Earlier, I wrote about how to implement a simple linear regression (SLR) model in python. SLR is probably the easiest model to implement among the most popular machine learning algorithms. In this post, we are going to take it one step further and instead of working with just one independent variable, we will be working with multiple independent variables. Such a model is called a multivariate linear regression (MLR) model.
How does the model work?
A multivariate linear model can be described by a linear equation consisting of multiple independent variables.
In this equation, ß (beta) defines all the coefficients, x defines all the independent variables and y defines dependent variable.
An SLR model is a simplified version of an MLR model where there is only one x. Linear regression models use a technique called Ordinary Least Squares (OLS) to find the optimum value for the betas. OLS consists of calculating the error which is the difference between predicted value and actual value and then taking square of it. The goal is to find the betas that minimize the sum of the squared errors.
If you want to learn more about SLM and OLS, I highly recommend this visual explanation.