I have 2 years of historical data of how much apples were sold at difference price points. Say at any given day, I have data that shows how much qty sold if I sold apples at \$1, how much qty if I sold apples at $2, etc. Say that prices are integers only, 1, 2, 3, 4, 5, 6. There is a lot of variance in the data, so it’s hard to say for example by just looking at the entire period, selling apples for \$4 maximizes revenue. What are methods that I can use to find the optimal price point for selling apples?
My thinking is to break the data into KFolds with K=10, and find out the optimal selling price in each fold’s training set, and taking the median of the optimal price of the 10 training sets to come to an optimal price for selling apples. However, I am not utilizing the test set at all, and I don’t know if I should be using it in my analysis. Is there a better way to look at this? Looking for any ideas enhancing the statistical framework.
The goal is the optimize revenue for selling apples by setting a sales price to use in the future.