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Kanika, SVM is used for encountering both regression and classification challenges. SVM is classified into 2 categories namely linear and non-linear categories. In linear SVM the classifiers can be separated using hyperplane and in non-linear SVM, it is not possible to separate the classifiers using hyperplane.  The advantage of using support vector machine is it provides classification performance, it doesn’t require any assumption and does not overfit the data. To know more about SVM visit https://towardsdatascience.com/support-vector-machine-introduction-to-machine-learning-algorithms-934a444fca47, https://machinelearningmastery.com/support-vector-machines-for-machine-learning/

Kanika, Support vector machine (SVM) is a form of supervised learning. This algorithm functions by classifying the information or data into separate classes. The data here is plotted as a point in n-dimensional space with each point being the value of a specific coordinate.  SVM  is also known as the kernel trick. Here, the functions with low dimension input space are transformed into high dimension space. In simple words, the non-separable problem is converted into a separable problem. It classifies the data into hyperplanes and maximises the distance between different classes involved.