Black Friday
Priyanka Mohanty
28 January 2018
1.Introduction
Dataset contains the information about numbers of the shoppers that have engaged in shopping on black friday from 2005 to 2013. Our data is stored based on product id i.e. if a person buying 10 products then his data will be stored in 10 observation and hence there will be repetition of many people age group. Research Question: The challenge is to predict the purchase amount of various products by users across categories given historic data of purchase amounts.
Data
Variable
User_ID - User ID
Product_ID - Product ID
Gender - Sex of User
Age - Age in bins
Occupation - Occupation (Masked)
City_Category - Category of the City (A,B,C)
Stay_In_Current_City_Years - Number of years stay in current city
Marital_Status - Marital Status
Product_Category_1 - Product Category (Masked)
Product_Category_2 - Product Category (Masked)
Product_Category_3 - Product Category (Masked)
Purchase - Purchase Amount (Target Variable)
2.Setting the environment
2.1. Loading packages
library(data.table)
library(ggplot2)
library(gridExtra)
library(corrplot)## corrplot 0.84 loadedlibrary(rpart)
library(randomForest)## randomForest 4.6-12## Type rfNews() to see new features/changes/bug fixes.##
## Attaching package: 'randomForest'## The following object is masked from 'package:gridExtra':
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## combine## The following object is masked from 'package:ggplot2':
##
## marginlibrary(stepPlr)
library(C50)
library(plyr)
library(MASS)
library(caret)## Loading required package: latticelibrary(caretEnsemble)##
## Attaching package: 'caretEnsemble'## The following object is masked from 'package:ggplot2':
##
## autoplotlibrary(dplyr)##
## Attaching package: 'dplyr'## The following object is masked from 'package:MASS':
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## select## The following objects are masked from 'package:plyr':
##
## arrange, count, desc, failwith, id, mutate, rename, summarise,
## summarize## The following object is masked from 'package:randomForest':
##
## combine## The following object is masked from 'package:gridExtra':
##
## combine## The following objects are masked from 'package:data.table':
##
## between, first, last## The following objects are masked from 'package:stats':
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## filter, lag## The following objects are masked from 'package:base':
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## intersect, setdiff, setequal, union2.2.Reading the data
train_set <- read.csv("~/train.csv")
View(train_set)
test_set <- read.csv("~/test.csv")
View(test_set)2.3.Datasets
2.3.1Training dataset
Structure of dataset
str(train_set)## 'data.frame': 550068 obs. of 12 variables:
## $ User_ID : int 1000001 1000001 1000001 1000001 1000002 1000003 1000004 1000004 1000004 1000005 ...
## $ Product_ID : Factor w/ 3631 levels "P00000142","P00000242",..: 673 2377 853 829 2735 1832 1746 3321 3605 2632 ...
## $ Gender : Factor w/ 2 levels "F","M": 1 1 1 1 2 2 2 2 2 2 ...
## $ Age : Factor w/ 7 levels "0-17","18-25",..: 1 1 1 1 7 3 5 5 5 3 ...
## $ Occupation : int 10 10 10 10 16 15 7 7 7 20 ...
## $ City_Category : Factor w/ 3 levels "A","B","C": 1 1 1 1 3 1 2 2 2 1 ...
## $ Stay_In_Current_City_Years: Factor w/ 5 levels "0","1","2","3",..: 3 3 3 3 5 4 3 3 3 2 ...
## $ Marital_Status : int 0 0 0 0 0 0 1 1 1 1 ...
## $ Product_Category_1 : int 3 1 12 12 8 1 1 1 1 8 ...
## $ Product_Category_2 : int NA 6 NA 14 NA 2 8 15 16 NA ...
## $ Product_Category_3 : int NA 14 NA NA NA NA 17 NA NA NA ...
## $ Purchase : int 8370 15200 1422 1057 7969 15227 19215 15854 15686 7871 ...Checking missing data
sapply(train_set, function(x) sum(is.na(x)))## User_ID Product_ID
## 0 0
## Gender Age
## 0 0
## Occupation City_Category
## 0 0
## Stay_In_Current_City_Years Marital_Status
## 0 0
## Product_Category_1 Product_Category_2
## 0 173638
## Product_Category_3 Purchase
## 383247 0summary(train_set)## User_ID Product_ID Gender Age
## Min. :1000001 P00265242: 1880 F:135809 0-17 : 15102
## 1st Qu.:1001516 P00025442: 1615 M:414259 18-25: 99660
## Median :1003077 P00110742: 1612 26-35:219587
## Mean :1003029 P00112142: 1562 36-45:110013
## 3rd Qu.:1004478 P00057642: 1470 46-50: 45701
## Max. :1006040 P00184942: 1440 51-55: 38501
## (Other) :540489 55+ : 21504
## Occupation City_Category Stay_In_Current_City_Years
## Min. : 0.000 A:147720 0 : 74398
## 1st Qu.: 2.000 B:231173 1 :193821
## Median : 7.000 C:171175 2 :101838
## Mean : 8.077 3 : 95285
## 3rd Qu.:14.000 4+: 84726
## Max. :20.000
##
## Marital_Status Product_Category_1 Product_Category_2 Product_Category_3
## Min. :0.0000 Min. : 1.000 Min. : 2.00 Min. : 3.0
## 1st Qu.:0.0000 1st Qu.: 1.000 1st Qu.: 5.00 1st Qu.: 9.0
## Median :0.0000 Median : 5.000 Median : 9.00 Median :14.0
## Mean :0.4097 Mean : 5.404 Mean : 9.84 Mean :12.7
## 3rd Qu.:1.0000 3rd Qu.: 8.000 3rd Qu.:15.00 3rd Qu.:16.0
## Max. :1.0000 Max. :20.000 Max. :18.00 Max. :18.0
## NA's :173638 NA's :383247
## Purchase
## Min. : 12
## 1st Qu.: 5823
## Median : 8047
## Mean : 9264
## 3rd Qu.:12054
## Max. :23961
## 2.3.2.Test Dataset
Structure of dataset
str(test_set)## 'data.frame': 233599 obs. of 11 variables:
## $ User_ID : int 1000004 1000009 1000010 1000010 1000011 1000013 1000013 1000013 1000015 1000022 ...
## $ Product_ID : Factor w/ 3491 levels "P00000142","P00000242",..: 1145 995 2673 1300 520 3241 1400 3438 1459 639 ...
## $ Gender : Factor w/ 2 levels "F","M": 2 2 1 1 1 2 2 2 2 2 ...
## $ Age : Factor w/ 7 levels "0-17","18-25",..: 5 3 4 4 3 5 5 5 3 2 ...
## $ Occupation : int 7 17 1 1 1 1 1 1 7 15 ...
## $ City_Category : Factor w/ 3 levels "A","B","C": 2 3 2 2 3 3 3 3 1 1 ...
## $ Stay_In_Current_City_Years: Factor w/ 5 levels "0","1","2","3",..: 3 1 5 5 2 4 4 4 2 5 ...
## $ Marital_Status : int 1 0 1 1 0 1 1 1 0 0 ...
## $ Product_Category_1 : int 1 3 5 4 4 2 1 2 10 5 ...
## $ Product_Category_2 : int 11 5 14 9 5 3 11 4 13 14 ...
## $ Product_Category_3 : int NA NA NA NA 12 15 15 9 16 NA ...Checking missing data
sapply(test_set, function(x) sum(is.na(x)))## User_ID Product_ID
## 0 0
## Gender Age
## 0 0
## Occupation City_Category
## 0 0
## Stay_In_Current_City_Years Marital_Status
## 0 0
## Product_Category_1 Product_Category_2
## 0 72344
## Product_Category_3
## 162562summary(test_set)## User_ID Product_ID Gender Age
## Min. :1000001 P00265242: 829 F: 57827 0-17 : 6232
## 1st Qu.:1001527 P00112142: 717 M:175772 18-25:42293
## Median :1003070 P00025442: 695 26-35:93428
## Mean :1003029 P00110742: 680 36-45:46711
## 3rd Qu.:1004477 P00046742: 646 46-50:19577
## Max. :1006040 P00184942: 626 51-55:16283
## (Other) :229406 55+ : 9075
## Occupation City_Category Stay_In_Current_City_Years
## Min. : 0.000 A:62524 0 :31318
## 1st Qu.: 2.000 B:98566 1 :82604
## Median : 7.000 C:72509 2 :43589
## Mean : 8.085 3 :40143
## 3rd Qu.:14.000 4+:35945
## Max. :20.000
##
## Marital_Status Product_Category_1 Product_Category_2 Product_Category_3
## Min. :0.0000 Min. : 1.000 Min. : 2.00 Min. : 3.00
## 1st Qu.:0.0000 1st Qu.: 1.000 1st Qu.: 5.00 1st Qu.: 9.00
## Median :0.0000 Median : 5.000 Median : 9.00 Median :14.00
## Mean :0.4101 Mean : 5.277 Mean : 9.85 Mean :12.67
## 3rd Qu.:1.0000 3rd Qu.: 8.000 3rd Qu.:15.00 3rd Qu.:16.00
## Max. :1.0000 Max. :18.000 Max. :18.00 Max. :18.00
## NA's :72344 NA's :1625623.Data Prepartion & Exploratory Data Analysis
Since our data is stored based on product id i.e. if a person buying 10 products then his data will be stored in 10 observation and hence there will be repetition of data.
train_set$User_ID <- as.factor(train_set$User_ID)
useridTable <- table(train_set$User_ID)
useridTable##
## 1000001 1000002 1000003 1000004 1000005 1000006 1000007 1000008 1000009
## 35 77 29 14 106 47 17 77 58
## 1000010 1000011 1000012 1000013 1000014 1000015 1000016 1000017 1000018
## 223 70 11 60 13 117 22 115 180
## 1000019 1000020 1000021 1000022 1000023 1000024 1000025 1000026 1000027
## 145 12 14 156 171 77 44 226 42
## 1000028 1000029 1000030 1000031 1000032 1000033 1000034 1000035 1000036
## 57 52 31 64 24 216 100 111 200
## 1000037 1000039 1000041 1000042 1000043 1000044 1000045 1000046 1000047
## 30 41 16 125 14 120 162 21 14
## 1000048 1000049 1000050 1000051 1000052 1000053 1000054 1000055 1000056
## 338 60 25 25 42 377 24 14 38
## 1000057 1000058 1000059 1000060 1000061 1000062 1000063 1000064 1000065
## 43 261 128 36 20 282 63 16 67
## 1000066 1000067 1000068 1000069 1000070 1000071 1000072 1000073 1000074
## 21 36 43 36 26 16 27 134 29
## 1000075 1000076 1000077 1000078 1000079 1000080 1000081 1000082 1000083
## 111 51 22 86 17 32 51 59 53
## 1000084 1000085 1000086 1000087 1000088 1000089 1000090 1000091 1000092
## 22 19 24 37 33 14 137 24 223
## 1000093 1000094 1000095 1000096 1000097 1000098 1000099 1000100 1000101
## 115 7 56 43 83 13 65 42 66
## 1000102 1000103 1000104 1000105 1000106 1000107 1000108 1000109 1000110
## 87 66 28 41 31 65 14 44 41
## 1000111 1000112 1000113 1000114 1000116 1000117 1000118 1000119 1000120
## 55 30 36 52 55 283 119 57 33
## 1000121 1000122 1000123 1000124 1000125 1000126 1000127 1000128 1000129
## 41 26 352 15 36 30 100 13 85
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## 80 153 45 93 102 39 227 125 37
## 1000139 1000140 1000141 1000142 1000143 1000144 1000145 1000146 1000147
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## 1000148 1000149 1000150 1000151 1000152 1000153 1000154 1000155 1000156
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## 1000157 1000158 1000159 1000160 1000161 1000162 1000163 1000164 1000165
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## 1001035 1001036 1001037 1001038 1001039 1001040 1001041 1001042 1001043
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## 118 28 18 377 180 81 58 82 431
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## 16 234 402 154 65 227 344 14 75
## 1001128 1001129 1001130 1001131 1001132 1001133 1001134 1001135 1001136
## 286 65 159 54 93 93 85 102 28
## 1001137 1001138 1001139 1001140 1001141 1001142 1001143 1001144 1001145
## 299 50 108 195 266 149 29 14 36
## 1001146 1001147 1001148 1001149 1001150 1001151 1001152 1001153 1001154
## 223 178 34 52 752 181 82 50 17
## 1001155 1001156 1001157 1001158 1001159 1001160 1001161 1001162 1001163
## 105 43 13 41 75 61 54 100 38
## 1001164 1001165 1001166 1001167 1001168 1001169 1001170 1001171 1001172
## 267 81 13 42 138 65 146 20 261
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## 1001182 1001183 1001184 1001185 1001186 1001187 1001188 1001189 1001190
## 207 113 155 47 119 21 15 20 17
## 1001191 1001192 1001193 1001194 1001195 1001196 1001197 1001198 1001199
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## 24 37 279 447 70 54 69 30 135
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## 25 235 38 18 27 64 101 34 120
## 1001219 1001220 1001221 1001222 1001223 1001224 1001225 1001226 1001227
## 134 108 132 41 75 338 14 18 76
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## 137 18 148 59 69 34 11 34 17
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## 16 30 50 14 116 362 207 296 337
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## 18 59 13 14 30 77 39 66 146
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## 1001338 1001339 1001340 1001341 1001342 1001343 1001344 1001345 1001346
## 75 115 451 51 51 153 36 112 18
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## 1004938 1004939 1004940 1004941 1004942 1004943 1004944 1004945 1004946
