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

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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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Looking for the code? Check out the article: ryannolandata.com/one-hot-encoder/
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14 авг 2023

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Комментарии : 39   
@RyanNolanData
@RyanNolanData 11 месяцев назад
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
@shivi_was_never_here 2 месяца назад
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
@shivi_was_never_here 2 месяца назад
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
@omer4826 3 месяца назад
thanks a lot dude! really helped me grasp the basics!
@RyanNolanData
@RyanNolanData 3 месяца назад
No problem
@A-K-I-R-A-
@A-K-I-R-A- 6 месяцев назад
Nice tutorial, clean and direct!
@RyanNolanData
@RyanNolanData 6 месяцев назад
Thank you
@message59
@message59 6 месяцев назад
Thanks a lot was a great help :) hope you have a good day
@RyanNolanData
@RyanNolanData 6 месяцев назад
Thanks for checking this out
@neerajchauhan1371
@neerajchauhan1371 6 дней назад
Thanks buudy
@shadrinan90
@shadrinan90 5 месяцев назад
Great explanation, thanks
@RyanNolanData
@RyanNolanData 5 месяцев назад
Thank you
@ginaross295
@ginaross295 6 месяцев назад
Thank you so much for this video !!!!
@RyanNolanData
@RyanNolanData 6 месяцев назад
Thanks for checking it out
@kablamo9999
@kablamo9999 Месяц назад
Thank you!
@RyanNolanData
@RyanNolanData Месяц назад
No problem
@onurbltc
@onurbltc 11 месяцев назад
Great video!
@RyanNolanData
@RyanNolanData 11 месяцев назад
Thanks!
@ayushparwal2210
@ayushparwal2210 6 месяцев назад
thanks buddy it helps me !:)
@RyanNolanData
@RyanNolanData 6 месяцев назад
Awesome glad you liked it
@user-xy8ev2tf4z
@user-xy8ev2tf4z 2 месяца назад
Thank you ❤
@RyanNolanData
@RyanNolanData 2 месяца назад
No problem
@Futureyouth-be1bo
@Futureyouth-be1bo 19 дней назад
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
@alonzoslim
@alonzoslim 7 месяцев назад
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
@RyanNolanData 7 месяцев назад
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
@eyadal-naimi3782
@eyadal-naimi3782 6 месяцев назад
protect this man
@RyanNolanData
@RyanNolanData 6 месяцев назад
haha I appreciate it
@swativarsha68
@swativarsha68 9 месяцев назад
lerant a lot! thanks!!
@RyanNolanData
@RyanNolanData 9 месяцев назад
Awesome! That’s the goal
@juanDoAs
@juanDoAs 8 месяцев назад
Trying your code I get this error: 'AttributeError: 'OneHotEncoder' object has no attribute 'set_output''. Any idea why this is?
@juanDoAs
@juanDoAs 8 месяцев назад
Nvm just needed to update scikit-learn
@RyanNolanData
@RyanNolanData 8 месяцев назад
Ok great. Everything else working properly?
@PhilTag-ml6wd
@PhilTag-ml6wd 3 месяца назад
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
@Aldotronix 2 месяца назад
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
@RyanNolanData Месяц назад
I have some projects that do. I may remake this video in the furture
@leodexter191
@leodexter191 Месяц назад
please go lil slow hard to understand
@RyanNolanData
@RyanNolanData Месяц назад
I'll have an article on this soon you can also check out
@leodexter191
@leodexter191 Месяц назад
@@RyanNolanData thank you