KLASIFIKASI DATA DENGAN NAIVE BAYES
RIZKI AYU FITRIAN SARI
16 Maret 2020
library(e1071)
library(caret)## Loading required package: lattice## Loading required package: ggplot2library(tidyverse)## -- Attaching packages ------------------------------------------ tidyverse 1.3.0 --## v tibble 2.1.3 v dplyr 0.8.5
## v tidyr 1.0.2 v stringr 1.4.0
## v readr 1.3.1 v forcats 0.5.0
## v purrr 0.3.3## -- Conflicts --------------------------------------------- tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()
## x purrr::lift() masks caret::lift()library(PerformanceAnalytics)## Loading required package: xts## Loading required package: zoo##
## Attaching package: 'zoo'## The following objects are masked from 'package:base':
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## as.Date, as.Date.numeric##
## Attaching package: 'xts'## The following objects are masked from 'package:dplyr':
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## first, last##
## Attaching package: 'PerformanceAnalytics'## The following objects are masked from 'package:e1071':
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## kurtosis, skewness## The following object is masked from 'package:graphics':
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## legendlibrary(corrplot)## corrplot 0.84 loadeddt<-read.csv("D:\\AYU\\SIT-IN\\UCI_Credit_Card.csv")
dt## ID LIMIT_BAL SEX EDUCATION MARRIAGE AGE PAY_0 PAY_2 PAY_3 PAY_4 PAY_5
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## [ reached 'max' / getOption("max.print") -- omitted 26001 rows ]head(dt)## ID LIMIT_BAL SEX EDUCATION MARRIAGE AGE PAY_0 PAY_2 PAY_3 PAY_4 PAY_5 PAY_6
