pyuplift 0.0.4.1

Uplift modeling implementation

Documentation • License • How to contribute • Uplift datasets • Inspiration

Installation

Install from PyPI

pip install pyuplift

Install from source code

git clone https://github.com/duketemon/pyuplift.git
cd pyuplift
python setup.py install

How to contribute

Any contributions are always welcomed. There is a lot of ways how you can help to the project.

• Contribute to the tests to make it more reliable.
• Contribute to the documentation to make it clearer for everyone.
• Contribute to the tutorials to share your experience with other users.
• Look for issues with tag "help wanted" and submit pull requests to address them.
• Open an issue to report problems or recommend new features.

Uplift datasets

• Criteo Uplift Prediction
• Hillstrom Email Marketing
• Lalonde NSW

Compatible with

• NumPy
• Scikit-learn

Inspiration

• Identifying Individuals Who Are Truly Impacted by Treatment
• Pinpointing the Persuadables: Convincing the Right Voters to Support Barack Obama
• Revenue Uplift Modeling

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, Guillén M, Pérez-Marín 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.