Missing Data Task
Group 17 1. Putu Calista Arthanti Dewi (5003231096) 2. Maitreya Zahwa Celesty S. (5003231043)
2025-09-17
Import Library
knitr::opts_chunk$set(echo = TRUE)
library(dplyr)##
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## intersect, setdiff, setequal, unionlibrary(ggplot2)
library(imputeTS) ## Warning: package 'imputeTS' was built under R version 4.5.1## Registered S3 method overwritten by 'quantmod':
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## cov, smooth, varDataset Preparation
data <- read.csv("data_missing.csv")
head(data)## Loan_ID Gender Married Dependents Education Self_Employed ApplicantIncome
## 1 LP001002 Male No 0 Graduate No 5849
## 2 LP001003 Male Yes 1 Graduate No 4583
## 3 LP001005 Male Yes 0 Graduate Yes 3000
## 4 LP001006 Male Yes 0 Not Graduate No 2583
## 5 LP001008 Male No 0 Graduate No 6000
## 6 LP001011 Male Yes 2 Graduate Yes 5417
## CoapplicantIncome LoanAmount Loan_Amount_Term Credit_History Property_Area
## 1 0 NA 360 1 Urban
## 2 1508 128 360 1 Rural
## 3 0 66 360 1 Urban
## 4 2358 120 360 1 Urban
## 5 0 141 360 1 Urban
## 6 4196 267 360 1 Urban
## Loan_Status
## 1 Y
## 2 N
## 3 Y
## 4 Y
## 5 Y
## 6 Y# Informasi terkait variabel dalam dataset
str(data)## 'data.frame': 614 obs. of 13 variables:
## $ Loan_ID : chr "LP001002" "LP001003" "LP001005" "LP001006" ...
## $ Gender : chr "Male" "Male" "Male" "Male" ...
## $ Married : chr "No" "Yes" "Yes" "Yes" ...
## $ Dependents : chr "0" "1" "0" "0" ...
## $ Education : chr "Graduate" "Graduate" "Graduate" "Not Graduate" ...
## $ Self_Employed : chr "No" "No" "Yes" "No" ...
## $ ApplicantIncome : int 5849 4583 3000 2583 6000 5417 2333 3036 4006 12841 ...
## $ CoapplicantIncome: num 0 1508 0 2358 0 ...
## $ LoanAmount : int NA 128 66 120 141 267 95 158 168 349 ...
## $ Loan_Amount_Term : int 360 360 360 360 360 360 360 360 360 360 ...
## $ Credit_History : int 1 1 1 1 1 1 1 0 1 1 ...
## $ Property_Area : chr "Urban" "Rural" "Urban" "Urban" ...
## $ Loan_Status : chr "Y" "N" "Y" "Y" ...# Summary data
summary(data)## Loan_ID Gender Married Dependents
## Length:614 Length:614 Length:614 Length:614
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
##
## Education Self_Employed ApplicantIncome CoapplicantIncome
## Length:614 Length:614 Min. : 150 Min. : 0
## Class :character Class :character 1st Qu.: 2878 1st Qu.: 0
## Mode :character Mode :character Median : 3812 Median : 1188
## Mean : 5403 Mean : 1621
## 3rd Qu.: 5795 3rd Qu.: 2297
## Max. :81000 Max. :41667
##
## LoanAmount Loan_Amount_Term Credit_History Property_Area
## Min. : 9.0 Min. : 12 Min. :0.0000 Length:614
## 1st Qu.:100.0 1st Qu.:360 1st Qu.:1.0000 Class :character
## Median :128.0 Median :360 Median :1.0000 Mode :character
## Mean :146.4 Mean :342 Mean :0.8422
## 3rd Qu.:168.0 3rd Qu.:360 3rd Qu.:1.0000
## Max. :700.0 Max. :480 Max. :1.0000
## NA's :22 NA's :14 NA's :50
## Loan_Status
## Length:614
## Class :character
## Mode :character
##
##
##
## # Lihat kategori dalam setiap kolom non-numerik
lapply(data[sapply(data, function(x) is.factor(x) | is.character(x))], unique)## $Loan_ID
## [1] "LP001002" "LP001003" "LP001005" "LP001006" "LP001008" "LP001011"
## [7] "LP001013" "LP001014" "LP001018" "LP001020" "LP001024" "LP001027"
## [13] "LP001028" "LP001029" "LP001030" "LP001032" "LP001034" "LP001036"
## [19] "LP001038" "LP001041" "LP001043" "LP001046" "LP001047" "LP001050"
## [25] "LP001052" "LP001066" "LP001068" "LP001073" "LP001086" "LP001087"
## [31] "LP001091" "LP001095" "LP001097" "LP001098" "LP001100" "LP001106"
## [37] "LP001109" "LP001112" "LP001114" "LP001116" "LP001119" "LP001120"
## [43] "LP001123" "LP001131" "LP001136" "LP001137" "LP001138" "LP001144"
## [49] "LP001146" "LP001151" "LP001155" "LP001157" "LP001164" "LP001179"
## [55] "LP001186" "LP001194" "LP001195" "LP001197" "LP001198" "LP001199"
## [61] "LP001205" "LP001206" "LP001207" "LP001213" "LP001222" "LP001225"
## [67] "LP001228" "LP001233" "LP001238" "LP001241" "LP001243" "LP001245"
## [73] "LP001248" "LP001250" "LP001253" "LP001255" "LP001256" "LP001259"
## [79] "LP001263" "LP001264" "LP001265" "LP001266" "LP001267" "LP001273"
## [85] "LP001275" "LP001279" "LP001280" "LP001282" "LP001289" "LP001310"
## [91] "LP001316" "LP001318" "LP001319" "LP001322" "LP001325" "LP001326"
## [97] "LP001327" "LP001333" "LP001334" "LP001343" "LP001345" "LP001349"
## [103] "LP001350" "LP001356" "LP001357" "LP001367" "LP001369" "LP001370"
## [109] "LP001379" "LP001384" "LP001385" "LP001387" "LP001391" "LP001392"
## [115] "LP001398" "LP001401" "LP001404" "LP001405" "LP001421" "LP001422"
## [121] "LP001426" "LP001430" "LP001431" "LP001432" "LP001439" "LP001443"
## [127] "LP001448" "LP001449" "LP001451" "LP001465" "LP001469" "LP001473"
## [133] "LP001478" "LP001482" "LP001487" "LP001488" "LP001489" "LP001491"
## [139] "LP001492" "LP001493" "LP001497" "LP001498" "LP001504" "LP001507"
## [145] "LP001508" "LP001514" "LP001516" "LP001518" "LP001519" "LP001520"
## [151] "LP001528" "LP001529" "LP001531" "LP001532" "LP001535" "LP001536"
## [157] "LP001541" "LP001543" "LP001546" "LP001552" "LP001560" "LP001562"
## [163] "LP001565" "LP001570" "LP001572" "LP001574" "LP001577" "LP001578"
## [169] "LP001579" "LP001580" "LP001581" "LP001585" "LP001586" "LP001594"
## [175] "LP001603" "LP001606" "LP001608" "LP001610" "LP001616" "LP001630"
## [181] "LP001633" "LP001634" "LP001636" "LP001637" "LP001639" "LP001640"
## [187] "LP001641" "LP001643" "LP001644" "LP001647" "LP001653" "LP001656"
## [193] "LP001657" "LP001658" "LP001664" "LP001665" "LP001666" "LP001669"
## [199] "LP001671" "LP001673" "LP001674" "LP001677" "LP001682" "LP001688"
## [205] "LP001691" "LP001692" "LP001693" "LP001698" "LP001699" "LP001702"
## [211] "LP001708" "LP001711" "LP001713" "LP001715" "LP001716" "LP001720"
## [217] "LP001722" "LP001726" "LP001732" "LP001734" "LP001736" "LP001743"
## [223] "LP001744" "LP001749" "LP001750" "LP001751" "LP001754" "LP001758"
## [229] "LP001760" "LP001761" "LP001765" "LP001768" "LP001770" "LP001776"
## [235] "LP001778" "LP001784" "LP001786" "LP001788" "LP001790" "LP001792"
## [241] "LP001798" "LP001800" "LP001806" "LP001807" "LP001811" "LP001813"
## [247] "LP001814" "LP001819" "LP001824" "LP001825" "LP001835" "LP001836"
## [253] "LP001841" "LP001843" "LP001844" "LP001846" "LP001849" "LP001854"
## [259] "LP001859" "LP001864" "LP001865" "LP001868" "LP001870" "LP001871"
## [265] "LP001872" "LP001875" "LP001877" "LP001882" "LP001883" "LP001884"
## [271] "LP001888" "LP001891" "LP001892" "LP001894" "LP001896" "LP001900"
## [277] "LP001903" "LP001904" "LP001907" "LP001908" "LP001910" "LP001914"
## [283] "LP001915" "LP001917" "LP001922" "LP001924" "LP001925" "LP001926"
## [289] "LP001931" "LP001935" "LP001936" "LP001938" "LP001940" "LP001945"
## [295] "LP001947" "LP001949" "LP001953" "LP001954" "LP001955" "LP001963"
## [301] "LP001964" "LP001972" "LP001974" "LP001977" "LP001978" "LP001990"
## [307] "LP001993" "LP001994" "LP001996" "LP001998" "LP002002" "LP002004"
## [313] "LP002006" "LP002008" "LP002024" "LP002031" "LP002035" "LP002036"
## [319] "LP002043" "LP002050" "LP002051" "LP002053" "LP002054" "LP002055"
## [325] "LP002065" "LP002067" "LP002068" "LP002082" "LP002086" "LP002087"
## [331] "LP002097" "LP002098" "LP002100" "LP002101" "LP002103" "LP002106"
## [337] "LP002110" "LP002112" "LP002113" "LP002114" "LP002115" "LP002116"
## [343] "LP002119" "LP002126" "LP002128" "LP002129" "LP002130" "LP002131"
## [349] "LP002137" "LP002138" "LP002139" "LP002140" "LP002141" "LP002142"
## [355] "LP002143" "LP002144" "LP002149" "LP002151" "LP002158" "LP002160"
## [361] "LP002161" "LP002170" "LP002175" "LP002178" "LP002180" "LP002181"
## [367] "LP002187" "LP002188" "LP002190" "LP002191" "LP002194" "LP002197"
## [373] "LP002201" "LP002205" "LP002209" "LP002211" "LP002219" "LP002223"
## [379] "LP002224" "LP002225" "LP002226" "LP002229" "LP002231" "LP002234"
## [385] "LP002236" "LP002237" "LP002239" "LP002243" "LP002244" "LP002250"
## [391] "LP002255" "LP002262" "LP002263" "LP002265" "LP002266" "LP002272"
## [397] "LP002277" "LP002281" "LP002284" "LP002287" "LP002288" "LP002296"
## [403] "LP002297" "LP002300" "LP002301" "LP002305" "LP002308" "LP002314"
## [409] "LP002315" "LP002317" "LP002318" "LP002319" "LP002328" "LP002332"
## [415] "LP002335" "LP002337" "LP002341" "LP002342" "LP002345" "LP002347"
## [421] "LP002348" "LP002357" "LP002361" "LP002362" "LP002364" "LP002366"
## [427] "LP002367" "LP002368" "LP002369" "LP002370" "LP002377" "LP002379"
## [433] "LP002386" "LP002387" "LP002390" "LP002393" "LP002398" "LP002401"
## [439] "LP002403" "LP002407" "LP002408" "LP002409" "LP002418" "LP002422"
## [445] "LP002424" "LP002429" "LP002434" "LP002435" "LP002443" "LP002444"
## [451] "LP002446" "LP002447" "LP002448" "LP002449" "LP002453" "LP002455"
## [457] "LP002459" "LP002467" "LP002472" "LP002473" "LP002478" "LP002484"
## [463] "LP002487" "LP002489" "LP002493" "LP002494" "LP002500" "LP002501"
## [469] "LP002502" "LP002505" "LP002515" "LP002517" "LP002519" "LP002522"
## [475] "LP002524" "LP002527" "LP002529" "LP002530" "LP002531" "LP002533"
## [481] "LP002534" "LP002536" "LP002537" "LP002541" "LP002543" "LP002544"
## [487] "LP002545" "LP002547" "LP002555" "LP002556" "LP002560" "LP002562"
## [493] "LP002571" "LP002582" "LP002585" "LP002586" "LP002587" "LP002588"
## [499] "LP002600" "LP002602" "LP002603" "LP002606" "LP002615" "LP002618"
## [505] "LP002619" "LP002622" "LP002624" "LP002625" "LP002626" "LP002634"
## [511] "LP002637" "LP002640" "LP002643" "LP002648" "LP002652" "LP002659"
## [517] "LP002670" "LP002682" "LP002683" "LP002684" "LP002689" "LP002690"
## [523] "LP002692" "LP002693" "LP002697" "LP002699" "LP002705" "LP002706"
## [529] "LP002714" "LP002716" "LP002717" "LP002720" "LP002723" "LP002729"
## [535] "LP002731" "LP002732" "LP002734" "LP002738" "LP002739" "LP002740"
## [541] "LP002741" "LP002743" "LP002753" "LP002755" "LP002757" "LP002767"
## [547] "LP002768" "LP002772" "LP002776" "LP002777" "LP002778" "LP002784"
## [553] "LP002785" "LP002788" "LP002789" "LP002792" "LP002794" "LP002795"
## [559] "LP002798" "LP002804" "LP002807" "LP002813" "LP002820" "LP002821"
## [565] "LP002832" "LP002833" "LP002836" "LP002837" "LP002840" "LP002841"
## [571] "LP002842" "LP002847" "LP002855" "LP002862" "LP002863" "LP002868"
## [577] "LP002872" "LP002874" "LP002877" "LP002888" "LP002892" "LP002893"
## [583] "LP002894" "LP002898" "LP002911" "LP002912" "LP002916" "LP002917"
## [589] "LP002925" "LP002926" "LP002928" "LP002931" "LP002933" "LP002936"
## [595] "LP002938" "LP002940" "LP002941" "LP002943" "LP002945" "LP002948"
## [601] "LP002949" "LP002950" "LP002953" "LP002958" "LP002959" "LP002960"
## [607] "LP002961" "LP002964" "LP002974" "LP002978" "LP002979" "LP002983"
## [613] "LP002984" "LP002990"
##
## $Gender
## [1] "Male" "Female" ""
##
## $Married
## [1] "No" "Yes" ""
##
## $Dependents
## [1] "0" "1" "2" "3+" ""
##
## $Education
## [1] "Graduate" "Not Graduate"
##
## $Self_Employed
## [1] "No" "Yes" ""
##
## $Property_Area
## [1] "Urban" "Rural" "Semiurban"
##
## $Loan_Status
## [1] "Y" "N"Exploratory Analysis of Missing Data
# Hitung missing data keseluruhan
sum(is.na(data))## [1] 86# Hitung jumlah missing tiap kolom
missing_summary <- data %>%
summarise(across(everything(), ~sum(is.na(.))))
missing_summary## Loan_ID Gender Married Dependents Education Self_Employed ApplicantIncome
## 1 0 0 0 0 0 0 0
## CoapplicantIncome LoanAmount Loan_Amount_Term Credit_History Property_Area
## 1 0 22 14 50 0
## Loan_Status
## 1 0# Hitung persentase missing tiap kolom
missing_percent <- data %>%
summarise(across(everything(), ~mean(is.na(.))*100))
missing_percent## Loan_ID Gender Married Dependents Education Self_Employed ApplicantIncome
## 1 0 0 0 0 0 0 0
## CoapplicantIncome LoanAmount Loan_Amount_Term Credit_History Property_Area
## 1 0 3.583062 2.28013 8.143322 0
## Loan_Status
## 1 0# Menggabungkan informasi terkait missing value
missing_table <- data.frame(
Variable = names(data),
Missing_Count = as.numeric(missing_summary[1,]),
Missing_Percent = as.numeric(missing_percent[1,])
)
missing_table## Variable Missing_Count Missing_Percent
## 1 Loan_ID 0 0.000000
## 2 Gender 0 0.000000
## 3 Married 0 0.000000
## 4 Dependents 0 0.000000
## 5 Education 0 0.000000
## 6 Self_Employed 0 0.000000
## 7 ApplicantIncome 0 0.000000
## 8 CoapplicantIncome 0 0.000000
## 9 LoanAmount 22 3.583062
## 10 Loan_Amount_Term 14 2.280130
## 11 Credit_History 50 8.143322
## 12 Property_Area 0 0.000000
## 13 Loan_Status 0 0.000000Pada dataset ini, missing value ditemukan dalam kolom LoanAmount, Loan_Amount_Term, dan Credit_History
# Visualisasi pattern missing data dengan library MICE
md.pattern(data)
## Loan_ID Gender Married Dependents Education Self_Employed ApplicantIncome
## 529 1 1 1 1 1 1 1
## 49 1 1 1 1 1 1 1
## 21 1 1 1 1 1 1 1
## 1 1 1 1 1 1 1 1
## 14 1 1 1 1 1 1 1
## 0 0 0 0 0 0 0
## CoapplicantIncome Property_Area Loan_Status Loan_Amount_Term LoanAmount
## 529 1 1 1 1 1
## 49 1 1 1 1 1
## 21 1 1 1 1 0
## 1 1 1 1 1 0
## 14 1 1 1 0 1
## 0 0 0 14 22
## Credit_History
## 529 1 0
## 49 0 1
## 21 1 1
## 1 0 2
## 14 1 1
## 50 86Hasil ini menampilkan missingness pattern dari dataset, namun variabel-variabelnya kurang terlihat karena jumlahnya cukup banyak. Maka dilakukan visualisasi kembali dengan memanfaatkan library VIM.
