Classification and Selection of Best Saving Service for Potential Investors using Decision Tree – Data Mining Algorithms
Amritpal Kaur1, Sandeep Singh2
1Amritpal Kaur, M.Sc Degree in IT from Khalsa College, Patiala, Punjab, Currently pursuing M.Tech Degree in Computer Science and Engineering at Lovely Professional University (LPU), Phagwara, Punjab, India.
2Sandeep Singh, Assistant Professor, Computer Science and Engineering at Lovely Professional University, Punjab. He received his B.TECH NIT Kurukshetra.
Manuscript received on March 02, 2013. | Revised Manuscript received on April 15, 2013. | Manuscript published on April 30, 2013. | PP: 80-82 | Volume-2, Issue-4, April 2013. | Retrieval Number: D1291042413/2013©BEIESP
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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: This research delineates a comprehensive and successful application of decision tree induction to large banking data set of different banking services obtained by numbers of customers. Complex interaction effects among banking services that lead to increased policy variability have been detected. The extracted information has been confirmed by the database managers, and used to improve the decision process. The research suggests that decision tree induction may be particularly useful when data is multidimensional, and the various process parameters and highly complex interactions. In order to classify and identify effective and beneficial saving service and design the appropriate criteria for selecting the right scheme for different persons having different taste, this study developed a data mining framework for analyzing banks and post office data, in which suitable[1] technique is employed to extract rules between present saving schemes. In other words, the objectives of this thesis are • To classify the available saving services of banks and post office to good, medium and bad level. • To select the best saving service according the investor’s choice and its preference. • To guide the potential investor to invest his money in the particular scheme so as to get more benefits. • To help to take the right decision for investment. • To reduce the time to take particular decision as there will be no need to analyze each and every available investment scheme thoroughly.
Keywords: CHAID, C4.5, Cluster, data mining, Decision tree induction, ID3.