关于r语言:R语言电信公司churn数据客户流失-k近邻knn模型预测分析

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原文链接：http://tecdat.cn/?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 

 

  1. 查看数据概览
  2. ## state account.length area.code phone.number
  3. ## WV : 158 Min. : 1.0 Min. :408.0 327-1058: 1
  4. ## MN : 125 1st Qu.: 73.0 1st Qu.:408.0 327-1319: 1
  5. ## AL : 124 Median :100.0 Median :415.0 327-2040: 1
  6. ## ID : 119 Mean :100.3 Mean :436.9 327-2475: 1
  7. ## VA : 118 3rd Qu.:127.0 3rd Qu.:415.0 327-3053: 1
  8. ## OH : 116 Max. :243.0 Max. :510.0 327-3587: 1
  9. ## (Other):4240 (Other) :4994
  10. ## international.plan voice.mail.plan number.vmail.messages
  11. ## no :4527 no :3677 Min. : 0.000
  12. ## yes: 473 yes:1323 1st Qu.: 0.000
  13. ## Median : 0.000
  14. ## Mean : 7.755
  15. ## 3rd Qu.:17.000
  16. ## Max. :52.000
  18. ## total.day.minutes total.day.calls total.day.charge total.eve.minutes
  19. ## Min. : 0.0 Min. : 0 Min. : 0.00 Min. : 0.0
  20. ## 1st Qu.:143.7 1st Qu.: 87 1st Qu.:24.43 1st Qu.:166.4
  21. ## Median :180.1 Median :100 Median :30.62 Median :201.0
  22. ## Mean :180.3 Mean :100 Mean :30.65 Mean :200.6
  23. ## 3rd Qu.:216.2 3rd Qu.:113 3rd Qu.:36.75 3rd Qu.:234.1
  24. ## Max. :351.5 Max. :165 Max. :59.76 Max. :363.7
  26. ## total.eve.calls total.eve.charge total.night.minutes total.night.calls
  27. ## Min. : 0.0 Min. : 0.00 Min. : 0.0 Min. : 0.00
  28. ## 1st Qu.: 87.0 1st Qu.:14.14 1st Qu.:166.9 1st Qu.: 87.00
  29. ## Median :100.0 Median :17.09 Median :200.4 Median :100.00
  30. ## Mean :100.2 Mean :17.05 Mean :200.4 Mean : 99.92
  31. ## 3rd Qu.:114.0 3rd Qu.:19.90 3rd Qu.:234.7 3rd Qu.:113.00
  32. ## Max. :170.0 Max. :30.91 Max. :395.0 Max. :175.00
  34. ## total.night.charge total.intl.minutes total.intl.calls total.intl.charge
  35. ## Min. : 0.000 Min. : 0.00 Min. : 0.000 Min. :0.000
  36. ## 1st Qu.: 7.510 1st Qu.: 8.50 1st Qu.: 3.000 1st Qu.:2.300
  37. ## Median : 9.020 Median :10.30 Median : 4.000 Median :2.780
  38. ## Mean : 9.018 Mean :10.26 Mean : 4.435 Mean :2.771
  39. ## 3rd Qu.:10.560 3rd Qu.:12.00 3rd Qu.: 6.000 3rd Qu.:3.240
  40. ## Max. :17.770 Max. :20.00 Max. :20.000 Max. :5.400
  42. ## number.customer.service.calls churn
  43. ## Min. :0.00 False.:4293
  44. ## 1st Qu.:1.00 True. : 707
  45. ## Median :1.00
  46. ## Mean :1.57
  47. ## 3rd Qu.:2.00
  48. ## Max. :9.00

 从数据概览中咱们能够发现没有缺失数据，同时能够发现电话号 地区代码是没有价值的变量，能够删去

 

Examine the variables graphically

 

   

从下面的后果中，咱们能够看到 churn 为 no 的样本数目要远远大于 churn 为 yes 的样本，因而所有样本中 churn 占多数。

 

