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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:
 Import modules
Step1:  Load data
For this exercise, we will be using a dataset of housing prices in Boston during the 1970s. Python's super-awesome sklearn package already has the data we need to get started. Below is the command to load the data. The data is stored as a dictionary. 
The 'DESCR' is a description of the data and the command for printing it is below. Note all the features we have to work with. From the dictionary, we need the data and the target variable (in this case, housing price). Store these as variables named "data" and "price", respectively. Once you have these, print their shapes to see all checks out with the DESCR.
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Python Code:
from sklearn.datasets import load_boston
from sklearn.cross_validation import train_test_split
from sklearn.preprocessing import scale
from sklearn.neighbors import KNeighborsRegressor
from sklearn.metrics import mean_squared_error
from sklearn.cross_validation import KFold
import matplotlib.pyplot as plt
import numpy as np
%matplotlib inline
Explanation: Import modules
End of explanation
boston = load_boston()
print boston.DESCR
Explanation: Load data
For this exercise, we will be using a dataset of housing prices in Boston during the 1970s. Python's super-awesome sklearn package already has the data we need to get started. Below is the command to load the data. The data is stored as a dictionary. 
The 'DESCR' is a description of the data and the command for printing it is below. Note all the features we have to work with. From the dictionary, we need the data and the target variable (in this case, housing price). Store these as variables named "data" and "price", respectively. Once you have these, print their shapes to see all checks out with the DESCR.
End of explanation
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