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) lreg.fit(x_train,y_train) # training the model pred = lreg.predict(x_cv) # predicting on cv mse = np.mean((pred - y_cv)**2) # calculating mse print("MSE=",mse) # calculating coefficients coeff = DataFrame(x_train.columns) coeff['Coefficient Estimate'] = Series(lreg.coef_) print(coeff[[0,"Coefficient Estimate"]]) y_cv_matrix=y_cv.as_matrix() 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