new_contents: from feature_format import featureFormat, targetFeatureSplit
  import pickle
  from sklearn.naive_bayes import GaussianNB
  
  # loading the enron data dictionary
  with open("final_project_dataset.pkl", "r") as data_file:
      data_dict = pickle.load(data_file)
  
  # removing 'TOTAL' outlier
  del data_dict['TOTAL']
  
  # selecting only 2 features - total_stock_value and bonus for now
  # total_stock_value - data available for all POIs and second best feature
  # bonus - data available for 16 out of 18 POIs and third best feature
  features_list = ['poi',
                   'total_stock_value',
                   'bonus']
  
  # creating list of labels and list of numpy arrays containing the features
  data = featureFormat(data_dict, features_list, sort_keys = True)
  labels, features = targetFeatureSplit(data)
  
  # Fitting and testing Gaussian Naive Bayes Classifier
  clf = GaussianNB()
  clf.fit(features, labels)
  print clf.score(features, labels)
subject: Create first pass Gaussian Naive Bayes Classifer