Write a program that splits a dataset into training and test set, builds a classification tree, and outputs a confusion matrix. The program should do the following:

load the iris.csv dataset

create a dataframe, x$,$ using the petal$\_$length and sepal$\_$length as features

create a dataframe, y$,$ using species

split the data into training and test sets with $0\mathrm{.}25$ test size and random$\_$state $=0$

standardize x$\_$train and x$\_$test

initialize the decision tree with criterion $=$ "gini", random$\_$state $=100,$ max$\_$depth$=3,$ min$\_$samples$\_$leaf$=5$

run the decision tree on x$\_$test

generate the confusion matrix

The output should be:

$[[1400]$

$[0131]$

$[019]]$