best teacher i have ever seen! Explaining in very proper way! in short time explaining exact things!!!

Data Mites is a hidden gem now but soon they will be a Brand for Data Science. Keep my note for Future.

A real pro! Subbed this channel after watching first 3 minutes. Glad to have found it.

Thank you, Ashok! This is an outstanding explanation of a complex subject. You make it all feel very intuitive. Awesome stuff - I will look for more DataMites videos in the future!

Amazing in depth explanation! I was exactly searching for this type of explanation.. Thanks for sharing

Great Ashok.!! Genuinely liked your way of explanation in depth and the solution... Glad i landed on your page... Thank You..!

Thanks Ashok, very clear and simple explanation.

Wow sir liked u r session .please continue posting such videos

This is really helpful and thank you again!

The best smote tutorial I've seen. Thanks

extreme clarification really superb teaching skills along with good communications

Nice and informative. Please keep up the good work.

Thank you so much, sir! I hope I see more videos

Such an amazing topic

Hello Sir, Thanks for explaining this very clearly.. keep it up....

wow...... i am very well impressed. well explained. thanks

Great video. Thanks

DataMites is like hidden pattern in unsupervised learning thank you so much ashok❤️❤️

Thank you very much

Thank you so much for the great tutorial.. As someone who does not have even the basic knowledge of python, I could learn many things from you, sir.

Excellent content, brilliantly presented. Thank you. Subscribed.

Great content sir !! Keep on spreading knowledge

Hai sir! thanks a lot for very simple and clear explanation.keep going we expect more videos from you...

Thank you so much!!! Really helpful. thanks

excellent explanation!

Nicely explained. Thanks!

Thank you ashok, clear explanation, but howto handle the imbalanced datasets if we have 4 classes?

very good explanation

Thank you for your sincere lecture sir

Great video. Great explanation. Thank you

Great video and explanation! Thanks.

hello sir, the material that you explain is very easy to understand. I want to ask about my project. I have imbalanced data, then I do smote and I model it with KNN, but why after smote does the accuracy go down? 79% to 78%, is there something wrong with my data? Can you help explain this? I am very grateful if you respond to my comment.

Great tutorial. Very good explanation sir.

Amazing in depth explanation

Thanks, nice explanation

Great job ....made it look very easy

very nicely explained sir ..thank you

wonderfully explained.thank you.

Very clear explanation. Thanks

Really appreciate sir..Lot off.🙏🏼🙏🏼🙏🏼🙏🏼🤗🤗🤗👌👌👌👌😊😊😊😊

Perfect video..thank you

thanks alot for this good tutorial.

Nice explanation .. Looking for more NLP related video

nice explanation

Amazing. Thanks a lot.

Firstly, Thank you for sharing. I wanna ask something about time series. I have lots of data. But datas are different frequency. I wonder how deal with all datas. And assume that datas edited to same frequency. By the way datas are not fitted normal distribution so imbalanced that's why i am asking. If datas be same frequency, Smote can be appliable for time series? If not how to resample my time series?

Thanks for sharing. Very helpful

Amazing Explanation!!! Thankyou.

This was a great lesson. Thanks a lot

in oversampling do you have to make the minority class instances equals the majority class instances ? for example: can it be 900 nc and 800 c

Awesome video

At 8.15 you have said it is taking the average of centroids which is completely wrong. SMOTE is calculated over the feature space...it goes like this 1. we take the feature vector of the minority class point. 2. we calculate the distance between the neighbours (neighbours=5). 3. we multiply the distance between the neighbours with a random number that is created between 0 &1. 4. Then we create the synthesized point. hope you got it 😀

how split stratify solves the problem?

thank you so much man. great thumbs up...

Nice video. After applying smote, balanced data was obtained. But balanced data (X_smote,y_smote) was not split (80:20) in to train n test data sets before reapplying classification model? Is it necessary or not to split the data again? Or orginal dataset itself was considered as test dataset.

