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In [46]:

    
import pandas as pd
import numpy as np



In [5]:

    
data = pd.read_csv('../data/telecom_churn.csv')



In [7]:

    
data.head()









    Out[7]:







  
    
      
      State
      Account length
      Area code
      International plan
      Voice mail plan
      Number vmail messages
      Total day minutes
      Total day calls
      Total day charge
      Total eve minutes
      Total eve calls
      Total eve charge
      Total night minutes
      Total night calls
      Total night charge
      Total intl minutes
      Total intl calls
      Total intl charge
      Customer service calls
      Churn
    
  
  
    
      0
      KS
      128
      415
      No
      Yes
      25
      265.1
      110
      45.07
      197.4
      99
      16.78
      244.7
      91
      11.01
      10.0
      3
      2.70
      1
      False
    
    
      1
      OH
      107
      415
      No
      Yes
      26
      161.6
      123
      27.47
      195.5
      103
      16.62
      254.4
      103
      11.45
      13.7
      3
      3.70
      1
      False
    
    
      2
      NJ
      137
      415
      No
      No
      0
      243.4
      114
      41.38
      121.2
      110
      10.30
      162.6
      104
      7.32
      12.2
      5
      3.29
      0
      False
    
    
      3
      OH
      84
      408
      Yes
      No
      0
      299.4
      71
      50.90
      61.9
      88
      5.26
      196.9
      89
      8.86
      6.6
      7
      1.78
      2
      False
    
    
      4
      OK
      75
      415
      Yes
      No
      0
      166.7
      113
      28.34
      148.3
      122
      12.61
      186.9
      121
      8.41
      10.1
      3
      2.73
      3
      False



In [10]:

    
data.shape









    Out[10]:





(3333, 20)



In [14]:

    
print(data.columns)









    



Index([u'State', u'Account length', u'Area code', u'International plan',
       u'Voice mail plan', u'Number vmail messages', u'Total day minutes',
       u'Total day calls', u'Total day charge', u'Total eve minutes',
       u'Total eve calls', u'Total eve charge', u'Total night minutes',
       u'Total night calls', u'Total night charge', u'Total intl minutes',
       u'Total intl calls', u'Total intl charge', u'Customer service calls',
       u'Churn'],
      dtype='object')



In [16]:

    
print(data.info())









    



<class 'pandas.core.frame.DataFrame'>
RangeIndex: 3333 entries, 0 to 3332
Data columns (total 20 columns):
State                     3333 non-null object
Account length            3333 non-null int64
Area code                 3333 non-null int64
International plan        3333 non-null object
Voice mail plan           3333 non-null object
Number vmail messages     3333 non-null int64
Total day minutes         3333 non-null float64
Total day calls           3333 non-null int64
Total day charge          3333 non-null float64
Total eve minutes         3333 non-null float64
Total eve calls           3333 non-null int64
Total eve charge          3333 non-null float64
Total night minutes       3333 non-null float64
Total night calls         3333 non-null int64
Total night charge        3333 non-null float64
Total intl minutes        3333 non-null float64
Total intl calls          3333 non-null int64
Total intl charge         3333 non-null float64
Customer service calls    3333 non-null int64
Churn                     3333 non-null bool
dtypes: bool(1), float64(8), int64(8), object(3)
memory usage: 498.1+ KB
None



In [23]:

    
data['Churn'] = data['Churn'].astype('int64')



In [24]:

    
print(data.describe())









    



       Account length    Area code  Number vmail messages  Total day minutes  \
count     3333.000000  3333.000000            3333.000000        3333.000000   
mean       101.064806   437.182418               8.099010         179.775098   
std         39.822106    42.371290              13.688365          54.467389   
min          1.000000   408.000000               0.000000           0.000000   
25%         74.000000   408.000000               0.000000         143.700000   
50%        101.000000   415.000000               0.000000         179.400000   
75%        127.000000   510.000000              20.000000         216.400000   
max        243.000000   510.000000              51.000000         350.800000   

