python实现房价预测，采用回归和随机梯度下降法

from sklearn.datasets import load_boston

boston = load_boston()

from sklearn.cross_validation import train_test_split

import numpy as np;

X = boston.data
y = boston.target

X_train, X_test, y_train, y_test = train_test_split(X, y, random_state = 33, test_size = 0.25)

print 'The max target value is: ', np.max(boston.target)
print 'The min target value is: ', np.min(boston.target)
print 'The average terget value is: ', np.mean(boston.target)

from sklearn.preprocessing import StandardScaler

ss_X = StandardScaler()
ss_y = StandardScaler()

X_train = ss_X.fit_transform(X_train)
X_test = ss_X.transform(X_test)
y_train = ss_y.fit_transform(y_train)
y_test = ss_y.transform(y_test)

from sklearn.linear_model import LinearRegression

lr = LinearRegression()

lr.fit(X_train, y_train)

lr_y_predict = lr.predict(X_test)

from sklearn.linear_model import SGDRegressor

sgdr = SGDRegressor()

sgdr.fit(X_train, y_train)

sgdr_y_predict = sgdr.predict(X_test)

print 'The value of default measurement of LinearRegression is: ', lr.score(X_test, y_test)

from sklearn.metrics import r2_score, mean_squared_error, mean_absolute_error

print 'The value of R-squared of LinearRegression is: ', r2_score(y_test, lr_y_predict)
print 'The mean squared error of LinearRegression is: ', mean_squared_error(ss_y.inverse_transform(y_test), ss_y.inverse_transform(lr_y_predict))
print 'The mean absolute error of LinearRegression is: ', mean_absolute_error(ss_y.inverse_transform(y_test), ss_y.inverse_transform(lr_y_predict))

print 'The value of default measurement of SGDRegression is: ', sgdr.score(X_test, y_test)
print 'The value of R-squared of SGDRegression is: ', r2_score(y_test, sgdr_y_predict)
print 'the value of mean squared error of SGDRgression is: ', mean_squared_error(ss_y.inverse_transform(y_test), ss_y.inverse_transform(sgdr_y_predict))
print 'the value of mean ssbsolute error of SGDRgression is: ', mean_absolute_error(ss_y.inverse_transform(y_test), ss_y.inverse_transform(sgdr_y_predict))