# Visualisasi missing data dengan library VIM
aggr_plot <- aggr(data, col=c('navyblue','red'),
numbers=TRUE, sortVars=TRUE,
labels=names(data), cex.axis=.7,
gap=3, ylab=c("Missing Data","Pattern"))
##
## Variables sorted by number of missings:
## Variable Count
## Credit_History 0.08143322
## LoanAmount 0.03583062
## Loan_Amount_Term 0.02280130
## Loan_ID 0.00000000
## Gender 0.00000000
## Married 0.00000000
## Dependents 0.00000000
## Education 0.00000000
## Self_Employed 0.00000000
## ApplicantIncome 0.00000000
## CoapplicantIncome 0.00000000
## Property_Area 0.00000000
## Loan_Status 0.00000000Dapat dilihat bahwa missing values yang terdapat dalam dataset relatif kecil yakni kurang dari 10%, jadi masih bisa ditangani dengan imputasi dan tidak perlu drop variabel. Variabel yang paling bermasalah adalah Credit_History, sehingga perlu perhatian khusus saat imputasi. Selain itu, pola missing value sebenarnya tidak terlalu kompleks (hanya 3 variabel yang terpengaruh), artinya imputasi sederhana yakni Ad-hoc methods (mean/median/mode) mungkin sudah cukup, tapi lebih baik dicoba juga MICE methods untuk hasil yang lebih akurat.
Imputation Methods
Drop row (without imputation)
# drop semua baris yang ada NA di kolom manapun
data_drop <- data %>% drop_na()# Cek setelah drop baris berisi missing value
colSums(is.na(data_drop))## Loan_ID Gender Married Dependents
## 0 0 0 0
## Education Self_Employed ApplicantIncome CoapplicantIncome
## 0 0 0 0
## LoanAmount Loan_Amount_Term Credit_History Property_Area
## 0 0 0 0
## Loan_Status
## 0Ad-hoc Methods
# Imputasi missing value dengan mean
data_imp_mean <- data %>%
mutate(across(where(is.numeric), ~na_mean(.)))# Imputasi missing value dengan median
data_imp_median <- data %>%
mutate(across(where(is.numeric), ~na_mean(., option = "median")))# Imputasi missing value dengan modus
# fungsi
get_mode <- function(x) {
ux <- unique(x[!is.na(x)])
ux[which.max(tabulate(match(x, ux)))]
}
# imputasi modus ke semua kolom
data_imp_mode <- data %>%
mutate(across(everything(), ~ifelse(is.na(.), get_mode(.), .)))# cek setelah imputasi mean
colSums(is.na(data_imp_mean))## Loan_ID Gender Married Dependents
## 0 0 0 0
## Education Self_Employed ApplicantIncome CoapplicantIncome
## 0 0 0 0
## LoanAmount Loan_Amount_Term Credit_History Property_Area
## 0 0 0 0
## Loan_Status
## 0# cek setelah imputasi mean
colSums(is.na(data_imp_median))## Loan_ID Gender Married Dependents
## 0 0 0 0
## Education Self_Employed ApplicantIncome CoapplicantIncome
## 0 0 0 0
## LoanAmount Loan_Amount_Term Credit_History Property_Area
## 0 0 0 0
## Loan_Status
## 0# cek setelah imputasi mean
colSums(is.na(data_imp_mode))## Loan_ID Gender Married Dependents
## 0 0 0 0
## Education Self_Employed ApplicantIncome CoapplicantIncome
## 0 0 0 0
## LoanAmount Loan_Amount_Term Credit_History Property_Area
## 0 0 0 0
## Loan_Status
## 0Multiple Imputation by Chained Equations (MICE) Methods
# Tentukan metode imputasi per kolom
method <- make.method(data)Kolom berisi missing values memiliki tipe data numerik untuk LoanAmount dan Loan_Amount_Term, namun kolom Credit_History memiliki tipe data kategorikal biner (0 dan 1). Sehingga sebaiknya diterapkan methods yang berbeda dalam imputasi missing values ini.
# Mengubah Credit_History menjadi faktor dengan 2 level (karena sebelumnya masih bertipe int di R)
data$Credit_History <- factor(data$Credit_History, levels = c(0,1))
# Atur methods sesuai hasil exploratory
method["LoanAmount"] <- "pmm" # numeric continuous
method["Loan_Amount_Term"] <- "pmm" # numeric integer
method["Credit_History"] <- "logreg" # binary categorical (0/1)# Jalankan mice
imp_mice <- mice(data, m = 5, method = method, maxit = 5, seed = 123)##
## iter imp variable
## 1 1 LoanAmount Loan_Amount_Term Credit_History
## 1 2 LoanAmount Loan_Amount_Term Credit_History
## 1 3 LoanAmount Loan_Amount_Term Credit_History
## 1 4 LoanAmount Loan_Amount_Term Credit_History
## 1 5 LoanAmount Loan_Amount_Term Credit_History
## 2 1 LoanAmount Loan_Amount_Term Credit_History
## 2 2 LoanAmount Loan_Amount_Term Credit_History
## 2 3 LoanAmount Loan_Amount_Term Credit_History
## 2 4 LoanAmount Loan_Amount_Term Credit_History
## 2 5 LoanAmount Loan_Amount_Term Credit_History
## 3 1 LoanAmount Loan_Amount_Term Credit_History
## 3 2 LoanAmount Loan_Amount_Term Credit_History
## 3 3 LoanAmount Loan_Amount_Term Credit_History
## 3 4 LoanAmount Loan_Amount_Term Credit_History
## 3 5 LoanAmount Loan_Amount_Term Credit_History
## 4 1 LoanAmount Loan_Amount_Term Credit_History
## 4 2 LoanAmount Loan_Amount_Term Credit_History
## 4 3 LoanAmount Loan_Amount_Term Credit_History
## 4 4 LoanAmount Loan_Amount_Term Credit_History
## 4 5 LoanAmount Loan_Amount_Term Credit_History
## 5 1 LoanAmount Loan_Amount_Term Credit_History
## 5 2 LoanAmount Loan_Amount_Term Credit_History
## 5 3 LoanAmount Loan_Amount_Term Credit_History
## 5 4 LoanAmount Loan_Amount_Term Credit_History
## 5 5 LoanAmount Loan_Amount_Term Credit_History## Warning: Number of logged events: 8data_imp_mice <- complete(imp_mice, 1)data_imp_mice## Loan_ID Gender Married Dependents Education Self_Employed
## 1 LP001002 Male No 0 Graduate No
## 2 LP001003 Male Yes 1 Graduate No
## 3 LP001005 Male Yes 0 Graduate Yes
## 4 LP001006 Male Yes 0 Not Graduate No
## 5 LP001008 Male No 0 Graduate No
## 6 LP001011 Male Yes 2 Graduate Yes
## 7 LP001013 Male Yes 0 Not Graduate No
## 8 LP001014 Male Yes 3+ Graduate No
## 9 LP001018 Male Yes 2 Graduate No
## 10 LP001020 Male Yes 1 Graduate No
## 11 LP001024 Male Yes 2 Graduate No
## 12 LP001027 Male Yes 2 Graduate
## 13 LP001028 Male Yes 2 Graduate No
## 14 LP001029 Male No 0 Graduate No
## 15 LP001030 Male Yes 2 Graduate No
## 16 LP001032 Male No 0 Graduate No
## 17 LP001034 Male No 1 Not Graduate No
## 18 LP001036 Female No 0 Graduate No
## 19 LP001038 Male Yes 0 Not Graduate No
## 20 LP001041 Male Yes 0 Graduate
## 21 LP001043 Male Yes 0 Not Graduate No
## 22 LP001046 Male Yes 1 Graduate No
## 23 LP001047 Male Yes 0 Not Graduate No
## 24 LP001050 Yes 2 Not Graduate No
## 25 LP001052 Male Yes 1 Graduate
## 26 LP001066 Male Yes 0 Graduate Yes
## 27 LP001068 Male Yes 0 Graduate No
## 28 LP001073 Male Yes 2 Not Graduate No
## 29 LP001086 Male No 0 Not Graduate No
## 30 LP001087 Female No 2 Graduate
## 31 LP001091 Male Yes 1 Graduate
## 32 LP001095 Male No 0 Graduate No
## 33 LP001097 Male No 1 Graduate Yes
## 34 LP001098 Male Yes 0 Graduate No
## 35 LP001100 Male No 3+ Graduate No
## 36 LP001106 Male Yes 0 Graduate No
## 37 LP001109 Male Yes 0 Graduate No
## 38 LP001112 Female Yes 0 Graduate No
## 39 LP001114 Male No 0 Graduate No
## 40 LP001116 Male No 0 Not Graduate No
## 41 LP001119 Male No 0 Graduate No
## 42 LP001120 Male No 0 Graduate No
## 43 LP001123 Male Yes 0 Graduate No
## 44 LP001131 Male Yes 0 Graduate No
## 45 LP001136 Male Yes 0 Not Graduate Yes
## 46 LP001137 Female No 0 Graduate No
## 47 LP001138 Male Yes 1 Graduate No
## 48 LP001144 Male Yes 0 Graduate No
## 49 LP001146 Female Yes 0 Graduate No
## 50 LP001151 Female No 0 Graduate No
## 51 LP001155 Female Yes 0 Not Graduate No
## 52 LP001157 Female No 0 Graduate No
## 53 LP001164 Female No 0 Graduate No
## 54 LP001179 Male Yes 2 Graduate No
## 55 LP001186 Female Yes 1 Graduate Yes
## 56 LP001194 Male Yes 2 Graduate No
## 57 LP001195 Male Yes 0 Graduate No
## 58 LP001197 Male Yes 0 Graduate No
## 59 LP001198 Male Yes 1 Graduate No
## 60 LP001199 Male Yes 2 Not Graduate No
## 61 LP001205 Male Yes 0 Graduate No
## 62 LP001206 Male Yes 3+ Graduate No
## 63 LP001207 Male Yes 0 Not Graduate Yes
## 64 LP001213 Male Yes 1 Graduate No
## 65 LP001222 Female No 0 Graduate No
## 66 LP001225 Male Yes 0 Graduate No
## 67 LP001228 Male No 0 Not Graduate No
## 68 LP001233 Male Yes 1 Graduate No
## 69 LP001238 Male Yes 3+ Not Graduate Yes
## 70 LP001241 Female No 0 Graduate No
## 71 LP001243 Male Yes 0 Graduate No
## 72 LP001245 Male Yes 2 Not Graduate Yes
## 73 LP001248 Male No 0 Graduate No
## 74 LP001250 Male Yes 3+ Not Graduate No
## 75 LP001253 Male Yes 3+ Graduate Yes
## 76 LP001255 Male No 0 Graduate No
## 77 LP001256 Male No 0 Graduate No
## 78 LP001259 Male Yes 1 Graduate Yes
## 79 LP001263 Male Yes 3+ Graduate No
## 80 LP001264 Male Yes 3+ Not Graduate Yes
## 81 LP001265 Female No 0 Graduate No
## 82 LP001266 Male Yes 1 Graduate Yes
## 83 LP001267 Female Yes 2 Graduate No
## 84 LP001273 Male Yes 0 Graduate No
## 85 LP001275 Male Yes 1 Graduate No
## 86 LP001279 Male No 0 Graduate No
## 87 LP001280 Male Yes 2 Not Graduate No
## 88 LP001282 Male Yes 0 Graduate No
## 89 LP001289 Male No 0 Graduate No
## 90 LP001310 Male Yes 0 Graduate No
## 91 LP001316 Male Yes 0 Graduate No
## 92 LP001318 Male Yes 2 Graduate No
## 93 LP001319 Male Yes 2 Not Graduate No
## 94 LP001322 Male No 0 Graduate No
## 95 LP001325 Male No 0 Not Graduate No
## 96 LP001326 Male No 0 Graduate
## 97 LP001327 Female Yes 0 Graduate No
## 98 LP001333 Male Yes 0 Graduate No
## 99 LP001334 Male Yes 0 Not Graduate No
## 100 LP001343 Male Yes 0 Graduate No
## 101 LP001345 Male Yes 2 Not Graduate No
## 102 LP001349 Male No 0 Graduate No
## 103 LP001350 Male Yes Graduate No
## 104 LP001356 Male Yes 0 Graduate No
## 105 LP001357 Male Graduate No
## 106 LP001367 Male Yes 1 Graduate No
## 107 LP001369 Male Yes 2 Graduate No
## 108 LP001370 Male No 0 Not Graduate
## 109 LP001379 Male Yes 2 Graduate No
## 110 LP001384 Male Yes 3+ Not Graduate No
## 111 LP001385 Male No 0 Graduate No
## 112 LP001387 Female Yes 0 Graduate
## 113 LP001391 Male Yes 0 Not Graduate No
## 114 LP001392 Female No 1 Graduate Yes
## 115 LP001398 Male No 0 Graduate
## 116 LP001401 Male Yes 1 Graduate No
## 117 LP001404 Female Yes 0 Graduate No
## 118 LP001405 Male Yes 1 Graduate No
## 119 LP001421 Male Yes 0 Graduate No
## 120 LP001422 Female No 0 Graduate No
## 121 LP001426 Male Yes Graduate No
## 122 LP001430 Female No 0 Graduate No
## 123 LP001431 Female No 0 Graduate No
## 124 LP001432 Male Yes 2 Graduate No
## 125 LP001439 Male Yes 0 Not Graduate No
## 126 LP001443 Female No 0 Graduate No
## 127 LP001448 Yes 3+ Graduate No
## 128 LP001449 Male No 0 Graduate No
## 129 LP001451 Male Yes 1 Graduate Yes
## 130 LP001465 Male Yes 0 Graduate No
## 131 LP001469 Male No 0 Graduate Yes
## 132 LP001473 Male No 0 Graduate No
## 133 LP001478 Male No 0 Graduate No
## 134 LP001482 Male Yes 0 Graduate Yes
## 135 LP001487 Male No 0 Graduate No
## 136 LP001488 Male Yes 3+ Graduate No
## 137 LP001489 Female Yes 0 Graduate No
## 138 LP001491 Male Yes 2 Graduate Yes
## 139 LP001492 Male No 0 Graduate No
## 140 LP001493 Male Yes 2 Not Graduate No
## 141 LP001497 Male Yes 2 Graduate No
## 142 LP001498 Male No 0 Graduate No
## 143 LP001504 Male No 0 Graduate Yes
## 144 LP001507 Male Yes 0 Graduate No
## 145 LP001508 Male Yes 2 Graduate No
## 146 LP001514 Female Yes 0 Graduate No
## 147 LP001516 Female Yes 2 Graduate No
## 148 LP001518 Male Yes 1 Graduate No
## 149 LP001519 Female No 0 Graduate No
## 150 LP001520 Male Yes 0 Graduate No
## 151 LP001528 Male No 0 Graduate No
## 152 LP001529 Male Yes 0 Graduate Yes
## 153 LP001531 Male No 0 Graduate No
## 154 LP001532 Male Yes 2 Not Graduate No
## 155 LP001535 Male No 0 Graduate No
## 156 LP001536 Male Yes 3+ Graduate No
## 157 LP001541 Male Yes 1 Graduate No
## 158 LP001543 Male Yes 1 Graduate No
## 159 