library$($DAAG$)$

library$($ggplot$2)$

library$($dplyr$)$

library$($tree$)$

library$($ISLR$2)$

library$($MASS$)$

library$($klaR$)$

library$($e$1071)$

df$=$data.frame$($nassCDS$)$

df$=$subset$($df$,$ select$=-$c$($caseid$))$

df $<-$ df $\%>\%$

group$\_$by$($yearVeh$)\%>\%$

mutate$($yearmod$=$case$\_$when$($

yearVeh$>=1953$ & yearVeh$<1958$ ~ $"1",$

yearVeh$>=1958$ & yearVeh$<1963$ ~ $"2",$

yearVeh$>=1963$ & yearVeh$<1968$ ~ $"3",$

yearVeh$>=1968$ & yearVeh$<1973$ ~ $"4",$

yearVeh$>=1973$ & yearVeh$<1978$ ~ $"5",$

yearVeh$>=1978$ & yearVeh$<1983$ ~ $"6",$

yearVeh$>=1983$ & yearVeh$<1988$ ~ $"7",$

yearVeh$>=1988$ & yearVeh$<1993$ ~ $"8",$

yearVeh$>=1993$ & yearVeh$<1998$ ~ $"9",$

yearVeh$>=1998$ ~ $"10"$

$))$

ca$<-$na$.$omit$($df$)$

attach$($ca$)$

table$($ca$dead$)$

table$($ca$sex$)$

ca$frontal$<-$as$.$factor$($ca$frontal$)$

ca$yearacc$<-$as$.$factor$($ca$yearacc$)$

ca$deploy$<-$as$.$factor$($ca$deploy$)$

ca$injSeverity$<-$as$.$factor$($ca$injSeverity$)$

ca$yearmod$<-$as$.$factor$($ca$yearmod$)$

ca$occRole$<-$as$.$factor$($ca$occRole$)$

ca$dvcat$<-$factor$($ca$dvcat$)$

ca$=$subset$($ca$,$ select$=-$c$($yearVeh$,$ abcat$))$

set.seed$(1)$

ttds$<-$sample$($c$($TRUE$,$ FALSE$),$ nrow$($ca$),$ replace$=$TRUE, prob$=$c$(0\mathrm{.}7,0\mathrm{.}3))$

train$<-$ca$[$ttds$,]$

test$<-$ca$[!$ttds$,]$

Please demonstrate how to fit a multi$-$layer neural network to predict the variable dead based on the provided code $($The dataset is one found in the package DAAG$).$ Determine how many hidden layers and neurons the neural net model should have in the code. Use the train dataset to train the neural net and test its accuracy using the test dataset.

Also please troubleshoot the code first before posting.