Відео

Thank you for the very clear explanation 🙂

Very well explained!!

short simple informative!

Thank you so much Rachit, video is really awesome

❤

but you can do getdummies before traintestsplit

i have an already scaled data with powertransformer, can you really scaled a new data outside the scaled data with scaled data as standard using fit.? because ive tried this and the data seems dont mach the scaled data. Thank you

Thanks! ❤

MICE part is good but the missingness definitions are all wrong.

excellent demonstration !I really appreciate your efforts. Very helpful for me as a beginner

why pd.get_dummies not working for me ?

Very clearly explained.Thank you.

Thank you so much!!

thank you for explaining this! :) poor PhDs are thankful for people like you who use their free time to do such videos!

in-depth and best explanation video

learned something new today. Thank you so much

If I want to use Simple Imputer on two different columns but with different strategies on each column then what should I do ?

You have explained so clearly

Wow now I completely understand pivoting. I was struggling to get the concepts, now its all clear. Thank you Rachit.

That's the best video I've seen! Thank you so much. But in this video, the "purchased" column is ignored because this is fully observed. So what happens if missing values are only present in the "age" column, I mean the "experience", "salary" and "purchased" are fully observed and for the same reason, we will ignore them so we only have the "age" column that can not use the regression? Please help me!

thanks a lot. really helpful

well explanation bro..i am wathching 5 6 vedios about split method but the only one vedio is helpful for me and that's is yours

Thanks Needed a clear explanation for my missed class😅

Bunch of thanks for the clear explanation❤

Wow, that really helped me in evaluating my master thesis!!!

Eknumber.

How imbalanced before AUPRC is preferred.

very simple explanation, thank you and subscribed!

Hey, the video was very helpful.. Can anyone explain me while implementing MICE in RStudio we get two columns Iteration & Imputation, how can we connect that with this video. Like in RStudio for each iteration we get 5 imputed dataset (by default). But from this video, we only get one dataset for a iteration.. It would be really helpful if anyone can explain me this. Thanks in advance

Can you turn on subtitle for the videos? Im bad at English listening

Thank your very much for this great explanation

What about 1 row missing value and 4th row missing value is those values to be filled necessary?

Great video bro...

very good video

Thanks, do you have example on how to incorporate LOG or SQRT transformation of features before modeling?

The word “namaste" in Sanskrit means “bowing to you". Muslims believe that one can bow/prostrate only to Allah. We don't bow down to any human. It's important to note that religious beliefs and practices can vary among individuals and communities.

Brother found some specific examples to explain MAR and MNAR 😅

Great explanation regarding column-transformer and pipeline, however how do you have the data frame column names back instead of numbers? Thank you!!

Nicely explained

Namaste, Awesome, Sir. I must addmit the best tutorial by far in pandas groupby I have ever accross. Keep it up

best explanation!!!

Sir how can i get the dataset

Very good explaination.I was struggling to get clearity on it .

This tutorial is awesome!!

(note: this is not an issue specific to your video, but something i have been getting confused by for a long time, this is just the first time I decided to stop and try ask about it in the comment section) It seems like it should be necessary (or maybe if not necessary, at least would be useful) to tell the model which column each imputed indicator is indicating for, right? But in the final dataset that you produce the imputed data indicators are all bunched up as the first four columns. How does the model know A) these features are imputed data indicators and, more importantly, B) which of the remaining 93 columns in the dataset each one is supposed to be for? They could be indicating for columns 5, 6, 7, and 8, or they could be indicating for columns 45, 72, 8, and 92, or any other combination of the remaining 93 feature columns. How does this not affect how the model trains? My brain is thinking that possibly the algorithm somehow susses that out on its own... but i don't understand how or why it can do that. Am i making much ado about nothing here?

i did not understand if s = df['population'] then how remove outlier from all dataset instead of population column.

Do we apply scaling techniques on categorical columns after encoding? Plz help

Very well explained video. Thank you!

I have also seen univariate imputation refer to a situation were you are only trying to impute one column instead of multiple columns that might more than one missing value

Great video, Rachit. Thank you. I also heard OneHotEncoding is not good for large categorical data in real world projects. Please which method do you advise or is there a video of you doing it that we can watch? Thank you so much