from sklearn.linear_model import LinearRegression Of linear regression class which will be the simple linear regressor. Will first fit the simple linear regression algorithm to the training set, andįor that, we first need to import a library linear_model from the scikit learn. That their power of prediction will be tested on a test set. The model will learn from the correlation between X_train Whereas X_test and Y_test form a test set. Here X_train and Y_train form a training set, ![]() It can be more clearly understood by the image given below: Positive Linear Relationship: If both dependent variable as well as independent variables increases, then a linear relationship is known as Positive Linear Relationship. The point where the line of regression crosses the Y-axis, and m calculates the The value of C is called as intercept shows ![]() In accordance to the change in the value of independent variables.Īs responses, m is the regression coefficient, X is the independent variable called Stated as the change (increase/decrease) in the value of the dependent variable So, we can say that the linear relation between two variables can be Learn the correlation between a dependent variable and one or more independentįeatures. ![]() Regression is the most important statistical algorithm in machine learning to
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