Classification of Bank Direct Marketing Data Using Subsets of Training Data

Abstract

Nowadays, most business organizations practice Direct Marketing. One of the promising application areas of this type of marketing practice is Banking and Financial Industry. A classification technique using subsets of training data has been proposed in this paper. We have used a real-world direct marketing campaign data for experimentation. This marketing campaign was a telemarketing campaign. The objective of our experiment is to forecast the probability of a term-deposit plan subscription. In our proposed method we have used customer segmentation process to group individual customers according to their demographic feature. We have used X-means clustering algorithm for customer segmentation process. We have extracted few appropriate collection of customers from the entire customer database using X-means cluster algorithm, on the basis of demographic feature of individual customers. We have tested our proposed method of training for classifier using three most widely used classifiers namely Naïve Bayes, Decision Tree and Support Vector Machine. It has been found that the result obtained using our proposed method for classification on the banking data is better compare to that reported in some previous work on the same data.