Please help fast urgent. Please asnwer BOTH QUESTIONS in python. Dataset $1$ has four columns that are called Mood, Effort, Score, Output. The mood is either Happy, Neutral or Sad. The effort is either High, Medium, or Low. The Score is numbers between $10-100.$ The output has only two values that are yes or no$.$ There are total of $19$ rows. Dataset $2$ has six columns called Age, Sex, BP$,$ Cholestrol, Na$\_$to$\_$K$,$ Output. The age has values between $10-100.$ The Sex is either F or M$.$ BP is either High, Normal, or Low. Cholestrol is either High, or Normal. Na$\_$to$\_$k has float values from $0-40.$ The output is either drugA, drugB, drugC, drugX, drugY. It has $200$ rows. Please help fast urgent.Problem $1$: $(15$ marks$)$

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 $-$

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$)$

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 $x=$YProblem $2$: $(10$ marks$)$

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 $-$

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

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

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

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$)$

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

representation. $(2$ marks$)$Problem $1$: $(15$ marks$)$

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 $-$

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$)$

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 $x=Y$