Statistical learning refers to a set of tools for modelling and understanding complex datasets. It is a recently developed area in statistics and blends with parallel developments in computer science and, in particular, machine learning. This paper aims to outline some of the key statistical learning methods in the areas of prediction and classification of data. The goal is to discuss the theory and methodology of Ordinary Least Squares Regression, Ridge Regression, Lasso Regression, Logistic Regression, K-Nearest Neighbours method of classification, Linear and Quadratic Discriminant analysis, and Classification Trees. We then discuss the idea of Cross Validation, and demonstrate these methods by applying them to two real-life datasets
