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#Importa os datasets que vem com o scikit learn
from sklearn import datasets
#importa a biblioteca pandas como pd
import pandas as pd
iris = datasets.load_iris()
irs = pd.DataFrame(iris.data, columns = iris.feature_names)
irs['class'] = iris.target
x = irs.iloc[:, :-1].values
y = irs.iloc[:, 4].values
from sklearn.model_selection import train_test_split
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = 0.20)
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
scaler.fit(x_train)
x_train = scaler.transform(x_train)
x_test = scaler.transform(x_test)
#importa o numpy como np
import numpy as np
from sklearn.neighbors import KNeighborsClassifier
error = []
# Calculando erro para valores de K entre 1 e 40
for i in range(1, 40):
knn = KNeighborsClassifier(n_neighbors=i)
knn.fit(x_train, y_train)
pred_i = knn.predict(x_test)
error.append(np.mean(pred_i != y_test))
#importa o matplotlib como plt
import matplotlib.pyplot as plt
plt.figure(figsize=(12, 6))
plt.plot(range(1, 40), error, color='red', linestyle='dashed', marker='o',
markerfacecolor='blue', markersize=10)
plt.title('Error Rate K Value')
plt.xlabel('K Value')
plt.ylabel('Mean Error')
#Para mostrar o gráfico
plt.show()
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