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https://github.com/station-10/awesome-marketing-machine-learning
A curated list of awesome machine learning libraries for marketing, including media mix models, multi touch attribution, causal inference and more
https://github.com/station-10/awesome-marketing-machine-learning
List: awesome-marketing-machine-learning
attribution awesome-list causal-inference clv machine-learning marketing media mmm mta recommendation-system synthetic-control time-series-forecasting
Last synced: 23 days ago
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A curated list of awesome machine learning libraries for marketing, including media mix models, multi touch attribution, causal inference and more
- Host: GitHub
- URL: https://github.com/station-10/awesome-marketing-machine-learning
- Owner: station-10
- License: mit
- Created: 2023-08-10T13:35:17.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-05-01T11:06:06.000Z (8 months ago)
- Last Synced: 2024-11-14T04:02:31.535Z (about 1 month ago)
- Topics: attribution, awesome-list, causal-inference, clv, machine-learning, marketing, media, mmm, mta, recommendation-system, synthetic-control, time-series-forecasting
- Homepage: http://www.station10.co.uk
- Size: 63.5 KB
- Stars: 88
- Watchers: 2
- Forks: 9
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- ultimate-awesome - awesome-marketing-machine-learning - A curated list of awesome machine learning libraries for marketing, including media mix models, multi touch attribution, causal inference and more. (Programming Language Lists / Python Lists)
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# awesome-marketing-machine-learning
A curated list of awesome machine learning libraries for marketing. Inspired by both
[awesome-production-machine-learning](https://github.com/EthicalML/awesome-production-machine-learning) and
[awesome-machine-learning](https://github.com/josephmisiti/awesome-machine-learning),
and created and maintained by [Station 10](https://station10.co.uk/).
Note that some packages could fit into more than one section. This has been noted in the descriptions so be sure to Ctrl + F as well as exploring by
sections.
Want to contribute? Please raise a Pull Request or an issue. If you find this useful please drop a ⭐️. This helps motivate us and others to update and
maintain the list.
All packages are Python based unless otherwise stated. We welcome contributions from R Users!
# Main Content
## Attribution
* [ChannelAttribution](https://github.com/DavideAltomare/ChannelAttribution/tree/master) 
Python and R library that employs a k-order Markov representation to identify structural correlations in customer journey data.
* [fractribution](https://github.com/google/fractribution) 
Data driven MTA by Google.
* [Marketing-Attribution-Models](https://github.com/DP6/Marketing-Attribution-Models) 
Heuristic and data driven Multi Touch Attribution.
* [markov-chain-attribution](https://github.com/jerednel/markov-chain-attribution) 
Leverages a first order Markov chain to reallocate conversions.
* [mta](https://github.com/eeghor/mta) 
Various data driven Multi Touch Attribution algorithms.
* [pychattr](https://github.com/jmwoloso/pychattr) 
Python implementation of the excellent R ChannelAttribution library.
* [shapley](https://github.com/hartmann-lars/shapley) 
Shapley Values For Attribution Modelling.
* [shapley-attribution-model-zhao-naive](https://github.com/ianchute/shapley-attribution-model-zhao-naive) 
Shapley Value Methods for Attribution Modeling (Naive, Set-based).
## Causal Inference
* [CausalImpact](https://github.com/google/CausalImpact) 
(R) Causal Inference using Bayesian structural time-series models by Google.
* [causalml](https://github.com/uber/causalml) 
Uplift modeling and causal inference with ML by Uber.
* [CausalPy](https://github.com/pymc-labs/CausalPy) 
Causal Inference & Synthetic Control. Supports fitting with `scikit-learn` and `PyMC` models.
* [dowhy](https://github.com/py-why/dowhy) 
Causal Inference that supports explicit modeling and testing of causal assumptions.
* [SyntheticControlMethods](https://github.com/OscarEngelbrektson/SyntheticControlMethods) 
Causal inference using Synthetic Control.
* [tfcausalimpact](https://github.com/WillianFuks/tfcausalimpact) 
Google's [CausalImpact](https://github.com/google/CausalImpact) Algorithm implemented on top of [TensorFlow Probability](https://github.com/tensorflow/probability).
* [upliftml](https://github.com/bookingcom/upliftml) 
Scalable unconstrained and constrained uplift modeling from experimental data using PySpark and H20.
* [scikit-uplift](https://github.com/maks-sh/scikit-uplift) 
* Uplift modeling python package that provides fast sklearn-style models implementation, evaluation metrics and visualization tools.
## Churn / CLV
* [btyd](https://github.com/ColtAllen/btyd) 
Buy Till You Die and CLV statistical models in Python.
* [lifetimes](https://github.com/CamDavidsonPilon/lifetimes) 
CLV and Churn modelling. Deprecated and incorporated into [pymc-marketing](https://github.com/pymc-labs/pymc-marketing).
* [lucius-ltv](https://github.com/plexagon/lucius-ltv) 
CLV for subscriptions.
## Data
* [gapandas4](https://github.com/practical-data-science/gapandas4) 
Python package for querying the Google Analytics Data API for GA4 and displaying the results in a Pandas dataframe.
## Econometrics
* [EconML](https://github.com/py-why/EconML) 
AI, Econometrics and Causal Inference modelling.
* [statsmodels](https://github.com/statsmodels/statsmodels) 
Statistical modeling including time series and econometrics.
## Geo Experimentation
* [trimmed_match](https://github.com/google/trimmed_match) 
Ad effectiveness through the design and analysis of randomized Geo Experiments by Google.
* [matched_markets](https://github.com/google/matched_markets) 
Time-Based regression matched markets approach for designing Geo Experiments by Google.
