Welcome back everyone to our Analytics Journey series. Those of us in Houston have been through a trying time, and our thoughts are with the community. We will try to return to a semblance of normalcy by continuing where we left off in our journey.
With all of our hard work in understanding and preparing the data during previous steps of the CRISP-DM method–exploring data, choosing a model space, removing NULLs, removing Multicollinearity–it’s time to have some fun with the Modeling stage. Today, we’ll look at an aspect of Multiple Linear Regression: Forward and Backward Selection.
Jason is a Junior Data Scientist at Ruths.ai with a Master’s degree in Predictive Analytics and Data Science from Northwestern University. He has experience with a multitude of machine learning techniques such as Random Forest, Neural Nets, and Hidden Markov Models. With a previous Master’s in Creative Writing, Jason is a fervent believer in the Oxford comma.