Kaggle competition: IEEE-CIS Fraud Detection
<SOS> metric: AUC Kaggle data source: ieee-cis-fraud-detection<prepare_x_and_y>
warnings.filterwarnings('ignore')
 for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename))
trainpredictions = None
testpredictions = None
features = list(blindtraindata.columns)
 features.remove('Response')
 features.remove('Id')
 dvisibletrain = \
xgb.DMatrix(blindtraindata[features],
blindtraindata.Response,
silent=True)
 dtest = \
xgb.DMatrix(testdata[features],
silent=True)
 folds = 1<train_model>
sub_1 = pd.read_csv('/kaggle/input/09522/submission2.csv')
 sub_2 = pd.read_csv('/kaggle/input/gmean-of-low-correlation-lb-0-952x/stack_gmean.csv' )
<compute_test_metric><EOS> 
score_1 = 0.95220
score_2 = 0.95240
sum_scores = score_1 + score_2
weight_1 = score_1/sum_scores
weight_2 = score_2/sum_scores
sub_1['isFraud'] = weight_1*sub_1['isFraud'] + weight_2*sub_2['isFraud']
sub_1.to_csv('submission-.9525.csv', index=False )