A Prediction to Choose Customers in Auto Ancillary Automotive Products using K-Tree-Bayes Model for Improving Business Profits and Retention
K. Shyamala1, C.S Padmasini2
1Dr K. Shyamala*, Associate Professor PG & Research Department of Computer Science,Dr.Ambedkar Government Arts College (Autonomous).
2C.S Padmasini, Assistant Professor, Department of Computer Science, M.O.P Vaishnav College for Women(Autonomous)
Manuscript received on November 12, 2019. | Revised Manuscript received on November 25, 2019. | Manuscript published on 30 November, 2019. | PP: 5189-5194 | Volume-8 Issue-4, November 2019. | Retrieval Number: D7391118419/2019©BEIESP | DOI: 10.35940/ijrte.D7391.118419
Open Access | Ethics and Policies | Cite | Mendeley | Indexing and Abstracting
© 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: Customers are of paramount importance for running business enterprises. A K-Tree-Bayes model, when applied for the purpose of customer retention and business promotion, it retains them and their favorite choices. This model is extended to work in various aspects as and when new customers as well as existing customers provide their wishes and those data will become impertinent to improve the product in all aspects right from the manufacturing to reach to the customers. The model shows reasonable accuracy to predict the changing customer choices towards their desire to buy any automotive as companies are investing heavily on customer prediction thereby providing automotive of their choices to retain them forever.
Keywords: Clustering, Pruning, Classification, Accuracy, Naïve Bayes
Scope of the Article: Classification.