Logistic Regression is a commonly used method in data mining for categorizing binary data. It can also be applied to multiple dependent variables. The main goal of this research is to explain the Logistic Regression model, its differences from Linear Regression, how it selects independent variables, and its primary principles. The study shows that Logistic Regression is useful for modeling data, especially when dealing with irregular or rare occurrences.