Linear regression models aim to predict outcomes by finding linear relationships between variables. These models work well when the target is linearly related to the features. By using the least squares method, researchers can find the best parameters for the model. This classic approach can be just as effective as more complex models. The simplicity and interpretability of linear regression make it a popular choice in the computer and big data fields. The article discusses simple linear regression with one feature, multiple linear regression with more than one feature, and shrinkage linear regression models.