We will explore a straightforward model known as Logistic Regression, which is a supervised machine learning algorithm designed for classification problems.

It is particularly useful for predicting binary outcomes (1 / 0, Yes / No, True / False) based on a set of independent variables. The algorithm constructs a model that calculates a probability for each potential outcome and makes predictions based on which outcome is most likely.

The model employs a logistic function to map input variables to probabilities that range between 0 and 1. While primarily used for binary classification, Logistic Regression can also be adapted for multi-class classification through the training of multiple binary classifiers and combining their outcomes. This method is widely utilized in various fields, including medical research, marketing, and social sciences.