Classification of Telemarketing Data using Different Classifier Algorithms
VenkateshYadav1, M. SreeLatha2, T. V. RajiniKanth3
1A.Venkatesh Yadav*, Research Scholar, Acharya Nagarjuna University: Guntur, Andhra Pradesh, India.
2Dr. M. SreeLatha, Professor & Head, RVR & JC Engg. College, Chowdavaram, Andhra Pradesh, India.
3Dr. T. V. Rajini Kanth, Professor & Dean R&D, SNIST, Hyderabad, Telangana, India.
Manuscript received on September 15, 2019. | Revised Manuscript received on 24 September, 2019. | Manuscript published on October 10, 2019. | PP: 1300-1307 | Volume-8 Issue-12, October 2019. | Retrieval Number: L39171081219/2019©BEIESP | DOI: 10.35940/ijitee.L3917.1081219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: IGlobalisation, growth of new technology usage and tough competition, made the banks to adopt new approaches to get competitive advantage to enlarge customer databases and also to generate customer satisfaction. In the present days the banks are trying to enhance customer base to meet their business targets for which they follow various approaches like Internet banking, Direct Tele Marketing, Mobile Banking, etc. Apart from banking services to customers, Banks are also selling Insurance policies to the customers through Tele Marketing and by which their business is expanding exponentially. In this paper, various Machine Learning Algorithms like Random Forest, Random Tree, Rep Tree, Naïve Baye’s, J48 Decision Tree before and after refinement of data and advanced Statistical techniques were applied for effective analysis of Bank’s Tele Marketing Data in order to enhance number of subscribing customer’s.
Keywords: Machine Learning Algorithms, Tele Banking, Subscribing Customers, Advanced Statistical techniques, Direct Tele Marketing
Scope of the Article: Machine Learning