lightweight_mmm
statsmodels
lightweight_mmm | statsmodels | |
---|---|---|
5 | 8 | |
797 | 9,591 | |
4.6% | 1.4% | |
5.5 | 9.4 | |
about 2 months ago | 4 days ago | |
Python | Python | |
Apache License 2.0 | BSD 3-clause "New" or "Revised" License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
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lightweight_mmm
- Lightweight (Bayesian) Marketing Mix Modeling (Google Unofficial)
- Show HN: Marketing software for solopreneurs who don't like marketing
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Mixed Marketing Modeling Approach for attribution?
Other packages to consider are LightweightMMM (from Google), which takes a Bayesian approach and is in Python (vs. R for Robyn). It's not as fully featured as Robyn (you don't get all the nice one-pagers and graphs output so easily), but it's still a great option, especially if you have some data scientists on the team who understand MMM.
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Is Hierarchical Bayesian Modelling used in industry?
LightweightMMM - python package
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Two More Years
Interestingly, Google did introduce an MMM framework: LightweightMMM 🦇 is a lightweight Bayesian Marketing Mix Modeling (MMM) library that allows users to easily train MMMs and obtain channel attribution information. https://github.com/google/lightweight_mmm
statsmodels
- statsmodels Release Candidate 0.14.0rc0 tagged
- How to generate Errors using Scipy Minimize with Powell Method
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[P] statsmodels.tsa.holtwinters.ExponentialSmoothing results in NaN forecasts and parameters when fitting on entire dataset using known parameters from training model.
I reckon you're more likely to get a good response on their Github page than here. Unless a dev happens to see this post.
- Statsmodels 0.13.3 released with Python 3.11 support
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First Year UG here, can someone offer any coding advice?
The method they use for computing the parameter covariance (in the code here, around line 330) involves some linear algebra, as they use the Moore-Penrose pseudo-inverse of the outputs.
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How do you usually build your models?
Since you are using python, pandas, scikit-learn, scipy, and statsmodels are what you are looking for
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Advice required to choose appropriate software for an assignment
Can't you get a student discount for Stata? R would definitely be able to handle everything. For Python, have a look through the statsmodel package https://github.com/statsmodels/statsmodels
- [C] I have an MS in Statistics - how can I get better at coding?
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.
SciPy - SciPy library main repository
mta - Multi-Touch Attribution
Numba - NumPy aware dynamic Python compiler using LLVM
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.
PyMC - Bayesian Modeling and Probabilistic Programming in Python
GeoexperimentsResearch - An open-source implementation of the geo experiment analysis methodology developed at Google. Disclaimer: This is not an official Google product.
Dask - Parallel computing with task scheduling
matched_markets - Matched Markets is a Python library for design and analysis of Geo experiments using Matched Markets and Time Based Regression.
Pandas - Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
orange - 🍊 :bar_chart: :bulb: Orange: Interactive data analysis
SymPy - A computer algebra system written in pure Python