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Package that allows both automated and customized treatment of missing values in datasets using Python.

Project description

This package allows both automated and customized treatment of missing values in datasets using Python. The treatments that are implemented in this package are:

  • Listwise deletion
  • Pairwise deletion
  • Dropping variables
  • Random sample imputation
  • Random hot-deck imputation
  • LOCF
  • NOCB
  • Most frequent substitution
  • Mean and median substitution
  • Constant value imputation
  • Random value imputation
  • Interpolation
  • Interpolation with seasonal adjustment
  • Linear regression imputation
  • Stochastic regression imputation
  • Logistic regression imputation
  • K-nearest neighbors imputation
  • Sequential regression multiple imputation
  • Multiple imputation by chained equations

All these treatments can be applied to whole datasets or parts of them and allow for extensive customization. The package can also recommend a treatment for a given dataset, inform about the treatments that are applicable to it, and automatically apply the best treatment.

Project details


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