Pitch Presentation - Developing Data Products

Using the famous (Fisher's or Anderson's) iris data set that measures in centimeters the variables sepal length and width and petal length and width, respectively, for 50 flowers from each of 3 species of iris. The species are Iris setosa, versicolor, and virginica.

In this Shiny app you can input the sepal and petal length and width changing the Sliders. Whith those information the app estimates with random forest the 3 different Species Iris setosa, versicolor, and virginica. The Predicted Species is stated at the bottom of the Side Panel. In the graphics you can see the Values of the data used in the model and the value you want to predict.

The app describe in this presentation can be see here https://rbkwork.shinyapps.io/FinalProject/

The model has an estimated error rate of 4%

library(randomForest)
randomForest(as.factor(Species)~ ., data= iris)

Call:
 randomForest(formula = as.factor(Species) ~ ., data = iris) 
               Type of random forest: classification
                     Number of trees: 500
No. of variables tried at each split: 2

        OOB estimate of  error rate: 4.67%
Confusion matrix:
           setosa versicolor virginica class.error
setosa         50          0         0        0.00
versicolor      0         47         3        0.06
virginica       0          4        46        0.08