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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:  Fit A Linear Regression
Step3:  View Intercept Term
Step4:  View Coefficients
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Python Code:
# Load libraries
from sklearn.linear_model import LinearRegression
from sklearn.datasets import load_boston
import warnings
# Suppress Warning
warnings.filterwarnings(action="ignore", module="scipy", message="^internal gelsd")
Explanation: Title: Linear Regression Using Scikit-Learn
Slug: linear_regression_using_scikit-learn
Summary: How to conduct linear 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
End of explanation
# Create linear regression
regr = LinearRegression()
# Fit the linear regression
model = regr.fit(X, y)
Explanation: Fit A Linear Regression
End of explanation
# View the intercept
model.intercept_
Explanation: View Intercept Term
End of explanation
# View the feature coefficients
model.coef_
Explanation: View Coefficients
End of explanation
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