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