There are three types of regressions:
Linear regression: Linear regression is one of the most basic and widely used machine learning algorithms. It is a predictive modeling technique used to predict a continuous dependent variable, given one or more independent variables. In a linear regression model, the relationship between the dependent and independent variable is always linear thus, when you try to plot their relationship, you’ll observe more of a straight line than a curved one.
Logistic Regression is a machine learning algorithm used to solve classification problems. It is called ‘Logistic Regression’, because its fundamental technique is quite similar to Linear Regression. Logistic Regression is a predictive analysis technique used to predict a dependent variable, given a set of independent variables, such that the dependent variable is categorical.
polynomial regression: Polynomial Regression is a method used to handle non-linear data. Non-linearly separable data is basically when you cannot draw out a straight line to study the relationship between the dependent and independent variables.