simple_knn_pipeline.py

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

# dataset
iris = load_iris()

# features e target
X = iris.data
y = iris.target

# treino e teste
X_train, X_test, y_train, y_test = train_test_split(X,y)

# instanciando o modelo
model = KNeighborsClassifier()

# treinando o modelo utilizando o conjunto de treino
model.fit(X_train,y_train)

# validando o modelo utilizando o conjunto de teste
accuracy = str(round(model.score(X_test,y_test) * 100, 2))+"%"

# imprimindo o resultado
print("A acurácia do modelo k-NN foi",accuracy)