Predicting Iris Species Using K-Nearest Neighbors Algorithm

• The iris dataset in base R consists of 150 observations of three different species of iris (virginica, setosa and versicolor). Each observation includes data for Petal Length, Petal Width, Sepal Length and Sepal Width.
• We use a k-nearest neighbors algorithm (k=13) to classify an unknown species type based upon the length and width measurements above.
• Finally, we build a Shiny app which allow users to interactively predict the species based on these measurement variables.

• CLICK HERE for the GitHub repo containing a README and full R scripts for our Shiny app.

• This example inspired by Jalayer Academy's tutorial video on Youtube: link

• Our Shiny app is deployed on RPubs at: THIS LINK
• It consists of ui.R and server.R files
  • ui.R creates the user interface including the four input measurements for petal and sepal
  • server.R provides reactive functions which take the four input measurements as inputs and immediately provide a species prediction using a k-nearest neighbors classification algorithm
• For higher accuracy, our algorithm nomalizes the predictor variables using the below function:

  normalize <- function(x) {
    return( (x - min(x)) / (max(x) - min(x)) ) 
  }

• Below is the knn() algorithm inside the reactive function:

library(class)
library(shiny)
  m1pred <- reactive({
    Sepal.Length.Input <- input$sliderSepalLength
    Sepal.Width.Input <- input$sliderSepalWidth
    Petal.Length.Input <- input$sliderPetalLength
    Petal.Width.Input <- input$sliderPetalWidth
    knn(train = iris_train, test = data.frame(
      (Sepal.Length.Input - min(iris$Sepal.Length)) / (max(iris$Sepal.Length) - min(iris$Sepal.Length)),
      (Sepal.Width.Input - min(iris$Sepal.Width)) / (max(iris$Sepal.Width) - min(iris$Sepal.Width)),
      (Petal.Length.Input - min(iris$Petal.Length)) / (max(iris$Petal.Length) - min(iris$Petal.Length)),
      (Petal.Width.Input - min(iris$Petal.Width)) / (max(iris$Petal.Width) - min(iris$Petal.Width))
      ), 
          cl = iris_train_target, k = 13
    )
  })

• This Shiny App demonstrates how we can pair classification algorithms with simple user interfaces to easily create accurate predictions about species type for irises.
• Classification algorithms have huge business value, e.g. detection of credit card fraud or spam email.
• For significantly more complex algorithms and and larger datasets we will want to integrate our app with some cloud service which can auto-scale and reduce trainig times.