• RINSON RAJU et al.


Nowadays, industries are using  product quality certifications to promote their product. This is a time taking process and requires the judgement given by human experts which makes the process very expensive. This paper explores the usage of r programming to predict the wine data quality with decision tree the main ambition of these study to predict wine quality data on psychochemical data. These data set contain 11 appearance of psychochemical data such as such as alcohol, chlorides, density, total sulphur dioxide, free sulphur dioxide .residual sugar and Ph. its where classified by using Random forest or decision tree. There are  6 quality classes of  red wine  and 7  quality classes  of white  wine. The  most successful classification  was obtained  by using Random  Forests Algorithm. In this study, it is also observed that the use of principal component analysis in the feature selection increases the success rate of classification random forest algorithm. and also decision tree is the major to finding which have better qualities and taste. Even up wine-drinkers generally agree that wines may be ranked by quality, wine-tasting is accurately subjective. There have been many attempts to construct a more methodical approach to the assessment of wines. We propose a method of judge  wine quality using a decision tree, and test it against the wine-quality dataset from the UC Irvine Machine Learning Repository