One Hot Encoder with Python Machine Learning (Scikit-Learn)

In this Python Machine Learning Tutorial, we take a look at how you can change categorical data to numeric with the help of One Hot Encoder
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[@RyanNolanData]

Have a need for a data project? Email me or fill out the form on my website.
Looking for the code? Check out the article: Looking for the code? Check out the article: ryannolandata.com/one-hot-encoder/

[@shivi_was_never_here]

Thanks a lot Ryan! This has to be one of the best videos out here dealing with encoders. If only others were this easy!
Thanks again.

[@shivi_was_never_here]

Also, do I have to fit and transform all my sets? Or only the training set? Do I have to fit the test set? Thanks again!

[@omer4826]

thanks a lot dude! really helped me grasp the basics!

[@RyanNolanData]

No problem

[@A-K-I-R-A-]

Nice tutorial, clean and direct!

[@RyanNolanData]

Thank you

[@neerajchauhan1371]

Thanks buudy

[@message59]

Thanks a lot was a great help :) hope you have a good day

[@RyanNolanData]

Thanks for checking this out

[@shadrinan90]

Great explanation, thanks

[@RyanNolanData]

Thank you

[@Futureyouth-be1bo]

dude how about if i have two different datasets while theier categorical values are different how can i do one hot encoding
the first one has 9349 rows × 17 columns
and the second one has 365 rows × 17 columns while if i make one hot encoding they will be produced
for the first one they become 611 columns of hot encoding
and the second one become 20 columns please help me how can i do this note the two datasets have Origin and destintion city names

[@ginaross295]

Thank you so much for this video !!!!

[@RyanNolanData]

Thanks for checking it out

[@kablamo9999]

Thank you!

[@RyanNolanData]

No problem

[@user-xy8ev2tf4z]

Thank you ❤

[@RyanNolanData]

No problem

[@ayushparwal2210]

thanks buddy it helps me !:)

[@RyanNolanData]

Awesome glad you liked it

[@alonzoslim]

This is a great video. Explained in a manner that a newbie like myself can understand. Thank you.
A question: What if the dataset contains multiple categorical variables (as well as numerical), and they are all required as input to make a prediction. How can one go about it?

[@RyanNolanData]

Thank you! There are multiple ways to one hot encode the categorical variables. Check out my titanic video and or the house predictions. I show a few different processes

[@onurbltc]

Great video!

[@RyanNolanData]

Thanks!

[@swativarsha68]

lerant a lot! thanks!!

[@RyanNolanData]

Awesome! That’s the goal

[@juanDoAs]

Trying your code I get this error: 'AttributeError: 'OneHotEncoder' object has no attribute 'set_output''. Any idea why this is?

[@juanDoAs]

Nvm just needed to update scikit-learn

[@RyanNolanData]

Ok great. Everything else working properly?

[@eyadal-naimi3782]

protect this man

[@RyanNolanData]

haha I appreciate it

[@PhilTag-ml6wd]

Stopped a bit short. Need to go through how to use the encoder for predicting and not just setting up for training. eg. enc.transform() on the features you need to run the prediction on . Has been a bit of a pain with the datatype.

[@Aldotronix]

I don’t know if i understand your comment but you can make a make_pipeline to build all preprocessing steps: use a ColumnTransformer to select the columns to one hot encode and use the one hot encoder. You can cross validate, fit and predict using the pipeline instead of building a model again.

[@RyanNolanData]

I have some projects that do. I may remake this video in the furture

[@leodexter191]

please go lil slow hard to understand

[@RyanNolanData]

I'll have an article on this soon you can also check out

[@leodexter191]

@@RyanNolanData thank you