Abstract
Data mining plays a vital role in the success of direct marketing campaigns by predicting which leads subscribe to a term deposit. This study is accomplished to illustrate with practical mining methods that the data are related to a Portuguese banking institution’s direct marketing campaign (phone calls). The algorithms are used: K-nearest neighbor, logistic regression, linear supported vector machines, and extreme gradient boosting to classify potential customers for long-term deposits finance products. Response coding is used to vectorize categorical data while solving a machine learning classification problem. Accuracy and AUC scores are key metrics to evaluate performance. We inherited selecting important features from previous research. This paper employed a better method by combining response coding techniques with practical algorithms in an unbalanced dataset. The best prediction model achieved 91.07% and 0.9324 of accuracy and AUC score, significantly higher than the prior of 79% and 0.8, respectively.
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References
Ling CX, Li C (1998) Data mining for direct marketing: problems and solutions. In: Proceedings of the international conference on knowledge discovery from data (KDD 98). New York City, pp 73–79
Elsalamony HA, Elsayad AM (2013) Bank direct marketing based on neural network. Int J Eng Adv Technol (IJEAT) 2(6):2249–8958
Ghoddusi H, Creamer GG, Rafizadeh N (2019) Machine learning in energy economics and finance: a review. Energy Econ 81:709–727. https://doi.org/10.1016/j.eneco.2019.05.006
Moro S, Cortez P, Rita P (2014) A data-driven approach to predict the success of bank telemarketing. Decis Support Syst 62:22–31. https://doi.org/10.1016/j.dss.2014.03.001
Miguéis VL, Camanho AS, Borges J (2017) Predicting direct marketing response in banking: comparison of class imbalance methods. Serv Bus 11(4):831–849. https://doi.org/10.1007/s11628-016-0332-3
Zhang X, Li X, Feng Y, Liu Z (2015) The use of ROC and AUC in the validation of objective image fusion evaluation metrics. Sign Process 115:38–48. https://doi.org/10.1016/j.sigpro.2015.03.007
Martens D, Vanthienen J, Verbeke W, Baesens B (2011) Performance of classification models from a user perspective. Decis Support Syst 51(4):782–793. https://doi.org/10.1016/j.dss.2011.01.013
Amponsah AA, Pabbi KA (2016) Enhancing direct marketing using data mining: a case of Yaa Asantewaa Rural Bank Ltd. in Ghana. Int J Comput Appl 153(7):6–12. https://doi.org/10.5120/ijca2016912092
Kozak J, Juszczuk P (2018) The ACDF algorithm in the stream data analysis for the bank telemarketing campaign. In: 2018 5th International conference on soft computing and machine intelligence (ISCMI), pp 49–53. https://doi.org/10.1109/iscmi.2018.8703246
Boryczka U, Kozak J (2012) Ant colony decision forest meta-ensemble. In: Computational collective intelligence. Technologies and applications, pp 473–482. https://doi.org/10.1007/978-3-642-34707-8_48
Ghatasheh N, Faris H, AlTaharwa I, Harb Y, Harb A (2020) Business analytics in telemarketing: cost-sensitive analysis of bank campaigns using artificial neural networks. Appl Sci 10(7):2581. https://doi.org/10.3390/app10072581
Moro S, Cortez P, Rita P (2017) A divide-and-conquer strategy using feature relevance and expert knowledge for enhancing a data mining approach to bank telemarketing. Expert Syst 35(3). https://doi.org/10.1111/exsy.12253
Moro S, Laureano R, Contez P (2012) Enhancing bank direct marketing through data mining. In: Proceedings of the forty-first international conference of the European marketing academy. European Marketing Academy, pp 1–8
Zhou S, Li K, Liu Y (2009) Text categorization based on topic model. Int J Comput Intell Syst 2(4):398–409. https://doi.org/10.1080/18756891.2009.9727671
He F, Ding X (2007) Improving naive bayes text classifier using smoothing methods. In: Lecture notes in computer science, vol 4425, pp 703–707. https://doi.org/10.1007/978-3-540-71496-5_73
Abdelmoula AK (2015) Bank credit risk analysis with k-nearest-neighbor classifier: case of Tunisian banks. Account Manage Inform Syst 14(1):79–106
Luu NT, Hung PD (2021) Loan default prediction using artificial intelligence for the borrow–lend collaboration. In: Luo Y (ed) Cooperative design, visualization, and engineering, CDVE 2021. Lecture notes in computer science, vol 12983. Springer, Cham. https://doi.org/10.1007/978-3-030-88207-5_26
Hung PD, Thinh TQ (2019) Cryptocurrencies price index prediction using neural networks on bittrex exchange. In: Dang T, KĂĽng J, Takizawa M, Bui S (eds) Future data and security engineering, FDSE 2019. Lecture notes in computer science, vol 11814. Springer, Cham. https://doi.org/10.1007/978-3-030-35653-8_43
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Huynh, L.D., Duong, P.T., Bach, K.D., Hung, P.D. (2023). Potential Customers Prediction in Bank Telemarketing. In: Saraswat, M., Chowdhury, C., Kumar Mandal, C., Gandomi, A.H. (eds) Proceedings of International Conference on Data Science and Applications. Lecture Notes in Networks and Systems, vol 552. Springer, Singapore. https://doi.org/10.1007/978-981-19-6634-7_4
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