Thanks so much bro..i have shown some data scientists used undersampling and oversampling before Splitting the dataset into training and testing..in my research paper we heve used NEARMISS technique to balance the dataset..i have got a good results with using cross validation Splitting and Extra tree classifier as model and also the same model to select the best importance features where my results are : (ACC 0.97 , F1 0.97 and AUC 0.99) are there results may be accepted for publishing?

Best Explanation sir ..............!

getting attribute error: 'SMOTE' object has no attribute 'fit_sample' but I have all the packages requirement satisfied still showing the error

Very good explanation Thanks. but this code, is applicable with text data (tweets) or not?

Very informative, i have a question sir, it is possible to set how many synthetic data created by smote ? in example i want to set n_sample increase to 200% so, how to put this parameters in pyhton code ?

Great Ashok. That was a well explained video. I tried the same thing on my data set but my accuracy came down from 94 to 86. What could be the cause?

Can you please explain this part of the code in the label encoder section:

great video, thank you!

Great video! Thanks a lot!!!

Thank you for the informative video! In this video, you have used SMOTE to rectify imbalance in target label. What methods can we use to deal with class imbalance in categorical features( input) in order to make the model more robust?

Great video on SMOTE. Do you have a video on undersampling? Can someone perform both undersampling and oversampling in one line of code??? THANKS.

Does Smote algorithm support Multi output classification?

Nicely explained

you haven't encoded the target variable?

Thank you very much for this video. I have a precipitation dataset containing 4 columns and 8000 rows, each of them has a lot of zeros and only a few continuous values. I would like to know if I can use smote in this case?

Oh! I got it. Don't worry. Thanks

which module is used for alternative module of imblearn in python sir(for handling imbalance dataset)

Hey Ashok, can u make a video on dsste algorithm for removing class imbalance?

what a crystal clear explanation. thank you.

Thank you so much. ^^

great video thank you. I am trying to figure out how to use this with a generator flowing from directory?

Running into a problem with sklearn 'support' column still looking unbalanced after smoting on print(classification_report(y_test, y_pred)) what gives?

SIr, I went on as per the recommended procedures but my jupyter environment giving an AttributeError that SMOTE object has no attribute '_validate_data'. Can you please help me with the.

i find this error >> plz help !

after I resample an imbalance dataset how can I download the resampled dataset from colab?

Hello Sir! can you please tell me how to generate images using smote technique ??? Thanks in advance...

While dividing training and test data shouldn't you be doing "stratify=y" ? To ensure test data and training data set have equal proportion of outcome variable?

Excellent!

Sir, Can I know how to run a logistic regression on the oversampled dataset?

hi sir i have question how did we implement those resampling technique in neural network, let say if we implement embedding layer and work with multiple kind of data is that resampling technique make our data losing such information?

Good

Any video where we use SMOTE for regression??

Cannot install imblearn. Kindly help me with this

Thank you sir...👍

hi sir need to join in ur classes how to approach you

Thank you for this nice explanation. I was making progress with the codes but when I tried to fit using the command X_train_smote, y_train_smote = smote.fit_sample(X_train.astype('float'),y_train), I got error saying AttributeError: 'SMOTE' object has no attribute 'fit_sample'. I need urgent help please. Thank you

AttributeError: 'SMOTE' object has no attribute 'fit_sample'

Amazing!

Hi sir what is the data type for outcome ? i think it is in object . Did u convert that into float or int?

subscribed for ur content

I have one doubt. What if data contains Nan values and you want to do under_sampling? If you impute Nan values with Mean() then there will be information leakage because we impute data before splitting it into train and test dataset. Could you please tell me what should be the possible solution in this case?

ur awesome

Getting an error: ValueError: Unknown label type: 'continuous-multioutput'

Hi, did anyone applied this concept to image dataset. please anyone let me know...

Pls share the notebook and input file