Inspect the dataset titled lab$01\_$dataset$\_1.$csv which has a mixture of numerical and

categorical data. Your task will be to write a function my$\_$ID$3()$ which can create a decision

tree for the given dataset using the ID$3$ algorithm. However, before doing that, you will be

have to perform some data processing tasks. Here are all the required tasks in order

$1.$ ID$3$ cannot handle continuous numerical data. Perform necessary operations to

handle all continuous$-$valued attributes. Do not forget to show the output i$.$e$.,$ the

updated dataset after handling continuous$-$valued attributes. $(2$ marks$)$

$2.$ Next, you will have to ensure the newly obtained dataset is optimal and free of

errors. Take appropriate actions based on the outcomes.

a$.$ Check if the dataset has any missing values. $(1$ mark$)$

b$.$ Check if the dataset has any redundant or repeated input sample. $(1$ mark$)$

c$.$ Check if the dataset has any contradicting pairs. $(1$ mark$)$

$3.$ Your function my$\_$ID$3()$ should operate in a manner such that after ever round of

decision making, it will output the attributes and its associated gain, with a message

stating Attribute X with Gain $=$ Y is chosen as the decision attribute. Once your

function completes, it should output the decision tree. The representation of the

decision tree is upto you. You can choose either a textual representation or a

graphical one; either is fine. $(10$ marks$)$

This dataset is relatively small and easy to understand just by looking at it$.$ But you must

perform all the above tasks via coding. Brute$-$forcing the answers or directly solving the

mathematics involved in ID$3$ without coding it in Python will not get you a score.