Kaggle competition: Don't Overfit! II
<SOS> metric: AUC Kaggle data source: dont-overfit!-ii<feature_engineering>
train = pd.read_csv(".. /input/train.csv" ).drop('id', axis=1)
 y_train = train['target']
X_train = train.drop('target', axis=1)
 test = pd.read_csv('.. /input/test.csv')
 X_test = test.drop('id', axis = 1)
 submission = pd.read_csv('.. /input/sample_submission.csv')
 clf = LogisticRegression(class_weight='balanced', penalty='l1', C=0.1, solver='liblinear' ).fit(X_train, y_train)
 submission['target'] = clf.predict_proba(X_test)[:,1]
submission.to_csv('submission.csv', index=False )