Can you go through how you answered each part of the following question?

Consider the traffic accident data set shown in the following table.

Let Crash Severity be the class label. Show the key steps for the following tasks.

(a) Using information gain as the attribute selection measure, construct the first level of the decision tree.

(b) If gain ratio is used as the attribute selection measure, will the first level of the decision tree be different from above?

(c) Given a traffic accident with the values Good, Sober, None, and Yes for the attributes Weather Condition, Drivers Condition, Traffic Violation, and Seat Belt, respectively, how would a nave Bayesian classifier determine whether Crash Severity would be Minor or Major?

Weather Condition Driver's Condition Sober Sober Sober Traffic Violation SeatCrash Belt Severity Yes Minor None Good Disobey stop signYes Bad Disobey stop sign Yes Minor Good Alcohol-impaired Ex NoMajor ceed speed limit Disobey traffic signal NoMajor Sober Bad GoodAlcohol-impaired Disobey stop sign GoodAlcohol-impaired Exceed speed limitYes| Major Bad Good GoodAlcohol-impaired Bad Good Yes Minor Alcohol-impaired Yes Major ignal Yes Major NoMajor None Sober Disobey traffic si None Sober Sober Disobey traffic signal No Major Exceed speed limitYesMajor