K-NeastNeighobr
算法思想：近朱者赤，近墨者黑。

1.加载数据

import pandas as pd
df=pd.read_csv('myData.csv')
df.head()

2.预处理

清洗

myResult=df.drop(['yingyu'],axis=1)
myResult.head()

筛选

测试集

train_x=df.iloc[0:80,2:4]
train_x.head()

train_y=df.iloc[0:80,4].values
train_y[0:5]

array([1, 2, 3, 3, 1], dtype=int64)

3.训练集

test_x=df.iloc[80:100,2:4]
test_x.head()

4.建模

from sklearn import neighbors
model=neighbors.KNeighborsClassifier()

model.fit(train_x,train_y)

输出：
KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',
           metric_params=None, n_jobs=1, n_neighbors=5, p=2,
           weights='uniform')
N默认为5

5.预测

model.predict(test_x)

输出：
array([2, 3, 3, 3, 1, 1, 3, 2, 3, 1, 3, 1, 1, 2, 3, 2, 2, 1, 2, 1],
      dtype=int64)

train_p=model.predict(train_x)
train_p

输出：
array([1, 2, 3, 3, 2, 3, 2, 3, 1, 2, 3, 3, 2, 3, 3, 1, 3, 1, 1, 3, 3, 1,
       1, 3, 1, 1, 2, 3, 2, 3, 2, 2, 3, 2, 1, 3, 3, 2, 3, 2, 2, 2, 2, 1,
       2, 1, 1, 3, 2, 3, 1, 2, 1, 3, 3, 3, 1, 2, 2, 3, 3, 3, 1, 1, 1, 3,
       2, 3, 3, 1, 3, 2, 1, 3, 3, 1, 2, 2, 1, 1], dtype=int64)

train_y