FAQ: Machine Learning Pipelines - Data Cleaning (Categorical)

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This community-built FAQ covers the “Data Cleaning (Categorical)” exercise from the lesson “Machine Learning Pipelines”.

Paths and Courses
This exercise can be found in the following Codecademy content:

Machine Learning/AI Engineer Career Path

FAQs on the exercise Data Cleaning (Categorical)

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This exercise is poorly written. It says that you should use sparse=‘False’, but sparse parameter does not take a string argument, but a bool. So it should be sparse=False.
Also, the exercise asks you to compare x_test_fill_missing_ohe with the transformation you get when calling the pipeline on x_test_fill_missing. However, it does not mention that the one hot encoder used to create x_test_fill_missing_ohe has a second parameter which is drop=‘first’. This lack of information causes confusion because when you try to compute the absolute difference between the 2 matrices, you get the error:

ValueError: operands could not be broadcast together with shapes (1045,2) (1045,3)

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Same for the one before this. Just coding the pipeline and comparing will not give you the same arrays. For 95% of the course we were fit and transforming the train data and validating on the test data. All of a sudden we’re fitting on the train data and trasforming on the test data? Or am I confused. The hints don’t really help either and you never receive a reply in the forums…I’m getting the feeling I wasted my money and time and that I could have better tried to learn on DeepLearning.com