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I have been studying data science for over two years now, and I often go back and refresh my knowledge on some of the popular machine learning algorithms. Each algorithm has been designed with a specific theoretical niche in mind, and it is for that reason that each algorithm must be covered in detail to gain a good understanding of it. In addition, a person could very well be asked to explain a certain algorithm during an interview, so it is a good idea to stay abreast of this subject.
Linear regression
Linear regression analysis is used to predict the value of a continuous variable of a variable based on the value of another variable. The variable that is predicted is the dependent, or output. The variable that is used to make the prediction is the independent variable, or input.
Linear regression fits a straight line or surface that minimises the discrepancies between predicted and actual output values.
Regression is used in many different fields, such as economics, computer science, and social science.
Linear regression is one of the most important and widely used regression techniques. It is one of the simplest regression models and it is easy to interpret the results of the regression.
