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I have watched only 4 mins so far i had to pulse and write this comment. I will say this is one of the best tutorial i have seen in data science. Sir you need to take this to another level. What a great teacher you are

@codebasics

4 жыл бұрын

That for the feedback my friend 😊👍

Wonderful Video. This so far the easiest explanation I have seen for one hot encoding. I have been struggling from very long to find a proper video on this topic and my quest ended today. Thanks a lot, sir.

For anyone stuck with the categorical features error. from sklearn.compose import ColumnTransformer ct = ColumnTransformer([("town", OneHotEncoder(), [0])], remainder = 'passthrough') X = ct.fit_transform(X) X Then you should be able to continue the tutorial without further issue.

@muhammadhattahakimkeren

4 жыл бұрын

thanks bro

@fatimahazzahra6181

4 жыл бұрын

thanks a lot! it helps

@souvikdas3189

11 ай бұрын

Thank you brother.

@User_2337random

10 ай бұрын

Hey, thank for the code. I tried using your code but it gives me an error, despite of converting it (X) to an array, it gives me this error. " TypeError: A sparse matrix was passed, but dense data is required. Use X.toarray() to convert to a dense numpy array. "

@TheSignatureGuy

10 ай бұрын

​@@User_2337random I know you said "despite converting X to an array", but just double check you have used the .toarray() method correctly. The error message seems pretty clear on this one. This function may help confirm that a dense numpy array is being passed. import numpy as np import scipy.sparse def is_dense(matrix): return isinstance(matrix, np.ndarray) Pass in X for matrix and it should return True. Good luck fixing this.

Exercise solution: github.com/codebasics/py/blob/master/ML/5_one_hot_encoding/Exercise/exercise_one_hot_encoding.ipynb Everyone, the error with catergorical_features is fixed. Check the new notebook on my github (link in video description). Thanks Kush Verma for giving me pull request for the fix.

@urveshdave1861

4 жыл бұрын

Thank you for the wonderful explanation sir. However I am getting an error as __init__() got an unexpected keyword argument 'catergorical_features' for the line for my code onehotencoder = OneHotEncoder(catergorical_features = [0]). Is it because of change of versions? what is the solution to this?

@bishwarupdey10

4 жыл бұрын

_init__() got an unexpected keyword argument 'categorical_features' sir I get this error when I specify categorical features

@sejalmittal1326

4 жыл бұрын

@@urveshdave1861 Have you got any answer for this? I am having the same error

@sejalmittal1326

4 жыл бұрын

@@urveshdave1861 okay .. i will do that. thanks

@tanvisingh9298

4 жыл бұрын

@@urveshdave1861 Hey I am also getting the same error. how did you resolve it?

Sir pls continue your machine learning tutorials ,yours tutorials are one of the best I have seen so far .

@codebasics

5 жыл бұрын

sure Gaurav, I just started deep learning series. check it out

@samrahafeez5001

3 жыл бұрын

@@codebasics Kindly explain the concept of dummies in deep learning as well

This was really well done! Kudos to you! It's hard to find clear and concise free tutorials nowadays. Subscribed and hope to see more awesome stuff!

Your ability to simplify things is amazing, thank you so much. You are a natural teacher.

This guy is AMAZING! I have spent 2 days trying decenes of other methods and this is the only one that worked for my data and didnøt come as an error, this guy totally saved my mental sanity, I was growing desperate as in DESPERATE! Thank you, thank you, thank you!

@codebasics

3 жыл бұрын

I am glad it was helpful to you 🙂👍

Hi, Your explanation is very simple and effective Ans for practice session A)Price of Mercedes Benz -4Yr old--mileage 45000= 36991.31721061 B)Price of BMW_X5 -7Yr old--mileage 86000=11080.74313219 C) Accuracy=0.9417050937281082(94 percent)

@ANIMESH_JAIN04

Ай бұрын

Same bro

@fathoniam8997

28 күн бұрын

same bro.... thx for replying so that i can check my results

This is the best machine learning playlist i have came across on youtube😃👍, Hats off to you sir.

