Linear Regression is a popular method used for predicting a numerical outcome based on one or more input variables.

Linear Regression assumes a linear relationship between the input variables and the outcome.

The goal of Linear Regression is to find the minimum distance between the predicted outcomes and the actual outcomes in the training data.

The line of best fit is defined by a slope and an intercept, which are estimated during training.

Linear Regression can be used for both simple regression (one feature) and multiple regression (more than one feature).

It is a parametric algorithm, meaning that it makes assumptions about the underlying distribution of the data.

Linear Regression can be sensitive to outliers and multicollinearity.

Regularisation techniques, such as L1 and L2 regularisation, can be used to reduce overfitting and improve generalisation performance.