SVM is a powerful technique and especially useful for data whose distribution is unknown (also known as non-regularity in data). Because the example considered here consisted of only two features, the SVM fitted by R here is also known as linear SVM. SVM is powered by a kernel for dealing with various kinds of data and its kernel can also be set during model tuning. Some such examples include gaussian and radial. Hence, SVM can also be used for non-linear data and does not require any assumptions about its functional form.

Click to learn more about author Kimberly Nevala. Artificial Intelligence (AI) provides exciting new opportunities for exploiting data and information. But is AI a revolution or natural evolution of existing BI/Analytics use cases? Certainly some of the core techniques that power AI (advanced Machine Learning and Deep Learning in particular) represent a fundamental shift in algorithmic programming. The application of AI, however, answers a need previous analytic generations could not.

Chaitanya Sagar is the Founder and CEO of Perceptive Analytics. Perceptive Analytics has been chosen as one of the top 10 analytics companies to watch out for by Analytics India Magazine.

There is so much of information lying in the text posts made by you and me and all others about all the trending topics today. Being in our respective firms, big or small, each of us collect some data related to our respective businesses and store it to analyze for various projects. At the same time, we all need this'unstructured data' to know and understand more about our clients, customers and the state of our company in the world today. However, working with this data is not easy. The data is not structured, every piece does not have all the information and each part is unique.

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.