PitchPresentation.Rmd

---
title: "IRISPredictor: Predicting Iris Flower Species"
author: "Daniel Spencer"
date: "4/5/2019"
output: 
    ioslides_presentation:
        logo: Logo.png
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = FALSE)
```

## Introduction to IRISPredictor

- A new application for easy and interactive predictions of iris flower species based on their measurements.
- Useful for casual and professional botanists alike.
<div class="red">
- No more searching through reference books to determine the species!
</div>

## Example

The below plot was created using the code for the new application server.

```{r example, cache = TRUE, message = FALSE}
library(caret)
library(ggplot2)

startTime <- proc.time()

model <- train(iris[1:4], iris$Species)

df <- data.frame(Sepal.Length = mean(iris$Sepal.Length),
                 Sepal.Width = mean(iris$Sepal.Width),
                 Petal.Length = mean(iris$Petal.Length),
                 Petal.Width = mean(iris$Petal.Width))
df <- cbind(df, Species = predict(model, df))

ggplot(iris, aes_string(x = "Sepal.Width", y = "Petal.Length", color = "Species")) +
    geom_point() +
    labs(x = "Sepal Width", y = "Petal Length") +
    scale_color_brewer(palette = "Dark2") +
    geom_point(data = df, size = 8, shape = 4)

endTime <- proc.time()
```

## Advantages

- Quick and easy to use
```{r timing, echo = TRUE}
endTime - startTime
```
- Beautiful interactive web interface makes viewing results aesthetically pleasing
- Customizable and easily extensible to other data sets in future

## Conclusions

- The application makes classification of iris flower species a trivial task
- Easy-to-use web interface can be used by anybody without machine learning knowledge

- Thank you for your time and I hope you will invest in IRISPredictor.