LP001546 Male No 0 Graduate
## 160 LP001552 Male Yes 0 Graduate No
## 161 LP001560 Male Yes 0 Not Graduate No
## 162 LP001562 Male Yes 0 Graduate No
## 163 LP001565 Male Yes 1 Graduate No
## 164 LP001570 Male Yes 2 Graduate No
## 165 LP001572 Male Yes 0 Graduate No
## 166 LP001574 Male Yes 0 Graduate No
## 167 LP001577 Female Yes 0 Graduate No
## 168 LP001578 Male Yes 0 Graduate No
## 169 LP001579 Male No 0 Graduate No
## 170 LP001580 Male Yes 2 Graduate No
## 171 LP001581 Male Yes 0 Not Graduate
## 172 LP001585 Yes 3+ Graduate No
## 173 LP001586 Male Yes 3+ Not Graduate No
## 174 LP001594 Male Yes 0 Graduate No
## 175 LP001603 Male Yes 0 Not Graduate Yes
## 176 LP001606 Male Yes 0 Graduate No
## 177 LP001608 Male Yes 2 Graduate No
## 178 LP001610 Male Yes 3+ Graduate No
## 179 LP001616 Male Yes 1 Graduate No
## 180 LP001630 Male No 0 Not Graduate No
## 181 LP001633 Male Yes 1 Graduate No
## 182 LP001634 Male No 0 Graduate No
## 183 LP001636 Male Yes 0 Graduate No
## 184 LP001637 Male Yes 1 Graduate No
## 185 LP001639 Female Yes 0 Graduate No
## 186 LP001640 Male Yes 0 Graduate Yes
## 187 LP001641 Male Yes 1 Graduate Yes
## 188 LP001643 Male Yes 0 Graduate No
## 189 LP001644 Yes 0 Graduate Yes
## 190 LP001647 Male Yes 0 Graduate No
## 191 LP001653 Male No 0 Not Graduate No
## 192 LP001656 Male No 0 Graduate No
## 193 LP001657 Male Yes 0 Not Graduate No
## 194 LP001658 Male No 0 Graduate No
## 195 LP001664 Male No 0 Graduate No
## 196 LP001665 Male Yes 1 Graduate No
## 197 LP001666 Male No 0 Graduate No
## 198 LP001669 Female No 0 Not Graduate No
## 199 LP001671 Female Yes 0 Graduate No
## 200 LP001673 Male No 0 Graduate Yes
## 201 LP001674 Male Yes 1 Not Graduate No
## 202 LP001677 Male No 2 Graduate No
## 203 LP001682 Male Yes 3+ Not Graduate No
## 204 LP001688 Male Yes 1 Not Graduate No
## 205 LP001691 Male Yes 2 Not Graduate No
## 206 LP001692 Female No 0 Not Graduate No
## 207 LP001693 Female No 0 Graduate No
## 208 LP001698 Male No 0 Not Graduate No
## 209 LP001699 Male No 0 Graduate No
## 210 LP001702 Male No 0 Graduate No
## 211 LP001708 Female No 0 Graduate No
## 212 LP001711 Male Yes 3+ Graduate No
## 213 LP001713 Male Yes 1 Graduate Yes
## 214 LP001715 Male Yes 3+ Not Graduate Yes
## 215 LP001716 Male Yes 0 Graduate No
## 216 LP001720 Male Yes 3+ Not Graduate No
## 217 LP001722 Male Yes 0 Graduate No
## 218 LP001726 Male Yes 0 Graduate No
## 219 LP001732 Male Yes 2 Graduate
## 220 LP001734 Female Yes 2 Graduate No
## 221 LP001736 Male Yes 0 Graduate No
## 222 LP001743 Male Yes 2 Graduate No
## 223 LP001744 Male No 0 Graduate No
## 224 LP001749 Male Yes 0 Graduate No
## 225 LP001750 Male Yes 0 Graduate No
## 226 LP001751 Male Yes 0 Graduate No
## 227 LP001754 Male Yes Not Graduate Yes
## 228 LP001758 Male Yes 2 Graduate No
## 229 LP001760 Male Graduate No
## 230 LP001761 Male No 0 Graduate Yes
## 231 LP001765 Male Yes 1 Graduate No
## 232 LP001768 Male Yes 0 Graduate
## 233 LP001770 Male No 0 Not Graduate No
## 234 LP001776 Female No 0 Graduate No
## 235 LP001778 Male Yes 1 Graduate No
## 236 LP001784 Male Yes 1 Graduate No
## 237 LP001786 Male Yes 0 Graduate
## 238 LP001788 Female No 0 Graduate Yes
## 239 LP001790 Female No 1 Graduate No
## 240 LP001792 Male Yes 1 Graduate No
## 241 LP001798 Male Yes 2 Graduate No
## 242 LP001800 Male Yes 1 Not Graduate No
## 243 LP001806 Male No 0 Graduate No
## 244 LP001807 Male Yes 2 Graduate Yes
## 245 LP001811 Male Yes 0 Not Graduate No
## 246 LP001813 Male No 0 Graduate Yes
## 247 LP001814 Male Yes 2 Graduate No
## 248 LP001819 Male Yes 1 Not Graduate No
## 249 LP001824 Male Yes 1 Graduate No
## 250 LP001825 Male Yes 0 Graduate No
## 251 LP001835 Male Yes 0 Not Graduate No
## 252 LP001836 Female No 2 Graduate No
## 253 LP001841 Male No 0 Not Graduate Yes
## 254 LP001843 Male Yes 1 Not Graduate No
## 255 LP001844 Male No 0 Graduate Yes
## 256 LP001846 Female No 3+ Graduate No
## 257 LP001849 Male No 0 Not Graduate No
## 258 LP001854 Male Yes 3+ Graduate No
## 259 LP001859 Male Yes 0 Graduate No
## 260 LP001864 Male Yes 3+ Not Graduate No
## 261 LP001865 Male Yes 1 Graduate No
## 262 LP001868 Male No 0 Graduate No
## 263 LP001870 Female No 1 Graduate No
## 264 LP001871 Female No 0 Graduate No
## 265 LP001872 Male No 0 Graduate Yes
## 266 LP001875 Male No 0 Graduate No
## 267 LP001877 Male Yes 2 Graduate No
## 268 LP001882 Male Yes 3+ Graduate No
## 269 LP001883 Female No 0 Graduate
## 270 LP001884 Female No 1 Graduate No
## 271 LP001888 Female No 0 Graduate No
## 272 LP001891 Male Yes 0 Graduate No
## 273 LP001892 Male No 0 Graduate No
## 274 LP001894 Male Yes 0 Graduate No
## 275 LP001896 Male Yes 2 Graduate No
## 276 LP001900 Male Yes 1 Graduate No
## 277 LP001903 Male Yes 0 Graduate No
## 278 LP001904 Male Yes 0 Graduate No
## 279 LP001907 Male Yes 0 Graduate No
## 280 LP001908 Female Yes 0 Not Graduate No
## 281 LP001910 Male No 1 Not Graduate Yes
## 282 LP001914 Male Yes 0 Graduate No
## 283 LP001915 Male Yes 2 Graduate No
## 284 LP001917 Female No 0 Graduate No
## 285 LP001922 Male Yes 0 Graduate No
## 286 LP001924 Male No 0 Graduate No
## 287 LP001925 Female No 0 Graduate Yes
## 288 LP001926 Male Yes 0 Graduate No
## 289 LP001931 Female No 0 Graduate No
## 290 LP001935 Male No 0 Graduate No
## 291 LP001936 Male Yes 0 Graduate No
## 292 LP001938 Male Yes 2 Graduate No
## 293 LP001940 Male Yes 2 Graduate No
## 294 LP001945 Female No Graduate No
## 295 LP001947 Male Yes 0 Graduate No
## 296 LP001949 Male Yes 3+ Graduate
## 297 LP001953 Male Yes 1 Graduate No
## 298 LP001954 Female Yes 1 Graduate No
## 299 LP001955 Female No 0 Graduate No
## 300 LP001963 Male Yes 1 Graduate No
## 301 LP001964 Male Yes 0 Not Graduate No
## 302 LP001972 Male Yes Not Graduate No
## 303 LP001974 Female No 0 Graduate No
## 304 LP001977 Male Yes 1 Graduate No
## 305 LP001978 Male No 0 Graduate No
## 306 LP001990 Male No 0 Not Graduate No
## 307 LP001993 Female No 0 Graduate No
## 308 LP001994 Female No 0 Graduate No
## 309 LP001996 Male No 0 Graduate No
## 310 LP001998 Male Yes 2 Not Graduate No
## 311 LP002002 Female No 0 Graduate No
## 312 LP002004 Male No 0 Not Graduate No
## 313 LP002006 Female No 0 Graduate No
## 314 LP002008 Male Yes 2 Graduate Yes
## 315 LP002024 Yes 0 Graduate No
## 316 LP002031 Male Yes 1 Not Graduate No
## 317 LP002035 Male Yes 2 Graduate No
## 318 LP002036 Male Yes 0 Graduate No
## 319 LP002043 Female No 1 Graduate No
## 320 LP002050 Male Yes 1 Graduate Yes
## 321 LP002051 Male Yes 0 Graduate No
## 322 LP002053 Male Yes 3+ Graduate No
## 323 LP002054 Male Yes 2 Not Graduate No
## 324 LP002055 Female No 0 Graduate No
## 325 LP002065 Male Yes 3+ Graduate No
## 326 LP002067 Male Yes 1 Graduate Yes
## 327 LP002068 Male No 0 Graduate No
## 328 LP002082 Male Yes 0 Graduate Yes
## 329 LP002086 Female Yes 0 Graduate No
## 330 LP002087 Female No 0 Graduate No
## 331 LP002097 Male No 1 Graduate No
## 332 LP002098 Male No 0 Graduate No
## 333 LP002100 Male No Graduate No
## 334 LP002101 Male Yes 0 Graduate
## 335 LP002103 Yes 1 Graduate Yes
## 336 LP002106 Male Yes Graduate Yes
## 337 LP002110 Male Yes 1 Graduate
## 338 LP002112 Male Yes 2 Graduate Yes
## 339 LP002113 Female No 3+ Not Graduate No
## 340 LP002114 Female No 0 Graduate No
## 341 LP002115 Male Yes 3+ Not Graduate No
## 342 LP002116 Female No 0 Graduate No
## 343 LP002119 Male Yes 1 Not Graduate No
## 344 LP002126 Male Yes 3+ Not Graduate No
## 345 LP002128 Male Yes 2 Graduate
## 346 LP002129 Male Yes 0 Graduate No
## 347 LP002130 Male Yes Not Graduate No
## 348 LP002131 Male Yes 2 Not Graduate No
## 349 LP002137 Male Yes 0 Graduate No
## 350 LP002138 Male Yes 0 Graduate No
## 351 LP002139 Male Yes 0 Graduate No
## 352 LP002140 Male No 0 Graduate No
## 353 LP002141 Male Yes 3+ Graduate No
## 354 LP002142 Female Yes 0 Graduate Yes
## 355 LP002143 Female Yes 0 Graduate No
## 356 LP002144 Female No Graduate No
## 357 LP002149 Male Yes 2 Graduate No
## 358 LP002151 Male Yes 1 Graduate No
## 359 LP002158 Male Yes 0 Not Graduate No
## 360 LP002160 Male Yes 3+ Graduate No
## 361 LP002161 Female No 1 Graduate No
## 362 LP002170 Male Yes 2 Graduate No
## 363 LP002175 Male Yes 0 Graduate No
## 364 LP002178 Male Yes 0 Graduate No
## 365 LP002180 Male No 0 Graduate Yes
## 366 LP002181 Male No 0 Not Graduate No
## 367 LP002187 Male No 0 Graduate No
## 368 LP002188 Male No 0 Graduate No
## 369 LP002190 Male Yes 1 Graduate No
## 370 LP002191 Male Yes 0 Graduate No
## 371 LP002194 Female No 0 Graduate Yes
## 372 LP002197 Male Yes 2 Graduate No
## 373 LP002201 Male Yes 2 Graduate Yes
## 374 LP002205 Male No 1 Graduate No
## 375 LP002209 Female No 0 Graduate
## 376 LP002211 Male Yes 0 Graduate No
## 377 LP002219 Male Yes 3+ Graduate No
## 378 LP002223 Male Yes 0 Graduate No
## 379 LP002224 Male No 0 Graduate No
## 380 LP002225 Male Yes 2 Graduate No
## 381 LP002226 Male Yes 0 Graduate
## 382 LP002229 Male No 0 Graduate No
## 383 LP002231 Female No 0 Graduate No
## 384 LP002234 Male No 0 Graduate Yes
## 385 LP002236 Male Yes 2 Graduate No
## 386 LP002237 Male No 1 Graduate
## 387 LP002239 Male No 0 Not Graduate No
## 388 LP002243 Male Yes 0 Not Graduate No
## 389 LP002244 Male Yes 0 Graduate No
## 390 LP002250 Male Yes 0 Graduate No
## 391 LP002255 Male No 3+ Graduate No
## 392 LP002262 Male Yes 3+ Graduate No
## 393 LP002263 Male Yes 0 Graduate No
## 394 LP002265 Male Yes 2 Not Graduate No
## 395 LP002266 Male Yes 2 Graduate No
## 396 LP002272 Male Yes 2 Graduate No
## 397 LP002277 Female No 0 Graduate No
## 398 LP002281 Male Yes 0 Graduate No
## 399 LP002284 Male No 0 Not Graduate No
## 400 LP002287 Female No 0 Graduate No
## 401 LP002288 Male Yes 2 Not Graduate No
## 402 LP002296 Male No 0 Not Graduate No
## 403 LP002297 Male No 0 Graduate No
## 404 LP002300 Female No 0 Not Graduate No
## 405 LP002301 Female No 0 Graduate Yes
## 406 LP002305 Female No 0 Graduate No
## 407 LP002308 Male Yes 0 Not Graduate No
## 408 LP002314 Female No 0 Not Graduate No
## 409 LP002315 Male Yes 1 Graduate No
## 410 LP002317 Male Yes 3+ Graduate No
## 411 LP002318 Female No 1 Not Graduate Yes
## 412 LP002319 Male Yes 0 Graduate
## 413 LP002328 Male Yes 0 Not Graduate No
## 414 LP002332 Male Yes 0 Not Graduate No
## 415 LP002335 Female Yes 0 Not Graduate No
## 416 LP002337 Female No 0 Graduate No
## 417 LP002341 Female No 1 Graduate No
## 418 LP002342 Male Yes 2 Graduate Yes
## 419 LP002345 Male Yes 0 Graduate No
## 420 LP002347 Male Yes 0 Graduate No
## 421 LP002348 Male Yes 0 Graduate No
## 422 LP002357 Female No 0 Not Graduate No
## 423 LP002361 Male Yes 0 Graduate No
## 424 LP002362 Male Yes 1 Graduate No
## 425 LP002364 Male Yes 0 Graduate No
## 426 LP002366 Male Yes 0 Graduate No
## 427 LP002367 Female No 1 Not Graduate No
## 428 LP002368 Male Yes 2 Graduate No
## 429 LP002369 Male Yes 0 Graduate No
## 430 LP002370 Male No 0 Not Graduate No
## 431 LP002377 Female No 1 Graduate Yes
## 432 LP002379 Male No 0 Graduate No
## 433 LP002386 Male No 0 Graduate
## 434 LP002387 Male Yes 0 Graduate No
## 435 LP002390 Male No 0 Graduate No
## 436 LP002393 Female Graduate No
## 437 LP002398 Male No 0 Graduate No
## 438 LP002401 Male Yes 0 Graduate No
## 439 LP002403 Male No 0 Graduate Yes
## 440 LP002407 Female Yes 0 Not Graduate Yes
## 441 LP002408 Male No 0 Graduate No
## 442 LP002409 Male Yes 0 Graduate No
## 443 