Performance Evaluation for Competency of Bank Telemarketing Prediction using Data Mining Techniques

Abstract

In today's market there is cut throat competition in the banks and struggling hard to gain competitive advantage over each other. The banking industry has undergone tremendous changes in the way business conducted. They realizes the needs and techniques of data mining which is helpful tool to gather, store, capture data and convert into knowledge. The application of data mining enhances the performance of telemarketing process in banking industry. It also provide an insight how these techniques effectively used in banking industry to make the decision making process easier and productive. This work describes a data mining approach to extract valuable knowledge and information from a bank telemarketing campaign data. At this time, the potential of five data mining methods was explored for forecasting of term deposit subscription. The presentation of these techniques was evaluated on fourteen different classifier parameters. The overall better performance achieved by J48 decision tree which reported 91.2% correctly classified with sensitivity, specificity and lowest error rate of 53.8, 95.9 and 8.8 % respectively