Metadata-Version: 2.1
Name: that-ml-library
Version: 0.0.6
Summary: A useful package for EDA and quick ML model building
Home-page: https://github.com/anhquan0412/that-ml-library
Author: Quan Tran
Author-email: anhquan0412@gmail.com
License: Apache Software License 2.0
Keywords: jupyter notebook python sklearn statistics statsmodels
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: License :: OSI Approved :: Apache Software License
Requires-Python: >=3.9
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: numpy >=1.23.0
Requires-Dist: pandas >=1.5.0
Requires-Dist: scikit-learn >=1.2.0
Requires-Dist: matplotlib >=3.7.0
Requires-Dist: seaborn >=0.12.0
Requires-Dist: plotly >=5.14.0
Requires-Dist: dtreeviz >=2.2
Requires-Dist: statsmodels >=0.13.0
Requires-Dist: pingouin >=0.5.3
Requires-Dist: yellowbrick >=1.5
Provides-Extra: dev

# that-ml-library

<!-- WARNING: THIS FILE WAS AUTOGENERATED! DO NOT EDIT! -->

## Install

``` sh
pip install that_ml_library
```

For tree visualization function
([`plot_tree_dtreeviz`](https://anhquan0412.github.io/that-ml-library/chart_plotting.html#plot_tree_dtreeviz)
or
[`plot_tree_sklearn`](https://anhquan0412.github.io/that-ml-library/chart_plotting.html#plot_tree_sklearn)),
you also need to install `graphviz`. Please follow the instruction
[here](https://github.com/parrt/dtreeviz#installation)

## How to use

Please visit <https://anhquan0412.github.io/that-ml-library/> for
tutorials and documentations

## A word of caution

This library should only be utilized solely for developing a proof of
concept or prototype for your machine learning model with your specific
dataset, with the aim of evaluating the model’s performance and
interpretability. For deployment in a production environment, opt for a
more organized methodology, such as
<https://scikit-learn.org/stable/modules/compose.html#pipeline>
