Income_Prediction_Group
Idris Yusuf , Nirmal Patel, Robindra and Hussain
2024-05-07
#Load all Libraries needed
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## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorslibrary(dplyr) # For data manipulation
library(ggplot2) # For visualization
library(broom) # For tidying model outputs
library(caret)
library(klaR)
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## +.gg ggplot2#import your data and preserve original copy
data <- read_csv("C:/GGTUAN/DREAMS/Yankee/TSU/MSc_TSU/Spring_2024/CS-583 Data Minning/income.csv")## Rows: 32561 Columns: 15
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (9): workclass, education, marital-status, occupation, relationship, rac...
## dbl (6): age, fnlwgt, education-num, capital-gain, capital-loss, hours-per-week
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.income <- data#Getting to familiarize with your data
### Getting familiar with our data
str(income)## spc_tbl_ [32,561 × 15] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## $ age : num [1:32561] 39 50 38 53 28 37 49 52 31 42 ...
## $ workclass : chr [1:32561] "State-gov" "Self-emp-not-inc" "Private" "Private" ...
## $ fnlwgt : num [1:32561] 77516 83311 215646 234721 338409 ...
## $ education : chr [1:32561] "Bachelors" "Bachelors" "HS-grad" "11th" ...
## $ education-num : num [1:32561] 13 13 9 7 13 14 5 9 14 13 ...
## $ marital-status: chr [1:32561] "Never-married" "Married-civ-spouse" "Divorced" "Married-civ-spouse" ...
## $ occupation : chr [1:32561] "Adm-clerical" "Exec-managerial" "Handlers-cleaners" "Handlers-cleaners" ...
## $ relationship : chr [1:32561] "Not-in-family" "Husband" "Not-in-family" "Husband" ...
## $ race : chr [1:32561] "White" "White" "White" "Black" ...
## $ sex : chr [1:32561] "Male" "Male" "Male" "Male" ...
## $ capital-gain : num [1:32561] 2174 0 0 0 0 ...
## $ capital-loss : num [1:32561] 0 0 0 0 0 0 0 0 0 0 ...
## $ hours-per-week: num [1:32561] 40 13 40 40 40 40 16 45 50 40 ...
## $ native-country: chr [1:32561] "United-States" "United-States" "United-States" "United-States" ...
## $ income : chr [1:32561] "<=50K" "<=50K" "<=50K" "<=50K" ...
## - attr(*, "spec")=
## .. cols(
## .. age = col_double(),
## .. workclass = col_character(),
## .. fnlwgt = col_double(),
## .. education = col_character(),
## .. `education-num` = col_double(),
## .. `marital-status` = col_character(),
## .. occupation = col_character(),
## .. relationship = col_character(),
## .. race = col_character(),
## .. sex = col_character(),
## .. `capital-gain` = col_double(),
## .. `capital-loss` = col_double(),
## .. `hours-per-week` = col_double(),
## .. `native-country` = col_character(),
## .. income = col_character()
## .. )
## - attr(*, "problems")=<externalptr>summary(income)## age workclass fnlwgt education
## Min. :17.00 Length:32561 Min. : 12285 Length:32561
## 1st Qu.:28.00 Class :character 1st Qu.: 117827 Class :character
## Median :37.00 Mode :character Median : 178356 Mode :character
## Mean :38.58 Mean : 189778
## 3rd Qu.:48.00 3rd Qu.: 237051
## Max. :90.00 Max. :1484705
## education-num marital-status occupation relationship
## Min. : 1.00 Length:32561 Length:32561 Length:32561
## 1st Qu.: 9.00 Class :character Class :character Class :character
## Median :10.00 Mode :character Mode :character Mode :character
## Mean :10.08
## 3rd Qu.:12.00
## Max. :16.00
## race sex capital-gain capital-loss
## Length:32561 Length:32561 Min. : 0 Min. : 0.0
## Class :character Class :character 1st Qu.: 0 1st Qu.: 0.0
## Mode :character Mode :character Median : 0 Median : 0.0
## Mean : 1078 Mean : 87.3
## 3rd Qu.: 0 3rd Qu.: 0.0
## Max. :99999 Max. :4356.0
## hours-per-week native-country income
## Min. : 1.00 Length:32561 Length:32561
## 1st Qu.:40.00 Class :character Class :character
## Median :40.00 Mode :character Mode :character
## Mean :40.44
## 3rd Qu.:45.00
## Max. :99.00head(income)## # A tibble: 6 × 15
## age workclass fnlwgt education `education-num` `marital-status` occupation
## <dbl> <chr> <dbl> <chr> <dbl> <chr> <chr>
## 1 39 State-gov 77516 Bachelors 13 Never-married Adm-cleri…
## 2 50 Self-emp-n… 83311 Bachelors 13 Married-civ-spo… Exec-mana…
## 3 38 Private 215646 HS-grad 9 Divorced Handlers-…
## 4 53 Private 234721 11th 7 Married-civ-spo… Handlers-…
## 5 28 Private 338409 Bachelors 13 Married-civ-spo… Prof-spec…
## 6 37 Private 284582 Masters 14 Married-civ-spo… Exec-mana…
## # ℹ 8 more variables: relationship <chr>, race <chr>, sex <chr>,
## # `capital-gain` <dbl>, `capital-loss` <dbl>, `hours-per-week` <dbl>,
## # `native-country` <chr>, income <chr>colnames(income)## [1] "age" "workclass" "fnlwgt" "education"
## [5] "education-num" "marital-status" "occupation" "relationship"
## [9] "race" "sex" "capital-gain" "capital-loss"
## [13] "hours-per-week" "native-country" "income"dim(income) ### 32561## [1] 32561 15#rename the column to remove special character
colnames(income)## [1] "age" "workclass" "fnlwgt" "education"
## [5] "education-num" "marital-status" "occupation" "relationship"
## [9] "race" "sex" "capital-gain" "capital-loss"
## [13] "hours-per-week" "native-country" "income"colnames(income) <- c("age", "workclass", "fnlwgt", "education", "education.num","marital.status", "occupation", "relationship" , "race", "sex", "capital.gain", "capital.loss", "hours.per.week", "native.country", "income" )
names(income) <- names(income) %>% make.names()
colnames(income)## [1] "age" "workclass" "fnlwgt" "education"
