I was trying to apply SVM into a dataset to find its accuracy. I also applied bagging and boosting on the same dataset and they worked properly. But when I tried to fit SVM into a model its not working. I couldn't understand whats the problem.
I'm providing the sample of my dataset
# define X and y
feature_cols = ['age','workclass','fnlwgt','education','education-num','marital-status','occupation','relationship','race','sex','capital-gain','capital-loss','hours-per-week','native-country']
# X is a matrix, hence we use  to access the features we want in feature_cols
X = train_new[feature_cols]
# y is a vector, hence we use dot to access 'label'
y = train_new['label']
from sklearn import svm
model = svm.SVC(kernel='linear', C=1, gamma=1)
and while trying to fit with model it's almost minutes passed and it's just look like,
What's the actual problem and could I solve it ?