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library(MASS)

# Tải dữ liệu Boston
data(Boston)

# Xây dựng mô hình hồi quy tuyến tính
model <- lm(medv ~ lstat + rm, data = Boston)

# Dự đoán giá trị
predictions <- predict(model, newdata = Boston)

# Tính MAE (Mean Absolute Error)
MAE <- mean(abs(predictions - Boston$medv))

# Tính MSE (Mean Squared Error)
MSE <- mean((predictions - Boston$medv)^2)

# In kết quả
print(paste("MAE:", MAE))

## [1] "MAE: 3.95258006711927"

print(paste("MSE:", MSE))

## [1] "MSE: 30.5124687772995"

library(ISLR)

## Warning: package 'ISLR' was built under R version 4.4.3

library(ggplot2)
library(caret)

## Warning: package 'caret' was built under R version 4.4.3

## Loading required package: lattice

library(pROC)

## Warning: package 'pROC' was built under R version 4.4.3

## Type 'citation("pROC")' for a citation.

## 
## Attaching package: 'pROC'

## The following objects are masked from 'package:stats':
## 
##     cov, smooth, var

# Load dữ liệu Default
data("Default")

# Chuyển đổi biến default thành dạng nhị phân
Default$default <- ifelse(Default$default == "Yes", 1, 0)

# Xây dựng mô hình hồi quy logistic
model <- glm(default ~ balance + income, data = Default, family = binomial)

# Dự đoán xác suất
pred_probs <- predict(model, type = "response")

# Tính ROC và AUC
roc_curve <- roc(Default$default, pred_probs)

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

auc_value <- auc(roc_curve)

# Vẽ ROC curve
plot(roc_curve, col = "blue", main = paste("ROC Curve - AUC:", round(auc_value, 2)))
abline(a = 0, b = 1, lty = 2, col = "gray")

# In giá trị AUC
print(paste("AUC:", round(auc_value, 2)))

## [1] "AUC: 0.95"

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