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Fitting the model, testing and getting the score or r2 does not give the error. But when I try to predict the actual data I get this ValueError:

#test and train shapes
test.shape, train.shape
((8523, 1606), (8523, 1606))

#creating dummies for the training dataset
X = train.drop('Item_Outlet_Sales_log', 1) #drop the log target column
y = train.Item_Outlet_Sales_log

X = pd.get_dummies(X)
train = pd.get_dummies(train)
test = pd.get_dummies(test)

#split the train data into train and test set in order to evaluate the model's accuracy
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression

#split the the train data into train and test
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 42, shuffle = True)
#model 2 fitted with raw data
regression_model_2 = LinearRegression()
#train
regression_model_2.fit(X_train, y_train)

pre = regression_model_2.predict(test)

Error enter image description here I tried different approaches but it still complains about the shapes

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    $\begingroup$ predict x_test not test. $\endgroup$
    – 10xAI
    Apr 11 at 16:43
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    $\begingroup$ test is the dataset that I must predict for submission. The x_test I did compute it and its working fine. #model_2 pred_regression_model_2 = regression_model_2.predict(X_test); $\endgroup$
    – SP_
    Apr 11 at 17:04
0
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Check whether dtypes for the test data matches the dtypes for the x_train.

For my case, prediction was fine on x_train. I then used the code below to receive new data(equivalent to your test data) for prediction

# Take input from user
sepal_length = float(input("Enter sepal_length: "))
sepal_width = float(input("Enter sepa_width: "))
petal_length = float(input("Enter petal_length: "))
petal_width = float(input("Enter petal_width: "))

I noticed that although I specified float() some ended up being int() and that was the problem.

Again you might be having a strange variable on the test data(not on included in x_train. This can happen when you create dummies from a categorical data where a category on the test data is not in x_train.

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