## [5] "education.num" "marital.status" "occupation" "relationship"
## [9] "race" "sex" "capital.gain" "capital.loss"
## [13] "hours.per.week" "native.country" "income"#### Check what distinct values types
table(income$workclass)##
## ? Federal-gov Local-gov Never-worked
## 1836 960 2093 7
## Private Self-emp-inc Self-emp-not-inc State-gov
## 22696 1116 2541 1298
## Without-pay
## 14prop.table(table(income$workclass))##
## ? Federal-gov Local-gov Never-worked
## 0.0563864746 0.0294831240 0.0642793526 0.0002149811
## Private Self-emp-inc Self-emp-not-inc State-gov
## 0.6970301895 0.0342741316 0.0780381438 0.0398636406
## Without-pay
## 0.0004299622table(income$education)##
## 10th 11th 12th 1st-4th 5th-6th 7th-8th
## 933 1175 433 168 333 646
## 9th Assoc-acdm Assoc-voc Bachelors Doctorate HS-grad
## 514 1067 1382 5355 413 10501
## Masters Preschool Prof-school Some-college
## 1723 51 576 7291prop.table(table(income$education))##
## 10th 11th 12th 1st-4th 5th-6th 7th-8th
## 0.028653911 0.036086115 0.013298117 0.005159547 0.010226959 0.019839686
## 9th Assoc-acdm Assoc-voc Bachelors Doctorate HS-grad
## 0.015785756 0.032769264 0.042443414 0.164460551 0.012683886 0.322502380
## Masters Preschool Prof-school Some-college
## 0.052916065 0.001566291 0.017689874 0.223918184table(income$marital.status )##
## Divorced Married-AF-spouse Married-civ-spouse
## 4443 23 14976
## Married-spouse-absent Never-married Separated
## 418 10683 1025
## Widowed
## 993prop.table(table(income$marital.status))##
## Divorced Married-AF-spouse Married-civ-spouse
## 0.1364515832 0.0007063665 0.4599367341
## Married-spouse-absent Never-married Separated
## 0.0128374436 0.3280918891 0.0314793772
## Widowed
## 0.0304966064table(income$occupation)##
## ? Adm-clerical Armed-Forces Craft-repair
## 1843 3770 9 4099
## Exec-managerial Farming-fishing Handlers-cleaners Machine-op-inspct
## 4066 994 1370 2002
## Other-service Priv-house-serv Prof-specialty Protective-serv
## 3295 149 4140 649
## Sales Tech-support Transport-moving
## 3650 928 1597prop.table(table(income$occupation))##
## ? Adm-clerical Armed-Forces Craft-repair
## 0.0566014557 0.1157826848 0.0002764043 0.1258867971
## Exec-managerial Farming-fishing Handlers-cleaners Machine-op-inspct
## 0.1248733147 0.0305273180 0.0420748749 0.0614845981
## Other-service Priv-house-serv Prof-specialty Protective-serv
## 0.1011946808 0.0045760265 0.1271459722 0.0199318203
## Sales Tech-support Transport-moving
## 0.1120972943 0.0285003532 0.0490464052table(income$relationship)##
## Husband Not-in-family Other-relative Own-child Unmarried
## 13193 8305 981 5068 3446
## Wife
## 1568prop.table(table(income$relationship))##
## Husband Not-in-family Other-relative Own-child Unmarried
## 0.40517797 0.25505973 0.03012807 0.15564633 0.10583213
## Wife
## 0.04815577table(income$race)##
## Amer-Indian-Eskimo Asian-Pac-Islander Black Other
## 311 1039 3124 271
## White
## 27816prop.table(table(income$race))##
## Amer-Indian-Eskimo Asian-Pac-Islander Black Other
## 0.009551304 0.031909339 0.095942999 0.008322840
## White
## 0.854273517table(income$sex)##
## Female Male
## 10771 21790prop.table(table(income$sex))##
## Female Male
## 0.3307945 0.6692055table(income$native.country)##
## ? Cambodia
## 583 19
## Canada China
## 121 75
## Columbia Cuba
## 59 95
## Dominican-Republic Ecuador
## 70 28
## El-Salvador England
## 106 90
## France Germany
## 29 137
## Greece Guatemala
## 29 64
## Haiti Holand-Netherlands
## 44 1
## Honduras Hong
## 13 20
## Hungary India
## 13 100
## Iran Ireland
## 43 24
## Italy Jamaica
## 73 81
## Japan Laos
## 62 18
## Mexico Nicaragua
## 643 34
## Outlying-US(Guam-USVI-etc) Peru
## 14 31
## Philippines Poland
## 198 60
## Portugal Puerto-Rico
## 37 114
## Scotland South
## 12 80
## Taiwan Thailand
## 51 18
## Trinadad&Tobago United-States
## 19 29170
## Vietnam Yugoslavia
## 67 16prop.table(table(income$native.country))##
## ? Cambodia
## 1.790486e-02 5.835202e-04
## Canada China
## 3.716102e-03 2.303369e-03
## Columbia Cuba
## 1.811984e-03 2.917601e-03
## Dominican-Republic Ecuador
## 2.149811e-03 8.599244e-04
## El-Salvador England
## 3.255428e-03 2.764043e-03
## France Germany
## 8.906360e-04 4.207487e-03
## Greece Guatemala
## 8.906360e-04 1.965542e-03
## Haiti Holand-Netherlands
## 1.351310e-03 3.071159e-05
## Honduras Hong
## 3.992506e-04 6.142317e-04
## Hungary India
## 3.992506e-04 3.071159e-03
## Iran Ireland
## 1.320598e-03 7.370781e-04
## Italy Jamaica
## 2.241946e-03 2.487639e-03
## Japan Laos
## 1.904118e-03 5.528086e-04
## Mexico Nicaragua
## 1.974755e-02 1.044194e-03
## Outlying-US(Guam-USVI-etc) Peru
## 4.299622e-04 9.520592e-04
## Philippines Poland
## 6.080894e-03 1.842695e-03
## Portugal Puerto-Rico
## 1.136329e-03 3.501121e-03
## Scotland South
## 3.685390e-04 2.456927e-03
## Taiwan Thailand
## 1.566291e-03 5.528086e-04
## Trinadad&Tobago United-States
## 5.835202e-04 8.958570e-01
## Vietnam Yugoslavia
## 2.057676e-03 4.913854e-04table(income$income)##
## <=50K >50K
## 24720 7841prop.table(table(income$income))##
## <=50K >50K
## 0.7591904 0.2408096#Standard Deviation
summary(income)## age workclass fnlwgt education
## Min. :17.00 Length:32561 Min. : 12285 Length:32561
## 1st Qu.:28.00 Class :character 1st Qu.: 117827 Class :character
## Median :37.00 Mode :character Median : 178356 Mode :character
## Mean :38.58 Mean : 189778
## 3rd Qu.:48.00 3rd Qu.: 237051
## Max. :90.00 Max. :1484705
## education.num marital.status occupation relationship
## Min. : 1.00 Length:32561 Length:32561 Length:32561
## 1st Qu.: 9.00 Class :character Class :character Class :character
## Median :10.00 Mode :character Mode :character Mode :character
## Mean :10.08
## 3rd Qu.:12.00
## Max. :16.00
## race sex capital.gain capital.loss
## Length:32561 Length:32561 Min. : 0 Min. : 0.0
## Class :character Class :character 1st Qu.: 0 1st Qu.: 0.0
## Mode :character Mode :character Median : 0 Median : 0.0
## Mean : 1078 Mean : 87.3
## 3rd Qu.: 0 3rd Qu.: 0.0
## Max. :99999 Max. :4356.0
## hours.per.week native.country income
## Min. : 1.00 Length:32561 Length:32561
## 1st Qu.:40.00 Class :character Class :character
## Median :40.00 Mode :character Mode :character
## Mean :40.44
## 3rd Qu.:45.00
## Max. :99.00sapply(income, sd)## Warning in var(if (is.vector(x) || is.factor(x)) x else as.double(x), na.rm =
## na.rm): NAs introduced by coercion
## Warning in var(if (is.vector(x) || is.factor(x)) x else as.double(x), na.rm =
## na.rm): NAs introduced by coercion
## Warning in var(if (is.vector(x) || is.factor(x)) x else as.double(x), na.rm =
## na.rm): NAs introduced by coercion
## Warning in var(if (is.vector(x) || is.factor(x)) x else as.double(x), na.rm =
## na.rm): NAs introduced by coercion
## Warning in var(if (is.vector(x) || is.factor(x)) x else as.double(x), na.rm =
## na.rm): NAs introduced by coercion
## Warning in var(if (is.vector(x) || is.factor(x)) x else as.double(x), na.rm =
## na.rm): NAs introduced by coercion
## Warning in var(if (is.vector(x) || is.factor(x)) x else as.double(x), na.rm =
## na.rm): NAs introduced by coercion
## Warning in var(if (is.vector(x) || is.factor(x)) x else as.double(x), na.rm =
## na.rm): NAs introduced by coercion
## Warning in var(if (is.vector(x) || is.factor(x)) x else as.double(x), na.rm =
## na.rm): NAs introduced by coercion## age workclass fnlwgt education education.num
## 13.64043 NA 105549.97770 NA 2.57272
## marital.status occupation relationship race sex
## NA NA NA NA NA
## capital.gain capital.loss hours.per.week native.country income
## 7385.29208 402.96022 12.34743 NA NA#Check NA , NULL and ?
sum(is.na(income))## [1] 0sum(is.null(income))## [1] 0sum(income=="?") ### Total of 4262 "question marks ?"## [1] 4262colSums(income=="?") #### ? found in columns - workclass -1836, occupation - 1843, native.country - 583 ## age workclass fnlwgt education education.num
## 0 1836 0 0 0
## marital.status occupation relationship race sex
## 0 1843 0 0 0
## capital.gain capital.loss hours.per.week native.country income
## 0 0 0 583 0#income_data <- income[!apply(income== '?', 1, any), ]
## Get unique values of affected columns
unique(income$workclass)## [1] "State-gov" "Self-emp-not-inc" "Private" "Federal-gov"
## [5] "Local-gov" "?" "Self-emp-inc" "Without-pay"
## [9] "Never-worked"unique(income$sex)## [1] "Male" "Female"unique(income$marital.status)## [1] "Never-married" "Married-civ-spouse" "Divorced"
## [4] "Married-spouse-absent" "Separated" "Married-AF-spouse"
## [7] "Widowed"table(income$workclass)##
## ? Federal-gov Local-gov Never-worked
## 1836 960 2093 7
## Private Self-emp-inc Self-emp-not-inc State-gov
## 22696 1116 2541 1298
## Without-pay
## 14table(income$occupation)##
## ? Adm-clerical Armed-Forces Craft-repair
## 1843 3770 9 4099
## Exec-managerial Farming-fishing Handlers-cleaners Machine-op-inspct
## 4066 994 1370 2002
## Other-service Priv-house-serv Prof-specialty Protective-serv
## 3295 149 4140 649
## Sales Tech-support Transport-moving
## 3650 928 1597table(income$native.country)##
## ? Cambodia
## 583 19
## Canada China
## 121 75
## Columbia Cuba
## 59 95
## Dominican-Republic Ecuador
## 70 28
## El-Salvador England
## 106 90
## France Germany
## 29 137
## Greece Guatemala
## 29 64
## Haiti Holand-Netherlands
## 44 1
## Honduras Hong
## 13 20
## Hungary India
## 13 100
## Iran Ireland
## 43 24
## Italy Jamaica
## 73 81
## Japan Laos
## 62 18
## Mexico Nicaragua
## 643 34
## Outlying-US(Guam-USVI-etc) Peru
## 14 31
## Philippines Poland
## 198 60
## Portugal Puerto-Rico
## 37 114
## Scotland South
## 12 80
## Taiwan Thailand
## 51 18
## Trinadad&Tobago United-States
## 19 29170
## Vietnam Yugoslavia
## 67 16#Data Cleansing
#### update data
income2 <- within(income, workclass[workclass == '?'] <- 'Unknown-workclass')
income2 <- within(income2, occupation[occupation == '?'] <- 'Unknown-occup')
income2 <- within(income2, native.country[native.country == '?'] <- 'Unknown-country') ## stop here
table(income2$workclass)##
## Federal-gov Local-gov Never-worked Private
## 960 2093 7 22696
## Self-emp-inc Self-emp-not-inc State-gov Unknown-workclass
## 1116 2541 1298 1836
## Without-pay
## 14table(income2$occupation)##
## Adm-clerical Armed-Forces Craft-repair Exec-managerial
## 3770 9 4099 4066
## Farming-fishing Handlers-cleaners Machine-op-inspct Other-service
## 994 1370 2002 3295
## Priv-house-serv Prof-specialty Protective-serv Sales
## 149 4140 649 3650
## Tech-support Transport-moving Unknown-occup
## 928 1597 1843table(income2$native.country)##
## Cambodia Canada
## 19 121
## China Columbia
## 75 59
## Cuba Dominican-Republic
## 95 70
## Ecuador El-Salvador
## 28 106
## England France
## 90 29
## Germany Greece
## 137 29
## Guatemala Haiti
## 64 44
## Holand-Netherlands Honduras
## 1 13
## Hong Hungary
## 20 13
## India Iran
## 100 43
## Ireland Italy
## 24 73
## Jamaica Japan
## 81 62
## Laos Mexico
## 18 643
## Nicaragua Outlying-US(Guam-USVI-etc)
## 34 14
## Peru Philippines
## 31 198
## Poland Portugal
## 60 37
## Puerto-Rico Scotland
## 114 12
## South Taiwan
## 80 51
## Thailand Trinadad&Tobago
## 18 19
## United-States Unknown-country
## 29170 583
## Vietnam Yugoslavia
## 67 16# income3 <- within(income, {
# f <- workclass == '?'
# workclass[f] <- 'Unknown-job'
# })
# table(income3$workclass)Exploratory Data Analysis and Visualization
sum(income2=="?")## [1] 0sum(is.na(income2))## [1] 0sum(is.null(income2))## [1] 0ggplot(data=income2, aes(age)) + geom_histogram(aes(fill=marital.status), binwidth = 1)
ggplot(data=income2, aes(age)) + geom_histogram(aes(fill=income), binwidth = 1)
ggplot(data=income2, aes(age)) + geom_histogram(aes(fill=education.num ), binwidth = 1)## Warning: The following aesthetics were dropped during statistical transformation: fill
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
hist(income2$education.num)
hist(income2$age)
#hist(income2$income)
##keep Income2 data into Income3
income3 <- income2
#### factor
income3$workclass <- factor(income3$workclass)
income3$education <- factor(income3$education)
income3$marital.status <- factor(income3$marital.status)
income3$occupation <- factor(income3$occupation)
income3$relationship <- factor(income3$relationship)
income3$race <- factor(income3$race)
income3$sex <- factor(income3$sex)
income3$native.country <- factor(income3$native.country)
#income$income <- as.integer(factor(income$income))
income3$income <- factor(income3$income)
###Correlation Plotting Visualization #########3
income4 <- income3
income4$workclass <- as.integer(factor(income4$workclass))
income4$education <- as.integer(factor(income4$education))
income4$marital.status <- as.integer(factor(income4$marital.status))
income4$occupation <- as.integer(factor(income4$occupation))
income4$relationship <- as.integer(factor(income4$relationship))
income4$race <- as.integer(factor(income4$race))
income4$sex <- as.integer(factor(income4$sex))
income4$native.country <- as.integer(factor(income4$native.country))
#income4$income <- as.integer(factor(income4$income))
income4$income <- factor(income3$income)
str(income4)## spc_tbl_ [32,561 × 15] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## $ age : num [1:32561] 39 50 38 53 28 37 49 52 31 42 ...
## $ workclass : int [1:32561] 7 6 4 4 4 4 4 6 4 4 ...
## $ fnlwgt : num [1:32561] 77516 83311 215646 234721 338409 ...
## $ education : int [1:32561] 10 10 12 2 10 13 7 12 13 10 ...
## $ education.num : num [1:32561] 13 13 9 7 13 14 5 9 14 13 ...
## $ marital.status: int [1:32561] 5 3 1 3 3 3 4 3 5 3 ...
## $ occupation : int [1:32561] 1 4 6 6 10 4 8 4 10 4 ...
## $ relationship : int [1:32561] 2 1 2 1 6 6 2 1 2 1 ...
## $ race : int [1:32561] 5 5 5 3 3 5 3 5 5 5 ...
## $ sex : int [1:32561] 2 2 2 2 1 1 1 2 1 2 ...
## $ capital.gain : num [1:32561] 2174 0 0 0 0 ...
## $ capital.loss : num [1:32561] 0 0 0 0 0 0 0 0 0 0 ...
## $ hours.per.week: num [1:32561] 40 13 40 40 40 40 16 45 50 40 ...
## $ native.country: int [1:32561] 39 39 39 39 5 39 23 39 39 39 ...
## $ income : Factor w/ 2 levels "<=50K",">50K": 1 1 1 1 1 1 1 2 2 2 ...
## - attr(*, "spec")=
## .. cols(
## .. age = col_double(),
## .. workclass = col_character(),
## .. fnlwgt = col_double(),
## .. education = col_character(),
## .. `education-num` = col_double(),
## .. `marital-status` = col_character(),
## .. occupation = col_character(),
## .. relationship = col_character(),
## .. race = col_character(),
## .. sex = col_character(),
## .. `capital-gain` = col_double(),
## .. `capital-loss` = col_double(),
## .. `hours-per-week` = col_double(),
## .. `native-country` = col_character(),
## .. income = col_character()
## .. )
## - attr(*, "problems")=<externalptr>cor(x = income4[-15], y = as.numeric(income4$income))## [,1]
## age 0.234037103
## workclass -0.048619885
## fnlwgt -0.009462557
## education 0.079316609
## education.num 0.335153953
## marital.status -0.199307009
## occupation 0.010801975
## relationship -0.250918142
## race 0.071845611
## sex 0.215980151
## capital.gain 0.223328818
## capital.loss 0.150526312
## hours.per.week 0.229689066
## native.country 0.022989504library(ggcorrplot)
model.matrix(~0+., data=income4) %>%
cor(use="pairwise.complete.obs") %>%
ggcorrplot(show.diag=FALSE, type="lower", lab=TRUE, lab_size=2)
#pairs.panels(income4)
# pairs.panels(income4[c(1,4,5,6,7,9,10,11,12,13)], bg=c("red","yellow","blue")[income4$income],
# pch=21+as.numeric(income4$income),main="income",hist.col="red")
#income.cor = cor(income2)
#corrplot(income.cor)
#income4.cor = cor(x = income4[-15], y = as.numeric(income4$income))#Split Data to Test and Train
#### Splitting of data to Training and Test Dataset ####
trainIndex <- createDataPartition(income4$income, p=0.80, list=FALSE)
dataTrainIncome4 <- income4[ trainIndex,]
dataTestIncome4 <- income4[-trainIndex,]
dim(dataTrainIncome4) ###26049 15## [1] 26049 15dim(dataTestIncome4) ###6512 15## [1] 6512 15head(dataTrainIncome4)## # A tibble: 6 × 15
## age workclass fnlwgt education education.num marital.status occupation
## <dbl> <int> <dbl> <int> <dbl> <int> <int>
## 1 39 7 77516 10 13 5 1
## 2 50 6 83311 10 13 3 4
## 3 38 4 215646 12 9 1 6
## 4 53 4 234721 2 7 3 6
## 5 37 4 284582 13 14 3 4
## 6 52 6 209642 12 9 3 4
## # ℹ 8 more variables: relationship <int>, race <int>, sex <int>,
## # capital.gain <dbl>, capital.loss <dbl>, hours.per.week <dbl>,
## # native.country <int>, income <fct>View(dataTrainIncome4)income_Tree <- income3
str(income_Tree)## spc_tbl_ [32,561 × 15] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## $ age : num [1:32561] 39 50 38 53 28 37 49 52 31 42 ...
