（一）导入并查看数据

import pandas as pd 
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split  
from sklearn.linear_model import LinearRegression  #线性回归库
from sklearn.datasets import load_boston   #导入波士顿数据集
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd

df = load_boston()
x = df.data  #数据
y = df.target #标签

x.shape

x_train,x_test,y_train,y_test = train_test_split(x,y,test_size = 0.2,random_state = 888)

(random_state为随机数种子)

 （二）训练

train = LinearRegression()
train.fit(x_train,y_train)

train.score(x_test,y_test) #查看准确率

train.coef_  #每一列数据斜率，对应x的系数，13位未知数

（三）预测

train.predict(x_test)

 （四）评估

dev = train.predict(x_test) - y_test #偏差
dev

 均方根误差

RMSE = np.sum(np.sqrt(dev**2))/102
RMSE

（五）保存结果