Linear regression

Linear regression is a statistical method used in data analysis and machine learning to model the relationship between a dependent variable (also known as the target or response variable) and one or more independent variables (predictors or features) by fitting a linear equation to the observed data. The primary goal of linear regression is to find the best-fitting linear relationship that describes the association between variables. This linear equation can then be used for prediction, understanding relationships, or making inferences about the data. Linear Equation: In simple linear regression, which involves one independent variable, the linear equation is represented as: Y = a + bX, Y represents the dependent variable, X represents the independent variable, “a” is the intercept, which represents the value of Y when X is 0, “b” is the slope, which represents the change in Y for a one-unit change in X.