Robyn
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Robyn | mta | |
---|---|---|
61 | 3 | |
3,548 | 91 | |
7.6% | - | |
9.1 | 0.0 | |
9 days ago | about 2 years ago | |
Python | Python | |
BSD 2-clause "Simplified" License | - |
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.
Robyn
- Robyn – Innovator Friendly, and Community Driven Python Web Framework
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Introducing Dependency Injections in Robyn with a Twist!
For those who might not be familiar, Robyn is a fast, asynchronous Python backend web framework that operates with a Rust runtime, combining the best of both worlds for efficient and robust web development.
- Robyn: A Fast, Innovator Friendly, and Community Driven Python Web Framework
- Robyn – Web Framework in Rust
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FastHttp for Python (64k requests/s)
If you're comparing web frameworks you might also like to look at robyn https://robyn.tech/, which claims impressive performance. It's always tricky tho' to go from benchmarks to a particular use case.
- Robyn: High-Performance and Community-Driven Python Web Framework
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Robyn passes 1M installs on PyPi.
Robyn's Link - https://github.com/sparckles/robyn
- Robyn v0.38.0 - An improved CLI for create-robyn-app
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Robyn Finds a New Nest: Joining the Sparckles Open-Source Organization
For the unaware, Robyn , is a High-Performance, Community-Driven, and Innovator Friendly Python Web Framework with a Rust runtime.
mta
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Is Hierarchical Bayesian Modelling used in industry?
Python library of a bunch of attribution models
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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.
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[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.
What are some alternatives?
fastapi - FastAPI framework, high performance, easy to learn, fast to code, ready for production
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.
uvicorn - An ASGI web server, for Python. 🦄
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.
MMM-BurnIn
lightweight_mmm - LightweightMMM 🦇 is a lightweight Bayesian Marketing Mix Modeling (MMM) library that allows users to easily train MMMs and obtain channel attribution information.
Prophet - Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.
GeoexperimentsResearch - An open-source implementation of the geo experiment analysis methodology developed at Google. Disclaimer: This is not an official Google product.
Python-Regex - A port of the Rust regex library to python for super speed linear matching.
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.
strawberry - A GraphQL library for Python that leverages type annotations 🍓
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.