python实现房价预测,采用回归和随机梯度下降法
2017-06-02 23:36
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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))
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