回归模型与房价预测

#导入boston房价数据集
from sklearn.datasets import load_boston
import numpy as np
boston = load_boston()
boston.keys()
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)
w = lineR.coef_  #x前的系数
b = lineR.intercept_  #截距
print(w)
print(b)

from matplotlib import pyplot as plt
x = boston.data[:,5] #变量
y = boston.target #房价
plt.figure(figsize=(10,6)) 
plt.scatter(x,y) 
plt.plot(x,9.1*x-34.6,‘r‘)
plt.show()

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

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()