#https://www.geeksforgeeks.org/naive-bayes-classifiers/

# load the iris dataset
from sklearn.datasets import load_iris
from sklearn.metrics import classification_report
iris = load_iris()
 
# store the feature matrix (X) and response vector (y)
X = iris.data
y = iris.target
 
# splitting X and y into training and testing sets
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=1)
 
# training the model on training set
from sklearn.naive_bayes import GaussianNB
gnb = GaussianNB()
gnb.fit(X_train, y_train)
 
# making predictions on the testing set
y_pred = gnb.predict(X_test)
 
# comparing actual response values (y_test) with predicted response values (y_pred)
from sklearn import metrics
print("Gaussian Naive Bayes model accuracy(in %):", metrics.accuracy_score(y_test, y_pred)*100)
print(classification_report(y_test, gnb.predict(X_test)))