Submitted by AutoModerator t3_zcdcoo in MachineLearning
I-am_Sleepy t1_j0phzjn wrote
Reply to comment by Maria_Adel in [D] Simple Questions Thread by AutoModerator
I'm not sure, but I think there are several ways to model product assortments
First, Demand forecasting - You predict demand of each product, and act accordingly. This usually can be done using time-series forecast, or
Second, personalize taste - You assume that each customer has their own fixed preference, and you modeled that. If you know the demographic of each customer, you would be able to estimate the demand from recommended products
But the later probably going to output a static distribution, so I think you can apply demand forecast on the second method to discount them correctly (I think)
However, every method need data. If you have a cold-start product, you might want to perform basic A/B testing first to get the initial data
Maria_Adel t1_j0s4l0o wrote
Thanks a lot. Data is available so that should not be a problem. What models would you suggest for demand forecasting of each product ( gradient boosting or hybrid deep learning models or ARIMA) and what key variables would you include in the model ( I’d suspect previous sales, price)
I-am_Sleepy t1_j0sh383 wrote
If you have a target variable, and other input features, you can treat this problem as a normal regression problem. Using model like linear regression, Random Forest Regression, or XGBoost is very straight forward from there
You can then look at feature importance to try to weed-out the uncorrelated features (if you want to). There are a few automated ml for timeseries, but currently I mostly use Pycaret
But if you suspect that your target variable autocorrelate, model like SARIMAX can be used instead. An automated version of that is Statsforecast e.g. AutoARIMA with exogenous variables (haven't used it though)
But noted that if you are in direct control of a few variables, and you want to predict want will happen, this is no longer a simple regression anymore i.e. the data distribution may shift. That would be in Casual Inference territory (see this handbook)
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