Metadata-Version: 2.1
Name: pyuplift
Version: 0.0.4.0
Summary: Uplift modeling implementation
Home-page: https://github.com/duketemon/pyuplift
Author: Artem Kuchumov
Author-email: kuchumov7@gmail.com
License: MIT License
Description: ![](https://github.com/duketemon/pyuplift/raw/master/resources/pyuplift-logo.png)
        
        [![Documentation Status](https://readthedocs.org/projects/pyuplift/badge/?version=latest)](https://pyuplift.readthedocs.io/en/latest/?badge=latest)
        [![Build Status](https://travis-ci.org/duketemon/pyuplift.svg?branch=master)](https://travis-ci.org/duketemon/pyuplift)
        [![PyPI - Python Version](https://img.shields.io/badge/python-3.5%20%7C%203.6%20%7C%203.7-blue.svg)](https://github.com/duketemon/pyuplift)
        [![GitHub](https://img.shields.io/github/license/duketemon/pyuplift.svg)](https://github.com/duketemon/pyuplift/blob/master/LICENSE)
        
        [Documentation](https://pyuplift.readthedocs.io) вЂў
        [License](https://github.com/duketemon/pyuplift/blob/master/LICENSE) вЂў
        [Uplift datasets](#uplift-datasets)
        
        ## Installation
        ### Install from PyPI
        ```bash
        pip install pyuplift
        ```
        ### Install from source code
        ```bash
        python setup.py install
        ```
        
        ## Examples of usage
        * [Synthetic dataset](https://github.com/duketemon/pyuplift/blob/master/examples/Synthetic_data-usage.ipynb)
        * [Hillstrom Email Marketing dataset](https://github.com/duketemon/pyuplift/blob/master/examples/Hillstrom_Email_Marketing-usage.ipynb)
        
        ## Uplift datasets
        * [Criteo Uplift Prediction](http://ailab.criteo.com/criteo-uplift-prediction-dataset)
        * [Hillstrom Email Marketing](https://blog.minethatdata.com/2008/05/best-answer-e-mail-analytics-challenge.html)
        
        ## Compatible with
        * [numpy](https://github.com/numpy/numpy)
        * [sklearn](https://github.com/scikit-learn/scikit-learn)
        
        ## References
        * Devriendt F, Moldovan D, Verbeke W. A literature survey and experimental evaluation of the state-of-the-art in uplift modeling: A stepping stone toward the development of prescriptive analytics. Big data. 2018 Mar 1;6(1):13-41.
        * Weisberg HI, Pontes VP. Post hoc subgroups in clinical trials: Anathema or analytics?. Clinical trials. 2015 Aug;12(4):357-64.
        * Lo VS. The true lift model: a novel data mining approach to response modeling in database marketing. ACM SIGKDD Explorations Newsletter. 2002 Dec 1;4(2):78-86.
        * Guelman L, GuillГ©n M, PГ©rez-MarГ­n AM. A decision support framework to implement optimal personalized marketing interventions. Decision Support Systems. 2015 Apr 1;72:24-32.
        * Tian L, Alizadeh AA, Gentles AJ, Tibshirani R. A simple method for estimating interactions between a treatment and a large number of covariates. Journal of the American Statistical Association. 2014 Oct 2;109(508):1517-32.
        
Keywords: uplift modeling,machine learning,true response modeling,incremental value marketing
Platform: UNKNOWN
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Education
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Topic :: Software Development :: Libraries
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Description-Content-Type: text/markdown
Provides-Extra: tests
