heregamma ranges from to we need to manually specify it in the learning algorithm good default value of gamma is as we implemented svm for linearly separable datawe can implement it in python for the data that is not linearly separable it can be done by using kernels example the following is an example for creating an svm classifier by using kernels we will be using iris dataset from scikit-learnwe will start by importing following packagesimport pandas as pd import numpy as np from sklearn import svmdatasets import matplotlib pyplot as plt nowwe need to load the input datairis datasets load_iris(from this datasetwe are taking first two features as followsx iris data[:: iris target nextwe will plot the svm boundaries with original data as followsx_minx_max [: min( [: max( y_miny_max [: min( [: max( (x_max x_min)/ xxyy np meshgrid(np arange(x_minx_maxh)np arange(y_miny_maxh)x_plot np c_[xx ravel()yy ravel()nowwe need to provide the value of regularization parameter as followsc nextsvm classifier object can be created as followssvc_classifier svm svc(kernel='linear' =cfit(xyz svc_classifier predict(x_plotz reshape(xx shapeplt figure(figsize=( )plt subplot(