Regression is a type of supervised machine learning problem, under which the relationship between features in the data-space and the outcome is established, to predict the values for the outcome, which are continuous by nature. So let’s dive right in! Source: What is Regression? In part 2, we’ll discuss gradient descent (coding it from scratch), regularization, and other relevant concepts. This is a two-part article, and this part encompasses the basics of feature engineering and regression. Specifically, we’re going to walk through the basics with a practical example in Python, and learn about applying feature engineering to the dataset we work with. In this post, we’re going to learn about the most basic regressor in machine learning- linear regression. Exploring regression and feature engineering on the facebook-comment-prediction dataset Photo by Ridham Nagralawala on Unsplash
