HyperImpute: sklearn-style library for handling missing data using novel algorithms

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There are many data imputation algorithms for machine learning. However, benchmarking them can be complicated, mainly because most implementations stay just as research code to reproduce the experiments in the papers. Moreover, when dealing with tabular data, you need to handle continuous/discrete/categorical data correctly -- not just let some regressor approximate everything.


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