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Logistic regression is a supervised learning algorithm used for binary classification tasks. That means it's used when the output variable can take on only two possible values—like true or false, yes or no, or one and zero. Unlike linear regression, which predicts a continuous value, logistic regression predicts the probability that a given input belongs to a particular class. This probability is then used to make a final classification decision.

It works by applying a transformation to the result of a linear combination of input features. This transformation ensures the output is always between zero and one, making it interpretable as a probability. A threshold, typically set at 0.5, is then used to convert this probability into a predicted class.

One of the key strengths of logistic regression is that it provides insight into feature importance. Each feature’s impact on the final prediction is represented by a coefficient, which tells us how strongly that feature is associated with the outcome.

Logistic regression is widely used in fields like finance, healthcare, and marketing—for example, to predict loan defaults, diagnose medical conditions, or estimate customer churn. Although it's a relatively simple model, logistic regression performs well when the relationship between the inputs and the outcome is approximately linear in the log-odds. It's also computationally efficient and easy to implement, making it a solid baseline for many classification problems.