my_xgboost_test.py

# First XGBoost model for Pima Indians dataset
from numpy import loadtxt
from xgboost import XGBClassifier
from sklearn.cross_validation import train_test_split
from sklearn.metrics import accuracy_score
# load data
dataset = loadtxt('datasets/training_dataset_04.csv', delimiter="|")
# split data into X and y
X = dataset[:,0:46]
Y = dataset[:,46]
# split data into train and test sets
seed=7
test_size = 0.33
X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=test_size,
    random_state=seed)
# fit model no training data
model = XGBClassifier()
model.fit(X_train, y_train)
# make predictions for test data
y_pred = model.predict(X_test)
predictions = [round(value) for value in y_pred]
# evaluate predictions
accuracy = accuracy_score(y_test, predictions)
print("Accuracy: %.2f%%" % (accuracy * 100.0))