Question No $05$:

Iris dataset has $50$ samples for each of three different species of Iris flower $($total

number of samples is $150).$ For each data sample, you have sepal length, sepal

width, petal length and petal width and a species name $($class$/$label$).$ Figure below

shows Iris flower and features in dataset:

Perform the following tasks:

Load the given Iris dataset

Split it into a training set and a test set

Preprocess the data

Build a Decision Tree classifier

Then train a Random Forest classifier

How much better does it perform compared to the Decision Tree

classifier?