Identify an iris from four measurements

• The app accepts sepal length and width, plus petal length and width.
• A classification decision tree predicts Setosa, Versicolor, or Virginica.
• The goal is a small, understandable demonstration of interactive data science.

Why iris? R includes the classic 150-row iris dataset, so users do not need to upload data or configure a data source.

Simple inputs, immediate feedback

1. Move four sliders to enter measurements in centimetres.
2. The app sends those values to the server.
3. The server calculates a prediction and class probabilities.
4. The result and probability chart update automatically.

The How to use tab inside the app explains the controls, dataset and limitations for first-time users.

A reproducible classification tree

The app trains an rpart decision tree on the built-in data. The same core steps are reproduced below.

library(rpart)

iris_tree <- rpart(
  Species ~ Sepal.Length + Sepal.Width + Petal.Length + Petal.Width,
  data = iris,
  method = "class",
  control = rpart.control(cp = 0.01, minsplit = 10)
)

example_flower <- data.frame(
  Sepal.Length = 5.1,
  Sepal.Width = 3.5,
  Petal.Length = 1.4,
  Petal.Width = 0.2
)

predict(iris_tree, newdata = example_flower, type = "class")

##      1 
## setosa 
## Levels: setosa versicolor virginica

The code chunk is evaluated when the presentation is knitted, making the displayed example reproducible.

A compact, accessible data-science project

• Interactive: users can experiment with measurements.
• Reactive: the prediction and probability chart refresh when inputs change.
• Reproducible: the app uses a built-in dataset and documented R code.
• Transparent: the app explains the model and its educational limitations.

Try the app: paste your deployed shinyapps.io URL here.

Source code: paste your GitHub repository URL here.

This is an educational prediction tool; its estimated probabilities are not guarantees of correctness.