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Linear regression is used to represent the relationship between two continuous variables by minimising the space between the observed and anticipated values of the variables by the application of a straight line fit to the data.

Assuming a linear relationship between independent and dependent variables is at the heart of linear regression analysis, y=mx+b.

Different from Multiple Linear Regression, in which only one independent variable is used to make predictions about the dependent variable, Simple Linear Regression focuses on modelling the connection between two independent variables.

The main objective of linear regression is to create a model that best fits the data, minimizing the difference between predicted and actual values.

Linear Regression models are evaluated using metrics like Mean Absolute Error, Mean Squared Error, and Root Mean Squared Error, which quantify the prediction accuracy.

The benefits of using linear regression stem from the fact that it is easy to understand and apply. Its popularity can be attributed to the ease with which it presents the connections between different variables.

Linear Regression is well-suited to continuous inputs and continuous data because it models a relationship between variables as a straight line.

The accuracy of predictions made by linear regression is limited by its focus on linear correlations, its susceptibility to outliers, and its reliance on the selection of independent variables.

Logistic Regression is used for predicting binary or categorical outcomes, making it applicable to classification tasks, while Linear Regression is used to predict continuous outcomes.

Ridge Regression balances model complexity and accuracy in the face of multicollinearity by minimising residuals and adding a regularisation term to prevent overfitting.

Using L1 regularisation, Lasso Regression promotes sparsity and enables feature selection. In order to prevent multicollinearity and strike a balance in terms of model complexity, Ridge Regression employs L2 regularisation.

To model non-linear interactions between variables and account for curves and bends in the data, Polynomial Regression goes beyond Linear Regression.

In contrast to Maximum Likelihood Linear Regression, Bayesian Linear Regression uses previous ideas about the parameters to provide a more thorough probabilistic framework.

Multi-task Linear Regression can handle a number of interconnected jobs since it takes use of task correlations. Multi-output Although it may deal with numerous outputs, linear regression generally treats them as independent variables.

By penalising extremely large coefficients, L2 regularisation is used to prevent overfitting in Ridge Regression. By using L1 regularisation, Lasso Regression promotes sparsity and makes it possible to pick features by setting some coefficients to zero.

Elastic Net combines L1 and L2 regularisation, whereas Ridge Regression uses L2 regularisation; both are useful for feature selection and dealing with multicollinearity.

The flexibility of Linear Regression is expanded in Generalised Linear Models (GLMs) by accommodating non-normal error distributions and other types of response variables.

Assuming a straight line in its analysis, linear regression ignores any curves or bends in the data, while non-linear regression takes them into account.

Results having linear relationships can be predicted using linear regression. In order to simulate multiplicative relationships, such as percentages, Log-linear Regression logarithmically transforms variables.