Linear Regression

Linear regression fits a straight-line equation to observed data to predict the value of a dependent variable based on one or more independent variables. The model learns the line that best represents the relationship by minimizing the difference between predicted and actual values. When there is one input variable, it is called simple linear regression. When multiple input variables are used, it is called multiple linear regression.

Linear regression is one of the most widely used predictive modeling techniques because it is simple, interpretable, and computationally efficient. It provides insights into how input variables influence outcomes and often serves as a baseline model before applying more complex machine learning algorithms.