LP002418 Male No 3+ Not Graduate No
## 444 LP002422 Male No 1 Graduate No
## 445 LP002424 Male Yes 0 Graduate No
## 446 LP002429 Male Yes 1 Graduate Yes
## 447 LP002434 Male Yes 2 Not Graduate No
## 448 LP002435 Male Yes 0 Graduate
## 449 LP002443 Male Yes 2 Graduate No
## 450 LP002444 Male No 1 Not Graduate Yes
## 451 LP002446 Male Yes 2 Not Graduate No
## 452 LP002447 Male Yes 2 Not Graduate No
## 453 LP002448 Male Yes 0 Graduate No
## 454 LP002449 Male Yes 0 Graduate No
## 455 LP002453 Male No 0 Graduate Yes
## 456 LP002455 Male Yes 2 Graduate No
## 457 LP002459 Male Yes 0 Graduate No
## 458 LP002467 Male Yes 0 Graduate No
## 459 LP002472 Male No 2 Graduate No
## 460 LP002473 Male Yes 0 Graduate No
## 461 LP002478 Yes 0 Graduate Yes
## 462 LP002484 Male Yes 3+ Graduate No
## 463 LP002487 Male Yes 0 Graduate No
## 464 LP002489 Female No 1 Not Graduate
## 465 LP002493 Male No 0 Graduate No
## 466 LP002494 Male No 0 Graduate No
## 467 LP002500 Male Yes 3+ Not Graduate No
## 468 LP002501 Yes 0 Graduate No
## 469 LP002502 Female Yes 2 Not Graduate
## 470 LP002505 Male Yes 0 Graduate No
## 471 LP002515 Male Yes 1 Graduate Yes
## 472 LP002517 Male Yes 1 Not Graduate No
## 473 LP002519 Male Yes 3+ Graduate No
## 474 LP002522 Female No 0 Graduate Yes
## 475 LP002524 Male No 2 Graduate No
## 476 LP002527 Male Yes 2 Graduate Yes
## 477 LP002529 Male Yes 2 Graduate No
## 478 LP002530 Yes 2 Graduate No
## 479 LP002531 Male Yes 1 Graduate Yes
## 480 LP002533 Male Yes 2 Graduate No
## 481 LP002534 Female No 0 Not Graduate No
## 482 LP002536 Male Yes 3+ Not Graduate No
## 483 LP002537 Male Yes 0 Graduate No
## 484 LP002541 Male Yes 0 Graduate No
## 485 LP002543 Male Yes 2 Graduate No
## 486 LP002544 Male Yes 1 Not Graduate No
## 487 LP002545 Male No 2 Graduate No
## 488 LP002547 Male Yes 1 Graduate No
## 489 LP002555 Male Yes 2 Graduate Yes
## 490 LP002556 Male No 0 Graduate No
## 491 LP002560 Male No 0 Not Graduate No
## 492 LP002562 Male Yes 1 Not Graduate No
## 493 LP002571 Male No 0 Not Graduate No
## 494 LP002582 Female No 0 Not Graduate Yes
## 495 LP002585 Male Yes 0 Graduate No
## 496 LP002586 Female Yes 1 Graduate No
## 497 LP002587 Male Yes 0 Not Graduate No
## 498 LP002588 Male Yes 0 Graduate No
## 499 LP002600 Male Yes 1 Graduate Yes
## 500 LP002602 Male No 0 Graduate No
## 501 LP002603 Female No 0 Graduate No
## 502 LP002606 Female No 0 Graduate No
## 503 LP002615 Male Yes 2 Graduate No
## 504 LP002618 Male Yes 1 Not Graduate No
## 505 LP002619 Male Yes 0 Not Graduate No
## 506 LP002622 Male Yes 2 Graduate No
## 507 LP002624 Male Yes 0 Graduate No
## 508 LP002625 No 0 Graduate No
## 509 LP002626 Male Yes 0 Graduate Yes
## 510 LP002634 Female No 1 Graduate No
## 511 LP002637 Male No 0 Not Graduate No
## 512 LP002640 Male Yes 1 Graduate No
## 513 LP002643 Male Yes 2 Graduate No
## 514 LP002648 Male Yes 0 Graduate No
## 515 LP002652 Male No 0 Graduate No
## 516 LP002659 Male Yes 3+ Graduate No
## 517 LP002670 Female Yes 2 Graduate No
## 518 LP002682 Male Yes Not Graduate No
## 519 LP002683 Male No 0 Graduate No
## 520 LP002684 Female No 0 Not Graduate No
## 521 LP002689 Male Yes 2 Not Graduate No
## 522 LP002690 Male No 0 Graduate No
## 523 LP002692 Male Yes 3+ Graduate Yes
## 524 LP002693 Male Yes 2 Graduate Yes
## 525 LP002697 Male No 0 Graduate No
## 526 LP002699 Male Yes 2 Graduate Yes
## 527 LP002705 Male Yes 0 Graduate No
## 528 LP002706 Male Yes 1 Not Graduate No
## 529 LP002714 Male No 1 Not Graduate No
## 530 LP002716 Male No 0 Not Graduate No
## 531 LP002717 Male Yes 0 Graduate No
## 532 LP002720 Male Yes 3+ Graduate No
## 533 LP002723 Male No 2 Graduate No
## 534 LP002729 Male No 1 Graduate No
## 535 LP002731 Female No 0 Not Graduate Yes
## 536 LP002732 Male No 0 Not Graduate
## 537 LP002734 Male Yes 0 Graduate No
## 538 LP002738 Male No 2 Graduate No
## 539 LP002739 Male Yes 0 Not Graduate No
## 540 LP002740 Male Yes 3+ Graduate No
## 541 LP002741 Female Yes 1 Graduate No
## 542 LP002743 Female No 0 Graduate No
## 543 LP002753 Female No 1 Graduate
## 544 LP002755 Male Yes 1 Not Graduate No
## 545 LP002757 Female Yes 0 Not Graduate No
## 546 LP002767 Male Yes 0 Graduate No
## 547 LP002768 Male No 0 Not Graduate No
## 548 LP002772 Male No 0 Graduate No
## 549 LP002776 Female No 0 Graduate No
## 550 LP002777 Male Yes 0 Graduate No
## 551 LP002778 Male Yes 2 Graduate Yes
## 552 LP002784 Male Yes 1 Not Graduate No
## 553 LP002785 Male Yes 1 Graduate No
## 554 LP002788 Male Yes 0 Not Graduate No
## 555 LP002789 Male Yes 0 Graduate No
## 556 LP002792 Male Yes 1 Graduate No
## 557 LP002794 Female No 0 Graduate No
## 558 LP002795 Male Yes 3+ Graduate Yes
## 559 LP002798 Male Yes 0 Graduate No
## 560 LP002804 Female Yes 0 Graduate No
## 561 LP002807 Male Yes 2 Not Graduate No
## 562 LP002813 Female Yes 1 Graduate Yes
## 563 LP002820 Male Yes 0 Graduate No
## 564 LP002821 Male No 0 Not Graduate Yes
## 565 LP002832 Male Yes 2 Graduate No
## 566 LP002833 Male Yes 0 Not Graduate No
## 567 LP002836 Male No 0 Graduate No
## 568 LP002837 Male Yes 3+ Graduate No
## 569 LP002840 Female No 0 Graduate No
## 570 LP002841 Male Yes 0 Graduate No
## 571 LP002842 Male Yes 1 Graduate No
## 572 LP002847 Male Yes Graduate No
## 573 LP002855 Male Yes 2 Graduate No
## 574 LP002862 Male Yes 2 Not Graduate No
## 575 LP002863 Male Yes 3+ Graduate No
## 576 LP002868 Male Yes 2 Graduate No
## 577 LP002872 Yes 0 Graduate No
## 578 LP002874 Male No 0 Graduate No
## 579 LP002877 Male Yes 1 Graduate No
## 580 LP002888 Male No 0 Graduate
## 581 LP002892 Male Yes 2 Graduate No
## 582 LP002893 Male No 0 Graduate No
## 583 LP002894 Female Yes 0 Graduate No
## 584 LP002898 Male Yes 1 Graduate No
## 585 LP002911 Male Yes 1 Graduate No
## 586 LP002912 Male Yes 1 Graduate No
## 587 LP002916 Male Yes 0 Graduate No
## 588 LP002917 Female No 0 Not Graduate No
## 589 LP002925 No 0 Graduate No
## 590 LP002926 Male Yes 2 Graduate Yes
## 591 LP002928 Male Yes 0 Graduate No
## 592 LP002931 Male Yes 2 Graduate Yes
## 593 LP002933 No 3+ Graduate Yes
## 594 LP002936 Male Yes 0 Graduate No
## 595 LP002938 Male Yes 0 Graduate Yes
## 596 LP002940 Male No 0 Not Graduate No
## 597 LP002941 Male Yes 2 Not Graduate Yes
## 598 LP002943 Male No Graduate No
## 599 LP002945 Male Yes 0 Graduate Yes
## 600 LP002948 Male Yes 2 Graduate No
## 601 LP002949 Female No 3+ Graduate
## 602 LP002950 Male Yes 0 Not Graduate
## 603 LP002953 Male Yes 3+ Graduate No
## 604 LP002958 Male No 0 Graduate No
## 605 LP002959 Female Yes 1 Graduate No
## 606 LP002960 Male Yes 0 Not Graduate No
## 607 LP002961 Male Yes 1 Graduate No
## 608 LP002964 Male Yes 2 Not Graduate No
## 609 LP002974 Male Yes 0 Graduate No
## 610 LP002978 Female No 0 Graduate No
## 611 LP002979 Male Yes 3+ Graduate No
## 612 LP002983 Male Yes 1 Graduate No
## 613 LP002984 Male Yes 2 Graduate No
## 614 LP002990 Female No 0 Graduate Yes
## ApplicantIncome CoapplicantIncome LoanAmount Loan_Amount_Term
## 1 5849 0.00 100 360
## 2 4583 1508.00 128 360
## 3 3000 0.00 66 360
## 4 2583 2358.00 120 360
## 5 6000 0.00 141 360
## 6 5417 4196.00 267 360
## 7 2333 1516.00 95 360
## 8 3036 2504.00 158 360
## 9 4006 1526.00 168 360
## 10 12841 10968.00 349 360
## 11 3200 700.00 70 360
## 12 2500 1840.00 109 360
## 13 3073 8106.00 200 360
## 14 1853 2840.00 114 360
## 15 1299 1086.00 17 120
## 16 4950 0.00 125 360
## 17 3596 0.00 100 240
## 18 3510 0.00 76 360
## 19 4887 0.00 133 360
## 20 2600 3500.00 115 360
## 21 7660 0.00 104 360
## 22 5955 5625.00 315 360
## 23 2600 1911.00 116 360
## 24 3365 1917.00 112 360
## 25 3717 2925.00 151 360
## 26 9560 0.00 191 360
## 27 2799 2253.00 122 360
## 28 4226 1040.00 110 360
## 29 1442 0.00 35 360
## 30 3750 2083.00 120 360
## 31 4166 3369.00 201 360
## 32 3167 0.00 74 360
## 33 4692 0.00 106 360
## 34 3500 1667.00 114 360
## 35 12500 3000.00 320 360
## 36 2275 2067.00 100 360
## 37 1828 1330.00 100 360
## 38 3667 1459.00 144 360
## 39 4166 7210.00 184 360
## 40 3748 1668.00 110 360
## 41 3600 0.00 80 360
## 42 1800 1213.00 47 360
## 43 2400 0.00 75 360
## 44 3941 2336.00 134 360
## 45 4695 0.00 96 360
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## Credit_History Property_Area Loan_Status
## 1 1 Urban Y
## 2 1 Rural N
## 3 1 Urban Y
## 4 1 Urban Y
## 5 1 Urban Y
## 6 1 Urban Y
## 7 1 Urban Y
## 8 0 Semiurban N
## 9 1 Urban Y
## 10 1 Semiurban N
## 11 1 Urban Y
## 12 1 Urban Y
## 13 1 Urban Y
## 14 1 Rural N
## 15 1 Urban Y
## 16 1 Urban Y
## 17 1 Urban Y
## 18 0 Urban N
## 19 1 Rural N
## 20 1 Urban Y
## 21 0 Urban N
## 22 1 Urban Y
## 23 0 Semiurban N
## 24 0 Rural N
## 25 0 Semiurban N
## 26 1 Semiurban Y
## 27 1 Semiurban Y
## 28 1 Urban Y
## 29 1 Urban N
## 30 1 Semiurban Y
## 31 1 Urban N
## 32 1 Urban N
## 33 1 Rural N
## 34 1 Semiurban Y
## 35 1 Rural N
## 36 1 Urban Y
## 37 0 Urban N
## 38 1 Semiurban Y
## 39 1 Urban Y
## 40 1 Semiurban Y
## 41 1 Urban N
## 42 1 Urban Y
## 43 1 Urban Y
## 44 1 Semiurban Y
## 45 1 Urban Y
## 46 1 Urban Y
## 47 1 Urban Y
## 48 1 Urban Y
## 49 0 Urban N
## 50 1 Semiurban Y
## 51 1 Semiurban Y
## 52 1 Semiurban Y
## 53 1 Semiurban N
## 54 1 Urban N
## 55 0 Urban N
## 56 1 Semiurban Y
## 57 1 Semiurban Y
## 58 1 Rural N
## 59 1 Urban Y
## 60 1 Urban Y
## 61 1 Urban Y
## 62 1 Urban Y
## 63 0 Rural N
## 64 0 Rural N
## 65 0 Semiurban N
## 66 1 Semiurban N
## 67 0 Urban N
## 68 1 Urban Y
## 69 1 Urban Y
## 70 0 Semiurban N
## 71 1 Urban Y
## 72 1 Semiurban Y
## 73 1 Semiurban Y
## 74 0 Semiurban N
## 75 1 Semiurban Y
## 76 1 Urban N
## 77 1 Urban N
## 78 1 Urban N
## 79 0 Semiurban N
## 80 0 Semiurban Y
## 81 1 Semiurban Y
## 82 1 Semiurban Y
## 83 1 Urban N
## 84 1 Semiurban N
## 85 1 Urban Y
## 86 1 Semiurban Y
## 87 1 Semiurban Y
## 88 1 Semiurban Y
## 89 1 Urban Y
## 90 1 Semiurban Y
## 91 1 Semiurban Y
## 92 1 Semiurban Y
## 93 1 Urban Y
## 94 1 Semiurban Y
## 95 1 Semiurban Y
## 96 1 Urban N
## 97 1 Semiurban Y
## 98 1 Semiurban Y
## 99 1 Semiurban Y
## 100 1 Semiurban Y
## 101 1 Urban Y
## 102 1 Semiurban Y
## 103 1 Urban Y
## 104 1 Semiurban Y
## 105 1 Urban Y
## 106 1 Urban Y
## 107 1 Urban Y
## 108 1 Rural N
## 109 0 Urban N
## 110 1 Semiurban Y
## 111 1 Urban Y
## 112 1 Semiurban Y
## 113 0 Rural N
## 114 1 Semiurban Y
## 115 1 Semiurban Y
## 116 1 Rural Y
## 117 1 Semiurban Y
## 118 1 Urban Y
## 119 1 Rural N
## 120 1 Urban Y
## 121 1 Rural Y
## 122 1 Semiurban Y
## 123 0 Semiurban Y
## 124 1 Semiurban Y
## 125 1 Rural Y
## 126 1 Rural Y
## 127 1 Rural Y
## 128 1 Rural Y
## 129 0 Urban N
## 130 1 Rural N
## 131 1 Urban Y
## 132 1 Urban Y
## 133 1 Semiurban Y
## 134 1 Semiurban Y
## 135 1 Semiurban Y
## 136 1 Semiurban N
## 137 1 Rural N
## 138 1 Urban Y
## 139 0 Semiurban N
## 140 1 Rural N
## 141 1 Rural N
## 142 1 Urban Y
## 143 1 Semiurban Y
## 144 1 Semiurban Y
## 145 1 Urban Y
## 146 1 Semiurban Y
## 147 1 Urban Y
## 148 1 Urban Y
## 149 1 Rural N
## 150 1 Semiurban Y
## 151 0 Rural N
## 152 1 Rural Y
## 153 1 Urban N
## 154 1 Rural N
## 155 1 Urban Y
## 156 0 Semiurban Y
## 157 1 Rural Y
## 158 1 Urban Y
## 159 1 Rural Y
## 160 1 Semiurban Y
## 161 1 Semiurban Y
## 162 1 Urban N
## 163 0 Semiurban N
## 164 1 Rural Y
## 165 1 Urban Y
## 166 1 Rural Y
## 167 1 Rural N
## 168 1 Rural Y
## 169 0 Semiurban N
## 170 1 Semiurban Y
## 171 1 Rural Y
## 172 1 Urban Y
## 173 1 Rural N
## 174 1 Semiurban Y
## 175 1 Semiurban N
## 176 1 Rural Y
## 177 1 Rural Y
## 178 0 Semiurban N
## 179 1 Semiurban Y
## 180 0 Urban N
## 181 0 Urban N
## 182 0 Rural N
## 183 1 Semiurban Y
## 184 1 Semiurban N
## 185 1 Semiurban Y
## 186 1 Semiurban Y
## 187 0 Rural N
## 188 1 Rural Y
## 189 1 Rural Y
## 190 1 Rural Y
## 191 1 Rural Y
## 192 1 Semiurban N
## 193 1 Urban N
## 194 1 Semiurban Y
## 195 1 Rural Y
## 196 1 Semiurban N
## 197 1 Rural Y
## 198 1 Urban Y
## 199 1 Semiurban Y
## 200 1 Urban N
## 201 1 Semiurban Y
## 202 0 Semiurban Y
## 203 1 Urban N
## 204 1 Urban Y
## 205 1 Semiurban Y
## 206 1 Semiurban Y
## 207 1 Urban Y
## 208 1 Rural Y
## 209 1 Urban Y
## 210 1 Semiurban N
## 211 1 Semiurban N
## 212 0 Semiurban N
## 213 1 Urban Y
## 214 1 Rural Y
## 215 1 Urban Y
## 216 1 Semiurban Y
## 217 1 Rural N
## 218 1 Semiurban Y
## 219 0 Semiurban N
## 220 0 Semiurban Y
## 221 0 Urban N
## 222 1 Semiurban Y
## 223 1 Semiurban Y
## 224 1 Semiurban Y
## 225 1 Semiurban Y
## 226 1 Rural N
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## 382 1 Semiurban Y
