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Missing data refers to the absence of values in a dataset where information is expected to be present. Missing data can occur for various reasons, including data entry errors, equipment malfunction, survey non-response, or intentional omission. Handling missing data is crucial for accurate and meaningful data analysis and modeling. Here are some common techniques for dealing with missing data:
Deletion:
Imputation:
Interpolation:
Predictive Modeling:
Missing-Value Indicators:
Domain-Specific Imputation:
Hot-Deck Imputation:
Data Augmentation:
Bootstrap Imputation:
Deep Learning Imputation:
The choice of method depends on the nature of the data, the reason for missingness, and the impact on downstream analysis or modeling tasks. It's essential to carefully evaluate the implications of the chosen method and consider potential biases introduced during the imputation process. Additionally, documenting the imputation strategy is crucial for transparency and reproducibility in data analysis.
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