model.py

from sklearn.model_selection import train_test_split
from xboost import XGBClassifier

X = #yours data
y = #labels


train_X, test_X, train_y, test_y = train_test_split(X, y, test_size=0.3, random_state=100)


parameters = {'nthread':[6], 
              'objective':['binary:logistic'],
              'learning_rate': [0.01, 0.1],
              'max_depth': [5,8,13],
              'n_estimators': [200,500,1000,3000],
              'seed': [1337]}

clf = XGBClassifier(param_grid=parameters)
clf.fit(X_train, y_train)
predictions = clf.predict(test_X)