Even in 23 your video is such a relief..kudos to your teaching.

I was shocked after the first 5 minutes of the video and have never thought it would be so easy and fast! Thanks ALOT1

@codebasics

4 жыл бұрын

Miyuki... I am glad you liked it

You teach with passion! thank you for the series!

I must say this is the best course I've come across so far.

Anyone can be a teacher , but real teacher eliminates the fear from students .. you did the same !! Excellent knowledge and skills

@codebasics

5 жыл бұрын

Sreenivasulu, your comment means a lot to me, thanks 😊

this ML tutorial is by far the best one i have seen it is so easy to learn and understand and your exersise also helps me to apply what i have learn so far thank you.

@codebasics

3 жыл бұрын

Glad it helped!

thank you, this helped me so much with multivariate regression with many categorical features!

I was confuse from where to start studying ml and then my friend suggested this series.... It's great :-)

@rishabhjain7572

3 жыл бұрын

any other courses or source you are following? and any development you have begun ?

@sauravmaurya6097

2 жыл бұрын

want to know how much this playlist is helpful? kindly reply.

@carti8778

2 жыл бұрын

@@sauravmaurya6097 its quite helpful if u are a beginner. Beginner in sense of {not from engineering or programming background }. U can accompany this with coursera’s andrew ng course.

@carti8778

2 жыл бұрын

@@sauravmaurya6097 if u already know calculus and python programming (intermediate level) , ML would feel easy . After doing this go to the deep learning series bcz thats what used in industries.

You are doing a wonderful job, people like you inspire me to learn and share the knowledge i gain. It is very useful for me. All the best.

definitely one of the best videos to learn from!

I wish I could give this videos 2 thumbs up! Great explanation of all the steps in one-hot encoding! Thank you!!

your are the best teacher on youtube , i have never seen before

The import linear regression statement lol. Amazing tutorial. :D

15:50 write your code like this: ct = ColumnTransformer( [('one_hot_encoder', OneHotEncoder(categories='auto'), [0])], remainder='passthrough' ) X = ct.fit_transform(X) X Ok so it will work fine otherwise it will give an error.

@AxelWolf26

2 жыл бұрын

what is the use of this " (categories='auto') " and " 'one_hot_encoder' "

@jollycolours

2 жыл бұрын

Thank you, you're a lifesaver! I was trying multiple ways since categorical_features has now been depreciated.

@adilmajeed8439

2 жыл бұрын

@@jollycolours correct, the categorical_features parameter is deprecated and for the same following are the steps needs to be followed; from sklearn.compose import ColumnTransformer ct = ColumnTransformer([('one_hot_encoder', OneHotEncoder(), [0])], remainder='passthrough') X = np.array(ct.fit_transform(X), dtype=float)

Simply excellent explanation with very simple examples!

I will say this is one of the best tutorial i have seen in ML

I learned a lot from the exercise that you gave at the end of the video, thank you so much sir!

You have gift of explaining things even to the layman. Big Up to you

@codebasics

4 жыл бұрын

Thanks a ton Wangs for your kind words of appreciation.

Excellent video - thank you!

Merc: 36991.317 BMW: 11080.743 Score: 94.17%

@codebasics

4 жыл бұрын

Your answer is perfect Ankit. Good job, here is my answer sheet for comparison: github.com/codebasics/py/blob/master/ML/5_one_hot_encoding/Exercise/exercise_one_hot_encoding.ipynb

@vishalrai2859

4 жыл бұрын

thanks for posting the answer bro

@mutiulmuhaimin9156

4 жыл бұрын

Could we upvote this comment to the top? Been looking for this for quite some time now. This is important, and this comment matters.

@Augustus1003

4 жыл бұрын

@@codebasics I used pandas dummy variable instead of using onehotencoding, because it is too confusing.

@clashcosmos4641

3 жыл бұрын

Got the same answer using OneHotEncoder after correcting tons of errors and watching videos over and over.

