The Iris Species Predictor is a Shiny application that classifies iris flowers into three species based on physical measurements.

Key Features: - Simple, intuitive interface - Real-time species prediction
- Visual representation of results - Educational documentation

How It Works: Users input four measurements and get instant species classification with probability scores.

## Dataset:  150 flowers with 5 measurements each

Problem Solved: - Helps botanists and students quickly identify iris species - Demonstrates machine learning in an accessible way - Provides educational value about classification algorithms

Technical Foundation: - Built with R Shiny framework - Uses k-nearest neighbors algorithm - Trained on Fisher’s classic Iris dataset

## Model Accuracy:  96.7 % (5-fold cross-validation)

User Inputs: - Sepal Length & Width sliders - Petal Length & Width sliders
- Predict button

Real-time Outputs: - Predicted species name - Classification probability - Interactive scatter plot

## Sample Flower Measurements:

## Sepal Length:  6.3 cm

## Sepal Width:   2.8 cm

## Petal Length:  4.9 cm

## Petal Width:   1.8 cm

## 
## Predicted Species: versicolor

## Confidence: 92%