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$R^2$ known as Coefficient of Determination, indicates the amount of variation in dependent variable Y with respect to the independent variable X. It is a goodness of fit measure for linear regression which measures the strength of the relationship between the two variables. Higher R -squared values represent smaller differences between the observed values and fitted values. It denotes the strength of the linear association between X and Y.

R-squared is the percentage of the dependent variable variation that a linear model explains. $R^2$ is always between 0 and 100% where, 0% represents a model that does not explain any of the variation in the dependent variable, whereas 100% represents a model that explains all the variation in the dependent variable with respect to the independent variable.