## 1 1 20000 2 2 1 24 2 2 -1 -1 -2 -2
## 2 2 120000 2 2 2 26 -1 2 0 0 0 2
## 3 3 90000 2 2 2 34 0 0 0 0 0 0
## 4 4 50000 2 2 1 37 0 0 0 0 0 0
## 5 5 50000 1 2 1 57 -1 0 -1 0 0 0
## 6 6 50000 1 1 2 37 0 0 0 0 0 0
## BILL_AMT1 BILL_AMT2 BILL_AMT3 BILL_AMT4 BILL_AMT5 BILL_AMT6 PAY_AMT1 PAY_AMT2
## 1 3913 3102 689 0 0 0 0 689
## 2 2682 1725 2682 3272 3455 3261 0 1000
## 3 29239 14027 13559 14331 14948 15549 1518 1500
## 4 46990 48233 49291 28314 28959 29547 2000 2019
## 5 8617 5670 35835 20940 19146 19131 2000 36681
## 6 64400 57069 57608 19394 19619 20024 2500 1815
## PAY_AMT3 PAY_AMT4 PAY_AMT5 PAY_AMT6 default.payment.next.month
## 1 0 0 0 0 1
## 2 1000 1000 0 2000 1
## 3 1000 1000 1000 5000 0
## 4 1200 1100 1069 1000 0
## 5 10000 9000 689 679 0
## 6 657 1000 1000 800 0str(dt)## 'data.frame': 30000 obs. of 25 variables:
## $ ID : int 1 2 3 4 5 6 7 8 9 10 ...
## $ LIMIT_BAL : num 20000 120000 90000 50000 50000 50000 500000 100000 140000 20000 ...
## $ SEX : int 2 2 2 2 1 1 1 2 2 1 ...
## $ EDUCATION : int 2 2 2 2 2 1 1 2 3 3 ...
## $ MARRIAGE : int 1 2 2 1 1 2 2 2 1 2 ...
## $ AGE : int 24 26 34 37 57 37 29 23 28 35 ...
## $ PAY_0 : int 2 -1 0 0 -1 0 0 0 0 -2 ...
## $ PAY_2 : int 2 2 0 0 0 0 0 -1 0 -2 ...
## $ PAY_3 : int -1 0 0 0 -1 0 0 -1 2 -2 ...
## $ PAY_4 : int -1 0 0 0 0 0 0 0 0 -2 ...
## $ PAY_5 : int -2 0 0 0 0 0 0 0 0 -1 ...
## $ PAY_6 : int -2 2 0 0 0 0 0 -1 0 -1 ...
## $ BILL_AMT1 : num 3913 2682 29239 46990 8617 ...
## $ BILL_AMT2 : num 3102 1725 14027 48233 5670 ...
## $ BILL_AMT3 : num 689 2682 13559 49291 35835 ...
## $ BILL_AMT4 : num 0 3272 14331 28314 20940 ...
## $ BILL_AMT5 : num 0 3455 14948 28959 19146 ...
## $ BILL_AMT6 : num 0 3261 15549 29547 19131 ...
## $ PAY_AMT1 : num 0 0 1518 2000 2000 ...
## $ PAY_AMT2 : num 689 1000 1500 2019 36681 ...
## $ PAY_AMT3 : num 0 1000 1000 1200 10000 657 38000 0 432 0 ...
## $ PAY_AMT4 : num 0 1000 1000 1100 9000 ...
## $ PAY_AMT5 : num 0 0 1000 1069 689 ...
## $ PAY_AMT6 : num 0 2000 5000 1000 679 ...
## $ default.payment.next.month: int 1 1 0 0 0 0 0 0 0 0 ...dt[,c(3:5,7:12,25)]<-lapply(dt[,c(3:5,7:12,25)], as.factor)
dt## ID LIMIT_BAL SEX EDUCATION MARRIAGE AGE PAY_0 PAY_2 PAY_3 PAY_4 PAY_5
## 1 1 20000 2 2 1 24 2 2 -1 -1 -2
## 2 2 120000 2 2 2 26 -1 2 0 0 0
## 3 3 90000 2 2 2 34 0 0 0 0 0
## 4 4 50000 2 2 1 37 0 0 0 0 0
## 5 5 50000 1 2 1 57 -1 0 -1 0 0
## 6 6 50000 1 1 2 37 0 0 0 0 0
## 7 7 500000 1 1 2 29 0 0 0 0 0
## 8 8 100000 2 2 2 23 0 -1 -1 0 0
## 9 9 140000 2 3 1 28 0 0 2 0 0
## 10 10 20000 1 3 2 35 -2 -2 -2 -2 -1
## 11 11 200000 2 3 2 34 0 0 2 0 0
## 12 12 260000 2 1 2 51 -1 -1 -1 -1 -1
## 13 13 630000 2 2 2 41 -1 0 -1 -1 -1
## 14 14 70000 1 2 2 30 1 2 2 0 0
## 15 15 250000 1 1 2 29 0 0 