#Randomized the records in the data set and
sz <- dim(faithful)[1]
q <- order(runif(sz))
r.data <- faithful[q, ]

#Generates the 80%train and 20%test sets
train <- r.data[1:floor(0.80*sz), ]
test <- r.data[floor(0.80*sz): (sz), ]

#Generates a Linear Regresion model from the train data
model <- lm(waiting~eruptions, data = train)

summary(model)
data.pred <- predict(model, test)
cor(data.pred, test$waiting, method = "pearson")

#dividing the dataset
part1 <- faithful[1:floor(sz/2), ]
part2 <- faithful[floor(sz/2):(sz), ]
q.1 <- order(runif(dim(part1)[1]))
q.2 <- order(runif(dim(part2)[1]))
part1 <- part1[q.1, ]
part2 <- part2[q.2, ]

part1.train <- part1[1:floor(0.80*dim(part1)[1]), ]
part2.train <- part2[1:floor(0.80*dim(part1)[1]), ]
test1 <- part1[floor(0.80*dim(part1)[1]): (dim(part1)[1]), ]
test2 <- part2[floor(0.80*dim(part2)[1]): (dim(part2)[1]), ]
part1.model <- lm(waiting~eruptions, data = part1.train)
part2.model <- lm(waiting~eruptions, data = part2.train)

part1.pred <- predict(part1.model, test)
part2.pred <- predict(part2.model, test)

cor(part1.pred, test$waiting, method = "pearson")
cor(part2.pred, test$waiting, method = "pearson")