Linear regression is a popular statistical tool used in data science, thanks to the several benefits it offers, such as: Hence, it is called the ‘best fit line.’ The goal of the linear regression algorithm is to find this best fit line seen in the above figure. Here, a line is plotted for the given data points that suitably fit all the issues.
Line of regression = Best fit line for a model This analysis method is advantageous when at least two variables are available in the data, as observed in stock market forecasting, portfolio management, scientific analysis, etc.Ī sloped straight line represents the linear regression model.īest Fit Line for a Linear Regression Model Thus, linear regression is a supervised learning algorithm that simulates a mathematical relationship between variables and makes predictions for continuous or numeric variables such as sales, salary, age, product price, etc. The regression model predicts the value of the dependent variable, which is the response or outcome variable being analyzed or studied. However, the dependent variable changes with fluctuations in the independent variable. The independent variable is also the predictor or explanatory variable that remains unchanged due to the change in other variables. It is a statistical method used in data science and machine learning for predictive analysis. Linear regression is an algorithm that provides a linear relationship between an independent variable and a dependent variable to predict the outcome of future events. Linear Regression Best Practices for 2022.Types of Linear Regression with Examples.
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