KNN算法为按距离进行分类的，对于已知的分类，根据欧式距离，最靠近那个分类就被预测为那个分类。

本文只是简单展示一下实现代码，具体的特征和分类，还得自己根据实际场景去调整。

在开始之前注意看看导入的包是否都存在，如不存在的化，请先安装相应的包

# -*- coding:utf-8 -*-
import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn.model_selection import cross_val_score
import matplotlib.pyplot as plt
import joblib
import pandas as pd

#加载iris数据集
iris = datasets.load_iris()
iris_X = iris.data
iris_y = iris.target
#print(iris_X[:4,:])

#数据分割
X_train,X_test,y_train,y_test = train_test_split(iris_X,iris_y,test_size=1/3,random_state=3)

#==========交叉验证============================
#cv_scores = []
#k_range = range(1,31)

'''
#此处为交叉验证，看KNN的k取什么值的时候效果最好
for n in k_range:
    knn = KNeighborsClassifier(n_neighbors=n)
    scores = cross_val_score(knn,X_train,y_train,cv=10,scoring='accuracy')
    cv_scores.append(scores.mean())
plt.plot(k_range,cv_scores)
plt.xlabel('K')
plt.ylabel('Accuracy')
plt.show()
'''

#模型训练
'''
best_knn = KNeighborsClassifier(n_neighbors=3)    # 选择最优的K=3传入模型
best_knn.fit(X_train,y_train)            #训练模型
print(best_knn.score(X_test,y_test))    #看看评分

#模型本地保存
joblib.dump(best_knn, 'D:/Users/wangkangren729/PycharmProjects/iris/model/best_knn.pkl',compress=3)
#load model
'''
bknn = joblib.load('D:/Users/wangkangren729/PycharmProjects/iris/model/best_knn.pkl')

#读取本地新数据
data = pd.read_csv('predict.data')
#print(data.head(5))

attributes=data[['sl','sw','pl','pw']]  #前四列属性简化为sl，sw，pl，pw
types=data['type'] #第5列属性为鸢尾花的类别

#print(type(attributes))
#data_frame = attributes.loc[0,:].to_frame()

#print(attributes)
#print(type(attributes[i]))    
#预测新数据
print(bknn.predict(attributes))
#print(type([[4.1, 2.2, 2.3, 5.4]]))
#print([[4.1, 2.2, 2.3, 5.4]])
#print(bknn.predict([[4.1, 2.2, 2.3, 5.4]]))
#print(types)
#print(bknn.predict(attributes))