We will only explore an easy model, Logistic Regression. Logistic Regression is a supervised machine learning algorithm for classification problems.

It is used to predict a binary outcome (1 / 0, Yes / No, True / False) given a set of independent variables. The algorithm builds a model that assigns a probability to each outcome and makes a prediction based on which outcome has the highest probability.

The model is represented by a logistic function that maps the input variables to a probability between 0 and 1. Logistic Regression can be used for binary classification, but it can also be extended to multi-class classification by training multiple binary classifiers and combining their results. Logistic Regression is widely used in various fields, including medical research, marketing, and social sciences.