Kaggle competition: Microsoft Malware Prediction
<SOS> metric: AUC Kaggle data source: microsoft-malware-prediction<load_from_csv>
import os
train = pd.read_csv('/kaggle/input/digit-recognizer/train.csv')
 test = pd.read_csv('/kaggle/input/digit-recognizer/test.csv')
 sub = pd.read_csv('/kaggle/input/digit-recognizer/sample_submission.csv' )<prepare_x_and_y>
a = pd.read_csv('.. /input/nffm-baseline-0-690-on-lb/nffm_submission.csv')
 b = pd.read_csv('.. /input/ensemble-lb-score-0-697/sub.csv')
 d = pd.read_csv('.. /input/ms-malware-starter/ms_malware.csv')
 e = pd.read_csv('.. /input/simple-blend-upvote-0-697-accuracy/sub.csv')
 f = pd.read_csv('.. /input/why-no-blend/super_blend.csv' )
X = train.drop(['label'], 1 ).values
y = train['label'].values
test_x = test.values
print(X)
 np.shape(X )<categorify>
submission = a[['MachineIdentifier']]
submission['HasDetections'] =(a['HasDetections']+b['HasDetections']+d['HasDetections']+e['HasDetections']+f['HasDetections'])/6
submission.to_csv('submission.csv', index=False)
 submission.head()