garve / mamimo Goto Github PK
View Code? Open in Web Editor NEWA package to compute a marketing mix model.
A package to compute a marketing mix model.
As a first step, I'm unable to install mamimo library. I'm getting below error:
Error
Defaulting to user installation because normal site-packages is not writeable
ERROR: Could not find a version that satisfies the requirement mamimo
ERROR: No matching distribution found for mamimo
Note: you may need to restart the kernel to use updated packages.
ERROR: Could not find a version that satisfies the requirement mamimo
ERROR: No matching distribution found for mamimo
Here is my Python Version Details
/opt/anaconda3/bin/python
3.8.8 (default, Apr 13 2021, 12:59:45)
[Clang 10.0.0 ]
sys.version_info(major=3, minor=8, micro=8, releaselevel='final', serial=0)
Hi Sr.Garve
Congralutations for your project! It´s fantastic :)
Although it is not a question especially of the mamimo package, I would like to know if you could pass me the code to be able to represent the saturation and carryover effects of your article in towardsdatascienc:
https://towardsdatascience.com/an-upgraded-marketing-mix-modeling-in-python-5ebb3bddc1b6
Specifically these that I show below
I can't figure out how to get this data to plot the graph and check both fantastic effects shown here.
Thanks for your help :)
Hello Dr. Robert, I found your article very useful, thank you alot for the sharing.
By the way, I am trying to follow your steps in the article, and when I pip install mamimo
, it shows me 2 errors,
ERROR: Could not find a version that satisfies the requirement mamimo (from versions: none) ERROR: No matching distribution found for mamimo
Do you know why is it?
Hi, and thank you for the great package. It is very intuitive and has helped me a lot.
Looking into the README file, in the section Training The Model, the RandomizedSearchCV
first splits the initial X
and y
to train and test sets, and then applies the preprocessing pipeline (carryover, saturation, and the model) to train set and test set separately.
But this results in missing all the carryover effects that would be caused from the media at the end of the train set to the beginning of the test set, which I think decreases the validity and accuracy of the model.
Shouldn't the preprocessing be applied first, and then pass the preprocessed dataset to the grid search?
Thanks!
Hi Dr. Kubler,
I was trying to download MaMiMo to give it a try but when trying to do pip install it gave me 2 errors.
Any help you can provide is greatly appreciated!
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