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I think you were right with your initial consideration of Poisson. I think that you can assume that the frequency with which people go shopping for a given good can be considered independent. Of course this assumption might not be perfect (like maybe you could argue that Person A buys item X every 3 days. However, if there is enough people I don't think this ...


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I was discussing this with somebody else who argued that t needs to be considered as hyperparameter and this parameter needs to be tuned separately. In your exercise, you are actually doing the same thing. Getting the best t. So, I don't think you need anything extra. What I see missing in your steps - - No steps to get the best K(nearest_neighbours) for ...


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Your chart seems to show that light GBM models are very inconsistent in terms of F1 score. The other two types of model tend to have lower validation accuracy than training accuracy, suggesting overfitting is occurring to some extent (but this is ubiquitous in machine learning so it’s not a deal breaker by any means). The best median validation performance ...


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This is a non-trivial question. There is no general law to find the "best" algorithm or the "correct" amount of clusters (assuming you don't know the correct number of clusters). As you already mentioned, certain algorithms make assumptions on the shape of your clusters or your data in general. One thing I suggest is to look at your data, ...


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The model built using only positive value will be biased towards positive value and will always predict for the customers who bought any items before. But customers who didn't buy anything, the model won't be able to predict anything.


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