       Total day calls  Total day charge  Total eve minutes  Total eve calls  \
count      3333.000000       3333.000000        3333.000000      3333.000000   
mean        100.435644         30.562307         200.980348       100.114311   
std          20.069084          9.259435          50.713844        19.922625   
min           0.000000          0.000000           0.000000         0.000000   
25%          87.000000         24.430000         166.600000        87.000000   
50%         101.000000         30.500000         201.400000       100.000000   
75%         114.000000         36.790000         235.300000       114.000000   
max         165.000000         59.640000         363.700000       170.000000   

       Total eve charge  Total night minutes  Total night calls  \
count       3333.000000          3333.000000        3333.000000   
mean          17.083540           200.872037         100.107711   
std            4.310668            50.573847          19.568609   
min            0.000000            23.200000          33.000000   
25%           14.160000           167.000000          87.000000   
50%           17.120000           201.200000         100.000000   
75%           20.000000           235.300000         113.000000   
max           30.910000           395.000000         175.000000   

       Total night charge  Total intl minutes  Total intl calls  \
count         3333.000000         3333.000000       3333.000000   
mean             9.039325           10.237294          4.479448   
std              2.275873            2.791840          2.461214   
min              1.040000            0.000000          0.000000   
25%              7.520000            8.500000          3.000000   
50%              9.050000           10.300000          4.000000   
75%             10.590000           12.100000          6.000000   
max             17.770000           20.000000         20.000000   

       Total intl charge  Customer service calls        Churn  
count        3333.000000             3333.000000  3333.000000  
mean            2.764581                1.562856     0.144914  
std             0.753773                1.315491     0.352067  
min             0.000000                0.000000     0.000000  
25%             2.300000                1.000000     0.000000  
50%             2.780000                1.000000     0.000000  
75%             3.270000                2.000000     0.000000  
max             5.400000                9.000000     1.000000



In [22]:

    
print(data.describe(include=['bool', 'object']))









    



       State International plan Voice mail plan
count   3333               3333            3333
unique    51                  2               2
top       WV                 No              No
freq     106               3010            2411



In [33]:

    
data['Churn'].value_counts()









    Out[33]:





0    2850
1     483
Name: Churn, dtype: int64



In [35]:

    
data['Area code'].value_counts(normalize = True)









    Out[35]:





415    0.496550
510    0.252025
408    0.251425
Name: Area code, dtype: float64



In [37]:

    
data.sort_values(by="Total day charge", ascending=False).head()









    Out[37]:







  
    
      
      State
      Account length
      Area code
      International plan
      Voice mail plan
      Number vmail messages
      Total day minutes
      Total day calls
      Total day charge
      Total eve minutes
      Total eve calls
      Total eve charge
      Total night minutes
      Total night calls
      Total night charge
      Total intl minutes
      Total intl calls
      Total intl charge
      Customer service calls
      Churn
    
  
  
    
      365
      CO
      154
      415
      No
      No
      0
      350.8
      75
      59.64
      216.5
      94
      18.40
      253.9
      100
      11.43
      10.1
      9
      2.73
      1
      1
    
    
      985
      NY
      64
      415
      Yes
      No
      0
      346.8
      55
      58.96
      249.5
      79
      21.21
      275.4
      102
      12.39
      13.3
      9
      3.59
      1
      1
    
    
      2594
      OH
      115
      510
      Yes
      No
      0
      345.3
      81
      58.70
      203.4
      106
      17.29
      217.5
      107
      9.79
      11.8
      8
      3.19
      1
      1
    
    
      156
      OH
      83
      415
      No
      No
      0
      337.4
      120
      57.36
      227.4
      116
      19.33
      153.9
      114
      6.93
      15.8
      7
      4.27
      0
      1
    
    
      605
      MO
      112
      415
      No
      No
      0
      335.5
      77
      57.04
      212.5
      109
      18.06
      265.0
      132
      11.93
      12.7
      8
      3.43
      2
      1



In [38]:

    
data['Churn'].mean()









