机器学习之Hello World kNN

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本文通过使用K近邻(KNN)算法对经典的鸢尾花数据集进行分类预测，介绍了数据预处理、特征规范化、训练模型及评估准确率的全过程。实验中采用Python的pandas库读取数据，利用sklearn库实现KNN算法。

# -*- coding:utf-8 -*-
import numpy as np
import pandas as pd
from sklearn.neighbors import KNeighborsClassifier

model = KNeighborsClassifier()

origin_data = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data',names=['sepal_length','sepal_width','petal_length','petal_width','class_name'])

norm_data = origin_data.apply(lambda v : v if v.name == 'class_name' else (v - np.min(v))/(np.max(v) - np.min(v)))
train_data = norm_data.sample(frac=0.7)
test_data = norm_data.drop(train_data.index)

model.fit(X=train_data.iloc[:,:-1],y=np.array(train_data.iloc[:,-1:]).astype(str).ravel())
accuracy = model.score(X=test_data.iloc[:,:-1],y=np.array(test_data.iloc[:,-1:]).astype(str).ravel())
print(accuracy)
just_try = model.predict(X=test_data.iloc[:,:-1])
print(just_try)