Contributed by Aravind Kolumum Raja and Thomas Kolasa. They are currently in the NYC Data Science Academy 12 week full time Data Science Bootcamp program taking place between January 11th to April 1st, 2016. This post is based on their fourth class project - machine learning (due on the 8th week of the program).
For our machine learning project, we classified forest cover types for data from Roosevelt National Forest in northern Colorado. The data consist of over forty categorical and continuous environmental and cartographical variables. With more than half a million observations, the data is apt for machine learning. We created visualizations, logistic regressions, neural networks, tree based methods, and ensembling using R.
Due to formatting constrictions involving three-dimensional plots and the size of the HTML, we published our blog post at the following URL:
Topics from this blog: R Machine Learning Student Works