Composite Model Fabrication of Classification with Transformed Target Regressor for Customer Segmentation using Machine Learning
Rincy Merlin Mathew1, R. Suguna2, M. Shyamala Devi3
1Rincy Merlin Mathew, Lecturer, Department of Computer Science, College of Science and Arts, Khamis Mushayt, King Khalid university, Abha, Asir, Saudi Arabia.
2R. Suguna, Professor, Computer Science and Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Avadi, Chennai, Tamil Nadu, India.
3M. Shyamala Devi, Associate Professor, Computer Science and Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Avadi, Chennai, Tamil Nadu, India.
Manuscript received on July 20, 2019. | Revised Manuscript received on August 10, 2019. | Manuscript published on August 30, 2019. | PP: 962-966 | Volume-8 Issue-6, August 2019. | Retrieval Number: F8257088619/2019©BEIESP | DOI: 10.35940/ijeat.F8257.088619
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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: In Current internet world, the customers prefer to buy the products through online rather than spending their time on show rooms. The online customers of wine increases day by day due to the availability of high brands in online sellers. So the customers buy the wine products based on the product description and the satisfaction of other customers those who have bought before. This makes the industries to focus on machine learning that concentrates on target transformation of the dependent variable. This paper endeavor to forecast the customer segmentation for the wine data set extracted from UCI Machine learning repository. The raw wine data set is subjected to target transformation for various classifiers like Huber Regressor, SGD Regressor, Ridge CV Regression, Logistic Regression CV and Passive Aggressive Regressor. The performance of the various classifiers is analyzed with and without target transformation using the metrics like Mean Absolute Error and R2 Score. The implementation is done in Anaconda Navigator with Python. Experimental results shows that after applying target transformation Ridge CV Regression is found to be effective with the R2 Score of 82% and Mean Absolute Error of 0.0 compared to other classifiers.
Keywords: Machine Learning, Target Transformation, Classifier, Mean Absolute Error and R2 Score.