Multiple Linear Regression is an extension of Simple Linear regression as it takes more than one predictor variable to predict the response variable. It is an important regression algorithm that models the linear relationship between a single dependent continuous variable and more than one independent variable. It uses two or more independent variables to predict a dependent variable by fitting a best linear relationship.
It has two or more independent variables (X) and one dependent variable (Y), where Y is the value to be predicted. Thus, it is an approach for predicting a quantitative response using multiple features.
Equation: Y = β0 + β1X1 + β2X2 + β3X3 + … + βnXn + e
Y = Dependent variable / Target variable
β0 = Intercept of the regression line
β1, β2, β3, …. βn = Slope of the regression line which tells whether the line is increasing or decreasing
X1, X2, X3, ….Xn = Independent variable / Predictor variable
e = Error