0 0 0
## 16 16 50000 2 3 3 23 1 2 0 0 0
## 17 17 20000 1 1 2 24 0 0 2 2 2
## 18 18 320000 1 1 1 49 0 0 0 -1 -1
## 19 19 360000 2 1 1 49 1 -2 -2 -2 -2
## 20 20 180000 2 1 2 29 1 -2 -2 -2 -2
## 21 21 130000 2 3 2 39 0 0 0 0 0
## 22 22 120000 2 2 1 39 -1 -1 -1 -1 -1
## 23 23 70000 2 2 2 26 2 0 0 2 2
## 24 24 450000 2 1 1 40 -2 -2 -2 -2 -2
## 25 25 90000 1 1 2 23 0 0 0 -1 0
## 26 26 50000 1 3 2 23 0 0 0 0 0
## 27 27 60000 1 1 2 27 1 -2 -1 -1 -1
## 28 28 50000 2 3 2 30 0 0 0 0 0
## 29 29 50000 2 3 1 47 -1 -1 -1 -1 -1
## 30 30 50000 1 1 2 26 0 0 0 0 0
## 31 31 230000 2 1 2 27 -1 -1 -1 -1 -1
## 32 32 50000 1 2 2 33 2 0 0 0 0
## 33 33 100000 1 1 2 32 0 0 0 0 0
## 34 34 500000 2 2 1 54 -2 -2 -2 -2 -2
## 35 35 500000 1 1 1 58 -2 -2 -2 -2 -2
## 36 36 160000 1 1 2 30 -1 -1 -2 -2 -2
## 37 37 280000 1 2 1 40 0 0 0 0 0
## 38 38 60000 2 2 2 22 0 0 0 0 0
## 39 39 50000 1 1 2 25 1 -1 -1 -2 -2
## 40 40 280000 1 1 2 31 -1 -1 2 -1 0
## 41 41 360000 1 1 2 33 0 0 0 0 0
## 42 42 70000 2 1 2 25 0 0 0 0 0
## 43 43 10000 1 2 2 22 0 0 0 0 0
## 44 44 140000 2 2 1 37 0 0 0 0 0
## 45 45 40000 2 1 2 30 0 0 0 2 0
## 46 46 210000 1 1 2 29 -2 -2 -2 -2 -2
## 47 47 20000 2 1 2 22 0 0 2 -1 0
## 48 48 150000 2 5 2 46 0 0 -1 0 0
## 49 49 380000 1 2 2 32 -1 -1 -1 -1 -1
## 50 50 20000 1 1 2 24 0 0 0 0 0
## 51 51 70000 1 3 2 42 1 2 2 2 2
## 52 52 100000 2 3 3 43 0 0 0 0 0
## 53 53 310000 2 2 1 49 -2 -2 -2 -2 -2
## 54 54 180000 2 1 2 25 1 2 0 0 0
## 55 55 150000 2 1 2 29 2 0 0 0 0
## 56 56 500000 2 1 1 45 -2 -2 -2 -2 -2
## 57 57 180000 2 3 1 34 0 0 0 -1 -1
## 58 58 180000 2 2 1 34 0 0 0 0 0
## 59 59 200000 2 1 2 34 -1 3 2 2 2
## 60 60 400000 2 2 1 29 0 0 0 0 0
## 61 61 500000 2 3 1 28 0 0 0 0 0
## 62 62 70000 1 2 1 39 0 0 0 0 0
## 63 63 50000 1 1 2 29 2 2 2 2 2
## 64 64 50000 2 2 1 46 0 0 0 -2 -2
## 65 65 130000 2 2 1 51 -1 -1 -2 -2 -1
## 66 66 200000 1 1 1 57 -2 -2 -2 -1 2
## 67 67 10000 1 2 1 56 2 2 2 0 0
## 68 68 210000 2 1 2 30 2 -1 -1 -1 -1
## 69 69 130000 2 3 2 29 1 -2 -2 -1 2
## 70 70 20000 1 5 2 22 2 0 0 0 0
## 71 71 80000 1 1 2 31 -1 -1 -1 -1 -1
## 72 72 320000 1 2 2 29 2 2 2 2 2
## 73 73 200000 2 2 1 32 -1 -1 -1 -1 2
## 74 74 290000 2 1 2 37 1 -2 -1 -1 -1
## 75 75 340000 1 1 2 32 -1 -1 -1 -1 -1
## 76 76 20000 1 2 2 24 0 0 2 0 0
## 77 77 50000 1 3 2 25 -1 0 0 0 0
## 78 78 300000 2 1 1 45 -1 -1 -1 -1 -1
## 79 79 30000 2 2 2 22 0 0 0 0 0
## 80 80 240000 2 2 2 44 1 -2 -2 -2 -2
## 81 81 470000 2 3 3 33 0 0 0 0 0
## 82 82 360000 2 1 2 26 0 0 0 0 0
## 83 83 60000 1 3 2 30 0 0 0 0 0
## 84 84 400000 2 2 1 44 0 0 2 0 0
## 85 85 50000 2 3 2 49 0 0 0 0 0
## 86 86 160000 1 2 2 33 0 0 0 0 0
## 87 87 360000 2 1 1 45 -1 -1 2 0 -1
## 88 88 160000 2 2 2 32 0 0 0 0 0
## 89 89 130000 2 1 1 35 0 0 0 -1 -1
## 90 