## 14 97 149 15 160 13 14 83 154
## 1004947 1004948 1004949 1004950 1004951 1004952 1004953 1004954 1004955
## 58 288 19 251 178 46 113 79 17
## 1004956 1004957 1004958 1004960 1004961 1004962 1004963 1004964 1004965
## 130 416 257 42 91 136 79 246 72
## 1004966 1004967 1004968 1004969 1004970 1004971 1004972 1004973 1004974
## 31 59 11 19 20 15 189 102 17
## 1004975 1004976 1004977 1004978 1004979 1004980 1004982 1004983 1004984
## 88 33 102 46 498 112 24 85 116
## 1004985 1004986 1004987 1004988 1004989 1004990 1004991 1004992 1004993
## 87 17 42 17 42 30 7 11 29
## 1004994 1004995 1004996 1004997 1004998 1004999 1005000 1005001 1005002
## 68 210 13 30 38 35 216 116 143
## 1005003 1005004 1005005 1005006 1005007 1005008 1005009 1005010 1005011
## 21 25 242 22 15 115 37 34 329
## 1005012 1005013 1005014 1005015 1005016 1005017 1005018 1005019 1005020
## 12 45 77 348 41 19 143 104 52
## 1005021 1005023 1005024 1005025 1005026 1005027 1005028 1005029 1005030
## 103 69 49 66 465 13 15 85 14
## 1005031 1005032 1005033 1005034 1005035 1005036 1005037 1005038 1005039
## 28 60 28 83 137 25 67 124 206
## 1005040 1005041 1005042 1005043 1005044 1005045 1005046 1005047 1005048
## 62 55 205 9 79 21 494 187 49
## 1005049 1005050 1005051 1005052 1005053 1005054 1005055 1005056 1005057
## 117 48 15 14 128 409 26 103 46
## 1005058 1005059 1005060 1005061 1005062 1005063 1005064 1005065 1005066
## 29 80 49 111 13 62 62 148 20
## 1005067 1005068 1005069 1005070 1005071 1005072 1005074 1005075 1005076
## 22 38 18 278 42 32 427 34 61
## 1005077 1005078 1005079 1005080 1005081 1005082 1005083 1005084 1005085
## 238 39 42 47 60 123 152 146 118
## 1005086 1005087 1005088 1005089 1005090 1005091 1005092 1005094 1005095
## 100 167 18 20 254 161 45 33 23
## 1005096 1005097 1005098 1005099 1005100 1005101 1005102 1005103 1005104
## 153 77 109 120 479 33 84 168 32
## 1005105 1005106 1005107 1005108 1005109 1005110 1005111 1005112 1005113
## 52 50 350 215 68 13 475 178 286
## 1005114 1005115 1005116 1005117 1005118 1005119 1005120 1005121 1005122
## 80 21 15 9 69 14 111 107 12
## 1005123 1005124 1005125 1005126 1005127 1005128 1005129 1005131 1005132
## 59 161 18 80 28 55 62 215 38
## 1005133 1005134 1005135 1005136 1005137 1005138 1005139 1005140 1005141
## 35 54 45 123 67 88 73 222 24
## 1005142 1005143 1005144 1005145 1005146 1005147 1005148 1005149 1005150
## 185 58 19 11 18 26 57 18 19
## 1005151 1005152 1005153 1005154 1005155 1005156 1005157 1005158 1005159
## 80 18 258 22 13 183 54 103 12
## 1005160 1005161 1005162 1005163 1005164 1005165 1005166 1005167 1005168
## 16 191 21 292 45 62 68 84 13
## 1005169 1005170 1005171 1005172 1005173 1005174 1005175 1005176 1005177
## 70 42 10 254 75 11 18 25 56
## 1005178 1005179 1005180 1005181 1005182 1005183 1005184 1005185 1005186
## 157 20 71 26 161 55 180 153 43
## 1005187 1005190 1005191 1005192 1005193 1005194 1005195 1005196 1005197
## 72 21 24 15 115 36 43 35 69
## 1005198 1005199 1005200 1005201 1005202 1005203 1005204 1005205 1005206
## 158 24 32 59 20 19 24 218 12
## 1005207 1005208 1005209 1005210 1005212 1005213 1005214 1005215 1005216
## 13 74 80 35 19 21 21 10 12
## 1005217 1005218 1005219 1005220 1005221 1005222 1005223 1005224 1005225
## 82 28 67 390 16 179 134 16 115
## 1005226 1005228 1005229 1005230 1005231 1005232 1005233 1005234 1005235
## 45 14 36 75 18 74 60 109 21
## 1005236 1005237 1005238 1005239 1005240 1005241 1005242 1005243 1005244
## 71 33 11 164 23 59 70 16 25
## 1005245 1005246 1005247 1005248 1005249 1005250 1005251 1005252 1005253
## 15 68 20 61 199 168 95 37 78
## 1005254 1005255 1005256 1005257 1005258 1005259 1005260 1005261 1005262
## 47 92 467 47 14 23 114 61 33
## 1005263 1005264 1005265 1005266 1005267 1005269 1005270 1005271 1005272
## 67 85 161 21 54 193 58 224 54
## 1005273 1005274 1005275 1005276 1005277 1005278 1005279 1005280 1005281
## 179 12 56 47 179 96 31 16 146
## 1005282 1005283 1005284 1005285 1005286 1005287 1005288 1005290 1005291
## 182 176 39 29 109 125 23 24 41
## 1005292 1005293 1005294 1005295 1005296 1005297 1005298 1005299 1005300
## 22 40 79 13 38 21 26 28 170
## 1005301 1005302 1005303 1005304 1005305 1005306 1005307 1005308 1005309
## 79 136 26 21 136 345 37 23 15
## 1005310 1005311 1005312 1005313 1005314 1005315 1005316 1005317 1005318
## 55 27 417 49 13 20 20 295 57
## 1005319 1005320 1005321 1005322 1005323 1005324 1005325 1005326 1005327
## 230 12 167 54 145 91 29 67 100
## 1005328 1005329 1005330 1005332 1005333 1005334 1005335 1005336 1005337
## 24 307 36 39 472 33 91 30 97
## 1005338 1005339 1005340 1005341 1005342 1005343 1005344 1005345 1005346
## 50 62 113 69 61 17 121 197 27
## 1005347 1005348 1005349 1005350 1005351 1005352 1005353 1005355 1005357
## 144 25 140 127 12 124 63 113 28
## 1005358 1005359 1005360 1005361 1005362 1005363 1005364 1005365 1005366
## 15 291 23 115 56 41 220 168 70
## 1005367 1005368 1005369 1005370 1005371 1005372 1005373 1005374 1005375
## 612 53 142 15 198 20 18 25 21
## 1005376 1005377 1005378 1005379 1005380 1005381 1005382 1005383 1005384
## 134 22 110 22 15 17 35 20 26
## 1005385 1005386 1005387 1005388 1005389 1005390 1005391 1005392 1005393
## 28 86 438 15 80 124 7 13 123
## 1005394 1005395 1005396 1005397 1005398 1005399 1005400 1005401 1005402
## 475 67 43 201 16 33 17 85 53
## 1005403 1005404 1005406 1005407 1005408 1005409 1005410 1005411 1005412
## 93 118 69 15 59 11 25 14 149
## 1005413 1005414 1005415 1005416 1005417 1005418 1005419 1005420 1005421
## 125 51 32 26 32 124 48 101 40
## 1005422 1005423 1005424 1005425 1005426 1005427 1005428 1005429 1005430
## 104 64 83 27 169 46 217 69 81
## 1005431 1005432 1005433 1005434 1005435 1005436 1005437 1005438 1005439
## 53 100 388 144 29 23 81 50 12
## 1005440 1005441 1005442 1005443 1005444 1005445 1005446 1005447 1005448
## 25 108 43 225 24 80 59 78 207
## 1005449 1005450 1005451 1005452 1005453 1005454 1005455 1005456 1005457
## 68 178 45 162 136 33 84 23 99
## 1005458 1005459 1005460 1005461 1005462 1005463 1005464 1005465 1005466
## 227 101 49 36 25 156 61 16 120
## 1005467 1005468 1005469 1005470 1005471 1005472 1005473 1005474 1005475
## 17 222 64 60 12 177 68 26 216
## 1005476 1005477 1005478 1005479 1005480 1005481 1005482 1005483 1005484
## 16 15 20 60 84 29 201 134 44
## 1005485 1005486 1005487 1005488 1005489 1005490 1005491 1005492 1005493
## 68 22 26 139 40 77 116 239 308
## 1005494 1005495 1005496 1005497 1005498 1005499 1005500 1005501 1005502
## 39 19 44 75 29 41 38 309 67
## 1005503 1005504 1005505 1005506 1005507 1005508 1005509 1005510 1005511
## 28 331 43 28 29 46 63 35 383
## 1005512 1005513 1005514 1005515 1005516 1005517 1005518 1005519 1005520
## 122 34 25 29 82 179 65 196 108
## 1005521 1005522 1005523 1005524 1005525 1005526 1005527 1005528 1005529
## 26 307 202 90 13 162 16 34 16
## 1005530 1005531 1005532 1005533 1005534 1005535 1005536 1005537 1005538
## 365 85 54 11 38 100 343 37 99
## 1005539 1005540 1005541 1005542 1005543 1005544 1005545 1005546 1005547
## 271 43 18 29 190 27 85 23 87
## 1005548 1005549 1005550 1005551 1005552 1005553 1005554 1005555 1005556
## 105 19 363 30 34 144 55 310 161
## 1005557 1005558 1005559 1005560 1005561 1005562 1005563 1005564 1005565
## 119 138 26 48 118 46 20 24 107
## 1005566 1005567 1005568 1005570 1005571 1005572 1005573 1005574 1005575
## 72 228 195 176 58 44 91 104 154
## 1005576 1005577 1005578 1005579 1005580 1005581 1005582 1005583 1005584
## 178 20 51 17 223 22 81 70 47
## 1005585 1005586 1005587 1005588 1005589 1005590 1005591 1005592 1005593
## 119 18 33 26 21 20 29 70 98
## 1005594 1005595 1005596 1005597 1005598 1005599 1005600 1005601 1005602
## 70 122 115 113 76 121 157 46 46
## 1005604 1005605 1005606 1005607 1005608 1005609 1005610 1005611 1005612
## 137 419 16 92 7 24 24 23 27
## 1005613 1005615 1005616 1005617 1005618 1005619 1005620 1005621 1005622
## 39 183 77 36 63 56 138 261 53
## 1005623 1005624 1005625 1005626 1005627 1005628 1005629 1005630 1005631
## 155 63 90 101 460 10 52 152 42
## 1005632 1005633 1005634 1005635 1005636 1005637 1005638 1005639 1005640
## 37 21 167 15 482 24 61 24 19
## 1005641 1005642 1005643 1005644 1005645 1005646 1005647 1005648 1005649
## 83 30 573 42 154 59 87 70 122
## 1005650 1005651 1005652 1005653 1005654 1005655 1005656 1005657 1005658
## 166 23 58 126 163 64 18 106 70
## 1005659 1005660 1005661 1005662 1005663 1005664 1005665 1005667 1005668
## 133 18 79 46 138 158 29 199 52
## 1005669 1005670 1005671 1005672 1005673 1005674 1005675 1005676 1005677
## 31 44 58 15 37 30 321 92 137
## 1005678 1005679 1005680 1005681 1005682 1005683 1005684 1005685 1005686
## 76 98 92 20 344 67 282 22 298
## 1005687 1005688 1005689 1005690 1005691 1005692 1005693 1005694 1005695
## 48 56 280 15 74 32 23 15 18
## 1005696 1005697 1005698 1005699 1005700 1005701 1005702 1005703 1005704
## 13 16 187 169 23 68 74 43 52
## 1005705 1005708 1005709 1005710 1005711 1005712 1005713 1005714 1005716
## 86 59 77 43 25 50 15 43 120
## 1005717 1005718 1005719 1005720 1005721 1005722 1005724 1005725 1005726
## 225 102 15 190 23 210 53 13 90
## 1005727 1005728 1005729 1005730 1005731 1005732 1005733 1005734 1005735
## 28 178 37 43 39 22 124 126 11
## 1005736 1005737 1005738 1005739 1005740 1005741 1005742 1005743 1005744
## 59 51 295 27 57 32 24 14 239
## 1005745 1005746 1005747 1005748 1005749 1005750 1005751 1005752 1005753
## 38 45 315 35 231 68 51 74 50
## 1005754 1005755 1005756 1005757 1005758 1005759 1005760 1005761 1005762
## 178 289 19 58 78 330 18 136 158
## 1005763 1005764 1005765 1005766 1005767 1005768 1005769 1005770 1005771
## 400 35 135 56 201 43 82 83 141
## 1005772 1005773 1005774 1005775 1005776 1005777 1005778 1005779 1005780
## 17 13 28 97 100 76 34 215 140
## 1005781 1005782 1005783 1005784 1005785 1005786 1005787 1005788 1005789
## 13 63 22 14 75 181 236 465 28
## 1005790 1005792 1005793 1005794 1005795 1005796 1005797 1005798 1005799
## 48 135 18 176 729 17 73 68 20
## 1005800 1005801 1005802 1005803 1005804 1005805 1005806 1005807 1005808
## 14 87 22 38 11 90 115 36 42
## 1005809 1005810 1005811 1005812 1005813 1005814 1005815 1005816 1005817
## 118 7 165 499 54 17 45 23 14
## 1005818 1005819 1005820 1005821 1005822 1005823 1005824 1005825 1005826
## 18 17 26 36 41 62 307 66 127
## 1005827 1005828 1005829 1005830 1005831 1005832 1005833 1005834 1005835
## 62 14 46 26 727 39 53 17 17
## 1005836 1005837 1005838 1005839 1005840 1005841 1005842 1005843 1005844
## 31 307 130 13 130 189 30 121 46
## 1005846 1005847 1005848 1005849 1005850 1005851 1005852 1005853 1005854
## 35 222 243 23 33 87 192 83 234
## 1005855 1005856 1005857 1005858 1005859 1005860 1005861 1005862 1005863
## 30 38 114 119 19 36 104 103 13
## 1005864 1005865 1005866 1005867 1005868 1005869 1005871 1005872 1005873
## 23 55 15 17 40 46 56 138 20
## 1005874 1005875 1005876 1005877 1005878 1005879 1005880 1005881 1005882
## 185 84 12 13 339 32 425 159 25
## 1005883 1005884 1005885 1005886 1005887 1005888 1005889 1005890 1005891
## 14 49 72 271 49 341 82 191 35
## 1005892 1005893 1005894 1005895 1005896 1005897 1005898 1005899 1005900
## 61 91 75 33 124 34 16 11 63
## 1005901 1005902 1005903 1005904 1005905 1005906 1005907 1005908 1005909
## 18 50 48 8 87 65 28 120 17
## 1005910 1005911 1005912 1005913 1005914 1005915 1005916 1005917 1005918
## 58 97 60 50 20 160 378 127 13
## 1005919 1005920 1005921 1005922 1005923 1005924 1005925 1005926 1005927
## 22 77 24 139 20 92 118 37 121
## 1005928 1005929 1005930 1005931 1005932 1005933 1005934 1005935 1005936
## 27 63 51 42 15 25 22 22 21
## 1005937 1005938 1005939 1005940 1005941 1005942 1005943 1005944 1005945
## 61 115 59 29 18 103 39 15 19
## 1005946 1005947 1005948 1005949 1005950 1005951 1005952 1005953 1005954
## 55 74 221 106 263 30 72 58 557
## 1005955 1005956 1005957 1005958 1005959 1005960 1005961 1005962 1005963
## 206 167 138 70 42 109 173 16 97
## 1005964 1005965 1005966 1005967 1005968 1005969 1005970 1005971 1005972
## 334 23 39 13 18 55 22 95 242
## 1005973 1005974 1005975 1005976 1005977 1005978 1005979 1005980 1005981
## 20 128 28 67 30 228 20 118 161
## 1005982 1005983 1005984 1005985 1005986 1005987 1005988 1005989 1005990
## 65 19 16 50 69 31 44 50 168
## 1005991 1005992 1005993 1005994 1005995 1005996 1005997 1005998 1005999
## 54 34 18 42 103 329 29 95 18
## 1006000 1006001 1006002 1006003 1006004 1006005 1006006 1006007 1006008
## 281 120 182 150 34 50 69 175 20
## 1006009 1006010 1006011 1006012 1006013 1006014 1006015 1006016 1006017
## 32 177 105 16 75 59 32 504 18
## 1006018 1006019 1006020 1006021 1006022 1006023 1006024 1006025 1006026
## 82 54 23 69 27 88 60 151 47
## 1006027 1006028 1006029 1006030 1006031 1006032 1006033 1006034 1006035
## 23 28 17 59 31 55 36 12 152
## 1006036 1006037 1006038 1006039 1006040
## 514 122 12 74 180train_set$Product_ID <- as.factor(train_set$Product_ID)
productidTable <- table(train_set$Product_ID)
productidTable##