This is an amazing tutorial! saved me so much time and brought so much clarity!!! Thank you!

the best video series on ML sir ....Thank you very much sir....

Excellent as usual!

You make it easy with your explanation !! Thank you !!

Hi Dhaval, your explanation on all the topics is crystal clear. Can you please make videos on NLP also

@codebasics , I am following this tutorial implementation into my own project , however , i faced an issue , i cannot numpy.ndarry.drop(["Feature1","Feature1_None"...] i cant follow your example as i am unaware of which columns i should drop , any advice? I am using KNN algorithm , does it auto drop one column for me?

Awesome, you're explaining concepts in very simple manner.

@codebasics

4 жыл бұрын

Vishwa I am happy to help 👍

Sir I have read the onehotencoder documentation , it says it has drop function which can be set and it will drop Column to prevent from dummy variable trap. Can you please provide some insight on it and in which version it is available

Would you get the same prediction results if you keep all of the dummy columns and in the first deep layer, remove the bias scalar (b)?

difficult topics are easily understood, Thank you so much for the content sir

This was ridiculously helpful. Thank you so much!!

@codebasics

4 жыл бұрын

Netté, I am glad you liked it

Thank you! Very helpful!

the god of data science......Amazing explanation sir..kudos to your patience in explanation

@codebasics

3 жыл бұрын

Glad it was helpful!

Please make regression video using preprocessing library with standaridization and normalization variables

Thanks for the excellent tutorial.... I see there is a decrease in score between this and the exercise data. Maybe due to an extra column in exercise data ? With increase in columns on X, Will the linearRegression score decrease ?

First of all, 1000*Thanks for sharing such content on youtube.. I got an accuracy of 94.17% on training data.

@codebasics

4 жыл бұрын

Bandham, I am glad you liked it buddy 👍

Great, concise video!

maximum how much distinct categorical variable we can apply onehotencoding?

sir i have a problem in a last some lines of your code suggest me when i have greater no of towns features of then how to write predict the house price need some hep

Thanks for the video! I want to ask why it has to be converted into array after OHE? Apologies if it’s a fun question. I’m real new in Python. Thanks!

Thank you for this series. Such great help

@codebasics

3 жыл бұрын

Glad it was helpful!

Beautiful explanation, very helpful

Many Thanks ! Great Explanation :)

How can I like this video more than 100 times!

@codebasics

3 жыл бұрын

I am happy this was helpful to you.

If you don't mind me asking, when calculating the score do you pass the train set or the test set ?

One of the best explanation for Encoding 👌👍

@codebasics

3 жыл бұрын

Glad it was helpful!

Thank you! great video!

thank you! For making it possible to understand:)

There is one query when i plugs the same values from the dataframe for the car model the predicted value is much differ from the actual value of the car model. why this is happeneing...

Excellent video.., thank you so much.

Thank you so much for the detailed step by step explanation.

@codebasics

4 жыл бұрын

Glad it was helpful!

Thank you for great videos

so in dummy variable trap do we always have to keep 2 columns in our original dataset? let's say we have one another town named A_town and when we call get_dummies() and concat() methods we will have 4 more columns. and we need to drop some of the columns bc these 4 columns basically same and that will occur a problem for our model. which columns will be drop in these situation?

Sir, If we remove one of the dummy variable manually & fit the dataset to the LinearRegression class of sklearn.linear_model library then what the regressor will do? Can the regressor automatically detect that one of the dummy variable is already removed or the regressor removes one more dummy variable.

What would help inform a decision to drop one of the dummy variables? You mentioned the linear regression classifier will typically be able to handle and nonlinear interaction between most dummy variables. When should we drop one?

