Answer: Linear regression is used to predict continuous variables, while logistic regression is used to predict categorical variables.

Linear regression is used to predict continuous variables, while logistic regression is used to predict categorical variables. Linear regression is a type of regression analysis that is used to predict a continuous target variable based on one or more input features. Logistic regression, on the other hand, is a classification algorithm that is used to predict a binary outcome (such as "yes" or "no") based on one or more input features. **Why other options are incorrect:** - **B is incorrect:** Both linear and logistic regressions are supervised machine learning techniques. - **C is incorrect:** Linear regression is instead used for regression, while logistic regression is used for classification. - **D is incorrect:** Linear regression cannot be used for classification. When using a linear regression model for binary classification, where the dependent variable Y can only be 0 or 1, it is possible for the model to predict probabilities outside of the range of 0 to 1. This occurs because the model is attempting to fit a straight line to the data, and the predicted values may not be restricted to the valid range of probabilities. As a result, the model may produce predictions that are less than zero or greater than one.

Author: Tanishq Prabhu