An overview of the workflow to generate a tested and tuned machine learning algorithm that takes recent information about sold in Tucson, AZ and accurately predicts the price they sold for. This is the first step in building an interactive app that people can use to determine the likely sale price of a house.
This is the workshop I recently ran for the iSpace Workshop Series at the University of Arizona Science and Engineering Laboratory. We used the caret pacakage to go through an example classification problem and cover loading data, preProcessing data, model comparison and prediction on test data.
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, 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.
