KOMPUTASI STATISTIKA
~ Ujian Tengah Semester ~
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Data Set
Tugas 1
Lakukan proses persiapan data dengan R dan Python, dengan beberapa langkah berikut:
Import Data
loan_train <- read.csv("loan-train.csv", stringsAsFactors = T, na.strings=c("","","NA"))Penanganan Data Hilang
Kita cek tipe data dan nilai NA dari data :
summary(loan_train)## ï..Loan_ID Gender Married Dependents Education
## LP001002: 1 Female:112 No :213 0 :345 Graduate :480
## LP001003: 1 Male :489 Yes :398 1 :102 Not Graduate:134
## LP001005: 1 NA's : 13 NA's: 3 2 :101
## LP001006: 1 3+ : 51
## LP001008: 1 NA's: 15
## LP001011: 1
## (Other) :608
## Self_Employed ApplicantIncome CoapplicantIncome LoanAmount
## No :500 Min. : 150 Min. : 0 Min. : 9.0
## Yes : 82 1st Qu.: 2878 1st Qu.: 0 1st Qu.:100.0
## NA's: 32 Median : 3812 Median : 1188 Median :128.0
## Mean : 5403 Mean : 1621 Mean :146.4
## 3rd Qu.: 5795 3rd Qu.: 2297 3rd Qu.:168.0
## Max. :81000 Max. :41667 Max. :700.0
## NA's :22
## Loan_Amount_Term Credit_History Property_Area Loan_Status
## Min. : 12 Min. :0.0000 Rural :179 N:192
## 1st Qu.:360 1st Qu.:1.0000 Semiurban:233 Y:422
## Median :360 Median :1.0000 Urban :202
## Mean :342 Mean :0.8422
## 3rd Qu.:360 3rd Qu.:1.0000
## Max. :480 Max. :1.0000
## NA's :14 NA's :50glimpse(loan_train)## Rows: 614
## Columns: 13
## $ ï..Loan_ID <fct> LP001002, LP001003, LP001005, LP001006, LP001008, LP~
## $ Gender <fct> Male, Male, Male, Male, Male, Male, Male, Male, Male~
## $ Married <fct> No, Yes, Yes, Yes, No, Yes, Yes, Yes, Yes, Yes, Yes,~
## $ Dependents <fct> 0, 1, 0, 0, 0, 2, 0, 3+, 2, 1, 2, 2, 2, 0, 2, 0, 1, ~
## $ Education <fct> Graduate, Graduate, Graduate, Not Graduate, Graduate~
## $ Self_Employed <fct> No, No, Yes, No, No, Yes, No, No, No, No, No, NA, No~
## $ ApplicantIncome <int> 5849, 4583, 3000, 2583, 6000, 5417, 2333, 3036, 4006~
## $ CoapplicantIncome <dbl> 0, 1508, 0, 2358, 0, 4196, 1516, 2504, 1526, 10968, ~
## $ LoanAmount <int> NA, 128, 66, 120, 141, 267, 95, 158, 168, 349, 70, 1~
## $ Loan_Amount_Term <int> 360, 360, 360, 360, 360, 360, 360, 360, 360, 360, 36~
## $ Credit_History <int> 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, NA, ~
## $ Property_Area <fct> Urban, Rural, Urban, Urban, Urban, Urban, Urban, Sem~
## $ Loan_Status <fct> Y, N, Y, Y, Y, Y, Y, N, Y, N, Y, Y, Y, N, Y, Y, Y, N~anyNA(loan_train)## [1] TRUEcolSums(is.na(loan_train))## ï..Loan_ID Gender Married Dependents
## 0 13 3 15
## Education Self_Employed ApplicantIncome CoapplicantIncome
## 0 32 0 0
## LoanAmount Loan_Amount_Term Credit_History Property_Area
## 22 14 50 0
## Loan_Status
## 0Ada dua tipe data yang perlu diubah:
- Loan_Amount_Term : Ubah sebagai tipe data faktor
- Credit_History : Ubah sebagai tipe data faktor
names(loan_train)[1] <- "Loan_ID"
loan.train <- loan_train %>%
dplyr::select(-Loan_ID) %>%
mutate(Loan_Amount_Term = as.factor(Loan_Amount_Term),
Credit_History = as.factor(Credit_History))
head(loan.train)Ada juga nilai NA berdasarkan pemeriksaan awal pada:
- LoanAmount
- Loan_Amount_Term
- Credit_History
Fungsi untuk data cleansing :
Mode = function(x){
a = table(x)
b = max(a)
if(all(a == b))
mod = NA
else if(is.numeric(x))
mod = as.numeric(names(a))[a==b]
else
mod = names(a)[a==b]
return(mod)
}Untuk membuat hasil keseluruhan yang lebih baik, kita akan mencoba mengganti nilai yang hilang/ NA berdasarkan tipenya:
- Data dengan nilai tipe Numerik yang hilang akan diganti dengan nilai rata-ratanya (menggunakan fungsi mean()).
- Nilai data dengan tipe data faktor akan diganti dengan nilai yang memiliki jumlah kemunculan tertinggi dalam kumpulan datanya (menggunakan fungsi mode()).
loan.train$Gender[is.na(loan.train$Gender)] <- Mode(loan.train$Gender)
loan.train$Married[is.na(loan.train$Married)] <- Mode(loan.train$Married)
loan.train$Dependents[is.na(loan.train$Dependents)] <- Mode(loan.train$Dependents)
loan.train$Credit_History[is.na(loan.train$Credit_History)] <- Mode(loan.train$Credit_History)loan.train$LoanAmount[is.na(loan.train$LoanAmount)] <- mean(loan.train$LoanAmount, na.rm = T)
loan.train$Loan_Amount_Term[is.na(loan.train$Loan_Amount_Term)] <- mean(loan.train$Loan_Amount_Term, na.rm = T)
summary(loan.train)## Gender Married Dependents Education Self_Employed
## Female:112 No :213 0 :360 Graduate :480 No :500
## Male :502 Yes:401 1 :102 Not Graduate:134 Yes : 82
## 2 :101 NA's: 32
## 3+: 51
##
##
##
## ApplicantIncome CoapplicantIncome LoanAmount Loan_Amount_Term
## Min. : 150 Min. : 0 Min. : 9.0 360 :512
## 1st Qu.: 2878 1st Qu.: 0 1st Qu.:100.2 180 : 44
## Median : 3812 Median : 1188 Median :129.0 480 : 15
## Mean : 5403 Mean : 1621 Mean :146.4 300 : 13
## 3rd Qu.: 5795 3rd Qu.: 2297 3rd Qu.:164.8 84 : 4
## Max. :81000 Max. :41667 Max. :700.0 (Other): 12
## NA's : 14
## Credit_History Property_Area Loan_Status
## 0: 89 Rural :179 N:192
## 1:525 Semiurban:233 Y:422
## Urban :202
##
##
##
## na.omit(loan.train)Periksa Data Duplikat
sum(duplicated(loan.train))## [1] 0Tidak ada data duplikat
Pemisahan Data Kategori dan Numerik
Kategori
Cat_data <- loan.train%>% dplyr::select_if(is.factor)
names(Cat_data)## [1] "Gender" "Married" "Dependents" "Education"
## [5] "Self_Employed" "Loan_Amount_Term" "Credit_History" "Property_Area"
## [9] "Loan_Status"Numerik
Num_data <- loan.train%>% dplyr::select_if(is.numeric)
names(Num_data)## [1] "ApplicantIncome" "CoapplicantIncome" "LoanAmount"Penanganan Data Numerik
Penganann Data Pencilan
Penanganan Data Kategorikal
Tugas 2
Lakukan Proses Visualisasi Data dengan menggunakan R dan Python dengan beberapa langkah berikut:
Visualisasi Univariabel
Categorical
Gender
plotdata <- loan.train %>%
count(Gender) %>%
mutate(pct = n / sum(n),
pctlabel = paste0(round(pct*100), "%"))
ggplot(plotdata,
aes(x = reorder(Gender, -pct),
y = pct)) +
geom_bar(stat = "identity",
color = "azure4") +
geom_text(aes(label = pctlabel),
vjust = -0.25) +
theme_minimal() + # use a minimal theme
scale_y_continuous(labels = percent) +
labs(x = "Gender",
y = "Percent",
title = "Loan by gender")
Married
plotdata <- loan.train %>%
count(Married) %>%
mutate(pct = n / sum(n),
pctlabel = paste0(round(pct*100), "%"))
ggplot(plotdata,
aes(x = reorder(Married, -pct),
y = pct)) +
geom_bar(stat = "identity",
color = "azure4") +
geom_text(aes(label = pctlabel),
vjust = -0.25) +
theme_minimal() + # use a minimal theme
scale_y_continuous(labels = percent) +
labs(x = "Married",
y = "Percent",
title = "Loan by married")
Dependents
plotdata <- loan.train %>%
count(Dependents) %>%
mutate(pct = n / sum(n),
pctlabel = paste0(round(pct*100), "%"))
ggplot(plotdata,
aes(x = reorder(Dependents, -pct),
y = pct)) +
geom_bar(stat = "identity",
color = "azure4") +
geom_text(aes(label = pctlabel),
vjust = -0.25) +
theme_minimal() + # use a minimal theme
scale_y_continuous(labels = percent) +
labs(x = "Dependents",
y = "Percent",
title = "Loan by Dependents")
Education
plotdata <- loan.train %>%
count(Education) %>%
mutate(pct = n / sum(n),
pctlabel = paste0(round(pct*100), "%"))
ggplot(plotdata,
aes(x = reorder(Education, -pct),
y = pct)) +
geom_bar(stat = "identity",
color = "azure4") +
geom_text(aes(label = pctlabel),
vjust = -0.25) +
theme_minimal() + # use a minimal theme
scale_y_continuous(labels = percent) +
labs(x = "Education",
y = "Percent",
title = "Loan by Education")
Self_employed
plotdata <- loan.train %>%
count(Self_Employed) %>%
mutate(pct = n / sum(n),
pctlabel = paste0(round(pct*100), "%"))
ggplot(plotdata,
aes(x = reorder(Self_Employed, -pct),
y = pct)) +
geom_bar(stat = "identity",
color = "azure4") +
geom_text(aes(label = pctlabel),
vjust = -0.25) +
theme_minimal() + # use a minimal theme
scale_y_continuous(labels = percent) +
labs(x = "Self_Employed",
y = "Percent",
title = "Loan by Self_Employed")
Loan_Amount_Term
plotdata <- loan.train %>%
count(Loan_Amount_Term) %>%
mutate(pct = n / sum(n),
pctlabel = paste0(round(pct*100), "%"))
ggplot(plotdata,
aes(x = reorder(Loan_Amount_Term, -pct),
y = pct)) +
geom_bar(stat = "identity",
color = "azure4") +
geom_text(aes(label = pctlabel),
vjust = -0.25) +
theme_minimal() +
scale_y_continuous(labels = percent) +
labs(x = "Loan_Amount_Term",
y = "Percent",
title = "Loan by Loan_Amount_Term")
Credit_History
plotdata <- loan.train %>%
count(Credit_History) %>%
mutate(pct = n / sum(n),
pctlabel = paste0(round(pct*100), "%"))
ggplot(plotdata,
aes(x = reorder(Credit_History, -pct),
y = pct)) +
geom_bar(stat = "identity",
color = "azure4") +
geom_text(aes(label = pctlabel),
vjust = -0.25) +
theme_minimal() +
scale_y_continuous(labels = percent) +
labs(x = "Credit_History",
y = "Percent",
title = "Loan by Credit_History")
Property_Area
plotdata <- loan.train %>%
count(Property_Area) %>%
mutate(pct = n / sum(n),
pctlabel = paste0(round(pct*100), "%"))
ggplot(plotdata,
aes(x = reorder(Property_Area, -pct),
y = pct)) +
geom_bar(stat = "identity",
color = "azure4") +
geom_text(aes(label = pctlabel),
vjust = -0.25) +
theme_minimal() +
scale_y_continuous(labels = percent) +
labs(x = "Property_Area",
y = "Percent",
title = "Loan by Property_Area")
Numerical
ApplicantIncome
ggplot(loan.train, aes(x = ApplicantIncome)) +
geom_histogram(fill = "cornflowerblue",
color = "white",bins = 20) +
theme_minimal() + # use a minimal theme
labs(title="Loan by ApplicantIncome",
x = "ApplicantIncome")
CoapplicantIncome
ggplot(loan.train, aes(x = CoapplicantIncome)) +
geom_histogram(fill = "cornflowerblue",
color = "white",bins = 20) +
theme_minimal() + # use a minimal theme
labs(title="Loan by CoapplicantIncome",
x = "CoapplicantIncome")
LoanAmount
ggplot(loan.train, aes(x = LoanAmount)) +
geom_histogram(fill = "cornflowerblue",
color = "white",bins = 20) +
theme_minimal() + # use a minimal theme
labs(title="Loan by LoanAmount",
x = "LoanAmount")
Visualisasi Bivariabel
Categorical vs Categorical
Gender vs Married
ggplot(loan.train,
aes(x = Gender,
fill = Married)) +
geom_bar(position = "fill") +
theme_minimal() +
labs(y = "Proportion")
Gender vs Education
ggplot(loan.train,
aes(x = Gender,
fill = Education)) +
geom_bar(position = "fill") +
theme_minimal() +
labs(y = "Proportion")
Gender vs Dependents
ggplot(loan.train,
aes(x = Gender,
fill = Dependents)) +
geom_bar(position = "fill") +
theme_minimal() +
labs(y = "Proportion")
Gender vs Self_Employed
ggplot(loan.train,
aes(x = Gender,
fill = Self_Employed)) +
geom_bar(position = "fill") +
theme_minimal() +
labs(y = "Proportion")
Gender vs loanAmountTerm
ggplot(loan.train,
aes(x = Gender,
fill = Loan_Amount_Term)) +
geom_bar(position = "fill") +
theme_minimal() +
labs(y = "Proportion")
Gender vs credit_history
ggplot(loan.train,
aes(x = Gender,
fill = Credit_History)) +
geom_bar(position = "fill") +
theme_minimal() +
labs(y = "Proportion")
Gender vs PropertyArea
ggplot(loan.train,
aes(x = Gender,
fill = Property_Area)) +
geom_bar(position = "fill") +
theme_minimal() +
labs(y = "Proportion")
Gender vs LoanStatus
ggplot(loan.train,
aes(x = Gender,
fill = Loan_Status )) +
geom_bar(position = "fill") +
theme_minimal() +
labs(y = "Proportion")
Married vs Dependents
ggplot(loan.train,
aes(x = Married,
fill = Education)) +
geom_bar(position = "fill") +
theme_minimal() +
labs(y = "Proportion")
Married vs Education
ggplot(loan.train,
aes(x = Married,
fill = Dependents)) +
geom_bar(position = "fill") +
theme_minimal() +
labs(y = "Proportion")
Married vs Self_Employed
ggplot(loan.train,
aes(x = Married,
fill = Self_Employed)) +
geom_bar(position = "fill") +
theme_minimal() +
labs(y = "Proportion")
Married vs loanAmountTerm
ggplot(loan.train,
aes(x = Married,
fill = Loan_Amount_Term)) +
geom_bar(position = "fill") +
theme_minimal() +
labs(y = "Proportion")
Married vs credit_history
ggplot(loan.train,
aes(x = Married,
fill = Credit_History)) +
geom_bar(position = "fill") +
theme_minimal() +
labs(y = "Proportion")
Married vs PropertyArea
ggplot(loan.train,
aes(x = Married,
fill = Property_Area)) +
geom_bar(position = "fill") +
theme_minimal() +
labs(y = "Proportion")
Married vs LoanStatus
ggplot(loan.train,
aes(x = Married,
fill = Loan_Status )) +
geom_bar(position = "fill") +
theme_minimal() +
labs(y = "Proportion")
Dependents vs Education
ggplot(loan.train,
aes(x = Dependents,
fill = Education)) +
geom_bar(position = "fill") +
theme_minimal() +
labs(y = "Proportion")
Dependents vs Self_Employed
ggplot(loan.train,
aes(x = Dependents,
fill = Self_Employed)) +
geom_bar(position = "fill") +
theme_minimal() +
labs(y = "Proportion")
Dependents vs loanAmountTerm
ggplot(loan.train,
aes(x = Dependents,
fill = Loan_Amount_Term)) +
geom_bar(position = "fill") +
theme_minimal() +
labs(y = "Proportion")
Dependents vs credit_history
ggplot(loan.train,
aes(x = Dependents,
fill = Credit_History)) +
geom_bar(position = "fill") +
theme_minimal() +
labs(y = "Proportion")
Dependents vs PropertyArea
ggplot(loan.train,
aes(x = Dependents,
fill = Property_Area)) +
geom_bar(position = "fill") +
theme_minimal() +
labs(y = "Proportion")
Dependents vs LoanStatus
ggplot(loan.train,
aes(x = Dependents,
fill = Loan_Status )) +
geom_bar(position = "fill") +
theme_minimal() +
labs(y = "Proportion")
Education vs Self_Employed
ggplot(loan.train,
aes(x = Education,
fill = Self_Employed)) +
geom_bar(position = "fill") +
theme_minimal() +
labs(y = "Proportion")
Education vs loanAmountTerm
ggplot(loan.train,
aes(x = Education,
fill = Loan_Amount_Term)) +
geom_bar(position = "fill") +
theme_minimal() +
labs(y = "Proportion")
Education vs credit_history
ggplot(loan.train,
aes(x = Education,
fill = Credit_History)) +
geom_bar(position = "fill") +
theme_minimal() +
labs(y = "Proportion")
Education vs PropertyArea
ggplot(loan.train,
aes(x = Education,
fill = Property_Area)) +
geom_bar(position = "fill") +
theme_minimal() +
labs(y = "Proportion")
Education vs LoanStatus
ggplot(loan.train,
aes(x = Education,
fill = Loan_Status )) +
geom_bar(position = "fill") +
theme_minimal() +
labs(y = "Proportion")
Self_Employed vs loanAmountTerm
ggplot(loan.train,
aes(x = Self_Employed,
fill = Loan_Amount_Term)) +
geom_bar(position = "fill") +
theme_minimal() +
labs(y = "Proportion")
Self_Employed vs credit_history
ggplot(loan.train,
aes(x = Self_Employed,
fill = Credit_History)) +
geom_bar(position = "fill") +
theme_minimal() +
labs(y = "Proportion")
Self_Employed vs PropertyArea
ggplot(loan.train,
aes(x = Self_Employed,
fill = Property_Area)) +
geom_bar(position = "fill") +
theme_minimal() +
labs(y = "Proportion")
Self_Employed vs LoanStatus
ggplot(loan.train,
aes(x = Self_Employed,
fill = Loan_Status )) +
geom_bar(position = "fill") +
theme_minimal() +
labs(y = "Proportion")
Loan_Amount_Term vs credit_history
ggplot(loan.train,
aes(x = Loan_Amount_Term,
fill = Credit_History)) +
geom_bar(position = "fill") +
theme_minimal() +
labs(y = "Proportion")
Loan_Amount_Term vs PropertyArea
ggplot(loan.train,
aes(x = Loan_Amount_Term,
fill = Property_Area)) +
geom_bar(position = "fill") +
theme_minimal() +
labs(y = "Proportion")
Loan_Amount_Term vs LoanStatus
ggplot(loan.train,
aes(x = Loan_Amount_Term,
fill = Loan_Status )) +
geom_bar(position = "fill") +
theme_minimal() +
labs(y = "Proportion")
Credit_History vs PropertyArea
ggplot(loan.train,
aes(x = Credit_History,
fill = Property_Area)) +
geom_bar(position = "fill") +
theme_minimal() +
labs(y = "Proportion")
Credit_History vs LoanStatus
ggplot(loan.train,
aes(x = Credit_History,
fill = Loan_Status )) +
geom_bar(position = "fill") +
theme_minimal() +
labs(y = "Proportion")
Property_Area vs LoanStatus
ggplot(loan.train,
aes(x = Property_Area,
fill = Loan_Status )) +
geom_bar(position = "fill") +
theme_minimal() +
labs(y = "Proportion")
Numerical vs Numerical
ApplicantIncome vs CoapplicantIncome
ggplot(loan.train,
aes(x = ApplicantIncome,
y = CoapplicantIncome)) +
geom_point(color="cornflowerblue",
size = 1.5,
alpha=.8) +
scale_y_continuous(label = scales::dollar,
limits = c(0, 50000)) +
scale_x_continuous(breaks = seq(0, 40000, 5000),
limits=c(0, 50000)) +
theme_minimal() + # use a minimal theme
labs(x = "ApplicantIncome",
y = "",
title = "ApplicantIncome vs CoapplicantIncome")
CoapplicantIncome vs LoanAmount
ggplot(loan.train,
aes(x = CoapplicantIncome,
y = LoanAmount)) +
geom_point(color="cornflowerblue",
size = 1.5,
alpha=.8) +
scale_y_continuous(label = scales::dollar,
limits = c(0, 800)) +
scale_x_continuous(breaks = seq(0, 40000, 5000),
limits=c(0, 50000)) +
theme_minimal() + # use a minimal theme
labs(x = "CoapplicantIncome",
y = "",
title = "CoapplicantIncome vs LoanAmount")
ApplicantIncome vs LoanAmount
ggplot(loan.train,
aes(x = ApplicantIncome,
y = LoanAmount)) +
geom_point(color="cornflowerblue",
size = 1,
alpha=.8) +
scale_y_continuous(label = scales::dollar,
limits = c(0, 800)) +
scale_x_continuous(breaks = seq(0, 40000, 5000),
limits=c(0, 50000)) +
theme_minimal() + # use a minimal theme
labs(x = "ApplicantIncome",
y = "",
title = "ApplicantIncome vs LoanAmount")
Categorical vs Numerical
ApplicantIncome vs Gender
ggplot(loan.train,
aes(x = ApplicantIncome,
fill = Gender)) +
geom_density(alpha = 0.4) +
theme_minimal() +
labs(title = "Applicant Income by Gender")
ApplicantIncome vs Married
ggplot(loan.train,
aes(x = ApplicantIncome,
fill = Married)) +
geom_density(alpha = 0.4) +
theme_minimal() +
labs(title = "Applicant Income by Married")
ApplicantIncome vs Dependents
ggplot(loan.train,
aes(x = ApplicantIncome,
fill = Dependents)) +
geom_density(alpha = 0.4) +
theme_minimal() +
labs(title = "Applicant Income by Dependents")
ApplicantIncome vs Education
ggplot(loan.train,
aes(x = ApplicantIncome,
fill = Education)) +
geom_density(alpha = 0.4) +
theme_minimal() +
labs(title = "Applicant Income by Education")
ApplicantIncome vs Self_Employed
ggplot(loan.train,
aes(x = ApplicantIncome,
fill = Self_Employed)) +
geom_density(alpha = 0.4) +
theme_minimal() +
labs(title = "Applicant Income by Self_Employed")
ApplicantIncome vs Loan_Amount_Term
library(ggridges) # to handle overlapping visulization
ggplot(loan.train,
aes(x = ApplicantIncome,
y = Loan_Amount_Term,
fill = Loan_Amount_Term)) +
geom_density_ridges(alpha = 0.7) +
theme_ridges() +
theme(legend.position = "none")
ApplicantIncome vs Credit_History
library(ggridges) # to handle overlapping visulization
ggplot(loan.train,
aes(x = ApplicantIncome,
y = Credit_History,
fill = Credit_History)) +
geom_density_ridges(alpha = 0.7) +
theme_ridges() +
theme(legend.position = "none")
ApplicantIncome vs Property_Area
library(ggridges) # to handle overlapping visulization
ggplot(loan.train,
aes(x = ApplicantIncome,
y = Property_Area,
fill = Property_Area)) +
geom_density_ridges(alpha = 0.7) +
theme_ridges() +
theme(legend.position = "none")
ApplicantIncome vs Loan_Status
library(ggridges) # to handle overlapping visulization
ggplot(loan.train,
aes(x = ApplicantIncome,
y = Loan_Status,
fill = Loan_Status)) +
geom_density_ridges(alpha = 0.7) +
theme_ridges() +
theme(legend.position = "none")
CoapplicantIncome vs Gender
ggplot(loan.train,
aes(x = CoapplicantIncome,
fill = Gender)) +
geom_density(alpha = 0.4) +
theme_minimal() +
labs(title = "CoapplicantIncome by Gender")
CoapplicantIncome vs Married
ggplot(loan.train,
aes(x = CoapplicantIncome,
fill = Married)) +
geom_density(alpha = 0.4) +
theme_minimal() +
labs(title = "CoapplicantIncome by Married")
CoapplicantIncome vs Dependents
ggplot(loan.train,
aes(x = CoapplicantIncome,
fill = Dependents)) +
geom_density(alpha = 0.4) +
theme_minimal() +
labs(title = "CoapplicantIncome by Dependents")
CoapplicantIncome vs Education
ggplot(loan.train,
aes(x = CoapplicantIncome,
fill = Education)) +
geom_density(alpha = 0.4) +
theme_minimal() +
labs(title = "CoapplicantIncome by Education")
CoapplicantIncome vs Self_Employed
ggplot(loan.train,
aes(x = ApplicantIncome,
fill = Self_Employed)) +
geom_density(alpha = 0.4) +
theme_minimal() +
labs(title = "CoapplicantIncome by Self_Employed")
CoapplicantIncome vs Loan_Amount_Term
library(ggridges) # to handle overlapping visulization
ggplot(loan.train,
aes(x = CoapplicantIncome,
y = Loan_Amount_Term,
fill = Loan_Amount_Term)) +
geom_density_ridges(alpha = 0.7) +
theme_ridges() +
theme(legend.position = "none")
CoapplicantIncome vs Credit_History
library(ggridges) # to handle overlapping visulization
ggplot(loan.train,
aes(x = CoapplicantIncome,
y = Credit_History,
fill = Credit_History)) +
geom_density_ridges(alpha = 0.7) +
theme_ridges() +
theme(legend.position = "none")
CoapplicantIncome vs Property_Area
library(ggridges) # to handle overlapping visulization
ggplot(loan.train,
aes(x = CoapplicantIncome,
y = Property_Area,
fill = Property_Area)) +
geom_density_ridges(alpha = 0.7) +
theme_ridges() +
theme(legend.position = "none")
CoapplicantIncome vs Loan_Status
library(ggridges) # to handle overlapping visulization
ggplot(loan.train,
aes(x = CoapplicantIncome,
y = Loan_Status,
fill = Loan_Status)) +
geom_density_ridges(alpha = 0.7) +
theme_ridges() +
theme(legend.position = "none")
LoanAmount vs Gender
ggplot(loan.train,
aes(x = LoanAmount,
fill = Gender)) +
geom_density(alpha = 0.4) +
theme_minimal() +
labs(title = "LoanAmount by Gender")
LoanAmount vs Married
ggplot(loan.train,
aes(x = LoanAmount,
fill = Married)) +
geom_density(alpha = 0.4) +
theme_minimal() +
labs(title = "LoanAmount by Married")
LoanAmount vs Dependents
ggplot(loan.train,
aes(x = LoanAmount,
fill = Dependents)) +
geom_density(alpha = 0.4) +
theme_minimal() +
labs(title = "LoanAmount by Dependents")
LoanAmount vs Education
ggplot(loan.train,
aes(x = LoanAmount,
fill = Education)) +
geom_density(alpha = 0.4) +
theme_minimal() +
labs(title = "LoanAmount by Education")
LoanAmount vs Self_Employed
ggplot(loan.train,
aes(x = LoanAmount,
fill = Self_Employed)) +
geom_density(alpha = 0.4) +
theme_minimal() +
labs(title = "LoanAmount by Self_Employed")
LoanAmount vs Loan_Amount_Term
library(ggridges) # to handle overlapping visulization
ggplot(loan.train,
aes(x = LoanAmount,
y = Loan_Amount_Term,
fill = Loan_Amount_Term)) +
geom_density_ridges(alpha = 0.7) +
theme_ridges() +
theme(legend.position = "none")
LoanAmount vs Credit_History
library(ggridges) # to handle overlapping visulization
ggplot(loan.train,
aes(x = CoapplicantIncome,
y = Credit_History,
fill = Credit_History)) +
geom_density_ridges(alpha = 0.7) +
theme_ridges() +
theme(legend.position = "none")
LoanAmount vs Property_Area
library(ggridges) # to handle overlapping visulization
ggplot(loan.train,
aes(x = LoanAmount,
y = Property_Area,
fill = Property_Area)) +
geom_density_ridges(alpha = 0.7) +
theme_ridges() +
theme(legend.position = "none")
LoanAmount vs Loan_Status
library(ggridges) # to handle overlapping visulization
ggplot(loan.train,
aes(x = LoanAmount,
y = Loan_Status,
fill = Loan_Status)) +
geom_density_ridges(alpha = 0.7) +
theme_ridges() +
theme(legend.position = "none")
- Visualisasi Multivariabel
Visualisasi Multivariabel
ApplicantIncome by Married, Gender, and Loan Term
ggplot(loan.train,
aes(x = Loan_Amount_Term,
y = ApplicantIncome,
color = Married,
shape = Gender)) +
geom_point(size = 3, alpha = .6) +
theme_minimal() +
labs(title = "ApplicantIncome by Married, Gender, and Loan Term")
ApplicantIncome by Education, Gender, and Loan Term
ggplot(loan.train,
aes(x = Loan_Amount_Term,
y = ApplicantIncome,
color = Education,
shape = Gender)) +
geom_point(size = 3, alpha = .6) +
theme_minimal() +
labs(title = "ApplicantIncome by Married, Gender, and Loan Term")
ApplicantIncome by Dependents, Gender, and Loan Term
ggplot(loan.train,
aes(x = Loan_Amount_Term,
y = ApplicantIncome,
color = Dependents,
shape = Gender)) +
geom_point(size = 3, alpha = .6) +
theme_minimal() +
labs(title = "ApplicantIncome by Dependents, Gender, and Loan Term")
ApplicantIncome by Self_Employed, Gender, and Loan Term
ggplot(loan.train,
aes(x = Loan_Amount_Term,
y = ApplicantIncome,
color = Self_Employed,
shape = Gender)) +
geom_point(size = 3, alpha = .6) +
theme_minimal() +
labs(title = "ApplicantIncome by Self_Employed, Gender, and Loan Term")
ApplicantIncome by Credit_History, Gender, and Loan Term
ggplot(loan.train,
aes(x = Loan_Amount_Term,
y = ApplicantIncome,
color = Credit_History,
shape = Gender)) +
geom_point(size = 3, alpha = .6) +
theme_minimal() +
labs(title = "ApplicantIncome by Credit_History, Gender, and Loan Term")
ApplicantIncome by Property_Area, Gender, and Loan Term
ggplot(loan.train,
aes(x = Loan_Amount_Term,
y = ApplicantIncome,
color = Property_Area,
shape = Gender)) +
geom_point(size = 3, alpha = .6) +
theme_minimal() +
labs(title = "ApplicantIncome by Property_Area, Gender, and Loan Term")
ApplicantIncome by Loan_Status, Gender, and Loan Term
ggplot(loan.train,
aes(x = Loan_Amount_Term,
y = ApplicantIncome,
color = Loan_Status,
shape = Gender)) +
geom_point(size = 3, alpha = .6) +
theme_minimal() +
labs(title = "ApplicantIncome by Loan_Status, Gender, and Loan Term")
CoapplicantIncome by Married, Gender, and Loan Term
ggplot(loan.train,
aes(x = Loan_Amount_Term,
y = CoapplicantIncome,
color = Married,
shape = Gender)) +
geom_point(size = 3, alpha = .6) +
theme_minimal() +
labs(title = "CoapplicantIncome by Married, Gender, and Loan Term")
CoapplicantIncome by Education, Gender, and Loan Term
ggplot(loan.train,
aes(x = Loan_Amount_Term,
y = CoapplicantIncome,
color = Education,
shape = Gender)) +
geom_point(size = 3, alpha = .6) +
theme_minimal() +
labs(title = "CoapplicantIncome by Married, Gender, and Loan Term")
CoapplicantIncome by Dependents, Gender, and Loan Term
ggplot(loan.train,
aes(x = Loan_Amount_Term,
y = CoapplicantIncome,
color = Dependents,
shape = Gender)) +
geom_point(size = 3, alpha = .6) +
theme_minimal() +
labs(title = "CoapplicantIncome by Dependents, Gender, and Loan Term")
CoapplicantIncome by Self_Employed, Gender, and Loan Term
ggplot(loan.train,
aes(x = Loan_Amount_Term,
y = CoapplicantIncome,
color = Self_Employed,
shape = Gender)) +
geom_point(size = 3, alpha = .6) +
theme_minimal() +
labs(title = "CoapplicantIncome by Self_Employed, Gender, and Loan Term")
CoapplicantIncome by Credit_History, Gender, and Loan Term
ggplot(loan.train,
aes(x = Loan_Amount_Term,
y = CoapplicantIncome,
color = Credit_History,
shape = Gender)) +
geom_point(size = 3, alpha = .6) +
theme_minimal() +
labs(title = "CoapplicantIncome by Credit_History, Gender, and Loan Term")
CoapplicantIncome by Property_Area, Gender, and Loan Term
ggplot(loan.train,
aes(x = Loan_Amount_Term,
y = CoapplicantIncome,
color = Property_Area,
shape = Gender)) +
geom_point(size = 3, alpha = .6) +
theme_minimal() +
labs(title = "CoapplicantIncome by Property_Area, Gender, and Loan Term")
CoapplicantIncome by Loan_Status, Gender, and Loan Term
ggplot(loan.train,
aes(x = Loan_Amount_Term,
y = CoapplicantIncome,
color = Loan_Status,
shape = Gender)) +
geom_point(size = 3, alpha = .6) +
theme_minimal() +
labs(title = "CoapplicantIncome by Loan_Status, Gender, and Loan Term")
LoanAmount by Married, Gender, and Loan Term
ggplot(loan.train,
aes(x = Loan_Amount_Term,
y = LoanAmount,
color = Married,
shape = Gender)) +
geom_point(size = 3, alpha = .6) +
theme_minimal() +
labs(title = "LoanAmount by Married, Gender, and Loan Term")
LoanAmount by Education, Gender, and Loan Term
ggplot(loan.train,
aes(x = Loan_Amount_Term,
y = LoanAmount,
color = Education,
shape = Gender)) +
geom_point(size = 3, alpha = .6) +
theme_minimal() +
labs(title = "LoanAmount by Married, Gender, and Loan Term")
LoanAmount by Dependents, Gender, and Loan Term
ggplot(loan.train,
aes(x = Loan_Amount_Term,
y = LoanAmount,
color = Dependents,
shape = Gender)) +
geom_point(size = 3, alpha = .6) +
theme_minimal() +
labs(title = "LoanAmount by Dependents, Gender, and Loan Term")
LoanAmount by Self_Employed, Gender, and Loan Term
ggplot(loan.train,
aes(x = Loan_Amount_Term,
y = LoanAmount,
color = Self_Employed,
shape = Gender)) +
geom_point(size = 3, alpha = .6) +
theme_minimal() +
labs(title = "LoanAmount by Self_Employed, Gender, and Loan Term")
LoanAmount by Credit_History, Gender, and Loan Term
ggplot(loan.train,
aes(x = Loan_Amount_Term,
y = LoanAmount,
color = Credit_History,
shape = Gender)) +
geom_point(size = 3, alpha = .6) +
theme_minimal() +
labs(title = "LoanAmount by Credit_History, Gender, and Loan Term")
LoanAmount by Property_Area, Gender, and Loan Term
ggplot(loan.train,
aes(x = Loan_Amount_Term,
y = LoanAmount,
color = Property_Area,
shape = Gender)) +
geom_point(size = 3, alpha = .6) +
theme_minimal() +
labs(title = "LoanAmount by Property_Area, Gender, and Loan Term")
LoanAmount by Loan_Status, Gender, and Loan Term
ggplot(loan.train,
aes(x = Loan_Amount_Term,
y = LoanAmount,
color = Loan_Status,
shape = Gender)) +
geom_point(size = 3, alpha = .6) +
theme_minimal() +
labs(title = "LoanAmount by Loan_Status, Gender, and Loan Term")
Tugas 3
Lakukan proses analisa data secara deskriptif menggunakan R dan Python dengan beberapa langkah berikut:
Kualitatif
Kategori Univariat
Loan_ID
Cat1 <- table(loan.train$Gender)
Cat1##
## Female Male
## 112 502Cat2 <- table(loan.train$Married)
Cat2##
## No Yes
## 213 401Cat3 <- table(loan.train$Dependents)
Cat3##
## 0 1 2 3+
## 360 102 101 51Cat4 <- table(loan.train$Education)
Cat4##
## Graduate Not Graduate
## 480 134Cat5 <- table(loan.train$Self_Employed)
Cat5##
## No Yes
## 500 82Cat6 <- table(loan.train$Loan_Amount_Term)
Cat6##
## 12 36 60 84 120 180 240 300 360 480
## 1 2 2 4 3 44 4 13 512 15Cat7 <- table(loan.train$Credit_History)
Cat7##
## 0 1
## 89 525Cat8 <- table(loan.train$Property_Area)
Cat8##
## Rural Semiurban Urban
## 179 233 202Cat9 <- table(loan.train$Loan_Status)
Cat9##
## N Y
## 192 422Kategori Bivariat
bicat1<- loan.train %>%dplyr::select(Gender, Married) %>%table()
bicat1## Married
## Gender No Yes
## Female 80 32
## Male 133 369bicat2<- loan.train %>%dplyr::select(Gender, Dependents) %>%table()
bicat2## Dependents
## Gender 0 1 2 3+
## Female 83 19 7 3
## Male 277 83 94 48bicat3<- loan.train %>%dplyr::select(Gender, Education) %>%table()
bicat3## Education
## Gender Graduate Not Graduate
## Female 92 20
## Male 388 114bicat4<- loan.train %>%dplyr::select(Gender, Self_Employed) %>%table()
bicat4## Self_Employed
## Gender No Yes
## Female 89 15
## Male 411 67bicat5<- loan.train %>%dplyr::select(Gender, Loan_Amount_Term) %>%table()
bicat5## Loan_Amount_Term
## Gender 12 36 60 84 120 180 240 300 360 480
## Female 0 1 0 1 0 3 1 1 98 4
## Male 1 1 2 3 3 41 3 12 414 11bicat6<- loan.train %>%dplyr::select(Gender, Credit_History) %>%table()
bicat6## Credit_History
## Gender 0 1
## Female 17 95
## Male 72 430bicat7<- loan.train %>%dplyr::select(Gender, Property_Area) %>%table()
bicat7## Property_Area
## Gender Rural Semiurban Urban
## Female 24 55 33
## Male 155 178 169bicat8<- loan.train %>%dplyr::select(Gender, Loan_Status) %>%table()
bicat8## Loan_Status
## Gender N Y
## Female 37 75
## Male 155 347bicat9<- loan.train %>%dplyr::select(Married, Dependents) %>%table()
bicat9## Dependents
## Married 0 1 2 3+
## No 175 23 8 7
## Yes 185 79 93 44bicat10<- loan.train %>%dplyr::select(Married, Education) %>%table()
bicat10## Education
## Married Graduate Not Graduate
## No 168 45
## Yes 312 89bicat11<- loan.train %>%dplyr::select(Married, Self_Employed) %>%table()
bicat11## Self_Employed
## Married No Yes
## No 171 28
## Yes 329 54bicat12<- loan.train %>%dplyr::select(Married, Loan_Amount_Term) %>%table()
bicat12## Loan_Amount_Term
## Married 12 36 60 84 120 180 240 300 360 480
## No 0 2 1 0 1 8 1 3 183 9
## Yes 1 0 1 4 2 36 3 10 329 6bicat13<- loan.train %>%dplyr::select(Married, Credit_History) %>%table()
bicat13## Credit_History
## Married 0 1
## No 32 181
## Yes 57 344bicat14<- loan.train %>%dplyr::select(Married, Property_Area) %>%table()
bicat14## Property_Area
## Married Rural Semiurban Urban
## No 63 80 70
## Yes 116 153 132bicat15<- loan.train %>%dplyr::select(Married, Loan_Status) %>%table()
bicat15## Loan_Status
## Married N Y
## No 79 134
## Yes 113 288bicat16<- loan.train %>%dplyr::select(Dependents, Education) %>%table()
bicat16## Education
## Dependents Graduate Not Graduate
## 0 286 74
## 1 81 21
## 2 77 24
## 3+ 36 15bicat17<- loan.train %>%dplyr::select(Dependents, Self_Employed) %>%table()
bicat17## Self_Employed
## Dependents No Yes
## 0 302 39
## 1 76 20
## 2 80 16
## 3+ 42 7bicat18<- loan.train %>%dplyr::select(Dependents, Loan_Amount_Term) %>%table()
bicat18## Loan_Amount_Term
## Dependents 12 36 60 84 120 180 240 300 360 480
## 0 1 1 1 0 2 19 1 6 306 11
## 1 0 1 0 2 0 11 2 2 82 1
## 2 0 0 0 2 1 6 1 3 86 2
## 3+ 0 0 1 0 0 8 0 2 38 1bicat19<- loan.train %>%dplyr::select(Dependents, Credit_History) %>%table()
bicat19## Credit_History
## Dependents 0 1
## 0 50 310
## 1 14 88
## 2 14 87
## 3+ 11 40bicat20<- loan.train %>%dplyr::select(Dependents, Property_Area) %>%table()
bicat20## Property_Area
## Dependents Rural Semiurban Urban
## 0 111 136 113
## 1 21 40 41
## 2 29 37 35
## 3+ 18 20 13bicat21<- loan.train %>%dplyr::select(Dependents, Loan_Status) %>%table()
bicat21## Loan_Status
## Dependents N Y
## 0 113 247
## 1 36 66
## 2 25 76
## 3+ 18 33bicat22<- loan.train %>%dplyr::select(Education, Self_Employed) %>%table()
bicat22## Self_Employed
## Education No Yes
## Graduate 389 65
## Not Graduate 111 17bicat23<- loan.train %>%dplyr::select(Education, Loan_Amount_Term) %>%table()
bicat23## Loan_Amount_Term
## Education 12 36 60 84 120 180 240 300 360 480
## Graduate 1 1 1 4 2 28 3 10 411 11
## Not Graduate 0 1 1 0 1 16 1 3 101 4bicat25<- loan.train %>%dplyr::select(Education, Credit_History) %>%table()
bicat25## Credit_History
## Education 0 1
## Graduate 63 417
## Not Graduate 26 108bicat26<- loan.train %>%dplyr::select(Education, Property_Area) %>%table()
bicat26## Property_Area
## Education Rural Semiurban Urban
## Graduate 131 187 162
## Not Graduate 48 46 40bicat27<- loan.train %>%dplyr::select(Education, Loan_Status) %>%table()
bicat27## Loan_Status
## Education N Y
## Graduate 140 340
## Not Graduate 52 82bicat28<- loan.train %>%dplyr::select(Self_Employed, Loan_Amount_Term) %>%table()
bicat28## Loan_Amount_Term
## Self_Employed 12 36 60 84 120 180 240 300 360 480
## No 1 2 1 3 2 35 3 10 418 14
## Yes 0 0 1 1 1 5 1 3 67 1bicat29<- loan.train %>%dplyr::select(Self_Employed, Credit_History) %>%table()
bicat29## Credit_History
## Self_Employed 0 1
## No 76 424
## Yes 12 70bicat30<- loan.train %>%dplyr::select(Self_Employed, Property_Area) %>%table()
bicat30## Property_Area
## Self_Employed Rural Semiurban Urban
## No 143 191 166
## Yes 26 32 24bicat31<- loan.train %>%dplyr::select(Self_Employed, Loan_Status) %>%table()
bicat31## Loan_Status
## Self_Employed N Y
## No 157 343
## Yes 26 56bicat32<- loan.train %>%dplyr::select(Loan_Amount_Term, Credit_History) %>%table()
bicat32## Credit_History
## Loan_Amount_Term 0 1
## 12 0 1
## 36 0 2
## 60 0 2
## 84 0 4
## 120 0 3
## 180 10 34
## 240 0 4
## 300 3 10
## 360 66 446
## 480 4 11bicat33<- loan.train %>%dplyr::select(Loan_Amount_Term, Property_Area) %>%table()
bicat33## Property_Area
## Loan_Amount_Term Rural Semiurban Urban
## 12 0 0 1
## 36 0 2 0
## 60 0 0 2
## 84 2 1 1
## 120 0 2 1
## 180 11 10 23
## 240 0 2 2
## 300 4 6 3
## 360 156 200 156
## 480 2 7 6bicat34<- loan.train %>%dplyr::select(Loan_Amount_Term, Loan_Status) %>%table()
bicat34## Loan_Status
## Loan_Amount_Term N Y
## 12 0 1
## 36 2 0
## 60 0 2
## 84 1 3
## 120 0 3
## 180 15 29
## 240 1 3
## 300 5 8
## 360 153 359
## 480 9 6bicat35<- loan.train %>%dplyr::select(Credit_History, Property_Area) %>%table()
bicat35## Property_Area
## Credit_History Rural Semiurban Urban
## 0 28 30 31
## 1 151 203 171bicat36<- loan.train %>%dplyr::select(Credit_History, Loan_Status) %>%table()
bicat36## Loan_Status
## Credit_History N Y
## 0 82 7
## 1 110 415bicat37<- loan.train %>%dplyr::select(Property_Area, Loan_Status) %>%table()
bicat37## Loan_Status
## Property_Area N Y
## Rural 69 110
## Semiurban 54 179
## Urban 69 133Kategori Multivariat
mulcat1 <- loan.train %>%dplyr::select(Gender, Married, Dependents) %>% ftable()
mulcat1## Dependents 0 1 2 3+
## Gender Married
## Female No 62 13 2 3
## Yes 21 6 5 0
## Male No 113 10 6 4
## Yes 164 73 88 44mulcat2 <- loan.train %>%dplyr::select(Gender, Married, Education) %>% ftable()
mulcat2## Education Graduate Not Graduate
## Gender Married
## Female No 66 14
## Yes 26 6
## Male No 102 31
## Yes 286 83mulcat3 <- loan.train %>%dplyr::select(Gender, Married, Self_Employed) %>% ftable()
mulcat3## Self_Employed No Yes
## Gender Married
## Female No 63 11
## Yes 26 4
## Male No 108 17
## Yes 303 50mulcat4 <- loan.train %>%dplyr::select(Gender, Married, Loan_Amount_Term) %>% ftable()
mulcat4## Loan_Amount_Term 12 36 60 84 120 180 240 300 360 480
## Gender Married
## Female No 0 1 0 0 0 2 0 1 70 3
## Yes 0 0 0 1 0 1 1 0 28 1
## Male No 0 1 1 0 1 6 1 2 113 6
## Yes 1 0 1 3 2 35 2 10 301 5mulcat5 <- loan.train %>%dplyr::select(Gender, Married, Credit_History) %>% ftable()
mulcat5## Credit_History 0 1
## Gender Married
## Female No 13 67
## Yes 4 28
## Male No 19 114
## Yes 53 316mulcat6 <- loan.train %>%dplyr::select(Gender, Married, Property_Area) %>% ftable()
mulcat6## Property_Area Rural Semiurban Urban
## Gender Married
## Female No 19 34 27
## Yes 5 21 6
## Male No 44 46 43
## Yes 111 132 126mulcat7 <- loan.train %>%dplyr::select(Gender, Married, Loan_Status) %>% ftable()
mulcat7## Loan_Status N Y
## Gender Married
## Female No 29 51
## Yes 8 24
## Male No 50 83
## Yes 105 264mulcat8 <- loan.train %>%dplyr::select(Married, Dependents, Education) %>% ftable()
mulcat8## Education Graduate Not Graduate
## Married Dependents
## No 0 139 36
## 1 16 7
## 2 8 0
## 3+ 5 2
## Yes 0 147 38
## 1 65 14
## 2 69 24
## 3+ 31 13mulcat9 <- loan.train %>%dplyr::select(Married, Dependents, Self_Employed) %>% ftable()
mulcat9## Self_Employed No Yes
## Married Dependents
## No 0 145 21
## 1 14 6
## 2 7 0
## 3+ 5 1
## Yes 0 157 18
## 1 62 14
## 2 73 16
## 3+ 37 6mulcat11 <- loan.train %>%dplyr::select(Married, Dependents, Loan_Amount_Term) %>% ftable()
mulcat11## Loan_Amount_Term 12 36 60 84 120 180 240 300 360 480
## Married Dependents
## No 0 0 1 1 0 1 5 0 3 150 9
## 1 0 1 0 0 0 2 1 0 19 0
## 2 0 0 0 0 0 0 0 0 8 0
## 3+ 0 0 0 0 0 1 0 0 6 0
## Yes 0 1 0 0 0 1 14 1 3 156 2
## 1 0 0 0 2 0 9 1 2 63 1
## 2 0 0 0 2 1 6 1 3 78 2
## 3+ 0 0 1 0 0 7 0 2 32 1mulcat12 <- loan.train %>%dplyr::select(Married, Dependents, Credit_History) %>% ftable()
mulcat12## Credit_History 0 1
## Married Dependents
## No 0 26 149
## 1 2 21
## 2 3 5
## 3+ 1 6
## Yes 0 24 161
## 1 12 67
## 2 11 82
## 3+ 10 34mulcat13 <- loan.train %>%dplyr::select(Married, Dependents, Property_Area) %>% ftable()
mulcat13## Property_Area Rural Semiurban Urban
## Married Dependents
## No 0 53 63 59
## 1 3 12 8
## 2 4 3 1
## 3+ 3 2 2
## Yes 0 58 73 54
## 1 18 28 33
## 2 25 34 34
## 3+ 15 18 11mulcat14 <- loan.train %>%dplyr::select(Married, Dependents, Loan_Status) %>% ftable()
mulcat14## Loan_Status N Y
## Married Dependents
## No 0 63 112
## 1 10 13
## 2 3 5
## 3+ 3 4
## Yes 0 50 135
## 1 26 53
## 2 22 71
## 3+ 15 29mulcat15 <- loan.train %>%dplyr::select( Dependents, Self_Employed, Loan_Amount_Term) %>% ftable()
mulcat15## Loan_Amount_Term 12 36 60 84 120 180 240 300 360 480
## Dependents Self_Employed
## 0 No 1 1 1 0 1 14 1 5 259 10
## Yes 0 0 0 0 1 3 0 1 31 1
## 1 No 0 1 0 2 0 8 2 1 60 1
## Yes 0 0 0 0 0 2 0 1 17 0
## 2 No 0 0 0 1 1 6 0 2 68 2
## Yes 0 0 0 1 0 0 1 1 13 0
## 3+ No 0 0 0 0 0 7 0 2 31 1
## Yes 0 0 1 0 0 0 0 0 6 0mulcat16 <- loan.train %>%dplyr::select(Dependents, Self_Employed, Credit_History) %>% ftable()
mulcat16## Credit_History 0 1
## Dependents Self_Employed
## 0 No 45 257
## Yes 5 34
## 1 No 9 67
## Yes 5 15
## 2 No 11 69
## Yes 2 14
## 3+ No 11 31
## Yes 0 7mulcat17 <- loan.train %>%dplyr::select(Dependents, Self_Employed, Property_Area) %>% ftable()
mulcat17## Property_Area Rural Semiurban Urban
## Dependents Self_Employed
## 0 No 93 117 92
## Yes 10 15 14
## 1 No 15 28 33
## Yes 5 9 6
## 2 No 19 30 31
## Yes 9 4 3
## 3+ No 16 16 10
## Yes 2 4 1mulcat18 <- loan.train %>%dplyr::select(Dependents, Self_Employed, Loan_Status) %>% ftable()
mulcat18## Loan_Status N Y
## Dependents Self_Employed
## 0 No 97 205
## Yes 11 28
## 1 No 24 52
## Yes 10 10
## 2 No 19 61
## Yes 5 11
## 3+ No 17 25
## Yes 0 7mulcat25 <- loan.train %>%dplyr::select(Dependents, Self_Employed, Married) %>% ftable()
mulcat25## Married No Yes
## Dependents Self_Employed
## 0 No 145 157
## Yes 21 18
## 1 No 14 62
## Yes 6 14
## 2 No 7 73
## Yes 0 16
## 3+ No 5 37
## Yes 1 6mulcat26 <- loan.train %>%dplyr::select(Dependents, Self_Employed, Gender) %>% ftable()
mulcat26## Gender Female Male
## Dependents Self_Employed
## 0 No 70 232
## Yes 10 29
## 1 No 12 64
## Yes 5 15
## 2 No 5 75
## Yes 0 16
## 3+ No 2 40
## Yes 0 7mulcat27 <- loan.train %>%dplyr::select(Dependents, Self_Employed, Education) %>% ftable()
mulcat27## Education Graduate Not Graduate
## Dependents Self_Employed
## 0 No 241 61
## Yes 30 9
## 1 No 59 17
## Yes 17 3
## 2 No 59 21
## Yes 14 2
## 3+ No 30 12
## Yes 4 3mulcat19 <- loan.train %>%dplyr::select(Self_Employed, Credit_History, Education) %>% ftable()
mulcat19## Education Graduate Not Graduate
## Self_Employed Credit_History
## No 0 52 24
## 1 337 87
## Yes 0 10 2
## 1 55 15mulcat20 <- loan.train %>%dplyr::select(Self_Employed, Credit_History, Property_Area) %>% ftable()
mulcat20## Property_Area Rural Semiurban Urban
## Self_Employed Credit_History
## No 0 23 27 26
## 1 120 164 140
## Yes 0 5 2 5
## 1 21 30 19mulcat21 <- loan.train %>%dplyr::select(Self_Employed, Credit_History, Loan_Status) %>% ftable()
mulcat21## Loan_Status N Y
## Self_Employed Credit_History
## No 0 69 7
## 1 88 336
## Yes 0 12 0
## 1 14 56mulcat22 <- loan.train %>%dplyr::select(Self_Employed, Credit_History, Married) %>% ftable()
mulcat22## Married No Yes
## Self_Employed Credit_History
## No 0 28 48
## 1 143 281
## Yes 0 4 8
## 1 24 46mulcat23 <- loan.train %>%dplyr::select(Self_Employed, Credit_History, Gender) %>% ftable()
mulcat23## Gender Female Male
## Self_Employed Credit_History
## No 0 14 62
## 1 75 349
## Yes 0 3 9
## 1 12 58mulcat24 <- loan.train %>%dplyr::select(Self_Employed, Credit_History, Loan_Amount_Term) %>% ftable()
mulcat24## Loan_Amount_Term 12 36 60 84 120 180 240 300 360 480
## Self_Employed Credit_History
## No 0 0 0 0 0 0 8 0 2 56 4
## 1 1 2 1 3 2 27 3 8 362 10
## Yes 0 0 0 0 0 0 2 0 1 9 0
## 1 0 0 1 1 1 3 1 2 58 1mulcat28 <- loan.train %>%dplyr::select(Credit_History, Property_Area, Education) %>% ftable()
mulcat28## Education Graduate Not Graduate
## Credit_History Property_Area
## 0 Rural 20 8
## Semiurban 24 6
## Urban 19 12
## 1 Rural 111 40
## Semiurban 163 40
## Urban 143 28mulcat29 <- loan.train %>%dplyr::select(Credit_History, Property_Area, Dependents) %>% ftable()
mulcat29## Dependents 0 1 2 3+
## Credit_History Property_Area
## 0 Rural 15 5 6 2
## Semiurban 16 3 5 6
## Urban 19 6 3 3
## 1 Rural 96 16 23 16
## Semiurban 120 37 32 14
## Urban 94 35 32 10mulcat30 <- loan.train %>%dplyr::select(Credit_History, Property_Area, Loan_Status) %>% ftable()
mulcat30## Loan_Status N Y
## Credit_History Property_Area
## 0 Rural 26 2
## Semiurban 26 4
## Urban 30 1
## 1 Rural 43 108
## Semiurban 28 175
## Urban 39 132mulcat31 <- loan.train %>%dplyr::select(Credit_History, Property_Area, Married) %>% ftable()
mulcat31## Married No Yes
## Credit_History Property_Area
## 0 Rural 8 20
## Semiurban 12 18
## Urban 12 19
## 1 Rural 55 96
## Semiurban 68 135
## Urban 58 113mulcat32 <- loan.train %>%dplyr::select(Credit_History, Property_Area, Gender) %>% ftable()
mulcat32## Gender Female Male
## Credit_History Property_Area
## 0 Rural 1 27
## Semiurban 8 22
## Urban 8 23
## 1 Rural 23 128
## Semiurban 47 156
## Urban 25 146mulcat33 <- loan.train %>%dplyr::select(Credit_History, Property_Area, Loan_Amount_Term) %>% ftable()
mulcat3## Self_Employed No Yes
## Gender Married
## Female No 63 11
## Yes 26 4
## Male No 108 17
## Yes 303 50Memahami distribusi data terhadap status pinjaman (yaitu variabel target) memvisualisasikan variabel kategori dan status pinjaman
train.new <- loan.train %>%
filter(Loan_Status!="test") %>%
mutate(Loan_Status=ifelse(Loan_Status=="Y",1,0))
train.new %>%
group_by(Loan_Status) %>%
summarise(n.count=n()) %>%
mutate(percent=round(n.count/nrow(train.new)*100,1),
Loan_Status=as.factor(Loan_Status)) %>%
ungroup() %>%
ggplot(aes(x=Loan_Status, y=percent, fill=Loan_Status)) +
geom_bar(stat="identity")+
theme_economist_white()
# buat fungsi untuk memplot banyak variabel kategori dan bagaimana mereka berinteraksi dengan variabel target
PlotSimple <- function(dataframe,x,y){
aaa <- enquo(x)
bbb <- enquo(y)
dataframe %>%
filter(!is.na(!! aaa), !is.na(!! bbb)) %>%
group_by(!! aaa,!! bbb) %>%
summarise(n=n())%>%
mutate(percent=n/nrow(dataframe)) %>%
ggplot(aes_(fill=aaa, y=~percent, x=bbb)) +
geom_bar(position="dodge", stat="identity") +
theme_economist_white()
}
xvars <- list(as.name("Married"),
as.name("Credit_History"),
as.name("Gender"),
as.name("Education"),
as.name("Self_Employed"),
as.name("Property_Area"))
cat.data <- loan.train%>% dplyr::select_if(is.factor)
all_plots<-lapply (xvars, PlotSimple, dataframe=cat.data, y =Loan_Status)
cowplot::plot_grid(plotlist = all_plots)
60% dari klien memiliki pinjaman mereka disetujui. Demikian pula, 60% klien yang memiliki riwayat kredit kemungkinan besar akan menyetujui pinjaman mereka. Ini merupakan indikasi sejarah kredit dan persetujuan pinjaman memiliki beberapa korelasi. 29,2% Pemohon yang tinggal di area properti semi-perkotaan cenderung menyetujui pinjaman mereka. Menikah cenderung memiliki status pinjaman disetujui
Kuantitatif
Univariat numerik
Mean
mean(loan.train$ApplicantIncome)## [1] 5403.459mean(loan.train$CoapplicantIncome)## [1] 1621.246mean(loan.train$LoanAmount)## [1] 146.4122Quantile
quantile(loan.train$ApplicantIncome)## 0% 25% 50% 75% 100%
## 150.0 2877.5 3812.5 5795.0 81000.0quantile(loan.train$CoapplicantIncome)## 0% 25% 50% 75% 100%
## 0.00 0.00 1188.50 2297.25 41667.00quantile(loan.train$LoanAmount)## 0% 25% 50% 75% 100%
## 9.00 100.25 129.00 164.75 700.00Median
median(loan.train$ApplicantIncome)## [1] 3812.5median(loan.train$CoapplicantIncome)## [1] 1188.5median(loan.train$LoanAmount)## [1] 129Mode
mode(loan.train$ApplicantIncome)## [1] "numeric"mode(loan.train$CoapplicantIncome)## [1] "numeric"mode(loan.train$LoanAmount)## [1] "numeric"loantrain <- loan.train%>% dplyr::select_if(is.numeric)
summary(loantrain)## ApplicantIncome CoapplicantIncome LoanAmount
## Min. : 150 Min. : 0 Min. : 9.0
## 1st Qu.: 2878 1st Qu.: 0 1st Qu.:100.2
## Median : 3812 Median : 1188 Median :129.0
## Mean : 5403 Mean : 1621 Mean :146.4
## 3rd Qu.: 5795 3rd Qu.: 2297 3rd Qu.:164.8
## Max. :81000 Max. :41667 Max. :700.0Var
var(loan.train$ApplicantIncome)## [1] 37320390var(loan.train$CoapplicantIncome)## [1] 8562930var(loan.train$LoanAmount)## [1] 7062.296standar deviation
sd(loan.train$ApplicantIncome)## [1] 6109.042sd(loan.train$CoapplicantIncome)## [1] 2926.248sd(loan.train$LoanAmount)## [1] 84.03747Media Absolute Deviation
mad(loan.train$ApplicantIncome)## [1] 1822.857mad(loan.train$CoapplicantIncome)## [1] 1762.07mad(loan.train$LoanAmount)## [1] 45.2193IQR
IQR(loan.train$ApplicantIncome)## [1] 2917.5IQR(loan.train$CoapplicantIncome)## [1] 2297.25IQR(loan.train$LoanAmount)## [1] 64.5Skewness
library(e1071)
skewness(loan.train$ApplicantIncome)## [1] 6.507596skewness(loan.train$CoapplicantIncome)## [1] 7.454967skewness(loan.train$LoanAmount)## [1] 2.713293Kurtosis
kurtosis(loan.train$ApplicantIncome)## [1] 59.83387kurtosis(loan.train$CoapplicantIncome)## [1] 83.97239kurtosis(loan.train$LoanAmount)## [1] 10.75326Bivariat numerik
Z-score
cov(loan.train$ApplicantIncome,loan.train$CoapplicantIncome)## [1] -2084490cov(loan.train$ApplicantIncome,loan.train$LoanAmount)## [1] 290383cov(loan.train$CoapplicantIncome,loan.train$LoanAmount)## [1] 46189.73cor(loan.train$ApplicantIncome,loan.train$CoapplicantIncome)## [1] -0.1166046cor(loan.train$ApplicantIncome,loan.train$LoanAmount)## [1] 0.5656205cor(loan.train$CoapplicantIncome,loan.train$LoanAmount)## [1] 0.1878284zscore_applicantincome=(loan.train$ApplicantIncome-mean(loan.train$ApplicantIncome))/sd(loan.train$ApplicantIncome)
zscore_coapplicantincome=(loan.train$CoapplicantIncome-mean(loan.train$CoapplicantIncome))/sd(loan.train$CoapplicantIncome)
zscore_LoanAmount=(loan.train$LoanAmount-mean(loan.train$LoanAmount))/sd(loan.train$LoanAmount)Multivariat numerik
cov(loantrain)## ApplicantIncome CoapplicantIncome LoanAmount
## ApplicantIncome 37320390 -2084490.34 290382.977
## CoapplicantIncome -2084490 8562929.52 46189.726
## LoanAmount 290383 46189.73 7062.296cor(loantrain)## ApplicantIncome CoapplicantIncome LoanAmount
## ApplicantIncome 1.0000000 -0.1166046 0.5656205
## CoapplicantIncome -0.1166046 1.0000000 0.1878284
## LoanAmount 0.5656205 0.1878284 1.0000000train.new <- train.new %>%
mutate(Loan_Status=as.factor(Loan_Status))
varlist <- c("ApplicantIncome", "CoapplicantIncome", "LoanAmount")
PlotFast <- function(varName) {
train.new %>%
group_by_("Loan_Status") %>%
dplyr::select_("Loan_Status",varName) %>%
ggplot(aes_string("Loan_Status",varName,fill="Loan_Status")) +
geom_boxplot() +
theme_economist_white()
}
all_plot_cont<-lapply(varlist,PlotFast)
cowplot::plot_grid(plotlist = all_plot_cont, ncol=3)
rm(train.new)Sulit untuk melihat pola khusus di antara variabel kontinu saat ini. Ini mungkin berarti bahwa kasus yang disetujui dan tidak disetujui memiliki jumlah pinjaman yang sama, pendapatan pemohon/pemohon.
EDA dengan cara Malas
library(funModeling)
library(tidyverse)
library(Hmisc)
library(skimr)
basic_eda <- function(loan.train)
{
glimpse(loan.train)
skim(loan.train)
df_status(loan.train)
freq(loan.train)
profiling_num(loan.train)
plot_num(loan.train)
describe(loan.train)
}
basic_eda(loan.train)## Rows: 614
## Columns: 12
## $ Gender <fct> Male, Male, Male, Male, Male, Male, Male, Male, Male~
## $ Married <fct> No, Yes, Yes, Yes, No, Yes, Yes, Yes, Yes, Yes, Yes,~
## $ Dependents <fct> 0, 1, 0, 0, 0, 2, 0, 3+, 2, 1, 2, 2, 2, 0, 2, 0, 1, ~
## $ Education <fct> Graduate, Graduate, Graduate, Not Graduate, Graduate~
## $ Self_Employed <fct> No, No, Yes, No, No, Yes, No, No, No, No, No, NA, No~
## $ ApplicantIncome <int> 5849, 4583, 3000, 2583, 6000, 5417, 2333, 3036, 4006~
## $ CoapplicantIncome <dbl> 0, 1508, 0, 2358, 0, 4196, 1516, 2504, 1526, 10968, ~
## $ LoanAmount <dbl> 146.4122, 128.0000, 66.0000, 120.0000, 141.0000, 267~
## $ Loan_Amount_Term <fct> 360, 360, 360, 360, 360, 360, 360, 360, 360, 360, 36~
## $ Credit_History <fct> 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0~
## $ Property_Area <fct> Urban, Rural, Urban, Urban, Urban, Urban, Urban, Sem~
## $ Loan_Status <fct> Y, N, Y, Y, Y, Y, Y, N, Y, N, Y, Y, Y, N, Y, Y, Y, N~
## variable q_zeros p_zeros q_na p_na q_inf p_inf type unique
## 1 Gender 0 0.00 0 0.00 0 0 factor 2
## 2 Married 0 0.00 0 0.00 0 0 factor 2
## 3 Dependents 360 58.63 0 0.00 0 0 factor 4
## 4 Education 0 0.00 0 0.00 0 0 factor 2
## 5 Self_Employed 0 0.00 32 5.21 0 0 factor 2
## 6 ApplicantIncome 0 0.00 0 0.00 0 0 integer 505
## 7 CoapplicantIncome 273 44.46 0 0.00 0 0 numeric 287
## 8 LoanAmount 0 0.00 0 0.00 0 0 numeric 204
## 9 Loan_Amount_Term 0 0.00 14 2.28 0 0 factor 10
## 10 Credit_History 89 14.50 0 0.00 0 0 factor 2
## 11 Property_Area 0 0.00 0 0.00 0 0 factor 3
## 12 Loan_Status 0 0.00 0 0.00 0 0 factor 2
## Gender frequency percentage cumulative_perc
## 1 Male 502 81.76 81.76
## 2 Female 112 18.24 100.00
## Married frequency percentage cumulative_perc
## 1 Yes 401 65.31 65.31
## 2 No 213 34.69 100.00
## Dependents frequency percentage cumulative_perc
## 1 0 360 58.63 58.63
## 2 1 102 16.61 75.24
## 3 2 101 16.45 91.69
## 4 3+ 51 8.31 100.00
## Education frequency percentage cumulative_perc
## 1 Graduate 480 78.18 78.18
## 2 Not Graduate 134 21.82 100.00
## Self_Employed frequency percentage cumulative_perc
## 1 No 500 81.43 81.43
## 2 Yes 82 13.36 94.79
## 3 <NA> 32 5.21 100.00
## Loan_Amount_Term frequency percentage cumulative_perc
## 1 360 512 83.39 83.39
## 2 180 44 7.17 90.56
## 3 480 15 2.44 93.00
## 4 <NA> 14 2.28 95.28
## 5 300 13 2.12 97.40
## 6 84 4 0.65 98.05
## 7 240 4 0.65 98.70
## 8 120 3 0.49 99.19
## 9 36 2 0.33 99.52
## 10 60 2 0.33 99.85
## 11 12 1 0.16 100.00
## Credit_History frequency percentage cumulative_perc
## 1 1 525 85.5 85.5
## 2 0 89 14.5 100.0
## Property_Area frequency percentage cumulative_perc
## 1 Semiurban 233 37.95 37.95
## 2 Urban 202 32.90 70.85
## 3 Rural 179 29.15 100.00
## Loan_Status frequency percentage cumulative_perc
## 1 Y 422 68.73 68.73
## 2 N 192 31.27 100.00
## loan.train
##
## 12 Variables 614 Observations
## --------------------------------------------------------------------------------
## Gender
## n missing distinct
## 614 0 2
##
## Value Female Male
## Frequency 112 502
## Proportion 0.182 0.818
## --------------------------------------------------------------------------------
## Married
## n missing distinct
## 614 0 2
##
## Value No Yes
## Frequency 213 401
## Proportion 0.347 0.653
## --------------------------------------------------------------------------------
## Dependents
## n missing distinct
## 614 0 4
##
## Value 0 1 2 3+
## Frequency 360 102 101 51
## Proportion 0.586 0.166 0.164 0.083
## --------------------------------------------------------------------------------
## Education
## n missing distinct
## 614 0 2
##
## Value Graduate Not Graduate
## Frequency 480 134
## Proportion 0.782 0.218
## --------------------------------------------------------------------------------
## Self_Employed
## n missing distinct
## 582 32 2
##
## Value No Yes
## Frequency 500 82
## Proportion 0.859 0.141
## --------------------------------------------------------------------------------
## ApplicantIncome
## n missing distinct Info Mean Gmd .05 .10
## 614 0 505 1 5403 4183 1898 2216
## .25 .50 .75 .90 .95
## 2878 3812 5795 9460 14583
##
## lowest : 150 210 416 645 674, highest: 39147 39999 51763 63337 81000
## --------------------------------------------------------------------------------
## CoapplicantIncome
## n missing distinct Info Mean Gmd .05 .10
## 614 0 287 0.912 1621 2118 0 0
## .25 .50 .75 .90 .95
## 0 1188 2297 3782 4997
##
## lowest : 0.00 16.12 189.00 240.00 242.00
## highest: 10968.00 11300.00 20000.00 33837.00 41667.00
## --------------------------------------------------------------------------------
## LoanAmount
## n missing distinct Info Mean Gmd .05 .10
## 614 0 204 1 146.4 77.79 57.3 72.3
## .25 .50 .75 .90 .95
## 100.2 129.0 164.8 229.4 293.4
##
## lowest : 9 17 25 26 30, highest: 500 570 600 650 700
## --------------------------------------------------------------------------------
## Loan_Amount_Term
## n missing distinct
## 600 14 10
##
## lowest : 12 36 60 84 120, highest: 180 240 300 360 480
##
## Value 12 36 60 84 120 180 240 300 360 480
## Frequency 1 2 2 4 3 44 4 13 512 15
## Proportion 0.002 0.003 0.003 0.007 0.005 0.073 0.007 0.022 0.853 0.025
## --------------------------------------------------------------------------------
## Credit_History
## n missing distinct
## 614 0 2
##
## Value 0 1
## Frequency 89 525
## Proportion 0.145 0.855
## --------------------------------------------------------------------------------
## Property_Area
## n missing distinct
## 614 0 3
##
## Value Rural Semiurban Urban
## Frequency 179 233 202
## Proportion 0.292 0.379 0.329
## --------------------------------------------------------------------------------
## Loan_Status
## n missing distinct
## 614 0 2
##
## Value N Y
## Frequency 192 422
## Proportion 0.313 0.687
## --------------------------------------------------------------------------------Tugas 4
Lakukan pemeriksaan distribusi densitas menggunakan R dan Python pada setiap variabel kuantitatif dengan beberapa bagian sebagai berikut:
Univariat numerik
Applicant Income
ggplot(loan.train, aes(x = ApplicantIncome)) +
geom_density()
Coapplicant Income
ggplot(loan.train, aes(x = CoapplicantIncome)) +
geom_density()
Loan Amount
ggplot(loan.train, aes(x = LoanAmount)) +
geom_density()
Bivariat numerik
Applicant Income vs Coapplicant Income
p1 <- ggplot(loan.train, aes(x = ApplicantIncome, y = CoapplicantIncome)) +
geom_point(alpha = .5) +
geom_density_2d()
ggplotly(p1)Coapplicant Income vs LoanAmount
p2 <-ggplot(loan.train, aes(x = CoapplicantIncome, y = LoanAmount)) +
geom_point(alpha = .5) +
geom_density_2d()
ggplotly(p2)ApplicantIncome vs LoanAmount
p3 <- ggplot(loan.train, aes(x = ApplicantIncome, y = LoanAmount)) +
geom_point(alpha = .5) +
geom_density_2d()
ggplotly(p3)Multivariat numerik
library(GGally)
ggpairs(loantrain)
Tugas 5
Lakukan proses pengujian Hipotesis menggunakan R dan Python pada setiap variabel kuantitatif dengan beberapa bagian sebagai berikut:
Hitunglah margin of error dan estimasi interval untuk proporsi peminjam bejenis kelamin perempuan dalam pada tingkat kepercayaan 95%.
library(MASS)
k = sum(loan.train$Gender == "Female")
n = length(loan.train$Gender)
pbar = k/n
SE = sqrt(pbar*(1-pbar)/n); SE ## [1] 0.01558505E = qnorm(0.975)*SE; E## [1] 0.03054614pbar + c(-E, E)## [1] 0.1518643 0.2129566Pada tingkat kepercayaan 95%, antara 15,2% dan 21,3% peminjam bejenis kelamin perempuan, dan margin of error adalah 3,05%.
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)
p = 0.5
E = 0.05
zstar^2*p*(1-p)/E^2 ## [1] 384.1459Lakukan pembuktian kebenaran assumsi dengan tingakat signifikansi 0.05, jika Bank mengklaim bahwa pinjaman rata-rata konsumen adalah:
Lebih besar $ 150.
mu0 = 150
xbar = mean(loan.train$LoanAmount)
s = sd(loan.train$LoanAmount)
n = length(loan.train$LoanAmount)
t = (xbar-mu0)/(s/sqrt(n)) ; t## [1] -1.057899alpha = 0.05
t.alpha = qt(1-alpha, df=n-1)
-t.alpha## [1] -1.647343Karena \(\mu_0 \ge \mu\), dalam hal ini kita harus fokus pada nilai kritis left tail. Di sini, ditemukan bahwa statistik uji -1.057899 lebih besar dari nilai kritis -1.644854. Akibatnya, pada tingkat signifikansi 0,05, kami menolak klaim bahwa rata-rata pinjaman konsumen lebih dari 150 dolar.
Lebih kecil $ 150
mu0 = 150
xbar = mean(loan.train$LoanAmount)
s = sd(loan.train$LoanAmount)
n = length(loan.train$LoanAmount)
t = (xbar-mu0)/(s/sqrt(n)) ; t## [1] -1.057899alpha = 0.05
t.alpha = qt(1-alpha, df=n-1)
t.alpha## [1] 1.647343Nilai statistiknya -1.058 lebih kecil dari nilai kritis yaitu 1.645. maka pada tingkat signifikan 0.05, kita menerima bahwa rata-rata pinjaman konsumen kurang dari 150 dolar.
Sama dengan $ 150.
mu0 = 150
xbar = mean(loan.train$LoanAmount)
s = sd(loan.train$LoanAmount)
n = length(loan.train$LoanAmount)
t = (xbar-mu0)/(s/sqrt(n)) ; t## [1] -1.057899alpha = .05
t.half.alpha = qt(1-alpha/2, df=n-1)
c(-t.half.alpha, t.half.alpha) ## [1] -1.963841 1.963841Statistik uji -1.057899 terletak di antara nilai kritis -1,96 dan 1,96. Oleh karena itu, pada tingkat signifikansi 0,05, kita tidak menolak hipotesis nol bahwa rata-rata penguin tidak jauh berbeda dari 150.
Lakukan pembuktian kebenaran assumsi dengan tingakat signifikansi 0.05, seperti diatas jika diketahui simpangan baku pinjaman adalah $ 85.
Lebih besar $ 150.
mu0 = 150
xbar = mean(loan.train$LoanAmount)
sigma = 85
n = length(loan.train$LoanAmount)
z = (xbar-mu0)/(sigma/sqrt(n)) ; z## [1] -1.045919alpha = 0.05
z.alpha = qnorm(1-alpha)
-z.alpha## [1] -1.644854Karena \(\mu_0 \ge \mu\), dalam hal ini kita harus fokus pada nilai kritis left tail. Di sini, ditemukan bahwa statistik uji -1.045 lebih besar dari nilai kritis -1.644854. Akibatnya, pada tingkat signifikansi 0,05, kami menolak klaim bahwa rata-rata pinjaman konsumen lebih dari 150 dolar.
Lebih kecil $ 150
mu0 = 150
xbar = mean(loan.train$LoanAmount)
sigma = 85
n = length(loan.train$LoanAmount)
z = (xbar-mu0)/(sigma/sqrt(n)) ; z## [1] -1.045919alpha = 0.05
z.alpha = qnorm(1-alpha)
z.alpha## [1] 1.644854Nilai statistiknya -1.046 lebih kecil dari nilai kritis yaitu 1.645. maka pada tingkat signifikan 0.05, kita menerima bahwa rata-rata pinjaman konsumen kurang dari 150 dolar.
Sama dengan $ 150.
mu0 = 150
xbar = mean(loan.train$LoanAmount)
sigma = 85
n = length(loan.train$LoanAmount)
z = (xbar-mu0)/(sigma/sqrt(n)) ; z## [1] -1.045919alpha = .05
z.half.alpha = qnorm(1-alpha/2)
c(-z.half.alpha, z.half.alpha) ## [1] -1.959964 1.959964Statistik uji -1.046 terletak di antara nilai kritis -1,96 dan 1,96. Oleh karena itu, pada tingkat signifikansi 0,05, kita tidak menolak hipotesis nol bahwa rata-rata penguin tidak jauh berbeda dari 150.
---
title: "KOMPUTASI STATISTIKA"
subtitle: "~ Ujian Tengah Semester ~"
author: "Vanessa SUpit"
date:  "`r format(Sys.Date(), '%B %d, %Y')`"
output:
  rmdformats::readthedown:   # https://github.com/juba/rmdformats
    self_contained: true
    thumbnails: true
    lightbox: true
    gallery: true
    lib_dir: libs
    df_print: "paged"
    code_folding: "show"
    code_download: yes

---

```{r include=FALSE}
knitr::opts_chunk$set(class.source = "nocopy",
                      class.output = "nocopy",
                      message = F,
                      warning = F)

library(reticulate)
library(Rcpp)
library(dplyr)
library(ggplot2)
library(plotly)
library(mvtnorm)
library(MASS)
library(ggthemes)
library(rpart.plot)
library(cowplot)
```

<br>


|
:---- |:----
**Kontak**| **: $\downarrow$**
Email| dsciencelabs@outlook.com
Instagram | https://www.instagram.com/dsciencelabs/ 
RPubs  | https://rpubs.com/dsciencelabs/ 

***

# Data Set



# Tugas 1

Lakukan proses persiapan data dengan R dan Python, dengan beberapa langkah berikut:

## Import Data

```{r}
loan_train <- read.csv("loan-train.csv", stringsAsFactors = T, na.strings=c("","","NA"))
```

## Penanganan Data Hilang

Kita cek tipe data dan nilai NA dari data :
```{r}
summary(loan_train)
glimpse(loan_train)
```

```{r}
anyNA(loan_train)
colSums(is.na(loan_train))
```

Ada dua tipe data yang perlu diubah: 

* Loan_Amount_Term : Ubah sebagai tipe data faktor 
* Credit_History : Ubah sebagai tipe data faktor

```{r}
names(loan_train)[1] <- "Loan_ID"
loan.train <- loan_train %>% 
  dplyr::select(-Loan_ID) %>% 
  mutate(Loan_Amount_Term = as.factor(Loan_Amount_Term),
         Credit_History = as.factor(Credit_History))

head(loan.train)
```
Ada juga nilai NA berdasarkan pemeriksaan awal pada:

* LoanAmount
* Loan_Amount_Term
* Credit_History


Fungsi untuk data cleansing :
```{r}
Mode = function(x){
  a = table(x)
  b = max(a)
  if(all(a == b))
    mod = NA
  else if(is.numeric(x))
    mod = as.numeric(names(a))[a==b]
    else
      mod = names(a)[a==b]
  return(mod)
}
```

Untuk membuat hasil keseluruhan yang lebih baik, kita akan mencoba mengganti nilai yang hilang/ NA berdasarkan tipenya: 

* Data dengan nilai tipe Numerik yang hilang akan diganti dengan nilai rata-ratanya (menggunakan fungsi mean()). 
* Nilai data dengan tipe data faktor akan diganti dengan nilai yang memiliki jumlah kemunculan tertinggi dalam kumpulan datanya (menggunakan fungsi mode()).

```{r}
loan.train$Gender[is.na(loan.train$Gender)] <-  Mode(loan.train$Gender)
loan.train$Married[is.na(loan.train$Married)] <- Mode(loan.train$Married)
loan.train$Dependents[is.na(loan.train$Dependents)] <-  Mode(loan.train$Dependents)
loan.train$Credit_History[is.na(loan.train$Credit_History)] <- Mode(loan.train$Credit_History)
```

```{r}
loan.train$LoanAmount[is.na(loan.train$LoanAmount)] <- mean(loan.train$LoanAmount, na.rm = T)
loan.train$Loan_Amount_Term[is.na(loan.train$Loan_Amount_Term)] <- mean(loan.train$Loan_Amount_Term, na.rm = T)
summary(loan.train)
na.omit(loan.train)
```

## Periksa Data Duplikat

```{r}
sum(duplicated(loan.train))
```
Tidak ada data duplikat

## Pemisahan Data Kategori dan Numerik

Kategori
```{r}
Cat_data <- loan.train%>% dplyr::select_if(is.factor)
names(Cat_data)
```

Numerik
```{r}
Num_data <- loan.train%>% dplyr::select_if(is.numeric)
names(Num_data)
```

## Penanganan Data Numerik
## Penganann Data Pencilan

## Penanganan Data Kategorikal


# Tugas 2

Lakukan Proses Visualisasi Data dengan menggunakan R dan Python dengan beberapa langkah berikut:

## Visualisasi Univariabel

### Categorical 

```{r include=FALSE}
library(ggplot2)
library(dplyr)
library(scales) 
```

**Gender**

```{r}
plotdata <- loan.train %>% 
  count(Gender) %>%
  mutate(pct = n / sum(n),
         pctlabel = paste0(round(pct*100), "%"))

ggplot(plotdata, 
       aes(x = reorder(Gender, -pct),
           y = pct)) + 
  geom_bar(stat = "identity", 
          color = "azure4") +
  geom_text(aes(label = pctlabel), 
            vjust = -0.25) +
  theme_minimal() +                                  # use a minimal theme
  scale_y_continuous(labels = percent) +
  labs(x = "Gender", 
       y = "Percent", 
       title  = "Loan by gender")

```


**Married** 

```{r}
plotdata <- loan.train %>% 
  count(Married) %>%
  mutate(pct = n / sum(n),
         pctlabel = paste0(round(pct*100), "%"))

ggplot(plotdata, 
       aes(x = reorder(Married, -pct),
           y = pct)) + 
  geom_bar(stat = "identity", 
          color = "azure4") +
  geom_text(aes(label = pctlabel), 
            vjust = -0.25) +
  theme_minimal() +                                  # use a minimal theme
  scale_y_continuous(labels = percent) +
  labs(x = "Married", 
       y = "Percent", 
       title  = "Loan by married")

```

**Dependents** 

```{r}
plotdata <- loan.train %>% 
  count(Dependents) %>%
  mutate(pct = n / sum(n),
         pctlabel = paste0(round(pct*100), "%"))

ggplot(plotdata, 
       aes(x = reorder(Dependents, -pct),
           y = pct)) + 
  geom_bar(stat = "identity", 
          color = "azure4") +
  geom_text(aes(label = pctlabel), 
            vjust = -0.25) +
  theme_minimal() +                                  # use a minimal theme
  scale_y_continuous(labels = percent) +
  labs(x = "Dependents", 
       y = "Percent", 
       title  = "Loan by Dependents")

```

**Education** 

```{r}
plotdata <- loan.train %>% 
  count(Education) %>%
  mutate(pct = n / sum(n),
         pctlabel = paste0(round(pct*100), "%"))

ggplot(plotdata, 
       aes(x = reorder(Education, -pct),
           y = pct)) + 
  geom_bar(stat = "identity", 
          color = "azure4") +
  geom_text(aes(label = pctlabel), 
            vjust = -0.25) +
  theme_minimal() +                                  # use a minimal theme
  scale_y_continuous(labels = percent) +
  labs(x = "Education", 
       y = "Percent", 
       title  = "Loan by Education")

```

**Self_employed**


```{r}
plotdata <- loan.train %>% 
  count(Self_Employed) %>%
  mutate(pct = n / sum(n),
         pctlabel = paste0(round(pct*100), "%"))

ggplot(plotdata, 
       aes(x = reorder(Self_Employed, -pct),
           y = pct)) + 
  geom_bar(stat = "identity", 
          color = "azure4") +
  geom_text(aes(label = pctlabel), 
            vjust = -0.25) +
  theme_minimal() +                                  # use a minimal theme
  scale_y_continuous(labels = percent) +
  labs(x = "Self_Employed", 
       y = "Percent", 
       title  = "Loan by Self_Employed")

```

**Loan_Amount_Term**


```{r}
plotdata <- loan.train %>% 
  count(Loan_Amount_Term) %>%
  mutate(pct = n / sum(n),
         pctlabel = paste0(round(pct*100), "%"))

ggplot(plotdata, 
       aes(x = reorder(Loan_Amount_Term, -pct),
           y = pct)) + 
  geom_bar(stat = "identity", 
          color = "azure4") +
  geom_text(aes(label = pctlabel), 
            vjust = -0.25) +
  theme_minimal() +                                  
  scale_y_continuous(labels = percent) +
  labs(x = "Loan_Amount_Term", 
       y = "Percent", 
       title  = "Loan by Loan_Amount_Term")

```

**Credit_History**

```{r}
plotdata <- loan.train %>% 
  count(Credit_History) %>%
  mutate(pct = n / sum(n),
         pctlabel = paste0(round(pct*100), "%"))

ggplot(plotdata, 
       aes(x = reorder(Credit_History, -pct),
           y = pct)) + 
  geom_bar(stat = "identity", 
          color = "azure4") +
  geom_text(aes(label = pctlabel), 
            vjust = -0.25) +
  theme_minimal() +                                  
  scale_y_continuous(labels = percent) +
  labs(x = "Credit_History", 
       y = "Percent", 
       title  = "Loan by Credit_History")

```


**Property_Area**

```{r}
plotdata <- loan.train %>% 
  count(Property_Area) %>%
  mutate(pct = n / sum(n),
         pctlabel = paste0(round(pct*100), "%"))

ggplot(plotdata, 
       aes(x = reorder(Property_Area, -pct),
           y = pct)) + 
  geom_bar(stat = "identity", 
          color = "azure4") +
  geom_text(aes(label = pctlabel), 
            vjust = -0.25) +
  theme_minimal() +                                  
  scale_y_continuous(labels = percent) +
  labs(x = "Property_Area", 
       y = "Percent", 
       title  = "Loan by Property_Area")

```

### Numerical 

**ApplicantIncome**

```{r}
ggplot(loan.train, aes(x = ApplicantIncome)) +
  geom_histogram(fill = "cornflowerblue", 
                 color = "white",bins = 20) + 
  theme_minimal() +                                  # use a minimal theme
  labs(title="Loan by ApplicantIncome",
       x = "ApplicantIncome")
```

**CoapplicantIncome**

```{r}
ggplot(loan.train, aes(x = CoapplicantIncome)) +
  geom_histogram(fill = "cornflowerblue", 
                 color = "white",bins = 20) + 
  theme_minimal() +                                  # use a minimal theme
  labs(title="Loan by CoapplicantIncome",
       x = "CoapplicantIncome")
```

**LoanAmount**

```{r}

ggplot(loan.train, aes(x = LoanAmount)) +
  geom_histogram(fill = "cornflowerblue", 
                 color = "white",bins = 20) + 
  theme_minimal() +                                  # use a minimal theme
  labs(title="Loan by LoanAmount",
       x = "LoanAmount")

```

## Visualisasi Bivariabel

### Categorical vs Categorical

**Gender vs Married**

```{r}

ggplot(loan.train, 
       aes(x = Gender, 
           fill = Married)) + 
  geom_bar(position = "fill") +
  theme_minimal() +                                  
  labs(y = "Proportion")
```

**Gender vs Education**

```{r}
ggplot(loan.train, 
       aes(x = Gender, 
           fill = Education)) + 
  geom_bar(position = "fill") +
  theme_minimal() +                                  
  labs(y = "Proportion")
```

**Gender vs Dependents**

```{r}
ggplot(loan.train, 
       aes(x = Gender, 
           fill = Dependents)) + 
  geom_bar(position = "fill") +
  theme_minimal() +                                  
  labs(y = "Proportion")
```

**Gender vs Self_Employed**

```{r}
ggplot(loan.train, 
       aes(x = Gender, 
           fill = Self_Employed)) + 
  geom_bar(position = "fill") +
  theme_minimal() +                                  
  labs(y = "Proportion")
```

