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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
 Title
Step1:  Load Boston Housing Dataset
Step2:  Standardize Features
Note
Step3:  Create Ridge Regression With Candidate Alpha Values
Step4:  Fit Ridge Regression
scikit-learn includes a RidgeCV method that allows us select the ideal value for $\alpha$
Step5:  View Best Model's Alpha Value
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Python Code:
# Load libraries
from sklearn.linear_model import RidgeCV
from sklearn.datasets import load_boston
from sklearn.preprocessing import StandardScaler
Explanation: Title: Selecting The Best Alpha Value In Ridge Regression  
Slug: selecting_best_alpha_value_in_ridge_regression  
Summary: How to select the best alpha value when conduct in ridge regression in scikit-learn for machine learning in Python.  
Date: 2017-09-18 12:00
Category: Machine Learning
Tags: Linear Regression 
Authors: Chris Albon
Preliminaries
End of explanation
# Load data
boston = load_boston()
X = boston.data
y = boston.target
Explanation: Load Boston Housing Dataset
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# Standarize features
scaler = StandardScaler()
X_std = scaler.fit_transform(X)
Explanation: Standardize Features
Note: Because in linear regression the value of the coefficients is partially determined by the scale of the feature, and in regularized models all coefficients are summed together, we must make sure to standardize the feature prior to training.
End of explanation
# Create ridge regression with three possible alpha values
regr_cv = RidgeCV(alphas=[0.1, 1.0, 10.0])
Explanation: Create Ridge Regression With Candidate Alpha Values
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# Fit the linear regression
model_cv = regr_cv.fit(X_std, y)
Explanation: Fit Ridge Regression
scikit-learn includes a RidgeCV method that allows us select the ideal value for $\alpha$:
End of explanation
# View alpha
model_cv.alpha_
Explanation: View Best Model's Alpha Value
End of explanation
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