Comments (2)
I also have some ideas on a Bayesian clustering variant.
My recent experience with an RFM segmentation project led me to discover the effectiveness of utilizing jenks breaks methodology over simple percentile-based segmentation. I found Jenks breaks logic to offer more meaningful segmentations. I suggest considering Jenks breaks as it could potentially enhance the segmentation process. There is a C-based implementation that can be found here.
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Hey @sarim-zafar,
Thanks for sharing. The Jenks Breaks methodology has some parallels with Dirichlet processes, which can be used to automatically infer the optimal number of clusters.
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Related Issues (20)
- test against actual mmm fit results
- Media transforms prior sampling & curve plotting HOT 1
- Default priors for saturation and adstock functions HOT 1
- Causality notebooks & Structural PyMC-Marketing equations HOT 2
- Prepare for PyMC 5.16 release HOT 1
- Fix `ParetoNBDModel` Docstring Typos HOT 2
- Expose minimization kwargs in budget optimization
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- Expected the # of transactions & CLV etc HOT 5
- Explicit dims in model config
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- Consolidate `ModelBuilder` InferenceData getters (and setters)
- Consider changing logo green color to redish-orange? HOT 8
- mark tests as slow
- Wrapper `Prior` / `Distribution` class HOT 4
- Pull out TVP as component
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