2026-07-30

Motivation

The Iris dataset contains measurements of three flower species:

  • Setosa
  • Versicolor
  • Virginica

This Shiny application allows users to explore the dataset and interactively predict iris species based on flower measurements.

Data and Model

The dataset contains 150 observations and four flower measurements:

  • Sepal length
  • Sepal width
  • Petal length
  • Petal width
##   Sepal.Length    Sepal.Width     Petal.Length    Petal.Width   
##  Min.   :4.300   Min.   :2.000   Min.   :1.000   Min.   :0.100  
##  1st Qu.:5.100   1st Qu.:2.800   1st Qu.:1.600   1st Qu.:0.300  
##  Median :5.800   Median :3.000   Median :4.350   Median :1.300  
##  Mean   :5.843   Mean   :3.057   Mean   :3.758   Mean   :1.199  
##  3rd Qu.:6.400   3rd Qu.:3.300   3rd Qu.:5.100   3rd Qu.:1.800  
##  Max.   :7.900   Max.   :4.400   Max.   :6.900   Max.   :2.500  
##        Species  
##  setosa    :50  
##  versicolor:50  
##  virginica :50  
##                 
##                 
##

A machine learning model (Random Forest or KNN) is used to predict the flower species.

Application Features

Prediction Tab

  • Users enter flower measurements
  • The model predicts the iris species

Explore Data Tab

  • Interactive visualizations show differences among species
  • Users can explore relationships between flower measurements

Summary Tab

  • Displays descriptive statistics of the Iris dataset

Conclusion