Assumptions of Linear Regression in Machine Learning

Linear Regression models give accurate results only when key assumptions are satisfied. This article explains all seven assumptions—linearity, independence, homoscedasticity, normality, multicollinearity, autocorrelation, and additivity—with examples and diagrams.