Here we will implement all regression models on 1 dataset and will analyze the performance of each model.
and we will discover which regression model is best for that data set by comparing R^2 coefficient of each model.
Adjusted R^2 = R2 shows how well terms (data points) fit a curve or line. Adjusted R2 also indicates how well terms fit a curve or line, but adjusts for the number of terms in a model. If you add more and more useless variables to a model, adjusted r-squared will decrease. If you add more useful variables, adjusted r-squared will increase. Adjusted R2 will always be less than or equal to R2.
where::-
N is the number of points in your data sample.
K is the number of independent regressors, i.e. the number of variables in your model, excluding the constant.
We found in the end that the highest performance for this dataset is given by Random Forest Regression Model with R^2 score of 0.96