This algorithm is used to solve the classification model problems. KNN is a Supervised Learning algorithm where the value or result that we want to predict is within the training data (labeled data) and the value which is in data that we want to study is known as target or test data.All the other columns in the dataset are known as the Feature or Predictor Variable or Independent Variable. K-NN algorithm basically creates an imaginary boundary to classify the data. When new data points come in, the algorithm will try to predict that to the nearest of the boundary line.
PCA is basically a dimension reduction process and this is basically a statistical procedure to convert a set of observation of possibly correlated variables into a set of values of linearly uncorrelated variables.Each of the principal components is chosen in such a way so that it would describe most of the still available variance and all these principal components are orthogonal to each other. Main task of PCA is to select a subset of variables from a larger set, based on which original variables have the highest correlation with the principal amount.
SVM is a supervised machine learning algorithms which are used both for classification and regression probelm. SVM used for linearly separable data .An SVM model is basically a representation of different classes in a hyperplane in multidimensional space. The hyperplane will be generated in an iterative manner by SVM so that the error can be minimized. The goal of SVM is to divide the datasets into classes to find a maximum marginal hyperplane (MMH). SVM will choose the hyperplane that separates the classes correctly. SVM uses various kernel like Linear Kernel ,Polynomial Kernel,Radial Basis Function (RBF) Kernel etc. in these kernel mostly used kernel is RBF.