One of the simplest Machine learning algorithms out there, Linear Regression is used to make predictions on continuous dependent variables with knowledge from independent variables. A dependent variable is the effect, in which its value depends on changes in the independent variable.
You may remember the line of best fit from school – this is what Linear Regression produces. A simple example is predicting one’s weight depending on their height.
Logistic Regression, similar to Linear Regression, is used to make predictions on categorical dependent variables with knowledge of independent variables. A categorical variable has two or more categories. Logistic Regression classifies outputs that can only be between 0 and 1.
For example, you can use Logistic Regression to determine whether a student will be admitted or not to a particular college depending on their grades – either Yes or No, or 0 or 1.