Deep Learning Based Methods for Processing Data in Telemarketing-Success Prediction

Abstract

In recent years, the importance of data has been increasing day by day. This has led companies to choose and use them actively, especially for reaching valuable information. Thanks to the interpretation of data, companies can save both time, labor, and costs for these operations in many application areas such as finance, security, e-commerce, data mining, etc. One critical area focuses on the use of finance, in which if the companies properly interpret and use this data, they can directly achieve more successful results in terms of their offering to customers with more accurate campaigns. In this paper, some deep learning methods (Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Simple Recurrent Neural Network (SimpleRNN)) are used to predict the possibility of subscribing to deposit after the customer is called within the scope of the bank telemarketing campaign. Implemented models are tested with the used dataset and experimental results were compared and interpreted. To improve the obtained accuracy level different approaches are applied to the dataset. Because of the unbalanced structure of the used dataset, SMOTE approach was used to reach more accurate results. After the dataset is processed to be a balanced form, some deep learning methods are applied to it. Obtained results had compared with other proposals. Experimental results showed that the proposed algorithms gave a very acceptable prediction, and it is expected to be used in the finance sector.

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