Predicting success of bank telemarketing with classification trees and logistic regression
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Success of bank marketing campaign is predicted with customer features, campaign information and economic attributes. To predict whether or not clients will subscribe long-term deposit, logistic regression is applied with backward variable selection and principal components analysis. Random forests and stochastic gradient boosting, as members of classification trees, are also built as comparisons. Based on visualization and quantitative predictive performance, gradient boosting (AUC = 0.791) is slightly better than the other two models. Variable importance from 3 models remains consistent for most variables. Social and economic attributes, such as euribor3m, are among top important variables.