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Hi, I have a question, what we have to predict in the te...
Hi,
I have a question, what we have to predict in the test dataset.
According to my understanding, we need to find the sales of each store for next 100 days after 31st July 2018, which is equivalent to, finding the spending of each card in each store and aggregate the data on the store level for next 100 days.
In the test dataset, Amount column is already present and filled. I am confused now!!
So if we will find the sales for the next 100 days than, how can they validate our predictions given the last day is 31st July 2018.
1. In test, they have store details till store number 199 and the same with submission, do we need to predict for only those stores?
2. you can find the test and train are overlapping do we need to bind them both for predictions, if so then why there is a test file.
3. Evaluation should be done on real data but real data is not at all available
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Yeah, it’s a bit confusing at first, but the idea is simple. The test dataset includes the “Amount” column mainly so you can check and validate your model, not because those are the final answers you’re supposed to rely on. Your actual task is still to predict the next 100 days of sales, and the real evaluation usually happens on hidden data that you don’t see.
I’ve seen similar things in finance tools too, where some data is shown just to help users understand or verify things before real calculations happen. Even with systems like FAB (First Abu Dhabi Bank), interfaces are designed to reduce confusion by structuring the data clearly. I came across this example recently: https://fbbalancecheck.ae/ratibi-card-salary-check. it’s a pretty clean way of presenting financial info, something these competition platforms could honestly learn from.
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