    Out[38]:





0.14491449144914492



In [40]:

    
data[data['Churn'] == 1].mean(), \
data[data['Churn'] == 0].mean()









    Out[40]:





(Account length            102.664596
 Area code                 437.817805
 Number vmail messages       5.115942
 Total day minutes         206.914079
 Total day calls           101.335404
 Total day charge           35.175921
 Total eve minutes         212.410145
 Total eve calls           100.561077
 Total eve charge           18.054969
 Total night minutes       205.231677
 Total night calls         100.399586
 Total night charge          9.235528
 Total intl minutes         10.700000
 Total intl calls            4.163561
 Total intl charge           2.889545
 Customer service calls      2.229814
 Churn                       1.000000
 dtype: float64, Account length            100.793684
 Area code                 437.074737
 Number vmail messages       8.604561
 Total day minutes         175.175754
 Total day calls           100.283158
 Total day charge           29.780421
 Total eve minutes         199.043298
 Total eve calls           100.038596
 Total eve charge           16.918909
 Total night minutes       200.133193
 Total night calls         100.058246
 Total night charge          9.006074
 Total intl minutes         10.158877
 Total intl calls            4.532982
 Total intl charge           2.743404
 Customer service calls      1.449825
 Churn                       0.000000
 dtype: float64)



In [42]:

    
data[data['Churn'] == 1]['Total day minutes'].mean()









    Out[42]:





206.91407867494823



In [44]:

    
data[(data['Churn'] == 0) & (data['International plan'] == 'No')]['Total intl minutes'].max()









    Out[44]:





18.9



In [47]:

    
data.apply(np.max)









    Out[47]:





State                        WY
Account length              243
Area code                   510
International plan          Yes
Voice mail plan             Yes
Number vmail messages        51
Total day minutes         350.8
Total day calls             165
Total day charge          59.64
Total eve minutes         363.7
Total eve calls             170
Total eve charge          30.91
Total night minutes         395
Total night calls           175
Total night charge        17.77
Total intl minutes           20
Total intl calls             20
Total intl charge           5.4
Customer service calls        9
Churn                         1
dtype: object



In [50]:

    
columns = ["Total day minutes", 'Total night minutes', 'Total intl minutes']
data.groupby(['Churn'])[columns].describe()









    Out[50]:







  
    
      
      Total day minutes
      Total night minutes
      Total intl minutes
    
    
      
      count
      mean
      std
      min
      25%
      50%
      75%
      max
      count
      mean
      ...
      75%
      max
      count
      mean
      std
      min
      25%
      50%
      75%
      max
    
    
      Churn
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    
  
  
    
      0
      2850.0
      175.175754
      50.181655
      0.0
      142.825
      177.2
      210.30
      315.6
      2850.0
      200.133193
      ...
      234.90
      395.0
      2850.0
      10.158877
      2.784489
      0.0
      8.4
      10.2
      12.0
      18.9
    
    
      1
      483.0
      206.914079
      68.997792
      0.0
      153.250
      217.6
      265.95
      350.8
      483.0
      205.231677
      ...
      239.85
      354.9
      483.0
      10.700000
      2.793190
      2.0
      8.8
      10.6
      12.8
      20.0
    
  

2 rows × 24 columns



In [52]:

    
data.groupby(['Churn'])[columns].agg([np.mean, np.std, np.min, np.max])









    Out[52]:







  
    
      
      Total day minutes
      Total night minutes
      Total intl minutes
    
    
      
      mean
      std
      amin
      amax
      mean
      std
      amin
      amax
      mean
      std
      amin
      amax
    
    
      Churn
      
      
      
      
      
      
      
      
      
      
      
      
    
  
  
    
      0
      175.175754
      50.181655
      0.0
      315.6
      200.133193
      51.105032
      23.2
      395.0
      10.158877
      2.784489
      0.0
      18.9
    
    
      1
      206.914079
      68.997792
      0.0
      350.8
      205.231677
      47.132825
      47.4
      354.9
      10.700000
      2.793190
      2.0
      20.0