## P00000142 P00000242 P00000342 P00000442 P00000542 P00000642 P00000742
## 1152 376 244 92 149 512 241
## P00000842 P00000942 P00001042 P00001142 P00001242 P00001342 P00001442
## 36 55 503 578 100 62 85
## P00001542 P00001642 P00001742 P00001842 P00001942 P00002042 P00002142
## 69 354 461 76 211 93 735
## P00002242 P00002342 P00002442 P00002542 P00002642 P00002742 P00002842
## 198 67 330 539 63 40 84
## P00002942 P00003042 P00003142 P00003242 P00003342 P00003442 P00003542
## 224 37 87 844 2 968 37
## P00003642 P00003742 P00003842 P00003942 P00004042 P00004142 P00004242
## 512 4 16 749 13 123 113
## P00004342 P00004442 P00004542 P00004642 P00004742 P00004842 P00004942
## 95 147 279 90 597 210 19
## P00005042 P00005142 P00005242 P00005342 P00005442 P00005542 P00005642
## 969 233 4 27 25 4 47
## P00005742 P00005842 P00005942 P00006042 P00006142 P00006242 P00006342
## 277 5 181 36 293 61 39
## P00006442 P00006542 P00006642 P00006742 P00006842 P00006942 P00007042
## 89 57 2 28 188 498 44
## P00007142 P00007242 P00007342 P00007442 P00007542 P00007642 P00007742
## 48 125 67 5 79 18 31
## P00007842 P00007942 P00008042 P00008142 P00008242 P00008342 P00008442
## 65 28 89 51 19 15 114
## P00008542 P00008642 P00008742 P00008842 P00008942 P00009042 P00009142
## 127 47 108 109 1 44 47
## P00009242 P00009342 P00009442 P00009542 P00009642 P00009742 P00010042
## 112 351 7 28 3 26 26
## P00010142 P00010242 P00010342 P00010442 P00010542 P00010642 P00010742
## 21 350 208 8 140 4 1350
## P00010842 P00010942 P00011042 P00011142 P00011242 P00011342 P00011442
## 675 305 32 6 27 41 36
## P00011542 P00011642 P00011742 P00011842 P00011942 P00012042 P00012142
## 5 3 20 91 62 5 188
## P00012242 P00012342 P00012442 P00012542 P00012742 P00012842 P00012942
## 51 1 7 5 9 43 1
## P00013142 P00013242 P00013342 P00013442 P00013542 P00013642 P00013742
## 71 7 3 1 1 67 357
## P00013842 P00013942 P00014042 P00014142 P00014242 P00014342 P00014442
## 1 236 194 9 105 10 24
## P00014542 P00014642 P00014742 P00014842 P00014942 P00015042 P00015142
## 672 319 38 412 94 48 32
## P00015242 P00015342 P00015442 P00015542 P00015642 P00015742 P00015842
## 104 147 58 295 419 241 267
## P00015942 P00016042 P00016142 P00016242 P00016342 P00016442 P00016542
## 205 447 29 2 191 63 159
## P00016642 P00016742 P00016842 P00016942 P00017042 P00017142 P00017242
## 51 266 315 43 128 129 67
## P00017342 P00017442 P00017542 P00017642 P00017742 P00017842 P00017942
## 11 27 244 51 35 17 13
## P00018042 P00018142 P00018242 P00018342 P00018442 P00018542 P00018642
## 303 110 60 108 9 49 43
## P00018742 P00018842 P00018942 P00019042 P00019142 P00019242 P00019342
## 2 95 120 75 316 9 330
## P00019442 P00019542 P00019642 P00019742 P00019842 P00019942 P00020042
## 60 5 18 13 113 107 48
## P00020142 P00020242 P00020342 P00020442 P00020542 P00020642 P00020742
## 49 117 358 31 20 22 7
## P00020842 P00020942 P00021042 P00021142 P00021242 P00021342 P00021442
## 64 15 80 187 40 113 10
## P00021542 P00021642 P00021742 P00021842 P00021942 P00022042 P00022142
## 17 180 776 200 140 1 75
## P00022242 P00022342 P00022442 P00022542 P00022642 P00022742 P00022842
## 10 68 186 370 187 127 89
## P00022942 P00023042 P00023142 P00023242 P00023342 P00023442 P00023542
## 527 184 155 18 92 20 14
## P00023642 P00023742 P00023842 P00023942 P00024042 P00024142 P00024242
## 46 5 6 6 394 259 79
## P00024342 P00024442 P00024542 P00024642 P00024742 P00024842 P00024942
## 150 15 10 190 415 18 41
## P00025042 P00025142 P00025242 P00025342 P00025442 P00025542 P00025642
## 117 56 73 55 1615 167 98
## P00025742 P00025842 P00025942 P00026042 P00026142 P00026242 P00026342
## 13 2 437 379 74 32 28
## P00026442 P00026542 P00026642 P00026742 P00026842 P00026942 P00027042
## 37 34 210 134 10 38 124
## P00027142 P00027242 P00027342 P00027442 P00027542 P00027642 P00027742
## 87 22 22 120 134 105 8
## P00027842 P00027942 P00028042 P00028142 P00028242 P00028342 P00028442
## 1 7 376 91 95 28 418
## P00028542 P00028642 P00028742 P00028842 P00028942 P00029042 P00029142
## 498 18 12 1197 14 14 75
## P00029242 P00029342 P00029442 P00029542 P00029642 P00029742 P00029842
## 369 26 45 218 22 76 187
## P00029942 P00030042 P00030142 P00030242 P00030442 P00030542 P00030642
## 115 109 26 2 55 29 156
## P00030742 P00030842 P00030942 P00031042 P00031142 P00031242 P00031342
## 77 592 363 1200 143 19 36
## P00031442 P00031542 P00031642 P00031742 P00031842 P00031942 P00032042
## 104 16 32 32 191 82 555
## P00032142 P00032242 P00032342 P00032442 P00032542 P00032642 P00032742
## 78 13 84 231 65 21 12
## P00032842 P00032942 P00033042 P00033142 P00033242 P00033342 P00033442
## 384 118 386 43 17 273 21
## P00033542 P00033642 P00033742 P00033842 P00033942 P00034042 P00034142
## 412 246 68 27 233 582 276
## P00034242 P00034342 P00034442 P00034542 P00034642 P00034742 P00034842
## 145 67 399 39 136 1212 682
## P00034942 P00035042 P00035142 P00035242 P00035342 P00035442 P00035542
## 72 12 65 153 134 34 602
## P00035642 P00035742 P00035842 P00035942 P00036042 P00036142 P00036242
## 36 135 637 435 26 216 129
## P00036342 P00036442 P00036542 P00036642 P00036742 P00036842 P00036942
## 223 150 107 9 185 884 81
## P00037042 P00037142 P00037242 P00037342 P00037442 P00037542 P00037642
## 221 753 111 152 152 25 5
## P00037742 P00037842 P00037942 P00038042 P00038142 P00038242 P00038342
## 21 58 47 10 28 25 13
## P00038442 P00038542 P00038642 P00038742 P00038842 P00039142 P00039242
## 63 14 1 7 1 3 111
## P00039342 P00039442 P00039542 P00039642 P00039742 P00039842 P00039942
## 7 107 35 22 252 8 167
## P00040042 P00040142 P00040242 P00040342 P00040442 P00040542 P00040642
## 147 51 94 49 115 13 73
## P00040742 P00040842 P00040942 P00041042 P00041142 P00041242 P00041342
## 175 52 35 127 40 57 155
## P00041442 P00041542 P00041642 P00041742 P00041842 P00041942 P00042042
## 98 140 102 12 188 220 22
## P00042142 P00042242 P00042342 P00042442 P00042542 P00042742 P00042842
## 388 280 80 31 78 504 363
## P00042942 P00043042 P00043142 P00043242 P00043342 P00043442 P00043542
## 350 12 47 53 132 13 132
## P00043642 P00043742 P00043842 P00043942 P00044042 P00044142 P00044242
## 38 117 52 18 37 450 147
## P00044342 P00044442 P00044542 P00044642 P00044742 P00044842 P00044942
## 59 1111 46 69 20 47 14
## P00045042 P00045142 P00045242 P00045342 P00045442 P00045542 P00045642
## 26 115 56 304 26 205 40
## P00045742 P00045842 P00045942 P00046042 P00046142 P00046242 P00046342
## 20 328 22 88 524 160 21
## P00046442 P00046542 P00046642 P00046742 P00046842 P00046942 P00047042
## 112 10 79 1438 161 92 46
## P00047142 P00047242 P00047342 P00047442 P00047542 P00047642 P00047742
## 19 276 30 28 27 55 94
## P00047842 P00047942 P00048042 P00048142 P00048242 P00048342 P00048442
## 117 200 165 204 26 21 349
## P00048542 P00048642 P00048742 P00048842 P00048942 P00049042 P00049142
## 30 27 446 63 87 8 87
## P00049242 P00049342 P00049442 P00049542 P00049642 P00049742 P00049842
## 36 65 171 315 331 26 80
## P00049942 P00050042 P00050142 P00050242 P00050342 P00050442 P00050542
## 105 37 184 239 135 213 80
## P00050642 P00050742 P00050842 P00050942 P00051042 P00051142 P00051242
## 104 205 56 41 60 259 13
## P00051342 P00051442 P00051542 P00051642 P00051742 P00051842 P00051942
## 4 1249 40 413 1 189 217
## P00052042 P00052142 P00052242 P00052342 P00052442 P00052542 P00052642
## 107 146 160 30 116 178 547
## P00052742 P00052842 P00052942 P00053042 P00053142 P00053242 P00053342
## 89 982 114 360 72 1 198
## P00053442 P00053542 P00053642 P00053742 P00053842 P00053942 P00054042
## 67 70 81 93 567 293 296
## P00054142 P00054242 P00054342 P00054442 P00054542 P00054642 P00054742
## 12 351 73 1 83 2 4
## P00054842 P00054942 P00055042 P00055142 P00055242 P00055342 P00055442
## 7 270 11 28 32 5 9
## P00055542 P00055642 P00055742 P00055842 P00055942 P00056042 P00056142
## 15 58 10 17 2 14 88
## P00056242 P00056342 P00056442 P00056542 P00056642 P00056742 P00056842
## 63 1 28 1 1 31 70
## P00057042 P00057242 P00057342 P00057442 P00057542 P00057642 P00057742
## 11 228 359 627 730 1470 823
## P00057842 P00057942 P00058042 P00058142 P00058242 P00058342 P00058442
## 1 785 1422 404 594 281 552
## P00058542 P00058642 P00058742 P00058942 P00059042 P00059142 P00059242
## 4 134 16 20 28 122 49
## P00059342 P00059442 P00059542 P00059642 P00059742 P00059842 P00059942
## 1 1406 28 356 121 43 45
## P00060042 P00060142 P00060242 P00060342 P00060442 P00060542 P00060642
## 5 31 150 6 5 18 24
## P00060742 P00060842 P00060942 P00061042 P00061142 P00061242 P00061342
## 1 1 13 68 259 5 44
## P00061442 P00061542 P00061742 P00061842 P00061942 P00062042 P00062142
## 8 11 42 70 11 41 39
## P00062342 P00062442 P00062642 P00062742 P00062842 P00062942 P00063042
## 1 1 13 258 847 7 50
## P00063242 P00063342 P00063442 P00063542 P00063642 P00063742 P00063842
## 5 322 1 20 1 1 12
## P00064042 P00064142 P00064242 P00064342 P00064442 P00064542 P00064642
## 288 70 163 5 19 1 28
## P00064742 P00064842 P00064942 P00065042 P00065142 P00065242 P00065342
## 19 12 32 270 1 313 214
## P00065442 P00065542 P00065642 P00065742 P00065842 P00065942 P00066042
## 162 1 37 6 3 1 15
## P00066142 P00066242 P00066342 P00066442 P00066542 P00066642 P00066742
## 2 114 1 63 53 75 72
## P00066842 P00066942 P00067042 P00067142 P00067342 P00067442 P00067542
## 3 43 4 76 18 3 22
## P00067642 P00067742 P00067842 P00067942 P00068042 P00068142 P00068242
## 22 2 148 371 76 29 25
## P00068342 P00068442 P00068542 P00068642 P00068742 P00068842 P00068942
## 1 90 28 16 1 17 135
## P00069042 P00069142 P00069242 P00069342 P00069442 P00069542 P00069642
## 227 14 228 45 3 128 1
## P00069742 P00069842 P00069942 P00070042 P00070142 P00070242 P00070342
## 1 28 4 743 1 58 598
## P00070442 P00070542 P00070642 P00070742 P00070842 P00070942 P00071042
## 156 173 111 53 3 379 19
## P00071142 P00071242 P00071342 P00071442 P00071542 P00071642 P00071742
## 26 227 3 737 10 16 7
## P00071842 P00071942 P00072042 P00072142 P00072242 P00072342 P00072442
## 3 2 1 1 33 20 70
## P00072542 P00072742 P00072842 P00072942 P00073042 P00073142 P00073242
## 160 34 86 69 15 6 6
## P00073342 P00073442 P00073542 P00073642 P00073742 P00073842 P00073942
## 1 3 2 422 30 936 53
## P00074042 P00074142 P00074242 P00074342 P00074442 P00074542 P00074642
## 80 198 211 415 248 1 517
## P00074742 P00074842 P00075042 P00075142 P00075242 P00075342 P00075442
## 1 1 1 6 2 110 182
## P00075542 P00075642 P00075742 P00075842 P00075942 P00076042 P00076142
## 351 63 233 34 121 208 31
## P00076242 P00076342 P00076442 P00076542 P00076642 P00076742 P00076942
## 6 16 51 71 5 28 5
## P00077042 P00077142 P00077242 P00077342 P00077442 P00077542 P00077742
## 97 2 1 1 13 1 25
## P00077842 P00077942 P00078042 P00078142 P00078242 P00078342 P00078442
## 75 155 10 316 44 2 59
## P00078542 P00078642 P00078742 P00078842 P00078942 P00079042 P00079142
## 139 118 282 72 24 12 89
## P00079342 P00079442 P00079542 P00079642 P00079742 P00079842 P00079942
## 2 17 5 84 269 18 67
## P00080042 P00080142 P00080242 P00080342 P00080442 P00080542 P00080642
## 336 2 4 1203 1 7 150
## P00080742 P00080842 P00080942 P00081142 P00081242 P00081342 P00081442
## 17 8 9 337 13 1 28
## P00081542 P00081642 P00081742 P00081842 P00081942 P00082042 P00082242
## 25 1 5 28 10 13 137
## P00082342 P00082442 P00082542 P00082742 P00082842 P00082942 P00083042
## 167 38 30 22 54 2 25
## P00083142 P00083242 P00083342 P00083442 P00083542 P00083642 P00083742
## 1 102 82 35 1 26 49
## P00083842 P00083942 P00084042 P00084142 P00084242 P00084342 P00084442
## 309 419 199 102 381 497 564
## P00084542 P00084642 P00084742 P00084842 P00084942 P00085042 P00085142
## 202 103 62 741 230 432 177
## P00085242 P00085342 P00085442 P00085542 P00085642 P00085742 P00085842
## 900 552 341 197 236 38 96
## P00085942 P00086042 P00086142 P00086242 P00086342 P00086442 P00086542
## 963 624 167 273 627 966 5
## P00086642 P00086742 P00086842 P00086942 P00087042 P00087142 P00087242
## 216 39 227 53 240 111 104
## P00087342 P00087442 P00087542 P00087642 P00087742 P00087842 P00087942
## 222 171 44 66 21 102 6
## P00088042 P00088142 P00088242 P00088342 P00088442 P00088542 P00088642
## 205 48 172 103 52 147 93
## P00088742 P00088842 P00088942 P00089042 P00089142 P00089242 P00089342
## 107 74 131 153 209 150 402
## P00089442 P00089542 P00089642 P00089742 P00089842 P00089942 P00090042
## 201 211 26 20 6 34 9
## P00090142 P00090242 P00090342 P00090442 P00090542 P00090642 P00090742
## 15 7 33 31 153 4 8
## P00090842 P00090942 P00091042 P00091142 P00091242 P00091342 P00091442
## 392 578 21 151 5 45 4
## P00091542 P00091642 P00091742 P00091842 P00091942 P00092042 P00092142
## 1 29 1 61 2 2 46
## P00092242 P00092342 P00092442 P00092542 P00092642 P00092842 P00092942
## 16 2 123 22 44 28 104
## P00093042 P00093142 P00093242 P00093342 P00093442 P00093542 P00093642
## 10 6 254 137 17 57 156
## P0009842 P0009942 P00100042 P00100142 P00100342 P00100442 P00100542
## 68 130 19 64 2 729 12
## P00100642 P00100742 P00100842 P00100942 P00101042 P00101142 P00101242
## 428 205 722 513 237 71 87
## P00101342 P00101442 P00101542 P00101642 P00101742 P00101842 P00101942
## 365 67 208 7 396 644 616
## P00102042 P00102142 P00102242 P00102342 P00102442 P00102542 P00102642
## 190 410 381 683 305 169 1246
## P00102742 P00102842 P00102942 P00103042 P00103142 P00103242 P00103342
## 2 103 232 571 3 228 281
## P00103442 P00103542 P00103642 P00103742 P00103842 P00103942 P00104042
## 24 3 50 18 23 20 1
## P00104142 P00104242 P00104342 P00104442 P00104542 P00104642 P00104742
## 6 8 1 13 238 8 21
## P00104842 P00104942 P00105042 P00105142 P00105242 P00105342 P00105442
## 224 209 168 917 98 487 125
## P00105542 P00105642 P00105842 P00105942 P00106042 P00106142 P00106342
## 108 125 2 221 905 4 1
## P00106442 P00106542 P00106642 P00106742 P00106842 P00106942 P00107042
## 6 2 151 491 2 81 17
## P00107142 P00107242 P00107342 P00107442 P00107542 P00107642 P00107742
## 15 4 5 1 48 56 12
## P00107842 P00107942 P00108042 P00108142 P00108242 P00108342 P00108442
## 1 1 47 10 16 10 237
## P00108542 P00108642 P00108742 P00108842 P00108942 P00109042 P00109142
## 350 105 2 201 70 78 115
## P00109242 P00109342 P00109442 P00109542 P00109642 P00109742 P00109842
## 458 23 7 563 47 249 320
## P00109942 P00110042 P00110142 P00110242 P00110342 P00110442 P00110542
## 76 357 152 74 121 59 958
## P00110642 P00110742 P00110842 P00110942 P00111042 P00111142 P00111242
## 106 1612 1281 1360 503 1015 435
## P00111342 P00111442 P00111542 P00111642 P00111742 P00111842 P00111942
## 36 323 445 119 689 514 646
## P00112042 P00112142 P00112242 P00112342 P00112442 P00112542 P00112642
## 91 1562 122 289 923 1131 509
## P00112742 P00112842 P00112942 P00113042 P00113142 P00113242 P00113342
## 45 184 122 670 759 933 675
## P00113442 