new_contents: # set plotting parameters
  plt.figure(figsize=(8,6))
  
  # create a regression friendly data frame
  y, x = dmatrices(formula_ml, data=df, return_type='matrix')
  
  # select which features we would like to analyze
  # try chaning the selection here for diffrent output.
  # Choose : [2,3] - pretty sweet DBs [3,1] --standard DBs [7,3] -very cool DBs, [3,6] -- very long complex dbs, could take over an hour to calculate! 
  feature_1 = 2
  feature_2 = 3
  
  X = np.asarray(x)
  X = X[:,[feature_1, feature_2]]  
  
  
  y = np.asarray(y)
  # needs to be 1 dimenstional so we flatten. it comes out of dmatirces with a shape. 
  y = y.flatten()      
  
  n_sample = len(X)
  
  np.random.seed(0)
  order = np.random.permutation(n_sample)
  
  X = X[order]
  y = y[order].astype(np.float)
  
  # do a cross validation
  nighty_precent_of_sample = int(.9 * n_sample)
  X_train = X[:nighty_precent_of_sample]
  y_train = y[:nighty_precent_of_sample]
  X_test = X[nighty_precent_of_sample:]
  y_test = y[nighty_precent_of_sample:]
  
  # create a list of the types of kerneks we will use for your analysis
  types_of_kernels = ['linear', 'rbf', 'poly']
  
  # specify our color map for plotting the results
  color_map = plt.cm.RdBu_r
  
  # fit the model
  for fig_num, kernel in enumerate(types_of_kernels):
      clf = svm.SVC(kernel=kernel, gamma=3)
      clf.fit(X_train, y_train)
  
      plt.figure(fig_num)
      plt.scatter(X[:, 0], X[:, 1], c=y, zorder=10, cmap=color_map)
  
      # circle out the test data
      plt.scatter(X_test[:, 0], X_test[:, 1], s=80, facecolors='none', zorder=10)
      
      plt.axis('tight')
      x_min = X[:, 0].min()
      x_max = X[:, 0].max()
      y_min = X[:, 1].min()
      y_max = X[:, 1].max()
  
      XX, YY = np.mgrid[x_min:x_max:200j, y_min:y_max:200j]
      Z = clf.decision_function(np.c_[XX.ravel(), YY.ravel()])
  
      # put the result into a color plot
      Z = Z.reshape(XX.shape)
      plt.pcolormesh(XX, YY, Z > 0, cmap=color_map)
      plt.contour(XX, YY, Z, colors=['k', 'k', 'k'], linestyles=['--', '-', '--'],
                 levels=[-.5, 0, .5])
  
      plt.title(kernel)
      plt.show()
  
subject: Create SVM.py