Linear Regression

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A method to find pattern with a “Best Fit Line” ( therefore, “Linear” get it ?) in your data. Linear Regression: The Easier Way | Towards Data Science

Linear regression is a common statistical method, which has been adopted in machine learning and enhanced with many new methods for fitting the line and measuring error. In the most basic sense, regression refers to prediction of a numeric target. Linear regression is still a good choice when you want a very simple model for a basic predictive task. Linear regression also tends to work well on high-dimensional, sparse data sets lacking complexity. Azure Machine Learning Studio supports a variety of regression models, in addition to linear regression. However, the term "regression" can be interpreted loosely, and some types of regression provided in other tools are not supported in Studio.

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Linear Regression algorithm will use the data points to find the best fit line to model the data. A line can be represented by the equation, y = m*x + c where y is the dependent variable and x is the independent variable. Basic calculus theories are applied to find the values for m and c using the given data set. Linear Regression has 2 types as Simple Linear Regression where only 1 independent variable is used and Multiple Linear Regression where multiple independent variables are defined. 10 Machine Learning Algorithms You need to Know | Sidath Asir @ Medium