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## 541 1 Semiurban Y
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## 544 1 Urban Y
## 545 1 Semiurban Y
## 546 1 Rural Y
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## 560 1 Semiurban Y
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## 589 1 Semiurban Y
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## 612 1 Urban Y
## 613 1 Urban Y
## 614 0 Semiurban Ncomplete(data_imp_mice)## Loan_ID Gender Married Dependents Education Self_Employed
## 1 LP001002 Male No 0 Graduate No
## 2 LP001003 Male Yes 1 Graduate No
## 3 LP001005 Male Yes 0 Graduate Yes
## 4 LP001006 Male Yes 0 Not Graduate No
## 5 LP001008 Male No 0 Graduate No
## 6 LP001011 Male Yes 2 Graduate Yes
## 7 LP001013 Male Yes 0 Not Graduate No
## 8 LP001014 Male Yes 3+ Graduate No
## 9 LP001018 Male Yes 2 Graduate No
## 10 LP001020 Male Yes 1 Graduate No
## 11 LP001024 Male Yes 2 Graduate No
## 12 LP001027 Male Yes 2 Graduate
## 13 LP001028 Male Yes 2 Graduate No
## 14 LP001029 Male No 0 Graduate No
## 15 LP001030 Male Yes 2 Graduate No
## 16 LP001032 Male No 0 Graduate No
## 17 LP001034 Male No 1 Not Graduate No
## 18 LP001036 Female No 0 Graduate No
## 19 LP001038 Male Yes 0 Not Graduate No
## 20 LP001041 Male Yes 0 Graduate
## 21 LP001043 Male Yes 0 Not Graduate No
## 22 LP001046 Male Yes 1 Graduate No
## 23 LP001047 Male Yes 0 Not Graduate No
## 24 LP001050 Yes 2 Not Graduate No
## 25 LP001052 Male Yes 1 Graduate
## 26 LP001066 Male Yes 0 Graduate Yes
## 27 LP001068 Male Yes 0 Graduate No
## 28 LP001073 Male Yes 2 Not Graduate No
## 29 LP001086 Male No 0 Not Graduate No
## 30 LP001087 Female No 2 Graduate
## 31 LP001091 Male Yes 1 Graduate
## 32 LP001095 Male No 0 Graduate No
## 33 LP001097 Male No 1 Graduate Yes
## 34 LP001098 Male Yes 0 Graduate No
## 35 LP001100 Male No 3+ Graduate No
## 36 LP001106 Male Yes 0 Graduate No
## 37 LP001109 Male Yes 0 Graduate No
## 38 LP001112 Female Yes 0 Graduate No
## 39 LP001114 Male No 0 Graduate No
## 40 LP001116 Male No 0 Not Graduate No
## 41 LP001119 Male No 0 Graduate No
## 42 LP001120 Male No 0 Graduate No
## 43 LP001123 Male Yes 0 Graduate No
## 44 LP001131 Male Yes 0 Graduate No
## 45 LP001136 Male Yes 0 Not Graduate Yes
## 46 LP001137 Female No 0 Graduate No
## 47 LP001138 Male Yes 1 Graduate No
## 48 LP001144 Male Yes 0 Graduate No
## 49 LP001146 Female Yes 0 Graduate No
## 50 LP001151 Female No 0 Graduate No
## 51 LP001155 Female Yes 0 Not Graduate No
## 52 LP001157 Female No 0 Graduate No
## 53 LP001164 Female No 0 Graduate No
## 54 LP001179 Male Yes 2 Graduate No
## 55 LP001186 Female Yes 1 Graduate Yes
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## 57 LP001195 Male Yes 0 Graduate No
## 58 LP001197 Male Yes 0 Graduate No
## 59 LP001198 Male Yes 1 Graduate No
## 60 LP001199 Male Yes 2 Not Graduate No
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## 62 LP001206 Male Yes 3+ Graduate No
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## 64 LP001213 Male Yes 1 Graduate No
## 65 LP001222 Female No 0 Graduate No
## 66 LP001225 Male Yes 0 Graduate No
## 67 LP001228 Male No 0 Not Graduate No
## 68 LP001233 Male Yes 1 Graduate No
## 69 LP001238 Male Yes 3+ Not Graduate Yes
## 70 LP001241 Female No 0 Graduate No
## 71 LP001243 Male Yes 0 Graduate No
## 72 LP001245 Male Yes 2 Not Graduate Yes
## 73 LP001248 Male No 0 Graduate No
## 74 LP001250 Male Yes 3+ Not Graduate No
## 75 LP001253 Male Yes 3+ Graduate Yes
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## 77 LP001256 Male No 0 Graduate No
## 78 LP001259 Male Yes 1 Graduate Yes
## 79 LP001263 Male Yes 3+ Graduate No
## 80 LP001264 Male Yes 3+ Not Graduate Yes
## 81 LP001265 Female No 0 Graduate No
## 82 LP001266 Male Yes 1 Graduate Yes
## 83 LP001267 Female Yes 2 Graduate No
## 84 LP001273 Male Yes 0 Graduate No
## 85 LP001275 Male Yes 1 Graduate No
## 86 LP001279 Male No 0 Graduate No
## 87 LP001280 Male Yes 2 Not Graduate No
## 88 LP001282 Male Yes 0 Graduate No
## 89 LP001289 Male No 0 Graduate No
## 90 LP001310 Male Yes 0 Graduate No
## 91 LP001316 Male Yes 0 Graduate No
## 92 LP001318 Male Yes 2 Graduate No
## 93 LP001319 Male Yes 2 Not Graduate No
## 94 LP001322 Male No 0 Graduate No
## 95 LP001325 Male No 0 Not Graduate No
## 96 LP001326 Male No 0 Graduate
## 97 LP001327 Female Yes 0 Graduate No
## 98 LP001333 Male Yes 0 Graduate No
## 99 LP001334 Male Yes 0 Not Graduate No
## 100 LP001343 Male Yes 0 Graduate No
## 101 LP001345 Male Yes 2 Not Graduate No
## 102 LP001349 Male No 0 Graduate No
## 103 LP001350 Male Yes Graduate No
## 104 LP001356 Male Yes 0 Graduate No
## 105 LP001357 Male Graduate No
## 106 LP001367 Male Yes 1 Graduate No
## 107 LP001369 Male Yes 2 Graduate No
## 108 LP001370 Male No 0 Not Graduate
## 109 LP001379 Male Yes 2 Graduate No
## 110 LP001384 Male Yes 3+ Not Graduate No
## 111 LP001385 Male No 0 Graduate No
## 112 LP001387 Female Yes 0 Graduate
## 113 LP001391 Male Yes 0 Not Graduate No
## 114 LP001392 Female No 1 Graduate Yes
## 115 LP001398 Male No 0 Graduate
## 116 LP001401 Male Yes 1 Graduate No
## 117 LP001404 Female Yes 0 Graduate No
## 118 LP001405 Male Yes 1 Graduate No
## 119 LP001421 Male Yes 0 Graduate No
## 120 LP001422 Female No 0 Graduate No
## 121 LP001426 Male Yes Graduate No
## 122 LP001430 Female No 0 Graduate No
## 123 LP001431 Female No 0 Graduate No
## 124 LP001432 Male Yes 2 Graduate No
## 125 LP001439 Male Yes 0 Not Graduate No
## 126 LP001443 Female No 0 Graduate No
## 127 LP001448 Yes 3+ Graduate No
## 128 LP001449 Male No 0 Graduate No
## 129 LP001451 Male Yes 1 Graduate Yes
## 130 LP001465 Male Yes 0 Graduate No
## 131 LP001469 Male No 0 Graduate Yes
## 132 LP001473 Male No 0 Graduate No
## 133 LP001478 Male No 0 Graduate No
## 134 LP001482 Male Yes 0 Graduate Yes
## 135 LP001487 Male No 0 Graduate No
## 136 LP001488 Male Yes 3+ Graduate No
## 137 LP001489 Female Yes 0 Graduate No
## 138 LP001491 Male Yes 2 Graduate Yes
## 139 LP001492 Male No 0 Graduate No
## 140 LP001493 Male Yes 2 Not Graduate No
## 141 LP001497 Male Yes 2 Graduate No
## 142 LP001498 Male No 0 Graduate No
## 143 LP001504 Male No 0 Graduate Yes
## 144 LP001507 Male Yes 0 Graduate No
## 145 LP001508 Male Yes 2 Graduate No
## 146 LP001514 Female Yes 0 Graduate No
## 147 LP001516 Female Yes 2 Graduate No
## 148 LP001518 Male Yes 1 Graduate No
## 149 LP001519 Female No 0 Graduate No
## 150 LP001520 Male Yes 0 Graduate No
## 151 LP001528 Male No 0 Graduate No
## 152 LP001529 Male Yes 0 Graduate Yes
## 153 LP001531 Male No 0 Graduate No
## 154 LP001532 Male Yes 2 Not Graduate No
## 155 LP001535 Male No 0 Graduate No
## 156 LP001536 Male Yes 3+ Graduate No
## 157 LP001541 Male Yes 1 Graduate No
## 158 LP001543 Male Yes 1 Graduate No
## 159 LP001546 Male No 0 Graduate
## 160 LP001552 Male Yes 0 Graduate No
## 161 LP001560 Male Yes 0 Not Graduate No
## 162 LP001562 Male Yes 0 Graduate No
## 163 LP001565 Male Yes 1 Graduate No
## 164 LP001570 Male Yes 2 Graduate No
## 165 LP001572 Male Yes 0 Graduate No
## 166 LP001574 Male Yes 0 Graduate No
## 167 LP001577 Female Yes 0 Graduate No
## 168 LP001578 Male Yes 0 Graduate No
## 169 LP001579 Male No 0 Graduate No
## 170 LP001580 Male Yes 2 Graduate No
## 171 LP001581 Male Yes 0 Not Graduate
## 172 LP001585 Yes 3+ Graduate No
## 173 LP001586 Male Yes 3+ Not Graduate No
## 174 LP001594 Male Yes 0 Graduate No
## 175 LP001603 Male Yes 0 Not Graduate Yes
## 176 LP001606 Male Yes 0 Graduate No
## 177 LP001608 Male Yes 2 Graduate No
## 178 LP001610 Male Yes 3+ Graduate No
## 179 LP001616 Male Yes 1 Graduate No
## 180 LP001630 Male No 0 Not Graduate No
## 181 LP001633 Male Yes 1 Graduate No
## 182 LP001634 Male No 0 Graduate No
## 183 LP001636 Male Yes 0 Graduate No
## 184 LP001637 Male Yes 1 Graduate No
## 185 LP001639 Female Yes 0 Graduate No
## 186 LP001640 Male Yes 0 Graduate Yes
## 187 LP001641 Male Yes 1 Graduate Yes
## 188 LP001643 Male Yes 0 Graduate No
## 189 LP001644 Yes 0 Graduate Yes
## 190 LP001647 Male Yes 0 Graduate No
## 191 LP001653 Male No 0 Not Graduate No
## 192 LP001656 Male No 0 Graduate No
## 193 LP001657 Male Yes 0 Not Graduate No
## 194 LP001658 Male No 0 Graduate No
## 195 LP001664 Male No 0 Graduate No
## 196 LP001665 Male Yes 1 Graduate No
## 197 LP001666 Male No 0 Graduate No
## 198 LP001669 Female No 0 Not Graduate No
## 199 LP001671 Female Yes 0 Graduate No
## 200 LP001673 Male No 0 Graduate Yes
## 201 LP001674 Male Yes 1 Not Graduate No
## 202 LP001677 Male No 2 Graduate No
## 203 LP001682 Male Yes 3+ Not Graduate No
## 204 LP001688 Male Yes 1 Not Graduate No
## 205 LP001691 Male Yes 2 Not Graduate No
## 206 LP001692 Female No 0 Not Graduate No
## 207 LP001693 Female No 0 Graduate No
## 208 LP001698 Male No 0 Not Graduate No
## 209 LP001699 Male No 0 Graduate No
## 210 LP001702 Male No 0 Graduate No
## 211 LP001708 Female No 0 Graduate No
## 212 LP001711 Male Yes 3+ Graduate No
## 213 LP001713 Male Yes 1 Graduate Yes
## 214 LP001715 Male Yes 3+ Not Graduate Yes
## 215 LP001716 Male Yes 0 Graduate No
## 216 LP001720 Male Yes 3+ Not Graduate No
## 217 LP001722 Male Yes 0 Graduate No
## 218 LP001726 Male Yes 0 Graduate No
## 219 LP001732 Male Yes 2 Graduate
## 220 LP001734 Female Yes 2 Graduate No
## 221 LP001736 Male Yes 0 Graduate No
## 222 LP001743 Male Yes 2 Graduate No
## 223 LP001744 Male No 0 Graduate No
## 224 LP001749 Male Yes 0 Graduate No
## 225 LP001750 Male Yes 0 Graduate No
## 226 LP001751 Male Yes 0 Graduate No
## 227 LP001754 Male Yes Not Graduate Yes
## 228 LP001758 Male Yes 2 Graduate No
## 229 LP001760 Male Graduate No
## 230 LP001761 Male No 0 Graduate Yes
## 231 LP001765 Male Yes 1 Graduate No
## 232 LP001768 Male Yes 0 Graduate
## 233 LP001770 Male No 0 Not Graduate No
## 234 LP001776 Female No 0 Graduate No
## 235 LP001778 Male Yes 1 Graduate No
## 236 LP001784 Male Yes 1 Graduate No
## 237 LP001786 Male Yes 0 Graduate
## 238 LP001788 Female No 0 Graduate Yes
## 239 LP001790 Female No 1 Graduate No
## 240 LP001792 Male Yes 1 Graduate No
## 241 LP001798 Male Yes 2 Graduate No
## 242 LP001800 Male Yes 1 Not Graduate No
## 243 LP001806 Male No 0 Graduate No
## 244 LP001807 Male Yes 2 Graduate Yes
## 245 LP001811 Male Yes 0 Not Graduate No
## 246 LP001813 Male No 0 Graduate Yes
## 247 