B$.$ Generating Classification Rules from a Decision Tree $(50$ points$)$

Given a database table containing weather data as follows:

$\backslash$table$[[(0$utlook,Temperature,Humidity,Windy,Class: Play$],[$Sunny$,$Hot,High,False,No$],[$Sunny$,$Hot,High,True,No$],[(0$vercast,Hot,High,False,Yes$],[$Rainy$,$Mild,High,False,Yes$],[$Rainy$,$Cool,Normal,False,Yes$],[$Rainy$,$Cool,Normal,True,No$],[(0$vercast,Cool,Normal,True,Yes$],[$Sunny$,$Mild,High,False,No$],[$Sunny$,$Cool,Normal,False,Yes$],[$Rainy$,$Mild,Normal,False,Yes$],[$Sunny$,$Mild,Normal,True,Yes$],[(1)$vercast$,$Mild,High,True,Yes$],[(0)$vercast$,$Hot,Normal,False,Yes$],[$Rainy$,$Mild,High,True,No$]]$

Please use Gini Index and the decision tree learning algorithm to induce a decision tree $($binary partition$)$ from the given training samples in the weather database table. $($Please provide step by step Gini Index calculation with its corresponding trees$).$

Please extract the classification rules from the generated decision tree in $1.$