ML Exp 2

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import Ridge, Lasso
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error, r2_score
df = pd.read_csv("Advertising.csv")
df = df.iloc[:, 1:]
display(df.head())
X=df.drop("sales",axis=1)
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)
# Ridge Regression
ridge = Ridge()
ridge.fit(X_train_scaled, y_train)
# Lasso Regression
lasso = Lasso()
lasso.fit(X_train_scaled, y_train)
ridge_preds = ridge.predict(X_test_scaled)
ridge_mse = mean_squared_error(y_test, ridge_preds)
ridge_r2 = r2_score(y_test, ridge_preds)

lasso_preds = lasso.predict(X_test_scaled)
lasso_mse = mean_squared_error(y_test, lasso_preds)
lasso_r2 = r2_score(y_test, lasso_preds)

print("📊 Model Comparison:")
if ridge_mse < lasso_mse:
    print(f"✅ Ridge has lower MSE ({ridge_mse:.4f}) than Lasso ({lasso_mse:.4f})")
else:
    print(f"✅ Lasso has lower MSE ({lasso_mse:.4f}) than Ridge ({ridge_mse:.4f})")

if ridge_r2 > lasso_r2:
    print(f"✅ Ridge has higher R² Score ({ridge_r2:.4f}) than Lasso ({lasso_r2:.4f})")
else:
    print(f"✅ Lasso has higher R² Score ({lasso_r2:.4f}) than Ridge ({ridge_r2:.4f})")

ridge_coef= ridge.coef_
lasso_coef = lasso.coef_
plt.plot(ridge_coef,'s',label="Ridge Coefficients")
plt.plot(lasso_coef,'x',label="Lasso Coefficients")
plt.xlabel("Coefficient index")
plt.ylabel("Coefficient magnitude")
plt.title("Comparison of Ridge and Lasso coefficients")
plt.legend()
plt.show()