision Tree
## 4 Discussion  
The real data used in this paper was the full bank marketing of 41188 instances in UCI from a Portuguese bank [11]. The main goal of this study was to understand the effectiveness of the J48 model at predicting the success of telemarketing calls for selling bank long-term deposits. We showed how the J48 classifier could be improved to achieve better evaluation metric results for the three used metrics. The metrics used for measuring the performance of the classifier were accuracy, specificity and sensitivity. Several studies have reported using J48 for bank direct marketing. After comparison, we proved that in this study the J48 performs better when using resampling and feature selection for the three metrics, with a result of 94.39% for accuracy, 0.975 for sensitivity and 0.709 for specificity. In today's competitive market, companies must make critical decisions to affect their future. That is the most common mistake made that causes the wrong marketing approaches. All managers wish to have faithful customers and increase revenue to attract customers, maintaining their presence is something else. Retaining customers and customer loyalty is more important in a competitive business where it is hard to stand out. In order to have customers who are satisfied and to solve the problem of customer loyalty and retention, the J48 classifier is suggested in order to predict the sale of long-term deposits. Figure 5: Compared models and methods after the last process. Figure 6: Shows matrix confusion at three levels when comparing the results. One of the most common problems of telemarketing is that customers may feel annoyed if they are not really interested in the product being offered. This paper achieved better results in comparison to other previous studies. In our preliminary study [35], five DM models: Naive Bayes (NB), logistic regression (LR), decision tree (DT), multilayer perceptron neural network (MLPNN) and support vector machine (SVM) were compared to predict the success of selling long-term deposits by telemarketing calls in bank marketing. LR has achieved the better performance than others based on accuracy: 91.21%, ROC 0.93 and sensitivity, 0.912%, from the same dataset [11]. Ref. [36] used a set of training data and tested it with C4.5 and Naive Bayes algorithms in a group of bank customers. Then, they were able to improve the accuracy results by about 1%. They obtained 93.961% for C4.5 and 87.724% for Naive Bayes. Ref. [37] cleaned and prepared the bank dataset [11] by considering data integrity, unified types, missing value imputation, data transformation, and data balancing, then applied five machine learning algorithms. Finally, they achieved the best accuracy by using the Gaussian NB algorithm, 88.86%. Ref. [38] proposed a geometric data perturbation (GDP) method using data partitioning and three-dimensional rotations. They divided attributes into three groups so that each group rotated into different axis to provide privacy, increased accuracy and efficiency. They used DT-IBK-J48-NB to improve the accuracy of the J48 with the three-dimensional rotation transformation (3 DRT) technique which is four times better than the 2 DRT. Ref. [39] analyzed bank customer information for predicting the success of bank telemarketing. They proposed the prediction model FMLP-SVM method. This model is using a fuzzy algorithm, it first fuzzifies input variables and uses fuzzified MLPNN as a function of the fuzzy SVM kernel function, then uses fuzzified data to predict the bank telemarketing. They used four algorithms (DT, SVM, Bayes net, FMLP-SVM) to increase the accurate result so as to increase the overall accuracy. Then, FLIP-SVM could gain the most accurate result at an accuracy level of 92.89% and DT could have an accuracy of 89.66%, with a 20% sample size. Another challenge of data mining is handling the problem of imbalanced data. A dataset is imbalanced when one class dataset has much fewer numbers of instances than the other class dataset [40]. Ref.