NEED A PYTHON CODE FOR THIS QUESTION PLEASE:

Automobile Accidents. The file accidentsFull.csv contains information on $42,183$ actual automobile accidents in $2001$ in the United States that involved one of three levels of injury: NO INJURY, INJURY, or FATALITY. For each accident, additional information is recorded, such as day of week, weather conditions, and road type. A firm might be interested in developing a system for quickly classifying the severity of an accident based on initial reports and associated data in the system $($some of which rely on GPS$-$assisted reporting$).$

Our goal here is to predict whether an accident just reported will involve an injury $($MAX$\_$SEV$\_$IR $=1$ or $2)$ or will not $($MAX$\_$SEV$\_$IR $=0).$ For this purpose, create a dummy variable called INJURY that takes the value $$yes$$ if MAX$\_$SEV$\_$IR $=1$ or $2,$ and otherwise $$no$.$

C$.$Let us now return to the entire dataset. Partition the data into training $(60\%)$ and validation $(40\%).$

i$.$ Assuming that no information or initial reports about the accident itself are available at the time of prediction $($only location characteristics, weather con$-$ ditions, etc.$),$ which predictors can we include in the analysis? $($Use the data descriptions page from www$.$dataminingbook.com.$)$

ii$.$ Run a naive Bayes classifier on the complete training set with the relevant pre$-$ dictors $($and INJURY as the response$).$ Note that all predictors are categorical. Show the confusion matrix.

iii. What is the overall error for the validations et$?$

iv$.$ What is the percent improvement relative to the naive rule $($using the validation

set$)?$

v$.$ Examine the conditional probabilities in the pivot tables. Why do we get a probability of zero for P$($INJURY $=$ No $|$ SPD$\_$LIM $=5)?$