All about missing value imputation techniques | missing value imputation in machine learning

[@UnfoldDataScience]

In the whiteboard topic name I wrote "Outlier treatment" by mistake - Sorry for that - It should be "missing value treatment"🙃

[@supreethalahari.sreeram5147]

Sir, Please do more videos on real-time projects with python from basic to experience level. Your explanation is too good we can understand it very well. 👏👏👏👏👏👏👏👏

[@sorourrostampour5741]

You explained it way better than my professor! Thanks, man! :)

[@georgechristy11]

please make an implementation of this using a csv file , especially the code snippet for deck imputation ( i have never about this). Multiple imputations is also something new. Can you make a part 2 of this video? You are a great teacher .

[@UnfoldDataScience]

Thanks Christy, I ll put the advised topic in video.

[@georgechristy11]

@@UnfoldDataScience Thanks !!

[@billinioreteachings]

You are explaining in way that we want sir thank you so much aman sir

[@UnfoldDataScience]

Thank you.

[@ashishraj5568]

This video very help full to understand missing value imputation if it is possible please make one practical video for better understanding

[@UnfoldDataScience]

Thanks Ashish, Sure.

[@souravbiswas6892]

Yes create videos on implementing deck imputation, model based(classification), imputing categorical variables with many classes such as pin codes and multiple imputation.

[@mehmetackgoz3521]

Thank you so much for the perfect explanation! I appreciate if you could explain the Simple and Multiple Imputation in your next session

[@UnfoldDataScience]

Thank you. Sure

[@amandakurowski8725]

This is fantastic, thank you. Why bother struggling through article after article when it's best explained through visuals and clear examples...Seriously, thanks.

[@UnfoldDataScience]

Glad it was helpful!

[@mateenahmed5401]

Very neatly explained. Thank you Aman.

[@UnfoldDataScience]

Thanks Mateen. You comment mean a lot to me.

[@SumitKumar-sj5xw]

Great Explanation Aman. Please make a video on multiple imputation

[@UnfoldDataScience]

Thanks sumit. It's already there just after this video on homepage

[@drumilbhalani7614]

Great video sir , Sir can you explain deck imputation in detail because generally when we have more missing values then it's become hard to think about how we can impute missing values😊😊😊

[@UnfoldDataScience]

Sure Drumil. Thank you

[@balajilp823]

I Really appreciate your work Thank you so much.. You are doing great job

[@UnfoldDataScience]

Most welcome

[@Abhishek-ek9gw]

Very helpful and explanatory video. Thank you!

[@UnfoldDataScience]

Glad it was helpful!

[@emanabdelhaleem7561]

Thanks a lot more details and some coding will be great too

[@RamanKumar-ss2ro]

Awesome again.

[@UnfoldDataScience]

Thank you

[@k_abhishek2056]

Waiting is over😊....

[@UnfoldDataScience]

Thanks Abhishek

[@radhakrishnanananthan1585]

Great explanation as always. 👍

[@UnfoldDataScience]

Thank byou.

[@sachinjare8627]

Thank you sir. I really appreciate your work. I like your teaching. Plz make a video on multiple Imputation and Deck Imputation.

[@UnfoldDataScience]

Thanks sachin. Will do

[@shubhampanwar3482]

Please explain deck imputation and also multi value imputation.

[@UnfoldDataScience]

Ok let's see what other say 😄

[@piyushgurung9690]

Should we impute missing values in target variable with respect to classification problem? If I don't do that, while splitting into test train set we encounter error that NaN values are present.

[@RajdeepBorgohainRajdeep]

You give good viewpoints about any given topics, Also cover one video on encoding, how we can use encoding on different types of models.

[@UnfoldDataScience]

Thanks Rajdeep.

[@praddyotahayaran9847]

SINGLE IMPUTATION VS MULTIPLE IMPUTATION kindly make a video.

[@mukeshkumar-kh2fh]

sir can we replace NaN value of column by mean in such a way that if other parameter value is in a particular range than find the mean and replace . Example..if column BMI has NaN value then if age of that person is 45 then we first find the mean BMI of people with a age of range 40 to 50 and replace with this.Similarly,for other person have NaN BMI ... then first check the age of that person and set an interval age and find mean and replace...

[@santoshkumargouda6270]

very good explanation, I am learning from you

[@UnfoldDataScience]

Thanks Santosh.

[@kousikdutta5889]

Kindly make a video on every imputation type with the help of datasets.

