Handling missing data in administrative records using statistical and machine learning–based imputation techniques. The project compares Mean, Median, KNN, and MICE across different missingness levels to evaluate their effectiveness in improving data quality and supporting reliable analysis and decision-making.
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Handling missing data in administrative records using statistical and machine learning–based imputation techniques. The project compares Mean, Median, KNN, and MICE across different missingness levels to evaluate their effectiveness in improving data quality and supporting reliable analysis and decision-making.
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Handling missing data in administrative records using statistical and machine learning–based imputation techniques. The project compares Mean, Median, KNN, and MICE across different missingness levels to evaluate their effectiveness in improving data quality and supporting reliable analysis and decision-making.
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