title: How to reshape for a Linear SVC
body: <p>I currently have a list of 35 features which we are using to predict and outcome. I understand the nature of the depreciation error, and my understanding of how to solve is by reshaping the np array to account for each feature:</p>
  
  <pre><code>X = X.reshape(len(FEATURES), -1)
  </code></pre>
  
  <p>However, I am unsure if this should be included in the Build_Data_Set function or in the Analysis function prior to the for loop?</p>
  
  <pre><code>FEATURES = [...#list of 35 columns for the below dataframe]
  
  def Build_Data_Set():
      data_df = pd.read_csv('{}{}'.format(path, key_stats_csv))
  
  
      #  data[features], means we take all of the 35 features of data, convert them to just those values
  
      X = np.array(data_df[FEATURES].values)
      y = (data_df['status'].replace('underperform', 0).replace('outperform', 1)
                                                       .values.tolist())
  
      X = preprocessing.scale(X)
      return X, y
  
  
  def Analysis():
  
      test_size = 1000
  
      X, y = Build_Data_Set()
  
      print(len(X))
  
      clf = svm.SVC(kernel='linear', C=1.0)
      clf.fit(X[:test_size], y[:test_size])
  
      correct_count = 0
  
      for x in range(1, test_size + 1):
          if clf.predict(X[-x])[0] == y[-x]:
              correct_count += 1
  
      print('Accuray: {}'.format((correct_count / test_size) * 100))
  
  
  Analysis()
  </code></pre>
  
  <p>Error:</p>
  
  <pre><code>DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17     
  and willraise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or 
  X.reshape(1, -1) if it contains a single sample.
  </code></pre>