ValueError: y should be a 1d array, got an array of shape (285, 30) instead I am using this data set below and I am trying to find the support vector machine of the data set. Also
I have my code and error below as well.
http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_breast_cancer.html#sklearn.datasets.load_breast_cancer

import numpy as np
from sklearn import svm,datasets
breastcancer = datasets.load_breast_cancer()
#print(breastcancer)
everydata = breastcancer.data
#print(everydata)
everytarget = breastcancer.target
traindata = []
traintarget = []
testdata = []
testclasses = []
#Class 0 data separtion
for i in range(0,140):
    traindata.append(everydata[i])
    traintarget.append(everytarget[i])
for i in range(140,212):
    testdata.append(everydata[i])
#Class 1 data separation
for i in range(212,357):
    traindata.append(everydata[i])
    traintarget.append(everytarget[i])
for i in range(357,569):
    testdata.append(everydata[i])
traindata = np.concatenate((everydata[:140, :],everydata[212:357, :]),axis=0)
traintarget = np.concatenate((everydata[:140],everydata[212:357]),axis=0)
testdata = np.concatenate((everydata[140:212, :],everydata[357:569, :]),axis=0)
print(len(traindata))
print(traintarget)
print(testdata)
dd = svm.SVC(kernel='linear')
dd.fit(traindata,traintarget)
decide = dd.predict(testdata)
print(decide)

Why am I getting this error is my concatenation incorrect. The results are supposed to output 0s and 1s at the end.
The problem is that you are using the everydaydata to build the traintarget dataset, but you should use the labels in everytarget. That is why is complaining abut the shape, because the labels should be one dimensional.
Try replacing this:
traintarget = np.concatenate((everydata[:140],everydata[212:357]),axis=0)

with this:
traintarget = np.concatenate((everytarget[:140],everytarget[212:357]),axis=0)