【大数据部落】R语言代写电信公司churn数据客户流失 k近邻（knn）模型预测分析

?p=5521

Data background

A telephone company is interested in determining which customer characteristics are useful for predicting churn, customers who will leave their service.

The data set is Churn . The fields are as follows:

State	discrete.
account length	continuous.
area code	continuous.
phone number	discrete.
international plan	discrete.
voice mail plan	discrete.
number vmail messages	continuous.
total day minutes	continuous.
total day calls	continuous.
total day charge	continuous.
total eve minutes	continuous.
total eve calls	continuous.
total eve charge	continuous.
total night minutes	continuous.
total night calls	continuous.
total night charge	continuous.
total intl minutes	continuous.
total intl calls	continuous.
total intl charge	continuous.
number customer service calls	continuous.
churn	Discrete

Data Preparation and Exploration

查看数据概览

##      state      account.length    area.code        phone.number 
##  WV     : 158   Min.   :  1.0   Min.   :408.0    327-1058:   1  
##  MN     : 125   1st Qu.: 73.0   1st Qu.:408.0    327-1319:   1  
##  AL     : 124   Median :100.0   Median :415.0    327-2040:   1  
##  ID     : 119   Mean   :100.3   Mean   :436.9    327-2475:   1  
##  VA     : 118   3rd Qu.:127.0   3rd Qu.:415.0    327-3053:   1  
##  OH     : 116   Max.   :243.0   Max.   :510.0    327-3587:   1  
##  (Other):4240                                   (Other)  :4994  
##  international.plan voice.mail.plan number.vmail.messages
##   no :4527           no :3677       Min.   : 0.000       
##   yes: 473           yes:1323       1st Qu.: 0.000       
##                                     Median : 0.000       
##                                     Mean   : 7.755       
##                                     3rd Qu.:17.000       
##                                     Max.   :52.000       
##                                                          
##  total.day.minutes total.day.calls total.day.charge total.eve.minutes
##  Min.   :  0.0     Min.   :  0     Min.   : 0.00    Min.   :  0.0    
##  1st Qu.:143.7     1st Qu.: 87     1st Qu.:24.43    1st Qu.:166.4    
##  Median :180.1     Median :100     Median :30.62    Median :201.0    
##  Mean   :180.3     Mean   :100     Mean   :30.65    Mean   :200.6    
##  3rd Qu.:216.2     3rd Qu.:113     3rd Qu.:36.75    3rd Qu.:234.1    
##  Max.   :351.5     Max.   :165     Max.   :59.76    Max.   :363.7    
##                                                                      
##  total.eve.calls total.eve.charge total.night.minutes total.night.calls
##  Min.   :  0.0   Min.   : 0.00    Min.   :  0.0       Min.   :  0.00   
##  1st Qu.: 87.0   1st Qu.:14.14    1st Qu.:166.9       1st Qu.: 87.00   
##  Median :100.0   Median :17.09    Median :200.4       Median :100.00   
##  Mean   :100.2   Mean   :17.05    Mean   :200.4       Mean   : 99.92   
##  3rd Qu.:114.0   3rd Qu.:19.90    3rd Qu.:234.7       3rd Qu.:113.00   
##  Max.   :170.0   Max.   :30.91    Max.   :395.0       Max.   :175.00   
##                                                                        
##  total.night.charge total.intl.minutes total.intl.calls total.intl.charge
##  Min.   : 0.000     Min.   : 0.00      Min.   : 0.000   Min.   :0.000    
##  1st Qu.: 7.510     1st Qu.: 8.50      1st Qu.: 3.000   1st Qu.:2.300    
##  Median : 9.020     Median :10.30      Median : 4.000   Median :2.780    
##  Mean   : 9.018     Mean   :10.26      Mean   : 4.435   Mean   :2.771    
##  3rd Qu.:10.560     3rd Qu.:12.00      3rd Qu.: 6.000   3rd Qu.:3.240    
##  Max.   :17.770     Max.   :20.00      Max.   :20.000   Max.   :5.400    
##                                                                          
##  number.customer.service.calls     churn     
##  Min.   :0.00                   False.:4293  
##  1st Qu.:1.00                   True. : 707  
##  Median :1.00                                
##  Mean   :1.57                                
##  3rd Qu.:2.00                                
##  Max.   :9.00                                
##

