Why missing data occurs can be attributed to various reasons, including human error, malfunctioning equipment, or even intentional omission. It is important to handle missing data because it can significantly impact the reliability and accuracy of any analysis or decision-making process. Without proper handling, missing data can lead to biased results, misleading conclusions, and flawed interpretations. Therefore, implementing effective strategies to address missing data is crucial in ensuring the integrity and validity of any data-driven insights and outcomes.
Comics: Introduction to Missing Data Analysis & Implementations
A comic guide to mean/median/mode imputation & Python codes
K-Nearest Neighbors (KNN) imputation in sklearn
Multiple Imputation with Chained Equations method & Python codes
Imputation using SoftImpute in python
More advanced:
What’s missing completely at random data
What’s Missing at Random (MAR)?
A comical guide to Missing Not At Random (MNAR)
why we can & probably should use missing at random imputation methods for data that’s not missing at random?
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