In [53]:

    
pd.crosstab(data['Churn'], data['International plan'])









    Out[53]:







  
    
      International plan
      No
      Yes
    
    
      Churn
      
      
    
  
  
    
      0
      2664
      186
    
    
      1
      346
      137



In [55]:

    
pd.crosstab(data['Churn'], data['Voice mail plan'], normalize=True)









    Out[55]:







  
    
      Voice mail plan
      No
      Yes
    
    
      Churn
      
      
    
  
  
    
      0
      0.602460
      0.252625
    
    
      1
      0.120912
      0.024002



In [59]:

    
data.pivot_table(['Total day calls', 'Total day calls', 'Total night calls'], ['Area code'], aggfunc='sum'), \
data.pivot_table(['Total day calls', 'Total day calls', 'Total night calls'], ['Area code'], aggfunc='mean')









    Out[59]:





(           Total day calls  Total day calls  Total night calls
 Area code                                                     
 408                  84216            84216              82995
 415                 166454           166454             166159
 510                  84082            84082              84505,
            Total day calls  Total day calls  Total night calls
 Area code                                                     
 408             100.496420       100.496420          99.039379
 415             100.576435       100.576435         100.398187
 510             100.097619       100.097619         100.601190)



In [61]:

    
data['Total calls'] = data['Total day calls'] + data['Total night calls'] + data['Total eve calls'] + data['Total intl calls']
data.head()









    Out[61]:







  
    
      
      State
      Account length
      Area code
      International plan
      Voice mail plan
      Number vmail messages
      Total day minutes
      Total day calls
      Total day charge
      Total eve minutes
      ...
      Total eve charge
      Total night minutes
      Total night calls
      Total night charge
      Total intl minutes
      Total intl calls
      Total intl charge
      Customer service calls
      Churn
      Total calls
    
  
  
    
      0
      KS
      128
      415
      No
      Yes
      25
      265.1
      110
      45.07
      197.4
      ...
      16.78
      244.7
      91
      11.01
      10.0
      3
      2.70
      1
      0
      303
    
    
      1
      OH
      107
      415
      No
      Yes
      26
      161.6
      123
      27.47
      195.5
      ...
      16.62
      254.4
      103
      11.45
      13.7
      3
      3.70
      1
      0
      332
    
    
      2
      NJ
      137
      415
      No
      No
      0
      243.4
      114
      41.38
      121.2
      ...
      10.30
      162.6
      104
      7.32
      12.2
      5
      3.29
      0
      0
      333
    
    
      3
      OH
      84
      408
      Yes
      No
      0
      299.4
      71
      50.90
      61.9
      ...
      5.26
      196.9
      89
      8.86
      6.6
      7
      1.78
      2
      0
      255
    
    
      4
      OK
      75
      415
      Yes
      No
      0
      166.7
      113
      28.34
      148.3
      ...
      12.61
      186.9
      121
      8.41
      10.1
      3
      2.73
      3
      0
      359
    
  

5 rows × 21 columns



In [62]:

    
data['Total charge'] = data['Total day charge'] + data['Total night charge'] + data['Total eve charge'] + data['Total intl charge']
data.head()









    Out[62]:







  
    
      
      State
      Account length
      Area code
      International plan
      Voice mail plan
      Number vmail messages
      Total day minutes
      Total day calls
      Total day charge
      Total eve minutes
      ...
      Total night minutes
      Total night calls
      Total night charge
      Total intl minutes
      Total intl calls
      Total intl charge
      Customer service calls
      Churn
      Total calls
      Total charge
    
  
  
    
      0
      KS
      128
      415
      No
      Yes
      25
      265.1
      110
      45.07
      197.4
      ...
      244.7
      91
      11.01
      10.0
      3
      2.70
      1
      0
      303
      75.56
    
    
      1
      OH
      107
      415
      No
      Yes
      26
      161.6
      123
      27.47
      195.5
      ...
      254.4
      103
      11.45
      13.7
      3
      3.70
      1
      0
      332
      59.24
    
