Kaggle competition: IEEE-CIS Fraud Detection
<SOS> metric: AUC Kaggle data source: ieee-cis-fraud-detection<count_missing_values>
import numpy as np
import pandas as pd
from shutil import copyfile
import xgboost as xgb
test.isnull().sum()<count_missing_values>
X_train, y_train, X_test, submission = a1w.quick_wrangle()
<groupby><EOS> 
for n in range(100, 501, 200):
clf = xgb.XGBClassifier(n_estimators=n, n_jobs=4, max_depth=10, learning_rate=0.03, subsample=0.9, colsample_bytree=0.9, missing=-999)
 clf.fit(X_train, y_train)
 submission['isFraud'] = clf.predict_proba(X_test)[:, 1]
submission.to_csv('XGBoost' + str(n)+ '.csv' )