fitthemodel.py

#fit the model 

from xgboost import XGBClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score

model = XGBClassifier()
model.fit(x_train, y_train)
accuracy = accuracy_score(y_test, predictions)
print("Accuracy: %.2f%%" % (accuracy * 100.0))

#evaluate the model 
import sklearn.metrics as metrics

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))
cm = confusion_matrix(y_test, y_pred)
print(cm)