Kaggle competition: Santander Customer Satisfaction
<SOS> metric: AUC Kaggle data source: santander-customer-satisfaction<count_values>
train_data = pd.read_csv('.. /input/santander-customer-satisfaction/train.csv',index_col=0)
 test_data = pd.read_csv('.. /input/santander-customer-satisfaction/test.csv', index_col=0)
 sample = pd.read_csv('.. /input/santander-customer-satisfaction/sample_submission.csv')
 X_train = train_data.iloc[:,:-1]
y_train = train_data['TARGET']
print("The most frequent value in 'Embarked' column is :", train_titanic['Embarked'].value_counts().idxmax() )<count_values>
classifier = HistGradientBoostingClassifier()
classifier.fit(X_train,y_train)
 CVscores = cross_val_score(classifier, X_train, y_train, scoring='roc_auc', cv=2)
 print("The average CV score is",CVscores.mean())
 predictions = classifier.predict_proba(test_data)[:,1]
<count_missing_values><EOS> 
sample.iloc[:,1:] = predictions
sample.to_csv('submission.csv',index=False )