Linear regression is a popular and widely used statistical method that is used to model the relationship between a dependent variable and one or more independent variables. In this technique, the goal is to find the best fit line that can explain the relationship between the independent and dependent variables. Linear regression is a supervised learning algorithm, meaning that it requires a labeled dataset to train the model. The labeled dataset consists of pairs of input-output data, where the input data represents the independent variables and the output data represents the dependent variable. The algorithm then learns the relationship between the independent and dependent variables by fitting a line to the data, minimizing the error between the predicted and actual output values. There are two types of linear regression: Simple Linear Regression: In simple linear regression, there is only one independent variable, and the relationship between the independent and dependent var...