python實現樓價預測，採用迴歸和隨機梯度下降法

from sklearn.datasets import load_bostonboston = load_boston()from sklearn.cross_validation import train_test_splitimport numpy as np;X = boston.datay = boston.targetX_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 StandardScalerss_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 LinearRegressionlr = LinearRegression()lr.fit(X_train, y_train)lr_y_predict = lr.predict(X_test)from sklearn.linear_model import SGDRegressorsgdr = 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_errorprint '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))