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.
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.
Finally, we get to some machine learning models. I go over using the functions in the caret package to build, test and tune a variety of models, and end up with a nice Random Forest model that does a very solid job predicting bean market prices 6 months in the future. We finish up with a little bit of data viz to assess our prediction power.
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.
An overview of my experience jumping into Machine Learning. The mind-switch from explanation to prediction, and a basic overview of my understanding of what Machine Learning is (and isn’t).