Logistic Regression is a popular supervised learning algorithm used for binary classification

It models the probability of the target variable given the values of one or more features.

Logistic Regression assumes that the relationship between the features and the target variable is log-linear,

The output of the logistic regression model is a probability value between 0 and 1

Logistic Regression calculates the best-fitting curve (sigmoid) that maximizes the likelihood of the observed data.

Logistic Regression can handle both numerical and categorical input features.

Logistic Regression can be regularized to reduce overfitting and improve generalization performance.

It is widely used in various applications, such as finance, healthcare, and social sciences, for predicting binary outcomes.