回归模型与房价预测

from sklearn.datasets import load_boston
boston=load_boston()
boston.keys()

print(boston.DESCR)

boston.data.shape

import pandas as pd
pd.DataFrame(boston.data)

import matplotlib.pyplot as plt
x=boston.data[:,5]
y=boston.target
plt.figure(figsize=(20,6))
plt.scatter(x,y)
plt.plot(x,9*x-20,‘r‘)
plt.show()
x.shape

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()

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_poly_pred,‘g‘)
plt.show()


from sklearn.preprocessing import PolynomialFeatures
poly = 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()