Linear regression is a fundamental supervised machine learning algorithm used for predicting continuous outcomes based on one or more input features. It establishes a linear relationship between the dependent variable (output) and independent variables (inputs) by fitting a straight line to the data points. The basic equation for simple linear regression is represented as (y = mx + b), where (m) is the slope and (b) is the intercept. In multivariable regression, the equation expands to include multiple features, such as (f(x,y,z) = w_1 x + w_2 y + w_3 z), where (w) represents the coefficients that the model learns during training ²⁴.

The goal of linear regression is to minimize the error between the predicted values and the actual outcomes. This is typically achieved using methods like gradient descent, which iteratively adjusts the weights to reduce the cost function, representing the error ²³. Linear regression is widely used in various fields, including economics, biology, and engineering, due to its simplicity and interpretability ³.

To effectively apply linear regression, the data should be continuous, and there should be a correlation between the independent and dependent variables ⁴.