数据挖掘目标（客户价值分析）

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import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

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data=pd.read_csv(r'../教师文件/air_data.csv')

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data.head()

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| | Start_time | End_time | Fare | City | Age | Flight_count | Avg_discount | Flight_mileage |
| 0 | 2011/08/18 | 2014/03/31 | 5860.0 | . | 35.0 | 10 | 0.973129 | 12560 |
| 1 | 2011/01/13 | 2014/03/31 | 5561.0 | 佛山 | 35.0 | 12 | 0.575906 | 21223 |
| 2 | 2012/08/15 | 2014/03/31 | 1089.0 | 北京 | 33.0 | 9 | 0.635025 | 19246 |
| 3 | 2012/10/17 | 2014/03/31 | 9626.0 | 绍兴县 | 53.0 | 7 | 0.868571 | 14070 |

4	2011/09/04	2014/03/31	4473.0	上海	34.0	13	0.703419	17373

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data.info()

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<class 'pandas.core.frame.DataFrame'>
RangeIndex: 15000 entries, 0 to 14999
Data columns (total 8 columns):
 #   Column          Non-Null Count  Dtype  
---  ------          --------------  -----  
 0   Start_time      15000 non-null  object 
 1   End_time        15000 non-null  object 
 2   Fare            14989 non-null  float64
 3   City            14490 non-null  object 
 4   Age             14907 non-null  float64
 5   Flight_count    15000 non-null  int64  
 6   Avg_discount    15000 non-null  float64
 7   Flight_mileage  15000 non-null  int64  
dtypes: float64(3), int64(2), object(3)
memory usage: 937.6+ KB

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data.describe()

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| | Fare | Age | Flight_count | Avg_discount | Flight_mileage |
| count | 14989.000000 | 14907.000000 | 15000.000000 | 15000.000000 | 15000.000000 |
| mean | 3761.743812 | 42.569531 | 9.057600 | 0.728391 | 12395.706800 |
| std | 2720.206579 | 9.807385 | 3.946338 | 0.163550 | 3588.357291 |
| min | 0.000000 | 16.000000 | 2.000000 | 0.136017 | 4040.000000 |
| 25% | 1709.000000 | 35.000000 | 6.000000 | 0.625525 | 9747.000000 |
| 50% | 3580.000000 | 41.000000 | 8.000000 | 0.713322 | 11986.500000 |
| 75% | 5452.000000 | 48.000000 | 11.000000 | 0.803840 | 14654.000000 |

max	36602.000000	110.000000	47.000000	1.500000	50758.000000

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data=data[data.Fare.notnull()]

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data=data[data.Fare!=0]

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for index,item in data.iterrows():
    s_year,s_month=item['Start_time'].split('/')[:2]
    e_year,e_month=item['End_time'].split('/')[:2]
    data.loc[index,'Months']=(int(e_year)-int(s_year))*12+(int(e_month)-int(s_month))
data=data.drop(['Start_time','End_time'],axis=1)

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data.info()

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<class 'pandas.core.frame.DataFrame'>
Int64Index: 13279 entries, 0 to 14998
Data columns (total 7 columns):
 #   Column          Non-Null Count  Dtype  
---  ------          --------------  -----  
 0   Fare            13279 non-null  float64
 1   City            12809 non-null  object 
 2   Age             13199 non-null  float64
 3   Flight_count    13279 non-null  int64  
 4   Avg_discount    13279 non-null  float64
 5   Flight_mileage  13279 non-null  int64  
 6   Months          13279 non-null  float64
dtypes: float64(4), int64(2), object(1)
memory usage: 1.3+ MB

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data=data.drop(['City'],axis=1)
data=(data-data.mean(axis=0))/data.std(axis=0)

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data.head()

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| | Fare | Age | Flight_count | Avg_discount | Flight_mileage | Months |
| 0 | 0.643204 | -0.781959 | 0.191752 | 1.539425 | 0.019051 | -0.616333 |
| 1 | 0.524036 | -0.781959 | 0.700041 | -0.935625 | 2.427818 | -0.357005 |
| 2 | -1.258303 | -0.985351 | -0.062393 | -0.567261 | 1.878109 | -1.060895 |
| 3 | 2.144162 | 1.048561 | -0.570681 | 0.887939 | 0.438910 | -1.134989 |

4	0.090408	-0.883655	0.954185	-0.141105	1.357317	-0.653379

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plt.figure(figsize=(10,10))
plt.title("Pearson Correlation of Features",y=1.05,size=15)
sns.heatmap(data.astype(float).corr(),linewidths=0.1,vmax=1,square=True,cmap=plt.cm.viridis,linecolor='white',annot=True)

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<AxesSubplot:title={'center':'Pearson Correlation of Features'}>

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data=data.drop(['Fare','Age'],axis=1)

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from sklearn.cluster import KMeans

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kmeans=KMeans(n_clusters=3).fit(data)

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kmeans.cluster_centers_

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array([[-0.56475974,  0.54131875, -0.70701626, -0.56628176],
       [-0.06513412, -0.03376272, -0.10437466,  1.24214471],
       [ 0.75090493, -0.63663316,  0.95977635, -0.37662422]])

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kmeans.labels_

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array([0, 2, 2, ..., 0, 0, 0])

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from collections import defaultdict

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label_dict=defaultdict(int)

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for label in kmeans.labels_:
    label_dict[label] += 1

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label_dict

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defaultdict(int, {0: 5287, 2: 4287, 1: 3705})

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kmeans.cluster_centers_

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array([[-0.56475974,  0.54131875, -0.70701626, -0.56628176],
       [-0.06513412, -0.03376272, -0.10437466,  1.24214471],
       [ 0.75090493, -0.63663316,  0.95977635, -0.37662422]])