    
      2
      NJ
      137
      415
      No
      No
      0
      243.4
      114
      41.38
      121.2
      ...
      162.6
      104
      7.32
      12.2
      5
      3.29
      0
      0
      333
      62.29
    
    
      3
      OH
      84
      408
      Yes
      No
      0
      299.4
      71
      50.90
      61.9
      ...
      196.9
      89
      8.86
      6.6
      7
      1.78
      2
      0
      255
      66.80
    
    
      4
      OK
      75
      415
      Yes
      No
      0
      166.7
      113
      28.34
      148.3
      ...
      186.9
      121
      8.41
      10.1
      3
      2.73
      3
      0
      359
      52.09
    
  

5 rows × 22 columns



In [68]:

    
data['Column for delete'] = 1
data.head()
data = data.drop(['Column for delete'], axis=1)
data.head()









    Out[68]:







  
    
      
      State
      Account length
      Area code
      International plan
      Voice mail plan
      Number vmail messages
      Total day minutes
      Total day calls
      Total day charge
      Total eve minutes
      ...
      Total night minutes
      Total night calls
      Total night charge
      Total intl minutes
      Total intl calls
      Total intl charge
      Customer service calls
      Churn
      Total calls
      Total charge
    
  
  
    
      0
      KS
      128
      415
      No
      Yes
      25
      265.1
      110
      45.07
      197.4
      ...
      244.7
      91
      11.01
      10.0
      3
      2.70
      1
      0
      303
      75.56
    
    
      1
      OH
      107
      415
      No
      Yes
      26
      161.6
      123
      27.47
      195.5
      ...
      254.4
      103
      11.45
      13.7
      3
      3.70
      1
      0
      332
      59.24
    
    
      2
      NJ
      137
      415
      No
      No
      0
      243.4
      114
      41.38
      121.2
      ...
      162.6
      104
      7.32
      12.2
      5
      3.29
      0
      0
      333
      62.29
    
    
      3
      OH
      84
      408
      Yes
      No
      0
      299.4
      71
      50.90
      61.9
      ...
      196.9
      89
      8.86
      6.6
      7
      1.78
      2
      0
      255
      66.80
    
    
      4
      OK
      75
      415
      Yes
      No
      0
      166.7
      113
      28.34
      148.3
      ...
      186.9
      121
      8.41
      10.1
      3
      2.73
      3
      0
      359
      52.09
    
  

5 rows × 22 columns



In [75]:

    
pd.crosstab(data['Churn'], data['International plan'], margins=True, normalize=True) * 100









    Out[75]:







  
    
      International plan
      No
      Yes
      All
    
    
      Churn
      
      
      
    
  
  
    
      0
      79.927993
      5.580558
      85.508551
    
    
      1
      10.381038
      4.110411
      14.491449
    
    
      All
      90.309031
      9.690969
      100.000000



In [74]:

    
pd.crosstab(data['Churn'], data['Customer service calls'], margins=True, normalize=True) * 100









    Out[74]:







  
    
      Customer service calls
      0
      1
      2
      3
      4
      5
      6
      7
      8
      9
      All
    
    
      Churn
      
      
      
      
      
      
      
      
      
      
      
    
  
  
    
      0
      18.151815
      31.773177
      20.162016
      11.551155
      2.700270
      0.780078
      0.240024
      0.120012
      0.030003
      0.000000
      85.508551
    
    
      1
      2.760276
      3.660366
      2.610261
      1.320132
      2.280228
      1.200120
      0.420042
      0.150015
      0.030003
      0.060006
      14.491449
    
    
      All
      20.912091
      35.433543
      22.772277
      12.871287
      4.980498
      1.980198
      0.660066
      0.270027
      0.060006
      0.060006
      100.000000



In [81]:

    
pd.crosstab(data[data['Churn'] == 0]['Churn'], data['Customer service calls'], margins=True, normalize=True) * 100









    Out[81]:







  
    
      Customer service calls
      0
      1
      2
      3
      4
      5
      6
      7
      8
      All
    
    
      Churn
      
      
      
      
      
      
      
      
      
      
    
  
  
    
      0
      21.22807
      37.157895
      23.578947
      13.508772
      3.157895
      0.912281
      0.280702
      0.140351
      0.035088
      100.0
    
    
      All
      21.22807
      37.157895
      23.578947
      13.508772
      3.157895
      0.912281
      0.280702
      0.140351
      0.035088
      100.0



In [80]:

    
pd.crosstab(data[data['Churn'] == 1]['Churn'], data['Customer service calls'], margins=True, normalize=True) * 100









    Out[80]:







  
    
      Customer service calls
      0
      1
      2
      3
      4
      5
      6
      7
      8
      9
      All
    
    
      Churn
      
      
      
      
      
      
      
      
      
      
      
    
  
  
    
      1
      19.047619
      25.258799
      18.012422
      9.109731
      15.73499
      8.281573
      2.898551
      1.035197
      0.207039
      0.414079
      100.0
    
    
      All
      19.047619
      25.258799
      18.012422
      9.109731
      15.73499
      8.281573
      2.898551
      1.035197
      0.207039
      0.414079
      100.0



In [95]:

    
data['Many_service_calls'] = (data['Customer service calls'] > 3).astype('int')
data.head()









    Out[95]:







  
    
      
      State
      Account length
      Area code
      International plan
      Voice mail plan
      Number vmail messages
      Total day minutes
      Total day calls
      Total day charge
      Total eve minutes
      ...
      Total night calls
      Total night charge
      Total intl minutes
      Total intl calls
      Total intl charge
      Customer service calls
      Churn
      Total calls
      Total charge
      Many_service_calls
    
  
  
    
      0
      KS
      128
      415
      No
      Yes
      25
      265.1
      110
      45.07
      197.4
      ...
      91
      11.01
      10.0
      3
      2.70
      1
      0
      303
      75.56
      0
    
    
      1
      OH
      107
      415
      No
      Yes
      26
      161.6
      123
      27.47
      195.5
      ...
      103
      11.45
      13.7
      3
      3.70
      1
      0
      332
      59.24
      0
    
    
      2
      NJ
      137
      415
      No
      No
      0
      243.4
      114
      41.38
      121.2
      ...
      104
      7.32
      12.2
      5
      3.29
      0
      0
      333
      62.29
      0
    
    
      3
      OH
      84
      408
      Yes
      No
      0
      299.4
      71
      50.90
      61.9
      ...
      89
      8.86
      6.6
      7
      1.78
      2
      0
      255
      66.80
      0
    
    
      4
      OK
      75
      415
      Yes
      No
      0
      166.7
      113
      28.34
      148.3
      ...
      121
      8.41
      10.1
      3
      2.73
      3
      0
      359
      52.09
      0
    
  

5 rows × 23 columns



In [96]:

    
pd.crosstab(data['Many_service_calls'], data['Churn'], margins=True)









    Out[96]:







  
    
      Churn
      0
      1
      All
    
    
      Many_service_calls
      
      
      
    
  
  
    
      0
      2721
      345
      3066
    
    
      1
      129
      138
      267
    
    
      All
      2850
      483
      3333



In [99]:

    
pd.crosstab(data['Many_service_calls'] & data['International plan'] , data['Churn'])

	State	Account length	Area code	International plan	Voice mail plan	Number vmail messages	Total day minutes	Total day calls	Total day charge	Total eve minutes	Total eve calls	Total eve charge	Total night minutes	Total night calls	Total night charge	Total intl minutes	Total intl calls	Total intl charge	Customer service calls	Churn
0	KS	128	415	No	Yes	25	265.1	110	45.07	197.4	99	16.78	244.7	91	11.01	10.0	3	2.70	1	False
1	OH	107	415	No	Yes	26	161.6	123	27.47	195.5	103	16.62	254.4	103	11.45	13.7	3	3.70	1	False
2	NJ	137	415	No	No	0	243.4	114	41.38	121.2	110	10.30	162.6	104	7.32	12.2	5	3.29	0	False
3	OH	84	408	Yes	No	0	299.4	71	50.90	61.9	88	5.26	196.9	89	8.86	6.6	7	1.78	2	False
4	OK	75	415	Yes	No	0	166.7	113	28.34	148.3	122	12.61	186.9	121	8.41	10.1	3	2.73	3	False

	State	Account length	Area code	International plan	Voice mail plan	Total day minutes	Total day calls	Total day charge	Total eve minutes	Total eve calls	Total eve charge	Total night minutes	Total night calls	Total night charge	Total intl minutes	Total intl calls	Total intl charge	Customer service calls	Churn
365	CO	154	415	No	No	350.8	75	59.64	216.5	94	18.40	253.9	100	11.43	10.1	9	2.73	1	1
985	NY	64	415	Yes	No	346.8	55	58.96	249.5	79	21.21	275.4	102	12.39	13.3	9	3.59	1	1
2594	OH	115	510	Yes	No	345.3	81	58.70	203.4	106	17.29	217.5	107	9.79	11.8	8	3.19	1	1
156	OH	83	415	No	No	337.4	120	57.36	227.4	116	19.33	153.9	114	6.93	15.8	7	4.27	0	1
605	MO	112	415	No	No	335.5	77	57.04	212.5	109	18.06	265.0	132	11.93	12.7	8	3.43	2	1

	Total day minutes								Total night minutes					Total intl minutes
	count	mean	std	min	25%	50%	75%	max	count	mean	...	75%	max	count	mean	std	min	25%	50%	75%	max
Churn
0	2850.0	175.175754	50.181655	0.0	142.825	177.2	210.30	315.6	2850.0	200.133193	...	234.90	395.0	2850.0	10.158877	2.784489	0.0	8.4	10.2	12.0	18.9
1	483.0	206.914079	68.997792	0.0	153.250	217.6	265.95	350.8	483.0	205.231677	...	239.85	354.9	483.0	10.700000	2.793190	2.0	8.8	10.6	12.8	20.0

	Total day minutes				Total night minutes				Total intl minutes
	mean	std	amin	amax	mean	std	amin	amax	mean	std	amin	amax
Churn
0	175.175754	50.181655	0.0	315.6	200.133193	51.105032	23.2	395.0	10.158877	2.784489	0.0	18.9
1	206.914079	68.997792	0.0	350.8	205.231677	47.132825	47.4	354.9	10.700000	2.793190	2.0	20.0

International plan	No	Yes	All
Churn
0	79.927993	5.580558	85.508551
1	10.381038	4.110411	14.491449
All	90.309031	9.690969	100.000000

Customer service calls	0	1	2	3	4	5	6	7	8	9	All
Churn
0	18.151815	31.773177	20.162016	11.551155	2.700270	0.780078	0.240024	0.120012	0.030003	0.000000	85.508551
1	2.760276	3.660366	2.610261	1.320132	2.280228	1.200120	0.420042	0.150015	0.030003	0.060006	14.491449
All	20.912091	35.433543	22.772277	12.871287	4.980498	1.980198	0.660066	0.270027	0.060006	0.060006	100.000000

Customer service calls	0	1	2	3	4	5	6	7	8	All
Churn
0	21.22807	37.157895	23.578947	13.508772	3.157895	0.912281	0.280702	0.140351	0.035088	100.0
All	21.22807	37.157895	23.578947	13.508772	3.157895	0.912281	0.280702	0.140351	0.035088	100.0

Customer service calls	0	1	2	3	4	5	6	7	8	9	All
Churn
1	19.047619	25.258799	18.012422	9.109731	15.73499	8.281573	2.898551	1.035197	0.207039	0.414079	100.0
All	19.047619	25.258799	18.012422	9.109731	15.73499	8.281573	2.898551	1.035197	0.207039	0.414079	100.0