• Set up Spark Context and Spark session
• Load the Data set
• Split the data into train and test set
• Fit the naive bayes model for classification
• Take the summary of the model
• Predict using the test set
• Evaluate the metrics

#Set up spark home
Sys.setenv(SPARK_HOME=”/…./spark-2.4.0-bin-hadoop2.7″)
.libPaths(c(file.path(Sys.getenv(“SPARK_HOME”), “R”, “lib”), .libPaths()))
#Load the library
library(SparkR)
#Initialize the Spark Context
#To run spark in a local node give master=”local”
sc #Start the SparkSQL Context
sqlContext #Load the data set
data = read.df(“file:///…../car.data”,”csv”,header = “true”, inferSchema = “true”, na.strings = “NA”)
#Split the data into train and test set
splt_data=randomSplit(data,c(7,3),42)
trainingData=splt_data[[1]]
testData=splt_data[[2]]
coln=columns(data)
xtest=select(testData,coln[1:6])
ytest=select(testData,”Class”)
#Build the model
nb summary(nb)
#Predict using the test data
pred=predict(nb,xtest)
showDF(pred,10)
#Convert the spark data frame to R data frame
y_pred=collect(select(pred,”prediction”),stringsAsFactors=FALSE)
y_true=collect(select(ytest,”Class”),stringsAsFactors=FALSE)
#Calculate the confusion matrix
conf_mat=confusionMatrix(as.factor(y_pred$prediction),as.factor(y_true$Class))
print(conf_mat)