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abdurahman-77 / ml-datamining-cw

rtools,Machine learning-based wine quality prediction model R Acknowledgement In this assignment, we consider a set of observations on a number of white wine varieties involving their chemical properties and ranking by tasters. Wine industry shows a recent growth spurt as social drinking is on the rise. The price of wine depends on a rather abstract concept of wine appreciation by wine tasters. Pricing of wine depends on such a volatile factor to some extent. Another key factor in wine certification and quality assessment is physicochemical tests which are laboratory-based and takes into account factors like acidity, pH level, presence of sugar and other chemical properties. For the wine market, it would be of interest if human quality of testing can be related to the chemical properties of wine so that certification and quality assessment and assurance process is more controlled. One dataset (whitewine_v2.xls) is available of which is on white wine and has 4710 varieties. All wines are produced in a particular area of Portugal. Data are collected on 12 different properties of the wines, one of which is Quality (i.e. the last column), based on sensory data, and the rest are on chemical properties of the wines including density, acidity, alcohol content etc. All chemical properties of wines are continuous variables. Quality is an ordinal variable with possible ranking from 1 (worst) to 10 (best). Each variety of wine is tasted by three independent tasters and the final rank assigned is the median rank given by the tasters.

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