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from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
# Load iris file
iris = load_iris()
iris.keys()
print(f"Target names: \n {iris.target_names} ")
print(f"\n Features: \n {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 = 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)
    f"\nNew array: \n {X_new}\n\nTarget Names Prediction: \n"
    f" {iris['target_names'][prediction]}"

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