A data modeling approach for classification problems: application to bank telemarketing prediction

In this paper, we present a new data modeling approach for five common classification algorithms to optimize the prediction of telemarketing target calls for selling bank long-term deposits. A Portuguese retail bank addressed, from 2008 until 2013, data on its clients, products and social-economic attributes including the effects of the financial crisis. An original set of 150 features has been explored and 21 features are retained for the proposed approach including label. This paper introduces a new modeling approach that preprocessed separately each type of features and normalize them to optimize prediction performance. To evaluate the proposed approach, this paper compares the results obtained with five most known machine learning techniques: Naïve Bayes (NB), Logistic Regression (LR), Decision Trees (DT), Artificial Neural Network (ANN) and Support Vector Machines (SVM) and it yielded better improved performances for all these algorithms in terms of accuracy and f-measure.

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