回归模型与房价预测
1 from sklearn.datasets import load_boston 2 boston = load_boston() 3 boston.keys()
print(boston.DESCR)
boston.target import pandas as pd df = pd.DataFrame(boston.data) df
from sklearn.linear_model import LinearRegression LineR = LinearRegression() LineR.fit(x.reshape(-1,1),y) LineR.coef_ LineR.intercept_ import matplotlib.pyplot as plt x=boston.data[:,5] y=boston.target plt.figure(figsize=(10,6)) plt.scatter(x,y) plt.plot(x,9.1*x-34,'r') plt.show()
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) plt.show()
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()