Shiny Application and Reproducible Pitch Presentation

Introduction

This application uses the Iris Flower Data Set to build 2 prediction models. The outcome is iris specie and the predictors are morphologic variations. Based on sepal and petal dimensions entered by the user, the application shows the most probable iris specie. Both models present an accuracy of around 96%. For few cases, they show different outcomes (iris species).

• To access the application: https://cleversonsch.shinyapps.io/myApp/

• Source code can be find here: https://github.com/cleversonsch/Developing-Data-Products-Week4-Project-

Data Set

head(iris)

  Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1          5.1         3.5          1.4         0.2  setosa
2          4.9         3.0          1.4         0.2  setosa
3          4.7         3.2          1.3         0.2  setosa
4          4.6         3.1          1.5         0.2  setosa
5          5.0         3.6          1.4         0.2  setosa
6          5.4         3.9          1.7         0.4  setosa

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  
                
                
                

Prediction

• Random Forest and Linear Discriminant Analysis were trained using 80% of the original data set (training data set) with 10-fold cross validations:

control <- trainControl(method="cv", number=10)
metric <- "Accuracy"
inTrain<-createDataPartition(y = iris$Species,p=0.8,list=FALSE)
training<- iris[inTrain,]
testing<- iris[-inTrain,]
model1 <- train(Species ~., data = training, method = "rf", metric = metric,
                trControl = control)
model2 <- train(Species ~., data = training, method = "lda", metric = metric,
                trControl = control)

• Models are tested on the testing data set:

model1_prediction <- predict(model1, testing)
model2_prediction <- predict(model2, testing)

• Accuracy is given by calculating the confusion matrix:

accuracy_model1 <- confusionMatrix(model1_prediction, testing$Species)
accuracy_model1 <- accuracy_model1$overall['Accuracy']
accuracy_model2 <- confusionMatrix(model2_prediction, testing$Species)
accuracy_model2 <- accuracy_model2$overall['Accuracy']

Application Interface