1. 导入boston房价数据集
from sklearn.datasets import load_boston
from sklearn.datasets import load_boston boston = load_boston() boston.keys()
print(boston.DESCR)
boston.feature_names
boston.target
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 pandas as pd df = pd.DataFrame(boston.data) df
import matplotlib.pyplot as plt x= boston.data[:,5] y= boston.target plt.figure(figsize=(10,6)) plt.scatter(x,y) plt.plot(x,9*x-34,'r') plt.show() x.shape
from sklearn.linear_model import LinearRegression lineR = LinearRegression() lineR.fit(x.reshape(-1,1),y) lineR.coef_
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_ w
y = wx+b
import matplotlib.pyplot as plt x= boston.data[:,12].reshape(-1,1) y= boston.target plt.figure(figsize=(10,6)) plt.scatter(x,y) from sklearn.linear_model import LinearRegression lineR = LinearRegression() lineR.fit(x,y) y_pred = lineR.predict(x) plt.plot(x,y_pred) 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 PolynomialFeatures poly = PolynomialFeatures(degree=2) poly.fit_transform(x)
from sklearn.preprocessing import PolynomialFeatures poly = 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.scatter(x,y_pred) plt.scatter(x,y_poly_pred) plt.show()
