回归模型与房价预测

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

dict_keys(['data', 'target', 'feature_names', 'DESCR'])

print(boston.DESCR)

data=boston.data
x=data[:,5]
y=boston.target
import matplotlib.pyplot as plt
plt.scatter(x,y)
plt.plot(x,w*x+b)
plt.show()


#加载分类库
from sklearn.linear_model import LinearRegression

LineR=LinearRegression()
#对库进行分类，从1开始，的第一列
LineR.fit(x.reshape(-1,1),y)
w=LineR.coef_#斜率
b=LineR.intercept_#截距

　　

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

import matplotlib.pyplot as plt
x=boston.data[:,12].reshape(-1,1)#拿出数据集的全部列进行分类预处理，做训练集
y=boston.target#划分测试集
plt.figure(figsize=(10,6)) #指定显示图大小
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,'green')
print(w,b)
plt.show()

　　

#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)
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.plot(x,y_poly_pred,'r')
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()