K-nearest neighbors (knn)

from sklearn import datasets
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler

# Collect data
iris = datasets.load_iris()
X = iris.data[:, [2, 3]]
y = iris.target

# Split data
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.3, random_state=1, stratify=y)

# Standardize features
sc = StandardScaler()
sc.fit(X_train)
X_train_std = sc.transform(X_train)
X_test_std = sc.transform(X_test)

from sklearn.neighbors import KNeighborsClassifier

knn = KNeighborsClassifier(n_neighbors=5, 
                           p=2, 
                           metric='minkowski')
knn.fit(X_train_std, y_train)
y_pred = knn.predict(X_test_std)

# Test prediction
from sklearn.metrics import accuracy_score
print(f'Accuracy: {accuracy_score(y_test, y_pred):.2%}')

# Visualize

X_combined_std = np.vstack((X_train_std, X_test_std))
y_combined = np.hstack((y_train, y_test))

plot_decision_regions(X_combined_std, y_combined, 
                      classifier=knn, test_idx=range(105, 150))

plt.xlabel('petal length [standardized]')
plt.ylabel('petal width [standardized]')
plt.legend(loc='upper left')
plt.tight_layout()
plt.show()