wine dataset contains the results of a chemical analysis of
wines grown in a specific area of Italy. Three types of wine are
represented in the 178 samples, with the results of 13 chemical
analyses recorded for each sample. The
Type variable has been
transformed into a categoric variable.
The data contains no missing values and consits of only numeric data,
with a three class target variable (
Type) for classification.
A data frame containing 178 observations of 13 variables.
The type of wine, into one of three classes, 1 (59 obs), 2(71 obs), and 3 (48 obs).
Alcalinity of ash
D280/OD315 of diluted wines.
The data was downloaded from the UCI Machine Learning Repository.
It was read as a CSV file with no header using
read.csv. The columns were then given the appropriate
colnames and the Type was transformed into a
as.factor. The compressed R data file was
1 2 3 4 5 6 7 8 9 10 11 12
UCI <- "http://archive.ics.uci.edu/ml" REPOS <- "machine-learning-databases" wine.url <- sprintf(" wine <- read.csv(wine.url, header=FALSE) colnames(wine) <- c('Type', 'Alcohol', 'Malic', 'Ash', 'Alcalinity', 'Magnesium', 'Phenols', 'Flavanoids', 'Nonflavanoids', 'Proanthocyanins', 'Color', 'Hue', 'Dilution', 'Proline') wine$Type <- as.factor(wine$Type) save(wine, file="wine.Rdata", compress=TRUE)
Asuncion, A. & Newman, D.J. (2007). UCI Machine Learning Repository [http://www.ics.uci.edu/~mlearn/MLRepository.html]. Irvine, CA: University of California, School of Information and Computer Science.
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