Question

我正在创建具有四个变量的预测机。当我添加变量时，一切都弄乱了，给了我：

ValueError: shapes (1,4) and (5,4) not aligned: 4 (dim 1) != 5 (dim 0)

代码

       import pandas as pd
       from pandas import DataFrame
       from sklearn import linear_model
       import tkinter as tk 
       import statsmodels.api as sm

＃方法1：将数据导入Python

Stock_Market = pd.read_csv(r'Training_Nis_New2.csv')
df = DataFrame(Stock_Market,columns=['Month 1','Month 2','Month 3','Month 
4','Month 5','Month 6','Month 7','Month 8',
                                 'Month 9','Month 10','Month 11','Month 
12','FSUTX','MMUKX','FUFRX','RYUIX','Interest R','Housing 
Sale','Unemployement Rate','Conus Average Temperature 
Rank','30FSUTX','30MMUKX','30FUFRX','30RYUIX'])


X = df[['Month 1','Interest R','Housing Sale','Unemployement Rate','Conus Average Temperature Rank']] 
# here we have 2 variables for multiple regression. If you just want to use one variable for simple linear regression, then use X = df['Interest_Rate'] for example.Alternatively,  you may add additional variables within the brackets

Y = df[['30FSUTX','30MMUKX','30FUFRX','30RYUIX']]

# with sklearn
regr = linear_model.LinearRegression()
regr.fit(X, Y)

print('Intercept: \n', regr.intercept_)
print('Coefficients: \n', regr.coef_)


# prediction with sklearn
# prediction with sklearn


HS=5.5
UR=6.7
CATR=8.9

New_Interest_R = 4.6



print('Predicted Stock Index Price: \n', regr.predict([[UR ,HS ,CATR 
,New_Interest_R]]))

# with statsmodel
X = df[['Month 1','Interest R','Housing Sale','Unemployement Rate','Conus Average Temperature Rank']]
Y = df['30FSUTX']
print('\n\n*** Fund = FSUTX')
X = sm.add_constant(X) # adding a constant

model = sm.OLS(Y, X).fit()
predictions = model.predict(X) 

print_model = model.summary()
print(print_model)

ValueError：将变量添加到预测机中时，形状（1,4）和（5,4）未对齐：4（dim 1）！= 5（dim 0）

0 个答案: