1. 数据准备与预处理

import numpy as np
import matplotlib.pyplot as plt2. KNN分类模型构建与评估 
from sklearn.datasets import load_digits
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
# 加载⼿写数字数据集
digits = load_digits()
X, y = digits.data, digits.target
# 数据可视化
plt.figure(figsize=(10, 8))
for i in range(25):
 plt.subplot(5, 5, i+1)
 plt.imshow(digits.images[i], cmap='binary')
 plt.title(f"Label: {y[i]}")
 plt.axis('off')
plt.tight_layout()
# 数据集划分
X_train, X_test, y_train, y_test = train_test_split(
 X, y, test_size=0.25, random_state=42
)
# 数据标准化
scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)