使用OLS迴歸（Python，StatsModels，Pandas）預測未來值

Question

我目前正試圖在Python中實現一個MLR，我不知道如何去應用我發現的未來值的係數。

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 

所以我們可以說我想預測出的「銷售」以下數據框：

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 

我一直在努力，我發現這裏的方法，但我似乎無法得到它的工作：Forecasting using Pandas OLS

謝謝！

Answer 1

假設DF2是你新出的樣本數據幀的：

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]) 

您可以直接將結果賦予DF2如下：

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

爲了填補從原來的數據幀與預測缺少銷售值從你的迴歸中，嘗試：

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)