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I'm trying to run a multivariate linear regression but I'm getting an error when trying to get the coefficients of the regression model.

The error I'm getting is this: AttributeError: 'numpy.ndarray' object has no attribute 'columns'

Here's the code I'm using:

import pandas as pd
import numpy as np  
import matplotlib.pyplot as plt  
import seaborn as seabornInstance 
from sklearn.model_selection import train_test_split 
from sklearn.linear_model import LinearRegression
from sklearn import metrics
%matplotlib inline

# Main files
dataset = pd.read_csv('namaste_econ_model.csv')
dataset.shape
dataset.describe()
dataset.isnull().any()

#Dividing data into "attributes" and "labels". X variable contains all the attributes and y variable contains labels.

X = dataset[['Read?', 'x1', 'x2', 'x3', 'x4', 'x5', 'x6' , 'x7','x8','x9','x10','x11','x12','x13','x14','x15','x16','x17','x18','x19','x20','x21','x22','x23','x24','x25','x26','x27','x28','x29','x30','x31','x32','x33','x34','x35','x36','x37','x38','x39','x40','x41','x42','x43','x44','x45','x46','x47']].values
y = dataset['Change in Profit (BP)'].values
plt.figure(figsize=(15,10))
plt.tight_layout()
seabornInstance.distplot(dataset['Change in Profit (BP)'])
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)
regressor = LinearRegression()  
regressor.fit(X_train, y_train)
coeff_df = pd.DataFrame(regressor.coef_, X.columns, columns=['Coefficient'])  
coeff_df

Full error:

Traceback (most recent call last):

File "", line 14, in coeff_df = pd.DataFrame(regressor.coef_, X.columns, columns=['Coefficient'])

AttributeError: 'numpy.ndarray' object has no attribute 'columns'

Any help on this will be highly appreciated!

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2 Answers 2

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Using .values on a pandas dataframe gives you a numpy array. This will not contain column names and such. You do this when setting X like this:

X = dataset[['Read?', 'x1', .. ,'x47']].values

But then you try to get the column names from X (which it does not have) by writing X.columns here:

coeff_df = pd.DataFrame(regressor.coef_, X.columns, columns=['Coefficient'])

So store your column names in a variable or input them again, like this:

coeff_df = pd.DataFrame(regressor.coef_, ['Read?', 'x1', .. ,'x47'], columns=['Coefficient'])
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  • $\begingroup$ Hi Simon, thank you so much for the support. You're right... that was totally the issue. Now I do have another question, sorry if I'm being opportunistic here... so the x1,x2, etc... are actually dummy variables (ref. prnt.sc/payj7q) that came from a categorical variable. So now my output for the coefficients, looks like this... prnt.sc/payio0 I'm doing something wrong here or where should I look to improve the model? Again, thank you so much for your support. $\endgroup$
    – Eduardo Martinez
    Commented Sep 25, 2019 at 19:29
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hi remove values method

X = dataset[['Read?', 'x1', 'x2', 'x3', 'x4', 'x5', 'x6' , 'x7','x8','x9','x10','x11','x12','x13','x14','x15','x16','x17','x18','x19','x20','x21','x22','x23','x24','x25','x26','x27','x28','x29','x30','x31','x32','x33','x34','x35','x36','x37','x38','x39','x40','x41','x42','x43','x44','x45','x46','x47']]

coeff_df = pd.DataFrame(regressor.coef_, X.columns, columns=['Coefficient'])
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