Abstract
This paper explores Data Mining (DM) techniques such as Decision Tree, Random Forest Decision Trees, and Artificial Neural Networks, employing Principal Component and Cluster Analysis for solving the problems of direct marketing. In our experiments the prediction accuracy 91% has been achieved with the DM models built for solving the problem. The experiments were run in RStudio which is a Popular DM platform, allowing analysts to efficiently carry out experiments and obtain the results. In our experiments we analysed patterns existing in the marketing benchmark data, which can affect the prediction accuracy. Boosting explored in our experiments has demonstrated an efficient reduction of the prediction error. Based on our results we conclude that the DT and ANN models provide analysts with understandable decision making concepts which can meet the realistic expectations of prediction accuracy.
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The authors are grateful to Dr Schetinin and Dr Jakaite from the University of Bedfordshire for useful comments, guidance and support.
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Fawei, T., Ludera, D.T.J. (2021). Data Mining Solutions for Direct Marketing Campaign. In: Arai, K., Kapoor, S., Bhatia, R. (eds) Intelligent Systems and Applications. IntelliSys 2020. Advances in Intelligent Systems and Computing, vol 1251. Springer, Cham. https://doi.org/10.1007/978-3-030-55187-2_46
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DOI: https://doi.org/10.1007/978-3-030-55187-2_46
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