LP001814 Male Yes 2 Graduate No
## 248 LP001819 Male Yes 1 Not Graduate No
## 249 LP001824 Male Yes 1 Graduate No
## 250 LP001825 Male Yes 0 Graduate No
## 251 LP001835 Male Yes 0 Not Graduate No
## 252 LP001836 Female No 2 Graduate No
## 253 LP001841 Male No 0 Not Graduate Yes
## 254 LP001843 Male Yes 1 Not Graduate No
## 255 LP001844 Male No 0 Graduate Yes
## 256 LP001846 Female No 3+ Graduate No
## 257 LP001849 Male No 0 Not Graduate No
## 258 LP001854 Male Yes 3+ Graduate No
## 259 LP001859 Male Yes 0 Graduate No
## 260 LP001864 Male Yes 3+ Not Graduate No
## 261 LP001865 Male Yes 1 Graduate No
## 262 LP001868 Male No 0 Graduate No
## 263 LP001870 Female No 1 Graduate No
## 264 LP001871 Female No 0 Graduate No
## 265 LP001872 Male No 0 Graduate Yes
## 266 LP001875 Male No 0 Graduate No
## 267 LP001877 Male Yes 2 Graduate No
## 268 LP001882 Male Yes 3+ Graduate No
## 269 LP001883 Female No 0 Graduate
## 270 LP001884 Female No 1 Graduate No
## 271 LP001888 Female No 0 Graduate No
## 272 LP001891 Male Yes 0 Graduate No
## 273 LP001892 Male No 0 Graduate No
## 274 LP001894 Male Yes 0 Graduate No
## 275 LP001896 Male Yes 2 Graduate No
## 276 LP001900 Male Yes 1 Graduate No
## 277 LP001903 Male Yes 0 Graduate No
## 278 LP001904 Male Yes 0 Graduate No
## 279 LP001907 Male Yes 0 Graduate No
## 280 LP001908 Female Yes 0 Not Graduate No
## 281 LP001910 Male No 1 Not Graduate Yes
## 282 LP001914 Male Yes 0 Graduate No
## 283 LP001915 Male Yes 2 Graduate No
## 284 LP001917 Female No 0 Graduate No
## 285 LP001922 Male Yes 0 Graduate No
## 286 LP001924 Male No 0 Graduate No
## 287 LP001925 Female No 0 Graduate Yes
## 288 LP001926 Male Yes 0 Graduate No
## 289 LP001931 Female No 0 Graduate No
## 290 LP001935 Male No 0 Graduate No
## 291 LP001936 Male Yes 0 Graduate No
## 292 LP001938 Male Yes 2 Graduate No
## 293 LP001940 Male Yes 2 Graduate No
## 294 LP001945 Female No Graduate No
## 295 LP001947 Male Yes 0 Graduate No
## 296 LP001949 Male Yes 3+ Graduate
## 297 LP001953 Male Yes 1 Graduate No
## 298 LP001954 Female Yes 1 Graduate No
## 299 LP001955 Female No 0 Graduate No
## 300 LP001963 Male Yes 1 Graduate No
## 301 LP001964 Male Yes 0 Not Graduate No
## 302 LP001972 Male Yes Not Graduate No
## 303 LP001974 Female No 0 Graduate No
## 304 LP001977 Male Yes 1 Graduate No
## 305 LP001978 Male No 0 Graduate No
## 306 LP001990 Male No 0 Not Graduate No
## 307 LP001993 Female No 0 Graduate No
## 308 LP001994 Female No 0 Graduate No
## 309 LP001996 Male No 0 Graduate No
## 310 LP001998 Male Yes 2 Not Graduate No
## 311 LP002002 Female No 0 Graduate No
## 312 LP002004 Male No 0 Not Graduate No
## 313 LP002006 Female No 0 Graduate No
## 314 LP002008 Male Yes 2 Graduate Yes
## 315 LP002024 Yes 0 Graduate No
## 316 LP002031 Male Yes 1 Not Graduate No
## 317 LP002035 Male Yes 2 Graduate No
## 318 LP002036 Male Yes 0 Graduate No
## 319 LP002043 Female No 1 Graduate No
## 320 LP002050 Male Yes 1 Graduate Yes
## 321 LP002051 Male Yes 0 Graduate No
## 322 LP002053 Male Yes 3+ Graduate No
## 323 LP002054 Male Yes 2 Not Graduate No
## 324 LP002055 Female No 0 Graduate No
## 325 LP002065 Male Yes 3+ Graduate No
## 326 LP002067 Male Yes 1 Graduate Yes
## 327 LP002068 Male No 0 Graduate No
## 328 LP002082 Male Yes 0 Graduate Yes
## 329 LP002086 Female Yes 0 Graduate No
## 330 LP002087 Female No 0 Graduate No
## 331 LP002097 Male No 1 Graduate No
## 332 LP002098 Male No 0 Graduate No
## 333 LP002100 Male No Graduate No
## 334 LP002101 Male Yes 0 Graduate
## 335 LP002103 Yes 1 Graduate Yes
## 336 LP002106 Male Yes Graduate Yes
## 337 LP002110 Male Yes 1 Graduate
## 338 LP002112 Male Yes 2 Graduate Yes
## 339 LP002113 Female No 3+ Not Graduate No
## 340 LP002114 Female No 0 Graduate No
## 341 LP002115 Male Yes 3+ Not Graduate No
## 342 LP002116 Female No 0 Graduate No
## 343 LP002119 Male Yes 1 Not Graduate No
## 344 LP002126 Male Yes 3+ Not Graduate No
## 345 LP002128 Male Yes 2 Graduate
## 346 LP002129 Male Yes 0 Graduate No
## 347 LP002130 Male Yes Not Graduate No
## 348 LP002131 Male Yes 2 Not Graduate No
## 349 LP002137 Male Yes 0 Graduate No
## 350 LP002138 Male Yes 0 Graduate No
## 351 LP002139 Male Yes 0 Graduate No
## 352 LP002140 Male No 0 Graduate No
## 353 LP002141 Male Yes 3+ Graduate No
## 354 LP002142 Female Yes 0 Graduate Yes
## 355 LP002143 Female Yes 0 Graduate No
## 356 LP002144 Female No Graduate No
## 357 LP002149 Male Yes 2 Graduate No
## 358 LP002151 Male Yes 1 Graduate No
## 359 LP002158 Male Yes 0 Not Graduate No
## 360 LP002160 Male Yes 3+ Graduate No
## 361 LP002161 Female No 1 Graduate No
## 362 LP002170 Male Yes 2 Graduate No
## 363 LP002175 Male Yes 0 Graduate No
## 364 LP002178 Male Yes 0 Graduate No
## 365 LP002180 Male No 0 Graduate Yes
## 366 LP002181 Male No 0 Not Graduate No
## 367 LP002187 Male No 0 Graduate No
## 368 LP002188 Male No 0 Graduate No
## 369 LP002190 Male Yes 1 Graduate No
## 370 LP002191 Male Yes 0 Graduate No
## 371 LP002194 Female No 0 Graduate Yes
## 372 LP002197 Male Yes 2 Graduate No
## 373 LP002201 Male Yes 2 Graduate Yes
## 374 LP002205 Male No 1 Graduate No
## 375 LP002209 Female No 0 Graduate
## 376 LP002211 Male Yes 0 Graduate No
## 377 LP002219 Male Yes 3+ Graduate No
## 378 LP002223 Male Yes 0 Graduate No
## 379 LP002224 Male No 0 Graduate No
## 380 LP002225 Male Yes 2 Graduate No
## 381 LP002226 Male Yes 0 Graduate
## 382 LP002229 Male No 0 Graduate No
## 383 LP002231 Female No 0 Graduate No
## 384 LP002234 Male No 0 Graduate Yes
## 385 LP002236 Male Yes 2 Graduate No
## 386 LP002237 Male No 1 Graduate
## 387 LP002239 Male No 0 Not Graduate No
## 388 LP002243 Male Yes 0 Not Graduate No
## 389 LP002244 Male Yes 0 Graduate No
## 390 LP002250 Male Yes 0 Graduate No
## 391 LP002255 Male No 3+ Graduate No
## 392 LP002262 Male Yes 3+ Graduate No
## 393 LP002263 Male Yes 0 Graduate No
## 394 LP002265 Male Yes 2 Not Graduate No
## 395 LP002266 Male Yes 2 Graduate No
## 396 LP002272 Male Yes 2 Graduate No
## 397 LP002277 Female No 0 Graduate No
## 398 LP002281 Male Yes 0 Graduate No
## 399 LP002284 Male No 0 Not Graduate No
## 400 LP002287 Female No 0 Graduate No
## 401 LP002288 Male Yes 2 Not Graduate No
## 402 LP002296 Male No 0 Not Graduate No
## 403 LP002297 Male No 0 Graduate No
## 404 LP002300 Female No 0 Not Graduate No
## 405 LP002301 Female No 0 Graduate Yes
## 406 LP002305 Female No 0 Graduate No
## 407 LP002308 Male Yes 0 Not Graduate No
## 408 LP002314 Female No 0 Not Graduate No
## 409 LP002315 Male Yes 1 Graduate No
## 410 LP002317 Male Yes 3+ Graduate No
## 411 LP002318 Female No 1 Not Graduate Yes
## 412 LP002319 Male Yes 0 Graduate
## 413 LP002328 Male Yes 0 Not Graduate No
## 414 LP002332 Male Yes 0 Not Graduate No
## 415 LP002335 Female Yes 0 Not Graduate No
## 416 LP002337 Female No 0 Graduate No
## 417 LP002341 Female No 1 Graduate No
## 418 LP002342 Male Yes 2 Graduate Yes
## 419 LP002345 Male Yes 0 Graduate No
## 420 LP002347 Male Yes 0 Graduate No
## 421 LP002348 Male Yes 0 Graduate No
## 422 LP002357 Female No 0 Not Graduate No
## 423 LP002361 Male Yes 0 Graduate No
## 424 LP002362 Male Yes 1 Graduate No
## 425 LP002364 Male Yes 0 Graduate No
## 426 LP002366 Male Yes 0 Graduate No
## 427 LP002367 Female No 1 Not Graduate No
## 428 LP002368 Male Yes 2 Graduate No
## 429 LP002369 Male Yes 0 Graduate No
## 430 LP002370 Male No 0 Not Graduate No
## 431 LP002377 Female No 1 Graduate Yes
## 432 LP002379 Male No 0 Graduate No
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## 435 LP002390 Male No 0 Graduate No
## 436 LP002393 Female Graduate No
## 437 LP002398 Male No 0 Graduate No
## 438 LP002401 Male Yes 0 Graduate No
## 439 LP002403 Male No 0 Graduate Yes
## 440 LP002407 Female Yes 0 Not Graduate Yes
## 441 LP002408 Male No 0 Graduate No
## 442 LP002409 Male Yes 0 Graduate No
## 443 LP002418 Male No 3+ Not Graduate No
## 444 LP002422 Male No 1 Graduate No
## 445 LP002424 Male Yes 0 Graduate No
## 446 LP002429 Male Yes 1 Graduate Yes
## 447 LP002434 Male Yes 2 Not Graduate No
## 448 LP002435 Male Yes 0 Graduate
## 449 LP002443 Male Yes 2 Graduate No
## 450 LP002444 Male No 1 Not Graduate Yes
## 451 LP002446 Male Yes 2 Not Graduate No
## 452 LP002447 Male Yes 2 Not Graduate No
## 453 LP002448 Male Yes 0 Graduate No
## 454 LP002449 Male Yes 0 Graduate No
## 455 LP002453 Male No 0 Graduate Yes
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## 457 LP002459 Male Yes 0 Graduate No
## 458 LP002467 Male Yes 0 Graduate No
## 459 LP002472 Male No 2 Graduate No
## 460 LP002473 Male Yes 0 Graduate No
## 461 LP002478 Yes 0 Graduate Yes
## 462 LP002484 Male Yes 3+ Graduate No
## 463 LP002487 Male Yes 0 Graduate No
## 464 LP002489 Female No 1 Not Graduate
## 465 LP002493 Male No 0 Graduate No
## 466 LP002494 Male No 0 Graduate No
## 467 LP002500 Male Yes 3+ Not Graduate No
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## 469 LP002502 Female Yes 2 Not Graduate
## 470 LP002505 Male Yes 0 Graduate No
## 471 LP002515 Male Yes 1 Graduate Yes
## 472 LP002517 Male Yes 1 Not Graduate No
## 473 LP002519 Male Yes 3+ Graduate No
## 474 LP002522 Female No 0 Graduate Yes
## 475 LP002524 Male No 2 Graduate No
## 476 LP002527 Male Yes 2 Graduate Yes
## 477 LP002529 Male Yes 2 Graduate No
## 478 LP002530 Yes 2 Graduate No
## 479 LP002531 Male Yes 1 Graduate Yes
## 480 LP002533 Male Yes 2 Graduate No
## 481 LP002534 Female No 0 Not Graduate No
## 482 LP002536 Male Yes 3+ Not Graduate No
## 483 LP002537 Male Yes 0 Graduate No
## 484 LP002541 Male Yes 0 Graduate No
## 485 LP002543 Male Yes 2 Graduate No
## 486 LP002544 Male Yes 1 Not Graduate No
## 487 LP002545 Male No 2 Graduate No
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## 489 LP002555 Male Yes 2 Graduate Yes
## 490 LP002556 Male No 0 Graduate No
## 491 LP002560 Male No 0 Not Graduate No
## 492 LP002562 Male Yes 1 Not Graduate No
## 493 LP002571 Male No 0 Not Graduate No
## 494 LP002582 Female No 0 Not Graduate Yes
## 495 LP002585 Male Yes 0 Graduate No
## 496 LP002586 Female Yes 1 Graduate No
## 497 LP002587 Male Yes 0 Not Graduate No
## 498 LP002588 Male Yes 0 Graduate No
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## 500 LP002602 Male No 0 Graduate No
## 501 LP002603 Female No 0 Graduate No
## 502 LP002606 Female No 0 Graduate No
## 503 LP002615 Male Yes 2 Graduate No
## 504 LP002618 Male Yes 1 Not Graduate No
## 505 LP002619 Male Yes 0 Not Graduate No
## 506 LP002622 Male Yes 2 Graduate No
## 507 LP002624 Male Yes 0 Graduate No
## 508 LP002625 No 0 Graduate No
## 509 LP002626 Male Yes 0 Graduate Yes
## 510 LP002634 Female No 1 Graduate No
## 511 LP002637 Male No 0 Not Graduate No
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## 525 LP002697 Male No 0 Graduate No
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## 527 LP002705 Male Yes 0 Graduate No
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## 529 LP002714 Male No 1 Not Graduate No
## 530 LP002716 Male No 0 Not Graduate No
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## 533 LP002723 Male No 2 Graduate No
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## 535 LP002731 Female No 0 Not Graduate Yes
## 536 LP002732 Male No 0 Not Graduate
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## 539 LP002739 Male Yes 0 Not Graduate No
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## 542 LP002743 Female No 0 Graduate No
