uplift-modeling
There are 35 repositories under uplift-modeling topic.
uber/causalml
Uplift modeling and causal inference with machine learning algorithms
maks-sh/scikit-uplift
:exclamation: uplift modeling in scikit-learn style in python :snake:
DataCanvasIO/YLearn
YLearn, a pun of "learn why", is a python package for causal inference
Minyus/causallift
CausalLift: Python package for causality-based Uplift Modeling in real-world business
DSXiangLi/Paper_CausalInference_abtest
因果推理&AB实验相关论文小书库
Nikhilkohli1/Digital-Marketing-Analytics
This contains projects based on Algorithmic Marketing like Marketing Mix Modeling, Attribution Modeling & Budget Optimization, RFM Analysis, Customer Segmentation, Recommendation Systems, and Social Media Analytics
haowei01/pytorch-examples
train models in pytorch, Learn to Rank, Collaborative Filter, Heterogeneous Treatment Effect, Uplift Modeling, etc
JackHCC/Awesome-Uplift-Model
🛠 How to Apply Causal ML to Real Scene Modeling？How to learn Causal ML？【✔从Causal ML到实际场景的Uplift建模】
rsyi/pylift
Uplift modeling and evaluation library. Actively maintained pypi version.
andrewtavis/causeinfer
Machine learning based causal inference/uplift in Python
kailiang-zhong/DESCN
Implementation of paper DESCN, which is accepted in SIGKDD 2022.
Nikhilkohli1/Olist-Marketing-Analytics
Algorithmic Marketing based Project to do Customer Segmentation using RFM Modeling and targeted Recommendations based on each segment
duketemon/pyuplift
Lightweight uplift modeling framework for Python
gdmarmerola/cfml_tools
My collection of causal inference algorithms built on top of accessible, simple, out-of-the-box ML methods, aimed at being explainable and useful in the business context
zahradbozorgi/CausalRulesDiscovery
https://arxiv.org/abs/2009.01561
jd-opensource/UTBoost
A powerful tree-based uplift modeling system.
google-marketing-solutions/fractional_uplift
A flexible python package for cost-aware uplift modelling.
jroessler/autoum
A Python Framework for Automatically Evaluating various Uplift Modeling Algorithms to Estimate Individual Treatment Effects
imsanjoykb/Sales-and-Marketing-Analytics
This repository consists of predicting dynamic pricing, churn predictions using sales and marketing data for understanding users' behaviour.
vub-dl/cs-um
Causal Simulations for Uplift Modeling
andypwyu/optimize_promotion_offers_for_Starbucks
DSND Term 2 Portfolio Exercise: Optimize promotion offers for Starbucks
tamasdinh/starbucks-targeting
Customer targeting model to optimize promotion targeting, on simulated data from Starbucks. (work in progress)
mazba-ahamad/Uplift-Modeling-for-Treatment-Non-Responders-through-Multifaceted-Intervention-for-Cost-Optimization
A modified uplift modeling technique to convert "treatment nonresponders" to "responders" is proposed through multifaceted interventions in market campaigns.
tongxinguo/Customer-Targeting-for-Email-Promotion
Marketing Analytics project : Promotion email targeting with uplift and causal forest model
AsmirMumin/Marketing-Campaign-Optimization-using-Profit-Uplift-Modeling
This is a project by Asmir Muminovic and Lukas Kolbe, which was created for the Applied Predictive Analytics class held by the Chair of Information Systems at the Humboldt University of Berlin
BasseyIsrael/Stock-Assets-Prediction
This repository provides a platform for the predicting of future stock prices based on historical stock prices. Time series analysis is extensively explored in this project. The repository also contains pipelines that can be reused for analyzing and predicting stock prices and feature extraction.
fanfanyjs/DSND-Starbucks-Marketing
Analysing promotion effectiveness on Starbucks' reward mobile app
minsoo9506/causality-study
study about causality
leowu4ever/causal-inference-items
Posts covering different causal inference topic.
mathicard/DSE-AdvMicro
Advanced Microeconomics final project for DSE
uzairahmadxy/email_marketing_causal_analysis
Statistical analysis to see effectiveness of email marketing campaign. Used regression, DoWhy & CausalML to calculate treatment effects. Feature importance & CATE, ITEs.
Villagomez-Joshi-cloud/Predictive_Accuracy
An ensemble is based on the notion of combining models. While uplift modeling combines supervised modeling with A-B testing, which is a simple type of randomized experiment.
imnikhilanand/Who-Is-More-Likely-To-Buy
Uplift Modeling to identify the pursuable group of users from all the users in order to send them encouragement (in terms of coupons or other offers) to buy the product more without spending resources to convert those users who are not willing or interested to buy the product even after encouragement.
mnlscn/predicting-modelling-INGBank
smote meets uplift modeling