Best Variables for classification and regression models

The objective of this packege is to simplify the usage of methods to make feature selection.

The Package bestvarspk is a Python module for machine learning built based on top of sklearn feature_selection and is distributed under the license.

The project was started in 2020 by Gutelvam as a Udacity Nanodegree of project.

Installation

Dependencies

bestvars_pk requires:

    -Python (>= 3.6)

    -NumPy (>= 1.13.3)

    -SciPy (>= 0.19.1)

    -joblib (>= 0.11)

    -threadpoolctl (>= 2.0.0)

    -scikit-learn (>=0.23.1)

    -matplotlib(>=3.2.2)

    -seaborn(>=0.10.1)

    -pandas(>=1.0.3)

User installation

If you already have a working installation of scikit-learn, the easiest way to install is using pip:

!pip install bestvarspk

How to use

    1. Instantiate an objet 'Selection'
            from bestvarspk.Variables_selection import Selection
            obj = Selecton(df, target)

where:

df is a dataframe

target is a string of target column name

    2. Use methods available.

            obj.corr_features()

            obj.importance_features()

            obj.rfe_features()

obs: Anytime you can check for help(?) to check docstrings.