Based Jupyter notebooks using sklearn library for programming multiple regression equation

First, import excel files and related libraries

import pandas;
import matplotlib;
from pandas.tools.plotting import scatter_matrix;
 
data = pandas.read_csv("D:\\面积距离车站.csv",engine='python',encoding='utf-8')

Show file size

data.shape

data

Second scattergram drawn between a plurality of variables twenty-two: scatter_matrix () method

#绘制多个变量两两之间的散点图：scatter_matrix（）方法
font = {
    'family' : 'SimHei'
}

matplotlib.rc('font', **font)
scatter_matrix(
    data[["area","distance", "money"]], 
    figsize=(10, 10), diagonal='kde'
)    #diagonal参数表示变量与变量本身之间的绘图方式，kde代表直方图
#求相关系数矩阵
data[["area", "distance", "money"]].corr()

x = data[["area", "distance"]]
y = data[["money"]]

Third, import sklearn

from sklearn.linear_model import LinearRegression

#建模
lrModel = LinearRegression()

#训练模型
lrModel.fit(x, y)

#评分
R2=lrModel.score(x, y)
print("R的平方:",R2)

#预测
lrModel.predict([[10, 110],[20, 110]])

#查看参数
lrModel.coef_

#查看截距
lrModel.intercept_

The results are as follows:
the regression equation: y = 41.51x1-0.34x2 + 65.32

Four, python all the code

import pandas;
import matplotlib;
from pandas.tools.plotting import scatter_matrix;

data.shape

#绘制多个变量两两之间的散点图：scatter_matrix（）方法
font = {
    'family' : 'SimHei'
}

matplotlib.rc('font', **font)
scatter_matrix(
    data[["area","distance", "money"]], 
    figsize=(10, 10), diagonal='kde'
)    #diagonal参数表示变量与变量本身之间的绘图方式，kde代表直方图
#求相关系数矩阵
data[["area", "distance", "money"]].corr()

x = data[["area", "distance"]]
y = data[["money"]]

from sklearn.linear_model import LinearRegression

#建模
lrModel = LinearRegression()

#训练模型
lrModel.fit(x, y)

#评分
R2=lrModel.score(x, y)
print("R的平方:",R2)

#预测
lrModel.predict([[10, 110],[20, 110]])

#查看参数
lrModel.coef_

#查看截距
lrModel.intercept_