FAQ: Machine Learning Pipelines - Column Transformer

FAQ Uncategorized
1

Community%20FAQs%20on%20Codecademy%20Exercises
Community%20FAQs%20on%20Codecademy%20Exercises1000×208 141 KB

This community-built FAQ covers the “Column Transformer” 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 Column Transformer

There are currently no frequently asked questions associated with this exercise – that’s where you come in! You can contribute to this section by offering your own questions, answers, or clarifications on this exercise. Ask or answer a question by clicking reply (reply) below.

If you’ve had an “aha” moment about the concepts, formatting, syntax, or anything else with this exercise, consider sharing those insights! Teaching others and answering their questions is one of the best ways to learn and stay sharp.

Join the Discussion. Help a fellow learner on their journey.

Ask or answer a question about this exercise by clicking reply (reply) below!
You can also find further discussion and get answers to your questions over in #get-help.

Agree with a comment or answer? Like (like) to up-vote the contribution!

Need broader help or resources? Head to #get-help and #community:tips-and-resources. If you are wanting feedback or inspiration for a project, check out #project.

Looking for motivation to keep learning? Join our wider discussions in #community

Learn more about how to use this guide.

Found a bug? Report it online, or post in #community:Codecademy-Bug-Reporting

Have a question about your account or billing? Reach out to our customer support team!

None of the above? Find out where to ask other questions here!

2

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
3

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

2 Likes
4

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.

2 Likes
5

Thank you for sharing your solution! :slight_smile: