KOMPUTASI STATISTIKA
~ Ujian Tengah Semester ~
| Kontak | : \(\downarrow\) |
| sherlytaurinsiri@gmail.com | |
| https://www.instagram.com/sherlytaurin/ | |
| RPubs | https://rpubs.com/sherlytaurin/ |
1 Data Set
Kumpulan data akan anda gunakan dalam ujian tengah semester ini adalah data konsumen yang melakukan pinjaman di suatu Bank. Dataset ini memiliki 613 observasi, 13 atribut sebagai berikut:
2 Tugas 1
Lakukan proses persiapan data dengan R dan Python, dengan beberapa langkah berikut:
2.1 Import Data
data <- read.csv("data/loan-train.csv", sep = ",")
data$Credit_History <- as.character(data$Credit_History)
data$Loan_Amount_Term <- as.character(data$Loan_Amount_Term)
data2.2 Penanganan Data Hilang
## [1] "Gender" "Married" "Dependents" "Self_Employed"data <- data %>%
mutate(Gender = replace(Gender, Gender == "", NA)) %>%
mutate(Married = replace(Married, Married == "", NA)) %>%
mutate(Dependents = replace(Dependents, Dependents == "", NA)) %>%
mutate(Self_Employed = replace(Self_Employed, Self_Employed == "", NA))%>%
mutate(LoanAmount = replace(LoanAmount, LoanAmount == "", NA))
list_na <- colnames(data)[ apply(data, 2, anyNA) ]
list_na## [1] "Gender" "Married" "Dependents" "Self_Employed"
## [5] "LoanAmount" "Loan_Amount_Term" "Credit_History"getmode <- function(v){
v=v[nchar(as.character(v))>0]
uniqv <- unique(v)
uniqv[which.max(tabulate(match(v, uniqv)))]
}
data_replace <- data %>%
mutate(LoanAmount = replace(LoanAmount, is.na(LoanAmount), mean(na.omit(data$LoanAmount))),
Loan_Amount_Term = replace(Loan_Amount_Term, is.na(Loan_Amount_Term), getmode(Loan_Amount_Term)),
Credit_History = replace(Credit_History, is.na(Credit_History), getmode(Credit_History)),
Gender = replace(Gender, is.na(Gender), getmode(Gender)),
Married = replace(Married, is.na(Married), getmode(Married)),
Dependents = replace(Dependents, is.na(Dependents), getmode(Dependents)),
Self_Employed = replace(Self_Employed, is.na(Self_Employed), getmode(Self_Employed))
)
sum(is.na(data_replace))## [1] 02.3 Periksa Data Duplikat
data.frame(
row_of_data = data_replace %>% nrow (),
row_of_unique.data = data_replace %>% distinct() %>% nrow()
)Karena row_of_data = row_of_unique.data maka dapat disimpulkan tidak ada data yang duplikat. Dengan itu, kita bisa lanjut ke yang selanutnya.
2.4 Pemisahan Data Kategori dan Numerik
2.5 Penanganan Data Numerik
2.6 Penanganan Data Pencilan
dataout <- data_replace
outliers <- function(x) {
Q1 <- quantile(x, probs=.25)
Q3 <- quantile(x, probs=.75)
iqr = Q3-Q1
upper_limit = Q3 + (iqr*1.5)
lower_limit = Q1 - (iqr*1.5)
x > upper_limit | x < lower_limit
}
outlier1 <- subset(dataout, outliers(dataout$LoanAmount))
outlier2 <- subset(dataout, outliers(dataout$ApplicantIncome))
outlier3 <- subset(dataout, outliers(dataout$CoapplicantIncome))
dataoutlier <- rbind(outlier1, outlier2, outlier3) %>% distinct()
dataoutlier## Joining, by = c("Loan_ID", "Gender", "Married", "Dependents", "Education", "Self_Employed", "ApplicantIncome", "CoapplicantIncome", "LoanAmount", "Loan_Amount_Term", "Credit_History", "Property_Area", "Loan_Status")2.7 Penanganan Data Kategorikal
2.7.1 Pelabelan
GenderLabel <-factor(data_cat$Gender, labels=c(0, 1)) # 0=Female 1=Male
MarriedLabel <-factor(data_cat$Married, labels=c(0, 1)) # 0=No 1=Yes
DependentsLabel <-factor(data_cat$Dependents, labels=c(0, 1, 2, 3)) # 0=0 1=1 2=2 3=3+
EducationLabel <-factor(data_cat$Education, labels=c(0, 1)) # 0=Graduate 1=Not Graduate
Self_EmployedLabel <-factor(data_cat$Self_Employed, labels=c(0, 1)) # 0=No 1=Yes
Loan_Amount_TermLabel <-factor(data_cat$Loan_Amount_Term, labels=c(0, 1, 2, 3, 4, 5, 6, 7, 8, 9)) # 0=12 1=120 2=180 3=240 4= 5=300 6=360 7=480 8=60 9=84
Credit_HistoryLabel <-factor(data_cat$Credit_History, labels=c(0, 1)) # 0=No 1=Yes
Property_AreaLabel <-factor(data_cat$Property_Area, labels=c(0, 1, 2)) # 0=Rural 1=Semiurban 2=Urban
Loan_StatusLabel <-factor(data_cat$Loan_Status, labels=c(0, 1)) # 0=No 1=Yes
data_cat_labeled <- data.frame("ID" = data_cat$Loan_ID, GenderLabel, MarriedLabel, DependentsLabel, EducationLabel, Self_EmployedLabel, Loan_Amount_TermLabel, Credit_HistoryLabel, Property_AreaLabel, Loan_StatusLabel)
data_cat_labeled3 Tugas 2
Lakukan Proses Visualisasi Data dengan menggunakan R dan Python dengan beberapa langkah berikut:
3.1 Visualisasi Univariabel
3.1.1 Data Kategori
plot_gender <- ggplot(dataakhir, aes(x = Gender))+geom_bar()
plot_married <- ggplot(dataakhir, aes(x = Married))+geom_bar()
plot_dependents <- ggplot(dataakhir, aes(x = Dependents))+geom_bar()
plot_education <- ggplot(dataakhir, aes(x = Education))+geom_bar()
plot_SE <- ggplot(dataakhir, aes(x = Self_Employed))+geom_bar()
plot_LAT <- ggplot(dataakhir, aes(x = Loan_Amount_Term))+geom_bar()+theme(axis.text.x = element_text(angle = 45, vjust = 0.5, hjust=0.5))
plot_CH <- ggplot(dataakhir, aes(x = Credit_History))+geom_bar()
plot_PA <- ggplot(dataakhir, aes(x = Property_Area))+geom_bar()
plot_LA <- ggplot(dataakhir, aes(x = Loan_Status))+geom_bar()
plot_gender + plot_married + plot_dependents + plot_education + plot_SE + plot_LAT + plot_CH + plot_PA + plot_LA + plot_layout(ncol = 3)
3.1.2 Data Numerik
dataakhir$APPCO_TotalIncome <- dataakhir$ApplicantIncome + dataakhir$CoapplicantIncome
plot_AI <- ggplot(dataakhir, aes(x = ApplicantIncome))+geom_histogram(bins = 12, colour = "white")
plot_APPCO <- ggplot(dataakhir, aes(x = APPCO_TotalIncome))+geom_histogram(bins = 12, colour = "white")
plot_LA <- ggplot(dataakhir, aes(x = LoanAmount))+geom_histogram(bins = 12, colour = "white")
plot_AI + plot_APPCO + plot_LA + plot_layout(ncol = 2)
3.2 Visualisasi Bivariabel
3.2.1 Dua data kategorikal
plot2_G_M <- ggplot(dataakhir, aes(x = Gender, fill = Married)) +
theme_minimal() + # use a minimal theme
geom_bar(position = position_dodge(preserve = "single"))
plot2_G_E <- ggplot(dataakhir, aes(x = Gender, fill = Education)) +
theme_minimal() + # use a minimal theme
geom_bar(position = position_dodge(preserve = "single"))
plot2_M_E <- ggplot(dataakhir, aes(x = Married, fill = Education)) +
theme_minimal() + # use a minimal theme
geom_bar(position = position_dodge(preserve = "single"))
Plot2_E_PA <- ggplot(dataakhir, aes(x = Education, fill = Property_Area)) +
theme_minimal() + # use a minimal theme
geom_bar(position = position_dodge(preserve = "single"))
plot2_G_M + plot2_G_E + plot2_M_E + Plot2_E_PA + plot_layout(ncol = 2)
3.2.2 Numerik vs Numerik
plot2_LA_ACT <- ggplot(dataakhir, aes(x = LoanAmount, y = APPCO_TotalIncome )) +
theme_minimal() + # use a minimal theme
geom_line()
plot2_AI_CI <- ggplot(dataakhir, aes(x = ApplicantIncome, y = CoapplicantIncome )) +
theme_minimal() + # use a minimal theme
geom_line()
plot2_AI_LA <- ggplot(dataakhir, aes(x = ApplicantIncome, y = LoanAmount )) +
theme_minimal() + # use a minimal theme
geom_line()
plot2_LA_ACT + plot2_AI_CI + plot2_AI_LA + plot_layout(ncol = 2)
3.2.3 Numerik vs Kategorikal
plot2_LA_LS <- ggplot(dataakhir,
aes(x = LoanAmount,
fill = Loan_Status)) +
geom_density(alpha = 0.3) +
theme_minimal() +
labs(title = "Loan Amount distribution by Loan Amount Term")
plot2_CI_M <- ggplot(dataakhir,
aes(x = CoapplicantIncome,
fill = Married)) +
geom_density(alpha = 0.3) +
theme_minimal() +
labs(title = "Coapplicant Income distribution by Married")
plot2_AI_E <- ggplot(dataakhir,
aes(x = ApplicantIncome,
fill = Education)) +
geom_density(alpha = 0.3) +
theme_minimal() +
labs(title = "Applicant Income distribution by Education")
plot2_LA_PA <- ggplot(dataakhir,
aes(x = ApplicantIncome,
fill = Property_Area)) +
geom_density(alpha = 0.3) +
theme_minimal() +
labs(title = "Applicant Income distribution by Property Area")
ggarrange(plot2_LA_LS, plot2_CI_M, plot2_AI_E, plot2_LA_PA)
3.3 Visualisasi Multivariabel
plot3_AI_LA_PA <-ggplot(dataakhir, aes(x=ApplicantIncome, y=LoanAmount, shape=Property_Area, colour=Property_Area))+geom_point()
plot3_AI_LA_LAT <-ggplot(dataakhir, aes(x=APPCO_TotalIncome, y=LoanAmount, shape=Loan_Amount_Term, colour=Property_Area))+geom_point()
plot3_AI_LA_E <-ggplot(dataakhir, aes(x=ApplicantIncome, y=LoanAmount, shape=Education, colour=Property_Area))+geom_point()
plot3_LA_CI_SE <-ggplot(dataakhir, aes(x=LoanAmount, y=APPCO_TotalIncome, shape=Self_Employed, colour=Property_Area))+geom_point()
ggarrange(plot3_AI_LA_PA, plot3_AI_LA_LAT, plot3_AI_LA_E, plot3_LA_CI_SE)
4 Tugas 3
Lakukan proses analisa data secara deskriptif menggunakan R dan Python dengan beberapa langkah berikut:
4.1 Kualitatif
4.1.1 Kategori Univariat
##
## Female Male
## 0.1869159 0.8130841##
## No Yes
## 0.3495327 0.6504673##
## 0 1 2 3+
## 0.60000000 0.15700935 0.16822430 0.07476636##
## Graduate Not Graduate
## 0.7551402 0.2448598##
## No Yes
## 0.8859813 0.1140187##
## 12 120 180 240 300 36
## 0.001869159 0.005607477 0.067289720 0.007476636 0.018691589 0.003738318
## 360 480 60 84
## 0.857943925 0.026168224 0.003738318 0.007476636##
## 0 1
## 0.1457944 0.8542056##
## Rural Semiurban Urban
## 0.2990654 0.3831776 0.3177570##
## N Y
## 0.3046729 0.69532714.1.2 Kategori Bivariat
## Married
## Gender No Yes
## Female 72 28
## Male 115 320## Education
## Gender Graduate Not Graduate
## Female 82 18
## Male 322 113## Property_Area
## Gender Rural Semiurban Urban
## Female 24 49 27
## Male 136 156 143## Self_Employed
## Education No Yes
## Graduate 358 46
## Not Graduate 116 15## Loan_Amount_Term
## Gender 12 120 180 240 300 36 360 480 60 84
## Female 0 0 2 1 1 1 90 4 0 1
## Male 1 3 34 3 9 1 369 10 2 3## Loan_Amount_Term
## Married 12 120 180 240 300 36 360 480 60 84
## No 0 1 7 1 3 2 164 8 1 0
## Yes 1 2 29 3 7 0 295 6 1 44.1.3 Kategori Multivariat
## Education Graduate Not Graduate
## Gender Married
## Female No 60 12
## Yes 22 6
## Male No 84 31
## Yes 238 82## Loan_Amount_Term 12 120 180 240 300 36 360 480 60 84
## Gender Married Education Property_Area
## Female No Graduate Rural 0 0 0 0 0 0 13 2 0 0
## Semiurban 0 0 0 0 1 1 23 0 0 0
## Urban 0 0 1 0 0 0 18 1 0 0
## Not Graduate Rural 0 0 0 0 0 0 4 0 0 0
## Semiurban 0 0 0 0 0 0 5 0 0 0
## Urban 0 0 0 0 0 0 3 0 0 0
## Yes Graduate Rural 0 0 0 0 0 0 3 0 0 0
## Semiurban 0 0 1 1 0 0 11 1 0 1
## Urban 0 0 0 0 0 0 4 0 0 0
## Not Graduate Rural 0 0 0 0 0 0 2 0 0 0
## Semiurban 0 0 0 0 0 0 4 0 0 0
## Urban 0 0 0 0 0 0 0 0 0 0
## Male No Graduate Rural 0 0 0 0 0 0 27 0 0 0
## Semiurban 0 0 1 0 1 0 23 2 0 0
## Urban 0 0 3 0 0 0 24 2 1 0
## Not Graduate Rural 0 0 0 0 1 0 12 0 0 0
## Semiurban 0 1 0 0 0 1 9 0 0 0
## Urban 0 0 2 1 0 0 3 1 0 0
## Yes Graduate Rural 0 0 7 0 1 0 57 0 0 2
## Semiurban 0 1 3 1 3 0 84 2 0 0
## Urban 1 1 5 1 1 0 67 0 0 1
## Not Graduate Rural 0 0 3 0 0 0 26 0 0 0
## Semiurban 0 0 2 0 1 0 19 2 0 0
## Urban 0 0 8 0 1 0 18 1 1 04.2 Kuantitatif
4.2.1 Univariat numerik
## ApplicantIncome CoapplicantIncome LoanAmount APPCO_TotalIncome
## Min. : 150 Min. : 0 Min. : 9.0 Min. : 1442
## 1st Qu.: 2752 1st Qu.: 0 1st Qu.:100.0 1st Qu.: 3900
## Median : 3598 Median :1260 Median :124.0 Median : 5000
## Mean : 4054 Mean :1323 Mean :127.0 Mean : 5377
## 3rd Qu.: 4891 3rd Qu.:2194 3rd Qu.:151.5 3rd Qu.: 6411
## Max. :10139 Max. :5701 Max. :260.0 Max. :13746f <- function(x){
data.frame(var(x),
sd(x),
mad(x),
IQR(x),
skewness(x),
kurtosis(x))
}
sapply(data_num_new,f)## ApplicantIncome CoapplicantIncome LoanAmount APPCO_TotalIncome
## var.x. 3435005 2019827 1990.793 4049507
## sd.x. 1853.377 1421.206 44.61831 2012.339
## mad.x. 1504.839 1868.076 38.5476 1813.22
## IQR.x. 2138.5 2194 51.5 2511
## skewness.x. 1.135837 0.8411675 0.3989309 0.9268307
## kurtosis.x. 1.192745 0.01943741 0.4724055 0.94869144.2.2 Bivariat numerik
## [1] -702662.6## [1] 18225.52## [1] 39323.8## [1] 57549.32## [1] -0.2667633## [1] 0.2874152## [1] 0.4755308## [1] 0.64095284.2.3 Multivariat numerik
## ApplicantIncome CoapplicantIncome LoanAmount
## ApplicantIncome 3435005.1 -702662.55 39323.796
## CoapplicantIncome -702662.6 2019826.68 18225.520
## LoanAmount 39323.8 18225.52 1990.793
## APPCO_TotalIncome 2732342.5 1317164.13 57549.316
## APPCO_TotalIncome
## ApplicantIncome 2732342.55
## CoapplicantIncome 1317164.13
## LoanAmount 57549.32
## APPCO_TotalIncome 4049506.68## ApplicantIncome CoapplicantIncome LoanAmount
## ApplicantIncome 1.0000000 -0.2667633 0.4755308
## CoapplicantIncome -0.2667633 1.0000000 0.2874152
## LoanAmount 0.4755308 0.2874152 1.0000000
## APPCO_TotalIncome 0.7326059 0.4605553 0.6409528
## APPCO_TotalIncome
## ApplicantIncome 0.7326059
## CoapplicantIncome 0.4605553
## LoanAmount 0.6409528
## APPCO_TotalIncome 1.00000004.3 EDA dengan cara Malas
basic_eda <- function(data)
{
glimpse(data)
skim(data)
df_status(data)
freq(data)
profiling_num(data)
plot_num(data)
describe(data)
}
basic_eda(dataakhir)## Rows: 535
## Columns: 14
## $ Loan_ID <chr> "LP001002", "LP001003", "LP001005", "LP001006", "LP0~
## $ Gender <chr> "Male", "Male", "Male", "Male", "Male", "Male", "Mal~
## $ Married <chr> "No", "Yes", "Yes", "Yes", "No", "Yes", "Yes", "Yes"~
## $ Dependents <chr> "0", "1", "0", "0", "0", "0", "3+", "2", "2", "2", "~
## $ Education <chr> "Graduate", "Graduate", "Graduate", "Not Graduate", ~
## $ Self_Employed <chr> "No", "No", "Yes", "No", "No", "No", "No", "No", "No~
## $ ApplicantIncome <int> 5849, 4583, 3000, 2583, 6000, 2333, 3036, 4006, 3200~
## $ CoapplicantIncome <dbl> 0, 1508, 0, 2358, 0, 1516, 2504, 1526, 700, 1840, 28~
## $ LoanAmount <dbl> 146.4122, 128.0000, 66.0000, 120.0000, 141.0000, 95.~
## $ Loan_Amount_Term <chr> "360", "360", "360", "360", "360", "360", "360", "36~
## $ Credit_History <chr> "1", "1", "1", "1", "1", "1", "0", "1", "1", "1", "1~
## $ Property_Area <chr> "Urban", "Rural", "Urban", "Urban", "Urban", "Urban"~
## $ Loan_Status <chr> "Y", "N", "Y", "Y", "Y", "Y", "N", "Y", "Y", "Y", "N~
## $ APPCO_TotalIncome <dbl> 5849, 6091, 3000, 4941, 6000, 3849, 5540, 5532, 3900~
## variable q_zeros p_zeros q_na p_na q_inf p_inf type unique
## 1 Loan_ID 0 0.00 0 0 0 0 character 535
## 2 Gender 0 0.00 0 0 0 0 character 2
## 3 Married 0 0.00 0 0 0 0 character 2
## 4 Dependents 321 60.00 0 0 0 0 character 4
## 5 Education 0 0.00 0 0 0 0 character 2
## 6 Self_Employed 0 0.00 0 0 0 0 character 2
## 7 ApplicantIncome 0 0.00 0 0 0 0 integer 438
## 8 CoapplicantIncome 229 42.80 0 0 0 0 numeric 259
## 9 LoanAmount 0 0.00 0 0 0 0 numeric 161
## 10 Loan_Amount_Term 0 0.00 0 0 0 0 character 10
## 11 Credit_History 78 14.58 0 0 0 0 character 2
## 12 Property_Area 0 0.00 0 0 0 0 character 3
## 13 Loan_Status 0 0.00 0 0 0 0 character 2
## 14 APPCO_TotalIncome 0 0.00 0 0 0 0 numeric 480
## Loan_ID frequency percentage cumulative_perc
## 1 LP001002 1 0.19 0.19
## 2 LP001003 1 0.19 0.38
## 3 LP001005 1 0.19 0.57
## 4 LP001006 1 0.19 0.76
## 5 LP001008 1 0.19 0.95
## 6 LP001013 1 0.19 1.14
## 7 LP001014 1 0.19 1.33
## 8 LP001018 1 0.19 1.52
## 9 LP001024 1 0.19 1.71
## 10 LP001027 1 0.19 1.90
## 11 LP001029 1 0.19 2.09
## 12 LP001030 1 0.19 2.28
## 13 LP001032 1 0.19 2.47
## 14 LP001034 1 0.19 2.66
## 15 LP001036 1 0.19 2.85
## 16 LP001038 1 0.19 3.04
## 17 LP001041 1 0.19 3.23
## 18 LP001043 1 0.19 3.42
## 19 LP001047 1 0.19 3.61
## 20 LP001050 1 0.19 3.80
## 21 LP001052 1 0.19 3.99
## 22 LP001066 1 0.19 4.18
## 23 LP001068 1 0.19 4.37
## 24 LP001073 1 0.19 4.56
## 25 LP001086 1 0.19 4.75
## 26 LP001087 1 0.19 4.94
## 27 LP001091 1 0.19 5.13
## 28 LP001095 1 0.19 5.32
## 29 LP001097 1 0.19 5.51
## 30 LP001098 1 0.19 5.70
## 31 LP001106 1 0.19 5.89
## 32 LP001109 1 0.19 6.08
## 33 LP001112 1 0.19 6.27
## 34 LP001116 1 0.19 6.46
## 35 LP001119 1 0.19 6.65
## 36 LP001120 1 0.19 6.84
## 37 LP001123 1 0.19 7.03
## 38 LP001131 1 0.19 7.22
## 39 LP001136 1 0.19 7.41
## 40 LP001137 1 0.19 7.60
## 41 LP001138 1 0.19 7.79
## 42 LP001144 1 0.19 7.98
## 43 LP001146 1 0.19 8.17
## 44 LP001151 1 0.19 8.36
## 45 LP001155 1 0.19 8.55
## 46 LP001157 1 0.19 8.74
## 47 LP001164 1 0.19 8.93
## 48 LP001179 1 0.19 9.12
## 49 LP001194 1 0.19 9.31
## 50 LP001195 1 0.19 9.50
## 51 LP001197 1 0.19 9.69
## 52 LP001198 1 0.19 9.88
## 53 LP001199 1 0.19 10.07
## 54 LP001205 1 0.19 10.26
## 55 LP001206 1 0.19 10.45
## 56 LP001207 1 0.19 10.64
## 57 LP001213 1 0.19 10.83
## 58 LP001222 1 0.19 11.02
## 59 LP001225 1 0.19 11.21
## 60 LP001228 1 0.19 11.40
## 61 LP001238 1 0.19 11.59
## 62 LP001241 1 0.19 11.78
## 63 LP001243 1 0.19 11.97
## 64 LP001245 1 0.19 12.16
## 65 LP001248 1 0.19 12.35
## 66 LP001250 1 0.19 12.54
## 67 LP001253 1 0.19 12.73
## 68 LP001255 1 0.19 12.92
## 69 LP001256 1 0.19 13.11
## 70 LP001259 1 0.19 13.30
## 71 LP001263 1 0.19 13.49
## 72 LP001264 1 0.19 13.68
## 73 LP001265 1 0.19 13.87
## 74 LP001266 1 0.19 14.06
## 75 LP001267 1 0.19 14.25
## 76 LP001275 1 0.19 14.44
## 77 LP001279 1 0.19 14.63
## 78 LP001280 1 0.19 14.82
## 79 LP001282 1 0.19 15.01
## 80 LP001289 1 0.19 15.20
## 81 LP001310 1 0.19 15.39
## 82 LP001316 1 0.19 15.58
## 83 LP001318 1 0.19 15.77
## 84 LP001319 1 0.19 15.96
## 85 LP001322 1 0.19 16.15
## 86 LP001325 1 0.19 16.34
## 87 LP001326 1 0.19 16.53
## 88 LP001327 1 0.19 16.72
## 89 LP001333 1 0.19 16.91
## 90 LP001334 1 0.19 17.10
## 91 LP001343 1 0.19 17.29
## 92 LP001345 1 0.19 17.48
## 93 LP001349 1 0.19 17.67
## 94 LP001356 1 0.19 17.86
## 95 LP001357 1 0.19 18.05
## 96 LP001367 1 0.19 18.24
## 97 LP001370 1 0.19 18.43
## 98 LP001379 1 0.19 18.62
## 99 LP001384 1 0.19 18.81
## 100 LP001385 1 0.19 19.00
## 101 LP001387 1 0.19 19.19
## 102 LP001391 1 0.19 19.38
## 103 LP001392 1 0.19 19.57
## 104 LP001398 1 0.19 19.76
## 105 LP001404 1 0.19 19.95
## 106 LP001405 1 0.19 20.14
## 107 LP001421 1 0.19 20.33
## 108 LP001426 1 0.19 20.52
## 109 LP001430 1 0.19 20.71
## 110 LP001432 1 0.19 20.90
## 111 LP001439 1 0.19 21.09
## 112 LP001443 1 0.19 21.28
## 113 LP001449 1 0.19 21.47
## 114 LP001465 1 0.19 21.66
## 115 LP001473 1 0.19 21.85
## 116 LP001478 1 0.19 22.04
## 117 LP001482 1 0.19 22.23
## 118 LP001487 1 0.19 22.42
## 119 LP001489 1 0.19 22.61
## 120 LP001491 1 0.19 22.80
## 121 LP001493 1 0.19 22.99
## 122 LP001497 1 0.19 23.18
## 123 LP001498 1 0.19 23.37
## 124 LP001504 1 0.19 23.56
## 125 LP001507 1 0.19 23.75
## 126 LP001514 1 0.19 23.94
## 127 LP001518 1 0.19 24.13
## 128 LP001519 1 0.19 24.32
## 129 LP001520 1 0.19 24.51
## 130 LP001528 1 0.19 24.70
## 131 LP001529 1 0.19 24.89
## 132 LP001531 1 0.19 25.08
## 133 LP001532 1 0.19 25.27
## 134 LP001535 1 0.19 25.46
## 135 LP001541 1 0.19 25.65
## 136 LP001543 1 0.19 25.84
## 137 LP001546 1 0.19 26.03
## 138 LP001552 1 0.19 26.22
## 139 LP001560 1 0.19 26.41
## 140 LP001565 1 0.19 26.60
## 141 LP001570 1 0.19 26.79
## 142 LP001572 1 0.19 26.98
## 143 LP001574 1 0.19 27.17
## 144 LP001577 1 0.19 27.36
## 145 LP001578 1 0.19 27.55
## 146 LP001579 1 0.19 27.74
## 147 LP001580 1 0.19 27.93
## 148 LP001581 1 0.19 28.12
## 149 LP001586 1 0.19 28.31
## 150 LP001594 1 0.19 28.50
## 151 LP001603 1 0.19 28.69
## 152 LP001606 1 0.19 28.88
## 153 LP001608 1 0.19 29.07
## 154 LP001616 1 0.19 29.26
## 155 LP001630 1 0.19 29.45
## 156 LP001634 1 0.19 29.64
## 157 LP001636 1 0.19 29.83
## 158 LP001639 1 0.19 30.02
## 159 LP001641 1 0.19 30.21
## 160 LP001643 1 0.19 30.40
## 161 LP001644 1 0.19 30.59
## 162 LP001647 1 0.19 30.78
## 163 LP001653 1 0.19 30.97
## 164 LP001657 1 0.19 31.16
## 165 LP001658 1 0.19 31.35
## 166 LP001664 1 0.19 31.54
## 167 LP001665 1 0.19 31.73
## 168 LP001666 1 0.19 31.92
## 169 LP001669 1 0.19 32.11
## 170 LP001671 1 0.19 32.30
## 171 LP001674 1 0.19 32.49
## 172 LP001677 1 0.19 32.68
## 173 LP001682 1 0.19 32.87
## 174 LP001688 1 0.19 33.06
## 175 LP001691 1 0.19 33.25
## 176 LP001692 1 0.19 33.44
## 177 LP001693 1 0.19 33.63
## 178 LP001698 1 0.19 33.82
## 179 LP001699 1 0.19 34.01
## 180 LP001702 1 0.19 34.20
## 181 LP001708 1 0.19 34.39
## 182 LP001711 1 0.19 34.58
## 183 LP001713 1 0.19 34.77
## 184 LP001715 1 0.19 34.96
## 185 LP001716 1 0.19 35.15
## 186 LP001720 1 0.19 35.34
## 187 LP001722 1 0.19 35.53
## 188 LP001726 1 0.19 35.72
## 189 LP001732 1 0.19 35.91
## 190 LP001734 1 0.19 36.10
## 191 LP001736 1 0.19 36.29
## 192 LP001743 1 0.19 36.48
## 193 LP001744 1 0.19 36.67
## 194 LP001749 1 0.19 36.86
## 195 LP001750 1 0.19 37.05
## 196 LP001751 1 0.19 37.24
## 197 LP001754 1 0.19 37.43
## 198 LP001758 1 0.19 37.62
## 199 LP001760 1 0.19 37.81
## 200 LP001761 1 0.19 38.00
## 201 LP001765 1 0.19 38.19
## 202 LP001768 1 0.19 38.38
## 203 LP001770 1 0.19 38.57
## 204 LP001778 1 0.19 38.76
## 205 LP001784 1 0.19 38.95
## 206 LP001786 1 0.19 39.14
## 207 LP001788 1 0.19 39.33
## 208 LP001790 1 0.19 39.52
## 209 LP001792 1 0.19 39.71
## 210 LP001798 1 0.19 39.90
## 211 LP001800 1 0.19 40.09
## 212 LP001806 1 0.19 40.28
## 213 LP001807 1 0.19 40.47
## 214 LP001811 1 0.19 40.66
## 215 LP001813 1 0.19 40.85
## 216 LP001814 1 0.19 41.04
## 217 LP001819 1 0.19 41.23
## 218 LP001824 1 0.19 41.42
## 219 LP001825 1 0.19 41.61
## 220 LP001835 1 0.19 41.80
## 221 LP001836 1 0.19 41.99
## 222 LP001841 1 0.19 42.18
## 223 LP001846 1 0.19 42.37
## 224 LP001849 1 0.19 42.56
## 225 LP001854 1 0.19 42.75
## 226 LP001864 1 0.19 42.94
## 227 LP001868 1 0.19 43.13
## 228 LP001870 1 0.19 43.32
## 229 LP001871 1 0.19 43.51
## 230 LP001872 1 0.19 43.70
## 231 LP001875 1 0.19 43.89
## 232 LP001877 1 0.19 44.08
## 233 LP001882 1 0.19 44.27
## 234 LP001883 1 0.19 44.46
## 235 LP001884 1 0.19 44.65
## 236 LP001888 1 0.19 44.84
## 237 LP001892 1 0.19 45.03
## 238 LP001894 1 0.19 45.22
## 239 LP001896 1 0.19 45.41
## 240 LP001900 1 0.19 45.60
## 241 LP001903 1 0.19 45.79
## 242 LP001904 1 0.19 45.98
## 243 LP001908 1 0.19 46.17
## 244 LP001910 1 0.19 46.36
## 245 LP001914 1 0.19 46.55
## 246 LP001915 1 0.19 46.74
## 247 LP001917 1 0.19 46.93
## 248 LP001924 1 0.19 47.12
## 249 LP001925 1 0.19 47.31
## 250 LP001926 1 0.19 47.50
## 251 LP001931 1 0.19 47.69
## 252 LP001935 1 0.19 47.88
## 253 LP001936 1 0.19 48.07
## 254 LP001938 1 0.19 48.26
## 255 LP001940 1 0.19 48.45
## 256 LP001945 1 0.19 48.64
## 257 LP001947 1 0.19 48.83
## 258 LP001949 1 0.19 49.02
## 259 LP001953 1 0.19 49.21
## 260 LP001954 1 0.19 49.40
## 261 LP001955 1 0.19 49.59
## 262 LP001963 1 0.19 49.78
## 263 LP001964 1 0.19 49.97
## 264 LP001972 1 0.19 50.16
## 265 LP001974 1 0.19 50.35
## 266 LP001977 1 0.19 50.54
## 267 LP001978 1 0.19 50.73
## 268 LP001990 1 0.19 50.92
## 269 LP001993 1 0.19 51.11
## 270 LP001994 1 0.19 51.30
## 271 LP001998 1 0.19 51.49
## 272 LP002002 1 0.19 51.68
## 273 LP002004 1 0.19 51.87
## 274 LP002006 1 0.19 52.06
## 275 LP002008 1 0.19 52.25
## 276 LP002024 1 0.19 52.44
## 277 LP002031 1 0.19 52.63
## 278 LP002035 1 0.19 52.82
## 279 LP002036 1 0.19 53.01
## 280 LP002043 1 0.19 53.20
## 281 LP002050 1 0.19 53.39
## 282 LP002051 1 0.19 53.58
## 283 LP002053 1 0.19 53.77
## 284 LP002054 1 0.19 53.96
## 285 LP002055 1 0.19 54.15
## 286 LP002068 1 0.19 54.34
## 287 LP002082 1 0.19 54.53
## 288 LP002086 1 0.19 54.72
## 289 LP002087 1 0.19 54.91
## 290 LP002097 1 0.19 55.10
## 291 LP002098 1 0.19 55.29
## 292 LP002100 1 0.19 55.48
## 293 LP002103 1 0.19 55.67
## 294 LP002106 1 0.19 55.86
## 295 LP002110 1 0.19 56.05
## 296 LP002112 1 0.19 56.24
## 297 LP002113 1 0.19 56.43
## 298 LP002114 1 0.19 56.62
## 299 LP002115 1 0.19 56.81
## 300 LP002116 1 0.19 57.00
## 301 LP002119 1 0.19 57.19
## 302 LP002126 1 0.19 57.38
## 303 LP002128 1 0.19 57.57
## 304 LP002129 1 0.19 57.76
## 305 LP002130 1 0.19 57.95
## 306 LP002131 1 0.19 58.14
## 307 LP002137 1 0.19 58.33
## 308 LP002139 1 0.19 58.52
## 309 LP002141 1 0.19 58.71
## 310 LP002142 1 0.19 58.90
## 311 LP002143 1 0.19 59.09
## 312 LP002144 1 0.19 59.28
## 313 LP002149 1 0.19 59.47
## 314 LP002151 1 0.19 59.66
## 315 LP002158 1 0.19 59.85
## 316 LP002160 1 0.19 60.04
## 317 LP002161 1 0.19 60.23
## 318 LP002170 1 0.19 60.42
## 319 LP002175 1 0.19 60.61
## 320 LP002178 1 0.19 60.80
## 321 LP002180 1 0.19 60.99
## 322 LP002181 1 0.19 61.18
## 323 LP002187 1 0.19 61.37
## 324 LP002188 1 0.19 61.56
## 325 LP002190 1 0.19 61.75
## 326 LP002197 1 0.19 61.94
## 327 LP002205 1 0.19 62.13
## 328 LP002209 1 0.19 62.32
## 329 LP002211 1 0.19 62.51
## 330 LP002219 1 0.19 62.70
## 331 LP002223 1 0.19 62.89
## 332 LP002224 1 0.19 63.08
## 333 LP002225 1 0.19 63.27
## 334 LP002226 1 0.19 63.46
## 335 LP002231 1 0.19 63.65
## 336 LP002234 1 0.19 63.84
## 337 LP002236 1 0.19 64.03
## 338 LP002237 1 0.19 64.22
## 339 LP002239 1 0.19 64.41
## 340 LP002243 1 0.19 64.60
## 341 LP002244 1 0.19 64.79
## 342 LP002250 1 0.19 64.98
## 343 LP002255 1 0.19 65.17
## 344 LP002263 1 0.19 65.36
## 345 LP002265 1 0.19 65.55
## 346 LP002266 1 0.19 65.74
## 347 LP002272 1 0.19 65.93
## 348 LP002277 1 0.19 66.12
## 349 LP002281 1 0.19 66.31
## 350 LP002284 1 0.19 66.50
## 351 LP002287 1 0.19 66.69
## 352 LP002288 1 0.19 66.88
## 353 LP002296 1 0.19 67.07
## 354 LP002300 1 0.19 67.26
## 355 LP002301 1 0.19 67.45
## 356 LP002305 1 0.19 67.64
## 357 LP002308 1 0.19 67.83
## 358 LP002314 1 0.19 68.02
## 359 LP002315 1 0.19 68.21
## 360 LP002318 1 0.19 68.40
## 361 LP002319 1 0.19 68.59
## 362 LP002328 1 0.19 68.78
## 363 LP002332 1 0.19 68.97
## 364 LP002335 1 0.19 69.16
## 365 LP002337 1 0.19 69.35
## 366 LP002341 1 0.19 69.54
## 367 LP002345 1 0.19 69.73
## 368 LP002347 1 0.19 69.92
## 369 LP002348 1 0.19 70.11
## 370 LP002357 1 0.19 70.30
## 371 LP002361 1 0.19 70.49
## 372 LP002362 1 0.19 70.68
## 373 LP002366 1 0.19 70.87
## 374 LP002367 1 0.19 71.06
## 375 LP002368 1 0.19 71.25
## 376 LP002369 1 0.19 71.44
## 377 LP002370 1 0.19 71.63
## 378 LP002377 1 0.19 71.82
## 379 LP002379 1 0.19 72.01
## 380 LP002387 1 0.19 72.20
## 381 LP002390 1 0.19 72.39
## 382 LP002393 1 0.19 72.58
## 383 LP002398 1 0.19 72.77
## 384 LP002401 1 0.19 72.96
## 385 LP002407 1 0.19 73.15
## 386 LP002408 1 0.19 73.34
## 387 LP002409 1 0.19 73.53
## 388 LP002418 1 0.19 73.72
## 389 LP002429 1 0.19 73.91
## 390 LP002434 1 0.19 74.10
## 391 LP002435 1 0.19 74.29
## 392 LP002443 1 0.19 74.48
## 393 LP002444 1 0.19 74.67
## 394 LP002446 1 0.19 74.86
## 395 LP002447 1 0.19 75.05
## 396 LP002448 1 0.19 75.24
## 397 LP002449 1 0.19 75.43
## 398 LP002453 1 0.19 75.62
## 399 LP002455 1 0.19 75.81
## 400 LP002459 1 0.19 76.00
## 401 LP002467 1 0.19 76.19
## 402 LP002472 1 0.19 76.38
## 403 LP002473 1 0.19 76.57
## 404 LP002478 1 0.19 76.76
## 405 LP002484 1 0.19 76.95
## 406 LP002487 1 0.19 77.14
## 407 LP002489 1 0.19 77.33
## 408 LP002493 1 0.19 77.52
## 409 LP002494 1 0.19 77.71
## 410 LP002500 1 0.19 77.90
## 411 LP002502 1 0.19 78.09
## 412 LP002505 1 0.19 78.28
## 413 LP002515 1 0.19 78.47
## 414 LP002517 1 0.19 78.66
## 415 LP002519 1 0.19 78.85
## 416 LP002522 1 0.19 79.04
## 417 LP002524 1 0.19 79.23
## 418 LP002529 1 0.19 79.42
## 419 LP002530 1 0.19 79.61
## 420 LP002533 1 0.19 79.80
## 421 LP002534 1 0.19 79.99
## 422 LP002536 1 0.19 80.18
## 423 LP002537 1 0.19 80.37
## 424 LP002543 1 0.19 80.56
## 425 LP002544 1 0.19 80.75
## 426 LP002545 1 0.19 80.94
## 427 LP002555 1 0.19 81.13
## 428 LP002556 1 0.19 81.32
## 429 LP002560 1 0.19 81.51
## 430 LP002562 1 0.19 81.70
## 431 LP002571 1 0.19 81.89
## 432 LP002585 1 0.19 82.08
## 433 LP002586 1 0.19 82.27
## 434 LP002587 1 0.19 82.46
## 435 LP002588 1 0.19 82.65
## 436 LP002600 1 0.19 82.84
## 437 LP002602 1 0.19 83.03
## 438 LP002603 1 0.19 83.22
## 439 LP002606 1 0.19 83.41
## 440 LP002615 1 0.19 83.60
## 441 LP002618 1 0.19 83.79
## 442 LP002619 1 0.19 83.98
## 443 LP002622 1 0.19 84.17
## 444 LP002625 1 0.19 84.36
## 445 LP002626 1 0.19 84.55
## 446 LP002637 1 0.19 84.74
## 447 LP002640 1 0.19 84.93
## 448 LP002643 1 0.19 85.12
## 449 LP002659 1 0.19 85.31
## 450 LP002670 1 0.19 85.50
## 451 LP002682 1 0.19 85.69
## 452 LP002683 1 0.19 85.88
## 453 LP002684 1 0.19 86.07
## 454 LP002689 1 0.19 86.26
## 455 LP002690 1 0.19 86.45
## 456 LP002692 1 0.19 86.64
## 457 LP002697 1 0.19 86.83
## 458 LP002705 1 0.19 87.02
## 459 LP002706 1 0.19 87.21
## 460 LP002714 1 0.19 87.40
## 461 LP002716 1 0.19 87.59
## 462 LP002717 1 0.19 87.78
## 463 LP002720 1 0.19 87.97
## 464 LP002723 1 0.19 88.16
## 465 LP002732 1 0.19 88.35
## 466 LP002738 1 0.19 88.54
## 467 LP002739 1 0.19 88.73
## 468 LP002740 1 0.19 88.92
## 469 LP002741 1 0.19 89.11
## 470 LP002743 1 0.19 89.30
## 471 LP002753 1 0.19 89.49
## 472 LP002755 1 0.19 89.68
## 473 LP002757 1 0.19 89.87
## 474 LP002767 1 0.19 90.06
## 475 LP002768 1 0.19 90.25
## 476 LP002772 1 0.19 90.44
## 477 LP002776 1 0.19 90.63
## 478 LP002777 1 0.19 90.82
## 479 LP002778 1 0.19 91.01
## 480 LP002784 1 0.19 91.20
## 481 LP002785 1 0.19 91.39
## 482 LP002788 1 0.19 91.58
## 483 LP002789 1 0.19 91.77
## 484 LP002792 1 0.19 91.96
## 485 LP002794 1 0.19 92.15
## 486 LP002795 1 0.19 92.34
## 487 LP002798 1 0.19 92.53
## 488 LP002804 1 0.19 92.72
## 489 LP002807 1 0.19 92.91
## 490 LP002820 1 0.19 93.10
## 491 LP002821 1 0.19 93.29
## 492 LP002832 1 0.19 93.48
## 493 LP002833 1 0.19 93.67
## 494 LP002836 1 0.19 93.86
## 495 LP002837 1 0.19 94.05
## 496 LP002840 1 0.19 94.24
## 497 LP002841 1 0.19 94.43
## 498 LP002842 1 0.19 94.62
## 499 LP002847 1 0.19 94.81
## 500 LP002862 1 0.19 95.00
## 501 LP002863 1 0.19 95.19
## 502 LP002868 1 0.19 95.38
## 503 LP002872 1 0.19 95.57
## 504 LP002874 1 0.19 95.76
## 505 LP002877 1 0.19 95.95
## 506 LP002888 1 0.19 96.14
## 507 LP002892 1 0.19 96.33
## 508 LP002894 1 0.19 96.52
## 509 LP002898 1 0.19 96.71
## 510 LP002911 1 0.19 96.90
## 511 LP002912 1 0.19 97.09
## 512 LP002916 1 0.19 97.28
## 513 LP002917 1 0.19 97.47
## 514 LP002925 1 0.19 97.66
## 515 LP002926 1 0.19 97.85
## 516 LP002928 1 0.19 98.04
## 517 LP002931 1 0.19 98.23
## 518 LP002936 1 0.19 98.42
## 519 LP002940 1 0.19 98.61
## 520 LP002941 1 0.19 98.80
## 521 LP002943 1 0.19 98.99
## 522 LP002945 1 0.19 99.18
## 523 LP002948 1 0.19 99.37
## 524 LP002950 1 0.19 99.56
## 525 LP002953 1 0.19 99.75
## 526 LP002958 1 0.19 99.94
## 527 LP002960 1 0.19 100.13
## 528 LP002961 1 0.19 100.32
## 529 LP002964 1 0.19 100.51
## 530 LP002974 1 0.19 100.70
## 531 LP002978 1 0.19 100.89
## 532 LP002979 1 0.19 101.08
## 533 LP002983 1 0.19 101.27
## 534 LP002984 1 0.19 101.46
## 535 LP002990 1 0.19 100.00
## Gender frequency percentage cumulative_perc
## 1 Male 435 81.31 81.31
## 2 Female 100 18.69 100.00
## Married frequency percentage cumulative_perc
## 1 Yes 348 65.05 65.05
## 2 No 187 34.95 100.00
## Dependents frequency percentage cumulative_perc
## 1 0 321 60.00 60.00
## 2 2 90 16.82 76.82
## 3 1 84 15.70 92.52
## 4 3+ 40 7.48 100.00
## Education frequency percentage cumulative_perc
## 1 Graduate 404 75.51 75.51
## 2 Not Graduate 131 24.49 100.00
## Self_Employed frequency percentage cumulative_perc
## 1 No 474 88.6 88.6
## 2 Yes 61 11.4 100.0
## Loan_Amount_Term frequency percentage cumulative_perc
## 1 360 459 85.79 85.79
## 2 180 36 6.73 92.52
## 3 480 14 2.62 95.14
## 4 300 10 1.87 97.01
## 5 240 4 0.75 97.76
## 6 84 4 0.75 98.51
## 7 120 3 0.56 99.07
## 8 36 2 0.37 99.44
## 9 60 2 0.37 99.81
## 10 12 1 0.19 100.00
## Credit_History frequency percentage cumulative_perc
## 1 1 457 85.42 85.42
## 2 0 78 14.58 100.00
## Property_Area frequency percentage cumulative_perc
## 1 Semiurban 205 38.32 38.32
## 2 Urban 170 31.78 70.10
## 3 Rural 160 29.91 100.00
## Loan_Status frequency percentage cumulative_perc
## 1 Y 372 69.53 69.53
## 2 N 163 30.47 100.00
## data
##
## 14 Variables 535 Observations
## --------------------------------------------------------------------------------
## Loan_ID
## n missing distinct
## 535 0 535
##
## lowest : LP001002 LP001003 LP001005 LP001006 LP001008
## highest: LP002978 LP002979 LP002983 LP002984 LP002990
## --------------------------------------------------------------------------------
## Gender
## n missing distinct
## 535 0 2
##
## Value Female Male
## Frequency 100 435
## Proportion 0.187 0.813
## --------------------------------------------------------------------------------
## Married
## n missing distinct
## 535 0 2
##
## Value No Yes
## Frequency 187 348
## Proportion 0.35 0.65
## --------------------------------------------------------------------------------
## Dependents
## n missing distinct
## 535 0 4
##
## Value 0 1 2 3+
## Frequency 321 84 90 40
## Proportion 0.600 0.157 0.168 0.075
## --------------------------------------------------------------------------------
## Education
## n missing distinct
## 535 0 2
##
## Value Graduate Not Graduate
## Frequency 404 131
## Proportion 0.755 0.245
## --------------------------------------------------------------------------------
## Self_Employed
## n missing distinct
## 535 0 2
##
## Value No Yes
## Frequency 474 61
## Proportion 0.886 0.114
## --------------------------------------------------------------------------------
## ApplicantIncome
## n missing distinct Info Mean Gmd .05 .10
## 535 0 438 1 4054 1986 1878 2200
## .25 .50 .75 .90 .95
## 2752 3598 4891 6467 8022
##
## lowest : 150 210 645 674 1000, highest: 9833 9963 10000 10047 10139
## --------------------------------------------------------------------------------
## CoapplicantIncome
## n missing distinct Info Mean Gmd .05 .10
## 535 0 259 0.922 1323 1528 0 0
## .25 .50 .75 .90 .95
## 0 1260 2194 3258 4130
##
## lowest : 0.00 16.12 189.00 240.00 242.00
## highest: 5500.00 5624.00 5625.00 5654.00 5701.00
## --------------------------------------------------------------------------------
## LoanAmount
## n missing distinct Info Mean Gmd .05 .10
## 535 0 161 1 127 49.51 55.7 71.0
## .25 .50 .75 .90 .95
## 100.0 124.0 151.5 185.6 205.6
##
## lowest : 9 17 25 26 30, highest: 253 255 258 259 260
## --------------------------------------------------------------------------------
## Loan_Amount_Term
## n missing distinct
## 535 0 10
##
## lowest : 12 120 180 240 300, highest: 36 360 480 60 84
##
## Value 12 120 180 240 300 36 360 480 60 84
## Frequency 1 3 36 4 10 2 459 14 2 4
## Proportion 0.002 0.006 0.067 0.007 0.019 0.004 0.858 0.026 0.004 0.007
## --------------------------------------------------------------------------------
## Credit_History
## n missing distinct
## 535 0 2
##
## Value 0 1
## Frequency 78 457
## Proportion 0.146 0.854
## --------------------------------------------------------------------------------
## Property_Area
## n missing distinct
## 535 0 3
##
## Value Rural Semiurban Urban
## Frequency 160 205 170
## Proportion 0.299 0.383 0.318
## --------------------------------------------------------------------------------
## Loan_Status
## n missing distinct
## 535 0 2
##
## Value N Y
## Frequency 163 372
## Proportion 0.305 0.695
## --------------------------------------------------------------------------------
## APPCO_TotalIncome
## n missing distinct Info Mean Gmd .05 .10
## 535 0 480 1 5377 2203 2718 3139
## .25 .50 .75 .90 .95
## 3900 5000 6411 8041 9517
##
## lowest : 1442 1830 1880 1950 1963, highest: 11500 11666 11904 12083 13746
## --------------------------------------------------------------------------------5 Tugas 4
Lakukan pemeriksaan distribusi densitas menggunakan R dan Python pada setiap variabel kuantitatif dengan beberapa bagian sebagai berikut:
5.1 Univariat numerik
library(ks)
library(MASS)
fhat <- kde(x=data_num_new[,3])
plot_1 <- plot(fhat, cont=50, col.cont=4, cont.lwd=2, xlab="Loan Amount", drawpoints=TRUE)
fhat1 <- kde(x=data_num_new[,2])
plot_2 <- plot(fhat1, cont=50, col.cont=4, cont.lwd=2, xlab="Coapplicant Income" , drawpoints=TRUE)
fhat2 <- kde(x=data_num_new[,4])
plot_3 <- plot(fhat2, cont=50, col.cont=4, cont.lwd=2, xlab="AppCoapplicant Income", drawpoints=TRUE)
fhat3 <- kde(x=data_num_new[,1])
plot_4 <- plot(fhat3, cont=50, col.cont=4, cont.lwd=2, xlab="Applicant Income", drawpoints=TRUE)
6 Tugas 5
Lakukan proses pengujian Hipotesis menggunakan R dan Python pada setiap variabel kuantitatif dengan beberapa bagian sebagai berikut:
6.1 Hitunglah margin of error dan estimasi interval untuk proporsi peminjam bejenis kelamin perempuan dalam pada tingkat kepercayaan 95%.
k = sum(dataakhir$Gender == "Female") # the sum of female responses
n = sum(count(dataakhir)) # total data
pbar = k/n # female student proportion in survey
SE = sqrt(pbar*(1-pbar)/n); SE # standard error ## [1] 0.01685443## [1] 0.03303407## [1] 0.1538818 0.21995006.2 Jika anda berencana menggunakan perkiraan proporsi 50% data konsumen berjenis kelamin perempuan, temukan ukuran sampel yang diperlukan untuk mencapai margin kesalahan 5% untuk data obeservasi pada tingkat kepercayaan 95%.
zstar = qnorm(.975) # quantiles (95% confidence level)
p = 0.5 # 50% planned proportion estimate
E = 0.05 # expected error
zstar^2*p*(1-p)/E^2 # sampling size## [1] 384.14596.3 Lakukan pembuktian kebenaran assumsi dengan tingakat signifikansi 0.05, jika Bank mengklaim bahwa pinjaman rata-rata konsumen adalah:
6.3.1 Lebih besar $ 150.
\[H_0 = \mu \leq \$150 \\ \] \[H_1 = \mu > \$150 \]
mu0 = 150 # hypothesized value
xbar = mean(DataSample$LoanAmount) # sample mean
s = sd(DataSample$LoanAmount) # sample standard deviation
n = sum(count(DataSample)) # sample size
t = (xbar-mu0)/(s/sqrt(n));t # test statistic ## [1] -2.520249alpha = .05 # use 0.05 left tail significant level
t.alpha = qt(1-alpha, df=n-1) # right tail critical value
t.alpha## [1] 1.699127Karena \(t_{hitung}<t_{table}\), maka terima \(H_0\). Sehingga dapat kita simpulkan bahwa rata-rata pinjaman konsumen lebih kecil sama dengan $150 dengan tingkat kepercayaan 95%.
6.3.2 Lebih kecil $150
\[H_0 = \mu \geq \$150 \\ \] \[H_1 = \mu < \$150 \]
mu0 = 150 # hypothesized value
xbar = mean(DataSample$LoanAmount) # sample mean
s = sd(DataSample$LoanAmount) # sample standard deviation
n = sum(count(DataSample)) # sample size
t = (xbar-mu0)/(s/sqrt(n));t # test statistic ## [1] -2.520249alpha = .05 # use 0.05 left tail significant level
t.alpha = qt(1-alpha, df=n-1) # right tail critical value
-t.alpha # left tail critical value ## [1] -1.699127Karena \(t_{hitung}<t_{table}\), maka tolak \(H_0\). Sehingga dapat kita simpulkan bahwa rata-rata pinjaman konsumen lebih kecil sama dengan $150 dengan tingkat kepercayaan 95%.
6.3.3 Sama dengan $ 150.
\[H_0 = \mu = \$150 \\ \] \[H_1 = \mu \neq \$150 \]
mu0 = 150 # hypothesized value
xbar = mean(DataSample$LoanAmount) # sample mean
s = sd(DataSample$LoanAmount) # sample standard deviation
n = sum(count(DataSample)) # sample size
t = (xbar-mu0)/(s/sqrt(n));t # test statistic ## [1] -2.520249alpha = .05 # use 0.05 left tail significant level
t.alpha = qt(1-alpha, df=n-1) # right tail critical value
t.alpha ## [1] 1.699127## [1] -1.699127Karena \(t_{hitung}\) berada diluar \(interval~t_{table}\), maka tolak \(H_0\). Sehingga dapat kita simpulkan bahwa rata-rata pinjaman konsumen tidak sama dengan $150 dengan tingkat kepercayaan 95%.
6.4 Lakukan pembuktian kebenaran assumsi dengan tingakat signifikansi 0.05, seperti diatas jika diketahui simpangan baku pinjaman adalah $ 85.
6.4.1 Lebih besar $ 150.
\[H_0 = \mu \leq \$150 \\ \] \[H_1 = \mu > \$150 \]
mu0 = 150 # hypothesized value
xbar = mean(DataSample$LoanAmount) # sample mean
s = 85 # sample standard deviation
n = sum(count(DataSample)) # sample size
t = (xbar-mu0)/(s/sqrt(n));t # test statistic ## [1] -1.546511alpha = .05 # use 0.05 left tail significant level
t.alpha = qt(1-alpha, df=n-1) # right tail critical value
t.alpha## [1] 1.699127Karena \(t_{hitung}<t_{table}\), maka terima \(H_0\). Sehingga dapat kita simpulkan bahwa rata-rata pinjaman konsumen lebih besar sama dengan $150 dengan tingkat kepercayaan 95%.
6.4.2 Lebih kecil $150
\[H_0 = \mu \geq \$150 \\ \] \[H_1 = \mu < \$150 \]
mu0 = 150 # hypothesized value
xbar = mean(DataSample$LoanAmount) # sample mean
s = 85 # sample standard deviation
n = sum(count(DataSample)) # sample size
t = (xbar-mu0)/(s/sqrt(n));t # test statistic ## [1] -1.546511alpha = .05 # use 0.05 left tail significant level
t.alpha = qt(1-alpha, df=n-1) # right tail critical value
-t.alpha # left tail critical value ## [1] -1.699127Karena \(t_{hitung} < t_{tabel}\), maka terima \(H_0\). Sehingga dapat kita simpulkan bahwa rata-rata pinjaman konsumen lebih kecil sama dengan $150 dengan tingkat kepercayaan 95%.
6.4.3 Sama dengan $ 150.
\[H_0 = \mu = \$150 \\ \] \[H_1 = \mu \neq \$150 \]
mu0 = 150 # hypothesized value
xbar = mean(DataSample$LoanAmount) # sample mean
s = 85 # sample standard deviation
n = sum(count(DataSample)) # sample size
t = (xbar-mu0)/(s/sqrt(n));t # test statistic ## [1] -1.546511alpha = .05 # use 0.05 left tail significant level
t.alpha = qt(1-alpha, df=n-1) # right tail critical value
t.alpha ## [1] 1.699127## [1] -1.699127Karena \(-t_{tabel} < t_{hitung} < t_{tabel}\) , maka terima \(H_0\). Sehingga dapat kita simpulkan bahwa rata-rata pinjaman konsumen sama dengan $150 dengan tingkat kepercayaan 95%.
7 Jawaban Python
8 Referensi
- https://bookdown.org/BaktiSiregar/data-science-for-beginners-part-2
- https://bookdown.org/BaktiSiregar/data-science-for-beginners/
- https://seaborn.pydata.org/generated/seaborn.jointplot.html
- https://www.python-graph-gallery.com/
- https://stackoverflow.com/
- https://rpubs.com/dsciencelabs/ks3
- https://rpubs.com/dsciencelabs/ks4
- https://rdrr.io/cran/ks/man/plot.kde.html