## $ workclass : Factor w/ 9 levels "Federal-gov",..: 7 6 4 4 4 4 4 6 4 4 ...
## $ fnlwgt : num [1:32561] 77516 83311 215646 234721 338409 ...
## $ education : Factor w/ 16 levels "10th","11th",..: 10 10 12 2 10 13 7 12 13 10 ...
## $ education.num : num [1:32561] 13 13 9 7 13 14 5 9 14 13 ...
## $ marital.status: Factor w/ 7 levels "Divorced","Married-AF-spouse",..: 5 3 1 3 3 3 4 3 5 3 ...
## $ occupation : Factor w/ 15 levels "Adm-clerical",..: 1 4 6 6 10 4 8 4 10 4 ...
## $ relationship : Factor w/ 6 levels "Husband","Not-in-family",..: 2 1 2 1 6 6 2 1 2 1 ...
## $ race : Factor w/ 5 levels "Amer-Indian-Eskimo",..: 5 5 5 3 3 5 3 5 5 5 ...
## $ sex : Factor w/ 2 levels "Female","Male": 2 2 2 2 1 1 1 2 1 2 ...
## $ capital.gain : num [1:32561] 2174 0 0 0 0 ...
## $ capital.loss : num [1:32561] 0 0 0 0 0 0 0 0 0 0 ...
## $ hours.per.week: num [1:32561] 40 13 40 40 40 40 16 45 50 40 ...
## $ native.country: Factor w/ 42 levels "Cambodia","Canada",..: 39 39 39 39 5 39 23 39 39 39 ...
## $ income : Factor w/ 2 levels "<=50K",">50K": 1 1 1 1 1 1 1 2 2 2 ...
## - attr(*, "spec")=
## .. cols(
## .. age = col_double(),
## .. workclass = col_character(),
## .. fnlwgt = col_double(),
## .. education = col_character(),
## .. `education-num` = col_double(),
## .. `marital-status` = col_character(),
## .. occupation = col_character(),
## .. relationship = col_character(),
## .. race = col_character(),
## .. sex = col_character(),
## .. `capital-gain` = col_double(),
## .. `capital-loss` = col_double(),
## .. `hours-per-week` = col_double(),
## .. `native-country` = col_character(),
## .. income = col_character()
## .. )
## - attr(*, "problems")=<externalptr>############################### Decision Tree Charting #####
income_Tree <- income3
trainTreeIndex <- createDataPartition(income_Tree$income, p=0.80, list=FALSE)
dataTrainIncomeTree <- income_Tree[ trainTreeIndex,]
dataTestIncomeTree <- income_Tree[-trainTreeIndex,]
dim(dataTrainIncomeTree) ###24131 15## [1] 26049 15dim(dataTestIncomeTree) ###6031 15## [1] 6512 15head(dataTrainIncomeTree)## # A tibble: 6 × 15
## age workclass fnlwgt education education.num marital.status occupation
## <dbl> <fct> <dbl> <fct> <dbl> <fct> <fct>
## 1 39 State-gov 77516 Bachelors 13 Never-married Adm-cleri…
## 2 38 Private 215646 HS-grad 9 Divorced Handlers-…
## 3 28 Private 338409 Bachelors 13 Married-civ-s… Prof-spec…
## 4 37 Private 284582 Masters 14 Married-civ-s… Exec-mana…
## 5 52 Self-emp-not-i… 209642 HS-grad 9 Married-civ-s… Exec-mana…
## 6 31 Private 45781 Masters 14 Never-married Prof-spec…
## # ℹ 8 more variables: relationship <fct>, race <fct>, sex <fct>,
## # capital.gain <dbl>, capital.loss <dbl>, hours.per.week <dbl>,
## # native.country <fct>, income <fct>#View(dataTestIncomeTree)
fit.rpart <- rpart(income ~ ., data=dataTrainIncomeTree, method="class" )
#fit <- rpart(T5.survived ~ T5.sex + T5.age + T5.sibsp + T5.parch + T5.fare + T5.embarked, data=training_dataset2, method="class")
fit.rpart## n= 26049
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 26049 6273 <=50K (0.75918461 0.24081539)
## 2) relationship=Not-in-family,Other-relative,Own-child,Unmarried 14230 948 <=50K (0.93338018 0.06661982)
## 4) capital.gain< 7073.5 13964 691 <=50K (0.95051561 0.04948439) *
## 5) capital.gain>=7073.5 266 9 >50K (0.03383459 0.96616541) *
## 3) relationship=Husband,Wife 11819 5325 <=50K (0.54945427 0.45054573)
## 6) education=10th,11th,12th,1st-4th,5th-6th,7th-8th,9th,Assoc-acdm,Assoc-voc,HS-grad,Preschool,Some-college 8289 2763 <=50K (0.66666667 0.33333333)
## 12) capital.gain< 5095.5 7863 2344 <=50K (0.70189495 0.29810505) *
## 13) capital.gain>=5095.5 426 7 >50K (0.01643192 0.98356808) *
## 7) education=Bachelors,Doctorate,Masters,Prof-school 3530 968 >50K (0.27422096 0.72577904) *####Ploting the fit treee
#png("C:/GGTUAN/DREAMS/Yankee/TSU/MSc_TSU/Spring_2024/CS-583 Data Minning/income_rpart2.png")
rpart.plot(fit.rpart, extra = 106)
#png("C:/GGTUAN/DREAMS/Yankee/TSU/MSc_TSU/Spring_2024/CS-583 Data Minning/fancy_rpart_tree.png")
fancyRpartPlot(fit.rpart, caption=NULL)
#dev.off()
fit.rpart$variable.importance## relationship marital.status capital.gain education education.num
## 1903.35338 1873.39960 858.82907 762.57974 762.57974
## sex occupation age hours.per.week native.country
## 594.40539 533.48871 435.77919 251.86942 16.85020
## capital.loss
## 13.82581names(fit.rpart)## [1] "frame" "where" "call"
## [4] "terms" "cptable" "method"
## [7] "parms" "control" "functions"
## [10] "numresp" "splits" "csplit"
## [13] "variable.importance" "y" "ordered"printcp(fit.rpart)##
## Classification tree:
## rpart(formula = income ~ ., data = dataTrainIncomeTree, method = "class")
##
## Variables actually used in tree construction:
## [1] capital.gain education relationship
##
## Root node error: 6273/26049 = 0.24082
##
## n= 26049
##
## CP nsplit rel error xerror xstd
## 1 0.127052 0 1.00000 1.00000 0.0110011
## 2 0.065678 2 0.74590 0.74590 0.0098766
## 3 0.039535 3 0.68022 0.68022 0.0095222
## 4 0.010000 4 0.64068 0.64068 0.0092938### Sex and age were dominant factors... Yes the slogan was true
###18 Prediction
PredictionTree <- predict(fit.rpart, dataTestIncomeTree, type = "class")
PredictionTree## 1 2 3 4 5 6 7 8 9 10 11 12 13
## >50K <=50K <=50K <=50K >50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K
## 14 15 16 17 18 19 20 21 22 23 24 25 26
## <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K
## 27 28 29 30 31 32 33 34 35 36 37 38 39
## <=50K <=50K >50K >50K <=50K <=50K <=50K <=50K >50K <=50K <=50K <=50K <=50K
## 40 41 42 43 44 45 46 47 48 49 50 51 52
## >50K <=50K >50K >50K <=50K <=50K <=50K >50K <=50K <=50K <=50K >50K <=50K
## 53 54 55 56 57 58 59 60 61 62 63 64 65
## <=50K <=50K <=50K <=50K <=50K >50K <=50K <=50K <=50K <=50K >50K <=50K <=50K
## 66 67 68 69 70 71 72 73 74 75 76 77 78
## <=50K <=50K >50K <=50K >50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K
## 79 80 81 82 83 84 85 86 87 88 89 90 91
## <=50K <=50K <=50K <=50K >50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K
## 92 93 94 95 96 97 98 99 100 101 102 103 104
## <=50K >50K >50K >50K <=50K >50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K
## 105 106 107 108 109 110 111 112 113 114 115 116 117
## <=50K <=50K <=50K >50K <=50K >50K <=50K <=50K <=50K <=50K >50K <=50K <=50K
## 118 119 120 121 122 123 124 125 126 127 128 129 130
## <=50K <=50K <=50K <=50K >50K <=50K >50K <=50K >50K <=50K <=50K <=50K <=50K
## 131 132 133 134 135 136 137 138 139 140 141 142 143
## <=50K <=50K <=50K <=50K >50K <=50K <=50K <=50K <=50K >50K <=50K >50K <=50K
## 144 145 146 147 148 149 150 151 152 153 154 155 156
## <=50K >50K <=50K <=50K <=50K <=50K <=50K >50K <=50K <=50K <=50K <=50K <=50K
## 157 158 159 160 161 162 163 164 165 166 167 168 169
## <=50K <=50K >50K >50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K
## 170 171 172 173 174 175 176 177 178 179 180 181 182
## <=50K <=50K >50K <=50K <=50K <=50K >50K <=50K <=50K <=50K <=50K <=50K <=50K
## 183 184 185 186 187 188 189 190 191 192 193 194 195
## <=50K >50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K >50K <=50K <=50K
## 196 197 198 199 200 201 202 203 204 205 206 207 208
## <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K
## 209 210 211 212 213 214 215 216 217 218 219 220 221
## <=50K <=50K <=50K <=50K >50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K
## 222 223 224 225 226 227 228 229 230 231 232 233 234
## <=50K <=50K >50K <=50K <=50K <=50K <=50K <=50K <=50K >50K <=50K <=50K <=50K
## 235 236 237 238 239 240 241 242 243 244 245 246 247
## <=50K <=50K <=50K <=50K <=50K >50K <=50K <=50K >50K <=50K >50K <=50K <=50K
## 248 249 250 251 252 253 254 255 256 257 258 259 260
## <=50K <=50K <=50K >50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K
## 261 262 263 264 265 266 267 268 269 270 271 272 273
## <=50K >50K <=50K <=50K <=50K <=50K >50K <=50K <=50K <=50K <=50K <=50K <=50K
## 274 275 276 277 278 279 280 281 282 283 284 285 286
## <=50K <=50K >50K <=50K <=50K >50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K
## 287 288 289 290 291 292 293 294 295 296 297 298 299
## <=50K <=50K <=50K >50K <=50K <=50K <=50K <=50K <=50K >50K <=50K <=50K <=50K
## 300 301 302 303 304 305 306 307 308 309 310 311 312
## <=50K <=50K >50K >50K <=50K <=50K <=50K <=50K >50K <=50K >50K <=50K >50K
## 313 314 315 316 317 318 319 320 321 322 323 324 325
## <=50K <=50K >50K <=50K <=50K >50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K
## 326 327 328 329 330 331 332 333 334 335 336 337 338
## <=50K <=50K <=50K <=50K <=50K <=50K <=50K >50K >50K >50K <=50K <=50K <=50K
## 339 340 341 342 343 344 345 346 347 348 349 350 351
## <=50K >50K <=50K >50K <=50K >50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K
## 352 353 354 355 356 357 358 359 360 361 362 363 364
## >50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K >50K <=50K >50K <=50K
## 365 366 367 368 369 370 371 372 373 374 375 376 377
## <=50K <=50K <=50K >50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K
## 378 379 380 381 382 383 384 385 386 387 388 389 390
## <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K >50K
## 391 392 393 394 395 396 397 398 399 400 401 402 403
## <=50K >50K <=50K <=50K <=50K >50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K
## 404 405 406 407 408 409 410 411 412 413 414 415 416
## <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K >50K
## 417 418 419 420 421 422 423 424 425 426 427 428 429
## <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K >50K >50K <=50K
## 430 431 432 433 434 435 436 437 438 439 440 441 442
## >50K <=50K >50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K
## 443 444 445 446 447 448 449 450 451 452 453 454 455
## <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K >50K <=50K <=50K <=50K
## 456 457 458 459 460 461 462 463 464 465 466 467 468
## <=50K <=50K <=50K <=50K <=50K <=50K >50K <=50K <=50K <=50K >50K >50K >50K
## 469 470 471 472 473 474 475 476 477 478 479 480 481
## <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K >50K <=50K <=50K <=50K <=50K
## 482 483 484 485 486 487 488 489 490 491 492 493 494
## >50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K >50K <=50K >50K <=50K
## 495 496 497 498 499 500 501 502 503 504 505 506 507
## <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K
## 508 509 510 511 512 513 514 515 516 517 518 519 520
## <=50K >50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K >50K <=50K <=50K >50K
## 521 522 523 524 525 526 527 528 529 530 531 532 533
## <=50K <=50K <=50K >50K <=50K <=50K >50K <=50K <=50K >50K <=50K <=50K <=50K
## 534 535 536 537 538 539 540 541 542 543 544 545 546
## <=50K <=50K <=50K >50K >50K <=50K <=50K <=50K >50K <=50K <=50K <=50K <=50K
## 547 548 549 550 551 552 553 554 555 556 557 558 559
## <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K
## 560 561 562 563 564 565 566 567 568 569 570 571 572
## >50K >50K <=50K <=50K <=50K <=50K >50K <=50K <=50K >50K <=50K <=50K >50K
## 573 574 575 576 577 578 579 580 581 582 583 584 585
## >50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K >50K
## 586 587 588 589 590 591 592 593 594 595 596 597 598
## <=50K <=50K <=50K >50K <=50K >50K <=50K <=50K <=50K >50K <=50K <=50K <=50K
## 599 600 601 602 603 604 605 606 607 608 609 610 611
## <=50K >50K <=50K <=50K >50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K
## 612 613 614 615 616 617 618 619 620 621 622 623 624
## <=50K <=50K <=50K <=50K >50K >50K <=50K >50K <=50K <=50K >50K >50K >50K
## 625 626 627 628 629 630 631 632 633 634 635 636 637
## <=50K <=50K <=50K <=50K >50K <=50K <=50K >50K <=50K >50K <=50K <=50K >50K
## 638 639 640 641 642 643 644 645 646 647 648 649 650
## <=50K <=50K >50K <=50K <=50K <=50K <=50K >50K <=50K <=50K <=50K <=50K <=50K
## 651 652 653 654 655 656 657 658 659 660 661 662 663
## <=50K <=50K <=50K <=50K <=50K >50K <=50K <=50K <=50K >50K <=50K >50K <=50K
## 664 665 666 667 668 669 670 671 672 673 674 675 676
## <=50K <=50K <=50K <=50K <=50K >50K >50K <=50K <=50K <=50K <=50K <=50K >50K
## 677 678 679 680 681 682 683 684 685 686 687 688 689
## <=50K <=50K <=50K >50K <=50K <=50K <=50K <=50K >50K <=50K <=50K >50K <=50K
## 690 691 692 693 694 695 696 697 698 699 700 701 702
## <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K >50K <=50K <=50K <=50K
## 703 704 705 706 707 708 709 710 711 712 713 714 715
## <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K
## 716 717 718 719 720 721 722 723 724 725 726 727 728
## >50K >50K <=50K <=50K <=50K <=50K >50K >50K <=50K <=50K <=50K <=50K >50K
## 729 730 731 732 733 734 735 736 737 738 739 740 741
## <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K >50K <=50K
## 742 743 744 745 746 747 748 749 750 751 752 753 754
## <=50K <=50K <=50K <=50K >50K <=50K <=50K >50K >50K >50K <=50K >50K <=50K
## 755 756 757 758 759 760 761 762 763 764 765 766 767
## <=50K <=50K <=50K <=50K <=50K >50K <=50K <=50K <=50K <=50K <=50K <=50K >50K
## 768 769 770 771 772 773 774 775 776 777 778 779 780
## <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K
## 781 782 783 784 785 786 787 788 789 790 791 792 793
## >50K <=50K <=50K <=50K <=50K <=50K >50K <=50K <=50K <=50K >50K <=50K <=50K
## 794 795 796 797 798 799 800 801 802 803 804 805 806
## <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K >50K <=50K <=50K
## 807 808 809 810 811 812 813 814 815 816 817 818 819
## <=50K <=50K <=50K <=50K <=50K <=50K >50K <=50K >50K >50K <=50K <=50K <=50K
## 820 821 822 823 824 825 826 827 828 829 830 831 832
## <=50K <=50K <=50K <=50K <=50K >50K <=50K <=50K <=50K <=50K <=50K <=50K >50K
## 833 834 835 836 837 838 839 840 841 842 843 844 845
## <=50K <=50K <=50K >50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K
## 846 847 848 849 850 851 852 853 854 855 856 857 858
## <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K
## 859 860 861 862 863 864 865 866 867 868 869 870 871
## <=50K <=50K >50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K
## 872 873 874 875 876 877 878 879 880 881 882 883 884
## <=50K >50K <=50K >50K <=50K <=50K <=50K <=50K <=50K >50K <=50K <=50K <=50K
## 885 886 887 888 889 890 891 892 893 894 895 896 897
## <=50K <=50K <=50K >50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K
## 898 899 900 901 902 903 904 905 906 907 908 909 910
## >50K >50K <=50K <=50K <=50K <=50K <=50K >50K <=50K <=50K <=50K <=50K <=50K
## 911 912 913 914 915 916 917 918 919 920 921 922 923
## <=50K <=50K <=50K >50K <=50K <=50K <=50K <=50K <=50K <=50K >50K >50K <=50K
## 924 925 926 927 928 929 930 931 932 933 934 935 936
## >50K <=50K <=50K <=50K >50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K
## 937 938 939 940 941 942 943 944 945 946 947 948 949
## <=50K <=50K <=50K <=50K <=50K <=50K <=50K >50K <=50K >50K <=50K <=50K <=50K
## 950 951 952 953 954 955 956 957 958 959 960 961 962
## <=50K <=50K >50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K
## 963 964 965 966 967 968 969 970 971 972 973 974 975
## <=50K <=50K >50K >50K <=50K >50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K
## 976 977 978 979 980 981 982 983 984 985 986 987 988
## <=50K <=50K <=50K >50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K
## 989 990 991 992 993 994 995 996 997 998 999 1000 1001
## <=50K <=50K >50K <=50K >50K >50K >50K >50K <=50K >50K <=50K <=50K <=50K
## 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014
## <=50K <=50K >50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K >50K
## 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025 1026 1027
## <=50K <=50K >50K <=50K >50K <=50K >50K <=50K <=50K <=50K <=50K <=50K <=50K
## 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040
## <=50K <=50K <=50K <=50K >50K <=50K <=50K <=50K >50K <=50K <=50K <=50K <=50K
## 1041 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053
## <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K >50K
## 1054 1055 1056 1057 1058 1059 1060 1061 1062 1063 1064 1065 1066
## <=50K <=50K <=50K <=50K >50K <=50K <=50K <=50K >50K <=50K <=50K <=50K >50K
## 1067 1068 1069 1070 1071 1072 1073 1074 1075 1076 1077 1078 1079
## <=50K <=50K >50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K >50K <=50K
## 1080 1081 1082 1083 1084 1085 1086 1087 1088 1089 1090 1091 1092
## <=50K <=50K <=50K >50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K
## 1093 1094 1095 1096 1097 1098 1099 1100 1101 1102 1103 1104 1105
## <=50K <=50K <=50K >50K >50K <=50K >50K <=50K >50K <=50K <=50K <=50K <=50K
## 1106 1107 1108 1109 1110 1111 1112 1113 1114 1115 1116 1117 1118
## <=50K <=50K <=50K <=50K <=50K >50K <=50K <=50K <=50K <=50K <=50K <=50K >50K
## 1119 1120 1121 1122 1123 1124 1125 1126 1127 1128 1129 1130 1131
## <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K
## 1132 1133 1134 1135 1136 1137 1138 1139 1140 1141 1142 1143 1144
## <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K >50K <=50K
## 1145 1146 1147 1148 1149 1150 1151 1152 1153 1154 1155 1156 1157
## <=50K <=50K <=50K <=50K >50K <=50K >50K <=50K <=50K <=50K <=50K <=50K <=50K
## 1158 1159 1160 1161 1162 1163 1164 1165 1166 1167 1168 1169 1170
## <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K
## 1171 1172 1173 1174 1175 1176 1177 1178 1179 1180 1181 1182 1183
## >50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K
## 1184 1185 1186 1187 1188 1189 1190 1191 1192 1193 1194 1195 1196
## <=50K >50K <=50K <=50K >50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K
## 1197 1198 1199 1200 1201 1202 1203 1204 1205 1206 1207 1208 1209
## <=50K <=50K <=50K >50K <=50K <=50K <=50K <=50K >50K <=50K <=50K <=50K <=50K
## 1210 1211 1212 1213 1214 1215 1216 1217 1218 1219 1220 1221 1222
## <=50K <=50K <=50K >50K <=50K >50K <=50K <=50K <=50K >50K <=50K <=50K <=50K
## 1223 1224 1225 1226 1227 1228 1229 1230 1231 1232 1233 1234 1235
## <=50K <=50K <=50K >50K <=50K >50K <=50K <=50K <=50K <=50K <=50K >50K <=50K
## 1236 1237 1238 1239 1240 1241 1242 1243 1244 1245 1246 1247 1248
## <=50K <=50K <=50K <=50K <=50K >50K >50K <=50K <=50K <=50K <=50K <=50K <=50K
## 1249 1250 1251 1252 1253 1254 1255 1256 1257 1258 1259 1260 1261
## <=50K >50K <=50K <=50K <=50K <=50K >50K <=50K <=50K <=50K <=50K >50K <=50K
## 1262 1263 1264 1265 1266 1267 1268 1269 1270 1271 1272 1273 1274
## <=50K <=50K >50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K >50K >50K
## 1275 1276 1277 1278 1279 1280 1281 1282 1283 1284 1285 1286 1287
## <=50K >50K <=50K <=50K >50K >50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K
## 1288 1289 1290 1291 1292 1293 1294 1295 1296 1297 1298 1299 1300
## <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K
## 1301 1302 1303 1304 1305 1306 1307 1308 1309 1310 1311 1312 1313
## >50K <=50K >50K >50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K >50K
## 1314 1315 1316 1317 1318 1319 1320 1321 1322 1323 1324 1325 1326
## >50K >50K <=50K <=50K >50K <=50K <=50K <=50K >50K <=50K <=50K >50K <=50K
## 1327 1328 1329 1330 1331 1332 1333 1334 1335 1336 1337 1338 1339
## <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K
## 1340 1341 1342 1343 1344 1345 1346 1347 1348 1349 1350 1351 1352
## <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K >50K
## 1353 1354 1355 1356 1357 1358 1359 1360 1361 1362 1363 1364 1365
## <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K >50K <=50K
## 1366 1367 1368 1369 1370 1371 1372 1373 1374 1375 1376 1377 1378
## >50K <=50K <=50K <=50K >50K >50K >50K <=50K <=50K <=50K <=50K <=50K <=50K
## 1379 1380 1381 1382 1383 1384 1385 1386 1387 1388 1389 1390 1391
## <=50K <=50K >50K <=50K <=50K <=50K <=50K <=50K >50K <=50K <=50K <=50K <=50K
## 1392 1393 1394 1395 1396 1397 1398 1399 1400 1401 1402 1403 1404
## <=50K <=50K <=50K >50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K >50K
## 1405 1406 1407 1408 1409 1410 1411 1412 1413 1414 1415 1416 1417
## >50K <=50K <=50K >50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K >50K <=50K
## 1418 1419 1420 1421 1422 1423 1424 1425 1426 1427 1428 1429 1430
## <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K >50K
## 1431 1432 1433 1434 1435 1436 1437 1438 1439 1440 1441 1442 1443
## <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K
## 1444 1445 1446 1447 1448 1449 1450 1451 1452 1453 1454 1455 1456
## <=50K <=50K <=50K >50K >50K <=50K <=50K <=50K <=50K <=50K <=50K >50K <=50K
## 1457 1458 1459 1460 1461 1462 1463 1464 1465 1466 1467 1468 1469
## <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K
## 1470 1471 1472 1473 1474 1475 1476 1477 1478 1479 1480 1481 1482
## <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K
## 1483 1484 1485 1486 1487 1488 1489 1490 1491 1492 1493 1494 1495
## >50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K >50K <=50K <=50K >50K <=50K
## 1496 1497 1498 1499 1500 1501 1502 1503 1504 1505 1506 1507 1508
## <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K
## 1509 1510 1511 1512 1513 1514 1515 1516 1517 1518 1519 1520 1521
## <=50K >50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K >50K <=50K
## 1522 1523 1524 1525 1526 1527 1528 1529 1530 1531 1532 1533 1534
## <=50K <=50K >50K <=50K <=50K <=50K >50K <=50K <=50K <=50K <=50K <=50K <=50K
## 1535 1536 1537 1538 1539 1540 1541 1542 1543 1544 1545 1546 1547
## <=50K >50K <=50K <=50K <=50K >50K >50K <=50K <=50K <=50K <=50K <=50K <=50K
## 1548 1549 1550 1551 1552 1553 1554 1555 1556 1557 1558 1559 1560
## <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K >50K <=50K
## 1561 1562 1563 1564 1565 1566 1567 1568 1569 1570 1571 1572 1573
## <=50K <=50K <=50K <=50K <=50K >50K <=50K <=50K <=50K <=50K <=50K <=50K >50K
## 1574 1575 1576 1577 1578 1579 1580 1581 1582 1583 1584 1585 1586
## <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K >50K
## 1587 1588 1589 1590 1591 1592 1593 1594 1595 1596 1597 1598 1599
## <=50K <=50K <=50K <=50K <=50K <=50K >50K <=50K <=50K <=50K <=50K <=50K <=50K
## 1600 1601 1602 1603 1604 1605 1606 1607 1608 1609 1610 1611 1612
## <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K >50K <=50K <=50K
## 1613 1614 1615 1616 1617 1618 1619 1620 1621 1622 1623 1624 1625
## <=50K >50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K
## 1626 1627 1628 1629 1630 1631 1632 1633 1634 1635 1636 1637 1638
## <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K
## 1639 1640 1641 1642 1643 1644 1645 1646 1647 1648 1649 1650 1651
## <=50K <=50K <=50K <=50K <=50K >50K <=50K <=50K >50K <=50K <=50K <=50K <=50K
## 1652 1653 1654 1655 1656 1657 1658 1659 1660 1661 1662 1663 1664
## <=50K <=50K >50K >50K <=50K <=50K <=50K <=50K <=50K >50K <=50K <=50K <=50K
## 1665 1666 1667 1668 1669 1670 1671 1672 1673 1674 1675 1676 1677
## <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K >50K <=50K <=50K
## 1678 1679 1680 1681 1682 1683 1684 1685 1686 1687 1688 1689 1690
## >50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K >50K <=50K >50K <=50K <=50K
## 1691 1692 1693 1694 1695 1696 1697 1698 1699 1700 1701 1702 1703
## <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K >50K <=50K
## 1704 1705 1706 1707 1708 1709 1710 1711 1712 1713 1714 1715 1716
## <=50K >50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K >50K >50K <=50K <=50K
## 1717 1718 1719 1720 1721 1722 1723 1724 1725 1726 1727 1728 1729
## >50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K
## 1730 1731 1732 1733 1734 1735 1736 1737 1738 1739 1740 1741 1742
## >50K <=50K <=50K <=50K <=50K <=50K <=50K >50K <=50K <=50K <=50K >50K >50K
## 1743 1744 1745 1746 1747 1748 1749 1750 1751 1752 1753 1754 1755
## <=50K <=50K <=50K >50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K
## 1756 1757 1758 1759 1760 1761 1762 1763 1764 1765 1766 1767 1768
## <=50K <=50K >50K >50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K
## 1769 1770 1771 1772 1773 1774 1775 1776 1777 1778 1779 1780 1781
## >50K <=50K <=50K <=50K >50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K
## 1782 1783 1784 1785 1786 1787 1788 1789 1790 1791 1792 1793 1794
## <=50K <=50K <=50K >50K <=50K <=50K >50K <=50K <=50K <=50K <=50K <=50K <=50K
## 1795 1796 1797 1798 1799 1800 1801 1802 1803 1804 1805 1806 1807
## <=50K >50K <=50K <=50K >50K <=50K <=50K <=50K <=50K <=50K <=50K >50K <=50K
## 1808 1809 1810 1811 1812 1813 1814 1815 1816 1817 1818 1819 1820
## <=50K >50K <=50K >50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K
## 1821 1822 1823 1824 1825 1826 1827 1828 1829 1830 1831 1832 1833
## >50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K
## 1834 1835 1836 1837 1838 1839 1840 1841 1842 1843 1844 1845 1846
## >50K <=50K <=50K <=50K <=50K >50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K
## 1847 1848 1849 1850 1851 1852 1853 1854 1855 1856 1857 1858 1859
## <=50K >50K >50K <=50K <=50K >50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K
## 1860 1861 1862 1863 1864 1865 1866 1867 1868 1869 1870 1871 1872
## <=50K <=50K <=50K <=50K >50K <=50K <=50K <=50K >50K <=50K >50K <=50K <=50K
## 1873 1874 1875 1876 1877 1878 1879 1880 1881 1882 1883 1884 1885
## <=50K <=50K <=50K <=50K <=50K <=50K >50K <=50K <=50K <=50K >50K <=50K <=50K
## 1886 1887 1888 1889 1890 1891 1892 1893 1894 1895 1896 1897 1898
## <=50K >50K <=50K <=50K <=50K <=50K >50K <=50K <=50K <=50K <=50K <=50K <=50K
## 1899 1900 1901 1902 1903 1904 1905 1906 1907 1908 1909 1910 1911
## <=50K <=50K <=50K >50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K
## 1912 1913 1914 1915 1916 1917 1918 1919 1920 1921 1922 1923 1924
## <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K >50K <=50K
## 1925 1926 1927 1928 1929 1930 1931 1932 1933 1934 1935 1936 1937
## <=50K <=50K <=50K <=50K <=50K <=50K >50K <=50K <=50K <=50K <=50K <=50K >50K
## 1938 1939 1940 1941 1942 1943 1944 1945 1946 1947 1948 1949 1950
## <=50K <=50K <=50K >50K <=50K >50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K
## 1951 1952 1953 1954 