P00113542 P00113642 P00113742 P00113842 P00113942 P00114042
## 254 212 747 129 136 371 708
## P00114142 P00114242 P00114342 P00114442 P00114542 P00114642 P00114742
## 403 26 542 553 378 49 182
## P00114842 P00114942 P00115042 P00115142 P00115242 P00115342 P00115442
## 199 1178 34 593 181 372 198
## P00115542 P00115642 P00115742 P00115842 P00115942 P00116042 P00116142
## 478 717 288 405 530 140 642
## P00116242 P00116342 P00116442 P00116542 P00116642 P00116742 P00116842
## 286 258 24 318 258 606 979
## P00116942 P00117042 P00117142 P00117242 P00117342 P00117442 P00117542
## 167 298 374 380 135 1260 550
## P00117642 P00117742 P00117842 P00117942 P00118042 P00118142 P00118242
## 395 161 375 1364 335 347 82
## P00118342 P00118442 P00118542 P00118642 P00118742 P00118842 P00118942
## 249 407 495 186 643 104 151
## P00119042 P00119142 P00119242 P00119342 P00119442 P00119542 P00119642
## 110 518 242 308 444 138 391
## P00119742 P00119842 P00119942 P00120042 P00120142 P00120242 P00120342
## 617 98 114 902 327 348 629
## P00120442 P00120542 P00120642 P00120742 P00120842 P00120942 P00121042
## 139 299 244 230 404 176 199
## P00121142 P00121242 P00121342 P00121442 P00121542 P00121642 P00121742
## 442 170 758 57 80 853 24
## P00121842 P00121942 P00122042 P00122142 P00122342 P00122442 P00122542
## 4 16 4 2 15 518 290
## P00122642 P00122742 P00122842 P00122942 P00123042 P00123142 P00123242
## 17 30 12 13 26 202 12
## P00123342 P00123442 P00123542 P00123642 P00123742 P00123842 P00123942
## 19 61 29 65 405 204 12
## P00124042 P00124142 P00124242 P00124342 P00124442 P00124542 P00124642
## 27 30 345 105 37 151 393
## P00124742 P00124842 P00124942 P00125042 P00125142 P00125242 P00125342
## 202 314 92 251 132 10 231
## P00125442 P00125542 P00125642 P00125742 P00125842 P00125942 P00126042
## 18 55 103 151 42 725 312
## P00126142 P00126242 P00126342 P00126442 P00126542 P00126642 P00126742
## 643 134 1 87 48 55 1
## P00126842 P00126942 P00127042 P00127142 P00127242 P00127342 P00127442
## 29 55 203 3 601 438 536
## P00127542 P00127642 P00127742 P00127842 P00127942 P00128042 P00128142
## 347 762 495 609 529 306 206
## P00128242 P00128342 P00128442 P00128542 P00128642 P00128742 P00128942
## 466 174 64 6 2 245 936
## P00129042 P00129142 P00129242 P00129342 P00129442 P00129542 P00129642
## 197 121 70 566 119 752 773
## P00129742 P00129842 P00129942 P00130042 P00130142 P00130242 P00130342
## 156 598 38 33 56 80 36
## P00130442 P00130542 P00130642 P00130742 P00130842 P00130942 P00131042
## 253 12 485 549 201 48 210
## P00131142 P00131242 P00131342 P00131442 P00131542 P00131642 P00131742
## 14 30 87 15 151 19 77
## P00131842 P00131942 P00132042 P00132142 P00132242 P00132342 P00132442
## 4 14 142 7 84 12 31
## P00132542 P00132642 P00132742 P00132842 P00132942 P00133042 P00133142
## 35 170 1 126 62 1 2
## P00133242 P00133342 P00133442 P00133542 P00133642 P00133742 P00133842
## 4 13 173 22 41 253 37
## P00133942 P00134042 P00134142 P00134242 P00134342 P00134442 P00134542
## 6 72 97 25 247 6 81
## P00134642 P00134742 P00134842 P00134942 P00135042 P00135142 P00135242
## 45 12 28 148 13 216 51
## P00135342 P00135442 P00135542 P00135642 P00135742 P00135842 P00135942
## 116 15 81 63 410 80 1
## P00136042 P00136142 P00136242 P00136342 P00136442 P00136542 P00136642
## 2 25 24 87 43 287 16
## P00136742 P00136842 P00136942 P00137042 P00137142 P00137242 P00137342
## 306 136 30 85 65 360 4
## P00137442 P00137542 P00137642 P00137742 P00137842 P00137942 P00138042
## 89 153 25 17 8 6 27
## P00138142 P00138242 P00138342 P00138442 P00138542 P00138642 P00138742
## 14 27 81 215 600 9 7
## P00138842 P00138942 P00139042 P00139142 P00139242 P00139442 P00139542
## 94 9 21 18 8 10 421
## P00139642 P00139742 P00139842 P00139942 P00140042 P00140142 P00140242
## 2 104 29 660 193 3 6
## P00140342 P00140442 P00140542 P00140642 P00140742 P00140942 P00141042
## 4 29 23 45 796 17 3
## P00141142 P00141242 P00141342 P00141442 P00141542 P00141642 P00141742
## 4 15 3 49 216 3 7
## P00141842 P00141942 P00142042 P00142142 P00142242 P00142342 P00142442
## 130 129 4 708 38 27 17
## P00142542 P00142642 P00142742 P00142842 P00142942 P00143042 P00143142
## 1 10 37 18 504 3 70
## P00143242 P00143342 P00143442 P00143542 P00143642 P00143742 P00143842
## 1 204 1 2 331 24 22
## P00143942 P00144042 P00144142 P00144242 P00144342 P00144442 P00144542
## 9 257 3 471 18 24 12
## P00144642 P00144742 P00144842 P00144942 P00145042 P00145142 P00145242
## 738 8 7 1 1406 28 33
## P00145342 P00145442 P00145542 P00145642 P00145742 P00145842 P00145942
## 15 750 13 308 305 154 283
## P00146042 P00146142 P00146242 P00146342 P00146442 P00146542 P00146642
## 279 243 73 79 166 39 16
## P00146742 P00146842 P00146942 P00147042 P00147142 P00147242 P00147342
## 448 65 37 46 37 23 194
## P00147442 P00147542 P00147642 P00147742 P00147842 P00147942 P00148042
## 37 45 60 573 34 911 131
## P00148142 P00148242 P00148342 P00148442 P00148542 P00148642 P00148742
## 29 15 249 130 192 1204 207
## P00148842 P00148942 P00149042 P00149142 P00149242 P00149342 P00149442
## 216 47 3 59 28 480 47
## P00149542 P00149642 P00149742 P00149842 P00149942 P00150042 P00150142
## 143 62 1 25 11 136 264
## P00150242 P00150342 P00150442 P00150542 P00150642 P00150742 P00150842
## 2 552 49 643 37 144 258
## P00150942 P00151042 P00151142 P00151242 P00151342 P00151442 P00151542
## 436 49 58 108 112 21 10
## P00151642 P00151742 P00151842 P00151942 P00152042 P00152142 P00152242
## 2 590 49 46 4 8 60
## P00152342 P00152442 P00152542 P00152642 P00152742 P00152842 P00152942
## 5 106 45 12 386 2 104
## P00153042 P00153142 P00153242 P00153342 P00153442 P00153542 P00153642
## 7 61 19 19 40 12 182
## P00153742 P00153842 P00153942 P00154042 P00154142 P00154242 P00154342
## 620 571 5 622 21 250 14
## P00154442 P00154542 P00154642 P00154742 P00154842 P00154942 P00155042
## 239 72 576 123 29 2 55
## P00155142 P00155242 P00155342 P00155442 P00155542 P00155642 P00155742
## 177 173 125 424 12 200 259
## P00155842 P00155942 P00156042 P00156142 P00156242 P00156342 P00156442
## 14 16 92 151 162 28 835
## P00156542 P00156642 P00156742 P00156842 P00157042 P00157142 P00157242
## 87 1 344 116 7 262 4
## P00157342 P00157442 P00157542 P00157642 P00157842 P00157942 P00158042
## 211 3 838 482 17 71 122
## P00158142 P00158242 P00158342 P00158442 P00158542 P00158642 P00158742
## 8 392 95 38 601 39 113
## P00158842 P00158942 P00159042 P00159142 P00159242 P00159342 P00159442
## 153 23 3 7 41 36 513
## P00159542 P00159642 P00159742 P00159842 P00159942 P00160042 P00160142
## 495 7 32 118 106 203 18
## P00160242 P00160342 P00160442 P00160542 P00160642 P00160742 P00160842
## 25 53 28 105 129 1 10
## P00160942 P00161042 P00161142 P00161242 P00161342 P00161442 P00161542
## 172 14 27 141 2 503 295
## P00161642 P00161742 P00161842 P00161942 P00162042 P00162142 P00162242
## 4 4 124 799 108 2 50
## P00162342 P00162442 P00162542 P00162642 P00162742 P00162842 P00162942
## 25 263 11 80 1 3 90
## P00163042 P00163142 P00163242 P00163342 P00163442 P00163542 P00163642
## 28 108 204 64 339 98 8
## P00163742 P00163842 P00163942 P00164042 P00164142 P00164242 P00164342
## 17 182 4 27 15 1 70
## P00164442 P00164642 P00164742 P00164842 P00164942 P00165042 P00165142
## 6 107 13 55 19 20 50
## P00165242 P00165442 P00165642 P00165742 P00165842 P00165942 P00166042
## 19 363 137 262 206 71 41
## P00166142 P00166242 P00166342 P00166442 P00166642 P00166742 P00166842
## 34 89 58 1 13 174 10
## P00166942 P00167042 P00167142 P00167242 P00167342 P00167442 P00167542
## 106 2 8 1 1 6 28
## P00167642 P00167742 P00167842 P00167942 P00168042 P00168142 P00168342
## 55 43 10 97 20 22 95
## P00168442 P00168542 P00168642 P00168742 P00168842 P00168942 P00169042
## 71 7 32 108 19 2 23
## P00169142 P00169242 P00169342 P00169442 P00169542 P00169642 P00169742
## 38 3 7 139 15 91 497
## P00169842 P00169942 P00170042 P00170142 P00170242 P00170342 P00170442
## 1 5 8 13 32 258 362
## P00170542 P00170642 P00170742 P00170842 P00170942 P00171042 P00171142
## 162 333 4 32 210 25 2
## P00171242 P00171342 P00171442 P00171542 P00171642 P00171742 P00171842
## 5 211 10 277 168 24 74
## P00171942 P00172042 P00172142 P00172242 P00172342 P00172442 P00172542
## 10 6 37 3 19 3 56
## P00172642 P00172742 P00172842 P00173042 P00173142 P00173242 P00173342
## 1 18 276 537 24 295 465
## P00173442 P00173542 P00173742 P00173842 P00173942 P00174042 P00174142
## 69 206 191 617 398 65 206
## P00174242 P00174342 P00174442 P00174542 P00174642 P00174742 P00174842
## 329 27 728 146 20 20 165
## P00174942 P00175042 P00175142 P00175242 P00175342 P00175442 P00175542
## 16 47 2 130 22 11 43
## P00175642 P00175742 P00175842 P00175942 P00176042 P00176142 P00176242
## 64 51 43 66 118 53 64
## P00176342 P00176442 P00176542 P00176642 P00176742 P00176842 P00176942
## 73 30 209 275 108 416 117
## P00177042 P00177142 P00177242 P00177342 P00177442 P00177542 P00177642
## 114 190 128 367 471 591 253
## P00177742 P00177842 P00177942 P00178042 P00178142 P00178242 P00178342
## 275 365 301 320 348 714 369
## P00178442 P00178542 P00178642 P00178742 P00178842 P00178942 P00179042
## 122 166 379 105 306 835 140
## P00179142 P00179242 P00179342 P00179442 P00179542 P00179642 P00179742
## 152 133 100 113 155 95 119
## P00179842 P00179942 P00180042 P00180142 P00180242 P00180342 P00180442
## 92 69 67 47 54 285 105
## P00180542 P00180642 P00180742 P00180842 P00180942 P00181042 P00181142
## 60 69 69 56 27 7 8
## P00181242 P00181342 P00181442 P00181542 P00181642 P00181742 P00181842
## 141 74 40 448 107 66 458
## P00181942 P00182042 P00182142 P00182242 P00182342 P00182442 P00182542
## 60 77 909 579 456 405 198
## P00182642 P00182742 P00182842 P00182942 P00183042 P00183142 P00183242
## 400 539 36 6 256 215 649
## P00183342 P00183442 P00183542 P00183642 P00183742 P00183842 P00183942
## 628 353 103 202 60 91 305
## P00184042 P00184142 P00184242 P00184342 P00184442 P00184542 P00184642
## 342 409 409 125 432 78 120
## P00184742 P00184842 P00184942 P00185042 P00185142 P00185242 P00185342
## 98 76 1440 45 19 20 101
## P00185442 P00185542 P00185642 P00185742 P00185842 P00185942 P00186042
## 158 32 49 21 108 1 61
## P00186142 P00186242 P00186342 P00186442 P00186542 P00186642 P00186742
## 78 101 89 25 31 300 7
## P00186842 P00186942 P00187042 P00187142 P00187242 P00187342 P00187442
## 68 23 54 58 116 200 537
## P00187542 P00187642 P00187742 P00187842 P00187942 P00188042 P00188142
## 22 7 54 446 146 108 2
## P00188242 P00188342 P00188442 P00188542 P00188642 P00188742 P00188842
## 38 4 463 278 67 315 60
## P00188942 P00189042 P00189142 P00189242 P00189342 P00189442 P00189542
## 79 139 100 160 37 11 50
## P00189642 P00189742 P00189842 P00189942 P00190042 P00190142 P00190242
## 348 73 358 7 465 570 190
## P00190342 P00190442 P00190542 P00190642 P00190742 P00190842 P00190942
## 145 41 314 14 321 263 135
## P00191042 P00191142 P00191242 P00191342 P00191442 P00191542 P00191642
## 131 114 96 141 415 64 262
## P00191742 P00191842 P00191942 P00192042 P00192142 P00192242 P00192342
## 113 43 111 630 6 110 19
## P00192442 P00192542 P00192642 P00192742 P00192842 P00192942 P00193042
## 31 528 95 107 461 316 204
## P00193142 P00193242 P00193342 P00193442 P00193542 P00193642 P00193742
## 184 206 39 133 615 249 148
## P00193842 P00193942 P00194042 P00194142 P00194242 P00194342 P00194442
## 194 85 145 126 123 112 391
## P00194542 P00194642 P00194742 P00194842 P00194942 P00195042 P00195142
## 241 268 3 19 4 165 14
## P00195242 P00195342 P00195442 P00195542 P00195642 P00195742 P00195842
## 161 311 276 131 132 269 178
## P00195942 P00196042 P00196142 P00196242 P00196342 P00196442 P00196542
## 196 363 219 107 188 396 369
## P00196642 P00196742 P00196842 P00196942 P00197042 P00197142 P00197242
## 198 125 95 52 370 73 43
## P00197342 P00197442 P00197542 P00197642 P00197742 P00197842 P00197942
## 146 85 152 7 13 12 119
## P00198042 P00198142 P00198242 P00198342 P00198442 P00198542 P00198642
## 391 404 90 110 14 60 78
## P00198742 P00198842 P00198942 P00199042 P00199142 P00199242 P00199342
## 436 42 75 67 101 2 61
## P00199442 P00199542 P00199642 P00199742 P00199842 P00199942 P00200042
## 794 7 95 46 48 27 33
## P00200142 P00200242 P00200342 P00200442 P00200542 P00200642 P00200742
## 68 60 86 83 9 269 15
## P00200842 P00200942 P00201042 P00201142 P00201242 P00201342 P00201442
## 141 12 29 3 3 419 596
## P00201542 P00201642 P00201742 P00201842 P00201942 P00202042 P00202142
## 83 15 9 2 3 10 121
## P00202242 P00202342 P00202442 P00202542 P00202642 P00202742 P00202842
## 122 49 13 95 4 117 7
## P00202942 P00203042 P00203142 P00203242 P00203442 P00203642 P00203742
## 29 7 64 1 6 1 4
## P00203842 P00203942 P00204042 P00204142 P00204242 P00204342 P00204442
## 6 1 1 14 182 125 1
## P00204542 P00204742 P00204842 P00204942 P00205042 P00205142 P00205242
## 5 111 22 45 50 130 74
## P00205342 P00205442 P00205542 P00205642 P00205742 P00205842 P00205942
## 3 436 4 290 8 200 500
## P00206042 P00206142 P00206242 P00206342 P00206442 P00206542 P00206642
## 115 25 1 109 248 1 45
## P00206742 P00206842 P00206942 P00207042 P00207142 