initial commit

code.R

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+library(ggplot2)
+library(gridExtra)
+library(PerformanceAnalytics)
+library(klaR)
+library(e1071)
+library(Metrics)
+library(party)
+
+#read dataset
+accid <- read.csv(file="C:\\Users\\Mortada\\Documents\\R\\win-library\\3.5\\nassCDS.csv", header=TRUE, stringsAsFactors = FALSE)
+
+#show name of coloums
+names(accid)
+
+#show details of each coloum
+str(accid)
+
+#detec missing values and remove it from dataset
+NoOfNull<-apply(accid,2,function(x)sum(is.na(x)))
+NoOfNull
+accid<-na.omit(accid)
+NoOfNull<-apply(accid,2,function(x)sum(is.na(x)))
+NoOfNull
+
+#delete wrong values of weight
+accid$weight[accid$weight>100]<-0
+accid$weight[accid$weight<10]<-0
+accid$weight[accid$weight==0]<-NA
+accid<-na.omit(accid)
+accid
+
+
+
+
+#barplot to show age of person in accident
+Person_Age<-table(accid$ageOFocc)
+barplot(Person_Age,main="Age of Person")
+
+
+
+
+#piechart to show distribution of year of accident
+
+#barplot of year accident
+year<-table(accid$yearacc)
+barplot(year,main="Year of accident")
+
+slices <- c(4000,4400,4500,4400, 4100, 4800)
+lbls <- c("1997","1998","1999","2000","2001","2002")
+pct <- slices/26000*100
+pcct<-as.integer(pct)
+lbls <- paste(lbls,pcct)
+lbls <- paste(lbls,"%",sep="") 
+pie(slices, labels = lbls, col= rainbow (length(lbls)), main="Pie Chart of Accident Year")
+
+
+
+#histogram to show the distribution model of cars
+hist(accid$yearVeh, col = rainbow(6),main = "Vehicle year Histogram  ", xlab = "Year of vehicle",ylab = "No of accident",breaks = 30,lwd=3)
+
+
+#densityplot show the injury severity
+plot(density(accid$injSeverity),main="Density plot of injury severiy",col="red")
+
+
+
+#barplot to determine no of alive and dead people
+d<-accid$dead
+barplot(table(d),main = "Dead or Alive",col = "blue")
+
+
+#barplot to determine no of male and female people
+s<-accid$sex
+barplot(table(s),main = "Male or female ",col = "Green")
+
+
+#histogram to show the distribution speed of cars
+accidtemp$dvcat<-as.numeric(as.factor(accidtemp$dvcat))
+hist(accidtemp$dvcat,col = rainbow(6),main = "Vehicle Speed Histogram  ", xaxt="n", ,xlab = "Distribution of speed",ylab = "No of accident",breaks = 30,lwd=3)
+axis(1,labels=c("1-9","10-24","25-39","40-54","+55"),at=1:5)
+
+
+#show details of age
+summary(accid$ageOFocc)
+
+
+#injury severity based on age
+ggplot(aes(x = injSeverity), data = accid) +
+geom_bar(color = "black", fill = "#993366") +
+ggtitle("Distribution of  injure sevirity ")
+boxplot(accid$ageOFocc ~ accid$injSeverity,main="Distribution of  injure sevirity according to age",col="red")
+
+
+#determine distribution of dead and alive based on their age
+p1<-plot(accid$dead,accid$ageOFocc)
+by(accid$ageOFocc, accid$dead, mean)
+
+
+#injury severity based on year of accident
+counts<-table(accid$injSeverity,accid$yearacc)
+barplot(counts,main = "YEAR OF ACCIDENT VS SEVERITY OF INJURY",
+        xlab ="year of accident",ylab = "severity",col = rainbow(7),
+        legend=rownames(counts),beside = T)
+
+
+
+# Classification with decision tree
+
+accid$dead<-as.integer(accid$dead)
+accid$airbag<-as.numeric(accid$airbag)
+accid$seatbelt<-as.numeric(accid$seatbelt)
+accid$dvcat<-as.numeric(accid$dvcat)
+accid$sex<-as.numeric(accid$sex)
+accid$occRole<-as.numeric(accid$occRole)
+accid$injSeverity<-as.numeric(accid$injSeverity)
+accid$ageOFocc<-as.numeric(accid$ageOFocc)
+accid$yearacc<-as.numeric(accid$yearacc)
+accid$yearVeh<-as.numeric(accid$yearVeh)
+
+ind<-sample(2,nrow(accid),prob=c(0.7,0.3),replace=TRUE)
+train.data<-accid[ind==1,]
+test.data<-accid[ind==2,]
+
+accid.tree <- ctree(dead~ageOFocc+weight+dvcat
+                      +airbag+seatbelt+yearacc+yearVeh+injSeverity ,data = train.data)
+
+table(predict(accid.tree),train.data$dead)
+testPred <-predict(accid.tree,newdata = test.data)
+table(testPred,test.data$dead)
+
+plot(accid.tree,type="simple")
+accuracy(predict(accid.tree,test.data),test.data[,"dead"])
+
+
+
+# Classification with naive bayesian
+accid$dead<-as.factor(accid$dead)
+accid$airbag<-as.factor(accid$airbag)
+accid$seatbelt<-as.factor(accid$seatbelt)
+accid$dvcat<-as.factor(accid$dvcat)
+accid$sex<-as.factor(accid$sex)
+accid$occRole<-as.factor(accid$occRole)
+accid$injSeverity<-as.factor(accid$injSeverity)
+accid$caseid<-as.integer(accid$caseid)
+
+nb_mod<-naiveBayes(accid$dead~.,data = accid)
+nb_mod
+
+prediction=predict(nb_mod,accid)
+table(prediction,accid$dead)
+
+prop.table(table(prediction,accid$dead),1)