AttributeError: 'DataFrame' object has no attribute 'mean_squared_error'

This error message indicates that you are trying to access an attribute called `mean_squared_error` on a pandas DataFrame object, but this attribute does not exist.

`mean_squared_error` is a function from the scikit-learn library that is used to calculate the mean squared error between two sets of data. To use this function, you need to import it from scikit-learn and pass in the actual values and predicted values as arguments.

Here's an example:

from sklearn.metrics import mean_squared_error

actual_values = [2, 4, 6, 8]
predicted_values = [1, 3, 5, 7]

mse = mean_squared_error(actual_values, predicted_values)
print("Mean squared error:", mse)

This will output:

Mean squared error: 1.0

Make sure that you have imported `mean_squared_error` from the correct library and that you are passing in the actual and predicted values correctly.