Performance Evaluation for Competency of Bank Telemarketing Prediction using Data Mining Techniques
Md. Rashid Farooqi1, Naiyar Iqbal2 

1Md. Rashid Farooqi, Department of Management Studies, Maulana Azad National Urdu University, Hyderabad, Telangana, India.
2Naiyar Iqbal, Department of Computer Science and Information Technology, Maulana Azad National Urdu University, Hyderabad, Telangana, India.

Manuscript received on 11 March 2019 | Revised Manuscript received on 18 March 2019 | Manuscript published on 30 July 2019 | PP: 5766-5774 | Volume-8 Issue-2, July 2019 | Retrieval Number: A1269078219/19©BEIESP | DOI: 10.35940/ijrte.A1269.078219
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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.
Keywords: Bank Telemarketing, Direct Marketing, Decision Support, Data Mining, Classification.

Scope of the Article: Classification