从数据概览中我们可以发现没有缺失数据，同时可以发现电话号地区代码是没有价值的变量，可以删去

Examine the variables graphically

从上面的结果中，我们可以看到churn为no的样本数目要远远大于churn为yes的样本，因此所有样本中churn占多数。

从上面的结果中，我们可以看到除了emailcode和areacode之外，其他数值变量近似符合正态分布。

##  account.length    area.code     number.vmail.messages total.day.minutes
##  Min.   :  1.0   Min.   :408.0   Min.   : 0.000        Min.   :  0.0    
##  1st Qu.: 73.0   1st Qu.:408.0   1st Qu.: 0.000        1st Qu.:143.7    
##  Median :100.0   Median :415.0   Median : 0.000        Median :180.1    
##  Mean   :100.3   Mean   :436.9   Mean   : 7.755        Mean   :180.3    
##  3rd Qu.:127.0   3rd Qu.:415.0   3rd Qu.:17.000        3rd Qu.:216.2    
##  Max.   :243.0   Max.   :510.0   Max.   :52.000        Max.   :351.5    
##  total.day.calls total.day.charge total.eve.minutes total.eve.calls
##  Min.   :  0     Min.   : 0.00    Min.   :  0.0     Min.   :  0.0  
##  1st Qu.: 87     1st Qu.:24.43    1st Qu.:166.4     1st Qu.: 87.0  
##  Median :100     Median :30.62    Median :201.0     Median :100.0  
##  Mean   :100     Mean   :30.65    Mean   :200.6     Mean   :100.2  
##  3rd Qu.:113     3rd Qu.:36.75    3rd Qu.:234.1     3rd Qu.:114.0  
##  Max.   :165     Max.   :59.76    Max.   :363.7     Max.   :170.0  
##  total.eve.charge total.night.minutes total.night.calls total.night.charge
##  Min.   : 0.00    Min.   :  0.0       Min.   :  0.00    Min.   : 0.000    
##  1st Qu.:14.14    1st Qu.:166.9       1st Qu.: 87.00    1st Qu.: 7.510    
##  Median :17.09    Median :200.4       Median :100.00    Median : 9.020    
##  Mean   :17.05    Mean   :200.4       Mean   : 99.92    Mean   : 9.018    
##  3rd Qu.:19.90    3rd Qu.:234.7       3rd Qu.:113.00    3rd Qu.:10.560    
##  Max.   :30.91    Max.   :395.0       Max.   :175.00    Max.   :17.770    
##  total.intl.minutes total.intl.calls total.intl.charge
##  Min.   : 0.00      Min.   : 0.000   Min.   :0.000    
##  1st Qu.: 8.50      1st Qu.: 3.000   1st Qu.:2.300    
##  Median :10.30      Median : 4.000   Median :2.780    
##  Mean   :10.26      Mean   : 4.435   Mean   :2.771    
##  3rd Qu.:12.00      3rd Qu.: 6.000   3rd Qu.:3.240    
##  Max.   :20.00      Max.   :20.000   Max.   :5.400    
##  number.customer.service.calls
##  Min.   :0.00                 
##  1st Qu.:1.00                 
##  Median :1.00                 
##  Mean   :1.57                 
##  3rd Qu.:2.00                 
##  Max.   :9.00

Relationships between variables

从结果中我们可以看到两者之间存在显著的正相关线性关系。

Using the statistics node, report

##                               account.length    area.code
## account.length                  1.0000000000 -0.018054187
## area.code                      -0.0180541874  1.000000000
## number.vmail.messages          -0.0145746663 -0.003398983
## total.day.minutes              -0.0010174908 -0.019118245
## total.day.calls                 0.0282402279 -0.019313854
## total.day.charge               -0.0010191980 -0.019119256
## total.eve.minutes              -0.0095913331  0.007097877
## total.eve.calls                 0.0091425790 -0.012299947
## total.eve.charge               -0.0095873958  0.007114130
## total.night.minutes             0.0006679112  0.002083626
## total.night.calls              -0.0078254785  0.014656846
## total.night.charge              0.0006558937  0.002070264
## total.intl.minutes              0.0012908394 -0.004153729
## total.intl.calls                0.0142772733 -0.013623309
## total.intl.charge               0.0012918112 -0.004219099
## number.customer.service.calls  -0.0014447918  0.020920513
##                               number.vmail.messages total.day.minutes
## account.length                        -0.0145746663      -0.001017491
## area.code                             -0.0033989831      -0.019118245
## number.vmail.messages                  1.0000000000       0.005381376
## total.day.minutes                      0.0053813760       1.000000000
## total.day.calls                        0.0008831280       0.001935149
## total.day.charge                       0.0053767959       0.999999951
## total.eve.minutes                      0.0194901208      -0.010750427
## total.eve.calls                       -0.0039543728       0.008128130
## total.eve.charge                       0.0194959757      -0.010760022
## total.night.minutes                    0.0055413838       0.011798660
## total.night.calls                      0.0026762202       0.004236100
## total.night.charge                     0.0055349281       0.011782533
## total.intl.minutes                     0.0024627018      -0.019485746
## total.intl.calls                       0.0001243302      -0.001303123
## total.intl.charge                      0.0025051773      -0.019414797
## number.customer.service.calls         -0.0070856427       0.002732576
##                               total.day.calls total.day.charge
## account.length                   0.0282402279     -0.001019198
## area.code                       -0.0193138545     -0.019119256
## number.vmail.messages            0.0008831280      0.005376796
## total.day.minutes                0.0019351487      0.999999951
## total.day.calls                  1.0000000000      0.001935884
## total.day.charge                 0.0019358844      1.000000000
## total.eve.minutes               -0.0006994115     -0.010747297
## total.eve.calls                  0.0037541787      0.008129319
## total.eve.charge                -0.0006952217     -0.010756893
## total.night.minutes              0.0028044650      0.011801434
## total.night.calls               -0.0083083467      0.004234934
## total.night.charge               0.0028018169      0.011785301
## total.intl.minutes               0.0130972198     -0.019489700
## total.intl.calls                 0.0108928533     -0.001306635
## total.intl.charge                0.0131613976     -0.019418755
## number.customer.service.calls   -0.0107394951      0.002726370
##                               total.eve.minutes total.eve.calls
## account.length                    -0.0095913331     0.009142579
## area.code                          0.0070978766    -0.012299947
## number.vmail.messages              0.0194901208    -0.003954373
## total.day.minutes                 -0.0107504274     0.008128130
## total.day.calls                   -0.0006994115     0.003754179
## total.day.charge                  -0.0107472968     0.008129319
## total.eve.minutes                  1.0000000000     0.002763019
## total.eve.calls                    0.0027630194     1.000000000
## total.eve.charge                   0.9999997749     0.002778097
## total.night.minutes               -0.0166391160     0.001781411
## total.night.calls                  0.0134202163    -0.013682341
## total.night.charge                -0.0166420421     0.001799380
## total.intl.minutes                 0.0001365487    -0.007458458
## total.intl.calls                   0.0083881559     0.005574500
## total.intl.charge                  0.0001593155    -0.007507151
## number.customer.service.calls     -0.0138234228     0.006234831
##                               total.eve.charge total.night.minutes
## account.length                   -0.0095873958        0.0006679112
## area.code                         0.0071141298        0.0020836263
## number.vmail.messages             0.0194959757        0.0055413838
## total.day.minutes                -0.0107600217        0.0117986600
## total.day.calls                  -0.0006952217        0.0028044650
## total.day.charge                 -0.0107568931        0.0118014339
## total.eve.minutes                 0.9999997749       -0.0166391160
## total.eve.calls                   0.0027780971        0.0017814106
## total.eve.charge                  1.0000000000       -0.0166489191
## total.night.minutes              -0.0166489191        1.0000000000
## total.night.calls                 0.0134220174        0.0269718182
## total.night.charge               -0.0166518367        0.9999992072
## total.intl.minutes                0.0001320238       -0.0067209669
## total.intl.calls                  0.0083930603       -0.0172140162
## total.intl.charge                 0.0001547783       -0.0066545873
## number.customer.service.calls    -0.0138363623       -0.0085325365

