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Data Interview: How to Decide Drop or Fill Missing Values? | MCAR MAR MNAR Explained

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Struggling with missing data in interviews or real projects? This video teaches you exactly how to decide whether to DROP or FILL missing values — using the "Missing Baby" storytelling method that makes it impossible to forget.

Master the 3 mechanisms of missingness:
→ MCAR – Missing Completely at Random
→ MAR – Missing at Random
→ MNAR – Missing Not at Random

Once you understand WHY data is missing, the drop vs fill decision becomes obvious. This is the exact framework top data scientists use — and interviewers love to test.

Perfect for data science interviews, ML projects, and Python/Pandas preprocessing.