1.匯入boston房價資料集

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

print(boston.DESCR)

boston.data.shape

boston.feature_names

2. 一元線性迴歸模型，建立一個變數與房價之間的預測模型，並圖形化顯示

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,'r')
plt.show()

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

lineR.intercept_ #計算b的值

3. 多元線性迴歸模型，建立13個變數與房價之間的預測模型，並檢測模型好壞，並圖形化顯示檢查結果

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

lineR.intercept_ #計算b的值

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()

4.  一元多項式迴歸模型，建立一個變數與房價之間的預測模型，並圖形化顯示

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,'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)   #多項迴歸
plt.show()