Machine Learning with R by 2013

Author:2013
Language: eng
Format: epub
Publisher: Packt Publishing

Note

To read more about the wine study, please refer to the publication Modeling wine preferences by data mining from physicochemical properties, Decision Support Systems, Vol. 47, pp. 547-553, by P. Cortez, A. Cerdeira, F. Almeida, T. Matos, and J. Reis (2009).

Step 2 – exploring and preparing the data

As usual, we will use the read.csv() function to load the data into R. Since all of the features are numeric, we can safely ignore the stringsAsFactors parameter.

> wine <- read.csv("whitewines.csv")

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