90 20000 1 3 2 44 2 2 0 0 0
## 91 91 200000 1 1 1 53 2 2 2 2 2
## 92 92 280000 2 1 2 39 -1 -1 -1 0 0
## 93 93 100000 2 1 2 27 -2 -2 -2 -2 -2
## 94 94 160000 2 2 1 37 -1 -1 -1 -1 -1
## 95 95 60000 2 2 2 23 0 0 0 0 0
## 96 96 90000 1 2 2 35 0 0 0 0 0
## 97 97 360000 1 1 1 43 -1 -1 -1 -1 -1
## 98 98 150000 1 1 2 27 0 0 0 0 0
## 99 99 50000 2 3 1 22 0 0 0 0 0
## 100 100 20000 1 2 1 38 0 0 0 0 0
## 101 101 140000 1 1 2 32 -2 -2 -2 -2 -2
## 102 102 380000 2 1 2 30 -2 -2 -1 0 0
## 103 103 480000 1 1 1 63 0 0 0 2 2
## 104 104 50000 2 3 2 22 0 0 0 0 0
## 105 105 60000 2 2 2 26 2 2 2 2 2
## 106 106 70000 2 2 2 24 -1 -1 -2 -2 -2
## 107 107 80000 2 2 1 36 -1 -1 -1 -1 -1
## 108 108 350000 1 1 2 52 -1 -1 -1 -1 -1
## 109 109 130000 1 2 2 38 0 0 0 -1 -1
## 110 110 360000 1 2 1 35 1 -2 -2 -2 -2
## 111 111 330000 2 1 1 31 0 0 2 0 0
## 112 112 50000 1 3 1 47 0 0 2 0 0
## 113 113 280000 1 2 1 41 2 2 2 2 2
## 114 114 100000 2 1 2 24 0 0 0 0 0
## 115 115 50000 1 2 2 41 0 0 0 0 0
## 116 116 30000 1 1 2 24 -1 2 0 0 3
## 117 117 240000 1 1 2 28 -1 -1 -1 -1 -1
## 118 118 80000 1 2 2 26 2 0 0 0 0
## 119 119 400000 1 2 1 34 -1 -1 -1 -1 -1
## 120 120 240000 2 2 2 38 0 0 0 0 -1
## 121 121 50000 1 3 2 37 2 2 2 3 2
## 122 122 450000 1 1 1 40 1 -2 -2 -2 -2
## 123 123 110000 2 1 1 48 1 -2 -2 -2 -2
## 124 124 310000 2 2 1 35 2 0 0 0 0
## 125 125 20000 1 1 2 27 0 0 0 0 0
## 126 126 20000 1 2 2 23 1 -2 -2 -2 -2
## 127 127 200000 1 3 2 52 0 0 0 0 0
## 128 128 180000 1 1 1 36 0 0 0 0 0
## 129 129 50000 1 2 1 51 0 0 0 0 0
## 130 130 60000 1 3 1 55 3 2 2 0 0
## 131 131 30000 2 1 2 23 1 -2 -2 -2 -1
## 132 132 240000 1 1 2 41 1 -1 -1 0 0
## 133 133 420000 1 2 1 34 0 0 0 0 0
## 134 134 330000 1 3 1 46 0 0 0 0 0
## 135 135 30000 2 2 2 22 0 0 0 0 0
## 136 136 240000 1 2 1 34 0 0 0 2 2
## 137 137 150000 1 1 2 27 0 0 0 0 0
## 138 138 210000 2 2 1 33 0 0 0 0 0
## 139 139 50000 2 3 1 51 -1 -1 -1 -1 -2
## 140 140 50000 1 1 2 24 0 0 0 0 0
## 141 141 240000 1 1 2 47 1 -2 -2 -2 -2
## 142 142 180000 1 2 2 28 -1 -1 -1 -1 -1
## 143 143 50000 1 2 2 23 1 2 2 2 0
## 144 144 170000 1 2 2 29 -2 -2 -2 -2 -2
## 145 145 20000 1 1 2 29 -1 -1 -1 -1 0
## 146 146 50000 1 1 2 28 0 0 0 0 0
## 147 147 170000 2 2 2 27 0 0 0 0 0
## 148 148 200000 1 1 2 34 1 2 0 0 0
## 149 149 80000 2 2 1 23 1 2 3 2 0
## 150 150 260000 2 1 1 60 1 -2 -1 -1 -1
## 151 151 140000 1 2 1 32 0 0 0 0 2
## 152 152 80000 1 1 2 25 0 0 0 0 0
## 153 153 350000 1 1 2 41 1 -1 -1 -1 -1
## 154 154 280000 2 2 1 56 0 0 0 0 0
## 155 155 30000 2 3 2 26 0 0 0 0 0
## 156 156 140000 1 1 1 34 0 0 0 0 0
## 157 157 200000 2 1 2 37 0 0 0 0 0
## 158 158 200000 2 3 2 30 0 0 0 0 0
## 159 159 210000 1 3 1 45 2 3 4 4 5
## 160 160 50000 1 3 1 57 3 2 0 0 0
## 161 161 30000 1 1 2 41 2 2 2 2 2
## 162 162 50000 1 2 2 27 2 -1 -1 -1 -1
## 163 163 290000 1 3 1 47 -1 -1 -1 -1 0
## 164 164 250000 2 1 1 34 0 0 2 0 0
## 165 165 60000 2 2 1 46 0 0 0 0 0
## 166 166 110000 2 1 2 27 0 0 0 0 0
## 167 167 370000 1 1 2 50 -2 -2 -2 -2 -2
## 168 168 100000 1 2 1 27 -1 2 2 0 0
## 169 169 90000 2 2 1 35 0 0 0 0 0
## 170 170 50000 2 2 2 22 0 0 0 0 0
## 171 171 270000 1 2 2 37 0 0 0 0 0
## 172 172 300000 2 1 2 30 -1 -1 -1 -1 -1
## 173 173 50000 2 2 2 22 -1 0 0 0 0
## 174 174 50000 2 1 2 24 1 -2 -2 -2 -2
## 175 175 360000 1 1 2 29 1 -2 -1 -1 -2
## 176 176 130000 1 3 1 56 1 2 2 2 2
## 177 177 80000 1 1 2 30 -2 -1 0 0 0
## 178 178 50000 1 2 2 30 1 2 0 0 0
## 179 179 20000 2 2 2 22 0 0 0 0 0
## 180 180 80000 2 2 1 29 0 0 2 0 0
## 181 181 240000 1 1 2 37 -1 -1 2 0 0
## 182 182 80000 2 3 2 35 0 -1 0 0 0
## 183 183 500000 2 1 1 47 0 0 0 0 0
## 184 184 60000 2 2 1 24 0 0 0 0 0
## 185 185 20000 1 2 2 25 0 0 0 0 0
## 186 186 100000 2 2 1 38 1 2 0 0 2
## 187 187 360000 2 1 2 32 1 -1 -1 -1 -1
## 188 188 200000 2 3 2 47 2 2 2 2 2
## 189 189 130000 2 2 1 34 1 -1 0 0 0
## 190 190 20000 2 2 2 31 1 5 4 4 3
## 191 191 310000 1 1 2 32 0 0 0 0 0
## 192 192 60000 2 1 2 27 2 0 0 0 2
## 193 193 180000 2 1 2 29 -1 -1 -1 -2 -1
## 194 194 180000 2 1 2 24 -1 -1 2 0 0
## 195 195 50000 1 2 1 36 0 0 0 0 -1
## 196 196 50000 2 1 2 24 1 2 2 2 2
## 197 197 150000 2 2 1 34 -2 -2 -2 -2 -2
## 198 198 20000 2 1 2 22 0 0 0 0 -1
## 199 199 500000 2 1 1 34 -2 -2 -2 -1 -1
## 200 200 30000 2 3 2 22 1 2 2 0 0
## 201 201 180000 2 1 1 38 -2 -2 -2 -2 -2
## 202 202 140000 1 1 2 31 0 0 2 0 0
## 203 203 140000 2 1 2 26 -1 0 -1 -1 -1
## 204 204 120000 1 2 2 26 0 0 0 0 0
## 205 205 360000 2 2 1 48 0 0 0 0 0
## 206 206 20000 1 3 1 43 -1 -1 0 0 0
## 207 207 100000 1 1 1 47 -1 -1 -1 -1 -1
## 208 208 210000 2 2 1 41 -1 -1 -1 -1 -1
## 209 209 80000 2 2 2 24 0 0 0 0 0
## 210 210 330000 2 1 1 50 -1 -1 -1 -1 -2
## 211 211 220000 1 1 2 24 0 0 0 -1 -1
## 212 212 210000 2 1 1 38 1 -2 -1 0 -1
## 213 213 40000 2 3 1 43 0 0 0 0 0
## 214 214 30000 1 2 2 39 0 0 0 2 0
## 215 215 470000 1 2 2 27 2 2 2 2 0
## 216 216 30000 1 3 1 42 -1 -1 -1 -1 -1
## 217 217 240000 2 2 1 36 1 -2 -2 -2 -2
## 218 218 80000 1 2 2 36 2 0 0 0 0
## 219 219 110000 2 3 0 31 0 0 0 0 0
## 220 220 310000 2 1 2 38 -1 -1 -1 -1 -2
## 221 221 360000 2 1 1 40 2 2 2 0 0
## 222 222 330000 2 3 2 45 0 0 0 0 0
## 223 223 300000 2 2 2 35 0 0 0 0 0
## 224 224 320000 2 2 2 33 0 0 0 0 0
## 225 225 50000 2 3 2 37 1 3 2 0 0
## 226 226 170000 2 1 2 28 0 0 0 0 0
## 227 227 350000 1 3 1 44 -1 -1 -1 -1 -1
## 228 228 20000 1 2 1 37 0 0 0 0 0
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## 1956 1956 10000 1 2 2 41 1 2 2 2 2
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## 1959 1959 220000 2 1 1 31 -2 -2 -2 -2 -2
## 1960 1960 70000 2 3 1 57 0 0 0 0 0
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## 1969 1969 70000 1 1 2 30 2 2 0 0 0
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## 1971 1971 200000 1 1 1 42 0 -1 0 0 -1
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## 1992 1992 360000 1 1 2 65 1 -2 -2 -2 -2
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## 2001 2001 10000 1 2 2 22 0 0 0 0 0
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## 3968 3968 50000 1 2 1 53 0 0 2 2 0
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## 3970 3970 90000 2 2 2 26 0 0 0 0 0
## 3971 3971 250000 1 1 1 31 -2 -2 -1 -1 -1
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## 3978 3978 310000 2 2 1 41 -1 -1 -1 -1 -1
## 3979 3979 10000 1 2 2 22 0 0 0 0 0
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## 3987 3987 500000 1 1 1 39 0 0 0 0 -1
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## 3994 3994 110000 2 2 2 23 0 0 0 0 -1
## 3995 3995 200000 1 1 3 43 -1 -1 -1 -1 -1
## 3996 3996 220000 1 3 2 33 1 -1 -1 0 0
## 3997 3997 50000 2 2 2 43 0 0 0 0 0
## 3998 3998 60000 1 2 2 26 0 0 0 0 0
## 3999 3999 20000 2 1 2 23 2 2 2 2 2
## PAY_6 BILL_AMT1 BILL_AMT2 BILL_AMT3 BILL_AMT4 BILL_AMT5 BILL_AMT6 PAY_AMT1
## 1 -2 3913 3102 689 0 0 0 0
## 2 2 2682 1725 2682 3272 3455 3261 0
## 3 0 29239 14027 13559 14331 14948 15549 1518
## 4 0 46990 48233 49291 28314 28959 29547 2000
## 5 0 8617 5670 35835 20940 19146 19131 2000
## 6 0 64400 57069 57608 19394 19619 20024 2500
## 7 0 367965 412023 445007 542653 483003 473944 55000
## 8 -1 11876 380 601 221 -159 567 380
## 9 0 11285 14096 12108 12211 11793 3719 3329
## 10 -1 0 0 0 0 13007 13912 0
## 11 -1 11073 9787 5535 2513 1828 3731 2306
## 12 2 12261 21670 9966 8517 22287 13668 21818
## 13 -1 12137 6500 6500 6500 6500 2870 1000
## 14 2 65802 67369 65701 66782 36137 36894 3200
## 15 0 70887 67060 63561 59696 56875 55512 3000
## 16 0 50614 29173 28116 28771 29531 30211 0
## 17 2 15376 18010 17428 18338 17905 19104 3200
## 18 -1 253286 246536 194663 70074 5856 195599 10358
## 19 -2 0 0 0 0 0 0 0
## 20 -2 0 0 0 0 0 0 0
## 21 -1 38358 27688 24489 20616 11802 930 3000
## 22 -1 316 316 316 0 632 316 316
## 23 2 41087 42445 45020 44006 46905 46012 2007
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## [ reached 'max' / getOption("max.print") -- omitted 26001 rows ]dt %>%
select_if(is.numeric) %>%
cor () %>%
corrplot(type="lower")
chisq.test(dt$SEX,dt$default.payment.next.month)##
## Pearson's Chi-squared test with Yates' continuity correction
##
## data: dt$SEX and dt$default.payment.next.month
## X-squared = 47.709, df = 1, p-value = 4.945e-12dt <- dt[,c(2:7,13,19:25)]
set.seed(111)
ind<-sample(2,nrow(dt), replace=T, prob = c(0.8,0.2))
train<-dt[ind==1, ]
test<-dt[ind==2, ]nb<-naiveBayes(default.payment.next.month ~ ., data=train)
nb##
## Naive Bayes Classifier for Discrete Predictors
##
## Call:
## naiveBayes.default(x = X, y = Y, laplace = laplace)
##
## A-priori probabilities:
## Y
## 0 1
## 0.777185 0.222815
##
## Conditional probabilities:
## LIMIT_BAL
## Y [,1] [,2]
## 0 178122.1 131815.2
## 1 130220.3 116165.0
##
## SEX
## Y 1 2
## 0 0.3863892 0.6136108
## 1 0.4326599 0.5673401
##
## EDUCATION
## Y 0 1 2 3 4
## 0 0.0006435352 0.3644554084 0.4577143776 0.1586850432 0.0050946533
## 1 0.0000000000 0.3050879162 0.5048634493 0.1857463524 0.0007482230
## EDUCATION
## Y 5 6
## 0 0.0115300048 0.0018769775
## 1 0.0024317247 0.0011223345
##
## MARRIAGE
## Y 0 1 2 3
## 0 0.002252373 0.448973025 0.538263528 0.010511074
## 1 0.000748223 0.480733259 0.506921062 0.011597456
##
## AGE
## Y [,1] [,2]
## 0 35.40382 9.076242
## 1 35.69398 9.703204
##
## PAY_0
## Y -2 -1 0 1 2
## 0 0.1028583686 0.2008365957 0.5512951145 0.1058079048 0.0337855955
## 1 0.0557426113 0.1419753086 0.2880658436 0.1866816311 0.2772166105
## PAY_0
## Y 3 4 5 6 7
## 0 0.0028959082 0.0010725586 0.0006971631 0.0002681396 0.0001072559
## 1 0.0364758698 0.0080433969 0.0018705574 0.0011223345 0.0011223345
## PAY_0
## Y 8
## 0 0.0003753955
## 1 0.0016835017
##
## BILL_AMT1
## Y [,1] [,2]
## 0 52228.02 74067.21
## 1 48731.51 73535.66
##
## PAY_AMT1
## Y [,1] [,2]
## 0 6208.930 17415.683
## 1 3433.244 9758.605
##
## PAY_AMT2
## Y [,1] [,2]
## 0 6667.594 26656.74
## 1 3362.495 11093.83
##
## PAY_AMT3
## Y [,1] [,2]
## 0 5767.316 18963.53
## 1 3352.416 13066.41
##
## PAY_AMT4
## Y [,1] [,2]
## 0 5332.654 17023.51
## 1 3174.941 11684.09
##
## PAY_AMT5
## Y [,1] [,2]
## 0 5260.668 16309.55
## 1 3308.797 12606.31
##
## PAY_AMT6
## Y [,1] [,2]
## 0 5733.044 18876.85
## 1 3542.988 14261.54pred<-predict(nb, newdata=test, type="class")
confusionMatrix(pred, test$default.payment.next.month)## Confusion Matrix and Statistics
##
## Reference
## Prediction 0 1
## 0 1289 138
## 1 3428 1152
##
## Accuracy : 0.4064
## 95% CI : (0.3939, 0.4189)
## No Information Rate : 0.7853
## P-Value [Acc > NIR] : 1
##
## Kappa : 0.0863
##
## Mcnemar's Test P-Value : <2e-16
##
## Sensitivity : 0.2733
## Specificity : 0.8930
## Pos Pred Value : 0.9033
## Neg Pred Value : 0.2515
## Prevalence : 0.7853
## Detection Rate : 0.2146
## Detection Prevalence : 0.2376
## Balanced Accuracy : 0.5831
##
## 'Positive' Class : 0
## head(pred);head(test$default.payment.next.month)## [1] 0 1 1 1 0 1
## Levels: 0 1## [1] 0 0 0 0 1 0
## Levels: 0 1table(test$default.payment.next.month)##
## 0 1
## 4717 1290