Pima.py

# import all packages
import pandas as pd
from numpy import loadtxt
from xgboost import XGBClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score

#Loading dataset and to retrieve first 5 rows
dataset = pd.read_csv('pima-indians-diabetes.csv')
dataset.head()

#Split the data into X and Y
X = dataset.iloc[:,0:8]
Y = dataset.iloc[:,8]

# split data into train and test sets with pre-defined "seed=7" for future reference
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)

# Creating XGBClassifier and fitting into training set of data 
# Used Scikit-learn to fit the model
model = XGBClassifier()
model.fit(X_train, y_train)

# Use Fitted training model to make predictions on test data
y_pred = model.predict(X_test)
predictions = [round(value) for value in y_pred]

#Evaluate the performance of the developed model after predictions
accuracy = accuracy_score(y_test, predictions)
print("Accuracy: %.2f%%" % (accuracy * 100.0))