#1.导入boston房价数据集from sklearn.datasets import load_bostonboston = load_boston()boston.keys()print(boston.DESCR)boston.data.shapeboston.feature_namesimport pandas as pdpd.DataFrame(boston.data)#2. 一元线性回归模型，建立一个变量与房价之间的预测模型，并图形化显示。import matplotlib.pyplot as pltx = boston.data[:,5]  y = boston.targetplt.figure(figsize=(10,6)) plt.scatter(x,y)plt.plot(x,9*x-20,'r')  plt.show()from sklearn.linear_model import LinearRegressionlineR=LinearRegression()lineR.fit(x.reshape(-1,1),y) w=lineR.coef_  b = lineR.intercept_ #3、多元线性回归模型，建立13个变量与房价之间的预测模型，并检测模型好坏，并图形化显示检查结果from sklearn.linear_model import LinearRegression  lineR = LinearRegression()lineR.fit(boston.data,y) w = lineR.coef_b = lineR.intercept_        import matplotlib.pyplot as pltx=boston.data[:,12].reshape(-1,1)y=boston.targetplt.figure(figsize=(10,6)) #指定显示图大小plt.scatter(x,y)from sklearn.linear_model import LinearRegressionlineR=LinearRegression()lineR.fit(x,y)y_pred=lineR.predict(x)plt.plot(x,y_pred,'green')print(lineR.coef_,lineR.intercept_)plt.show()#4.  一元多项式回归模型，建立一个变量与房价之间的预测模型，并图形化显示。from sklearn.preprocessing import PolynomialFeaturespoly = PolynomialFeatures(degree=2)x_poly = poly.fit_transform(x)lrp = LinearRegression()lrp.fit(x_poly,y)y_poly_pred = lrp.predict(x_poly)plt.scatter(x,y)plt.plot(x,y_poly_pred,'r')plt.show()from sklearn.preprocessing import PolynomialFeaturespoly = PolynomialFeatures(degree=2)x_poly = poly.fit_transform(x)lrp = LinearRegression()lrp.fit(x_poly,y)plt.scatter(x,y)plt.scatter(x,y_pred)plt.scatter(x,y_poly_pred)   #多项回归plt.show()