Metadata-Version: 2.1
Name: MissForest
Version: 1.1.1
Summary: nonparametric imputation on missing values.
Home-page: https://github.com/HindyDS/MissForest
Author: Hindy Yuen
Author-email: hindy888@hotmail.com
License: MIT
Keywords: MissForest aims to provide the most convenient way for the data science community to perform nonparametric imputation on missing values by using machine learning models
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Developers
Classifier: Topic :: Software Development :: Build Tools
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Requires-Python: >=3.6
Description-Content-Type: text/markdown
License-File: LICENSE.txt

# MissForest
Arguably the best missing values imputation method.

MissForest aims to provide the most convenient way for the data science community to perform nonparametric imputation on missing values by using machine learning models.

- **Examples:** https://github.com/HindyDS/MissFores/tree/main/examples
- **Email:** hindy888@hotmail.com
- **Source code:** https://github.com/HindyDS/MissForest/tree/main/MissForest
- **Bug reports:** https://github.com/HindyDS/MissForest/issues
- 
# Convenient
It only requires 3 arguments to run:

- x: dataset that being imputed
- feature_to_be_imputed (str): feature that being imputed
- estimator: machine learning model

Optional arguments:
- max_iter (int): maximum number of iterations

If you have any ideas for this packge please don't hesitate to bring forward!

# Flexible
You can implement other machine learning models besides RandomForest into MissForest

# Quick Start
    !pip install MissForestExtra
    
    from MissForestExtra import MissForestExtra

    mfe = MissForestExtra()

    mfe.single_impute(x, feature_to_be_imputed, estimator)

    # return the imputed pandas series

    mfe.impute(x, classifier, regressor)

     # return imputed dataframe