* [GeoexperimentsResearch](https://github.com/google/GeoexperimentsResearch) 
(R) Open-source implementation of the geo experiment analysis methodology developed at Google (Archived)
* [GeoLift](https://github.com/facebookincubator/GeoLift) 
Geo Experimentation methodology based on Synthetic Control Methods used to measure lift of ad campaigns by Facebook.
## Media / Marketing Mix Models
* [BayesianMMM](https://github.com/leopoldavezac/BayesianMMM) 
Bayesian Media Mix mMdelling with shape and carryover effect.
* [dammmdatagen](https://github.com/DoktorMike/dammmdatagen) 
(R) Media Mix Modeling Data Generator.
* [lightweight-mmm](https://github.com/google/lightweight_mmm) 
Bayesian Media Mix Models by Google.
* [mamimo](https://github.com/Garve/mamimo) 
Small Media Mix Models designed to be used in conjunction with ML libraries (e.g. SKL)
* [mmm-stan](https://github.com/sibylhe/mmm_stan) 
Multiplicative Media Media Mix Model.
* [pymc-marketing](https://github.com/pymc-labs/pymc-marketing) 
Bayesian Media Mix, Adstock, Saturation Customer Lifetime Value & Churn models.
* [Robyn](https://github.com/facebookexperimental/Robyn) 
(R) Bayesian Media Mix Models by Facebook.
## Personalisation / Segmentation
* [amazon-denseclus](https://github.com/awslabs/amazon-denseclus) 
Python module for clustering both categorical and numerical data using UMAP and HDBSCAN by Amazon.
* [rfm](https://github.com/sonwanesuresh95/rfm) 
RFM Analysis and Customer Segmentation.
* [retentioneering-tools](https://github.com/retentioneering/retentioneering-tools) 
Retentioneering: product analytics, data-driven customer journey map optimization, marketing analytics, web analytics, transaction analytics, graph visualization, and behavioral segmentation
* [ecommercetools](https://github.com/practical-data-science/ecommercetools) 
Data science toolkit for those working in technical ecommerce, marketing science, and technical seo and includes a wide range of features to aid analysis and model building.
## Recommendation Systems
* [lightfm](https://github.com/lyst/lightfm) 
Implementation of LightFM, a hybrid recommendation algorithm.
* [openrec](https://github.com/ylongqi/openrec) 
Open-source and modular library for neural network-inspired recommendation algorithms.
* [recmetrics](https://github.com/statisticianinstilettos/recmetrics) 
A library of metrics for evaluating recommender systems
* [recommenders](https://github.com/microsoft/recommenders) 
Best Practices on Recommendation Systems by Microsoft.
* [Surprise](https://github.com/NicolasHug/Surprise) 
Scikit for building and analyzing recommender systems that deal with explicit rating data.
## Time Series
* [darts](https://github.com/unit8co/darts) 
Python library for user-friendly forecasting and anomaly detection on time series built using SKL conventions.
* [gluonts](https://github.com/awslabs/gluonts) 
Probabilistic time series modeling, focusing on deep learning based models, based on PyTorch and MXNet.
* [neural_prophet](https://github.com/ourownstory/neural_prophet) 
Framework for interpretable time series forecasting built on PyTorch.
* [orbit](https://github.com/uber/orbit) 
Python package for Bayesian time series forecasting and inference by Uber.
* [pmdarima](https://github.com/alkaline-ml/pmdarima) 
* Pmdarima is a statistical library designed to fill the void in Python's time series analysis capabilities.
* [prophet](https://github.com/facebook/prophet) 
Additive time series modelling by Facebook.
* [sktime](https://github.com/sktime/sktime) 
A unified framework for ML with Time Eeries.
* [statsforecast](https://github.com/Nixtla/statsforecast) 
Lightning ⚡️ fast forecasting with statistical and econometric models.
* [stumpy](https://github.com/TDAmeritrade/stumpy) 
STUMPY computes something called the matrix profile, which is just an academic way of saying "for every subsequence automatically identify its corresponding nearest-neighbor"
* [temporian](https://github.com/google/temporian) 
Temporian is an open-source Python library for preprocessing ⚡ and feature engineering 🛠 temporal data 📈 for machine learning applications 🤖.
* [tbats](https://github.com/intive-DataScience/tbats) 
BATS and TBATS time series forecasting
* [tsfresh](https://github.com/blue-yonder/tsfresh) 
Time Series Feature extraction based on scalable hypothesis tests.
* [tslearn](https://github.com/tslearn-team/tslearn) 
The machine learning toolkit for time series analysis in Python.
## Survival Analysis
* [lifelines](https://github.com/CamDavidsonPilon/lifelines) 
lifelines is a pure Python implementation of the best parts of survival analysis.
* [pysurvival](https://github.com/square/pysurvival) 
An open source python package for Survival Analysis modeling.
* [scikit-survival](https://github.com/sebp/scikit-survival) 
Survival analysis built on top of scikit-learn.
## Synthetic Control
* [pysyncon](https://github.com/sdfordham/pysyncon) 
Multiple Synthetic Control implementations.
* [scpi](https://github.com/nppackages/scpi) 
Provides Python, R and Stata implementations of estimation and inference procedures for synthetic control methods.
* [SparseSC](https://github.com/microsoft/SparseSC) 
Sparse Synthetic Control Models in Python by Microsoft.
## Synthetic Data
* [Decoy](https://github.com/EqualExperts/decoy) 
Synthetic Data Generator using DuckDB at its core.
* [SDV](https://github.com/sdv-dev/SDV) 
Python library designed to be your one-stop shop for creating tabular synthetic data.