SAP/knn-sampler
Machine learning imputation method to recover the distribution of missing values, based on kNN. This method can be enabled to be used as multiple imputation and provide uncertainty quantification.
This tool helps data scientists and analysts clean up datasets that have gaps or missing entries. You provide a dataset with incomplete information, and it fills in those blanks using a sophisticated kNN-based method. The output is a complete dataset that better reflects the original data's underlying patterns, enabling more reliable analysis and modeling.
Use this if you need to fill in missing values in your datasets accurately, especially when preserving the original data distribution and understanding the uncertainty of those imputed values is important.
Not ideal if you're looking for a simple, quick fix for missing data without concern for statistical rigor or the underlying data distribution.
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Language
Jupyter Notebook
License
Apache-2.0
Category
Last pushed
Mar 13, 2026
Commits (30d)
0
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