Back to Basics, Part Tres: Logistic Regression

An illustrated guide on Logistic Regression, with code

Welcome back to the final installment of our Back to Basics series, where we’ll delve into another fundamental machine learning algorithm: Logistic Regression. In the previous two articles, we helped our friend Mark determine the ideal selling price for his 2400 feet² house using Linear Regression and Gradient Descent.

Today, Mark comes back to us again for help. He lives in a fancy neighborhood where he thinks houses below a certain size don’t sell, and he is worried that his house might not sell either. He asked us to help him determine how likely it is that his house will sell.

This is where Logistic Regression comes into play.

Logistic Regression is a type of algorithm that predicts the probability of a binary outcome, such as whether a house will sell or not. Unlike Linear Regression, Logistic Regression predicts probabilities using a range of 0% to 100%. Note the difference between predictions a linear regression model and logistic regression model make:

Let’s delve deeper into how logistic regression works by determining the probability of selling houses with varying sizes.