**Gender vs loanAmountTerm**

```{r}
ggplot(loan.train, 
       aes(x = Gender, 
           fill = Loan_Amount_Term)) + 
  geom_bar(position = "fill") +
  theme_minimal() +                                  
  labs(y = "Proportion")
```

**Gender vs credit_history**

```{r}
ggplot(loan.train, 
       aes(x = Gender, 
           fill = Credit_History)) + 
  geom_bar(position = "fill") +
  theme_minimal() +                                  
  labs(y = "Proportion")
```

**Gender vs PropertyArea**

```{r}
ggplot(loan.train, 
       aes(x = Gender, 
           fill = Property_Area)) + 
  geom_bar(position = "fill") +
  theme_minimal() +                                  
  labs(y = "Proportion")
```

**Gender vs LoanStatus**

```{r}
ggplot(loan.train, 
       aes(x = Gender, 
           fill = Loan_Status )) + 
  geom_bar(position = "fill") +
  theme_minimal() +                                  
  labs(y = "Proportion")
```

**Married vs Dependents**

```{r}
ggplot(loan.train, 
       aes(x = Married, 
           fill = Education)) + 
  geom_bar(position = "fill") +
  theme_minimal() +                                  
  labs(y = "Proportion")
```

**Married vs Education**

```{r}
ggplot(loan.train, 
       aes(x = Married, 
           fill = Dependents)) + 
  geom_bar(position = "fill") +
  theme_minimal() +                                  
  labs(y = "Proportion")
```

**Married vs Self_Employed**

```{r}
ggplot(loan.train, 
       aes(x = Married, 
           fill = Self_Employed)) + 
  geom_bar(position = "fill") +
  theme_minimal() +                                  
  labs(y = "Proportion")
```

**Married vs loanAmountTerm**

```{r}
ggplot(loan.train, 
       aes(x = Married, 
           fill = Loan_Amount_Term)) + 
  geom_bar(position = "fill") +
  theme_minimal() +                                  
  labs(y = "Proportion")
```

**Married vs credit_history**

```{r}
ggplot(loan.train, 
       aes(x = Married, 
           fill = Credit_History)) + 
  geom_bar(position = "fill") +
  theme_minimal() +                                  
  labs(y = "Proportion")
```

**Married vs PropertyArea**

```{r}
ggplot(loan.train, 
       aes(x = Married, 
           fill = Property_Area)) + 
  geom_bar(position = "fill") +
  theme_minimal() +                                  
  labs(y = "Proportion")
```

**Married vs LoanStatus**

```{r}
ggplot(loan.train, 
       aes(x = Married, 
           fill = Loan_Status )) + 
  geom_bar(position = "fill") +
  theme_minimal() +                                  
  labs(y = "Proportion")
```

**Dependents vs Education**

```{r}
ggplot(loan.train, 
       aes(x = Dependents, 
           fill = Education)) + 
  geom_bar(position = "fill") +
  theme_minimal() +                                  
  labs(y = "Proportion")
```

**Dependents vs Self_Employed**

```{r}
ggplot(loan.train, 
       aes(x = Dependents, 
           fill = Self_Employed)) + 
  geom_bar(position = "fill") +
  theme_minimal() +                                  
  labs(y = "Proportion")
```

**Dependents vs loanAmountTerm**

```{r}
ggplot(loan.train, 
       aes(x = Dependents, 
           fill = Loan_Amount_Term)) + 
  geom_bar(position = "fill") +
  theme_minimal() +                                  
  labs(y = "Proportion")
```

**Dependents vs credit_history**

```{r}
ggplot(loan.train, 
       aes(x = Dependents, 
           fill = Credit_History)) + 
  geom_bar(position = "fill") +
  theme_minimal() +                                  
  labs(y = "Proportion")
```

**Dependents vs PropertyArea**

```{r}
ggplot(loan.train, 
       aes(x = Dependents, 
           fill = Property_Area)) + 
  geom_bar(position = "fill") +
  theme_minimal() +                                  
  labs(y = "Proportion")
```

**Dependents vs LoanStatus**

```{r}
ggplot(loan.train, 
       aes(x = Dependents, 
           fill = Loan_Status )) + 
  geom_bar(position = "fill") +
  theme_minimal() +                                  
  labs(y = "Proportion")
```

**Education vs Self_Employed**

```{r}
ggplot(loan.train, 
       aes(x = Education, 
           fill = Self_Employed)) + 
  geom_bar(position = "fill") +
  theme_minimal() +                                  
  labs(y = "Proportion")
```

**Education vs loanAmountTerm**

```{r}
ggplot(loan.train, 
       aes(x = Education, 
           fill = Loan_Amount_Term)) + 
  geom_bar(position = "fill") +
  theme_minimal() +                                  
  labs(y = "Proportion")
```

**Education vs credit_history**

```{r}
ggplot(loan.train, 
       aes(x = Education, 
           fill = Credit_History)) + 
  geom_bar(position = "fill") +
  theme_minimal() +                                  
  labs(y = "Proportion")
```

**Education vs PropertyArea**

```{r}
ggplot(loan.train, 
       aes(x = Education, 
           fill = Property_Area)) + 
  geom_bar(position = "fill") +
  theme_minimal() +                                  
  labs(y = "Proportion")
```

**Education vs LoanStatus**

```{r}
ggplot(loan.train, 
       aes(x = Education, 
           fill = Loan_Status )) + 
  geom_bar(position = "fill") +
  theme_minimal() +                                  
  labs(y = "Proportion")
```

**Self_Employed vs loanAmountTerm**

```{r}
ggplot(loan.train, 
       aes(x = Self_Employed, 
           fill = Loan_Amount_Term)) + 
  geom_bar(position = "fill") +
  theme_minimal() +                                  
  labs(y = "Proportion")
```

**Self_Employed vs credit_history**

```{r}
ggplot(loan.train, 
       aes(x = Self_Employed, 
           fill = Credit_History)) + 
  geom_bar(position = "fill") +
  theme_minimal() +                                  
  labs(y = "Proportion")
```

**Self_Employed vs PropertyArea**

```{r}
ggplot(loan.train, 
       aes(x = Self_Employed, 
           fill = Property_Area)) + 
  geom_bar(position = "fill") +
  theme_minimal() +                                  
  labs(y = "Proportion")
```

**Self_Employed vs LoanStatus**

```{r}
ggplot(loan.train, 
       aes(x = Self_Employed, 
           fill = Loan_Status )) + 
  geom_bar(position = "fill") +
  theme_minimal() +                                  
  labs(y = "Proportion")
```

**Loan_Amount_Term vs credit_history**

```{r}
ggplot(loan.train, 
       aes(x = Loan_Amount_Term, 
           fill = Credit_History)) + 
  geom_bar(position = "fill") +
  theme_minimal() +                                  
  labs(y = "Proportion")
```

**Loan_Amount_Term vs PropertyArea**

```{r}
ggplot(loan.train, 
       aes(x = Loan_Amount_Term, 
           fill = Property_Area)) + 
  geom_bar(position = "fill") +
  theme_minimal() +                                  
  labs(y = "Proportion")
```

**Loan_Amount_Term vs LoanStatus**

```{r}
ggplot(loan.train, 
       aes(x = Loan_Amount_Term, 
           fill = Loan_Status )) + 
  geom_bar(position = "fill") +
  theme_minimal() +                                  
  labs(y = "Proportion")
```

**Credit_History vs PropertyArea**

```{r}
ggplot(loan.train, 
       aes(x = Credit_History, 
           fill = Property_Area)) + 
  geom_bar(position = "fill") +
  theme_minimal() +                                  
  labs(y = "Proportion")
```

**Credit_History vs LoanStatus**

```{r}
ggplot(loan.train, 
       aes(x = Credit_History, 
           fill = Loan_Status )) + 
  geom_bar(position = "fill") +
  theme_minimal() +                                  
  labs(y = "Proportion")
```

**Property_Area vs LoanStatus**

```{r}
ggplot(loan.train, 
       aes(x = Property_Area, 
           fill = Loan_Status )) + 
  geom_bar(position = "fill") +
  theme_minimal() +                                  
  labs(y = "Proportion")
```


### Numerical vs Numerical

**ApplicantIncome vs CoapplicantIncome**

```{r}
ggplot(loan.train, 
       aes(x = ApplicantIncome, 
           y = CoapplicantIncome)) +
  geom_point(color="cornflowerblue", 
             size = 1.5, 
             alpha=.8) +
  scale_y_continuous(label = scales::dollar, 
                     limits = c(0, 50000)) +
  scale_x_continuous(breaks = seq(0, 40000, 5000), 
                     limits=c(0, 50000)) +
  theme_minimal() +                                  # use a minimal theme
  labs(x = "ApplicantIncome",
       y = "",
       title = "ApplicantIncome vs CoapplicantIncome")
```

**CoapplicantIncome vs LoanAmount**

```{r}
ggplot(loan.train, 
       aes(x = CoapplicantIncome, 
           y = LoanAmount)) +
  geom_point(color="cornflowerblue", 
             size = 1.5, 
             alpha=.8) +
  scale_y_continuous(label = scales::dollar, 
                     limits = c(0, 800)) +
  scale_x_continuous(breaks = seq(0, 40000, 5000), 
                     limits=c(0, 50000)) +
  theme_minimal() +                                  # use a minimal theme
  labs(x = "CoapplicantIncome",
       y = "",
       title = "CoapplicantIncome vs LoanAmount")
```

**ApplicantIncome vs LoanAmount**

```{r}
ggplot(loan.train, 
       aes(x = ApplicantIncome, 
           y = LoanAmount)) +
  geom_point(color="cornflowerblue", 
             size = 1, 
             alpha=.8) +
  scale_y_continuous(label = scales::dollar, 
                     limits = c(0, 800)) +
  scale_x_continuous(breaks = seq(0, 40000, 5000), 
                     limits=c(0, 50000)) +
  theme_minimal() +                                  # use a minimal theme
  labs(x = "ApplicantIncome",
       y = "",
       title = "ApplicantIncome vs LoanAmount")
```

### Categorical vs Numerical

**ApplicantIncome vs Gender**

```{r}
ggplot(loan.train, 
       aes(x = ApplicantIncome, 
           fill = Gender)) +
  geom_density(alpha = 0.4) +
  theme_minimal() +
  labs(title = "Applicant Income by Gender")
```

**ApplicantIncome vs Married**

```{r}
ggplot(loan.train, 
       aes(x = ApplicantIncome, 
           fill = Married)) +
  geom_density(alpha = 0.4) +
  theme_minimal() +
  labs(title = "Applicant Income by Married")
```


**ApplicantIncome vs Dependents**

```{r}
ggplot(loan.train, 
       aes(x = ApplicantIncome, 
           fill = Dependents)) +
  geom_density(alpha = 0.4) +
  theme_minimal() +
  labs(title = "Applicant Income by Dependents")
```

**ApplicantIncome vs Education**

```{r}
ggplot(loan.train, 
       aes(x = ApplicantIncome, 
           fill = Education)) +
  geom_density(alpha = 0.4) +
  theme_minimal() +
  labs(title = "Applicant Income by Education")
```

**ApplicantIncome vs Self_Employed**

```{r}
ggplot(loan.train, 
       aes(x = ApplicantIncome, 
           fill = Self_Employed)) +
  geom_density(alpha = 0.4) +
  theme_minimal() +
  labs(title = "Applicant Income by Self_Employed")
```

**ApplicantIncome vs Loan_Amount_Term**

```{r}
library(ggridges)                                    # to handle overlapping visulization
ggplot(loan.train, 
       aes(x = ApplicantIncome, 
           y = Loan_Amount_Term, 
           fill = Loan_Amount_Term)) +
  geom_density_ridges(alpha = 0.7) + 
  theme_ridges() +
  theme(legend.position = "none")
```

**ApplicantIncome vs Credit_History**

```{r}
library(ggridges)                                    # to handle overlapping visulization
ggplot(loan.train, 
       aes(x = ApplicantIncome, 
           y = Credit_History, 
           fill = Credit_History)) +
  geom_density_ridges(alpha = 0.7) + 
  theme_ridges() +
  theme(legend.position = "none")
```

**ApplicantIncome vs Property_Area**

```{r}
library(ggridges)                                    # to handle overlapping visulization
ggplot(loan.train, 
       aes(x = ApplicantIncome, 
           y = Property_Area, 
           fill = Property_Area)) +
  geom_density_ridges(alpha = 0.7) + 
  theme_ridges() +
  theme(legend.position = "none")
```

**ApplicantIncome vs Loan_Status**

```{r}
library(ggridges)                                    # to handle overlapping visulization
ggplot(loan.train, 
       aes(x = ApplicantIncome, 
           y = Loan_Status, 
           fill = Loan_Status)) +
  geom_density_ridges(alpha = 0.7) + 
  theme_ridges() +
  theme(legend.position = "none")
```

**CoapplicantIncome vs Gender**

```{r}
ggplot(loan.train, 
       aes(x = CoapplicantIncome, 
           fill = Gender)) +
  geom_density(alpha = 0.4) +
  theme_minimal() +
  labs(title = "CoapplicantIncome by Gender")
```

**CoapplicantIncome vs Married**

```{r}
ggplot(loan.train, 
       aes(x = CoapplicantIncome, 
           fill = Married)) +
  geom_density(alpha = 0.4) +
  theme_minimal() +
  labs(title = "CoapplicantIncome by Married")
```


**CoapplicantIncome vs Dependents**

```{r}
ggplot(loan.train, 
       aes(x = CoapplicantIncome, 
           fill = Dependents)) +
  geom_density(alpha = 0.4) +
  theme_minimal() +
  labs(title = "CoapplicantIncome by Dependents")
```

**CoapplicantIncome vs Education**

```{r}
ggplot(loan.train, 
       aes(x = CoapplicantIncome, 
           fill = Education)) +
  geom_density(alpha = 0.4) +
  theme_minimal() +
  labs(title = "CoapplicantIncome by Education")
```

**CoapplicantIncome vs Self_Employed**

```{r}
ggplot(loan.train, 
       aes(x = ApplicantIncome, 
           fill = Self_Employed)) +
  geom_density(alpha = 0.4) +
  theme_minimal() +
  labs(title = "CoapplicantIncome by Self_Employed")
```

**CoapplicantIncome vs Loan_Amount_Term**

```{r}
library(ggridges)                                    # to handle overlapping visulization
ggplot(loan.train, 
       aes(x = CoapplicantIncome, 
           y = Loan_Amount_Term, 
           fill = Loan_Amount_Term)) +
  geom_density_ridges(alpha = 0.7) + 
  theme_ridges() +
  theme(legend.position = "none")
```

**CoapplicantIncome vs Credit_History**

```{r}
library(ggridges)                                    # to handle overlapping visulization
ggplot(loan.train, 
       aes(x = CoapplicantIncome, 
           y = Credit_History, 
           fill = Credit_History)) +
  geom_density_ridges(alpha = 0.7) + 
  theme_ridges() +
  theme(legend.position = "none")
```