## 543 LP002753 Female No 1 Graduate
## 544 LP002755 Male Yes 1 Not Graduate No
## 545 LP002757 Female Yes 0 Not Graduate No
## 546 LP002767 Male Yes 0 Graduate No
## 547 LP002768 Male No 0 Not Graduate No
## 548 LP002772 Male No 0 Graduate No
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## 552 LP002784 Male Yes 1 Not Graduate No
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## Credit_History Property_Area Loan_Status
## 1 1 Urban Y
## 2 1 Rural N
## 3 1 Urban Y
## 4 1 Urban Y
## 5 1 Urban Y
## 6 1 Urban Y
## 7 1 Urban Y
## 8 0 Semiurban N
## 9 1 Urban Y
## 10 1 Semiurban N
## 11 1 Urban Y
## 12 1 Urban Y
## 13 1 Urban Y
## 14 1 Rural N
## 15 1 Urban Y
## 16 1 Urban Y
## 17 1 Urban Y
## 18 0 Urban N
## 19 1 Rural N
## 20 1 Urban Y
## 21 0 Urban N
## 22 1 Urban Y
## 23 0 Semiurban N
## 24 0 Rural N
## 25 0 Semiurban N
## 26 1 Semiurban Y
## 27 1 Semiurban Y
## 28 1 Urban Y
## 29 1 Urban N
## 30 1 Semiurban Y
## 31 1 Urban N
## 32 1 Urban N
## 33 1 Rural N
## 34 1 Semiurban Y
## 35 1 Rural N
## 36 1 Urban Y
## 37 0 Urban N
## 38 1 Semiurban Y
## 39 1 Urban Y
## 40 1 Semiurban Y
## 41 1 Urban N
## 42 1 Urban Y
## 43 1 Urban Y
## 44 1 Semiurban Y
## 45 1 Urban Y
## 46 1 Urban Y
## 47 1 Urban Y
## 48 1 Urban Y
## 49 0 Urban N
## 50 1 Semiurban Y
## 51 1 Semiurban Y
## 52 1 Semiurban Y
## 53 1 Semiurban N
## 54 1 Urban N
## 55 0 Urban N
## 56 1 Semiurban Y
## 57 1 Semiurban Y
## 58 1 Rural N
## 59 1 Urban Y
## 60 1 Urban Y
## 61 1 Urban Y
## 62 1 Urban Y
## 63 0 Rural N
## 64 0 Rural N
## 65 0 Semiurban N
## 66 1 Semiurban N
## 67 0 Urban N
## 68 1 Urban Y
## 69 1 Urban Y
## 70 0 Semiurban N
## 71 1 Urban Y
## 72 1 Semiurban Y
## 73 1 Semiurban Y
## 74 0 Semiurban N
## 75 1 Semiurban Y
## 76 1 Urban N
## 77 1 Urban N
## 78 1 Urban N
## 79 0 Semiurban N
## 80 0 Semiurban Y
## 81 1 Semiurban Y
## 82 1 Semiurban Y
## 83 1 Urban N
## 84 1 Semiurban N
## 85 1 Urban Y
## 86 1 Semiurban Y
## 87 1 Semiurban Y
## 88 1 Semiurban Y
## 89 1 Urban Y
## 90 1 Semiurban Y
## 91 1 Semiurban Y
## 92 1 Semiurban Y
## 93 1 Urban Y
## 94 1 Semiurban Y
## 95 1 Semiurban Y
## 96 1 Urban N
## 97 1 Semiurban Y
## 98 1 Semiurban Y
## 99 1 Semiurban Y
## 100 1 Semiurban Y
## 101 1 Urban Y
## 102 1 Semiurban Y
## 103 1 Urban Y
## 104 1 Semiurban Y
## 105 1 Urban Y
## 106 1 Urban Y
## 107 1 Urban Y
## 108 1 Rural N
## 109 0 Urban N
## 110 1 Semiurban Y
## 111 1 Urban Y
## 112 1 Semiurban Y
## 113 0 Rural N
## 114 1 Semiurban Y
## 115 1 Semiurban Y
## 116 1 Rural Y
## 117 1 Semiurban Y
## 118 1 Urban Y
## 119 1 Rural N
## 120 1 Urban Y
## 121 1 Rural Y
## 122 1 Semiurban Y
## 123 0 Semiurban Y
## 124 1 Semiurban Y
## 125 1 Rural Y
## 126 1 Rural Y
## 127 1 Rural Y
## 128 1 Rural Y
## 129 0 Urban N
## 130 1 Rural N
## 131 1 Urban Y
## 132 1 Urban Y
## 133 1 Semiurban Y
## 134 1 Semiurban Y
## 135 1 Semiurban Y
## 136 1 Semiurban N
## 137 1 Rural N
## 138 1 Urban Y
## 139 0 Semiurban N
## 140 1 Rural N
## 141 1 Rural N
## 142 1 Urban Y
## 143 1 Semiurban Y
## 144 1 Semiurban Y
## 145 1 Urban Y
## 146 1 Semiurban Y
## 147 1 Urban Y
## 148 1 Urban Y
## 149 1 Rural N
## 150 1 Semiurban Y
## 151 0 Rural N
## 152 1 Rural Y
## 153 1 Urban N
## 154 1 Rural N
## 155 1 Urban Y
## 156 0 Semiurban Y
## 157 1 Rural Y
## 158 1 Urban Y
## 159 1 Rural Y
## 160 1 Semiurban Y
## 161 1 Semiurban Y
## 162 1 Urban N
## 163 0 Semiurban N
## 164 1 Rural Y
## 165 1 Urban Y
## 166 1 Rural Y
## 167 1 Rural N
## 168 1 Rural Y
## 169 0 Semiurban N
## 170 1 Semiurban Y
## 171 1 Rural Y
## 172 1 Urban Y
## 173 1 Rural N
## 174 1 Semiurban Y
## 175 1 Semiurban N
## 176 1 Rural Y
## 177 1 Rural Y
## 178 0 Semiurban N
## 179 1 Semiurban Y
## 180 0 Urban N
## 181 0 Urban N
## 182 0 Rural N
## 183 1 Semiurban Y
## 184 1 Semiurban N
## 185 1 Semiurban Y
## 186 1 Semiurban Y
## 187 0 Rural N
## 188 1 Rural Y
## 189 1 Rural Y
## 190 1 Rural Y
## 191 1 Rural Y
## 192 1 Semiurban N
## 193 1 Urban N
## 194 1 Semiurban Y
## 195 1 Rural Y
## 196 1 Semiurban N
## 197 1 Rural Y
## 198 1 Urban Y
## 199 1 Semiurban Y
## 200 1 Urban N
## 201 1 Semiurban Y
## 202 0 Semiurban Y
## 203 1 Urban N
## 204 1 Urban Y
## 205 1 Semiurban Y
## 206 1 Semiurban Y
## 207 1 Urban Y
## 208 1 Rural Y
## 209 1 Urban Y
## 210 1 Semiurban N
## 211 1 Semiurban N
## 212 0 Semiurban N
## 213 1 Urban Y
## 214 1 Rural Y
## 215 1 Urban Y
## 216 1 Semiurban Y
## 217 1 Rural N
## 218 1 Semiurban Y
## 219 0 Semiurban N
## 220 0 Semiurban Y
## 221 0 Urban N
## 222 1 Semiurban Y
## 223 1 Semiurban Y
## 224 1 Semiurban Y
## 225 1 Semiurban Y
## 226 1 Rural N
## 227 1 Urban N
## 228 1 Semiurban Y
## 229 1 Semiurban Y
## 230 1 Rural Y
## 231 1 Semiurban Y
## 232 1 Rural Y
## 233 1 Rural Y
## 234 1 Semiurban Y
## 235 1 Semiurban Y
## 236 1 Rural Y
## 237 1 Urban N
## 238 1 Urban Y
## 239 1 Rural Y
## 240 1 Semiurban Y
## 241 1 Rural Y
## 242 1 Urban N
## 243 1 Urban Y
## 244 1 Rural Y
## 245 1 Semiurban Y
## 246 1 Urban N
## 247 1 Urban Y
## 248 1 Urban Y
## 249 1 Semiurban Y
## 250 1 Urban Y
## 251 0 Semiurban N
## 252 1 Urban N
## 253 1 Rural Y
## 254 1 Semiurban Y
## 255 0 Urban N
## 256 1 Rural Y
## 257 0 Rural N
## 258 1 Urban N
## 259 1 Rural N
## 260 1 Semiurban N
## 261 1 Urban Y
## 262 1 Semiurban Y
## 263 1 Semiurban N
## 264 1 Rural Y
## 265 1 Semiurban Y
## 266 1 Rural Y
## 267 1 Semiurban Y
## 268 0 Urban Y
## 269 1 Rural N
## 270 1 Urban Y
## 271 1 Urban Y
## 272 1 Urban Y
## 273 1 Rural Y
## 274 1 Semiurban Y
## 275 1 Semiurban Y
## 276 1 Semiurban Y
## 277 1 Semiurban Y
## 278 1 Urban Y
## 279 1 Semiurban Y
## 280 1 Rural Y
## 281 0 Urban N
## 282 1 Semiurban Y
## 283 1 Urban Y
## 284 1 Urban Y
## 285 1 Rural N
## 286 1 Rural Y
## 287 1 Semiurban N
## 288 1 Rural Y
## 289 1 Semiurban Y
## 290 1 Rural Y
## 291 1 Rural Y
## 292 0 Semiurban N
## 293 1 Urban Y
## 294 0 Urban N
## 295 1 Semiurban Y
## 296 1 Urban Y
## 297 1 Semiurban Y
## 298 1 Urban Y
## 299 1 Rural N
## 300 1 Urban N
## 301 0 Urban N
## 302 1 Semiurban Y
## 303 1 Rural Y
## 304 1 Urban Y
## 305 1 Rural Y
## 306 1 Urban N
## 307 1 Rural Y
## 308 0 Urban N
## 309 1 Rural N
## 310 1 Rural Y
## 311 1 Semiurban Y
## 312 1 Semiurban Y
## 313 1 Rural Y
## 314 1 Rural Y
## 315 1 Rural N
## 316 1 Urban Y
## 317 1 Semiurban Y
## 318 1 Urban Y
## 319 1 Semiurban Y
## 320 1 Rural N
## 321 1 Semiurban Y
## 322 1 Semiurban Y
## 323 1 Rural Y
## 324 1 Rural Y
## 325 1 Rural Y
## 326 0 Rural N
## 327 0 Rural Y
## 328 1 Semiurban Y
## 329 1 Urban N
## 330 1 Urban Y
## 331 1 Urban Y
## 332 1 Semiurban Y
## 333 1 Urban Y
## 334 1 Urban Y
## 335 1 Urban Y
## 336 1 Semiurban Y
## 337 1 Rural Y
## 338 1 Rural Y
## 339 0 Urban N
## 340 1 Semiurban Y
## 341 1 Rural N
## 342 1 Rural N
## 343 1 Urban Y
## 344 1 Semiurban Y
## 345 1 Rural Y
## 346 1 Semiurban Y
## 347 0 Rural N
## 348 1 Urban Y
## 349 0 Semiurban Y
## 350 1 Rural Y
## 351 1 Semiurban Y
## 352 1 Rural N
## 353 1 Rural Y
## 354 0 Rural N
## 355 1 Semiurban Y
## 356 1 Urban Y
## 357 1 Rural Y
## 358 1 Urban N
## 359 0 Urban N
## 360 1 Semiurban Y
## 361 1 Semiurban N
## 362 1 Semiurban Y
## 363 1 Urban Y
## 364 0 Urban Y
## 365 1 Rural Y
## 366 1 Rural N
## 367 1 Semiurban N
## 368 0 Rural N
## 369 1 Semiurban Y
## 370 1 Rural N
## 371 1 Semiurban Y
## 372 1 Semiurban Y
## 373 1 Rural Y
## 374 0 Urban N
## 375 1 Urban Y
## 376 1 Urban Y
## 377 1 Rural Y
## 378 1 Semiurban Y
## 379 1 Urban N
## 380 1 Urban Y
## 381 1 Semiurban Y
## 382 1 Semiurban Y
## 383 1 Urban Y
## 384 1 Urban Y
## 385 1 Urban N
## 386 1 Urban Y
## 387 1 Semiurban Y
## 388 0 Urban N
## 389 1 Urban Y
## 390 1 Rural Y
## 391 1 Rural Y
## 392 1 Rural Y
## 393 1 Urban Y
## 394 1 Semiurban Y
## 395 1 Urban Y
## 396 1 Semiurban Y
## 397 0 Urban N
## 398 1 Urban Y
## 399 1 Rural Y
## 400 0 Semiurban N
## 401 0 Urban N
## 402 1 Rural N
## 403 1 Semiurban Y
## 404 1 Semiurban Y
## 405 1 Rural N
## 406 1 Semiurban Y
## 407 1 Urban Y
## 408 1 Rural Y
## 409 0 Semiurban N
## 410 0 Rural N
## 411 1 Semiurban N
## 412 1 Urban Y
## 413 0 Rural N
## 414 1 Rural Y
## 415 0 Semiurban N
## 416 1 Urban Y
## 417 1 Urban N
## 418 1 Urban N
## 419 1 Rural Y
## 420 1 Semiurban Y
## 421 1 Rural Y
## 422 0 Urban N
## 423 1 Urban Y
## 424 0 Urban N
## 425 1 Semiurban Y
## 426 1 Rural Y
## 427 1 Rural N
## 428 1 Semiurban Y
## 429 1 Rural Y
## 430 1 Urban Y
## 431 1 Semiurban Y
## 432 0 Rural N
## 433 1 Semiurban Y
## 434 1 Semiurban Y
## 435 1 Urban Y
## 436 1 Semiurban Y
## 437 1 Semiurban Y
## 438 1 Urban Y
## 439 0 Urban N
## 440 1 Rural Y
## 441 1 Semiurban Y
## 442 1 Rural Y
## 443 1 Semiurban Y
## 444 1 Semiurban Y
## 445 1 Rural Y
## 446 1 Rural Y
## 447 1 Rural Y
## 448 1 Rural N
## 449 0 Rural N
## 450 1 Semiurban N
## 451 0 Rural N
## 452 1 Urban Y
## 453 0 Rural N
## 454 0 Rural Y
## 455 1 Semiurban Y
## 456 1 Semiurban Y
## 457 1 Urban Y
## 458 1 Urban N
## 459 1 Rural Y
## 460 1 Semiurban N
## 461 1 Semiurban Y
## 462 1 Urban Y
## 463 1 Rural Y
## 464 1 Semiurban Y
## 465 0 Semiurban N
## 466 1 Rural Y
## 467 0 Urban N
## 468 1 Semiurban Y
## 469 1 Semiurban Y
## 470 1 Urban N
## 471 1 Semiurban Y
## 472 0 Rural N
## 473 1 Semiurban Y
## 474 1 Urban Y
## 475 1 Rural Y
## 476 1 Rural Y
## 477 1 Semiurban Y
## 478 0 Semiurban N
## 479 1 Semiurban Y
## 480 1 Urban N
## 481 1 Rural Y
## 482 1 Rural Y
## 483 1 Semiurban Y
## 484 1 Semiurban Y
## 485 1 Semiurban Y
## 486 1 Rural Y
## 487 0 Rural N
## 488 1 Urban N
## 489 1 Semiurban Y
## 490 1 Urban N
## 491 1 Semiurban Y
## 492 1 Urban Y
## 493 1 Rural Y
## 494 1 Semiurban Y
## 495 0 Rural N
## 496 1 Semiurban Y
## 497 1 Rural Y
## 498 1 Urban Y
## 499 1 Semiurban Y
## 500 0 Rural N
## 501 1 Rural Y
## 502 1 Semiurban Y
## 503 1 Semiurban Y
## 504 1 Rural N
## 505 1 Semiurban Y
## 506 1 Rural Y
## 507 1 Urban Y
## 508 1 Urban N
## 509 1 Urban Y
## 510 1 Urban Y
## 511 1 Rural N
## 512 1 Semiurban Y
## 513 1 Urban Y
## 514 1 Semiurban N
## 515 1 Rural N
## 516 1 Rural Y
## 517 1 Semiurban Y
## 518 0 Semiurban N
## 519 1 Semiurban N
## 520 1 Rural N
## 521 1 Semiurban Y
## 522 1 Semiurban Y
## 523 1 Rural Y
## 524 1 Rural Y
## 525 1 Semiurban N
## 526 1 Rural Y
## 527 1 Semiurban Y
## 528 0 Semiurban Y
## 529 1 Semiurban Y
## 530 1 Semiurban Y
## 531 1 Rural Y
## 532 1 Urban Y
## 533 0 Rural N
## 534 1 Semiurban N
## 535 1 Urban Y
## 536 1 Rural Y
## 537 1 Urban Y
## 538 1 Semiurban Y
## 539 1 Rural N
## 540 1 Rural Y
## 541 1 Semiurban Y
## 542 0 Semiurban N
## 543 1 Semiurban Y
## 544 1 Urban Y
## 545 1 Semiurban Y
## 546 1 Rural Y
## 547 1 Semiurban N
## 548 1 Rural Y
## 549 0 Semiurban N
## 550 1 Rural Y
## 551 0 Rural N
## 552 1 Rural Y
## 553 1 Urban Y
## 554 0 Urban N
## 555 0 Rural N
## 556 1 Semiurban Y
## 557 0 Urban Y
## 558 1 Semiurban Y
## 559 1 Semiurban Y
## 560 1 Semiurban Y
## 561 1 Semiurban Y
## 562 1 Semiurban Y
## 563 1 Rural Y
## 564 1 Semiurban Y
## 565 0 Urban N
## 566 1 Rural Y
## 567 1 Urban Y
## 568 0 Rural N
## 569 1 Urban N
## 570 0 Urban N
## 571 1 Urban Y
## 572 0 Urban N
## 573 1 Urban Y
## 574 1 Semiurban N
## 575 1 Semiurban N
## 576 1 Urban Y
## 577 0 Semiurban N
## 578 1 Urban Y
## 579 1 Rural Y
## 580 1 Urban Y
## 581 1 Semiurban Y
## 582 1 Urban N
## 583 1 Semiurban Y
## 584 0 Rural N
## 585 0 Rural N
## 586 1 Rural N
## 587 1 Urban Y
## 588 1 Semiurban Y
## 589 1 Semiurban Y
## 590 0 Semiurban N
## 591 1 Semiurban Y
## 592 1 Semiurban N
## 593 1 Semiurban Y
## 594 1 Rural Y
## 595 1 Urban Y
## 596 1 Rural Y
## 597 1 Rural N
## 598 0 Semiurban N
## 599 1 Rural Y
## 600 1 Urban Y
## 601 1 Urban N
## 602 1 Rural Y
## 603 1 Urban Y
## 604 1 Rural Y
## 605 1 Semiurban Y
## 606 1 Urban N
## 607 1 Semiurban Y
## 608 1 Rural Y
## 609 1 Rural Y
## 610 1 Rural Y
## 611 1 Rural Y
## 612 1 Urban Y
## 613 1 Urban Y
## 614 0 Semiurban Ndata_imp_mice$Loan_Status <- factor(data_imp_mice$Loan_Status, levels = c("N","Y"))
table(data_imp_mice$Loan_Status)##
