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 #檢視資料個數

import pandas as pd      #用pandas模型輸入資料
pd.DataFrame(boston.data)

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 matplotlib.pyplot as plt
x 
 
= boston.data[:,5]       #第五個因素與房價的關係
y = boston.target
plt.figure(figsize=(10,6)) #圖形大小
plt.scatter(x,y)           #散點圖
plt.plot(x,9*x-20,'r')     #一元線性迴歸線，斜率
plt.show()
x.shape

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。

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,'r')
print(lineR.coef_,lineR.intercept_)
plt.show()

#多元線性迴歸
from sklearn.linear_model import LinearRegression
lr 
 
= LinearRegression()
lr.fit(boston.data,y)
w = lr.coef_
print(w)

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)
poly.fit_transform(x)
lrp = LinearRegression()
lineR.fit(x,y)
y_pred = lineR.predict(x)
plt.plot(x,y_pred)

from sklearn.preprocessing import PolynomialFeatures
poly = PolynomialFeatures(degree=2)
x_poly = poly.fit_transform(x)

lrp = LinearRegression()
lrp.fit(x_poly,y)
y_pred = lineR.predict(x)
plt.plot(x,y_pred)

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_poly_pred = lrp.predict(x_poly)
plt.scatter(x,y)
plt.scatter(x,y_pred)
plt.scatter(x,y_poly_pred)
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_pred,'r')
plt.scatter(x,y_poly_pred,c='b')
plt.show()