# Load required library

library(e1071)

# Load the iris dataset

data(iris)

# Plot the entire iris dataset

plot(iris)

# Display the first 5 rows of the iris dataset

print(head(iris, 5))

# Scatter plot of Sepal.Length vs. Sepal.Width with colors based on Species

plot(iris$Sepal.Length, iris$Sepal.Width, col = iris$Species)

# Randomly sample 100 rows for training data

s = sample(nrow(iris), 100)

# Select relevant columns for training and testing datasets

col <- c("Petal.Length", "Petal.Width", "Species")

iris_train <- iris[s, col]

iris_test <- iris[-s, col]

# Fit a Support Vector Machine (SVM) model with a linear kernel

svmfit = svm(Species ~ ., data = iris_train, kernel = "linear")

# Display information about the SVM model

print(svmfit)

# Plot the SVM decision boundaries along with the training data

plot(svmfit, iris_train[, col])

# Make predictions on the test data

p = predict(svmfit, iris_test[, col], type = "class")

# Plot the predicted values

plot(p)

# Create a confusion table and display the classification accuracy

table(p, iris_test[, 3])

accuracy <- mean(p == iris_test[, 3])

print(paste("Classification Accuracy:", round(accuracy, 2)))