Metadata-Version: 2.4
Name: credit_scoring
Version: 0.2.0
Summary: This library help researcher have quick result with woe, iv, result reporting with credit scoring problem
Home-page: https://github.com/minhtcuet/creditscoring
Download-URL: https://github.com/minhtcuet/creditscoring/archive/v_01.tar.gz
Author: Minh Tran
Author-email: minhtc.uet@gmail.com
License: MIT
Keywords: Credit,Credit Score,WOE,IV,WOE-IV,GINI,KS,Lift,GAIN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Topic :: Software Development :: Build Tools
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Description-Content-Type: text/markdown
License-File: LICENSE.txt
Requires-Dist: numpy
Requires-Dist: pandas
Requires-Dist: plotly
Requires-Dist: sklearn
Requires-Dist: scipy
Dynamic: author
Dynamic: author-email
Dynamic: classifier
Dynamic: description
Dynamic: description-content-type
Dynamic: download-url
Dynamic: home-page
Dynamic: keywords
Dynamic: license
Dynamic: license-file
Dynamic: requires-dist
Dynamic: summary

## Project Description

#### Credit-Score

Credit Score is a Python module for credit scoring problems, help developers/ researchers applied woe-iv for easier.
More, It provides some importance reports about credit scoring contains: GINI, KS, LIFT, GAIN, IRL, LR, ... help users 
save time to choose the best model.

Currently, credit-scoring package handles only binary target

#### Installation

This package requires numpy, pandas, sklearn and plotly. We can use this package for Python 3, and pandas must be >= 1.0 

To install the package, run this command:

`python setup.py install `

or 

`pip install credit_scoring`

If you want install the development of this package, you can visit: 
_`https://github.com/minhtcuet/creditscoring`_

#### Usage 

This version, we focus on reporting first. You can have quick GINI, KS, LIFT, .... with 2 input parameters contains: 
y_target(binary type) and y_predict(probability)

#### Example

###### GINI Calculated

from credit_scoring.AUC import GINI

gini = GINI(y_predict, y_label)
print(gini)_


