Selection of important features and predicting wine quality using machine learning techniques

Nowadays, industries are using product quality certifications to promote their products. This is a time taking process and requires the assessment given by human experts which makes this process very expensive. This paper explores the usage of machine learning techniques such as linear regression, neural network and support vector machine for product quality in two ways. Firstly, determine the dependency of target variable on independent variables and secondly, predicting the value of target variable. In this paper, linear regression is used to determine the dependency of target variable on independent variables. On the basis of computed dependency, important variables are selected those make significant impact on dependent variable. Further, neural network and support vector machine are used to predict the values of dependent variable. All the experiments are performed on Red Wine and White Wine datasets. This paper proves that the better prediction can be made if selected features (variables) are being considered rather than considering all the features. (C) 2018 The Authors. Published by Elsevier B.V.