Linear regression

Introduction
1. Linear regression (LR) attempts to model the relationship between two variables by fitting a linear equation to the observed data. One variable is considered to be an explanatory variable, and the other is considered to be a dependent variable.
2. A linear regression line has an equation of the form Y = a + bX, where X is the explanatory variable and Y is the dependent variable. The slope of the line is b, and a is the intercept. On ViNAS, the LR algorithms is implemented using scikit-learn 0.24.1.