At the intersection of hunting and data analytics, there’s a crazy person out there who would look for ways to utilize their technological skills to improve their odds of a successful deer hunt. A person who straddles both seemingly dichotomous worlds of enjoying the outdoors and writing largely superfluous code. A person who, in an effort to learn more about nature, ended up spending hours upon hours pouring over his laptop.
That person is me.
According to the Nuclear Regulatory Commission, there are 31 nuclear research reactors in the United States. I happen to have a license to operate one of them, and in this article I’ll be demonstrating how I applied machine learning and general data analytics techniques to predict pulse power levels and increase the repeatability of our experiments.
A fission nuclear reactor works by harnessing the power of fissile atoms. When uranium-235 absorbs a neutron it has a chance to fission and split, releasing fission products, neutrons, and kinetic energy. This energy heats a coolant medium which is usually piped to a…
This project uses lending data from LendingClub.com to determine if potential customers will successfully pay off a loan after entering a lending agreement. Our main goal will be to compare two models: one created using a single decision tree, the other using a random forest.
Note: This project was covered in a Udemy “Python for Data Science & Machine Learning” course. My code and explanations are shown below. This post is for demonstration purposes only and is not meant to be a tutorial or exhaustive guide.
We’ll start off with the usual analytics workflow — import some relevant libraries, read…