Skip to content

A repo with functions for building various COMs and GCOMs quickly.

Notifications You must be signed in to change notification settings

hklchung/UpliftModelling

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

12 Commits
 
 
 
 
 
 
 
 

Repository files navigation

Contributors Forks Stargazers Issues

Python 3.6 Sklearn 0.22.1 Xgboost 1.1.0 License MIT


Uplift Modelling

Uplift Modelling - One line of code to get an uplift model.
Explore the docs »

View Demo · Report Bug · Request Feature

Table of Contents

About the Project

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.

Getting Started

Hope you are now excited with testing out Prism on your machine. To get started, please follow the below guidelines on prerequisites and installation.

Prerequisites

  • Sklearn==0.22.1
  • Xgboost==1.1.0
  • Numpy==1.18.2

Usage

See this jupyter notebook for further details on how to use this package.

Contributing

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.

Contact

Known Issues

n/a