ridge regressino with sklearn python in python

main.py
from sklearn.linear_model import Ridge
from sklearn.datasets import load_boston
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error

# Load the Boston Housing dataset
boston = load_boston()

# Split the data into training and test sets
X_train, X_test, y_train, y_test = train_test_split(boston.data, boston.target, random_state=0)

# Create a Ridge regression object
ridge = Ridge(alpha=0.5)

# Fit the model to the training data
ridge.fit(X_train, y_train)

# Predict the output for the test data
y_pred = ridge.predict(X_test)

# Calculate the error
mse = mean_squared_error(y_test, y_pred)
print("Mean Squared Error:", mse)
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