I have been working on a case study where I have been given some data about fruits. Features include fruit_id, fruit_type, color, demand_supply_ratio, date_posted, geo_country, is_purchased, fruit_price. Every seller in the market can post a fruit_price on a given day(date_posted captures this day). I want to build a predictive model so as to recommend a daily fruit price such that the revenue for that seller is optimized. How do I go about solving this problem?
fruit_id - unique ID describing the fruit fruit_type - categorical - A/B/C etc color - color of the fruit demand_supply_ratio - ratio of how many people saw that fruit to how many such fruits were available in the given country date_posted - timestamp of the date when the seller posted that fruit for sale is_purchased - 0 or 1. Was the fruit sold that day or not fruit_price - numerical variable.Price of the fruit posted by the seller
How do I come up with an equation such that when I as a
new-seller want to launch a fruit, the model should
recommend me an
optimal price for my fruit on that day.
Is it similar to dynamic pricing for companies like Airbnb where a host while in the process of posting their listing, is recommended a price which gives maximum returns to the host?