Kaggle competition: ASHRAE - Great Energy Predictor III
<define_variables><EOS> 
import pandas as pd
import numpy as np
<SOS> metric: RMSLE Kaggle data source: ashrae-great-energy-predictor-iii<save_to_csv>
import pandas as pd
import numpy as np
concat_sub['Predicted'] = concat_sub['m_mean']
concat_sub[['Id','Predicted']].to_csv('stack_mean.csv',
index=False, float_format='%.6f' )<save_to_csv>
fold_1 = pd.read_csv('/kaggle/input/lgbt-fold-1/submission.csv')
 fold_2 = pd.read_csv('/kaggle/input/lgbt-fold-2/submission.csv')
 fold_3 = pd.read_csv('/kaggle/input/lgbt-fold-3/submission.csv')
 fold_4 = pd.read_csv('/kaggle/input/lgbt-fold-4/submission.csv')
 fold_5 = pd.read_csv('/kaggle/input/lgbt-fold-5/submission.csv' )
concat_sub['Predicted'] = concat_sub['m_median']
concat_sub[['Id','Predicted']].to_csv('stack_median.csv',
index=False, float_format='%.6f' )<save_to_csv>
fold_1['meter_reading'] =(fold_1['meter_reading'] + fold_2['meter_reading'] + fold_3['meter_reading'] + fold_4['meter_reading'] + fold_5['meter_reading'])/5
concat_sub['Predicted'] = np.where(np.all(concat_sub.iloc[:,1:ncol] > cutoff_lo, axis=1), 1,
np.where(np.all(concat_sub.iloc[:,1:ncol] < cutoff_hi, axis=1),
0, concat_sub['m_median']))
concat_sub[['Id','Predicted']].to_csv('stack_pushout_median.csv',
index=False, float_format='%.6f' )<feature_engineering>
fold_1['meter_reading'] = np.expm1(fold_1['meter_reading'])
 fold_1.loc[fold_1['meter_reading'] < 0, 'meter_reading'] = 0
<feature_engineering><EOS> 
fold_1.to_csv('submission_fold_bland.csv',index=False )