Linear Vs Logistic Regression

A linear regression algorithm defines a linear relationship between independent and dependent variables. It uses a linear equation to identify the line of best fit (straight line) for a problem, which it then uses to predict the output of the dependent variables.

A logistic regression algorithm predicts a binary outcome for an event based on a dataset's previous observations. Its output lies between 0 and 1. The algorithm uses independent variables to predict the occurrence or failure of specific events.

You use linear regression when the outcome is a continuous value, such as price or temperature. You should use logistic regression when the outcome is a categorical value like spam/not spam, yes/no, etc.