Kaggle competition: IEEE-CIS Fraud Detection
pd.options.display.max_rows = 4000
def seed_everything(seed=0):
random.seed(seed)
 os.environ['PYTHONHASHSEED'] = str(seed)
 np.random.seed(seed)
 SEED = 42
seed_everything(SEED)
 START_DATE = datetime.datetime.strptime('2017-11-30', '%Y-%m-%d' )<load_from_csv>
m15 = pd.read_csv('/kaggle/input/ieee-top-models-blend/m15.csv')
 m17 = pd.read_csv('/kaggle/input/ieee-top-models-blend/m17.csv')
 m18 = pd.read_csv('/kaggle/input/ieee-top-models-blend/m18.csv')
 m19 = pd.read_csv('/kaggle/input/ieee-top-models-blend/m19.csv')
 m20 = pd.read_csv('/kaggle/input/ieee-top-models-blend/m20.csv')
 m16 = pd.read_csv('/kaggle/input/ieee-top-models-blend/m16.csv')
 m0 = pd.read_csv('/kaggle/input/ieee-top-models-blend/m0.csv')
 submission = pd.read_csv("/kaggle/input/ieee-fraud-detection/sample_submission.csv")
 submission.head()
<drop_column><EOS> 
submission['isFraud'] =(0.20*m15.isFraud)+ \ 
(0.20*m17.isFraud)+ \ 
(0.20*m18.isFraud)+ \ 
(0.20*m19.isFraud)+ \ 
(0.10)*m20.isFraud + \ 
(0.10*m16.isFraud)+ \ 
(0.0*m0.isFraud)
 submission.to_csv('my_blend_5.csv', index=False )