P00207242 P00207342
## 15 9 1 100 25 84 168
## P00207442 P00207542 P00207642 P00207742 P00207842 P00207942 P00208042
## 101 34 38 101 39 546 129
## P00208142 P00208242 P00208342 P00208442 P00208542 P00208642 P00208742
## 45 62 356 39 36 1 443
## P00208842 P00208942 P00209042 P00209142 P00209242 P00209342 P00209442
## 135 60 7 174 29 24 20
## P00209542 P00209642 P00209742 P00209842 P00209942 P00210042 P00210142
## 24 151 390 547 54 784 15
## P00210242 P00210342 P00210442 P00210542 P00210642 P00210742 P00210842
## 13 338 40 77 185 8 2
## P00210942 P00211042 P00211142 P00211242 P00211342 P00211442 P00211542
## 338 194 609 174 3 16 54
## P00211642 P00211742 P00211942 P00212042 P00212142 P00212242 P00212342
## 21 1 66 233 174 264 58
## P00212442 P00212542 P00212642 P00212742 P00212842 P00212942 P00213042
## 88 143 4 174 85 629 167
## P00213142 P00213242 P00213342 P00213442 P00213542 P00213642 P00213742
## 23 636 73 11 53 87 351
## P00213842 P00213942 P00214042 P00214142 P00214242 P00214342 P00214442
## 46 90 5 298 201 234 502
## P00214542 P00214642 P00214742 P00214842 P00214942 P00215042 P00215142
## 70 172 12 179 23 8 1
## P00215242 P00215342 P00215442 P00215542 P00215642 P00215742 P00215842
## 98 19 171 45 186 86 12
## P00215942 P00216042 P00216142 P00216242 P00216342 P00216442 P00216542
## 60 470 564 71 943 47 116
## P00216642 P00216742 P00216842 P00216942 P00217042 P00217142 P00217242
## 2 375 63 92 30 159 71
## P00217342 P00217442 P00217542 P00217642 P00217742 P00217842 P00217942
## 78 404 158 34 418 74 331
## P00218042 P00218142 P00218242 P00218342 P00218442 P00218542 P00218642
## 124 102 138 220 167 95 213
## P00218742 P00218842 P00218942 P00219042 P00219142 P00219242 P00219342
## 116 106 95 84 76 291 75
## P00219442 P00219542 P00219642 P00219742 P00219842 P00219942 P00220042
## 34 121 11 149 179 406 109
## P00220142 P00220242 P00220342 P00220442 P00220542 P00220642 P00220742
## 436 317 698 1282 6 197 120
## P00220842 P00220942 P00221042 P00221142 P00221242 P00221342 P00221442
## 25 162 217 218 164 383 737
## P00221542 P00221642 P00221742 P00221842 P00221942 P00222042 P00222142
## 549 136 242 225 222 147 241
## P00222242 P00222342 P00222442 P00222542 P00222642 P00222742 P00222842
## 205 9 190 38 57 40 269
## P00222942 P00223042 P00223142 P00223242 P00223342 P00223442 P00223542
## 271 148 272 462 31 16 450
## P00223642 P00223742 P00223842 P00223942 P00224042 P00224142 P00224242
## 266 104 78 252 88 342 29
## P00224342 P00224442 P00224542 P00224642 P00224742 P00224842 P00224942
## 64 62 45 1 104 23 13
## P00225042 P00225142 P00225242 P00225342 P00225442 P00225542 P00225642
## 88 65 3 40 32 138 98
## P00225742 P00225842 P00225942 P00226042 P00226142 P00226242 P00226342
## 20 211 65 8 107 262 599
## P00226442 P00226542 P00226642 P00226742 P00226842 P00226942 P00227042
## 164 137 77 132 64 31 25
## P00227142 P00227342 P00227442 P00227542 P00227642 P00227742 P00227842
## 256 26 4 177 190 260 511
## P00227942 P00228042 P00228142 P00228242 P00228342 P00228442 P00228542
## 219 58 91 181 86 146 20
## P00228642 P00228742 P00228842 P00228942 P00229042 P00229142 P00229342
## 318 26 1 17 16 14 222
## P00229442 P00229542 P00229642 P00229742 P00229842 P00229942 P00230042
## 3 4 43 443 58 87 16
## P00230142 P00230242 P00230342 P00230442 P00230542 P00230642 P00230742
## 16 56 195 91 52 33 69
## P00230842 P00230942 P00231042 P00231142 P00231242 P00231342 P00231442
## 334 465 5 105 163 83 28
## P00231542 P00231642 P00231742 P00231842 P00231942 P00232042 P00232142
## 8 1 43 12 170 68 31
## P00232242 P00232342 P00232442 P00232542 P00232642 P00232742 P00232842
## 32 141 160 52 92 42 43
## P00232942 P00233042 P00233142 P00233242 P00233342 P00233442 P00233542
## 22 144 80 72 351 338 636
## P00233642 P00233742 P00233842 P00233942 P00234042 P00234142 P00234242
## 199 169 196 251 9 67 17
## P00234342 P00234442 P00234542 P00234642 P00234742 P00234842 P00234942
## 18 3 348 87 314 349 1
## P00235042 P00235142 P00235242 P00235342 P00235442 P00235542 P00235642
## 10 3 86 63 98 99 104
## P00235742 P00235842 P00235942 P00236042 P00236242 P00236342 P00236442
## 23 126 80 81 2 127 15
## P00236542 P00236642 P00236742 P00236942 P00237042 P00237142 P00237242
## 100 65 4 216 22 159 112
## P00237342 P00237442 P00237542 P00237642 P00237742 P00237842 P00237942
## 4 53 1394 390 22 101 72
## P00238042 P00238142 P00238242 P00238342 P00238442 P00238542 P00238642
## 2 17 2 15 378 342 30
## P00238742 P00238842 P00238942 P00239042 P00239142 P00239242 P00239342
## 122 1 22 11 53 34 19
## P00239442 P00239642 P00239742 P00239842 P00239942 P00240042 P00240142
## 1 19 36 81 35 281 816
## P00240242 P00240342 P00240442 P00240542 P00240642 P00240742 P00240842
## 221 3 409 105 46 4 34
## P00240942 P00241042 P00241142 P00241242 P00241342 P00241442 P00241542
## 21 84 78 165 29 91 324
## P00241642 P00241742 P00241842 P00241942 P00242042 P00242142 P00242242
## 556 27 1 24 36 216 4
## P00242342 P00242442 P00242542 P00242642 P00242742 P00242842 P00242942
## 49 41 27 7 1215 82 25
## P00243042 P00243142 P00243242 P00243342 P00243442 P00243542 P00243642
## 2 4 98 52 15 10 18
## P00243742 P00243842 P00243942 P00244042 P00244142 P00244242 P00244342
## 22 92 688 466 285 181 125
## P00244442 P00244542 P00244642 P00244742 P00244842 P00244942 P00245042
## 61 17 23 226 36 20 53
## P00245142 P00245242 P00245342 P00245442 P00245542 P00245642 P00245742
## 34 26 77 28 33 691 7
## P00245842 P00245942 P00246042 P00246142 P00246242 P00246342 P00246442
## 34 75 46 322 25 343 39
## P00246542 P00246642 P00246742 P00246842 P00246942 P00247042 P00247142
## 19 36 150 66 133 407 214
## P00247242 P00247342 P00247442 P00247542 P00247642 P00247742 P00247842
## 5 16 8 159 130 17 2
## P00247942 P00248042 P00248142 P00248242 P00248342 P00248442 P00248542
## 23 48 815 4 319 232 377
## P00248642 P00248742 P00248842 P00248942 P00249042 P00249142 P00249242
## 21 241 13 581 113 448 7
## P00249342 P00249442 P00249542 P00249642 P00249742 P00249842 P00250042
## 43 25 785 721 476 206 18
## P00250142 P00250242 P00250342 P00250442 P00250542 P00250642 P00250742
## 4 768 341 24 123 672 11
## P00250842 P00250942 P00251042 P00251142 P00251242 P00251342 P00251442
## 528 211 47 14 1187 302 204
## P00251542 P00251642 P00251742 P00251842 P00251942 P00252042 P00252142
## 269 198 46 317 469 347 52
## P00252242 P00252342 P00252442 P00252542 P00252642 P00252742 P00252842
## 73 14 237 122 79 118 78
## P00252942 P00253042 P00253142 P00253242 P00253342 P00253442 P00253542
## 31 240 257 126 8 44 251
## P00253642 P00253742 P00253942 P00254042 P00254142 P00254242 P00254342
## 10 32 14 18 131 512 189
## P00254442 P00254542 P00254642 P00254742 P00254842 P00254942 P00255042
## 71 49 146 25 97 38 7
## P00255142 P00255242 P00255342 P00255442 P00255542 P00255642 P00255742
## 20 11 68 13 235 4 387
## P00255842 P00255942 P00256042 P00256142 P00256242 P00256342 P00256442
## 1383 561 154 3 20 12 10
## P00256542 P00256642 P00256742 P00256842 P00256942 P00257042 P00257142
## 39 498 101 133 9 38 8
## P00257242 P00257342 P00257442 P00257542 P00257642 P00257742 P00257842
## 8 3 17 182 42 31 58
## P00257942 P00258042 P00258142 P00258242 P00258342 P00258442 P00258542
## 3 35 11 31 68 234 95
## P00258642 P00258742 P00258842 P00258942 P00259042 P00259142 P00259242
## 26 964 300 137 202 294 128
## P00259342 P00259442 P00259542 P00259642 P00259742 P00259842 P00259942
## 836 92 56 11 48 162 199
## P00260042 P00260142 P00260242 P00260342 P00260442 P00260542 P00260642
## 754 234 87 47 193 13 26
## P00260742 P00260842 P00260942 P00261042 P00261142 P00261242 P00261342
## 1 23 16 4 146 75 62
## P00261442 P00261542 P00261642 P00261742 P00261842 P00261942 P00262042
## 32 174 33 2 145 2 7
## P00262142 P00262242 P00262342 P00262442 P00262542 P00262642 P00262742
## 2 419 189 74 88 8 2
## P00262842 P00262942 P00263042 P00263142 P00263242 P00263342 P00263442
## 2 9 35 93 13 6 221
## P00263542 P00263642 P00263742 P00263842 P00264042 P00264142 P00264242
## 288 25 11 42 83 14 18
## P00264342 P00264442 P00264542 P00264642 P00264742 P00264842 P00264942
## 8 2 78 76 15 4 7
## P00265042 P00265142 P00265242 P00265342 P00265442 P00265542 P00265642
## 41 219 1880 133 74 202 51
## P00265742 P00265842 P00265942 P00266042 P00266142 P00266242 P00266342
## 268 16 16 114 162 35 3
## P00266442 P00266542 P00266642 P00266742 P00266842 P00266942 P00267042
## 105 405 428 10 37 166 25
## P00267142 P00267242 P00267342 P00267442 P00267542 P00267642 P00267742
## 114 21 89 36 430 67 144
## P00267842 P00267942 P00268042 P00268142 P00268242 P00268342 P00268442
## 23 35 16 3 65 151 567
## P00268542 P00268642 P00268742 P00268842 P00269042 P00269142 P00269242
## 93 7 27 21 14 12 51
## P00269342 P00269442 P00269542 P00269642 P00269742 P00269842 P00269942
## 72 90 180 97 50 7 37
## P00270042 P00270142 P00270242 P00270442 P00270542 P00270642 P00270742
## 140 123 483 4 260 12 7
## P00270842 P00270942 P00271042 P00271142 P00271242 P00271342 P00271442
## 472 1152 289 791 202 56 199
## P00271542 P00271642 P00271742 P00271842 P00271942 P00272042 P00272142
## 131 7 67 28 239 60 44
## P00272242 P00272342 P00272442 P00272542 P00272642 P00272742 P00272842
## 113 2 2 66 5 2 81
## P00272942 P00273042 P00273142 P00273242 P00273342 P00273442 P00273542
## 81 53 22 28 60 159 211
## P00273642 P00273742 P00273842 P00273942 P00274042 P00274142 P00274242
## 88 392 18 178 475 407 358
## P00274342 P00274442 P00274542 P00274642 P00274742 P00274842 P00274942
## 192 280 116 194 2 74 793
## P00275042 P00275142 P00275242 P00275342 P00275442 P00275542 P00275642
## 1 169 7 9 8 38 230
## P00275742 P00275842 P00275942 P00276042 P00276142 P00276242 P00276342
## 65 560 35 51 171 31 264
## P00276442 P00276542 P00276642 P00276742 P00276842 P00276942 P00277042
## 35 60 305 22 61 24 7
## P00277142 P00277242 P00277342 P00277442 P00277542 P00277642 P00277742
## 22 40 7 657 275 989 62
## P00277842 P00277942 P00278042 P00278142 P00278242 P00278342 P00278442
## 216 197 220 13 239 5 182
## P00278542 P00278642 P00278742 P00278842 P00278942 P00279142 P00279242
## 73 1226 6 2 174 23 5
## P00279342 P00279442 P00279542 P00279642 P00279742 P00279842 P00279942
## 143 353 487 154 23 23 84
## P00280042 P00280142 P00280242 P00280342 P00280442 P00280542 P00280642
## 5 40 35 78 228 57 142
## P00280742 P00280842 P00280942 P00281042 P00281142 P00281242 P00281342
## 216 280 18 120 86 7 3
## P00281442 P00281542 P00281642 P00281742 P00281842 P00281942 P00282042
## 4 79 101 115 29 128 490
## P00282142 P00282242 P00282342 P00282442 P00282542 P00282642 P00282742
## 180 226 99 328 45 463 170
## P00282842 P00282942 P00283042 P00283142 P00283242 P00283342 P00283442
## 40 38 16 310 67 53 13
## P00283542 P00283642 P00283742 P00283842 P00283942 P00284042 P00284142
## 48 15 7 87 537 189 78
## P00284242 P00284342 P00284542 P00284642 P00284742 P00284842 P00284942
## 47 4 7 307 161 202 82
## P00285042 P00285142 P00285242 P00285342 P00285442 P00285542 P00285642
## 29 1 103 190 203 30 338
## P00285742 P00285842 P00285942 P00286042 P00286142 P00286242 P00286342
## 161 537 17 129 36 40 54
## P00286442 P00286542 P00286642 P00286742 P00286842 P00286942 P00287042
## 85 69 561 469 219 2 1
## P00287142 P00287242 P00287342 P00287442 P00287542 P00287642 P00287742
## 48 266 207 125 100 74 10
## P00287842 P00287942 P00288042 P00288142 P00288242 P00288342 P00288442
## 111 52 281 43 123 299 142
## P00288542 P00288642 P00288742 P00288842 P00288942 P00289042 P00289142
## 329 451 129 33 416 372 15
## P00289242 P00289342 P00289442 P00289542 P00289642 P00289742 P00289842
## 482 531 36 17 103 45 310
## P00289942 P00290042 P00290142 P00290242 P00290342 P00290442 P00290542
## 850 57 7 41 33 3 63
## P00290642 P00290742 P00290842 P00290942 P00291042 P00291142 P00291242
## 9 18 2 16 99 2 29
## P00291342 P00291442 P00291542 P00291642 P00291742 P00291842 P00291942
## 23 160 72 1 15 17 99
## P00292042 P00292142 P00292242 P00292342 P00292442 P00292542 P00292642
## 97 1 9 37 6 5 33
## P00292742 P00292842 P00292942 P00293042 P00293142 P00293242 P00293342
## 75 18 23 146 174 666 392
## P00293442 P00293542 P00293742 P00293842 P00293942 P00294042 P00294142
## 4 34 191 145 4 168 210
## P00294242 P00294342 P00294442 P00294542 P00294642 P00294742 P00294842
## 283 16 237 610 8 12 162
## P00295042 P00295142 P00295242 P00295342 P00295442 P00295542 P00295642
## 31 4 98 276 143 16 1
## P00295742 P00295842 P00295942 P00296042 P00296142 P00296242 P00296342
## 257 478 972 731 88 285 106
## P00296442 P00296542 P00296642 P00296742 P00296842 P00296942 P00297042
## 38 93 62 33 2 200 229
## P00297142 P00297242 P00297342 P00297442 P00297542 P00297642 P00297742
## 2 25 171 89 14 120 194
## P00297842 P00297942 P00298042 P00298142 P00298242 P00298342 P00298442
## 133 181 35 100 193 1 282
## P00298542 P00298742 P00298842 P00298942 P00299042 P00299142 P00299242
## 109 68 1 145 1 460 35
## P00299342 P00299442 P00299542 P00299642 P00299742 P00299842 P00299942
## 1 127 54 9 1 50 17
## P00300042 P00300242 P00300342 P00300442 P00300542 P00300642 P00300742
## 248 15 16 16 37 62 1
## P00300942 P00301042 P00301142 P00301242 P00301342 P00301442 P00301542
## 41 3 2 40 2 1 163
## P00301642 P00301742 P00301842 P00301942 P00302042 P00302142 P00302242
## 18 17 29 1 162 173 3
## P00302342 P00302442 P00302542 P00302642 P00302742 P00302842 P00302942
## 259 377 108 177 195 140 292
## P00303042 P00303142 P00303242 P00303342 P00303442 P00303542 P00303642
## 599 245 614 594 356 197 219
## P00303742 P00303842 P00303942 P00304042 P00304142 P00304242 P00304342
## 263 237 78 357 272 170 76
## P00304442 P00304542 P00304642 P00304742 P00304842 P00304942 P00305042
## 257 96 246 109 183 90 318
## P00305142 P00305242 P00305342 P00305442 P00305542 P00305642 P00305742
## 257 53 21 1 1 31 6
## P00305842 P00305942 P00306042 P00306142 P00306242 P00306342 P00306442
## 6 25 215 74 66 3 31
## P00306542 P00306742 P00306942 P00307042 P00307142 P00307242 P00307342
## 1 4 1 1 110 1 333
## P00307442 P00307542 P00307642 P00307742 P00307842 P00307942 P00308042
## 94 297 328 25 4 27 1
## P00308142 P00308242 P00308342 P00308442 P00308542 P00308642 P00308842
## 13 152 149 14 20 14 12
## P00308942 P00309042 P00309142 P00309242 P00309342 P00309442 P00309542
## 3 1 163 278 26 18 27
## P00309642 P00309742 P00309942 P00310042 P00310142 P00310242 P00310342
## 1 3 103 82 96 73 180
## P00310442 P00310542 P00310642 P00310742 P00310842 P00310942 P00311042
## 15 61 19 8 236 5 2
## P00311242 P00311342 P00311442 P00311542 P00311642 P00311742 P00311842
## 16 22 42 96 112 21 9
## P00311942 P00312042 P00312142 P00312242 P00312342 P00312442 P00312542
## 17 3 111 14 78 11 1
## P00312742 P00312842 P00312942 P00313042 P00313142 P00313242 P00313342
## 442 152 58 255 278 327 597
## P00313442 P00313542 P00313642 P00313742 P00313842 P00313942 P00314042
## 408 524 85 121 140 23 33
## P00314142 P00314242 P00314342 P00314542 P00314642 P00314742 P00314842
## 38 31 27 9 34 1 1
## P00314942 P00315042 P00315142 P00315242 P00315442 P00315542 P00315642
## 2 51 1 1 141 80 399
## P00315742 P00315842 P00315942 P00316042 P00316142 P00316242 P00316342
## 92 169 21 13 52 98 24
## P00316442 P00316542 P00316642 P00316742 P00316842 P00316942 P00317042
## 113 30 417 200 181 44 15
## P00317142 P00317242 P00317342 P00317442 P00317542 P00317642 P00317742
## 62 13 23 128 67 60 2
## P00317842 P00317942 P00318042 P00318142 P00318342 P00318442 P00318542
## 756 226 5 87 28 10 32
## P00318642 P00318742 P00318842 P00318942 P00319042 P00319142 P00319242
## 32 759 3 112 652 132 42
## P00319342 P00319442 P00319542 P00319642 P00319742 P00319842 P00319942
## 435 40 111 40 33 149 124
## P00320042 P00320142 P00320242 P00320342 P00320442 P00320542 P00320642
## 44 39 23 23 267 70 8
## P00320742 P00320842 P00320942 P00321042 P00321142 P00321242 P00321342
## 278 130 116 263 23 3 116
## P00321442 P00321542 P00321642 P00321742 P00321842 P00321942 P00322042
## 75 22 40 579 22 449 194
## P00322142 P00322242 P00322342 P00322442 P00322542 P00322742 P00322942
## 179 51 21 29 72 36 194
## P00323042 P00323142 P00323242 P00323342 P00323442 P00323542 P00323642
## 159 4 10 10 107 153 241
## P00323742 P00323842 P00323942 P00324042 P00324142 P00324242 P00324342
## 144 47 800 4 11 9 82
## P00324442 P00324542 P00324642 P00324742 P00324842 P00324942 P00325042
## 363 146 147 316 79 785 2
## P00325142 P00325242 P00325342 P00325442 P00325542 P00325642 P00325742
## 92 168 1 7 35 14 389
## P00325842 P00325942 P00326042 P00326242 P00326342 P00326442 P00326542
## 9 34 9 177 4 25 50
## P00326642 P00326742 P00326842 P00326942 P00327042 P00327142 P00327242
## 158 513 101 152 49 83 383
## P00327342 P00327442 P00327542 P00327642 P00327742 P00327842 P00327942
## 186 104 202 203 46 436 36
## P00328042 P00328142 P00328242 P00328342 P00328442 P00328642 P00328742
## 250 222 31 111 118 30 112
## P00328842 P00328942 P00329042 P00329142 P00329242 P00329342 P00329442
## 22 100 1 3 288 196 423
## P00329542 P00329642 P00329742 P00329842 P00329942 P00330042 P00330142
## 708 178 84 1 18 10 2
## P00330242 P00330342 P00330442 P00330542 P00330642 P00330742 P00330842
## 277 341 266 16 95 18 16
## P00331042 P00331142 P00331242 P00331342 P00331442 P00331542 P00331642
## 481 52 240 120 41 155 132
## P00331742 P00331842 P00331942 P00332042 P00332142 P00332242 P00332342
## 88 207 509 50 64 522 183
## P00332442 P00332542 P00332642 P00332742 P00332842 P00332942 P00333042
## 66 12 15 7 70 148 183
## P00333142 P00333242 P00333342 P00333442 P00333542 P00333642 P00333742
## 21 18 24 5 71 42 16
## P00333842 P00333942 P00334042 P00334142 P00334242 P00334342 P00334442
## 8 2 157 34 1093 35 14
## P00334542 P00334642 P00334742 P00334842 P00334942 P00335042 P00335142
## 5 74 11 15 19 20 44
## P00335242 P00335342 P00335442 P00335542 P00335642 P00335742 P00335842
## 364 2 190 105 1 38 20
## P00335942 P00336042 P00336142 P00336242 P00336342 P00336442 P00336542
## 93 54 75 1 32 3 75
## P00336642 P00336742 P00336942 P00337042 P00337142 P00337242 P00337342
## 27 83 451 4 19 11 13
## P00337442 P00337542 P00337642 P00337742 P00337842 P00337942 P00338042
## 44 295 178 81 239 190 50
## P00338142 P00338242 P00338342 P00338442 P00338542 P00338642 P00338742
## 28 1 48 731 417 9 26
## P00338842 P00338942 P00339042 P00339142 P00339242 P00339342 P00339442
## 67 50 154 1 23 186 86
## P00339542 P00339642 P00339742 P00339842 P00339942 P00340042 P00340142
## 264 6 15 333 238 5 3
## P00340242 P00340342 P00340442 P00340542 P00340642 P00340742 P00340842
## 12 56 11 12 98 1 6
## P00340942 P00341042 P00341142 P00341242 P00341342 P00341442 P00341542
## 159 2 2 95 256 30 1
## P00341642 P00341742 P00341842 P00341942 P00342042 P00342142 P00342242
## 2 40 24 84 44 52 37
## P00342342 P00342442 P00342542 P00342642 P00342742 P00342842 P00342942
## 32 14 2 19 205 40 13
## P00343042 P00343142 P00343242 P00343342 P00343442 P00343542 P00343642
## 594 84 15 19 150 113 26
## P00343742 P00343842 P00343942 P00344042 P00344142 P00344242 P00344342
## 52 32 28 171 119 338 214
## P00344442 P00344542 P00344742 P00344842 P00344942 P00345042 P00345142
## 282 239 186 90 58 19 29
## P00345242 P00345342 P00345442 P00345542 P00345642 P00345742 P00345842
## 100 32 29 37 349 393 468
## P00345942 P00346042 P00346142 P00346242 P00346342 P00346442 P00346542
## 139 273 593 557 388 199 254
## P00346642 P00346742 P00346842 P00346942 P00347042 P00347142 P00347242
## 126 152 62 107 18 188 47
## P00347342 P00347442 P00347542 P00347642 P00347742 P00347842 P00347942
## 35 46 38 325 37 27 49
## P00348042 P00348142 P00348242 P00348342 P00348442 P00348542 P00348642
## 17 1 148 101 59 59 80
## P00348742 P00348842 P00348942 P00349042 P00349142 P00349242 P00349342
## 73 133 247 22 40 146 31
## P00349442 P00349542 P00349642 P00349742 P00349842 P00349942 P00350042
## 287 19 31 50 25 326 122
## P00350142 P00350242 P00350342 P00350442 P00350542 P00350642 P00350742
## 134 52 247 295 31 80 1
## P00350842 P00350942 P00351042 P00351142 P00351242 P00351342 P00351442
## 12 735 317 642 115 558 15
## P00351542 P00351642 P00351742 P00351842 P00351942 P00352042 P00352142
## 11 58 19 171 14 1 383
## P00352242 P00352342 P00352442 P00352542 P00352642 P00352742 P00352842
## 132 25 114 59 47 116 187
## P00352942 P00353042 P00353142 P00353242 P00353342 P00353442 P00353542
## 86 1 92 32 12 17 7
## P00353642 P00353742 P00353842 P00353942 P00354042 P00354142 P00354242
## 3 1 24 17 25 136 94
## P00354342 P00354442 P00354542 P00354642 P00354742 P00354842 P00354942
## 324 100 30 133 84 29 249
## P00355042 P00355142 P00355242 P00355342 P00355442 P00355542 P00355642
## 37 811 66 36 4 21 260
## P00355742 P00355842 P00355942 P00356042 P00356142 P00356242 P00356342
## 60 2 118 177 6 20 15
## P00356442 P00356542 P00356642 P00356742 P00356842 P00356942 P00357042
## 31 69 18 463 46 186 154
## P00357142 P00357242 P00357342 P00357442 P00357542 P00357642 P00357742
## 41 120 27 9 1 128 20
## P00357842 P00357942 P00358042 P00358142 P00358242 P00358342 P00358442
## 134 44 35 71 122 224 264
## P00358542 P00358642 P00358742 P00358842 P00358942 P00359042 P00359142
## 2 8 101 13 26 98 100
## P00359242 P00359342 P00359442 P00359542 P00359642 P00359742 P00359942
## 191 61 24 28 58 171 19
## P00360042 P00360142 P00360242 P00360342 P00360442 P00360542 P00360642
## 234 13 77 11 10 25 7
## P00360742 P00360842 P00360942 P00361042 P00361142 P00361242 P00361342
## 20 115 6 9 18 30 36
## P00361442 P00361542 P00361642 P00361742 P00361842 P00361942 P00362042
## 49 7 136 11 353 80 79
## P00362142 P00362242 P00362342 P00362442 P00362542 P00362642 P00362742
## 4 4 362 238 24 154 61
## P00362842 P00362942 P00363042 P00363142 P00363242 P00363342 P00363442
## 153 13 3 2 104 6 62
## P00363542 P00363742 P00363842 P00363942 P00364042 P00364142 P00364242
## 9 124 7 23 16 18 3
## P00364342 P00364442 P00364542 P00364642 P00364742 P00364842 P00364942
## 1 73 1 3 6 321 11
## P00365042 P00365142 P00365242 P00365342 P00365442 P00365542 P00365642
## 50 95 521 25 2 23 51
## P00365742 P00365842 P00365942 P00366042 P00366142 P00366242 P00366342
## 11 28 1 6 16 49 34
## P00366442 P00366542 P00366642 P00366742 P00366842 P00366942 P00367042
## 126 361 24 14 40 63 224
## P00367142 P00367242 P00367342 P00367442 P00367542 P00367642 P00367742
## 160 89 51 38 21 23 60
## P00367842 P00367942 P00368042 P00368142 P00368242 P00368342 P00368442
## 14 66 93 193 115 82 127
## P00368542 P00368642 P00368742 P00368842 P00368942 P00369042 P00369142
## 20 130 8 14 10 65 78
## P00369242 P00369342 P00369442 P00369542 P00369642 P00369742 P00369842
## 15 15 8 14 12 49 5
## P00369942 P00370042 P00370142 P00370242 P00370293 P00370342 P00370442
## 24 46 30 463 785 166 25
## P00370542 P00370642 P00370853 P00371644 P00372445 P00375436 P0093742
## 23 217 818 899 837 814 10
## P0093842 P0093942 P0094042 P0094142 P0094242 P0094342 P0094442
## 5 66 57 78 46 121 48
## P0094542 P0094642 P0094742 P0094842 P0094942 P0095042 P0095142
## 174 133 72 167 154 84 114
## P0095242 P0095342 P0095442 P0095542 P0095642 P0095742 P0095842
## 84 145 62 311 210 115 313
## P0095942 P0096042 P0096142 P0096242 P0096342 P0096442 P0096542
## 111 68 165 2 180 532 329
## P0096642 P0096742 P0096842 P0096942 P0097042 P0097142 P0097242
## 174 175 298 154 87 488 912
## P0097342 P0097442 P0097542 P0097642 P0097742 P0097842 P0097942
## 364 6 3 1 254 279 62
## P0098042 P0098142 P0098242 P0098342 P0098442 P0098542 P0098642
## 20 43 233 255 72 46 27
## P0098742 P0098842 P0098942 P0099042 P0099142 P0099242 P0099342
## 47 10 33 144 7 257 440
## P0099442 P0099642 P0099742 P0099842 P0099942
## 200 13 126 102 14train_set$Marital_Status <- as.factor(ifelse(train_set$Marital_Status == 1, 'Married', 'Single'))
maritalstatusTable <- table(train_set$Marital_Status)
maritalstatusTable##
## Married Single
## 225337 324731train_set$Age <- as.factor(train_set$Age)
ageTable <- table(train_set$Age)
ageTable##
## 0-17 18-25 26-35 36-45 46-50 51-55 55+
## 15102 99660 219587 110013 45701 38501 21504train_set$Gender <- as.factor(ifelse(train_set$Gender=='M', 'Male', 'Female'))
genderTable <- table(train_set$Gender)
genderTable##
## Female Male
## 135809 414259train_set$Occupation <- as.factor(train_set$Occupation)
occupationTable <- table(train_set$Occupation)
occupationTable##
## 0 1 2 3 4 5 6 7 8 9 10 11
## 69638 47426 26588 17650 72308 12177 20355 59133 1546 6291 12930 11586
## 12 13 14 15 16 17 18 19 20
## 31179 7728 27309 12165 25371 40043 6622 8461 33562train_set$City_Category <- as.factor(train_set$City_Category)
citycategoryTable <- table(train_set$City_Category)
citycategoryTable##
## A B C
## 147720 231173 171175train_set$Stay_In_Current_City_Years <- as.factor(train_set$Stay_In_Current_City_Years)
stayTable <- table(train_set$Stay_In_Current_City_Years)
stayTable##
## 0 1 2 3 4+
## 74398 193821 101838 95285 84726EDA_Distinct <- distinct(train_set, User_ID, Age, Gender, Marital_Status, Occupation, City_Category, Stay_In_Current_City_Years)
head(EDA_Distinct)## User_ID Gender Age Occupation City_Category Stay_In_Current_City_Years
## 1 1000001 Female 0-17 10 A 2
## 2 1000002 Male 55+ 16 C 4+
## 3 1000003 Male 26-35 15 A 3
## 4 1000004 Male 46-50 7 B 2
## 5 1000005 Male 26-35 20 A 1
## 6 1000006 Female 51-55 9 A 1
## Marital_Status
## 1 Single
## 2 Single
## 3 Single
## 4 Married
## 5 Married
## 6 Single3.1.User_ID
head(train_set$User_ID,40)## [1] 1000001 1000001 1000001 1000001 1000002 1000003 1000004 1000004
## [9] 1000004 1000005 1000005 1000005 1000005 1000005 1000006 1000006
## [17] 1000006 1000006 1000007 1000008 1000008 1000008 1000008 1000008
## [25] 1000008 1000009 1000009 1000009 1000009 1000010 1000010 1000010
## [33] 1000010 1000010 1000010 1000010 1000010 1000010 1000010 1000010
## 5891 Levels: 1000001 1000002 1000003 1000004 1000005 1000006 ... 1006040head(test_set$User_ID,40)## [1] 1000004 1000009 1000010 1000010 1000011 1000013 1000013 1000013
## [9] 1000015 1000022 1000026 1000026 1000026 1000026 1000028 1000029
## [17] 1000033 1000033 1000034 1000035 1000036 1000036 1000042 1000045
## [25] 1000045 1000045 1000048 1000048 1000053 1000053 1000053 1000053
## [33] 1000053 1000059 1000062 1000063 1000063 1000065 1000068 1000069Here checking if the all user_id refer to other person or the same person.
