An updated random forest bean model with new data - now spanning 1987-2017. I show off the new model (which still has excellent predictive power, though somewhat less than the previous model) and demonstrate some new analytics to maximize the purchasing insight from the model outputs.
Here, I show a demonstration of a simple investing/selling simulation based on historic bean data and the Random Forest model we built two posts ago. The basic premise is to see how much money we could make if we had had access to the predictive model in the 80s to 2011, and had purchased and sold beans based on the predictions it makes.
Here, we take the next step in bean market prediction with machine learning project. I go through the steps I use to preprocess and split the data into training and test sets using the R package caret.
Beans I’m the son of a bean broker. Both my dad and his dad worked in the dry bean industry in the US - which seems niche, but it’s really fascinating. When I originally started thinking about applying data science tools to problems outside of academia (in my case, outside of plants and insects and ecology), I immediately thought of beans. It’s something my father and I have talked about frequently, and a world I’ve always been interested in.
