Iris dataset Prediciton modeling Application

The basic idea was to try to present an interactive tool that, based on the iris dataset, would be able to predict the width of the sepals/petals given their corresponding length

str(iris)

'data.frame':   150 obs. of  5 variables:
 $ Sepal.Length: num  5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...
 $ Sepal.Width : num  3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ...
 $ Petal.Length: num  1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...
 $ Petal.Width : num  0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...
 $ Species     : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 1 1 1 1 1 1 ...

The dataset consists of 150 observations of 5 variables. Or else, it consists of 50 observations of three different iris' species (setosa, versicolor, virginica) of 4 variables.

But the result of the prediction is also based on the observations that our user will use for its model. This model can be based on:

• 50 obs of iris setosa, 50 obs of iris versicolor or 50 obs of iris virginica
• 100 obs of iris setosa and iris versicolor, 100 obs of iris setosa and iris virginica or 100 obs of iris versicolor and iris virginica
• 150 obs of all three of them

plot(iris$Petal.Length, iris$Petal.Width, xlab="Petal Length (cm)", ylab="Petal Width (cm)", bty="n", pch=16, col=ifelse(iris$Species=="setosa","blueviolet",ifelse(iris$Species=="versicolor", "cyan3", "aquamarine4")), xlim=c(min(iris$Petal.Length), max(iris$Petal.Length)), ylim=c(min(iris$Petal.Width), max(iris$Petal.Width)))

… we created a Shiny application where the user can choose among the 14 above options in order to obtain not only the corresponding model plot but also —whether for sepals or petals— a precise width prediction for a length input of their own choice.

• https://gtsa.shinyapps.io/Iris_App/
• https://github.com/gtsa/Shiny/tree/master/Iris_App

Thank you very much !!!!