1955 1956 1957 1958 1959 1960 1961 1962 1963
## <=50K <=50K <=50K <=50K >50K <=50K <=50K <=50K <=50K >50K >50K <=50K <=50K
## 1964 1965 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976
## <=50K <=50K >50K >50K <=50K <=50K <=50K >50K <=50K >50K <=50K <=50K <=50K
## 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989
## <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K >50K <=50K <=50K <=50K
## 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002
## <=50K >50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K >50K >50K
## 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015
## <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K >50K <=50K
## 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028
## >50K <=50K <=50K <=50K <=50K >50K <=50K >50K <=50K <=50K <=50K >50K <=50K
## 2029 2030 2031 2032 2033 2034 2035 2036 2037 2038 2039 2040 2041
## <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K
## 2042 2043 2044 2045 2046 2047 2048 2049 2050 2051 2052 2053 2054
## <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K >50K <=50K
## 2055 2056 2057 2058 2059 2060 2061 2062 2063 2064 2065 2066 2067
## <=50K <=50K <=50K <=50K <=50K >50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K
## 2068 2069 2070 2071 2072 2073 2074 2075 2076 2077 2078 2079 2080
## >50K <=50K <=50K >50K <=50K <=50K <=50K <=50K <=50K >50K <=50K <=50K <=50K
## 2081 2082 2083 2084 2085 2086 2087 2088 2089 2090 2091 2092 2093
## <=50K <=50K >50K <=50K <=50K <=50K >50K <=50K <=50K <=50K <=50K <=50K <=50K
## 2094 2095 2096 2097 2098 2099 2100 2101 2102 2103 2104 2105 2106
## <=50K <=50K <=50K <=50K <=50K <=50K >50K <=50K <=50K <=50K <=50K <=50K >50K
## 2107 2108 2109 2110 2111 2112 2113 2114 2115 2116 2117 2118 2119
## <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K >50K <=50K >50K <=50K <=50K
## 2120 2121 2122 2123 2124 2125 2126 2127 2128 2129 2130 2131 2132
## <=50K <=50K <=50K <=50K >50K >50K <=50K <=50K <=50K <=50K <=50K >50K <=50K
## 2133 2134 2135 2136 2137 2138 2139 2140 2141 2142 2143 2144 2145
## <=50K <=50K >50K <=50K <=50K <=50K <=50K <=50K <=50K >50K >50K >50K <=50K
## 2146 2147 2148 2149 2150 2151 2152 2153 2154 2155 2156 2157 2158
## <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K >50K <=50K <=50K
## 2159 2160 2161 2162 2163 2164 2165 2166 2167 2168 2169 2170 2171
## <=50K <=50K <=50K >50K <=50K <=50K <=50K <=50K >50K <=50K >50K >50K >50K
## 2172 2173 2174 2175 2176 2177 2178 2179 2180 2181 2182 2183 2184
## >50K >50K <=50K <=50K <=50K <=50K >50K <=50K <=50K <=50K <=50K <=50K <=50K
## 2185 2186 2187 2188 2189 2190 2191 2192 2193 2194 2195 2196 2197
## <=50K <=50K <=50K <=50K <=50K <=50K <=50K >50K >50K >50K <=50K <=50K <=50K
## 2198 2199 2200 2201 2202 2203 2204 2205 2206 2207 2208 2209 2210
## <=50K <=50K >50K <=50K >50K <=50K <=50K <=50K <=50K >50K <=50K <=50K >50K
## 2211 2212 2213 2214 2215 2216 2217 2218 2219 2220 2221 2222 2223
## <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K >50K <=50K
## 2224 2225 2226 2227 2228 2229 2230 2231 2232 2233 2234 2235 2236
## >50K <=50K >50K <=50K <=50K <=50K <=50K <=50K <=50K >50K <=50K >50K >50K
## 2237 2238 2239 2240 2241 2242 2243 2244 2245 2246 2247 2248 2249
## <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K
## 2250 2251 2252 2253 2254 2255 2256 2257 2258 2259 2260 2261 2262
## <=50K <=50K <=50K <=50K >50K <=50K <=50K <=50K <=50K >50K <=50K <=50K <=50K
## 2263 2264 2265 2266 2267 2268 2269 2270 2271 2272 2273 2274 2275
## <=50K <=50K <=50K >50K <=50K <=50K <=50K <=50K <=50K <=50K >50K <=50K <=50K
## 2276 2277 2278 2279 2280 2281 2282 2283 2284 2285 2286 2287 2288
## <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K >50K <=50K <=50K >50K <=50K
## 2289 2290 2291 2292 2293 2294 2295 2296 2297 2298 2299 2300 2301
## >50K <=50K <=50K >50K <=50K <=50K <=50K >50K <=50K >50K <=50K >50K <=50K
## 2302 2303 2304 2305 2306 2307 2308 2309 2310 2311 2312 2313 2314
## <=50K <=50K <=50K <=50K <=50K <=50K >50K <=50K <=50K <=50K <=50K >50K <=50K
## 2315 2316 2317 2318 2319 2320 2321 2322 2323 2324 2325 2326 2327
## <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K
## 2328 2329 2330 2331 2332 2333 2334 2335 2336 2337 2338 2339 2340
## <=50K >50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K >50K >50K >50K
## 2341 2342 2343 2344 2345 2346 2347 2348 2349 2350 2351 2352 2353
## <=50K <=50K <=50K <=50K <=50K <=50K >50K <=50K <=50K <=50K <=50K >50K <=50K
## 2354 2355 2356 2357 2358 2359 2360 2361 2362 2363 2364 2365 2366
## <=50K >50K <=50K <=50K <=50K >50K <=50K <=50K <=50K <=50K >50K <=50K <=50K
## 2367 2368 2369 2370 2371 2372 2373 2374 2375 2376 2377 2378 2379
## <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K
## 2380 2381 2382 2383 2384 2385 2386 2387 2388 2389 2390 2391 2392
## <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K >50K >50K <=50K
## 2393 2394 2395 2396 2397 2398 2399 2400 2401 2402 2403 2404 2405
## <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K
## 2406 2407 2408 2409 2410 2411 2412 2413 2414 2415 2416 2417 2418
## <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K >50K >50K
## 2419 2420 2421 2422 2423 2424 2425 2426 2427 2428 2429 2430 2431
## <=50K >50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K
## 2432 2433 2434 2435 2436 2437 2438 2439 2440 2441 2442 2443 2444
## <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K >50K >50K <=50K <=50K <=50K
## 2445 2446 2447 2448 2449 2450 2451 2452 2453 2454 2455 2456 2457
## <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K >50K <=50K <=50K <=50K
## 2458 2459 2460 2461 2462 2463 2464 2465 2466 2467 2468 2469 2470
## <=50K >50K <=50K >50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K
## 2471 2472 2473 2474 2475 2476 2477 2478 2479 2480 2481 2482 2483
## >50K <=50K <=50K <=50K <=50K <=50K <=50K >50K <=50K <=50K <=50K <=50K <=50K
## 2484 2485 2486 2487 2488 2489 2490 2491 2492 2493 2494 2495 2496
## <=50K <=50K >50K >50K <=50K >50K <=50K <=50K <=50K >50K >50K <=50K <=50K
## 2497 2498 2499 2500 2501 2502 2503 2504 2505 2506 2507 2508 2509
## <=50K <=50K <=50K >50K <=50K <=50K <=50K <=50K >50K <=50K <=50K >50K >50K
## 2510 2511 2512 2513 2514 2515 2516 2517 2518 2519 2520 2521 2522
## <=50K <=50K <=50K <=50K <=50K <=50K >50K <=50K <=50K <=50K <=50K <=50K <=50K
## 2523 2524 2525 2526 2527 2528 2529 2530 2531 2532 2533 2534 2535
## <=50K <=50K <=50K <=50K >50K <=50K <=50K <=50K <=50K >50K <=50K <=50K >50K
## 2536 2537 2538 2539 2540 2541 2542 2543 2544 2545 2546 2547 2548
## <=50K <=50K <=50K >50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K >50K
## 2549 2550 2551 2552 2553 2554 2555 2556 2557 2558 2559 2560 2561
## <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K >50K <=50K <=50K <=50K
## 2562 2563 2564 2565 2566 2567 2568 2569 2570 2571 2572 2573 2574
## <=50K <=50K >50K <=50K <=50K <=50K <=50K <=50K >50K <=50K <=50K <=50K <=50K
## 2575 2576 2577 2578 2579 2580 2581 2582 2583 2584 2585 2586 2587
## <=50K <=50K >50K <=50K <=50K >50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K
## 2588 2589 2590 2591 2592 2593 2594 2595 2596 2597 2598 2599 2600
## >50K <=50K >50K <=50K <=50K >50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K
## 2601 2602 2603 2604 2605 2606 2607 2608 2609 2610 2611 2612 2613
## <=50K >50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K >50K
## 2614 2615 2616 2617 2618 2619 2620 2621 2622 2623 2624 2625 2626
## <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K >50K <=50K >50K <=50K >50K
## 2627 2628 2629 2630 2631 2632 2633 2634 2635 2636 2637 2638 2639
## <=50K <=50K >50K <=50K <=50K <=50K <=50K <=50K >50K <=50K >50K <=50K <=50K
## 2640 2641 2642 2643 2644 2645 2646 2647 2648 2649 2650 2651 2652
## <=50K <=50K <=50K >50K <=50K <=50K <=50K >50K <=50K >50K <=50K >50K <=50K
## 2653 2654 2655 2656 2657 2658 2659 2660 2661 2662 2663 2664 2665
## >50K <=50K <=50K >50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K
## 2666 2667 2668 2669 2670 2671 2672 2673 2674 2675 2676 2677 2678
## <=50K <=50K >50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K >50K
## 2679 2680 2681 2682 2683 2684 2685 2686 2687 2688 2689 2690 2691
## <=50K >50K >50K <=50K <=50K <=50K >50K <=50K <=50K <=50K <=50K >50K <=50K
## 2692 2693 2694 2695 2696 2697 2698 2699 2700 2701 2702 2703 2704
## <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K >50K >50K
## 2705 2706 2707 2708 2709 2710 2711 2712 2713 2714 2715 2716 2717
## <=50K >50K <=50K <=50K <=50K <=50K >50K <=50K <=50K <=50K <=50K <=50K <=50K
## 2718 2719 2720 2721 2722 2723 2724 2725 2726 2727 2728 2729 2730
## >50K <=50K <=50K <=50K >50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K >50K
## 2731 2732 2733 2734 2735 2736 2737 2738 2739 2740 2741 2742 2743
## <=50K <=50K <=50K <=50K >50K <=50K <=50K <=50K <=50K <=50K >50K <=50K <=50K
## 2744 2745 2746 2747 2748 2749 2750 2751 2752 2753 2754 2755 2756
## <=50K <=50K >50K >50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K
## 2757 2758 2759 2760 2761 2762 2763 2764 2765 2766 2767 2768 2769
## <=50K <=50K <=50K >50K <=50K <=50K >50K <=50K <=50K <=50K <=50K <=50K <=50K
## 2770 2771 2772 2773 2774 2775 2776 2777 2778 2779 2780 2781 2782
## <=50K <=50K >50K <=50K <=50K <=50K <=50K <=50K <=50K >50K <=50K <=50K <=50K
## 2783 2784 2785 2786 2787 2788 2789 2790 2791 2792 2793 2794 2795
## >50K <=50K >50K <=50K <=50K <=50K <=50K >50K <=50K <=50K >50K <=50K <=50K
## 2796 2797 2798 2799 2800 2801 2802 2803 2804 2805 2806 2807 2808
## <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K >50K <=50K <=50K
## 2809 2810 2811 2812 2813 2814 2815 2816 2817 2818 2819 2820 2821
## <=50K <=50K <=50K <=50K <=50K <=50K <=50K >50K <=50K <=50K <=50K <=50K <=50K
## 2822 2823 2824 2825 2826 2827 2828 2829 2830 2831 2832 2833 2834
## <=50K >50K >50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K >50K
## 2835 2836 2837 2838 2839 2840 2841 2842 2843 2844 2845 2846 2847
## <=50K <=50K <=50K <=50K <=50K >50K <=50K <=50K <=50K <=50K >50K <=50K >50K
## 2848 2849 2850 2851 2852 2853 2854 2855 2856 2857 2858 2859 2860
## <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K >50K <=50K <=50K <=50K >50K
## 2861 2862 2863 2864 2865 2866 2867 2868 2869 2870 2871 2872 2873
## <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K >50K <=50K <=50K <=50K
## 2874 2875 2876 2877 2878 2879 2880 2881 2882 2883 2884 2885 2886
## <=50K <=50K >50K <=50K <=50K <=50K >50K <=50K <=50K <=50K <=50K <=50K <=50K
## 2887 2888 2889 2890 2891 2892 2893 2894 2895 2896 2897 2898 2899
## <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K >50K >50K <=50K
## 2900 2901 2902 2903 2904 2905 2906 2907 2908 2909 2910 2911 2912
## <=50K <=50K >50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K >50K <=50K <=50K
## 2913 2914 2915 2916 2917 2918 2919 2920 2921 2922 2923 2924 2925
## <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K
## 2926 2927 2928 2929 2930 2931 2932 2933 2934 2935 2936 2937 2938
## >50K <=50K >50K <=50K >50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K
## 2939 2940 2941 2942 2943 2944 2945 2946 2947 2948 2949 2950 2951
## <=50K <=50K <=50K <=50K <=50K <=50K <=50K >50K <=50K <=50K <=50K <=50K <=50K
## 2952 2953 2954 2955 2956 2957 2958 2959 2960 2961 2962 2963 2964
## <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K
## 2965 2966 2967 2968 2969 2970 2971 2972 2973 2974 2975 2976 2977
## <=50K <=50K <=50K <=50K <=50K >50K <=50K >50K <=50K >50K <=50K <=50K <=50K
## 2978 2979 2980 2981 2982 2983 2984 2985 2986 2987 2988 2989 2990
## <=50K <=50K <=50K <=50K <=50K <=50K <=50K >50K <=50K <=50K <=50K <=50K <=50K
## 2991 2992 2993 2994 2995 2996 2997 2998 2999 3000 3001 3002 3003
## <=50K <=50K <=50K <=50K <=50K <=50K >50K <=50K <=50K >50K <=50K <=50K <=50K
## 3004 3005 3006 3007 3008 3009 3010 3011 3012 3013 3014 3015 3016
## <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K
## 3017 3018 3019 3020 3021 3022 3023 3024 3025 3026 3027 3028 3029
## <=50K <=50K <=50K <=50K <=50K >50K <=50K <=50K >50K <=50K <=50K <=50K >50K
## 3030 3031 3032 3033 3034 3035 3036 3037 3038 3039 3040 3041 3042
## <=50K >50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K
## 3043 3044 3045 3046 3047 3048 3049 3050 3051 3052 3053 3054 3055
## <=50K >50K >50K >50K <=50K <=50K <=50K <=50K <=50K <=50K >50K <=50K >50K
## 3056 3057 3058 3059 3060 3061 3062 3063 3064 3065 3066 3067 3068
## <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K >50K <=50K <=50K <=50K
## 3069 3070 3071 3072 3073 3074 3075 3076 3077 3078 3079 3080 3081
## >50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K >50K <=50K
## 3082 3083 3084 3085 3086 3087 3088 3089 3090 3091 3092 3093 3094
## <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K
## 3095 3096 3097 3098 3099 3100 3101 3102 3103 3104 3105 3106 3107
## <=50K <=50K <=50K <=50K <=50K >50K >50K >50K <=50K <=50K <=50K <=50K <=50K
## 3108 3109 3110 3111 3112 3113 3114 3115 3116 3117 3118 