Question

class on time on time on time none late none day season wind rain weekday spring none none weekday winter none slight weekday winter slight weekday winter heavy saturday summer normal none weekday autumn normal none holiday summer high slight sunday summer normal weekday winter high heavy weekday summer none slight saturday spring high heavy weekday summer high slight saturday winter normal weekday summer high none weekday winter normal heavy saturday autumn high slight weekday autumn none heavy holiday spring normal slight weekday spring normal none weekday spring normal slight on time very late on time on time very late on time cancelled on time late on time very late on time on time on time on time on time none Figure 3.1 The train Dataset

Answer 1

1. Reading the data file

> train=read.csv(file = "Data.csv")
> attach(train)

2. Loading the naive bayes package
> library(naivebayes)
naivebayes 0.9.7 loaded
Warning message:
package ‘naivebayes’ was built under R version 3.6.3

3. Building the model
> nbModelSum2020 <- naive_bayes(class~., train,laplace = 1)

4. Creating test dataset
> test<- train[,c(1,2,3,4)]

5. Adding new rows
> testNB1 <- rbind(test,c("weekday","winter","high","heavy"),
+ c("weekday","summer","high","heavy"),
+ c("Sunday","summer","normal","slight"))