R Programming

install.packages("arules")
install.packages("arulesViz")

library(arules)
library(arulesViz)

transactions_list <- list(
  c("Milk","Bread","Butter"),
  c("Milk","Bread"),
  c("Bread","Butter"),
  c("Milk","Butter"),
  c("Milk","Bread","Butter")
)

trans <- as(transactions_list, "transactions")

inspect(trans)

# Frequent Itemsets
itemsets <- apriori(
  trans,
  parameter = list(
    supp = 0.4,
    target = "frequent itemsets"
  )
)

inspect(itemsets)

# Association Rules
rules <- apriori(
  trans,
  parameter = list(
    supp = 0.4,
    conf = 0.7,
    target = "rules"
  )
)

inspect(rules)

# Sort Rules
rules_sorted <- sort(
  rules,
  by = "lift",
  decreasing = TRUE
)

inspect(rules_sorted)

# Plot Rules
plot(
  rules_sorted,
  method = "graph",
  engine = "htmlwidget"
)

install.packages("C50")

library(C50)

data(iris)

iris$Species <- as.factor(iris$Species)

set.seed(123)

train_index <- sample(
  1:nrow(iris),
  0.7 * nrow(iris)
)

train_data <- iris[train_index, ]

test_data <- iris[-train_index, ]

tree_model <- C5.0(
  Species ~ .,
  data = train_data
)

summary(tree_model)

predictions <- predict(
  tree_model,
  test_data
)

conf_matrix <- table(
  Predicted = predictions,
  Actual = test_data$Species
)

print(conf_matrix)

accuracy <- sum(diag(conf_matrix)) /
            sum(conf_matrix)

cat("Decision Tree Accuracy:",
    accuracy * 100, "%\n")