Coursera Developing Data Products Course Project

Shiny Application: Iris Species Prediction

December 18 2014

Building Iris Species Prediction Model

Step 1: Load iris data and partition the data into training and testing datasets

data(iris)
inTrain <- createDataPartition(y=iris$Species, p = 0.7, list=FALSE)
training <- iris[inTrain,]  
testing <- iris[-inTrain,]

Step 2: Build iris species prediction model using rpart method on the training dataset

modFit <- train(Species ~ ., method="rpart", data=training)

Building Iris Species Prediction Model

Step 3: Plot the model's classification tree

fancyRpartPlot(modFit$finalModel)

plot of chunk unnamed-chunk-4

Building Iris Species Prediction Model

Step 4: Use the model to predict iris species on one example from the testing dataset

userInput <-data.frame(6.9, 3.1, 5.1, 2.3)
names(userInput)<-c("Sepal.Length",
                    "Sepal.Width",
                    "Petal.Length",
                    "Petal.Width")
levels(iris$Species)[predict(modFit,newdata=userInput)]

[1] "virginica"

Iris Species Prediction Shiny Application

Server URL: https://lmcmahan.shinyapps.io/project/ alt text ui.R and server.R code available here on github