Logistic Regression from scratch

You can find working code examples (including this one) in my lab repository on GitHub. Sometimes it's necessary to split existing data into several classes in order to predict new, unseen data. This problem is called classification and one of the algorithms which can be used to learn those classes from data is called Logistic Regression. In this article we'll take a deep dive into the Logistic Regression model to learn how it differs from other regression models such as Linear- or Multiple Linear Regression, how to think about it from an intuitive perspective and how we can translate our learnings into code while implementing it from scratch. If you've read the post about Linear- and Multiple Linear Regression you might remember that the main objective of our algorithm was to find a best fitting line or hyperplane respectively.