Subscribing prediction of term deposit based on decision tree, random forest and support vector machine

Abstract

To classify customers and predict their behaviour based on some of their features and what they do before is most people want to do. This study finds a data set about bank customers from Kaggle and use three different classification models to classify customers and predict whether they will subscribe a term deposit based on some of their features. The three classification models are decision tree model, random forest model and support vector machine model. Firstly, using these models to get the feature importance and accuracy rate to evaluate the result. In addition, this study changes the parameters about these models and find how can get better result. Based on these models to process data set and getting the result, this paper also compared their results and find advantages and disadvantages of three models. Finally, this paper also discusses how to improve the models so that they can get the better result and solve some other problems.