Metadata-Version: 2.1
Name: bestvarspk
Version: 0.1
Summary: Bestvars feature_selection methods
Home-page: UNKNOWN
Author: Gutelvam Rodrigues de Jesus
Author-email: gutto.rdj@gmail.com
License: UNKNOWN
Description: <h1>Best Variables for classification and regression models</h1>
        
        The objective of this packege is to simplify the usage of methods to make feature selection.
        
        The Package **bestvars_pk** 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.
        
        <h4>Installation</h4>
        
        ***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 best-vars-pk
        
        <h2>How to use</h2>
        
                1. Instantiate an object '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.
Platform: UNKNOWN
Description-Content-Type: text/markdown
