第十三次作业——回归模型与房价预测
1. 导入boston房价数据集
from sklearn.datasets import load_boston boston = load_boston() boston.keys()
print(boston.DESCR)
boston.data.shape #查看数据个数 import pandas as pd #用pandas模型输出数据 pd.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 plt x = boston.data[:,5] #第五个因素与房价的关系 y = boston.target plt.figure(figsize=(10,6)) #图形大小 plt.scatter(x,y) #散点图 plt.plot(x,9*x-20,'r') #一元线性回归线,斜率 plt.show() x.shape
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。
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,'r') print(lineR.coef_,lineR.intercept_) plt.show() #多元线性回归 from sklearn.linear_model import LinearRegression lr = LinearRegression() lr.fit(boston.data,y) w = lr.coef_ print(w)
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) lrp = LinearRegression() lineR.fit(x,y) y_pred = lineR.predict(x) plt.plot(x,y_pred) from sklearn.preprocessing import PolynomialFeatures poly = PolynomialFeatures(degree=2) x_poly = poly.fit_transform(x) lrp = LinearRegression() lrp.fit(x_poly,y) y_pred = lineR.predict(x) plt.plot(x,y_pred) x_poly 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() from sklearn.preprocessing import PolynomialFeatures poly = PolynomialFeatures(degree=2) x_poly = poly.fit_transform(x) lp = LinearRegression() lp.fit(x_poly,y) y_poly_pred = lp.predict(x_poly) plt.scatter(x,y) plt.plot(x,y_pred,'r') plt.scatter(x,y_poly_pred,c='b') plt.show()