Getting error while fitting a linear regression model




I am trying to run the following Linear Regression code and I am getting the error as listed in the subject line. I have tried to research on the given error but I have not been able to figure out the reason.

Error that I am getting is

shapes (1,1705) and (2,) not aligned: 1705 (dim 1) != 2 (dim 0)

code is as follows

import numpy as np
import pandas as pd
from pandas import Series, DataFrame
from sklearn.model_selection import train_test_split
#import test and train file

from sklearn.linear_model import LinearRegression 

train = pd.read_csv('Train_Data.csv')
#test = pd.read_csv('test.csv')

lreg = LinearRegression() 

X = train.loc[:,['Outlet_Establishment_Year','Item_MRP']] # splitting into training and cv for cross validation

x_train, x_cv, y_train, y_cv = train_test_split(X,train.Item_Outlet_Sales,test_size=0.20,random_state=42),y_train) # training the model 

pred = lreg.predict(x_cv) #  predicting on cv

mse = np.mean((pred - y_cv)**2) # calculating mse

# calculating coefficients 
coeff = DataFrame(x_train.columns) 
coeff['Coefficient Estimate'] = Series(lreg.coef_)
print(coeff[[0,"Coefficient Estimate"]])


print(lreg.score(pred,y_cv_matrix)) # this line not working

I have tried to reshape and use np.newaxis as well but it doesn’t work

All the help is appreciated.

Thanks in advance



Hi mohit,

Can you print the shape of pred and y_cv_matrix ?