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).
Messy Excel Files So, as I discussed last time, the first big hurdle in starting to explore the domestic dry bean market data was overcoming the terror of working with a bunch of really messy, really gnarly excel files. The main one looks like this:Lots of problems, right? The data are in multiple sheets in a single workbook, they’re not uniform, etc. It’s an R-user’s nightmare, but the reality is that data often look like this.
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.
Hello! I’m an ecologist and data scientist working at the University of Arizona in Tucson. I’m currently a postdoctoral fellow with the National Institute of Health (NIH), but as that position is coming to a close, am on the job market looking for new projects and challenges. Throughout my fellowship at the University of Arizona, I’ve become increasingly interested in data science, and have taken the plunge in applying some of the techniques I’ve developed a scientist to outside projects.
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