In [1]:

    

import pandas as pd


    

In [5]:

    

data = pd.read_csv('resources/svm-data.csv', header=None)


    

In [20]:

    

target = data[0]
features = data.loc[:, 1:]


    

In [22]:

    

from sklearn.svm import SVC


    

In [24]:

    

svc = SVC(kernel='linear', C=100000, random_state=241)


    

In [28]:

    

svc.fit(features, target)


    

    
Out[28]:





SVC(C=100000, cache_size=200, class_weight=None, coef0=0.0,
  decision_function_shape='ovr', degree=3, gamma='auto', kernel='linear',
  max_iter=-1, probability=False, random_state=241, shrinking=True,
  tol=0.001, verbose=False)

In [32]:

    

svc.support_


    

    
Out[32]:





array([3, 4, 9], dtype=int32)

In [42]:

    

import matplotlib.pyplot as plt
%matplotlib inline

plt.plot(features,linestyle="",marker="o")


    

    
Out[42]:





[<matplotlib.lines.Line2D at 0x111633290>,
 <matplotlib.lines.Line2D at 0x111633310>]