Random Forest in Python – William Koehrsen – Medium

There has never been a better time to get into machine learning. With the learning resources available online, free open-source tools with implementations of any algorithm imaginable, and the cheap availability of computing power through cloud services such as AWS, machine learning is truly a field that has been democratized by the internet. Anyone with access to a laptop and a willingness to learn can try out state-of-the-art algorithms in minutes. With a little more time, you can develop practical models to help in your daily life or at work (or better yet, switch into the machine learning field and reap the economic benefits). This post will walk you through an end-to-end implementation of the powerful random forest machine learning model. It is meant to serve as a complement to my conceptual explanation of the random forest, but can be read entirely on its own as long as you have the basic idea of a decision tree and a random forest. There will of course be Python code here, however, it is not meant to intimate anyone, but rather to show how accessible machine learning is with the resources available today!