import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_absolute_error, mean_squared_error

df = pd.read_csv("https://raw.githubusercontent.com/justmarkham/scikit-learn-videos/master/data/Advertising.csv")
df = df.iloc[:, 1:]
#display(df)

X = df[['TV','Radio','Newspaper']]
y = df['Sales']

X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.2,random_state=42)

scaler = StandardScaler()
X_train_scaled = scaler.fit_transform(X_train)
X_test_scaled = scaler.transform(X_test)

mlr = LinearRegression()
mlr.fit(X_train_scaled, y_train)
y_pred = mlr.predict(X_test_scaled)

mae = mean_absolute_error(y_test, y_pred)
mse = mean_squared_error(y_test, y_pred)
rmse = np.sqrt(mse)

print(f"Mean Absolute Error (MAE): {mae:.3f}")
print(f"Mean Squared Error (MSE): {mse:.3f}")
print(f"Root Mean Squared Error (RMSE): {rmse:.3f}")