3119 3120
## >50K <=50K <=50K >50K >50K <=50K <=50K <=50K <=50K >50K <=50K <=50K <=50K
## 3121 3122 3123 3124 3125 3126 3127 3128 3129 3130 3131 3132 3133
## <=50K <=50K <=50K >50K <=50K <=50K <=50K <=50K <=50K >50K <=50K <=50K >50K
## 3134 3135 3136 3137 3138 3139 3140 3141 3142 3143 3144 3145 3146
## <=50K <=50K <=50K <=50K <=50K <=50K <=50K >50K <=50K >50K >50K >50K <=50K
## 3147 3148 3149 3150 3151 3152 3153 3154 3155 3156 3157 3158 3159
## <=50K >50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K >50K <=50K <=50K >50K
## 3160 3161 3162 3163 3164 3165 3166 3167 3168 3169 3170 3171 3172
## >50K >50K <=50K <=50K >50K >50K <=50K <=50K <=50K >50K <=50K <=50K <=50K
## 3173 3174 3175 3176 3177 3178 3179 3180 3181 3182 3183 3184 3185
## <=50K <=50K <=50K <=50K <=50K >50K >50K <=50K <=50K <=50K <=50K <=50K <=50K
## 3186 3187 3188 3189 3190 3191 3192 3193 3194 3195 3196 3197 3198
## >50K <=50K <=50K >50K <=50K <=50K <=50K <=50K >50K <=50K <=50K <=50K <=50K
## 3199 3200 3201 3202 3203 3204 3205 3206 3207 3208 3209 3210 3211
## <=50K <=50K <=50K <=50K <=50K <=50K >50K >50K <=50K <=50K <=50K >50K <=50K
## 3212 3213 3214 3215 3216 3217 3218 3219 3220 3221 3222 3223 3224
## <=50K <=50K <=50K >50K <=50K <=50K >50K >50K <=50K <=50K >50K <=50K <=50K
## 3225 3226 3227 3228 3229 3230 3231 3232 3233 3234 3235 3236 3237
## <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K >50K <=50K
## 3238 3239 3240 3241 3242 3243 3244 3245 3246 3247 3248 3249 3250
## <=50K <=50K <=50K >50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K >50K <=50K
## 3251 3252 3253 3254 3255 3256 3257 3258 3259 3260 3261 3262 3263
## <=50K <=50K <=50K >50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K
## 3264 3265 3266 3267 3268 3269 3270 3271 3272 3273 3274 3275 3276
## <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K
## 3277 3278 3279 3280 3281 3282 3283 3284 3285 3286 3287 3288 3289
## <=50K <=50K <=50K <=50K <=50K >50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K
## 3290 3291 3292 3293 3294 3295 3296 3297 3298 3299 3300 3301 3302
## <=50K >50K <=50K <=50K <=50K >50K >50K >50K <=50K <=50K >50K >50K <=50K
## 3303 3304 3305 3306 3307 3308 3309 3310 3311 3312 3313 3314 3315
## <=50K >50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K >50K
## 3316 3317 3318 3319 3320 3321 3322 3323 3324 3325 3326 3327 3328
## <=50K <=50K <=50K >50K <=50K <=50K <=50K <=50K >50K <=50K <=50K <=50K >50K
## 3329 3330 3331 3332 3333 3334 3335 3336 3337 3338 3339 3340 3341
## <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K >50K <=50K <=50K <=50K
## 3342 3343 3344 3345 3346 3347 3348 3349 3350 3351 3352 3353 3354
## <=50K >50K <=50K <=50K >50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K
## 3355 3356 3357 3358 3359 3360 3361 3362 3363 3364 3365 3366 3367
## <=50K <=50K >50K <=50K >50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K
## 3368 3369 3370 3371 3372 3373 3374 3375 3376 3377 3378 3379 3380
## <=50K <=50K <=50K <=50K >50K >50K <=50K <=50K <=50K <=50K <=50K <=50K >50K
## 3381 3382 3383 3384 3385 3386 3387 3388 3389 3390 3391 3392 3393
## <=50K <=50K <=50K <=50K <=50K <=50K <=50K >50K <=50K <=50K <=50K >50K <=50K
## 3394 3395 3396 3397 3398 3399 3400 3401 3402 3403 3404 3405 3406
## <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K >50K <=50K <=50K
## 3407 3408 3409 3410 3411 3412 3413 3414 3415 3416 3417 3418 3419
## >50K >50K >50K >50K >50K <=50K >50K <=50K <=50K >50K >50K <=50K <=50K
## 3420 3421 3422 3423 3424 3425 3426 3427 3428 3429 3430 3431 3432
## <=50K <=50K <=50K >50K <=50K >50K >50K <=50K <=50K <=50K <=50K <=50K <=50K
## 3433 3434 3435 3436 3437 3438 3439 3440 3441 3442 3443 3444 3445
## <=50K <=50K <=50K <=50K <=50K <=50K >50K <=50K <=50K <=50K <=50K <=50K <=50K
## 3446 3447 3448 3449 3450 3451 3452 3453 3454 3455 3456 3457 3458
## >50K <=50K >50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K
## 3459 3460 3461 3462 3463 3464 3465 3466 3467 3468 3469 3470 3471
## <=50K <=50K >50K <=50K <=50K <=50K <=50K >50K <=50K >50K <=50K <=50K <=50K
## 3472 3473 3474 3475 3476 3477 3478 3479 3480 3481 3482 3483 3484
## <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K >50K <=50K >50K <=50K <=50K
## 3485 3486 3487 3488 3489 3490 3491 3492 3493 3494 3495 3496 3497
## <=50K <=50K <=50K <=50K <=50K >50K <=50K <=50K <=50K <=50K <=50K >50K <=50K
## 3498 3499 3500 3501 3502 3503 3504 3505 3506 3507 3508 3509 3510
## <=50K >50K >50K <=50K <=50K <=50K <=50K <=50K <=50K >50K <=50K <=50K >50K
## 3511 3512 3513 3514 3515 3516 3517 3518 3519 3520 3521 3522 3523
## >50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K >50K
## 3524 3525 3526 3527 3528 3529 3530 3531 3532 3533 3534 3535 3536
## <=50K <=50K <=50K <=50K <=50K <=50K >50K <=50K <=50K <=50K <=50K >50K <=50K
## 3537 3538 3539 3540 3541 3542 3543 3544 3545 3546 3547 3548 3549
## >50K >50K >50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K
## 3550 3551 3552 3553 3554 3555 3556 3557 3558 3559 3560 3561 3562
## <=50K >50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K >50K
## 3563 3564 3565 3566 3567 3568 3569 3570 3571 3572 3573 3574 3575
## <=50K <=50K <=50K <=50K <=50K <=50K >50K <=50K <=50K <=50K <=50K <=50K >50K
## 3576 3577 3578 3579 3580 3581 3582 3583 3584 3585 3586 3587 3588
## <=50K <=50K <=50K >50K <=50K <=50K >50K <=50K <=50K <=50K >50K <=50K <=50K
## 3589 3590 3591 3592 3593 3594 3595 3596 3597 3598 3599 3600 3601
## <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K >50K <=50K
## 3602 3603 3604 3605 3606 3607 3608 3609 3610 3611 3612 3613 3614
## <=50K >50K <=50K <=50K >50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K >50K
## 3615 3616 3617 3618 3619 3620 3621 3622 3623 3624 3625 3626 3627
## <=50K <=50K <=50K >50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K >50K <=50K
## 3628 3629 3630 3631 3632 3633 3634 3635 3636 3637 3638 3639 3640
## >50K <=50K <=50K >50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K
## 3641 3642 3643 3644 3645 3646 3647 3648 3649 3650 3651 3652 3653
## <=50K >50K <=50K >50K >50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K
## 3654 3655 3656 3657 3658 3659 3660 3661 3662 3663 3664 3665 3666
## >50K <=50K <=50K <=50K >50K >50K >50K <=50K <=50K <=50K <=50K <=50K <=50K
## 3667 3668 3669 3670 3671 3672 3673 3674 3675 3676 3677 3678 3679
## >50K <=50K <=50K <=50K <=50K >50K <=50K >50K <=50K <=50K <=50K <=50K <=50K
## 3680 3681 3682 3683 3684 3685 3686 3687 3688 3689 3690 3691 3692
## <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K >50K <=50K <=50K <=50K <=50K
## 3693 3694 3695 3696 3697 3698 3699 3700 3701 3702 3703 3704 3705
## <=50K >50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K >50K <=50K <=50K
## 3706 3707 3708 3709 3710 3711 3712 3713 3714 3715 3716 3717 3718
## <=50K <=50K <=50K <=50K >50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K
## 3719 3720 3721 3722 3723 3724 3725 3726 3727 3728 3729 3730 3731
## <=50K <=50K >50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K
## 3732 3733 3734 3735 3736 3737 3738 3739 3740 3741 3742 3743 3744
## <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K >50K <=50K <=50K <=50K
## 3745 3746 3747 3748 3749 3750 3751 3752 3753 3754 3755 3756 3757
## >50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K >50K >50K <=50K <=50K
## 3758 3759 3760 3761 3762 3763 3764 3765 3766 3767 3768 3769 3770
## <=50K <=50K <=50K >50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K >50K <=50K
## 3771 3772 3773 3774 3775 3776 3777 3778 3779 3780 3781 3782 3783
## <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K >50K <=50K <=50K
## 3784 3785 3786 3787 3788 3789 3790 3791 3792 3793 3794 3795 3796
## <=50K <=50K >50K >50K >50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K
## 3797 3798 3799 3800 3801 3802 3803 3804 3805 3806 3807 3808 3809
## <=50K <=50K <=50K <=50K <=50K >50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K
## 3810 3811 3812 3813 3814 3815 3816 3817 3818 3819 3820 3821 3822
## >50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K >50K <=50K <=50K <=50K <=50K
## 3823 3824 3825 3826 3827 3828 3829 3830 3831 3832 3833 3834 3835
## <=50K <=50K <=50K >50K >50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K
## 3836 3837 3838 3839 3840 3841 3842 3843 3844 3845 3846 3847 3848
## >50K <=50K <=50K >50K <=50K >50K <=50K >50K <=50K <=50K <=50K <=50K <=50K
## 3849 3850 3851 3852 3853 3854 3855 3856 3857 3858 3859 3860 3861
## <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K
## 3862 3863 3864 3865 3866 3867 3868 3869 3870 3871 3872 3873 3874
## <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K
## 3875 3876 3877 3878 3879 3880 3881 3882 3883 3884 3885 3886 3887
## <=50K <=50K <=50K <=50K <=50K <=50K >50K <=50K <=50K <=50K <=50K >50K <=50K
## 3888 3889 3890 3891 3892 3893 3894 3895 3896 3897 3898 3899 3900
## <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K
## 3901 3902 3903 3904 3905 3906 3907 3908 3909 3910 3911 3912 3913
## <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K
## 3914 3915 3916 3917 3918 3919 3920 3921 3922 3923 3924 3925 3926
## >50K <=50K <=50K >50K >50K <=50K >50K <=50K <=50K <=50K <=50K <=50K <=50K
## 3927 3928 3929 3930 3931 3932 3933 3934 3935 3936 3937 3938 3939
## <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K
## 3940 3941 3942 3943 3944 3945 3946 3947 3948 3949 3950 3951 3952
## <=50K <=50K <=50K <=50K <=50K >50K >50K <=50K <=50K <=50K <=50K <=50K <=50K
## 3953 3954 3955 3956 3957 3958 3959 3960 3961 3962 3963 3964 3965
## <=50K >50K <=50K <=50K <=50K <=50K <=50K <=50K >50K <=50K <=50K <=50K <=50K
## 3966 3967 3968 3969 3970 3971 3972 3973 3974 3975 3976 3977 3978
## <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K
## 3979 3980 3981 3982 3983 3984 3985 3986 3987 3988 3989 3990 3991
## <=50K <=50K >50K <=50K >50K <=50K >50K <=50K <=50K <=50K <=50K <=50K <=50K
## 3992 3993 3994 3995 3996 3997 3998 3999 4000 4001 4002 4003 4004
## >50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K
## 4005 4006 4007 4008 4009 4010 4011 4012 4013 4014 4015 4016 4017
## <=50K <=50K <=50K <=50K <=50K <=50K >50K <=50K <=50K <=50K <=50K <=50K <=50K
## 4018 4019 4020 4021 4022 4023 4024 4025 4026 4027 4028 4029 4030
## <=50K <=50K <=50K <=50K <=50K >50K <=50K <=50K >50K <=50K <=50K <=50K >50K
## 4031 4032 4033 4034 4035 4036 4037 4038 4039 4040 4041 4042 4043
## <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K
## 4044 4045 4046 4047 4048 4049 4050 4051 4052 4053 4054 4055 4056
## <=50K <=50K <=50K <=50K <=50K <=50K <=50K >50K <=50K <=50K <=50K <=50K <=50K
## 4057 4058 4059 4060 4061 4062 4063 4064 4065 4066 4067 4068 4069
## <=50K <=50K <=50K >50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K
## 4070 4071 4072 4073 4074 4075 4076 4077 4078 4079 4080 4081 4082
## <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K >50K <=50K <=50K
## 4083 4084 4085 4086 4087 4088 4089 4090 4091 4092 4093 4094 4095
## <=50K <=50K <=50K <=50K >50K <=50K <=50K >50K <=50K <=50K <=50K <=50K <=50K
## 4096 4097 4098 4099 4100 4101 4102 4103 4104 4105 4106 4107 4108
## <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K
## 4109 4110 4111 4112 4113 4114 4115 4116 4117 4118 4119 4120 4121
## <=50K <=50K <=50K >50K <=50K <=50K <=50K >50K <=50K <=50K <=50K >50K <=50K
## 4122 4123 4124 4125 4126 4127 4128 4129 4130 4131 4132 4133 4134
## <=50K <=50K <=50K >50K <=50K <=50K <=50K >50K >50K <=50K <=50K <=50K <=50K
## 4135 4136 4137 4138 4139 4140 4141 4142 4143 4144 4145 4146 4147
## <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K
## 4148 4149 4150 4151 4152 4153 4154 4155 4156 4157 4158 4159 4160
## <=50K <=50K <=50K <=50K <=50K <=50K <=50K >50K <=50K <=50K <=50K <=50K <=50K
## 4161 4162 4163 4164 4165 4166 4167 4168 4169 4170 4171 4172 4173
## <=50K <=50K <=50K <=50K >50K <=50K <=50K <=50K <=50K <=50K <=50K >50K <=50K
## 4174 4175 4176 4177 4178 4179 4180 4181 4182 4183 4184 4185 4186
## <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K
## 4187 4188 4189 4190 4191 4192 4193 4194 4195 4196 4197 4198 4199
## <=50K <=50K <=50K <=50K <=50K <=50K >50K <=50K <=50K >50K <=50K <=50K <=50K
## 4200 4201 4202 4203 4204 4205 4206 4207 4208 4209 4210 4211 4212
## <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K >50K
## 4213 4214 4215 4216 4217 4218 4219 4220 4221 4222 4223 4224 4225
## <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K
## 4226 4227 4228 4229 4230 4231 4232 4233 4234 4235 4236 4237 4238
## <=50K <=50K <=50K <=50K <=50K >50K <=50K <=50K <=50K <=50K <=50K >50K <=50K
## 4239 4240 4241 4242 4243 4244 4245 4246 4247 4248 4249 4250 4251
## <=50K <=50K <=50K >50K <=50K <=50K >50K <=50K <=50K <=50K <=50K <=50K <=50K
## 4252 4253 4254 4255 4256 4257 4258 4259 4260 4261 4262 4263 4264
## >50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K >50K <=50K <=50K <=50K
## 4265 4266 4267 4268 4269 4270 4271 4272 4273 4274 4275 4276 4277
