I have a dataframe like:
>>> df [Output]: day spent_amount location prediction 1 10 US '0-2' 1 20 US '3-5' 1 30 US '3-5' 2 10 US '3-5' 2 20 US '3-5' 2 30 US '6+' 3 10 US '0-2' 3 20 US '0-2' 3 30 US '6+'
To sum up, I have 3 days, 3 spent_amounts, also 2 locations (I didn't add UK since that is pretty much similar to US) and predictions that are generated by a previous model. So the model predicted for instance on day 3, if you spent 20 for marketing in the US the predictions of sold goods will be between 0 and 2.
My goal is to maximize the value of sold goods based on the predictions and considering the daily budget as the limiting constraint. But since I need to quantify the predictions, I am confused about how to replace them. One idea is to replace them with the mean of two ends. For instance 1 for the prediction '0-2'. But that generates unrealistic results. How can I determine the numeric values to assign to each prediction group?