SVM is not fitted when tried to fit it into a model 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

train_new.head()



# 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 ?
15 attributes with 30161 instances are not much to cause this delay, I've trained with much more. Your system config also plays a major role in this. For instance if you are using anaconda on chrome and you have multiple processes going on then it would cause delay as distances are calculated on the fly which will be heavy on memory.

You can also try SGD as you are going for linear. It is faster than SVM.