## >50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K >50K >50K <=50K <=50K
## 4278 4279 4280 4281 4282 4283 4284 4285 4286 4287 4288 4289 4290
## >50K <=50K <=50K <=50K >50K >50K <=50K <=50K <=50K <=50K >50K <=50K <=50K
## 4291 4292 4293 4294 4295 4296 4297 4298 4299 4300 4301 4302 4303
## <=50K <=50K <=50K <=50K <=50K >50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K
## 4304 4305 4306 4307 4308 4309 4310 4311 4312 4313 4314 4315 4316
## >50K <=50K <=50K <=50K <=50K <=50K >50K <=50K <=50K <=50K <=50K <=50K <=50K
## 4317 4318 4319 4320 4321 4322 4323 4324 4325 4326 4327 4328 4329
## <=50K <=50K <=50K <=50K <=50K <=50K >50K <=50K >50K <=50K <=50K <=50K >50K
## 4330 4331 4332 4333 4334 4335 4336 4337 4338 4339 4340 4341 4342
## <=50K <=50K <=50K <=50K <=50K <=50K <=50K >50K <=50K <=50K <=50K >50K <=50K
## 4343 4344 4345 4346 4347 4348 4349 4350 4351 4352 4353 4354 4355
## >50K >50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K >50K <=50K <=50K
## 4356 4357 4358 4359 4360 4361 4362 4363 4364 4365 4366 4367 4368
## >50K <=50K <=50K <=50K <=50K <=50K <=50K >50K <=50K <=50K <=50K <=50K <=50K
## 4369 4370 4371 4372 4373 4374 4375 4376 4377 4378 4379 4380 4381
## <=50K <=50K <=50K <=50K >50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K
## 4382 4383 4384 4385 4386 4387 4388 4389 4390 4391 4392 4393 4394
## <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K >50K <=50K
## 4395 4396 4397 4398 4399 4400 4401 4402 4403 4404 4405 4406 4407
## <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K
## 4408 4409 4410 4411 4412 4413 4414 4415 4416 4417 4418 4419 4420
## >50K <=50K >50K <=50K <=50K <=50K <=50K >50K <=50K <=50K <=50K >50K <=50K
## 4421 4422 4423 4424 4425 4426 4427 4428 4429 4430 4431 4432 4433
## <=50K <=50K <=50K <=50K >50K <=50K >50K <=50K <=50K <=50K <=50K <=50K <=50K
## 4434 4435 4436 4437 4438 4439 4440 4441 4442 4443 4444 4445 4446
## <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K >50K <=50K
## 4447 4448 4449 4450 4451 4452 4453 4454 4455 4456 4457 4458 4459
## >50K <=50K >50K <=50K <=50K <=50K >50K >50K <=50K <=50K >50K <=50K <=50K
## 4460 4461 4462 4463 4464 4465 4466 4467 4468 4469 4470 4471 4472
## <=50K >50K <=50K <=50K <=50K <=50K <=50K >50K <=50K <=50K <=50K <=50K <=50K
## 4473 4474 4475 4476 4477 4478 4479 4480 4481 4482 4483 4484 4485
## <=50K <=50K <=50K <=50K <=50K <=50K >50K <=50K <=50K <=50K <=50K <=50K <=50K
## 4486 4487 4488 4489 4490 4491 4492 4493 4494 4495 4496 4497 4498
## >50K <=50K <=50K <=50K >50K <=50K <=50K <=50K <=50K >50K <=50K <=50K <=50K
## 4499 4500 4501 4502 4503 4504 4505 4506 4507 4508 4509 4510 4511
## <=50K <=50K <=50K <=50K <=50K <=50K <=50K >50K <=50K <=50K <=50K <=50K <=50K
## 4512 4513 4514 4515 4516 4517 4518 4519 4520 4521 4522 4523 4524
## <=50K <=50K <=50K <=50K >50K <=50K >50K <=50K <=50K <=50K <=50K <=50K >50K
## 4525 4526 4527 4528 4529 4530 4531 4532 4533 4534 4535 4536 4537
## <=50K <=50K >50K >50K <=50K >50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K
## 4538 4539 4540 4541 4542 4543 4544 4545 4546 4547 4548 4549 4550
## <=50K >50K <=50K <=50K <=50K <=50K <=50K >50K <=50K <=50K <=50K <=50K <=50K
## 4551 4552 4553 4554 4555 4556 4557 4558 4559 4560 4561 4562 4563
## <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K >50K <=50K <=50K
## 4564 4565 4566 4567 4568 4569 4570 4571 4572 4573 4574 4575 4576
## <=50K <=50K <=50K <=50K >50K <=50K <=50K >50K <=50K <=50K <=50K <=50K <=50K
## 4577 4578 4579 4580 4581 4582 4583 4584 4585 4586 4587 4588 4589
## <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K >50K <=50K <=50K <=50K
## 4590 4591 4592 4593 4594 4595 4596 4597 4598 4599 4600 4601 4602
## <=50K <=50K >50K <=50K <=50K <=50K <=50K >50K <=50K <=50K <=50K <=50K >50K
## 4603 4604 4605 4606 4607 4608 4609 4610 4611 4612 4613 4614 4615
## >50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K
## 4616 4617 4618 4619 4620 4621 4622 4623 4624 4625 4626 4627 4628
## <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K
## 4629 4630 4631 4632 4633 4634 4635 4636 4637 4638 4639 4640 4641
## <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K
## 4642 4643 4644 4645 4646 4647 4648 4649 4650 4651 4652 4653 4654
## <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K >50K <=50K <=50K <=50K <=50K
## 4655 4656 4657 4658 4659 4660 4661 4662 4663 4664 4665 4666 4667
## <=50K >50K <=50K >50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K >50K
## 4668 4669 4670 4671 4672 4673 4674 4675 4676 4677 4678 4679 4680
## <=50K <=50K <=50K <=50K <=50K <=50K >50K <=50K <=50K <=50K <=50K <=50K <=50K
## 4681 4682 4683 4684 4685 4686 4687 4688 4689 4690 4691 4692 4693
## >50K <=50K >50K <=50K >50K <=50K <=50K <=50K <=50K <=50K >50K <=50K <=50K
## 4694 4695 4696 4697 4698 4699 4700 4701 4702 4703 4704 4705 4706
## >50K <=50K <=50K <=50K >50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K
## 4707 4708 4709 4710 4711 4712 4713 4714 4715 4716 4717 4718 4719
## <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K
## 4720 4721 4722 4723 4724 4725 4726 4727 4728 4729 4730 4731 4732
## <=50K <=50K <=50K <=50K >50K >50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K
## 4733 4734 4735 4736 4737 4738 4739 4740 4741 4742 4743 4744 4745
## <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K >50K <=50K <=50K <=50K <=50K
## 4746 4747 4748 4749 4750 4751 4752 4753 4754 4755 4756 4757 4758
## <=50K <=50K >50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K
## 4759 4760 4761 4762 4763 4764 4765 4766 4767 4768 4769 4770 4771
## <=50K >50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K >50K <=50K <=50K
## 4772 4773 4774 4775 4776 4777 4778 4779 4780 4781 4782 4783 4784
## <=50K <=50K <=50K >50K <=50K <=50K <=50K >50K <=50K <=50K <=50K <=50K <=50K
## 4785 4786 4787 4788 4789 4790 4791 4792 4793 4794 4795 4796 4797
## <=50K <=50K >50K <=50K <=50K >50K >50K <=50K >50K <=50K <=50K <=50K <=50K
## 4798 4799 4800 4801 4802 4803 4804 4805 4806 4807 4808 4809 4810
## >50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K >50K
## 4811 4812 4813 4814 4815 4816 4817 4818 4819 4820 4821 4822 4823
## <=50K >50K <=50K <=50K <=50K <=50K >50K <=50K <=50K <=50K <=50K <=50K <=50K
## 4824 4825 4826 4827 4828 4829 4830 4831 4832 4833 4834 4835 4836
## <=50K <=50K >50K >50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K
## 4837 4838 4839 4840 4841 4842 4843 4844 4845 4846 4847 4848 4849
## <=50K <=50K <=50K <=50K >50K <=50K <=50K <=50K >50K >50K <=50K <=50K <=50K
## 4850 4851 4852 4853 4854 4855 4856 4857 4858 4859 4860 4861 4862
## >50K <=50K >50K <=50K <=50K <=50K >50K <=50K >50K <=50K <=50K <=50K <=50K
## 4863 4864 4865 4866 4867 4868 4869 4870 4871 4872 4873 4874 4875
## <=50K <=50K <=50K <=50K <=50K <=50K >50K <=50K <=50K <=50K <=50K <=50K >50K
## 4876 4877 4878 4879 4880 4881 4882 4883 4884 4885 4886 4887 4888
## >50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K
## 4889 4890 4891 4892 4893 4894 4895 4896 4897 4898 4899 4900 4901
## >50K <=50K <=50K >50K >50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K
## 4902 4903 4904 4905 4906 4907 4908 4909 4910 4911 4912 4913 4914
## <=50K <=50K <=50K >50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K
## 4915 4916 4917 4918 4919 4920 4921 4922 4923 4924 4925 4926 4927
## <=50K <=50K >50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K >50K
## 4928 4929 4930 4931 4932 4933 4934 4935 4936 4937 4938 4939 4940
## <=50K <=50K <=50K <=50K <=50K >50K <=50K <=50K <=50K <=50K <=50K <=50K >50K
## 4941 4942 4943 4944 4945 4946 4947 4948 4949 4950 4951 4952 4953
## <=50K <=50K <=50K <=50K >50K >50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K
## 4954 4955 4956 4957 4958 4959 4960 4961 4962 4963 4964 4965 4966
## <=50K >50K <=50K <=50K <=50K <=50K <=50K >50K <=50K <=50K <=50K <=50K <=50K
## 4967 4968 4969 4970 4971 4972 4973 4974 4975 4976 4977 4978 4979
## >50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K
## 4980 4981 4982 4983 4984 4985 4986 4987 4988 4989 4990 4991 4992
## <=50K <=50K <=50K >50K >50K <=50K <=50K <=50K <=50K >50K >50K >50K <=50K
## 4993 4994 4995 4996 4997 4998 4999 5000 5001 5002 5003 5004 5005
## <=50K <=50K <=50K <=50K <=50K <=50K <=50K >50K <=50K <=50K <=50K <=50K <=50K
## 5006 5007 5008 5009 5010 5011 5012 5013 5014 5015 5016 5017 5018
## <=50K <=50K <=50K <=50K <=50K >50K >50K <=50K <=50K <=50K >50K <=50K <=50K
## 5019 5020 5021 5022 5023 5024 5025 5026 5027 5028 5029 5030 5031
## >50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K
## 5032 5033 5034 5035 5036 5037 5038 5039 5040 5041 5042 5043 5044
## <=50K >50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K >50K
## 5045 5046 5047 5048 5049 5050 5051 5052 5053 5054 5055 5056 5057
## <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K >50K
## 5058 5059 5060 5061 5062 5063 5064 5065 5066 5067 5068 5069 5070
## <=50K <=50K <=50K <=50K <=50K <=50K >50K <=50K <=50K <=50K >50K <=50K <=50K
## 5071 5072 5073 5074 5075 5076 5077 5078 5079 5080 5081 5082 5083
## <=50K <=50K <=50K <=50K <=50K <=50K <=50K >50K <=50K >50K <=50K <=50K <=50K
## 5084 5085 5086 5087 5088 5089 5090 5091 5092 5093 5094 5095 5096
## <=50K <=50K >50K <=50K <=50K <=50K <=50K >50K <=50K <=50K <=50K <=50K >50K
## 5097 5098 5099 5100 5101 5102 5103 5104 5105 5106 5107 5108 5109
## <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K
## 5110 5111 5112 5113 5114 5115 5116 5117 5118 5119 5120 5121 5122
## <=50K <=50K <=50K >50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K >50K <=50K
## 5123 5124 5125 5126 5127 5128 5129 5130 5131 5132 5133 5134 5135
## <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K >50K <=50K
## 5136 5137 5138 5139 5140 5141 5142 5143 5144 5145 5146 5147 5148
## <=50K <=50K <=50K <=50K <=50K <=50K <=50K >50K <=50K <=50K <=50K <=50K <=50K
## 5149 5150 5151 5152 5153 5154 5155 5156 5157 5158 5159 5160 5161
## <=50K >50K <=50K <=50K <=50K >50K <=50K <=50K >50K >50K <=50K <=50K >50K
## 5162 5163 5164 5165 5166 5167 5168 5169 5170 5171 5172 5173 5174
## >50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K >50K
## 5175 5176 5177 5178 5179 5180 5181 5182 5183 5184 5185 5186 5187
## <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K
## 5188 5189 5190 5191 5192 5193 5194 5195 5196 5197 5198 5199 5200
## <=50K >50K <=50K >50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K
## 5201 5202 5203 5204 5205 5206 5207 5208 5209 5210 5211 5212 5213
## >50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K >50K <=50K <=50K <=50K <=50K
## 5214 5215 5216 5217 5218 5219 5220 5221 5222 5223 5224 5225 5226
## <=50K <=50K <=50K >50K <=50K <=50K >50K <=50K <=50K >50K >50K <=50K <=50K
## 5227 5228 5229 5230 5231 5232 5233 5234 5235 5236 5237 5238 5239
## <=50K <=50K >50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K >50K <=50K
## 5240 5241 5242 5243 5244 5245 5246 5247 5248 5249 5250 5251 5252
## <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K
## 5253 5254 5255 5256 5257 5258 5259 5260 5261 5262 5263 5264 5265
## <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K >50K
## 5266 5267 5268 5269 5270 5271 5272 5273 5274 5275 5276 5277 5278
## <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K
## 5279 5280 5281 5282 5283 5284 5285 5286 5287 5288 5289 5290 5291
## <=50K <=50K >50K >50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K >50K
## 5292 5293 5294 5295 5296 5297 5298 5299 5300 5301 5302 5303 5304
## <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K
## 5305 5306 5307 5308 5309 5310 5311 5312 5313 5314 5315 5316 5317
## <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K
## 5318 5319 5320 5321 5322 5323 5324 5325 5326 5327 5328 5329 5330
## <=50K <=50K <=50K <=50K <=50K <=50K <=50K >50K <=50K <=50K <=50K <=50K <=50K
## 5331 5332 5333 5334 5335 5336 5337 5338 5339 5340 5341 5342 5343
## <=50K >50K >50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K
## 5344 5345 5346 5347 5348 5349 5350 5351 5352 5353 5354 5355 5356
## <=50K <=50K <=50K <=50K <=50K <=50K >50K <=50K >50K <=50K <=50K <=50K <=50K
## 5357 5358 5359 5360 5361 5362 5363 5364 5365 5366 5367 5368 5369
## <=50K <=50K <=50K <=50K <=50K <=50K >50K >50K <=50K <=50K <=50K <=50K <=50K
## 5370 5371 5372 5373 5374 5375 5376 5377 5378 5379 5380 5381 5382
## <=50K <=50K <=50K <=50K >50K >50K <=50K <=50K <=50K <=50K <=50K >50K <=50K
## 5383 5384 5385 5386 5387 5388 5389 5390 5391 5392 5393 5394 5395
## <=50K >50K <=50K >50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K
## 5396 5397 5398 5399 5400 5401 5402 5403 5404 5405 5406 5407 5408
## >50K <=50K >50K <=50K <=50K >50K <=50K <=50K <=50K <=50K >50K <=50K <=50K
## 5409 5410 5411 5412 5413 5414 5415 5416 5417 5418 5419 5420 5421
## <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K >50K >50K <=50K >50K
## 5422 5423 5424 5425 5426 5427 5428 5429 5430 5431 5432 5433 5434
## >50K >50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K
## 5435 5436 5437 5438 5439 