Answer / Atul Goel

Handling missing or corrupted data is crucial to ensure the quality and reliability of Machine Learning models. There are several strategies for dealing with missing data: Imputation (replacing missing values with a value estimate such as mean, median or mode), Deletion (removing the entire row or column containing missing data), and Data Interpolation (filling in missing values by interpolating between existing data points). For corrupted data, techniques like outlier detection and removal can be applied to clean and preprocess the dataset.

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