[@aswinpadmanabhan92]

Plz put video related to imputation mainly multivariate imputation

[@danielrivaldo7756]

thanks bro, data scientist so cool

[@shalinigoud802]

Great content Aman could you please explain in detail about each imputation with examples

[@Maryashahere]

Good session, plz give practical session too

[@Maryashahere]

Sir Suppose we want to integrate few datasets, with different features, and if in one of that dataset already some imputation is done. Then how to do imputation on the integrated dataset without getting the effect of already imputed values?

[@shamsuzzoha-uz3tg]

sir please make a vedio on model based imputation analog and phython

[@ratnajyotibhowmick9801]

Could you please create video on Deck imputation?. Thanks.

[@shilpisinha921]

Please don't stop making videos

[@balajilp823]

Thank you amen, interesting, please make video about multiple imputations also

[@UnfoldDataScience]

Thanks Bala. Sure.

[@afeezlawal5167]

@@UnfoldDataScience sir, Have you made any video on those imputation yet ? Your students are calmly waiting sir ☺️

[@ajaykushwaha-je6mw]

Very informative, thank you for this. Can we expect practical implementation on these.

[@UnfoldDataScience]

Thanks Ajay, long time, hope u r doing awesome. Yes will create

[@sadhnarai8757]

Very good Aman

[@UnfoldDataScience]

Thank you

[@santhoshjayaraman1112]

Sir weather we need to do feature selection using correlation is after train test split or before

[@vivekkatole153]

Median mode mean imputation .plz make a video on that

[@swatisingh4041]

Please make a video on deck imputation with python code

[@anandhari5239]

Which method is best suitable for dealing missing values in longitudinal data analysis.

[@subhajitroy4869]

Please discuss multiple imputations and deck imputation.. and also please show the implementation of discussed methods on python.

[@UnfoldDataScience]

Thanks for watching , sure.

[@Graphite-G]

Thank you sir, please explain model based, and multiple imputation.

[@UnfoldDataScience]

Sure, thanks a lot

[@muskanrath7125]

Sir, can you please share approach on how to fill a column with NaN with immediate non null value till we encounter another non null value?

[@UnfoldDataScience]

please check this once if this is helpful stackoverflow.com/questions/43507767/pandas-fill-nans-with-next-non-nan-consecutive-nans

[@saravanankarunamurthy9854]

How to handle the missing value from target column? Assume, we have less no records and can't drop the values.. what is best approach of handling missing value from target column?

[@UnfoldDataScience]

Generally we do not impute targets as it may mislead training.

[@5775850]

Please discuss model based imputation for categorical variables with python codes

[@souravbiswas6892]

Hi Aman, in the video, the headline is shown as outlier treatment techniques. Was that a type?

[@mkentertainments7424]

Sir try to show it in the practical i mean show it in python too

[@shadiyapp5552]

Thank you sir♥️

[@billinioreteachings]

Aman sir you are gradually increasing your followers on KZbin 😁

[@UnfoldDataScience]

Thanks Hameed.

[@balajilp823]

If you add implementations also that would be helpfull for us

[@UnfoldDataScience]

Ok

[@TheStainlessFish]

Good content!

[@hiteshyerekar2204]

Hi Aman please make detail video on ruc and auc curve.most asked interview questions .

[@UnfoldDataScience]

Sure

[@modishubham1123]

Sir, please create a video on practical

[@UnfoldDataScience]

Sure, thank you

[@veenaev8222]

Multiple Imputation pls..

[@sriramswaroop9713]

Multivalue imputation and Deck imputation sessions please!

[@UnfoldDataScience]

Thanks, sure Sri ram

[@sachinjare8627]

Sir I have one simple question not related to this video. Can we use normalization and standardization scale at a time on same data set? I mean some feature with normalization scale and remaining with standardization.

[@UnfoldDataScience]

I think it should not create a problem but while prediction and retraining it may add more complexity still u can try

[@AnilKumar-im2ur]

Hi Aman, why can't we make multiple imputation and deck imputation in same video...please look into once.., Eagerly waiting for next explanation

[@UnfoldDataScience]

Sure, will explain thank you

[@clairetatham1457]

Structure missing and model based imputation.

[@amanpatyal2187]

Pls single and multiple imputation..

[@UnfoldDataScience]

Thanks for comment. Ok sure

[@bhavyaadesara7529]

Single and Multiple Imputation

[@UnfoldDataScience]

Sure, thanks for watching

[@anonanon6225]

Difference between NMAR and SM is not very clear

[@UnfoldDataScience]

Thank you for feedback, I will have a look and try to be better in next video.

[@Ankit-hs9nb]

I think what you told about NMAR is wrong

[@UnfoldDataScience]

Thanks for feedback, I will have a look, can you let me know wha texactly did i say wrong?