Q$3.(30$ pts$)$ Consider the following dataset that contains examples of the different conditions that associated with accidents.

$\backslash$table$[[$SNo$.,\backslash$table$[[$Weather$],[$condition$]],\backslash$table$[[$Road$],[$condition$]],\backslash$table$[[$Traffic$],[$condition$]],\backslash$table$[[$Engine$],[$problem$]],$Accident$],[1,$Rain,bad,high,no$,$yes$],[2,$snow,average,normal,yes,yes$],[3,$clear,bad,light,no$,$no$],[4,$clear,good,light,yes,yes$],[5,$snow,good,normal,no$,$no$],[6,$rain,average,light,no$,$no$],[7,$rain,good,normal,no$,$no$],[8,$snow,bad,high,no$,$yes$],[9,$clear,good,high,yes,no$],[10,$clear,bad,high,yes,yes$]]$

Build a Naive Bayes Classifier of conditional probabilities and absolute probabilities $($as is done in the slides$)$ for each of the four features in the above dataset and use these tables to predict whether an accident would occur or not for the following condition.

Weather Condition: Rain

Road condition: good

Traffic condition: normal

Engine problem: no