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=(10,6))
plt.scatter(x,y)
plt.plot(x,9.1*x-34,‘g‘)
plt.show()

from sklearn.linear_model import LinearRegression
lineR=LinearRegression()
lineR.fit(x.reshape(-1,1),y)
lineR.coef_

lineR.intercept_

from sklearn.linear_model import LinearRegression
lineR=LinearRegression()
lineR.fit(boston.data,y)
w=lineR.coef_
w

b=lineR.intercept_
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()

from sklearn.preprocessing import PolynomialFeatures
poly=PolynomialFeatures(degree=2)
x_poly=poly.fit_transform(x)
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_plot_pred=lrp.predict(x_poly)
plt.scatter(x,y)
plt.scatter(x,y_pred)
plt.scatter(x,y_plot_pred)
plt.show()