Uplift Modelling - One line of code to get an uplift model.
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Here I have created some code to help speed up the process of building uplift models. You will need to first prepare a modelling dataset yourself with features, a treatment (yes/no) column and a target/label column.
[23/02/2021] X-learner function added and tested
[23/03/2021] T-learner function added and tested
[19/04/2021] S-learner function added and tested
[Coming soon] ML interpretability -- functions that will enable users to see drivers for outcome
See this jupyter notebook for further details on how to use this package.
Hope you are now excited with testing out Prism on your machine. To get started, please follow the below guidelines on prerequisites and installation.
- Sklearn==0.22.1
- Xgboost==1.1.0
- Numpy==1.18.2
See this jupyter notebook for further details on how to use this package.
I welcome anyone to contribute to this project so if you are interested, feel free to add your code. Alternatively, if you are not a programmer but would still like to contribute to this project, please click on the request feature button at the top of the page and provide your valuable feedback.
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