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

[@RyanAndMattDataScience]

Hey guys I hope you enjoyed the video! If you did please subscribe to the channel!
If you want to watch a full course on Machine Learning check out Datacamp: datacamp.pxf.io/XYD7Qg
Want to solve Python data interview questions: stratascratch.com/?via=ryan
I'm also open to freelance data projects. Hit me up at [e-mail]
*Both Datacamp and Stratascratch are affiliate links.

[@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!

[@economicslover9534]

Thank u very much sir.. Love from india❤

[@RyanAndMattDataScience]

No problem

[@omer4826]

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

[@RyanAndMattDataScience]

No problem

[@RyanAndMattDataScience]

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/

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

Nice tutorial, clean and direct!

[@RyanAndMattDataScience]

Thank you

[@charlesmay1610]

Perfect explanation! very helpful :)

[@RyanAndMattDataScience]

Thank you

[@aniketshrikondawar6598]

Please make sure all cells are visible on screen. Sometimes not able to view end of cell content.

[@ahsanjamil1495]

in case if we have multiple variables which are non-ordinal, do we use the onehotencoder on all the variables at once by adding them to the list initially or do we do this one by one?

[@ShirHaShiurim-mq1zj]

This video was so helpful, thank you. Think you could also make one on frequency encoding and the other types of encoding?

[@RyanAndMattDataScience]

I can add them to my backlog after my stats series

[@message59]

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

[@RyanAndMattDataScience]

Thanks for checking this out

[@yasminwael-pl5fv]

thank you very much 💕

[@shadrinan90]

Great explanation, thanks

[@RyanAndMattDataScience]

Thank you

[@ginaross295]

Thank you so much for this video !!!!

[@RyanAndMattDataScience]

Thanks for checking it out

[@Only_Reality_True]

Hii...I have an error like OneHotEncoder._init_() got an unexpected keyword argument 'sparse'.... Also I already imported library which are necessary... please tell me what should I do😢

[@RyanAndMattDataScience]

Join our discord and post your notebook

[@Only_Reality_True]

@@RyanAndMattDataScience okay

[@La_mia-r5z]

Thank you ❤

[@RyanAndMattDataScience]

No problem

[@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?

[@RyanAndMattDataScience]

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

[@ayushparwal2004]

thanks buddy it helps me !:)

[@RyanAndMattDataScience]

Awesome glad you liked it

[@eyadal-naimi3782]

protect this man

[@RyanAndMattDataScience]

haha I appreciate it

[@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

[@williamalexander5089]

u can merge them first, encode it, then split it again

[@kablamo9999]

Thank you!

[@RyanAndMattDataScience]

No problem

[@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

[@RyanAndMattDataScience]

Ok great. Everything else working properly?

[@AtanasVekiev]

Very good tutorial, but what about the "dummy variable" trap? I think you should drop one of these new variables.

[@mehdialaoui1432]

you're right e should chose one feature as reference to avoid the trap bais of dummy variables

[@ttdddaa]

thanks dude

[@neerajchauhan1371]

Thanks buudy

[@onurdatascience]

Great video!

[@RyanAndMattDataScience]

Thanks!

[@swativarsha68]

lerant a lot! thanks!!

[@RyanAndMattDataScience]

Awesome! That’s the goal

[@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.

[@RyanAndMattDataScience]

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

[@peidomolhado7016]

skibi learn 😝😝😝

[@leodexter191]

please go lil slow hard to understand

[@RyanAndMattDataScience]

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

[@leodexter191]

@@RyanAndMattDataScience thank you

[@usamaspeakscricket]

Thanks buddy

[@RyanAndMattDataScience]

Np