Shiny Product Illustration

Part of Data Science Coursework @JHU on Coursera

The presented data product represent a simple prediction application based on IRIS dataset

• The application starts by showing a pair plot between all variables of IRIS dataset

ggpairs(data = iris, columns = 1:4, aes(colour = Species))

• The user has to enter 4 values of Sepal and Petal length & width respectively. 4 input fields are assigned for those values in the UI. The lower and higher limits of the input fields are the ranges of these values in IRIS dataset

• The 4 values are used to construct a data frame to apply the prediction model on it later

• Boosting algorithm is used to predict the type of flower corresponding to the supplied dimensions

• The algorithm is trained on the whole 150 observations of the dataset

mdl <- train(Species ~ ., method = "gbm", data = iris)

• The Boosting prediction algorithm is applied to the selected point data frame to get the class of flower correponding to the entered dimensions

• The output class is colorly coded according to the colors used in “ggpairs” plot (setosa, Versicolor & Virginica)