如果把高相关性的变量保存下来，可能会造成多重共线性问题，因此需要把高相关关系的变量删去。

Data Manipulation

从结果中可以看到，total.day.calls和total.day.charge之间存在一定的相关关系。

特别是voicemial为no的变量之间存在负相关关系。

Discretize (make categorical) a relevant numeric variable

`对变量进行离散化`

construct a distribution of the variable with a churn overlay

construct a histogram of the variable with a churn overlay

Find a pair of numeric variables which are interesting with respect to churn.

从结果中可以看到，total.day.calls和total.day.charge之间存在一定的相关关系。

Model Building

特别是churn为no的变量之间存在相关关系。

##                                 Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                    0.3082150  0.0735760   4.189 2.85e-05 ***
## stateAL                        0.0151188  0.0462343   0.327 0.743680    
## stateAR                        0.0894792  0.0490897   1.823 0.068399 .  
## stateAZ                        0.0329566  0.0494195   0.667 0.504883    
## stateCA                        0.1951511  0.0567439   3.439 0.000588 ***
## international.plan yes         0.3059341  0.0151677  20.170  < 2e-16 ***
## voice.mail.plan yes           -0.1375056  0.0337533  -4.074 4.70e-05 ***
## number.vmail.messages          0.0017068  0.0010988   1.553 0.120402    
## total.day.minutes              0.3796323  0.2629027   1.444 0.148802    
## total.day.calls                0.0002191  0.0002235   0.981 0.326781    
## total.day.charge              -2.2207671  1.5464583  -1.436 0.151056    
## total.eve.minutes              0.0288233  0.1307496   0.220 0.825533    
## total.eve.calls               -0.0001585  0.0002238  -0.708 0.478915    
## total.eve.charge              -0.3316041  1.5382391  -0.216 0.829329    
## total.night.minutes            0.0083224  0.0695916   0.120 0.904814    
## total.night.calls             -0.0001824  0.0002225  -0.820 0.412290    
## total.night.charge            -0.1760782  1.5464674  -0.114 0.909355    
## total.intl.minutes            -0.0104679  0.4192270  -0.025 0.980080    
## total.intl.calls              -0.0063448  0.0018062  -3.513 0.000447 ***
## total.intl.charge              0.0676460  1.5528267   0.044 0.965254    
## number.customer.service.calls  0.0566474  0.0033945  16.688  < 2e-16 ***
## total.day.minutes1medium       0.0502681  0.0160228   3.137 0.001715 ** 
## total.day.minutes1short        0.2404020  0.0322293   7.459 1.02e-13 ***

从结果中看，我们可以发现 state  total.intl.calls   、number.customer.service.calls 、 total.day.minutes1medium 、    total.day.minutes1short    的变量有重要的影响。

Use K-Nearest-Neighbors (K-NN) algorithm to develop a model for predicting Churn

##         Direction.2005
## knn.pred   1   2
##        1 760  97
##        2 100  43


 [1] 0.803

混淆矩阵（英语：confusion matrix）是可视化工具，特别用于监督学习，在无监督学习一般叫做匹配矩阵。 矩阵的每一列代表一个类的实例预测，而每一行表示一个实际的类的实例。

##         Direction.2005
## knn.pred   1   2
##        1 827 104
##        2  33  36
 


 [1] 0.863

从测试集的结果，我们可以看到准确度达到86%。

Findings

我们可以发现，total.day.calls和total.day.charge之间存在一定的相关关系。特别是churn为no的变量之间存在相关关系。同时我们可以发现 state total.intl.calls 、number.customer.service.calls 、 total.day.minutes1medium、 total.day.minutes1short 的变量有重要的影响。同时我们可以发现，total.day.calls和total.day.charge之间存在一定的相关关系。最后从knn模型结果中，我们可以发现从训练集的结果中，我们可以看到准确度有80%，从测试集的结果，我们可以看到准确度达到86%。说明模型有很好的预测效果。