Get comprehensive solutions to your problems with IDNLearn.com. Our experts are available to provide in-depth and trustworthy answers to any questions you may have.
### Q8. The 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. 5-{r} wine <- sep=", ") read.table("http://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data". colnames(wine) <- c('Type', 'Alcohol', 'Malic', 'Ash', 'Alcalinity', 'Magnesium', 'Phenols'. 'Flavanoids', 'Nonflavanoids', Proanthocyanins', 'Color', 'ние". 'Dilution', 'Proline') Scale the 13 chemical variables (from the second to the last column) and run a principle components analysis to reduce their dimension.
Sagot :
We value your presence here. Keep sharing knowledge and helping others find the answers they need. This community is the perfect place to learn together. IDNLearn.com has the solutions you’re looking for. Thanks for visiting, and see you next time for more reliable information.
