code: # baseline: constant 0
  # local score 4.668
  # kaggle score 4.69
  
  import sys  # pylint: disable=unused-import
  import numpy as np
  import pandas as pd
  from sklearn.metrics import mean_squared_error
  from time import time
  
  import os
  
  is_kaggle = os.environ['HOME'] == '/tmp'
  
  zipext = '' if is_kaggle else '.zip'
  
  # load data
  train = pd.read_csv(f'../input/train.csv{zipext}')
  test = pd.read_csv(f'../input/test.csv{zipext}')
  
  #-------- main
  
  start_time = time()
  
  target = 'meter_reading'
  
  result = 0
  
  train['predicted'] = result
  
  score = np.sqrt(mean_squared_error(np.log1p(train[target]), np.log1p(train.predicted)))
  
  print('score', score)
  
  test[target] = result
  
  predictions = test[['row_id', target]]
  
  predictions.to_csv('submission.csv', index=False)
  
  print('%.0f mins' % ((time() - start_time) / 60))
  
apis: pandas.read_csv numpy.log1p