1. Introduction

This report explores the Iris dataset and outlines plans for building a prediction algorithm and Shiny app.

Mathematical Explanation: We have a multi-class classification problem. Given a feature vector: X = [x₁, x₂, …, xₙ] where xᵢ represents flower measurements (e.g., sepal length, petal width), we aim to predict the species label y ∈ {setosa, versicolor, virginica}.

We will use Random Forest, which builds multiple decision trees and outputs the most frequent class. The error function is: Error = (1/N) Σ I(ŷᵢ ≠ yᵢ) where ŷᵢ is the predicted label and yᵢ is the true label.

2. Data Loading and Overview

data(iris)
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 ...
summary(iris)
##   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  
##                 
##                 
##

3. Summary Statistics

ggplot(iris, aes(x = Species)) +
  geom_bar(fill = "steelblue") +
  theme_minimal() +
  labs(title = "Species Frequency", x = "Species", y = "Count")

4. Interesting Findings

ggplot(iris, aes(x = Species, y = Sepal.Length)) +
  geom_boxplot(fill = "lightgreen") +
  theme_minimal() +
  labs(title = "Sepal Length by Species", x = "Species", y = "Sepal Length")

5. Future Plans

We plan to: - Train a Random Forest model to predict species based on flower measurements. - Create a Shiny app with: - File upload for user data - Real-time prediction output - Interactive plots

6. Shiny App Skeleton

library(shiny)

ui <- fluidPage(
  titlePanel("Iris Species Prediction App"),
  sidebarLayout(
    sidebarPanel(
      fileInput("file", "Upload Iris Data"),
      actionButton("predict", "Predict Species")
    ),
    mainPanel(
      textOutput("prediction"),
      plotOutput("plot")
    )
  )
)

server <- function(input, output) {
  output$prediction <- renderText({"Prediction will appear here"})
  output$plot <- renderPlot({hist(rnorm(100))})
}

shinyApp(ui = ui, server = server)