from numpy import loadtxt
from xgboost import XGBClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
# load data
dataset = loadtxt('pima-indians-diabetes.csv', delimiter=",")
# split data into X and y
X = dataset[:, 0:8]
y = dataset[:, 8]
# fit model 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)
prediction = [round(value) for value in y_pred]
# evaluate predictions
accuracy = accuracy_score(y_test, prediction)
# print(prediction, end='\n')
for i in prediction:
    print(i)

print("Accuracy:%.2f%%" % (accuracy * 100.0))