Question

scribed previously. on time on time very late on time on time very late summer day season wind rain class weekday spring none none on time weekday winter none slight on time weekday winter none slight weekday winter high heavy late saturday summer normal none weekday autumn normal none holiday summer high slight sunday normal none weekday winter high heavy weekday summer none slight on time saturday spring high heavy cancelled weekday summer high slight on time saturday winter normal none late weekday summer high none on time weekday winter normal heavy saturday autumn high slight on time weekday autumn heavy on time holiday spring normal slight on time weekday spring normal none on time weekday spring normal slight on time very late 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"))
6. Predicting from the model

> New_Column=predict(nbModelSum2020,testNB1,type = "class")

7. Values of Class

> Final_Data= testNB1
> Final_Data[5]=New_Column
> names(Final_Data)[names(Final_Data) == "V5"] <- "Predicted Class"
> Final_Data

Day Season Wind rain Predicted Class
1 weekday spring none none on time
2 weekday winter none slight on time
3 weekday winter none slight on time
4 weekday winter high heavy very late
5 Saturday summer normal none on time
6 weekday autumn normal none on time
7 holiday summer high slight on time
8 Sunday summer normal none on time
9 weekday winter high heavy very late
10 weekday summer none slight on time
11 Saturday spring high heavy cancelled
12 weekday summer high slight on time
13 Saturday winter normal none late
14 weekday summer high none on time
15 weekday winter normal heavy very late
16 Saturday autumn high slight on time
17 weekday autumn none heavy on time
18 holiday spring normal slight on time
19 weekday spring normal none on time
20 weekday spring normal slight on time
21 weekday winter high heavy very late
22 weekday summer high heavy on time
23 Sunday summer normal slight on time