application_vnd.jupyter.stderr_output_1.png
from sklearn import svm

import numpy as np # linear algebra

import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)

data = pd.read_csv('../input/mushrooms.csv')

data = np.array(data)
train, test = (data[0:8000,], data[8000:,])
Xtrain, ytrain = (train[:, 0:-1], train[:, -1])
Xtest, ytest = (test[:, 0:-1], test[:, -1])

from sklearn import svm
model = svm.SVC(kernel='linear', gamma=1)
model.fit(Xtrain, ytrain)