Logistic regression is a type of classification algorithm used to predict the probability of a class or event existing. Unlike linear regression, which predicts a continuous number, logistic regression predicts a discrete outcome—a sort of yes or no, true or false, or in data terms, class 0 or class 1.

For example, imagine you have data on whether an email is spam or not. Logistic regression can help you predict whether a new email is spam based on its content.

You'll use logistic regression when you need to classify data into categories. Real-life examples include:

• Predicting if a student will pass or fail
• Determining whether an email is spam
• Diagnosing whether a patient has a certain disease

Logistic regression works by fitting a logistic function (also known as the sigmoid function) to the data. The sigmoid function is defined as:

$\sigma(z) = \frac{1}{1 + e^{-z}}$