5440 5441 5442 5443 5444 5445 5446 5447
## <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K >50K <=50K
## 5448 5449 5450 5451 5452 5453 5454 5455 5456 5457 5458 5459 5460
## <=50K <=50K <=50K <=50K <=50K <=50K >50K <=50K >50K <=50K <=50K <=50K <=50K
## 5461 5462 5463 5464 5465 5466 5467 5468 5469 5470 5471 5472 5473
## <=50K <=50K <=50K <=50K >50K <=50K <=50K >50K >50K <=50K <=50K <=50K <=50K
## 5474 5475 5476 5477 5478 5479 5480 5481 5482 5483 5484 5485 5486
## >50K <=50K <=50K <=50K <=50K <=50K <=50K >50K <=50K <=50K <=50K <=50K <=50K
## 5487 5488 5489 5490 5491 5492 5493 5494 5495 5496 5497 5498 5499
## <=50K <=50K <=50K <=50K <=50K >50K <=50K >50K <=50K <=50K <=50K <=50K <=50K
## 5500 5501 5502 5503 5504 5505 5506 5507 5508 5509 5510 5511 5512
## <=50K >50K <=50K <=50K <=50K <=50K <=50K <=50K >50K <=50K <=50K <=50K <=50K
## 5513 5514 5515 5516 5517 5518 5519 5520 5521 5522 5523 5524 5525
## >50K <=50K <=50K <=50K <=50K <=50K >50K <=50K >50K >50K <=50K <=50K <=50K
## 5526 5527 5528 5529 5530 5531 5532 5533 5534 5535 5536 5537 5538
## <=50K <=50K <=50K <=50K <=50K <=50K >50K <=50K <=50K >50K <=50K <=50K <=50K
## 5539 5540 5541 5542 5543 5544 5545 5546 5547 5548 5549 5550 5551
## >50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K >50K <=50K <=50K
## 5552 5553 5554 5555 5556 5557 5558 5559 5560 5561 5562 5563 5564
## <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K >50K <=50K <=50K <=50K <=50K
## 5565 5566 5567 5568 5569 5570 5571 5572 5573 5574 5575 5576 5577
## <=50K <=50K <=50K <=50K <=50K >50K <=50K <=50K >50K >50K <=50K <=50K <=50K
## 5578 5579 5580 5581 5582 5583 5584 5585 5586 5587 5588 5589 5590
## <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K >50K
## 5591 5592 5593 5594 5595 5596 5597 5598 5599 5600 5601 5602 5603
## <=50K <=50K >50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K
## 5604 5605 5606 5607 5608 5609 5610 5611 5612 5613 5614 5615 5616
## <=50K <=50K >50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K
## 5617 5618 5619 5620 5621 5622 5623 5624 5625 5626 5627 5628 5629
## <=50K <=50K <=50K >50K <=50K <=50K <=50K <=50K >50K <=50K >50K <=50K <=50K
## 5630 5631 5632 5633 5634 5635 5636 5637 5638 5639 5640 5641 5642
## <=50K <=50K <=50K <=50K <=50K >50K <=50K <=50K <=50K <=50K <=50K >50K >50K
## 5643 5644 5645 5646 5647 5648 5649 5650 5651 5652 5653 5654 5655
## >50K >50K <=50K <=50K >50K <=50K <=50K >50K <=50K <=50K <=50K <=50K <=50K
## 5656 5657 5658 5659 5660 5661 5662 5663 5664 5665 5666 5667 5668
## <=50K <=50K <=50K <=50K >50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K >50K
## 5669 5670 5671 5672 5673 5674 5675 5676 5677 5678 5679 5680 5681
## <=50K >50K <=50K >50K <=50K <=50K <=50K >50K <=50K <=50K <=50K <=50K <=50K
## 5682 5683 5684 5685 5686 5687 5688 5689 5690 5691 5692 5693 5694
## <=50K <=50K >50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K >50K <=50K
## 5695 5696 5697 5698 5699 5700 5701 5702 5703 5704 5705 5706 5707
## >50K >50K <=50K >50K <=50K <=50K <=50K <=50K <=50K >50K <=50K <=50K <=50K
## 5708 5709 5710 5711 5712 5713 5714 5715 5716 5717 5718 5719 5720
## <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K >50K <=50K <=50K <=50K <=50K
## 5721 5722 5723 5724 5725 5726 5727 5728 5729 5730 5731 5732 5733
## <=50K <=50K <=50K >50K <=50K <=50K <=50K <=50K <=50K >50K <=50K <=50K <=50K
## 5734 5735 5736 5737 5738 5739 5740 5741 5742 5743 5744 5745 5746
## <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K >50K <=50K
## 5747 5748 5749 5750 5751 5752 5753 5754 5755 5756 5757 5758 5759
## <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K >50K <=50K <=50K <=50K <=50K
## 5760 5761 5762 5763 5764 5765 5766 5767 5768 5769 5770 5771 5772
## <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K
## 5773 5774 5775 5776 5777 5778 5779 5780 5781 5782 5783 5784 5785
## <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K >50K <=50K <=50K
## 5786 5787 5788 5789 5790 5791 5792 5793 5794 5795 5796 5797 5798
## <=50K <=50K <=50K >50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K
## 5799 5800 5801 5802 5803 5804 5805 5806 5807 5808 5809 5810 5811
## <=50K >50K <=50K <=50K <=50K <=50K <=50K >50K <=50K <=50K >50K <=50K <=50K
## 5812 5813 5814 5815 5816 5817 5818 5819 5820 5821 5822 5823 5824
## >50K <=50K >50K >50K <=50K >50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K
## 5825 5826 5827 5828 5829 5830 5831 5832 5833 5834 5835 5836 5837
## >50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K >50K <=50K >50K <=50K
## 5838 5839 5840 5841 5842 5843 5844 5845 5846 5847 5848 5849 5850
## <=50K >50K <=50K <=50K <=50K >50K <=50K <=50K <=50K <=50K >50K <=50K <=50K
## 5851 5852 5853 5854 5855 5856 5857 5858 5859 5860 5861 5862 5863
## <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K
## 5864 5865 5866 5867 5868 5869 5870 5871 5872 5873 5874 5875 5876
## <=50K <=50K <=50K <=50K <=50K <=50K <=50K >50K >50K <=50K <=50K <=50K <=50K
## 5877 5878 5879 5880 5881 5882 5883 5884 5885 5886 5887 5888 5889
## <=50K <=50K <=50K <=50K >50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K >50K
## 5890 5891 5892 5893 5894 5895 5896 5897 5898 5899 5900 5901 5902
## <=50K <=50K <=50K <=50K <=50K <=50K >50K <=50K <=50K >50K <=50K <=50K <=50K
## 5903 5904 5905 5906 5907 5908 5909 5910 5911 5912 5913 5914 5915
## <=50K <=50K <=50K <=50K <=50K >50K >50K <=50K <=50K <=50K <=50K <=50K <=50K
## 5916 5917 5918 5919 5920 5921 5922 5923 5924 5925 5926 5927 5928
## <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K
## 5929 5930 5931 5932 5933 5934 5935 5936 5937 5938 5939 5940 5941
## <=50K <=50K <=50K <=50K <=50K <=50K <=50K >50K <=50K <=50K >50K <=50K >50K
## 5942 5943 5944 5945 5946 5947 5948 5949 5950 5951 5952 5953 5954
## <=50K >50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K
## 5955 5956 5957 5958 5959 5960 5961 5962 5963 5964 5965 5966 5967
## <=50K <=50K <=50K <=50K <=50K >50K <=50K <=50K <=50K <=50K >50K <=50K <=50K
## 5968 5969 5970 5971 5972 5973 5974 5975 5976 5977 5978 5979 5980
## <=50K >50K <=50K <=50K >50K <=50K <=50K <=50K >50K >50K <=50K <=50K <=50K
## 5981 5982 5983 5984 5985 5986 5987 5988 5989 5990 5991 5992 5993
## <=50K <=50K >50K >50K <=50K <=50K <=50K >50K <=50K <=50K <=50K <=50K <=50K
## 5994 5995 5996 5997 5998 5999 6000 6001 6002 6003 6004 6005 6006
## >50K <=50K <=50K >50K >50K <=50K <=50K <=50K >50K <=50K <=50K >50K >50K
## 6007 6008 6009 6010 6011 6012 6013 6014 6015 6016 6017 6018 6019
## <=50K <=50K <=50K <=50K <=50K >50K <=50K <=50K <=50K <=50K <=50K <=50K >50K
## 6020 6021 6022 6023 6024 6025 6026 6027 6028 6029 6030 6031 6032
## <=50K >50K <=50K <=50K >50K >50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K
## 6033 6034 6035 6036 6037 6038 6039 6040 6041 6042 6043 6044 6045
## <=50K >50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K >50K <=50K <=50K <=50K
## 6046 6047 6048 6049 6050 6051 6052 6053 6054 6055 6056 6057 6058
## >50K <=50K >50K >50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K
## 6059 6060 6061 6062 6063 6064 6065 6066 6067 6068 6069 6070 6071
## <=50K <=50K <=50K >50K <=50K <=50K <=50K <=50K >50K <=50K <=50K <=50K >50K
## 6072 6073 6074 6075 6076 6077 6078 6079 6080 6081 6082 6083 6084
## >50K <=50K >50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K >50K <=50K
## 6085 6086 6087 6088 6089 6090 6091 6092 6093 6094 6095 6096 6097
## >50K <=50K <=50K <=50K <=50K <=50K >50K >50K <=50K <=50K <=50K <=50K >50K
## 6098 6099 6100 6101 6102 6103 6104 6105 6106 6107 6108 6109 6110
## <=50K <=50K <=50K <=50K <=50K <=50K >50K <=50K <=50K <=50K <=50K <=50K <=50K
## 6111 6112 6113 6114 6115 6116 6117 6118 6119 6120 6121 6122 6123
## <=50K <=50K >50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K >50K <=50K <=50K
## 6124 6125 6126 6127 6128 6129 6130 6131 6132 6133 6134 6135 6136
## <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K
## 6137 6138 6139 6140 6141 6142 6143 6144 6145 6146 6147 6148 6149
## <=50K >50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K >50K
## 6150 6151 6152 6153 6154 6155 6156 6157 6158 6159 6160 6161 6162
## <=50K <=50K <=50K <=50K <=50K <=50K <=50K >50K <=50K >50K <=50K <=50K <=50K
## 6163 6164 6165 6166 6167 6168 6169 6170 6171 6172 6173 6174 6175
## <=50K <=50K >50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K
## 6176 6177 6178 6179 6180 6181 6182 6183 6184 6185 6186 6187 6188
## <=50K <=50K <=50K >50K >50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K
## 6189 6190 6191 6192 6193 6194 6195 6196 6197 6198 6199 6200 6201
## <=50K <=50K <=50K >50K <=50K <=50K <=50K <=50K >50K <=50K <=50K >50K <=50K
## 6202 6203 6204 6205 6206 6207 6208 6209 6210 6211 6212 6213 6214
## <=50K <=50K <=50K <=50K >50K >50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K
## 6215 6216 6217 6218 6219 6220 6221 6222 6223 6224 6225 6226 6227
## <=50K <=50K >50K <=50K <=50K <=50K <=50K >50K <=50K >50K <=50K <=50K <=50K
## 6228 6229 6230 6231 6232 6233 6234 6235 6236 6237 6238 6239 6240
## <=50K >50K <=50K <=50K >50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K
## 6241 6242 6243 6244 6245 6246 6247 6248 6249 6250 6251 6252 6253
## <=50K >50K <=50K >50K <=50K <=50K <=50K <=50K >50K >50K >50K <=50K <=50K
## 6254 6255 6256 6257 6258 6259 6260 6261 6262 6263 6264 6265 6266
## <=50K <=50K >50K <=50K <=50K >50K <=50K >50K <=50K >50K <=50K <=50K <=50K
## 6267 6268 6269 6270 6271 6272 6273 6274 6275 6276 6277 6278 6279
## <=50K <=50K >50K <=50K <=50K >50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K
## 6280 6281 6282 6283 6284 6285 6286 6287 6288 6289 6290 6291 6292
## <=50K <=50K <=50K <=50K <=50K >50K <=50K <=50K <=50K <=50K >50K <=50K <=50K
## 6293 6294 6295 6296 6297 6298 6299 6300 6301 6302 6303 6304 6305
## <=50K <=50K >50K <=50K <=50K <=50K <=50K <=50K <=50K >50K <=50K >50K <=50K
## 6306 6307 6308 6309 6310 6311 6312 6313 6314 6315 6316 6317 6318
## >50K <=50K <=50K <=50K <=50K >50K >50K >50K <=50K >50K <=50K <=50K <=50K
## 6319 6320 6321 6322 6323 6324 6325 6326 6327 6328 6329 6330 6331
## <=50K <=50K <=50K <=50K >50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K >50K
## 6332 6333 6334 6335 6336 6337 6338 6339 6340 6341 6342 6343 6344
## <=50K <=50K <=50K <=50K <=50K >50K >50K <=50K <=50K <=50K <=50K >50K <=50K
## 6345 6346 6347 6348 6349 6350 6351 6352 6353 6354 6355 6356 6357
## <=50K <=50K <=50K >50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K
## 6358 6359 6360 6361 6362 6363 6364 6365 6366 6367 6368 6369 6370
## <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K >50K
## 6371 6372 6373 6374 6375 6376 6377 6378 6379 6380 6381 6382 6383
## <=50K >50K >50K <=50K <=50K <=50K <=50K <=50K >50K >50K <=50K <=50K <=50K
## 6384 6385 6386 6387 6388 6389 6390 6391 6392 6393 6394 6395 6396
## <=50K <=50K >50K <=50K <=50K >50K <=50K <=50K <=50K <=50K >50K <=50K <=50K
## 6397 6398 6399 6400 6401 6402 6403 6404 6405 6406 6407 6408 6409
## <=50K <=50K <=50K >50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K
## 6410 6411 6412 6413 6414 6415 6416 6417 6418 6419 6420 6421 6422
## <=50K <=50K >50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K >50K >50K
## 6423 6424 6425 6426 6427 6428 6429 6430 6431 6432 6433 6434 6435
## <=50K <=50K <=50K <=50K <=50K <=50K >50K <=50K >50K <=50K >50K <=50K <=50K
## 6436 6437 6438 6439 6440 6441 6442 6443 6444 6445 6446 6447 6448
## <=50K <=50K <=50K <=50K <=50K >50K <=50K <=50K <=50K <=50K >50K >50K <=50K
## 6449 6450 6451 6452 6453 6454 6455 6456 6457 6458 6459 6460 6461
## <=50K >50K >50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K >50K <=50K <=50K
## 6462 6463 6464 6465 6466 6467 6468 6469 6470 6471 6472 6473 6474
## <=50K <=50K <=50K >50K <=50K >50K <=50K <=50K <=50K <=50K <=50K <=50K >50K
## 6475 6476 6477 6478 6479 6480 6481 6482 6483 6484 6485 6486 6487
## <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K
## 6488 6489 6490 6491 6492 6493 6494 6495 6496 6497 6498 6499 6500
## <=50K <=50K <=50K <=50K <=50K <=50K <=50K <=50K >50K <=50K <=50K <=50K <=50K
## 6501 6502 6503 6504 6505 6506 6507 6508 6509 6510 6511 6512
## <=50K <=50K <=50K <=50K <=50K >50K <=50K <=50K <=50K <=50K >50K <=50K
## Levels: <=50K >50Ksummary(PredictionTree)## <=50K >50K
## 5462 1050# Confusion Matrix
cf_DecisionTree <- confusionMatrix(PredictionTree, dataTestIncomeTree$income)
cf_DecisionTree## Confusion Matrix and Statistics
##
## Reference
## Prediction <=50K >50K
## <=50K 4681 781
## >50K 263 787
##
## Accuracy : 0.8397
## 95% CI : (0.8305, 0.8485)
## No Information Rate : 0.7592
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.5058
##
## Mcnemar's Test P-Value : < 2.2e-16
##
## Sensitivity : 0.9468
## Specificity : 0.5019
## Pos Pred Value : 0.8570
## Neg Pred Value : 0.7495
## Prevalence : 0.7592
## Detection Rate : 0.7188
## Detection Prevalence : 0.8388
## Balanced Accuracy : 0.7244
##
## 'Positive' Class : <=50K
## #################################End of DecisionTree ##########