setwd("<your local directory>")
# Let us get the appropriate libraries loaded for NB Classifier. Select a mirror closer to 
# your location to download the packages
install.packages("e1071")
library("e1071")
# read the data into a table from the file
sample <- read.table("sample.csv",header=TRUE,sep=",")
# we will now define the data frames to use the NB classifier
traindata <- as.data.frame(sample[1:14,])
testdata <- as.data.frame(sample[15,])
#Display data frames
traindata
testdata
#Bulid the model maually
tprior <- table(traindata$Enrolls)
tprior <- tprior/sum(tprior)

ageCounts <-table(traindata[,c("Enrolls", "Age")])
ageCounts <- ageCounts/rowSums(ageCounts)
incomeCounts <- table(traindata[,c("Enrolls", "Income")])
incomeCounts <- incomeCounts/rowSums(incomeCounts)
jsCounts <- table(traindata[,c("Enrolls", "Jobsatisfaction")])
jsCounts<-jsCounts/rowSums(jsCounts)
desireCounts <- table(traindata[,c("Enrolls", "Desire")])
desireCounts <- desireCounts/rowSums(desireCounts)
#Predict - Compute the probabilities for Age<=30, Income = Medium,
#Jobsatisfaction = yes and Desire = Fair
pyes <- 
         ageCounts["Yes","<=30"]*
         incomeCounts["Yes","Medium"]*
         jsCounts["Yes","Yes"]*
         desireCounts["Yes","Fair"]*
         tprior["Yes"]
pno <- 
         ageCounts["No","<=30"]*
         incomeCounts["No","Medium"]*
         jsCounts["No","Yes"]*
         desireCounts["No","Fair"]*
         tprior["No"]
print (pyes)
print (pno)
print(max(pyes,pno))
# use the NB classifier 
model <- naiveBayes(Enrolls ~.,traindata)
# display model
model
# predict with testdata
results <- predict (model,testdata)
# display results
results

# use the NB classifier with Lapalce smoothing
model1 <- naiveBayes(Enrolls ~.,traindata,laplace=.01)
# display model
model1
# predict with testdata
results1 <- predict (model1,testdata)
# display results
results1