K-Nearest Neighbors (KNN) and Support Vector Machines (SVMs)

K-Nearest Neighbors (KNN) and Support Vector Machines (SVMs) are a set of learning methods used for regression and classification (also density estimation in the case of KNN). The aim of this experience is to apply both KNN and SVM to the same dataset, analysing a set of parameters (k in the case of KNN and C, Gamma for SVM) in order to achieve the best accuracy on the validation set and then trying to predict correctly the labels on the test set. At the end, K-fold Cross-Validation will be applied to improve the quality of the classification.