Principal Components Analysis (PCA) for Wine Dataset

This project will use Principal Components Analysis (PCA) technique to do data exploration on the Wine dataset and then use PCA conponents as predictors in RandomForest to predict wine types.

The shiny app for this project is at https://suteak.shinyapps.io/dev_data_product/

The source code for this project are at https://github.com/eakalak-suthampan/dev_data_product

The presentation for this project is at http://rpubs.com/suteak/253795

There are 13 predictors to predict Wine Types (3 Types)

data(wine, package = 'rattle')
str(wine,list.len = 5)

'data.frame':   178 obs. of  14 variables:
 $ Type           : Factor w/ 3 levels "1","2","3": 1 1 1 1 1 1 1 1 1 1 ...
 $ Alcohol        : num  14.2 13.2 13.2 14.4 13.2 ...
 $ Malic          : num  1.71 1.78 2.36 1.95 2.59 1.76 1.87 2.15 1.64 1.35 ...
 $ Ash            : num  2.43 2.14 2.67 2.5 2.87 2.45 2.45 2.61 2.17 2.27 ...
 $ Alcalinity     : num  15.6 11.2 18.6 16.8 21 15.2 14.6 17.6 14 16 ...
  [list output truncated]

2 PCA variables will be used as predictors in RandomForest to predict wine types.

rfFit <- randomForest(Type ~ PC1 + PC2, data = pca.train.df)
paste("training accuracy is",confusionMatrix(training$Type,predict(rfFit))$overall[1]*100,"%")

[1] "training accuracy is 95.0617283950617 %"

paste("testing accuracy is",confusionMatrix(testing$Type,predict(rfFit,pca.test.df))$overall[1]*100,"%")

[1] "testing accuracy is 100 %"