从下面的后果中，咱们能够看到除了 emailcode 和 areacode 之外，其余数值变量近似合乎正态分布。

  1. ## account.length area.code number.vmail.messages total.day.minutes
  2. ## Min. : 1.0 Min. :408.0 Min. : 0.000 Min. : 0.0
  3. ## 1st Qu.: 73.0 1st Qu.:408.0 1st Qu.: 0.000 1st Qu.:143.7
  4. ## Median :100.0 Median :415.0 Median : 0.000 Median :180.1
  5. ## Mean :100.3 Mean :436.9 Mean : 7.755 Mean :180.3
  6. ## 3rd Qu.:127.0 3rd Qu.:415.0 3rd Qu.:17.000 3rd Qu.:216.2
  7. ## Max. :243.0 Max. :510.0 Max. :52.000 Max. :351.5
  8. ## total.day.calls total.day.charge total.eve.minutes total.eve.calls
  9. ## Min. : 0 Min. : 0.00 Min. : 0.0 Min. : 0.0
  10. ## 1st Qu.: 87 1st Qu.:24.43 1st Qu.:166.4 1st Qu.: 87.0
  11. ## Median :100 Median :30.62 Median :201.0 Median :100.0
  12. ## Mean :100 Mean :30.65 Mean :200.6 Mean :100.2
  13. ## 3rd Qu.:113 3rd Qu.:36.75 3rd Qu.:234.1 3rd Qu.:114.0
  14. ## Max. :165 Max. :59.76 Max. :363.7 Max. :170.0
  15. ## total.eve.charge total.night.minutes total.night.calls total.night.charge
  16. ## Min. : 0.00 Min. : 0.0 Min. : 0.00 Min. : 0.000
  17. ## 1st Qu.:14.14 1st Qu.:166.9 1st Qu.: 87.00 1st Qu.: 7.510
  18. ## Median :17.09 Median :200.4 Median :100.00 Median : 9.020
  19. ## Mean :17.05 Mean :200.4 Mean : 99.92 Mean : 9.018
  20. ## 3rd Qu.:19.90 3rd Qu.:234.7 3rd Qu.:113.00 3rd Qu.:10.560
  21. ## Max. :30.91 Max. :395.0 Max. :175.00 Max. :17.770
  22. ## total.intl.minutes total.intl.calls total.intl.charge
  23. ## Min. : 0.00 Min. : 0.000 Min. :0.000
  24. ## 1st Qu.: 8.50 1st Qu.: 3.000 1st Qu.:2.300
  25. ## Median :10.30 Median : 4.000 Median :2.780
  26. ## Mean :10.26 Mean : 4.435 Mean :2.771
  27. ## 3rd Qu.:12.00 3rd Qu.: 6.000 3rd Qu.:3.240
  28. ## Max. :20.00 Max. :20.000 Max. :5.400
  29. ## number.customer.service.calls
  30. ## Min. :0.00
  31. ## 1st Qu.:1.00
  32. ## Median :1.00
  33. ## Mean :1.57
  34. ## 3rd Qu.:2.00
  35. ## Max. :9.00

Relationships between variables

从后果中咱们能够看到两者之间存在显著的正相干线性关系。


 