## N Y
## 192 422Model Building
Logistic regression model without imputation (drop row)
#menyiapkan data
data_drop$Loan_Status <- factor(data_drop$Loan_Status, levels = c("N","Y"))
#split train test
set.seed(123)
trainIndex <- createDataPartition(data_drop$Loan_Status, p = 0.7, list = FALSE)
trainData <- data_drop[trainIndex, ]
testData <- data_drop[-trainIndex, ]
#logistic regression
model_logit <- glm(Loan_Status ~ Gender + Married + Dependents +
Education + Self_Employed + Property_Area,
data = trainData,
family = binomial)
summary(model_logit)##
## Call:
## glm(formula = Loan_Status ~ Gender + Married + Dependents + Education +
## Self_Employed + Property_Area, family = binomial, data = trainData)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 14.6145 618.3503 0.024 0.98114
## GenderFemale 0.3562 0.9543 0.373 0.70891
## GenderMale 0.2134 0.9072 0.235 0.81404
## MarriedNo -14.5633 618.3498 -0.024 0.98121
## MarriedYes -14.0722 618.3498 -0.023 0.98184
## Dependents0 0.6016 0.8408 0.716 0.47427
## Dependents1 0.5647 0.8827 0.640 0.52234
## Dependents2 0.8438 0.8800 0.959 0.33766
## Dependents3+ 0.8013 0.9256 0.866 0.38663
## EducationNot Graduate -0.2705 0.2748 -0.984 0.32489
## Self_EmployedNo -0.8327 0.6626 -1.257 0.20883
## Self_EmployedYes -0.7152 0.7237 -0.988 0.32300
## Property_AreaSemiurban 0.8438 0.2889 2.921 0.00349 **
## Property_AreaUrban 0.3265 0.2847 1.147 0.25145
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 460.10 on 371 degrees of freedom
## Residual deviance: 440.27 on 358 degrees of freedom
## AIC: 468.27
##
## Number of Fisher Scoring iterations: 13#evaluasi model
pred_prob <- predict(model_logit, newdata = testData, type = "response")
pred_class <- ifelse(pred_prob > 0.5, "Y", "N")
conf_mat <- confusionMatrix(factor(pred_class, levels=c("N","Y")),
testData$Loan_Status)
#plot ROC
roc_obj <- roc(testData$Loan_Status, pred_prob, levels = c("N","Y"))## Setting direction: controls < casesplot(roc_obj, col="blue", main="ROC Curve Logistic Regression (data_drop)")
Logistic regression model with ad-hoc imputation dataset
Mean
# Menyiapkan data (imputasi mean)
data_imp_mean$Loan_Status <- factor(data_imp_mean$Loan_Status, levels = c("N","Y"))
# Split train-test
set.seed(123)
trainIndex <- createDataPartition(data_imp_mean$Loan_Status, p = 0.7, list = FALSE)
trainData <- data_imp_mean[trainIndex, ]
testData <- data_imp_mean[-trainIndex, ]
# Logistic Regression
model_logit <- glm(Loan_Status ~ Gender + Married + Dependents +
Education + Self_Employed + Property_Area,
data = trainData,
family = binomial)
# Ringkasan model
summary(model_logit)##
## Call:
## glm(formula = Loan_Status ~ Gender + Married + Dependents + Education +
## Self_Employed + Property_Area, family = binomial, data = trainData)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 14.506093 624.180234 0.023 0.9815
## GenderFemale 0.008733 0.764410 0.011 0.9909
## GenderMale 0.021016 0.724329 0.029 0.9769
## MarriedNo -14.470290 624.179931 -0.023 0.9815
## MarriedYes -13.872183 624.179937 -0.022 0.9823
## Dependents0 0.973541 0.700480 1.390 0.1646
## Dependents1 0.427600 0.730082 0.586 0.5581
## Dependents2 1.084227 0.751006 1.444 0.1488
## Dependents3+ 0.905950 0.800151 1.132 0.2575
## EducationNot Graduate -0.394731 0.261427 -1.510 0.1311
## Self_EmployedNo -0.632675 0.533292 -1.186 0.2355
## Self_EmployedYes -0.844949 0.586554 -1.441 0.1497
## Property_AreaSemiurban 0.677748 0.277877 2.439 0.0147 *
## Property_AreaUrban -0.035147 0.259942 -0.135 0.8924
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 535.87 on 430 degrees of freedom
## Residual deviance: 509.06 on 417 degrees of freedom
## AIC: 537.06
##
## Number of Fisher Scoring iterations: 13Median
data_imp_median$Loan_Status <- factor(data_imp_median$Loan_Status, levels = c("N","Y"))
set.seed(123)
trainIndex <- createDataPartition(data_imp_median$Loan_Status, p = 0.7, list = FALSE)
trainData <- data_imp_median[trainIndex, ]
testData <- data_imp_median[-trainIndex, ]
model_median <- glm(Loan_Status ~ Gender + Married + Dependents +
Education + Self_Employed + Property_Area,
data = trainData,
family = binomial)
summary(model_median)##
## Call:
## glm(formula = Loan_Status ~ Gender + Married + Dependents + Education +
## Self_Employed + Property_Area, family = binomial, data = trainData)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 14.506093 624.180234 0.023 0.9815
## GenderFemale 0.008733 0.764410 0.011 0.9909
## GenderMale 0.021016 0.724329 0.029 0.9769
## MarriedNo -14.470290 624.179931 -0.023 0.9815
## MarriedYes -13.872183 624.179937 -0.022 0.9823
## Dependents0 0.973541 0.700480 1.390 0.1646
## Dependents1 0.427600 0.730082 0.586 0.5581
## Dependents2 1.084227 0.751006 1.444 0.1488
## Dependents3+ 0.905950 0.800151 1.132 0.2575
## EducationNot Graduate -0.394731 0.261427 -1.510 0.1311
## Self_EmployedNo -0.632675 0.533292 -1.186 0.2355
## Self_EmployedYes -0.844949 0.586554 -1.441 0.1497
## Property_AreaSemiurban 0.677748 0.277877 2.439 0.0147 *
## Property_AreaUrban -0.035147 0.259942 -0.135 0.8924
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 535.87 on 430 degrees of freedom
## Residual deviance: 509.06 on 417 degrees of freedom
## AIC: 537.06
##
## Number of Fisher Scoring iterations: 13Mode
data_imp_mode$Loan_Status <- factor(data_imp_mode$Loan_Status, levels = c("N","Y"))
set.seed(123)
trainIndex <- createDataPartition(data_imp_mode$Loan_Status, p = 0.7, list = FALSE)
trainData <- data_imp_mode[trainIndex, ]
testData <- data_imp_mode[-trainIndex, ]
model_mode <- glm(Loan_Status ~ Gender + Married + Dependents +
Education + Self_Employed + Property_Area,
data = trainData,
family = binomial)
summary(model_mode)##
## Call:
## glm(formula = Loan_Status ~ Gender + Married + Dependents + Education +
## Self_Employed + Property_Area, family = binomial, data = trainData)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 14.506093 624.180234 0.023 0.9815
## GenderFemale 0.008733 0.764410 0.011 0.9909
## GenderMale 0.021016 0.724329 0.029 0.9769
## MarriedNo -14.470290 624.179931 -0.023 0.9815
## MarriedYes -13.872183 624.179937 -0.022 0.9823
## Dependents0 0.973541 0.700480 1.390 0.1646
## Dependents1 0.427600 0.730082 0.586 0.5581
## Dependents2 1.084227 0.751006 1.444 0.1488
## Dependents3+ 0.905950 0.800151 1.132 0.2575
## EducationNot Graduate -0.394731 0.261427 -1.510 0.1311
## Self_EmployedNo -0.632675 0.533292 -1.186 0.2355
## Self_EmployedYes -0.844949 0.586554 -1.441 0.1497
## Property_AreaSemiurban 0.677748 0.277877 2.439 0.0147 *
## Property_AreaUrban -0.035147 0.259942 -0.135 0.8924
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 535.87 on 430 degrees of freedom
## Residual deviance: 509.06 on 417 degrees of freedom
## AIC: 537.06
##
## Number of Fisher Scoring iterations: 13Logistic regression model with MICE imputation dataset
set.seed(123)
trainIndex <- createDataPartition(data_imp_mice$Loan_Status, p = 0.7, list = FALSE)
trainData <- data_imp_mice[trainIndex, ]
testData <- data_imp_mice[-trainIndex, ]
# Model logistic regression
model_mice <- glm(Loan_Status ~ Gender + Married + Dependents +
Education + Self_Employed + Property_Area,
data = trainData,
family = binomial)
summary(model_mice)##
## Call:
## glm(formula = Loan_Status ~ Gender + Married + Dependents + Education +
## Self_Employed + Property_Area, family = binomial, data = trainData)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 14.506093 624.180234 0.023 0.9815
## GenderFemale 0.008733 0.764410 0.011 0.9909
## GenderMale 0.021016 0.724329 0.029 0.9769
## MarriedNo -14.470290 624.179931 -0.023 0.9815
## MarriedYes -13.872183 624.179937 -0.022 0.9823
## Dependents0 0.973541 0.700480 1.390 0.1646
## Dependents1 0.427600 0.730082 0.586 0.5581
## Dependents2 1.084227 0.751006 1.444 0.1488
## Dependents3+ 0.905950 0.800151 1.132 0.2575
## EducationNot Graduate -0.394731 0.261427 -1.510 0.1311
## Self_EmployedNo -0.632675 0.533292 -1.186 0.2355
## Self_EmployedYes -0.844949 0.586554 -1.441 0.1497
## Property_AreaSemiurban 0.677748 0.277877 2.439 0.0147 *
## Property_AreaUrban -0.035147 0.259942 -0.135 0.8924
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 535.87 on 430 degrees of freedom
## Residual deviance: 509.06 on 417 degrees of freedom
## AIC: 537.06
##
## Number of Fisher Scoring iterations: 13# Prediksi probabilitas
pred_prob <- predict(model_mice, newdata = testData, type = "response")
# ROC Curve
roc_obj <- roc(testData$Loan_Status, pred_prob, levels = c("N","Y"))## Setting direction: controls > casesplot(roc_obj, col="blue", main="ROC Curve Logistic Regression (Imputasi MICE)")
## Model intepretation
Berdasarkan hasil analisis regresi logistik dengan berbagai metode penanganan missing value, diperoleh bahwa secara umum performa model tidak menunjukkan perbedaan yang signifikan antara pendekatan drop, adhoc, dan mice. Secara keseluruhan, regresi logistik pada dataset ini hanya memiliki kemampuan prediksi yang terbatas, dan peningkatan performa kemungkinan besar dapat dicapai melalui eksplorasi variabel prediktor tambahan
Model Evaluation
cv_logit <- function(dataset, dataset_name){
dataset$Loan_Status <- factor(dataset$Loan_Status, levels = c("N","Y"))
set.seed(123)
train_control <- trainControl(method = "cv", number = 5)
model <- train(
Loan_Status ~ Gender + Married + Dependents +
Education + Self_Employed + Property_Area,
data = dataset,
method = "glm",
family = binomial,
trControl = train_control
)
results <- data.frame(
Dataset = dataset_name,
Accuracy = mean(model$resample$Accuracy),
Kappa = mean(model$resample$Kappa)
)
return(results)
}
res_drop <- cv_logit(data_drop, "Drop NA")
res_mean <- cv_logit(data_imp_mean, "Mean Imputation")
res_median <- cv_logit(data_imp_median, "Median Imputation")
res_mode <- cv_logit(data_imp_mode, "Mode Imputation")
res_mice <- cv_logit(data_imp_mice, "MICE Imputation")
all_results <- rbind(res_drop, res_mean, res_median, res_mode, res_mice)
print(all_results)## Dataset Accuracy Kappa
## 1 Drop NA 0.6767900 0.02851803
## 2 Mean Imputation 0.6742503 0.02127239
## 3 Median Imputation 0.6742503 0.02127239
## 4 Mode Imputation 0.6742503 0.02127239
## 5 MICE Imputation 0.6742503 0.02127239Conclusion
Berdasarkan hasil cross valiadation dapat diketahui bahwa tidak ada perbedaan signifikan antara imputasi mean, median, mode, dan MICE semua menghasilkan akurasi yang hampir identik. Selain itu, imputasi dengan metode drop justru memberi hasil yang sedikit lebih baik, meski perbedaan akurasi sangat kecil (0.003). Dengan demikian, dalam konteks dataset ini, strategi penghapusan data hilang masih dapat diterima karena proporsi data yang hilang relatif kecil, sedangkan imputasi cenderung menambah noise dan tidak meningkatkan kualitas prediksi.