mta
pycave
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
mta
-
Is Hierarchical Bayesian Modelling used in industry?
Python library of a bunch of attribution models
-
What are some applications of Data Science in Digital Marketing?
Some other marketing topics to be aware of: forecasting - Prophet is an interesting library for this, you'll definitely need some domain knowledge to fit the forecast, it really shouldn't be used to just fit and go otherwise you'll probably end up with some bad results, Media Mix Modeling - FB-Robyn is a library with quite a bit of potential, Multi-Touch Attribution - MTA is a decent python library for this, but you'll have pretty significant data requirements to actually have accurate results, these approaches tend to be pretty susceptible to survivorship/selection bias, survival analysis - Lifelines is a pretty good python package for this, this sort of analysis is useful for determining churn likelihood or time until next purchase.
-
[Marketing Attribution Model for B2B] How to assign revenue based on the lead source?
This is a nice library that implements several multi-touch attribution models beyond the simpler heuristic based ones. One word of caution about these sort of attribution models is the attribution always adds up to 100%. Attribution models typically don't take exogenous factors into account - things that potentially influence whether the customer would have purchased anyway regardless of marketing touchpoints. They also tend to be quite sensitive to selection bias. If you have a touchpoint that requires a customer perform some behavior that can be related to a base level of interest, the model will overweight the attribution of that touchpoint - think things like an abandoned cart remarketing journey. The customer has already shown an inherent interest in the product by placing the product in the cart.
pycave
-
Traditional Machine Learning Models on GPU
I've just published a new major revision of a library I've been working on, PyCave.
What are some alternatives?
Robyn - Robyn is an experimental, AI/ML-powered and open sourced Marketing Mix Modeling (MMM) package from Meta Marketing Science. Our mission is to democratise modeling knowledge, inspire the industry through innovation, reduce human bias in the modeling process & build a strong open source marketing science community.
d2l-en - Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 500 universities from 70 countries including Stanford, MIT, Harvard, and Cambridge.
trimmed_match - This Python library implements Trimmed Match for analyzing randomized paired geo experiments and also implements Trimmed Match Design for designing randomized paired geo experiments.
jina - ☁️ Build multimodal AI applications with cloud-native stack
lightweight_mmm - LightweightMMM 🦇 is a lightweight Bayesian Marketing Mix Modeling (MMM) library that allows users to easily train MMMs and obtain channel attribution information.
traingenerator - 🧙 A web app to generate template code for machine learning
GeoexperimentsResearch - An open-source implementation of the geo experiment analysis methodology developed at Google. Disclaimer: This is not an official Google product.
lightning-flash - Your PyTorch AI Factory - Flash enables you to easily configure and run complex AI recipes for over 15 tasks across 7 data domains
EconML - ALICE (Automated Learning and Intelligence for Causation and Economics) is a Microsoft Research project aimed at applying Artificial Intelligence concepts to economic decision making. One of its goals is to build a toolkit that combines state-of-the-art machine learning techniques with econometrics in order to bring automation to complex causal inference problems. To date, the ALICE Python SDK (econml) implements orthogonal machine learning algorithms such as the double machine learning work of Chernozhukov et al. This toolkit is designed to measure the causal effect of some treatment variable(s) t on an outcome variable y, controlling for a set of features x.
pytorch-forecasting - Time series forecasting with PyTorch
tensor-house - A collection of reference Jupyter notebooks and demo AI/ML applications for enterprise use cases: marketing, pricing, supply chain, smart manufacturing, and more.
quaterion - Blazing fast framework for fine-tuning similarity learning models