Dealing With Missing Data Part I

[@eligirl100]

You are a hidden gem of the data science KZbin community

[@ritvikmath]

thanks!

[@zhaoqian58]

you have natural talent in teaching and simplifying concepts!

[@ritvikmath]

Wow, thank you!

[@haraldurkarlsson1147]

Hotdeck imputation is used by the Census Bureau amongst other methods for instance. (There is also cold deck - meaning that you use a previous dataset of a similar nature. The analogy , hot vs cold, is taken from the old computer cards that were hot when they were run and cold after they sat out).

[@gamespauls7765]

Quick, simple, and awesome - thank you!

[@cheettaah]

Great easy-to-follow video!

[@brendali5803]

Thanks for the nice sharing and clear explanation!

[@elmoreglidingclub3030]

Very nice explanation! Can you comment on variance and hot deck, please? Doing the quick math, the variance of the initial data set is 0.7. With the mean added it is 0.46. With hot deck it is 0.49. So hit deck solves the variance con a bit, obviously, but is that perception generalizable?? Great stuff!!

[@mixalisk.5413]

Thanks for the amazing videos. Just one quick question. If this was a machine learning problem and sex was our target variable would it still be ok to apply this method without having concerns of introducing bias to our model?

[@farahadibahadnan1648]

Good sharing

[@areebaniaz6429]

Excellent

[@juvenalmachado7693]

Hey, I am currently doing my thesis and working with economic surveys on a few countries and need to apply a hotdeck with 5 variables to impute declared income. I was wondering whether you do paid tasks such as this one I'm describing? Thanks

[@ayselceferzade8587]

awesome! thanks

[@alecvan7143]

Awesome!!

[@anvarisamukhamedov4961]

love it, thanks.

[@logicboard7746]

Hot deck @5:00

[@charyyevasuray148]

Thank you!

[@johnsoncy]

Thank you!