new_contents: 
  """ Part 1 """
  # Load data
  import numpy as np
  from sklearn import datasets
  iris = datasets.load_iris()
  iris_X = iris.data
  iris_y = iris.target
  
  print(iris.feature_names)
  print(iris.target_names)
  print(np.unique(iris_y))
  
  
  
  # Visualize data
  import matplotlib.pyplot as plt
  from mpl_toolkits.mplot3d import Axes3D
  X = iris.data[:, :2]  # we only take the first two features.
  y = iris.target
  x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5
  y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5
  
  
  
  # Plot the training points
  plt.figure(2, figsize=(8, 6))
  plt.clf()
  plt.scatter(X[:, 0], X[:, 1], c=y, cmap=plt.cm.Set1, edgecolor='k')
  plt.xlabel('Sepal length')
  plt.ylabel('Sepal width')
  plt.xlim(x_min, x_max)
  plt.ylim(y_min, y_max)
  plt.xticks(())
  plt.yticks(())
  plt.show()
  
  
  
  # Plot the distribution in boxplot
  plt.boxplot(iris.data)
  plt.xlabel('Features')
  plt.ylabel('Cm')
  plt.show()
  
  
  """ Part 2 """
  # KNN Classification
  # shuffle the data
  np.random.seed(0)
  indices = np.random.permutation(len(iris_X))
  # Split iris data in train and test data
  # A random permutation, to split the data randomly
  iris_X_train = iris_X[indices[:-10]]
  iris_y_train = iris_y[indices[:-10]]
  iris_X_test  = iris_X[indices[-10:]]
  iris_y_test  = iris_y[indices[-10:]]
  
  # Create and fit a nearest-neighbor classifier
  from sklearn.neighbors import KNeighborsClassifier
  knn = KNeighborsClassifier()
  knn.fit(iris_X_train, iris_y_train) 
  
  # Do prediction on the test data
  knn.predict(iris_X_test)
  print iris_y_test
  
  
  
  # KNN Visualization.
  n_neighbors = 15
  h = .02  # step size in the mesh
  
  # Create color maps
  from matplotlib.colors import ListedColormap
  cmap_light = ListedColormap(['#FFAAAA', '#AAFFAA', '#AAAAFF'])
  cmap_bold = ListedColormap(['#FF0000', '#00FF00', '#0000FF'])
  
  for weights in ['uniform', 'distance']:
      # we create an instance of Neighbours Classifier and fit the data.
      knn = KNeighborsClassifier(n_neighbors, weights=weights)
      knn.fit(X, y)
  
      # Plot the decision boundary. For that, we will assign a color to each
      # point in the mesh [x_min, x_max]x[y_min, y_max].
      xx, yy = np.meshgrid(np.arange(x_min, x_max, h),
                           np.arange(y_min, y_max, h))
      Z = knn.predict(np.c_[xx.ravel(), yy.ravel()])
  
      # Put the result into a color plot
      Z = Z.reshape(xx.shape)
      plt.figure()
      plt.pcolormesh(xx, yy, Z, cmap=cmap_light)
  
      # Plot also the training points
      plt.scatter(X[:, 0], X[:, 1], c=y, cmap=cmap_bold,
                  edgecolor='k', s=20)
      plt.xlim(xx.min(), xx.max())
      plt.ylim(yy.min(), yy.max())
      plt.title("3-Class classification (k = %i, weights = '%s')"
                % (n_neighbors, weights))
  
  plt.show()
  
  
  
  """ Part 3 """
  from sklearn import svm
  svc = svm.SVC(kernel='linear')
  svc.fit(iris_X_train, iris_y_train)  
  svc.predict(iris_X_test)
  print iris_y_test
  
  
  ### Your Job: get the SVC visualization for different kernels.
  
subject: Create supervised.py