Knowledge Extraction for Business Information System using C5.0 Tree Algorithm
Anitha A1, Mathivanan R2, Sundarraj R3, Indhulekha J4
1Anitha A*, Information Technology, Francis Xavier Engineering college , Tirunelveli, India.
2Mathivanan R, Information Technology, Francis Xavier Engineering College , Tirunelveli, India.
3Sundarraj R, Information Technology, Francis Xavier Engineering College , Tirunelveli, India.
4Indhulekha J , Information Technology, Francis Xavier Engineering College , Tirunelveli, India.
Manuscript received on January 23, 2020. | Revised Manuscript received on February 05, 2020. | Manuscript published on February 30, 2020. | PP: 3703-3707 | Volume-9 Issue-3, February 2020. | Retrieval Number: C6288029320/2020©BEIESP | DOI: 10.35940/ijeat.C6288.029320
Open Access | Ethics and Policies | Cite | Mendeley
© 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: Usually people can predict that some products will be purchase by the males and some will be purchased by the females, but there are some hidden factors behind the data. When the data was analyzed ,Analysts comes to know those hidden factors in the dataset. In this study,C5.0 algorithm is used which is highly approachable compare to other decision tree algorithms. So that it is easy understand the data patterns and the decision that can be made by the Entrepreneur. Normally the products like beer, meat, crispy chips and so on will be purchased by the males and the products like chocolates, soft drinks will be purchased by the females, but when the data was analyzed it is predicted that which gender would buy which product that can’t be predicted by the normal peoples . In this project, it is proposed to apply C5.0 algorithm for finding the target customer group. Identifying specific customer group is necessary to improve profit in sales domain. Accuracy attained with proposed model is 81.6%. For each category of product, the interested gender group is identified.
Keywords: Accuracy, Decision Tree, Knowledge Extraction, Prediction.