Understanding the Distinction: Linear Regression vs Logistic Regression and How to Apply Each Model

At its heart, the difference between linear regression and logistic regression lies in the nature of the dependent variable they are each designed to predict. Linear regression is used to forecast a continuous dependent variable using a linear relationship with one or more independent variables. Conversely, logistic regression is used primarily for classification problems, predicting the probability that a given input falls into one of two categories, making it indispensable in binary classification scenarios. This fundamental distinction influences the algorithmic approach and use cases of each regression model. <a href="https://blog.mirkopeters.com/understanding-the-distinction-linear-regression-vs-logistic-regression-and-how-to-apply-each-model-09b4977d5f94">Click Here</a>