过程分为五步：
1. 获取数据
2. 划分数据集
3. 特征工程：标准化
4. KNN算法估计器
5. 模型评估（对比真实值和估计值 ； 计算准确率）
#coding=UTF-8
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.neighbors import KNeighborsClassifier
def knn_selector():
'''
用KNN对数据进行分类
'''
#(1) 获取数据
iris = load_iris()
#(2) 划分数据集
x_train,x_test,y_train,y_test = train_test_split(iris.data , iris.target , random_state= 6)
#(3) 特征工程：标准化
transfer = StandardScaler()
x_train = transfer.fit_transform(x_train)
x_test= transfer.transform(x_test)
#(4) KNN算法预估器
estimator = KNeighborsClassifier(n_neighbors= 3)
estimator.fit(x_train, y_train)
#(5) 模型评估
score = estimator.score(x_test,y_test)
print("准确率为：\n", score)
if __name__=="__main__":
knn_selector()
