Composite Model Fabrication of Classification with Transformed Target Regressor for Customer Segmentation using Machine Learning

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, RidgeCV Regression, Logistic RegressionCV 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 RidgeCV Regression is found to be effective with the R2 Score of 82% and Mean Absolute Error of 0.0 compared to other classifiers.