Logistic Regression  Algorithm Concept of Logistic Regression A machine learning approach called logistic regression is used to model the likelihood of a binary outcome based on one or more independent factors. The goal of logistic regression is to find the best-fitting logistic function that maps the input variables to a probability output between 0 and 1. The logistic function, also known as the sigmoid function, takes the form of:   sigmoid(z) = 1 / (1 + e^-z)   where z is a linear combination of the input variables and their coefficients. For example, let's say we have a dataset of customer information, including their age and whether they have purchased a product. We can use logistic regression to predict the probability of a customer making a purchase based on their age. Logistic Regression  Algorithm: Define the problem and collect data. Choose a hypothesis class (e.g., logistic regression). Define a cost function to measure the difference between predicted and actual