Logistic Regression is a statistical method for analyzing a dataset in which there are one or more independent variables that determine an outcome. Unlike linear regression, which predicts continuous values, logistic regression predicts the probability of a binary outcome (e.g., success/failure, yes/no). The model uses the logistic function to map predicted values to probabilities, ensuring they fall within the range [0, 1]. Logistic regression is widely used in fields such as healthcare, finance, and marketing for tasks like disease diagnosis, credit scoring, and customer churn prediction. It provides coefficients that describe the impact of each independent variable on the probability of the outcome, making it interpretable and useful for understanding relationships in the data.