Inspect the dataset titled lab$01\_$dataset$\_2.$csv which also has a mixture of numerical and categorical data. For this problem, you will use decision tree classifiers for supervised learning. In particular, you will be using the functionalities of the sklearn.tree library. The classification task using sklearn libraries work only on numerical$-$valued attributes, and not on categorical ones. $($What to do now? Hint: Look up One$-$hot Encoding and Integer Encoding$).$ Here are all the required tasks $$

$1.$ Restructure the dataset such that it has all numerical$-$valued attributes. $(2$ marks$)$

$2.$ Perform supervised learning using decision tree classifiers. Employ the train$-$test

split approach during the learning. $(4$ marks$)$

$3.$ After the learning is complete, show the results by predicting the class of the test

set. Display the results of the prediction and test set side$-$by$-$side. $(2$ marks$)$

$4.$ Output the decision tree; it can be either a textual representation or a graphical

representation. $(2$ marks$)$