from sklearn.model_selection import train_test_split
from sklearn.datasets import load_iris
from sklearn.neighbors import KNeighborsClassifier

#Load iris file
iris = load_iris()
iris.keys()


print('Target names: \n {} '.format(iris.target_names))
print('\n Features: \n {}'.format(iris.feature_names))

#Train set e Test set
X_train, X_test, y_train, y_test = train_test_split(iris['data'],iris['target'], random_state=4)

#KNN

knn = KNeighborsClassifier (n_neighbors = 1)
knn.fit(X_train, y_train)

#new array to test
X_new = [[1,2,1,4],
		 [2,3,4,5]]	

prediction = knn.predict(X_new)

print('\nNew array: \n {}'
	  '\n\nTarget Names Prediction: \n {}'.format(X_new, iris['target_names'][prediction]))