# Load necessary libraries
library(caret)
library(Metrics)

# Sample data
x <- c(1, 2, 3, 4, 5)
y <- c(1.2, 1.9, 3.1, 4.5, 6.8)
data <- data.frame(x, y)

# Define polynomial degree
degree <- 2

# Create polynomial features
data$poly_x <- poly(data$x, degree, raw = TRUE)

# Fit the model
model <- lm(y ~ poly_x, data = data)

# Make predictions
y_pred <- predict(model, data)

# Calculate Mean Squared Error
mse <- mse(y, y_pred)

# Calculate R^2 Score
r2 <- summary(model)$r.squared

print(paste("Mean Squared Error:", mse))
print(paste("R^2 Score:", r2))