Quick but detailed solution with the tree please

Question $2$B $-$ Decision Tree Learning $[25\%]$

You have been given $5$ training examples representing various cars and their attributes. The target attribute is 'Safety Rating,$\text{'}$ which can have only two values: 'Good' or 'Poor.$\text{'}$ This is to be predicted based on the other attributes of the car Type.

Here's a $5$ training samples from the dataset:

$\backslash$table$[[$Car Type,Engine Size,Airbags,Anti$-$lock Brakes,Safety Rating$],[$Sedan$,$Medium,Yes,Yes,Good$],[$SUV$,$Large,Yes,Yes,Good$],[$Hatchback$,$Small,No$,$No$,$Poor$],[$SUV$,$Medium,Yes,Yes,Good$],[$Sedan$,$Large,Yes,Yes,Poor$]]$

a$)$ Calculate the entropy of the target attribute. Recall that

Entropy $(S)={?}^{\text{d}\text{e}\text{f}}{\displaystyle \underset{i=1}{\overset{c}{}}}{p}_{i}lo{g}_2(\frac{1}{p_i})={\displaystyle \underset{i=1}{\overset{c}{}}}-p_{i}lo{g}_2(p_i)$

Where $p_1$ is the proportion of $S$ belonging to class i$.$ Also note that $lo{g}_2(x)=\frac{lo{g}_{10}(x)}{lo{g}_{10}(2)}$

b$)$ Show the value of the information gain for each candidate attribute at each step in the construction of the tree.

c$)$ Construct the decision tree structure from the above examples, which would be learned by the ID$3$ algorithm.

In your report you need to include all details of the tree construction process following steps $1-3.$