#creating a new data frame to store the number of purchase made by each user
userIDCount <- as.data.frame(table(train_set$User_ID))
names(userIDCount) <- c("User_ID","User_Purchase_Count")
head(userIDCount)## User_ID User_Purchase_Count
## 1 1000001 35
## 2 1000002 77
## 3 1000003 29
## 4 1000004 14
## 5 1000005 106
## 6 1000006 47train_set <- merge(x = train_set, y = userIDCount, by = "User_ID", all.x = TRUE)
str(train_set)## 'data.frame': 550068 obs. of 13 variables:
## $ User_ID : Factor w/ 5891 levels "1000001","1000002",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ Product_ID : Factor w/ 3631 levels "P00000142","P00000242",..: 673 2377 853 829 250 723 2041 1560 1028 834 ...
## $ Gender : Factor w/ 2 levels "Female","Male": 1 1 1 1 1 1 1 1 1 1 ...
## $ Age : Factor w/ 7 levels "0-17","18-25",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ Occupation : Factor w/ 21 levels "0","1","2","3",..: 11 11 11 11 11 11 11 11 11 11 ...
## $ City_Category : Factor w/ 3 levels "A","B","C": 1 1 1 1 1 1 1 1 1 1 ...
## $ Stay_In_Current_City_Years: Factor w/ 5 levels "0","1","2","3",..: 3 3 3 3 3 3 3 3 3 3 ...
## $ Marital_Status : Factor w/ 2 levels "Married","Single": 2 2 2 2 2 2 2 2 2 2 ...
## $ Product_Category_1 : int 3 1 12 12 1 3 14 8 8 2 ...
## $ Product_Category_2 : int NA 6 NA 14 2 4 NA NA NA 4 ...
## $ Product_Category_3 : int NA 14 NA NA 9 12 NA NA NA 8 ...
## $ Purchase : int 8370 15200 1422 1057 15416 10572 11011 10003 8094 12842 ...
## $ User_Purchase_Count : int 35 35 35 35 35 35 35 35 35 35 ...test_set <- merge(x = test_set, y = userIDCount, by = "User_ID", all.x = TRUE)
#Now we can remove the UserIDCount dataframe
rm(userIDCount)
'%!in%' <- function(x,y)!('%in%'(x,y))
#if(test_set$User_ID %!in% train_set$User_ID){ assign(test_set$userIDCount, 0)}
test_set[is.na(test_set$User_Purchase_Count), "User_Purchase_Count"] <- 1
class(test_set$User_Purchase_Count)## [1] "numeric"str(test_set)## 'data.frame': 233599 obs. of 12 variables:
## $ User_ID : int 1000001 1000001 1000001 1000001 1000001 1000001 1000001 1000001 1000001 1000001 ...
## $ Product_ID : Factor w/ 3491 levels "P00000142","P00000242",..: 2679 1016 1976 559 663 553 3457 1942 2393 966 ...
## $ Gender : Factor w/ 2 levels "F","M": 1 1 1 1 1 1 1 1 1 1 ...
## $ Age : Factor w/ 7 levels "0-17","18-25",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ Occupation : int 10 10 10 10 10 10 10 10 10 10 ...
## $ City_Category : Factor w/ 3 levels "A","B","C": 1 1 1 1 1 1 1 1 1 1 ...
## $ Stay_In_Current_City_Years: Factor w/ 5 levels "0","1","2","3",..: 3 3 3 3 3 3 3 3 3 3 ...
## $ Marital_Status : int 0 0 0 0 0 0 0 0 0 0 ...
## $ Product_Category_1 : int 8 8 3 3 3 3 4 5 5 8 ...
## $ Product_Category_2 : int NA NA 4 4 5 4 5 NA 9 NA ...
## $ Product_Category_3 : int NA NA 5 12 16 5 12 NA NA NA ...
## $ User_Purchase_Count : num 35 35 35 35 35 35 35 35 35 35 ...test_set$User_Purchase_Count <- as.integer(test_set$User_Purchase_Count)#Updating EDA_Distinct dataframe
EDA_Distinct <- distinct(train_set, User_ID, Age, Gender, Marital_Status, Occupation, City_Category, Stay_In_Current_City_Years, User_Purchase_Count)d1 <- summary(EDA_Distinct$User_Purchase_Count)
d1## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 6.00 26.00 54.00 93.37 117.00 1026.00p1 <- ggplot(EDA_Distinct, aes(x=User_Purchase_Count)) +geom_density(fill="red", col="black", alpha=0.80) + annotate(geom = "text", x = 6, y = 0.0125, label = "Min") + annotate(geom = "text", x = 24, y = 0.013, label = "1st Qu.") + annotate(geom = "text", x = 50, y = 0.0125, label = "Median") + annotate(geom = "text", x = 90, y = 0.013, label = "Mean") + annotate(geom = "text", x = 112, y = 0.0125, label = "3rd Qu.") + annotate(geom = "text", x = 1015, y = 0.0125, label = "Max") + geom_vline(xintercept = c(6, 26, 54, 93.37, 117, 1026), size = 0.2, col = 'black') #+ lims(x = )
p1
p2 <- ggplot(EDA_Distinct, aes(x=User_Purchase_Count)) +geom_histogram(fill="red", col="black", alpha=0.80)
p2## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
p3 <- ggplot(EDA_Distinct,aes(x= Age,y=User_Purchase_Count, fill=Age)) + geom_boxplot() + facet_grid(Gender~Marital_Status) + labs(x="Age",y="Customer Purchase Count")
p3
p4 <- ggplot(EDA_Distinct,aes(x= Occupation,y=User_Purchase_Count, fill=Occupation)) + geom_boxplot() + facet_grid(Gender~Marital_Status) + labs(x="Occupation",y="Customer Purchase Count")
p4
p5 <- ggplot(EDA_Distinct,aes(x=Age,y=User_Purchase_Count,fill=Stay_In_Current_City_Years))+geom_boxplot()+facet_grid(City_Category~ Stay_In_Current_City_Years) + labs(x="Age",y="Customer Purchase Count")
p5
p5i <- ggplot(EDA_Distinct,aes(x=Age,y=User_Purchase_Count,fill=Stay_In_Current_City_Years))+geom_boxplot()+facet_grid( Stay_In_Current_City_Years ~ City_Category) + labs(x="Age",y="Customer Purchase Count")
p5i
p6 <- ggplot(EDA_Distinct,aes(x=Age,y=User_Purchase_Count,fill=Marital_Status))+geom_boxplot()+facet_grid(Gender~City_Category) + scale_fill_manual(values=c("tan4","limegreen")) + labs(x="Age",y="Customer Purchase Count")
p6
3.2.Product_ID
head(train_set$Product_ID,15)## [1] P00069042 P00248942 P00087842 P00085442 P00025442 P00074142 P00214842
## [8] P00165942 P00111842 P00085942 P00297042 P00117942 P00258742 P00142242
## [15] P00178242
## 3631 Levels: P00000142 P00000242 P00000342 P00000442 P00000542 ... P0099942head(test_set$Product_ID,15)## [1] P00289042 P00115542 P00216342 P00058242 P00070942 P00057542 P0096442
## [8] P00212942 P00259342 P00110542 P00144042 P00157542 P00322042 P00161942
## [15] P00046742
## 3491 Levels: P00000142 P00000242 P00000342 P00000442 P00000542 ... P0099942i.e. there are a total of 3631 number of product or we can say the product line consist of 3631 products.
#creating a new data frame to stor the number of purchase made by each user
ProductIDCount <- as.data.frame(table(train_set$Product_ID))
names(ProductIDCount) <- c("Product_ID","Product_Sold_Count")
head(ProductIDCount)## Product_ID Product_Sold_Count
## 1 P00000142 1152
## 2 P00000242 376
## 3 P00000342 244
## 4 P00000442 92
## 5 P00000542 149
## 6 P00000642 512# joining i.e. storing the Product sold count in original data frame
train_set <- merge(x = train_set, y = ProductIDCount, by = "Product_ID", all.x = TRUE)
str(train_set)## 'data.frame': 550068 obs. of 14 variables:
## $ Product_ID : Factor w/ 3631 levels "P00000142","P00000242",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ User_ID : Factor w/ 5891 levels "1000001","1000002",..: 3000 3309 235 5439 2386 612 1907 4255 4639 4588 ...
## $ Gender : Factor w/ 2 levels "Female","Male": 2 2 2 1 2 1 1 2 2 2 ...
## $ Age : Factor w/ 7 levels "0-17","18-25",..: 3 4 2 2 3 1 2 4 4 2 ...
## $ Occupation : Factor w/ 21 levels "0","1","2","3",..: 8 21 20 2 1 11 2 1 18 21 ...
## $ City_Category : Factor w/ 3 levels "A","B","C": 2 2 1 3 2 3 2 3 3 3 ...
## $ Stay_In_Current_City_Years: Factor w/ 5 levels "0","1","2","3",..: 2 5 2 2 1 3 1 2 2 2 ...
## $ Marital_Status : Factor w/ 2 levels "Married","Single": 1 2 2 1 1 2 1 2 2 2 ...
## $ Product_Category_1 : int 3 3 3 3 3 3 3 3 3 3 ...
## $ Product_Category_2 : int 4 4 4 4 4 4 4 4 4 4 ...
## $ Product_Category_3 : int 5 5 5 5 5 5 5 5 5 5 ...
## $ Purchase : int 13642 8335 13374 10883 10843 11041 13206 10568 13578 10784 ...
## $ User_Purchase_Count : int 197 171 39 51 282 129 287 73 62 175 ...
## $ Product_Sold_Count : int 1152 1152 1152 1152 1152 1152 1152 1152 1152 1152 ...# Product which are sold most frequent and least frequent
head(ProductIDCount[order(-ProductIDCount$Product_Sold_Count),])## Product_ID Product_Sold_Count
## 2537 P00265242 1880
## 250 P00025442 1615
## 1017 P00110742 1612
## 1031 P00112142 1562
## 566 P00057642 1470
## 1746 P00184942 1440tail(ProductIDCount[order(-ProductIDCount$Product_Sold_Count),])## Product_ID Product_Sold_Count
## 3397 P00353742 1
## 3435 P00357542 1
## 3501 P00364342 1
## 3503 P00364542 1
## 3517 P00365942 1
## 3609 P0097642 1# writing code such that if a new user comes for the first time his count is set to one in test dataset
test_set <- merge(x = test_set, y = ProductIDCount, by = "Product_ID", all.x = TRUE)
#Now we can remove the UserIDCount dataframe
rm(ProductIDCount)
test_set[is.na(test_set$User_Purchase_Count), "User_Purchase_Count"] <- 1
str(test_set)## 'data.frame': 233599 obs. of 13 variables:
## $ Product_ID : Factor w/ 3491 levels "P00000142","P00000242",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ User_ID : int 1003796 1002934 1004633 1002682 1003292 1005650 1001593 1001803 1003281 1005543 ...
## $ Gender : Factor w/ 2 levels "F","M": 2 1 2 2 1 1 2 2 2 2 ...
## $ Age : Factor w/ 7 levels "0-17","18-25",..: 5 4 1 2 6 4 3 3 3 3 ...
## $ Occupation : int 14 20 0 4 6 12 16 2 17 17 ...
## $ City_Category : Factor w/ 3 levels "A","B","C": 3 2 2 3 2 2 3 2 3 1 ...
## $ Stay_In_Current_City_Years: Factor w/ 5 levels "0","1","2","3",..: 5 4 2 4 4 3 2 2 4 3 ...
## $ Marital_Status : int 1 0 0 1 0 1 1 0 0 0 ...
## $ Product_Category_1 : int 3 3 3 3 3 3 3 3 3 3 ...
## $ Product_Category_2 : int 4 4 4 4 4 4 4 4 4 4 ...
## $ Product_Category_3 : int 5 5 5 5 5 5 5 5 5 5 ...
## $ User_Purchase_Count : num 120 363 46 56 502 166 112 149 93 190 ...
## $ Product_Sold_Count : int 1152 1152 1152 1152 1152 1152 1152 1152 1152 1152 ...3.3.Gender and Martial Status
head(train_set$Gender)## [1] Male Male Male Female Male Female
## Levels: Female Malehead(train_set$Marital_Status)## [1] Married Single Single Married Married Single
## Levels: Married Singled3 <- table(EDA_Distinct$Gender, EDA_Distinct$Marital_Status)
d3##
## Married Single
## Female 719 947
## Male 1755 2470p8 <- ggplot(EDA_Distinct, aes(x=Gender, fill= Marital_Status)) + geom_bar(position = "dodge") + ggtitle("") + labs(x="Gender",y="No. of distinct Sales") + annotate(geom = "text", x = 0.775, y = 619, label = "719") + annotate(geom = "text", x = 1.225, y = 847, label = "947") + annotate(geom = "text", x = 1.775, y = 1655, label = "1755") + annotate(geom = "text", x = 2.225, y = 2370, label = "2470") + scale_fill_manual(values=c("tan4","limegreen"))
p8
3.4.Age
head(train_set, 10)## Product_ID User_ID Gender Age Occupation City_Category
## 1 P00000142 1003081 Male 26-35 7 B
## 2 P00000142 1003402 Male 36-45 20 B
## 3 P00000142 1000239 Male 18-25 19 A
## 4 P00000142 1005578 Female 18-25 1 C
## 5 P00000142 1002457 Male 26-35 0 B
## 6 P00000142 1000629 Female 0-17 10 C
## 7 P00000142 1001962 Female 18-25 1 B
## 8 P00000142 1004367 Male 36-45 0 C
## 9 P00000142 1004757 Male 36-45 17 C
## 10 P00000142 1004704 Male 18-25 20 C
## Stay_In_Current_City_Years Marital_Status Product_Category_1
## 1 1 Married 3
## 2 4+ Single 3
## 3 1 Single 3
## 4 1 Married 3
## 5 0 Married 3
## 6 2 Single 3
## 7 0 Married 3
## 8 1 Single 3
## 9 1 Single 3
## 10 1 Single 3
## Product_Category_2 Product_Category_3 Purchase User_Purchase_Count
## 1 4 5 13642 197
## 2 4 5 8335 171
## 3 4 5 13374 39
## 4 4 5 10883 51
## 5 4 5 10843 282
## 6 4 5 11041 129
## 7 4 5 13206 287
## 8 4 5 10568 73
## 9 4 5 13578 62
## 10 4 5 10784 175
## Product_Sold_Count
## 1 1152
## 2 1152
## 3 1152
## 4 1152
## 5 1152
## 6 1152
## 7 1152
## 8 1152
## 9 1152
## 10 1152Distribution of distinct customers
d4 <- table(EDA_Distinct$Age)
d4##
## 0-17 18-25 26-35 36-45 46-50 51-55 55+
## 218 1069 2053 1167 531 481 372p9 <- ggplot(EDA_Distinct, aes(x=Age)) + geom_bar(fill=rainbow(7), col="black") + ggtitle("") + labs(x="Age Group",y="No. of distinct buyer") + annotate(geom = "text", x = 1, y = 168, label = "218") + annotate(geom = "text", x = 2, y = 1019, label = "1069") + annotate(geom = "text", x = 3, y = 2000, label = "2053") + annotate(geom = "text", x = 4, y = 1117, label = "1167") + annotate(geom = "text", x = 5, y = 481, label = "531") + annotate(geom = "text", x = 6, y = 431, label = "481") + annotate(geom = "text", x = 7, y = 322, label = "372")
p9
d5 <- table(EDA_Distinct$Marital_Status, EDA_Distinct$Gender, EDA_Distinct$Age)
d5## , , = 0-17
##
##
## Female Male
## Married 0 0
## Single 78 140
##
## , , = 18-25
##
##
## Female Male
## Married 70 174
## Single 217 608
##
## , , = 26-35
##
##
## Female Male
## Married 225 584
## Single 320 924
##
## , , = 36-45
##
##
## Female Male
## Married 131 331
## Single 202 503
##
## , , = 46-50
##
##
## Female Male
## Married 133 242
## Single 49 107
##
## , , = 51-55
##
##
## Female Male
## Married 93 252
## Single 49 87
##
## , , = 55+
##
##
## Female Male
## Married 67 172
## Single 32 101p10 <- ggplot(EDA_Distinct, aes(x= Age,fill= Gender, col= Marital_Status)) + geom_bar(position = "dodge", size=1.25) + labs(x="Age Group",y="No. of distinct buyer") + scale_fill_manual(values=c("hotpink", "royalblue")) + scale_color_manual(values=c("tan4","limegreen")) + ggtitle("")
p10
p11 <- ggplot(EDA_Distinct,aes(x=Age,fill=Marital_Status))+geom_bar(position = "dodge")+facet_grid(Gender~.) + scale_fill_manual(values=c("tan4","limegreen"))
p11
3.5.Occupation
head(train_set$Occupation, 10)## [1] 7 20 19 1 0 10 1 0 17 20
## Levels: 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20d6 <- table(EDA_Distinct$Occupation)
d6##
## 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
## 688 517 256 170 740 111 228 669 17 88 192 128 376 140 294 140 235 491
## 18 19 20
## 67 71 273d7 <- table(EDA_Distinct$Gender, EDA_Distinct$Occupation)
d7##
## 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
## Female 226 203 88 98 228 31 99 137 3 85 66 22 46 33 78 28
## Male 462 314 168 72 512 80 129 532 14 3 126 106 330 107 216 112
##
## 16 17 18 19 20
## Female 49 50 4 15 77
## Male 186 441 63 56 196p12 <- ggplot(EDA_Distinct, aes(x=Occupation, fill=Gender)) + geom_bar( col="black") + ggtitle("") + labs(x="Occupation",y="No. of distinct people") + scale_fill_manual(values=c("hotpink", "royalblue"))
p12
d8 <- table(EDA_Distinct$Marital_Status, EDA_Distinct$Occupation)
d8##
## 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
## Married 286 238 112 80 196 46 126 314 6 47 8 55 157 90 143 67
## Single 402 279 144 90 544 65 102 355 11 41 184 73 219 50 151 73
##
## 16 17 18 19 20
## Married 125 203 32 14 129
## Single 110 288 35 57 144p13 <- ggplot(EDA_Distinct, aes(x=Occupation, fill=Marital_Status)) + geom_bar( col="black") + ggtitle("") + labs(x="Occupation",y="No. of distinct people") + scale_fill_manual(values=c("tan4","limegreen"))
p13
p14 <- ggplot(EDA_Distinct,aes(x=Occupation, fill=Age))+geom_bar()+facet_grid(Gender~Marital_Status)
p14
3.6.City Category and Stay In Current City Years
head(train_set$Stay_In_Current_City_Years, 10)## [1] 1 4+ 1 1 0 2 0 1 1 1
## Levels: 0 1 2 3 4+head(train_set$City_Category, 10)## [1] B B A C B C B C C C
## Levels: A B Cd9 <- table(EDA_Distinct$City_Category, EDA_Distinct$Stay_In_Current_City_Years)
d9##
## 0 1 2 3 4+
## A 147 370 183 180 165
## B 211 608 342 295 251
## C 414 1108 620 504 493p15 <- ggplot(EDA_Distinct, aes(x=Stay_In_Current_City_Years, fill=City_Category)) + geom_bar( col="black") + ggtitle("") + labs(x="Stay in Current City (Years)",y="No. of distinct people")
p15
p16 <- ggplot(EDA_Distinct,aes(City_Category,fill=Age))+geom_bar()
p16
p17 <- ggplot(EDA_Distinct,aes(x=Age,fill=Stay_In_Current_City_Years))+geom_bar()+facet_grid(City_Category~ Stay_In_Current_City_Years)
p17
p18 <- ggplot(EDA_Distinct,aes(x=Age,fill=Marital_Status))+geom_bar()+facet_grid(Gender~City_Category) + scale_fill_manual(values=c("tan4","limegreen"))
p18
3.7.Product Category
head(as.factor(train_set$Product_Category_1))## [1] 3 3 3 3 3 3
## Levels: 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20head(as.factor(train_set$Product_Category_2))## [1] 4 4 4 4 4 4
## Levels: 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18head(as.factor(train_set$Product_Category_3))## [1] 5 5 5 5 5 5
## Levels: 3 4 5 6 8 9 10 11 12 13 14 15 16 17 18Discussion
It can be seen that there are 20 product categories for the product and to deal with the missing data we can do either of the two things described below: 1. We can create 20 binary variables for the 20 product type. 2. We can assign the missing value equal to product category of 1 i.e. same product category in the missing position.
