Using Logistic Regression Model to Predict the Success of Bank Telemarketing
International Journal on Data Science and Technology
Volume 4, Issue 1, March 2018, Pages: 35-41
Received: Jun. 20, 2018; Published: Jun. 21, 2018
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Author
Yiyan Jiang, School of Data Science, Zhejiang University of Finance and Economics, Hangzhou, China
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Abstract
Term deposit is always an essential business of a bank and a good market campaign plays an essential role in financial selling. Nowadays, the telephone marketing, which can assist consulting institution to extract potential clients, has been one of the most general marketing campaigns. Previous research shows that data mining has gradually stood out on the era of Big Data and has been incorporated to deal with massive data precisely. The purpose of this study is to predict the success of bank telemarketing to select the best consumer set. A relationship is observed between success and other factors through constructing logistic regression model. To validate the effectiveness of prediction, some basic classification models have been compared in this study, including Bayes, Support Vector Machine, Neural Network and Decision Tree. As a result, the prediction accuracy and the area under ROC curve prove the logistic regression model performs best in classifying than other models. All of the experiments are implemented by R language software. And the experimental results can provide some suggestions and instructions towards the management of the bank.
Keywords
Term Deposit, Data Mining, Prediction, Logistic Regression Model, R Language
To cite this article
Yiyan Jiang, Using Logistic Regression Model to Predict the Success of Bank Telemarketing, International Journal on Data Science and Technology. Vol. 4, No. 1, 2018, pp. 35-41. doi: 10.11648/j.ijdst.20180401.15
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