Kaggle competition: Toxic Comment Classification Challenge
X_test_scaled = scaler.transform(X_test)
 submission['time_to_failure'] = m.predict(X_test_scaled)
 submission.to_csv('submission.csv' )<import_modules>
f_nbsvm = '.. /input/nb-svm-strong-linear-baseline/submission.csv'
f_bi_lstm_dual = '.. /input/bi-gru-lstm-dual-embedding-with-mish/submission.csv'
warnings.filterwarnings("ignore" )<load_from_csv>
p_nbsvm = pd.read_csv(f_nbsvm)
 p_bi_lstm_dual = pd.read_csv(f_bi_lstm_dual )
%%time
train_df = pd.read_csv(os.path.join(PATH,'train.csv'),
dtype={'acoustic_data': np.int16, 'time_to_failure': np.float32} )<create_dataframe>
label_cols = ['toxic', 'severe_toxic', 'obscene', 'threat', 'insult', 'identity_hate']
p_res = p_nbsvm.copy()
p_res[label_cols] =(p_nbsvm[label_cols] + p_bi_lstm_dual[label_cols])/ 2
<feature_engineering><EOS> 
p_res.to_csv('submission.csv', index=False )