We could have some problem while using the second method when one of the value is missing out of three. So lets use the first solution. ####Feature manuplation and Creation - Training Set
train_set$Product_Category_1 <- as.factor(train_set$Product_Category_1)
train_set$Product_Category_2 <- as.factor(train_set$Product_Category_2)
train_set$Product_Category_3 <- as.factor(train_set$Product_Category_3)train_set$Product_Category_2 <- factor(train_set$Product_Category_2, levels=c(levels(train_set$Product_Category_2), "0"))
train_set[is.na(train_set$Product_Category_2), "Product_Category_2"] <-"0"train_set$Product_Category_3 <- factor(train_set$Product_Category_3, levels=c(levels(train_set$Product_Category_3), "0"))
train_set[is.na(train_set$Product_Category_3), "Product_Category_3"] <-"0"# Creating new binary variable based on product category
train_set$Cat_1 <- as.factor(ifelse((train_set$Product_Category_1=='1' | train_set$Product_Category_2=='1' | train_set$Product_Category_3=='1'), 1,0))
for(i in 2:20)
{
assign(paste("Cat_", as.character(i), sep=""),as.factor(ifelse((train_set$Product_Category_1==i | train_set$Product_Category_2==i | train_set$Product_Category_3==i), 1,0)))
}
train_set <- cbind(train_set, Cat_2, Cat_3, Cat_4, Cat_5, Cat_6, Cat_7, Cat_8, Cat_9, Cat_10, Cat_11, Cat_12, Cat_13, Cat_14, Cat_15, Cat_16, Cat_17, Cat_18, Cat_19, Cat_20)# Dropping the unnecessary variables
to_drop <- c("Product_Category_1", "Product_Category_2", "Product_Category_3")
train_set <- train_set[,!names(train_set)%in% to_drop]
rm(Cat_2, Cat_3, Cat_4, Cat_5, Cat_6, Cat_7, Cat_8, Cat_9, Cat_10, Cat_11, Cat_12, Cat_13, Cat_14, Cat_15, Cat_16, Cat_17, Cat_18, Cat_19, Cat_20)#Checking the final structure of the dataset
dim(train_set)## [1] 550068 31as.matrix(sapply(train_set, function(x) class(x)))## [,1]
## Product_ID "factor"
## User_ID "factor"
## Gender "factor"
## Age "factor"
## Occupation "factor"
## City_Category "factor"
## Stay_In_Current_City_Years "factor"
## Marital_Status "factor"
## Purchase "integer"
## User_Purchase_Count "integer"
## Product_Sold_Count "integer"
## Cat_1 "factor"
## Cat_2 "factor"
## Cat_3 "factor"
## Cat_4 "factor"
## Cat_5 "factor"
## Cat_6 "factor"
## Cat_7 "factor"
## Cat_8 "factor"
## Cat_9 "factor"
## Cat_10 "factor"
## Cat_11 "factor"
## Cat_12 "factor"
## Cat_13 "factor"
## Cat_14 "factor"
## Cat_15 "factor"
## Cat_16 "factor"
## Cat_17 "factor"
## Cat_18 "factor"
## Cat_19 "factor"
## Cat_20 "factor"Feature manuplation and Creation - Test Set
test_set$Product_Category_1 <- as.factor(test_set$Product_Category_1)
test_set$Product_Category_2 <- as.factor(test_set$Product_Category_2)
test_set$Product_Category_3 <- as.factor(test_set$Product_Category_3)test_set$Product_Category_2 <- factor(test_set$Product_Category_2, levels=c(levels(test_set$Product_Category_2), "0"))
test_set[is.na(test_set$Product_Category_2), "Product_Category_2"] <-"0"test_set$Product_Category_3 <- factor(test_set$Product_Category_3, levels=c(levels(test_set$Product_Category_3), "0"))
test_set[is.na(test_set$Product_Category_3), "Product_Category_3"] <-"0"# Creating new binary variable based on product category
for(i in 1:20)
{
assign(paste("Cat_", as.character(i), sep=""),as.factor(ifelse((test_set$Product_Category_1==i | test_set$Product_Category_2==i | test_set$Product_Category_3==i), 1,0)))
}
test_set <- cbind(test_set, Cat_1, Cat_2, Cat_3, Cat_4, Cat_5, Cat_6, Cat_7, Cat_8, Cat_9, Cat_10, Cat_11, Cat_12, Cat_13, Cat_14, Cat_15, Cat_16, Cat_17, Cat_18, Cat_19, Cat_20)# Dropping the unnecessary variables
to_drop <- c("Product_Category_1", "Product_Category_2", "Product_Category_3")
test_set <- test_set[,!names(test_set)%in% to_drop]
rm(Cat_1, Cat_2, Cat_3, Cat_4, Cat_5, Cat_6, Cat_7, Cat_8, Cat_9, Cat_10, Cat_11, Cat_12, Cat_13, Cat_14, Cat_15, Cat_16, Cat_17, Cat_18, Cat_19, Cat_20)#Checking the final structure of the dataset
dim(test_set)## [1] 233599 30as.matrix(sapply(test_set, function(x) class(x)))## [,1]
## Product_ID "factor"
## User_ID "integer"
## Gender "factor"
## Age "factor"
## Occupation "integer"
## City_Category "factor"
## Stay_In_Current_City_Years "factor"
## Marital_Status "integer"
## User_Purchase_Count "numeric"
## Product_Sold_Count "integer"
## Cat_1 "factor"
## Cat_2 "factor"
## Cat_3 "factor"
## Cat_4 "factor"
## Cat_5 "factor"
## Cat_6 "factor"
## Cat_7 "factor"
## Cat_8 "factor"
## Cat_9 "factor"
## Cat_10 "factor"
## Cat_11 "factor"
## Cat_12 "factor"
## Cat_13 "factor"
## Cat_14 "factor"
## Cat_15 "factor"
## Cat_16 "factor"
## Cat_17 "factor"
## Cat_18 "factor"
## Cat_19 "factor"
## Cat_20 "factor"#To remove some workspace
rm(d1,d2,d3,d4,d5,d6,d7,d8,d9,p1,p2,p3,p4,p5,p6,p7,p8,p9,p10,p11,p12,p13,p14,p15,p16,p17,p18,i,to_drop)## Warning in rm(d1, d2, d3, d4, d5, d6, d7, d8, d9, p1, p2, p3, p4, p5, p6, :
## object 'd2' not found## Warning in rm(d1, d2, d3, d4, d5, d6, d7, d8, d9, p1, p2, p3, p4, p5, p6, :
## object 'p7' not found3.9.Final View
str(train_set)## 'data.frame': 550068 obs. of 31 variables:
## $ Product_ID : Factor w/ 3631 levels "P00000142","P00000242",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ User_ID : Factor w/ 5891 levels "1000001","1000002",..: 3000 3309 235 5439 2386 612 1907 4255 4639 4588 ...
## $ Gender : Factor w/ 2 levels "Female","Male": 2 2 2 1 2 1 1 2 2 2 ...
## $ Age : Factor w/ 7 levels "0-17","18-25",..: 3 4 2 2 3 1 2 4 4 2 ...
## $ Occupation : Factor w/ 21 levels "0","1","2","3",..: 8 21 20 2 1 11 2 1 18 21 ...
## $ City_Category : Factor w/ 3 levels "A","B","C": 2 2 1 3 2 3 2 3 3 3 ...
## $ Stay_In_Current_City_Years: Factor w/ 5 levels "0","1","2","3",..: 2 5 2 2 1 3 1 2 2 2 ...
## $ Marital_Status : Factor w/ 2 levels "Married","Single": 1 2 2 1 1 2 1 2 2 2 ...
## $ Purchase : int 13642 8335 13374 10883 10843 11041 13206 10568 13578 10784 ...
## $ User_Purchase_Count : int 197 171 39 51 282 129 287 73 62 175 ...
## $ Product_Sold_Count : int 1152 1152 1152 1152 1152 1152 1152 1152 1152 1152 ...
## $ Cat_1 : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ Cat_2 : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ Cat_3 : Factor w/ 2 levels "0","1": 2 2 2 2 2 2 2 2 2 2 ...
## $ Cat_4 : Factor w/ 2 levels "0","1": 2 2 2 2 2 2 2 2 2 2 ...
## $ Cat_5 : Factor w/ 2 levels "0","1": 2 2 2 2 2 2 2 2 2 2 ...
## $ Cat_6 : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ Cat_7 : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ Cat_8 : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ Cat_9 : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ Cat_10 : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ Cat_11 : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ Cat_12 : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ Cat_13 : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ Cat_14 : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ Cat_15 : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ Cat_16 : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ Cat_17 : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ Cat_18 : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ Cat_19 : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ Cat_20 : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...sapply(train_set, function(x) sum(is.na(x)))## Product_ID User_ID
## 0 0
## Gender Age
## 0 0
## Occupation City_Category
## 0 0
## Stay_In_Current_City_Years Marital_Status
## 0 0
## Purchase User_Purchase_Count
## 0 0
## Product_Sold_Count Cat_1
## 0 0
## Cat_2 Cat_3
## 0 0
## Cat_4 Cat_5
## 0 0
## Cat_6 Cat_7
## 0 0
## Cat_8 Cat_9
## 0 0
## Cat_10 Cat_11
## 0 0
## Cat_12 Cat_13
## 0 0
## Cat_14 Cat_15
## 0 0
## Cat_16 Cat_17
## 0 0
## Cat_18 Cat_19
## 0 0
## Cat_20
## 0 str(test_set)## 'data.frame': 233599 obs. of 30 variables:
## $ Product_ID : Factor w/ 3491 levels "P00000142","P00000242",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ User_ID : int 1003796 1002934 1004633 1002682 1003292 1005650 1001593 1001803 1003281 1005543 ...
## $ Gender : Factor w/ 2 levels "F","M": 2 1 2 2 1 1 2 2 2 2 ...
## $ Age : Factor w/ 7 levels "0-17","18-25",..: 5 4 1 2 6 4 3 3 3 3 ...
## $ Occupation : int 14 20 0 4 6 12 16 2 17 17 ...
## $ City_Category : Factor w/ 3 levels "A","B","C": 3 2 2 3 2 2 3 2 3 1 ...
## $ Stay_In_Current_City_Years: Factor w/ 5 levels "0","1","2","3",..: 5 4 2 4 4 3 2 2 4 3 ...
## $ Marital_Status : int 1 0 0 1 0 1 1 0 0 0 ...
## $ User_Purchase_Count : num 120 363 46 56 502 166 112 149 93 190 ...
## $ Product_Sold_Count : int 1152 1152 1152 1152 1152 1152 1152 1152 1152 1152 ...
## $ Cat_1 : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ Cat_2 : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ Cat_3 : Factor w/ 2 levels "0","1": 2 2 2 2 2 2 2 2 2 2 ...
## $ Cat_4 : Factor w/ 2 levels "0","1": 2 2 2 2 2 2 2 2 2 2 ...
## $ Cat_5 : Factor w/ 2 levels "0","1": 2 2 2 2 2 2 2 2 2 2 ...
## $ Cat_6 : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ Cat_7 : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ Cat_8 : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ Cat_9 : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ Cat_10 : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ Cat_11 : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ Cat_12 : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ Cat_13 : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ Cat_14 : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ Cat_15 : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ Cat_16 : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ Cat_17 : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ Cat_18 : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ Cat_19 : Factor w/ 1 level "0": 1 1 1 1 1 1 1 1 1 1 ...
## $ Cat_20 : Factor w/ 1 level "0": 1 1 1 1 1 1 1 1 1 1 ... sapply(test_set, function(x) sum(is.na(x)))## Product_ID User_ID
## 0 0
## Gender Age
## 0 0
## Occupation City_Category
## 0 0
## Stay_In_Current_City_Years Marital_Status
## 0 0
## User_Purchase_Count Product_Sold_Count
## 0 61
## Cat_1 Cat_2
## 0 0
## Cat_3 Cat_4
## 0 0
## Cat_5 Cat_6
## 0 0
## Cat_7 Cat_8
## 0 0
## Cat_9 Cat_10
## 0 0
## Cat_11 Cat_12
## 0 0
## Cat_13 Cat_14
## 0 0
## Cat_15 Cat_16
## 0 0
## Cat_17 Cat_18
## 0 0
## Cat_19 Cat_20
## 0 04.Conclusion
From the various plot plotted, various kind of associations and the inferences can easily be observed.