Thank you! it was really well explained

Hi Sir, any specific reason for first doing labelencoder before applying one hot encoder?. can we apply one hot encoder directly without doing label encoding?.

nicely explained👌

@codebasics I'm trying to plot scatter plot in PyCharm using plt.scatter(df.Mileage,df.SellPrice($)) But it is giving me below stated error , Traceback (most recent call last): return object.__getattribute__(self, name) AttributeError: 'DataFrame' object has no attribute 'Age' But when I remove ($) it doesn't give the error and plots it. why is this happening, same problem with Age(yrs), Shoud we remove (yrs) and ($) while using plotting plt.scatter(df.Mileage,df.SellPrice)

Could you explain to me, why is the train_test_split not in this program?

perfection

I am getting 84% accuracy without encoding variable, but after encoding i am getting 94% accuracy on model. Thank you for your teaching. Doing great Job

❤🎉🎉 Thank you. You earned a subscriber

I'm getting Error while get_dummies() function like this: TypeError: 'DataFrame' objects are mutable, thus they cannot be hashed

Thank you sir🎉. You made my ML Journey Better.. 🤩

Thank you. Please could you make more videos I understand your timing problem but still please:))

This helped me a lot in my assignment, thank you so much code basics

@codebasics

3 жыл бұрын

Glad it helped!

Sir , how can we create dummy variables for numerical values (ex:: if there are 1,2 ,3 classes in a ship or train).

hi there brother I have a lot of data related to the exercise with models engine type a lot of attributes and instances some of them are numerical data and some are categorical how can I apply one-hot encoding on that data please replay

the categorical features is droped by sklearn so my program isnt running are there any changes to be be done in the program in oorder to maki it work

Hi, Since OneHotEncoder's categorical_features has been deprecated... Can you please mention here how to proceed?

Hi if we want to do one hot coding for multiple categorical columns we need to give multiple index numbers ?

sir if we have output variable as a multiclass categorial variable, then after applying label encoding, will priority effect matter. I dont think it will because there will be no effect in algorithm maths, as its an output variable.

Excellent!

nice teaching, really outstanding thanks a lot

Very nice explanation, appreciated

Hello sir. Thank you for such wonderful content. I am curious to know how we can plot the graph and analyze this data and our predictions.

The Data Science GOAT! One day I will send you a nice donation for all that you have contributed to my journey sir!

Wait wait... I don't see the point 😕 The first half of the video does the same thing as one hot encoding(the second half of video)but second half is more tedious and takes more steps Then why not use the pd.get_dummies instead of onehotencoding??? What's the advantage of using onehot?

@codebasics

5 жыл бұрын

I personally like pd.get_dummies as it is convenient to use. I wanted to just show two different ways of doing same thing and there are some subtle differences between the two. Check this: stackoverflow.com/questions/36631163/pandas-get-dummies-vs-sklearns-onehotencoder-what-is-more-efficient

@armagaan007

5 жыл бұрын

@@codebasics thank you :]... btw you make grt videos

thank you so much it has helped me in my work

@codebasics

4 жыл бұрын

Hey Eugene, I am glad to hear that it helped you in your work. Stay in touch for more videos and share our channel if you really find it worth.

can we use either one for encoding ? or is there a deciding factor of when to use one hot encoder and when to use dummy encoding ?

Hi, can we know which interger values are assigned to the categories through 'LabelEncoder'. Here, only three categories are available, so it can be easily distinguished Encoded values. What if there are 10 categories available, and how to know the exact Encoded values with respect to Categories?

@rajeshmandapati4407

2 жыл бұрын

yes sir same question

What is the work done by label encoder just showing the values into a 2D array but never used??

Even if I don't perform dummy variable drop method for 'homeprices.csv', am getting same accuracy & predicted price as with Onehotencoder. Is it because the no.of dummy variables category is less (say 3 here)? Or this method will be effective for large no. of dummy variables?

@codebasics what if we have some new another category in test data set. How we can handly that.please reply.

why did we drop and separate X and Y columns?cant we apply linera regression on the same dataset

Thank you so mush, very clear

model is also predicting, When we give two categories as 1. That is not true because either car is mercedes or it is BMW X5. Please sir help me out how to solve this error?

I achieved the same result using a different method that doesn't require dropping columns or concatenating dataframes. This alternative approach can lead to cleaner and more efficient code df=pd.get_dummies(df, columns=['CarModel'],drop_first=True)