Nonlinear Modeling in R with GAMs

GAMs are useful for inference and prediction. GAMs are flexible and more understandable than traditional linear models. GAMs model data relationships using nonlinear functions. This allows them to be used for data science and other data problems. This course will show you how GAMs can be constructed using the popular mgcv software. Learn how to analyse, explain, visualize, and diagnose model problems. GAMs are useful in many different situations. To build mixed linear and nonlinear models, you will need data sets like automobile performance data. Geospatial models can be built using soil pollution data. To classify and predict consumer purchasing data. This course will teach you how to solve data science problems.