Performance Analysis of Support Vector Machine Linear Kernel Towards Machine Learning

Support Vector Machine or SVM is one of the most popular Supervised Learning algorithms, which is used for classification as well as regression problems. The important concepts in SVM includes Support Vectors, data points that are closest to the hyper plane is called support vectors. Separating line will be defined with the help of these data points. Margin, it may be defined as the gap between two lines on the closet data points of different classes. It can be calculated as the perpendicular distance from the line to the support vectors. Large margin is considered as a good margin and small margin is considered as a bad margin. There are some problem needs to consider, while implementing SVM for a data set. There are multiple hyper planes that separate the data exists, which one to choose. Target classes are overlapping. In cases where the number of features for each data point exceeds the number of training data samples. In this paper we used linear kernel based SVM and develop to models first model used heart disease data with 14 attribute. Last attribute is classify the records based on SVM model that patient belong to yes or no class. In second model we used Social data set which has five attribute customers ID, Gender, Age Salary, purchased, the last class is a binary class. Based on salary and age we need to classify the into purchased class (Yes or No). We used R language to implement both the model. We divided the data set into training and testing data set. First we create model using training data set. After that we apply the testing data set. We create Confusion Matrix for both models after allying testing data set to find out the accuracy of the models. We calculate the accuracy and use graph to the show the number of records classifies correctly by both model. Real life data set is used in both the models.