R, Machine Learning, Nonlinear Regression.
CONCLUSION We have discussed alternative methods that can be used to model nonlinear relationships and compared them in terms of RMSE and R-squared criteria. As a result, we can say that polynomial regression, spline regression and generalized additive models can be preferred for this data set. See