#'loan_advances',
#'long_term_incentive',
'poi_messages_pctg',
#'restricted_stock',
#'salary',
#'shared_receipt_with_poi',
'total_payments',
'total_stock_value'
]
### Load the dictionary containing the dataset
data_dict = pickle.load(open("final_project_dataset.pkl", "r"))
### Task 2: Remove outliers
outliers = ['TOTAL', 'THE TRAVEL AGENCY IN THE PARK', 'LOCKHART EUGENE E']
remove_outliers(data_dict, outliers)
### Task 3: Create new feature(s)
### Fields to be considered as cash received
cash_fields = ['salary','bonus','exercised_stock_options','loan_advances']
### Add cash received fields
calc_cash_received(data_dict, cash_fields)
### Add POI message percentage
calc_poi_messages_pctg(data_dict)
### Store to my_dataset for easy export below
my_dataset = data_dict
### Extract features and labels from dataset for local testing
data = featureFormat(my_dataset, features_list, sort_keys = True)
labels, features = targetFeatureSplit(data)
# Create Min/Max Scaler
unsorted_pairs = zip(features_list[1:], scores)
# Sort list
sorted_pairs = list(reversed(sorted(unsorted_pairs, key=lambda x: x[1])))
# Create dict
if k == 'all':
k_best_features = dict(sorted_pairs)
else:
k_best_features = dict(sorted_pairs[:k])
return k_best_features
if __name__ == '__main__':
# Load the dictionary containing the dataset
data_dict = pickle.load(open("../project/final_project_dataset.pkl", "r"))
outliers = ['TOTAL', 'THE TRAVEL AGENCY IN THE PARK', 'LOCKHART EUGENE E']
# Remove outliers
remove_outliers(data_dict, outliers)
# Fields to be considered as cash received
cash_fields = ['salary','bonus','exercised_stock_options','loan_advances']
# Add cash received fields
calc_cash_received(data_dict, cash_fields)
# Add POI message %
calc_poi_messages_pctg(data_dict)
#pprint(data_dict)
# Create features list
features_list = ['poi',
'bonus',
'cash_received',
'cash_received_pctg',
'deferral_payments',
'deferred_income',
'director_fees',