Building Regression Models in R using Support Vector Regression

The article studies the advantage of Support Vector Regression (SVR) over Simple Linear Regression (SLR) models. SVR uses the same basic idea as Support Vector Machine (SVM), a classification algorithm, but applies it to predict real values rather than a class. SVR acknowledges the presence of non-linearity in the data and provides a proficient prediction model. Along with the thorough understanding of SVR, we also provide the reader with hands on experience of preparing the model on R. We perform SLR and SVR on the same dataset and make a comparison. The article is organized as follows; Section 1 provides a quick review of SLR and its implementation on R. Section 2 discusses the theoretical aspects of SVR and the steps to fit SVR on R. It also covers the basics of tuning SVR model.