Dealing with Missing Values in Machine Learning: Easy Explanation for Data Science Interviews

In this video, I’m going to tackle a simple, common machine learning interview question: how to deal with missing values in a dataset. This problem impacts the quality of a dataset, and it can even bias the results of the machine learning model trained based on the data. This is a question that is often asked in Data Science interviews, so we’ll cover why there may be missing values in your data set, and how to deal with them.
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Contents of this video:
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00:00 Introduction
00:44 Missing Values
02:09 Data Point Omission
02:58 Feature Omission
03:26 Imputation
04:44 Missing Values
05:04 Offer Your Feedback

[@MrFromminsk]

The best video on handling missing values in DSs

[@user-wy4ge3yu4h]

Good explanation

[@edwinsimjaya4541]

Your explanation is very clear Emma, thank you so much!

[@emma_ding]

Happy to help! Thanks for watching. 😊

[@ArtificiallyConcious]

Excellently explained!

[@louisforlibertarian]

Love the vid! Can't wait for more in this ML interview question series!

[@emma_ding]

Thanks for following along, Louis! 💛

[@jameswright1848]

Thanks Emma! Very clear, easy to understand and very helpful!

[@emma_ding]

So glad to be of assistance, James! 😊

[@tejasphirke3436]

Wonderfully explained 😀

[@lisayang9256]

👍 thank you

[@chandansagar212]

thanks !

[@kelseyarthur6421]

What machine learning algorithms would you use to try to fill in missing values?

[@dishashah7127]

Regression can be used

[@chillfill4866]

I would consider the apriori algorithm