tensor-house
EconML
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tensor-house | EconML | |
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4 | 8 | |
1,162 | 3,550 | |
- | 2.6% | |
7.5 | 8.5 | |
3 months ago | 4 days ago | |
Jupyter Notebook | Jupyter Notebook | |
Apache License 2.0 | GNU General Public License v3.0 or later |
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tensor-house
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Supply Chain Uses Cases
I still have this on my reading list, it has quite some interesting SC applications. https://github.com/ikatsov/tensor-house
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How to use the code to do analysis with my data
Hi, how can i use the code of an analysis like this https://github.com/ikatsov/tensor-house/blob/master/pricing/price-optimization-multiple-time-intervals.ipynb but with my data?
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What are some applications of Data Science in Digital Marketing?
This is the companion github to the book, it doesn't have all the use cases, but there are a decent amount of code samples to get you started.
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Machine learning Applications in Marketing
Happy to help out! That website I linked has a link to the book PDF, so you can check it out yourself. I guess the Amazon reviews must have missed it, but there is a companion github for a selection of models in the book that may be helpful.
EconML
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[D] What approach to decide which class is most optimal for recovery?
A good package with many of the tools used for this type of problem as well as pretty good documentation about how it all works is https://github.com/microsoft/EconML
- Getting treatment effects from a random forest
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Data Science and Marketing
Uplift Modeling (python): CausalML, EconML
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EconML: A Python Package for ML-Based Heterogeneous Treatment Effects Estimation
Github: https://github.com/microsoft/EconML
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UpliftML: An uplift modeling library that handles web scale datasets
Many libraries have recently emerged that offer implementations of algorithms for heterogeneous treatment effect estimation (or, CATE estimation). The most well-known examples are Microsoft's EconML (https://github.com/microsoft/EconML) and Uber's CausalML (https://github.com/uber/causalml). Existing libraries require all data to fit in memory, which is often a limitation for industry applications on web scale datasets. Booking.com's new library offers similar functionality on top of Spark, enabling web scale uplift modeling.
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[q] before/after test
EconML and CausalLift are pretty good python packages that help you build uplift models. scikit-uplift is a decent sklearn style wrapper package that can be helpful as well. One of the drawbacks of these packages is they only allow for the modeling of a single treatment. mr-uplift is a newer package that allows you to model the multiple treatment effects simultaneously. I haven't used it personally, but it does look fairly interesting.
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[N] Spotify Confidence - open source for analyzing a/b test data
Can't see how this adds to decades of causal inference packages development in stats oriented frameworks like R/Stata/EViews etc and the ongoing effort of porting this to Python. If you want something fancy there's already EconML.
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What are some applications of Data Science in Digital Marketing?
Uplift Modeling - This is a very powerful technique aimed at discovering the customers who are most likely to respond to your marketing efforts. Some good python libraries for this are EconML and mr-uplift
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.
causalml - Uplift modeling and causal inference with machine learning algorithms
models - A collection of pre-trained, state-of-the-art models in the ONNX format
upliftml - UpliftML: A Python Package for Scalable Uplift Modeling
Workshops - Workshops organized to introduce students to security, AI, blockchain, AR/VR, hardware and software
models - Models and examples built with TensorFlow
causalglm - Interpretable and model-robust causal inference for heterogeneous treatment effects using generalized linear working models with targeted machine-learning
vectordb-recipes - High quality resources & applications for LLMs, multi-modal models and VectorDBs
causallift - CausalLift: Python package for causality-based Uplift Modeling in real-world business
mta - Multi-Touch Attribution
Robyn - Robyn is a Super Fast Async Python Web Framework with a Rust runtime.