Kaggle competition: SIIM-ISIC Melanoma Classification
<SOS> metric: MCAUC Kaggle data source: siim-isic-melanoma-classification<feature_engineering>
import numpy as np
import pandas as pd
for df in data_cleaner:
df['CabinMissing'] = df.Cabin == 'M'<count_missing_values>
data1 = pd.read_csv('.. /input/minmax-ensemble-0-9526-lb/submission.csv')
 data2 = pd.read_csv('.. /input/stacking-ensemble-on-my-submissions/submission_mean.csv')
 data3 = pd.read_csv('.. /input/stacking-ensemble-on-my-submissions/submission_median.csv')
 data4 = pd.read_csv('.. /input/analysis-of-melanoma-metadata-and-effnet-ensemble/ensembled.csv')
 data5 = pd.read_csv('.. /input/new-basline-np-log2-ensemble-top-10/submission.csv')
 submission = data1.copy()
for df in data_cleaner:
print('-'*20)
 print(df.isnull().any() )<categorify>
submission['target'] = 2/6 * data1['target'] + 1/6 * data2['target'] + 1/6 * data3['target'] + 1/6 * data4['target'] + 1/6 * data5['target']
<define_variables><EOS> 
submission.to_csv('submission.csv', index=False, float_format='%.6f' )