naive bayes classification with cv

tugas-mandat-naive-bayes-cv.r

library(e1071)
library(gmodels)
library(caret)

# 1. Load dataset from CSV

bank_csv <- read.csv("./Downloads/bank-additional/bank-additional-full.csv", header=TRUE, sep=";")
bank_df <- as.data.frame(bank_csv[1:32951,])
summary(bank_df)

# 2. Do the preprocessing
# 2.1. todo
# 2.2. todo
# 2.3. todo
# 2.4. todo

# 3. Train the model

model <- naiveBayes(bank_df, bank_df$y, 'nb', laplace=1, trControl=trainControl(method='cv',number=50))

# 4. Test the model

term_deposit_test <- predict(model, bank_csv[32952:41188,1:20])

# 5. Evaluate the model

CrossTable(term_deposit_test, bank_csv[32952:41188,21], prop.chisq = FALSE, prop.t = FALSE, dnn = c('predicted', 'actual'))