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Missing values and outliers: imputation and robust pipelines

Missing data: you cannot just delete—fill wisely

Real data always has missing values. The customer did not enter age. The sensor failed. A transaction was lost. Dropping rows with missing values wastes data and introduces systematic bias. Filling with zero gives the model false information. The right strategy hinges on one question: why are the data missing?

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