使用OLS回归预测未来值（Python，StatsModels，Pandas）

Question

我目前正在尝试在Python中实现MLR，但不确定如何将找到的系数应用于将来的值。

import pandas as pd
import statsmodels.formula.api as sm
import statsmodels.api as sm2

TV = [230.1, 44.5, 17.2, 151.5, 180.8]
Radio = [37.8,39.3,45.9,41.3,10.8]
Newspaper = [69.2,45.1,69.3,58.5,58.4]
Sales = [22.1, 10.4, 9.3, 18.5,12.9]
df = pd.DataFrame({'TV': TV, 
                   'Radio': Radio, 
                   'Newspaper': Newspaper, 
                   'Sales': Sales})

Y = df.Sales
X = df[['TV','Radio','Newspaper']]
X = sm2.add_constant(X)
model = sm.OLS(Y, X).fit()
>>> model.params
const       -0.141990
TV           0.070544
Radio        0.239617
Newspaper   -0.040178
dtype: float64

因此，假设我要为以下DataFrame预测“销售”：

EDIT

TV     Radio    Newspaper    Sales
230.1  37,8       69.2       22.4
44.5   39.3       45.1       10.1
...    ...        ...        ...
25      15        15
30      20        22
35      22        36

我一直在尝试在这里找到的方法，但似乎无法正常工作： 使用Pandas OLS进行预测

谢谢！

Answer 1

假设df2是您新的示例DataFrame：

model = sm.OLS(Y, X).fit()
new_x = df2.loc[df.Sales.notnull(), ['TV', 'Radio', 'Newspaper']].values
new_x = sm2.add_constant(new_x)  # sm2 = statsmodels.api
y_predict = model.predict(new_x)

>>> y_predict
array([ 4.61319034,  5.88274588,  6.15220225])

您可以将结果直接分配给df2，如下所示：

df2.loc[:, 'Sales'] = model.predict(new_x)

要使用回归预测来填充原始DataFrame中缺少的Sales值，请尝试：

X = df.loc[df.Sales.notnull(), ['TV', 'Radio', 'Newspaper']]
X = sm2.add_constant(X)
Y = df[df.Sales.notnull()].Sales

model = sm.OLS(Y, X).fit()
new_x = df.loc[df.Sales.isnull(), ['TV', 'Radio', 'Newspaper']]
new_x = sm2.add_constant(new_x)  # sm2 = statsmodels.api

df.loc[df.Sales.isnull(), 'Sales'] = model.predict(new_x)