lightweight_mmm
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LightweightMMM 🦇 is a lightweight Bayesian Marketing Mix Modeling (MMM) library that allows users to easily train MMMs and obtain channel attribution information.
Hi team, I have some question regarding the model. I would be very happy if you could have a look at them :) 1. In the theory section of the...
### Discussed in https://github.com/google/lightweight_mmm/discussions/49 Originally posted by **xijianlim** August 1, 2022 Hi all, this is something I've been noticing the provided code base using the adstock-hill . The response curves...
Hi Team, 1. find_optimal_budgets current function value returning nan 2. previous and. optimal budget allocation values are always equal how much i change the values and range 3. one of...
Dear team I got the RuntimeError in hill_adstock. ```python --------------------------------------------------------------------------- RuntimeError Traceback (most recent call last) Cell In [8], line 3 1 SEED = 123 2 mmm = lightweight_mmm.LightweightMMM(model_name="hill_adstock") ---->...
Hi, I am running "simple_end_to_end_demo" notebook on a GCP instance. All of the code runs fine except that I get the following error when running the code. Quick google search...
Hi Team, Can you please provide the way to generate the CSV files for response curve plots and actual vs predicted plots?
Line 107 was missing a # to denote it being a comment, causes problems with copy-pasting the code.
Getting Negative Values in Pre optimization predicted Target vs Post optimization predicted Target graph.  Code Snippet: ``` solution, kpi_without_optim, previous_media_allocation = optimize_media.find_optimal_budgets( n_time_periods=n_time_periods, media_mix_model=mmm, extra_features=extra_features_train[:n_time_periods], budget=budget, prices=prices, media_scaler=media_scaler, target_scaler=target_scaler,...
I have a data frame which looks like this: date | channel name | cost | leads Some of the channels' cost is zero. The number of leads per channel...