Using the statistics node, report

  1. ## account.length area.code
  2. ## account.length 1.0000000000 -0.018054187
  3. ## area.code -0.0180541874 1.000000000
  4. ## number.vmail.messages -0.0145746663 -0.003398983
  5. ## total.day.minutes -0.0010174908 -0.019118245
  6. ## total.day.calls 0.0282402279 -0.019313854
  7. ## total.day.charge -0.0010191980 -0.019119256
  8. ## total.eve.minutes -0.0095913331 0.007097877
  9. ## total.eve.calls 0.0091425790 -0.012299947
  10. ## total.eve.charge -0.0095873958 0.007114130
  11. ## total.night.minutes 0.0006679112 0.002083626
  12. ## total.night.calls -0.0078254785 0.014656846
  13. ## total.night.charge 0.0006558937 0.002070264
  14. ## total.intl.minutes 0.0012908394 -0.004153729
  15. ## total.intl.calls 0.0142772733 -0.013623309
  16. ## total.intl.charge 0.0012918112 -0.004219099
  17. ## number.customer.service.calls -0.0014447918 0.020920513
  18. ## number.vmail.messages total.day.minutes
  19. ## account.length -0.0145746663 -0.001017491
  20. ## area.code -0.0033989831 -0.019118245
  21. ## number.vmail.messages 1.0000000000 0.005381376
  22. ## total.day.minutes 0.0053813760 1.000000000
  23. ## total.day.calls 0.0008831280 0.001935149
  24. ## total.day.charge 0.0053767959 0.999999951
  25. ## total.eve.minutes 0.0194901208 -0.010750427
  26. ## total.eve.calls -0.0039543728 0.008128130
  27. ## total.eve.charge 0.0194959757 -0.010760022
  28. ## total.night.minutes 0.0055413838 0.011798660
  29. ## total.night.calls 0.0026762202 0.004236100
  30. ## total.night.charge 0.0055349281 0.011782533
  31. ## total.intl.minutes 0.0024627018 -0.019485746
  32. ## total.intl.calls 0.0001243302 -0.001303123
  33. ## total.intl.charge 0.0025051773 -0.019414797
  34. ## number.customer.service.calls -0.0070856427 0.002732576
  35. ## total.day.calls total.day.charge
  36. ## account.length 0.0282402279 -0.001019198
  37. ## area.code -0.0193138545 -0.019119256
  38. ## number.vmail.messages 0.0008831280 0.005376796
  39. ## total.day.minutes 0.0019351487 0.999999951
  40. ## total.day.calls 1.0000000000 0.001935884
  41. ## total.day.charge 0.0019358844 1.000000000
  42. ## total.eve.minutes -0.0006994115 -0.010747297
  43. ## total.eve.calls 0.0037541787 0.008129319
  44. ## total.eve.charge -0.0006952217 -0.010756893
  45. ## total.night.minutes 0.0028044650 0.011801434
  46. ## total.night.calls -0.0083083467 0.004234934
  47. ## total.night.charge 0.0028018169 0.011785301
  48. ## total.intl.minutes 0.0130972198 -0.019489700
  49. ## total.intl.calls 0.0108928533 -0.001306635
  50. ## total.intl.charge 0.0131613976 -0.019418755
  51. ## number.customer.service.calls -0.0107394951 0.002726370
  52. ## total.eve.minutes total.eve.calls
  53. ## account.length -0.0095913331 0.009142579
  54. ## area.code 0.0070978766 -0.012299947
  55. ## number.vmail.messages 0.0194901208 -0.003954373
  56. ## total.day.minutes -0.0107504274 0.008128130
  57. ## total.day.calls -0.0006994115 0.003754179
  58. ## total.day.charge -0.0107472968 0.008129319
  59. ## total.eve.minutes 1.0000000000 0.002763019
  60. ## total.eve.calls 0.0027630194 1.000000000
  61. ## total.eve.charge 0.9999997749 0.002778097
  62. ## total.night.minutes -0.0166391160 0.001781411
  63. ## total.night.calls 0.0134202163 -0.013682341
  64. ## total.night.charge -0.0166420421 0.001799380
  65. ## total.intl.minutes 0.0001365487 -0.007458458
  66. ## total.intl.calls 0.0083881559 0.005574500
  67. ## total.intl.charge 0.0001593155 -0.007507151
  68. ## number.customer.service.calls -0.0138234228 0.006234831
  69. ## total.eve.charge total.night.minutes
  70. ## account.length -0.0095873958 0.0006679112
  71. ## area.code 0.0071141298 0.0020836263
  72. ## number.vmail.messages 0.0194959757 0.0055413838
  73. ## total.day.minutes -0.0107600217 0.0117986600
  74. ## total.day.calls -0.0006952217 0.0028044650
  75. ## total.day.charge -0.0107568931 0.0118014339
  76. ## total.eve.minutes 0.9999997749 -0.0166391160
  77. ## total.eve.calls 0.0027780971 0.0017814106
  78. ## total.eve.charge 1.0000000000 -0.0166489191
  79. ## total.night.minutes -0.0166489191 1.0000000000
  80. ## total.night.calls 0.0134220174 0.0269718182
  81. ## total.night.charge -0.0166518367 0.9999992072
  82. ## total.intl.minutes 0.0001320238 -0.0067209669
  83. ## total.intl.calls 0.0083930603 -0.0172140162
  84. ## total.intl.charge 0.0001547783 -0.0066545873
  85. ## number.customer.service.calls -0.0138363623 -0.0085325365
 
 如果把高相关性的变量保留下来，可能会造成多重共线性问题，因而须要把高相干关系的变量删去。

Data Manipulation

 
 从后果中能够看到，total.day.calls 和 total.day.charge 之间存在肯定的相干关系。
 特地是 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. 

 
 从后果中能够看到，total.day.calls 和 total.day.charge 之间存在肯定的相干关系。
 

Model Building

特地是 churn 为 no 的变量之间存在相干关系。
 

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

 

 从后果中看，咱们能够发现 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

  1. ## Direction.2005
  2. ## knn.pred 1 2
  3. ## 1 760 97
  4. ## 2 100 43
  5. [1] 0.803
 
 混同矩阵（英语：confusion matrix）是可视化工具，特地用于监督学习，在无监督学习个别叫做匹配矩阵。矩阵的每一列代表一个类的实例预测，而每一行示意一个理论的类的实例。
  1. ## Direction.2005
  2. ## knn.pred 1 2
  3. ## 1 827 104
  4. ## 2 33 36
  5. [1] 0.863

 

 从测试集的后果，咱们能够看到准确度达到 86%。

 

Findings

 

咱们能够发现，total.day.calls 和 total.day.charge 之间存在肯定的相干关系。特地是 churn 为 no 的变量之间存在相干关系。同时咱们能够发现 state  total.intl.calls、number.customer.service.calls、total.day.minutes1medium、total.day.minutes1short    的变量有重要的影响。同时咱们能够发现，total.day.calls 和 total.day.charge 之间存在肯定的相干关系。最初从 knn 模型后果中，咱们能够发现从训练集的后果中，咱们能够看到准确度有 80%，从测试集的后果，咱们能够看到准确度达到 86%。阐明模型有很好的预测成果。
 

相干文章：

 Python 中用 PyTorch 机器学习分类预测银行_客户散失_模型

决策树算法建设电信_客户散失_模型

【大数据部落】(数据挖掘) 如何用大数据做用户异样行为

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r语言
发表至： r语言
2020-08-05
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