FAQ: Machine Learning Pipelines - Column Transformer

This community-built FAQ covers the “Column Transformer” exercise from the lesson “Machine Learning Pipelines”.

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This exercise can be found in the following Codecademy content:

Machine Learning/AI Engineer Career Path

FAQs on the exercise Column Transformer

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This course has been launched while it is incomplete. For all the 8 pages of parctice code lesson, no code solution is provided so the learner cannot check the diff between their solution and code academy solution.

Page 8 - no solution - the CA solution is empty

Page 4 - col transformers

1 Like

it is still the same, please improve this course and its solutions.


cat_vals = Pipeline([(“imputer”,SimpleImputer(strategy=‘most_frequent’)), (“ohe”,OneHotEncoder(sparse=False, drop=‘first’))])
num_vals = Pipeline([(“imputer”,SimpleImputer(strategy=‘mean’)), (“scale”,StandardScaler())])

#Create the column transformer with the categorical and numerical processes
preprocess = ColumnTransformer(transformers=[
(“cat_process”, cat_vals, cat_cols),
(“num_process”, num_vals, num_cols)

#fit the preprocess transformer
preprocess.fit(x_train, y_train)

^^ That’s how I solved it.


Thank you for sharing your solution! :slight_smile: