The R-square is a measure of how well the linear regression model fits the observed data. It is calculated by squaring the correlation coefficient and dividing by the standard deviation of errors. It is the square of the correlation coefficient divided by its standard deviation (r2/s2). The R-square value of 1 indicates that the model explains 100% of the variation in Y. The R-square values greater than 1 indicate that the model explains more than 100% of the variation in Y.

Regression is the method used to predict the continuous variable in the target column or dependent variable based on independent features. It falls under the supervised technique. It is a statistical tool used to find out the relationship between the outcome variable, the dependent variable, and one or more variables often called independent variables. Linear regression is used for finding the linear relationship between the target and one or more predictors. Simple linear regression finds the relationship between the dependent (Y) and independent (X) and it tries to find the best fit line by minimizing the errors this fitness function says how good your model is, or you can define a cost function that measures how bad it is.