**CoapplicantIncome vs Property_Area**

```{r}
library(ggridges)                                    # to handle overlapping visulization
ggplot(loan.train, 
       aes(x = CoapplicantIncome, 
           y = Property_Area, 
           fill = Property_Area)) +
  geom_density_ridges(alpha = 0.7) + 
  theme_ridges() +
  theme(legend.position = "none")
```

**CoapplicantIncome vs Loan_Status**

```{r}
library(ggridges)                                    # to handle overlapping visulization
ggplot(loan.train, 
       aes(x = CoapplicantIncome, 
           y = Loan_Status, 
           fill = Loan_Status)) +
  geom_density_ridges(alpha = 0.7) + 
  theme_ridges() +
  theme(legend.position = "none")
```

**LoanAmount vs Gender**

```{r}
ggplot(loan.train, 
       aes(x = LoanAmount, 
           fill = Gender)) +
  geom_density(alpha = 0.4) +
  theme_minimal() +
  labs(title = "LoanAmount by Gender")
```

**LoanAmount vs Married**

```{r}
ggplot(loan.train, 
       aes(x = LoanAmount, 
           fill = Married)) +
  geom_density(alpha = 0.4) +
  theme_minimal() +
  labs(title = "LoanAmount by Married")
```


**LoanAmount vs Dependents**

```{r}
ggplot(loan.train, 
       aes(x = LoanAmount, 
           fill = Dependents)) +
  geom_density(alpha = 0.4) +
  theme_minimal() +
  labs(title = "LoanAmount by Dependents")
```

**LoanAmount vs Education**

```{r}
ggplot(loan.train, 
       aes(x = LoanAmount, 
           fill = Education)) +
  geom_density(alpha = 0.4) +
  theme_minimal() +
  labs(title = "LoanAmount by Education")
```

**LoanAmount vs Self_Employed**

```{r}
ggplot(loan.train, 
       aes(x = LoanAmount, 
           fill = Self_Employed)) +
  geom_density(alpha = 0.4) +
  theme_minimal() +
  labs(title = "LoanAmount by Self_Employed")
```

**LoanAmount vs Loan_Amount_Term**

```{r}
library(ggridges)                                    # to handle overlapping visulization
ggplot(loan.train, 
       aes(x = LoanAmount, 
           y = Loan_Amount_Term, 
           fill = Loan_Amount_Term)) +
  geom_density_ridges(alpha = 0.7) + 
  theme_ridges() +
  theme(legend.position = "none")
```

**LoanAmount vs Credit_History**

```{r}
library(ggridges)                                    # to handle overlapping visulization
ggplot(loan.train, 
       aes(x = CoapplicantIncome, 
           y = Credit_History, 
           fill = Credit_History)) +
  geom_density_ridges(alpha = 0.7) + 
  theme_ridges() +
  theme(legend.position = "none")
```

**LoanAmount vs Property_Area**

```{r}
library(ggridges)                                    # to handle overlapping visulization
ggplot(loan.train, 
       aes(x = LoanAmount, 
           y = Property_Area, 
           fill = Property_Area)) +
  geom_density_ridges(alpha = 0.7) + 
  theme_ridges() +
  theme(legend.position = "none")
```

**LoanAmount vs Loan_Status**
```{r}
library(ggridges)                                    # to handle overlapping visulization
ggplot(loan.train, 
       aes(x = LoanAmount, 
           y = Loan_Status, 
           fill = Loan_Status)) +
  geom_density_ridges(alpha = 0.7) + 
  theme_ridges() +
  theme(legend.position = "none")
```

* Visualisasi Multivariabel


## Visualisasi Multivariabel


**ApplicantIncome by Married, Gender, and Loan Term**

```{r}
ggplot(loan.train, 
       aes(x = Loan_Amount_Term, 
           y = ApplicantIncome, 
           color = Married, 
           shape = Gender)) +
  geom_point(size = 3, alpha = .6) +
  theme_minimal() +
  labs(title = "ApplicantIncome by Married, Gender, and Loan Term")
```

**ApplicantIncome by Education, Gender, and Loan Term**

```{r}
ggplot(loan.train, 
       aes(x = Loan_Amount_Term, 
           y = ApplicantIncome, 
           color = Education, 
           shape = Gender)) +
  geom_point(size = 3, alpha = .6) +
  theme_minimal() +
  labs(title = "ApplicantIncome by Married, Gender, and Loan Term")
```

**ApplicantIncome by Dependents, Gender, and Loan Term**

```{r}
ggplot(loan.train, 
       aes(x = Loan_Amount_Term, 
           y = ApplicantIncome, 
           color = Dependents, 
           shape = Gender)) +
  geom_point(size = 3, alpha = .6) +
  theme_minimal() +
  labs(title = "ApplicantIncome by Dependents, Gender, and Loan Term")
```

**ApplicantIncome by Self_Employed, Gender, and Loan Term**

```{r}
ggplot(loan.train, 
       aes(x = Loan_Amount_Term, 
           y = ApplicantIncome, 
           color = Self_Employed, 
           shape = Gender)) +
  geom_point(size = 3, alpha = .6) +
  theme_minimal() +
  labs(title = "ApplicantIncome by Self_Employed, Gender, and Loan Term")
```

**ApplicantIncome by Credit_History, Gender, and Loan Term**

```{r}
ggplot(loan.train, 
       aes(x = Loan_Amount_Term, 
           y = ApplicantIncome, 
           color = Credit_History, 
           shape = Gender)) +
  geom_point(size = 3, alpha = .6) +
  theme_minimal() +
  labs(title = "ApplicantIncome by Credit_History, Gender, and Loan Term")
```

**ApplicantIncome by Property_Area, Gender, and Loan Term**

```{r}
ggplot(loan.train, 
       aes(x = Loan_Amount_Term, 
           y = ApplicantIncome, 
           color = Property_Area, 
           shape = Gender)) +
  geom_point(size = 3, alpha = .6) +
  theme_minimal() +
  labs(title = "ApplicantIncome by Property_Area, Gender, and Loan Term")
```

**ApplicantIncome by Loan_Status, Gender, and Loan Term**

```{r}
ggplot(loan.train, 
       aes(x = Loan_Amount_Term, 
           y = ApplicantIncome, 
           color = Loan_Status, 
           shape = Gender)) +
  geom_point(size = 3, alpha = .6) +
  theme_minimal() +
  labs(title = "ApplicantIncome by Loan_Status, Gender, and Loan Term")
```

**CoapplicantIncome by Married, Gender, and Loan Term**

```{r}
ggplot(loan.train, 
       aes(x = Loan_Amount_Term, 
           y = CoapplicantIncome, 
           color = Married, 
           shape = Gender)) +
  geom_point(size = 3, alpha = .6) +
  theme_minimal() +
  labs(title = "CoapplicantIncome by Married, Gender, and Loan Term")
```

**CoapplicantIncome by Education, Gender, and Loan Term**

```{r}
ggplot(loan.train, 
       aes(x = Loan_Amount_Term, 
           y = CoapplicantIncome, 
           color = Education, 
           shape = Gender)) +
  geom_point(size = 3, alpha = .6) +
  theme_minimal() +
  labs(title = "CoapplicantIncome by Married, Gender, and Loan Term")
```

**CoapplicantIncome by Dependents, Gender, and Loan Term**

```{r}
ggplot(loan.train, 
       aes(x = Loan_Amount_Term, 
           y = CoapplicantIncome, 
           color = Dependents, 
           shape = Gender)) +
  geom_point(size = 3, alpha = .6) +
  theme_minimal() +
  labs(title = "CoapplicantIncome by Dependents, Gender, and Loan Term")
```

**CoapplicantIncome by Self_Employed, Gender, and Loan Term**

```{r}
ggplot(loan.train, 
       aes(x = Loan_Amount_Term, 
           y = CoapplicantIncome, 
           color = Self_Employed, 
           shape = Gender)) +
  geom_point(size = 3, alpha = .6) +
  theme_minimal() +
  labs(title = "CoapplicantIncome by Self_Employed, Gender, and Loan Term")
```

**CoapplicantIncome by Credit_History, Gender, and Loan Term**

```{r}
ggplot(loan.train, 
       aes(x = Loan_Amount_Term, 
           y = CoapplicantIncome, 
           color = Credit_History, 
           shape = Gender)) +
  geom_point(size = 3, alpha = .6) +
  theme_minimal() +
  labs(title = "CoapplicantIncome by Credit_History, Gender, and Loan Term")
```

**CoapplicantIncome by Property_Area, Gender, and Loan Term**

```{r}
ggplot(loan.train, 
       aes(x = Loan_Amount_Term, 
           y = CoapplicantIncome, 
           color = Property_Area, 
           shape = Gender)) +
  geom_point(size = 3, alpha = .6) +
  theme_minimal() +
  labs(title = "CoapplicantIncome by Property_Area, Gender, and Loan Term")
```

**CoapplicantIncome by Loan_Status, Gender, and Loan Term**

```{r}
ggplot(loan.train, 
       aes(x = Loan_Amount_Term, 
           y = CoapplicantIncome, 
           color = Loan_Status, 
           shape = Gender)) +
  geom_point(size = 3, alpha = .6) +
  theme_minimal() +
  labs(title = "CoapplicantIncome by Loan_Status, Gender, and Loan Term")
```

**LoanAmount by Married, Gender, and Loan Term**

```{r}
ggplot(loan.train, 
       aes(x = Loan_Amount_Term, 
           y = LoanAmount, 
           color = Married, 
           shape = Gender)) +
  geom_point(size = 3, alpha = .6) +
  theme_minimal() +
  labs(title = "LoanAmount by Married, Gender, and Loan Term")
```

**LoanAmount by Education, Gender, and Loan Term**

```{r}
ggplot(loan.train, 
       aes(x = Loan_Amount_Term, 
           y = LoanAmount, 
           color = Education, 
           shape = Gender)) +
  geom_point(size = 3, alpha = .6) +
  theme_minimal() +
  labs(title = "LoanAmount by Married, Gender, and Loan Term")
```

**LoanAmount by Dependents, Gender, and Loan Term**

```{r}
ggplot(loan.train, 
       aes(x = Loan_Amount_Term, 
           y = LoanAmount, 
           color = Dependents, 
           shape = Gender)) +
  geom_point(size = 3, alpha = .6) +
  theme_minimal() +
  labs(title = "LoanAmount by Dependents, Gender, and Loan Term")
```

**LoanAmount by Self_Employed, Gender, and Loan Term**

```{r}
ggplot(loan.train, 
       aes(x = Loan_Amount_Term, 
           y = LoanAmount, 
           color = Self_Employed, 
           shape = Gender)) +
  geom_point(size = 3, alpha = .6) +
  theme_minimal() +
  labs(title = "LoanAmount by Self_Employed, Gender, and Loan Term")
```

**LoanAmount by Credit_History, Gender, and Loan Term**

```{r}
ggplot(loan.train, 
       aes(x = Loan_Amount_Term, 
           y = LoanAmount, 
           color = Credit_History, 
           shape = Gender)) +
  geom_point(size = 3, alpha = .6) +
  theme_minimal() +
  labs(title = "LoanAmount by Credit_History, Gender, and Loan Term")
```

**LoanAmount by Property_Area, Gender, and Loan Term**

```{r}
ggplot(loan.train, 
       aes(x = Loan_Amount_Term, 
           y = LoanAmount, 
           color = Property_Area, 
           shape = Gender)) +
  geom_point(size = 3, alpha = .6) +
  theme_minimal() +
  labs(title = "LoanAmount by Property_Area, Gender, and Loan Term")
```

**LoanAmount by Loan_Status, Gender, and Loan Term**

```{r}
ggplot(loan.train, 
       aes(x = Loan_Amount_Term, 
           y = LoanAmount, 
           color = Loan_Status, 
           shape = Gender)) +
  geom_point(size = 3, alpha = .6) +
  theme_minimal() +
  labs(title = "LoanAmount by Loan_Status, Gender, and Loan Term")
```



# Tugas 3

Lakukan proses analisa data secara deskriptif menggunakan R dan Python dengan beberapa langkah berikut:

## Kualitatif
### Kategori Univariat
Loan_ID           <fct> LP001002, LP001003, LP001005, LP001006, LP001008, LP001011,~
$ Gender            <fct> Male, Male, Male, Male, Male, Male, Male, Male, Male, Male,~
$ Married           <fct> No, Yes, Yes, Yes, No, Yes, Yes, Yes, Yes, Yes, Yes, Yes, Y~
$ Dependents        <fct> 0, 1, 0, 0, 0, 2, 0, 3+, 2, 1, 2, 2, 2, 0, 2, 0, 1, 0, 0, 0~
$ Education         <fct> Graduate, Graduate, Graduate, Not Graduate, Graduate, Gradu~
$ Self_Employed     <fct> No, No, Yes, No, No, Yes, No, No, No, No, No, NA, No, No, N~
$ ApplicantIncome   <int> 5849, 4583, 3000, 2583, 6000, 5417, 2333, 3036, 4006, 12841~
$ CoapplicantIncome <dbl> 0, 1508, 0, 2358, 0, 4196, 1516, 2504, 1526, 10968, 700, 18~
$ LoanAmount        <int> NA, 128, 66, 120, 141, 267, 95, 158, 168, 349, 70, 109, 200~
$ Loan_Amount_Term  <int> 360, 360, 360, 360, 360, 360, 360, 360, 360, 360, 360, 360,~
$ Credit_History    <int> 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, NA, 0, 1, 1~
$ Property_Area     <fct> Urban, Rural, Urban, Urban, Urban, Urban, Urban, Semiurban,~
$ Loan_Status       <fct> Y, N, Y, Y, Y, Y, Y, N, Y, N, Y, Y, Y, N, Y, Y, Y, N, N, Y,~
```{r}
Cat1 <- table(loan.train$Gender)
Cat1
Cat2 <- table(loan.train$Married)
Cat2
Cat3 <- table(loan.train$Dependents)
Cat3
Cat4 <- table(loan.train$Education)
Cat4
Cat5 <- table(loan.train$Self_Employed)
Cat5
Cat6 <- table(loan.train$Loan_Amount_Term)
Cat6
Cat7 <- table(loan.train$Credit_History)
Cat7
Cat8 <- table(loan.train$Property_Area)
Cat8
Cat9 <- table(loan.train$Loan_Status)
Cat9
```

### Kategori Bivariat
```{r}
bicat1<- loan.train %>%dplyr::select(Gender, Married) %>%table()
bicat1
bicat2<- loan.train %>%dplyr::select(Gender, Dependents) %>%table()
bicat2
bicat3<- loan.train %>%dplyr::select(Gender, Education) %>%table()
bicat3
bicat4<- loan.train %>%dplyr::select(Gender, Self_Employed) %>%table()
bicat4
bicat5<- loan.train %>%dplyr::select(Gender, Loan_Amount_Term) %>%table()
bicat5
bicat6<- loan.train %>%dplyr::select(Gender, Credit_History) %>%table()
bicat6
bicat7<- loan.train %>%dplyr::select(Gender, Property_Area) %>%table()
bicat7
bicat8<- loan.train %>%dplyr::select(Gender, Loan_Status) %>%table()
bicat8

bicat9<- loan.train %>%dplyr::select(Married, Dependents) %>%table()
bicat9
bicat10<- loan.train %>%dplyr::select(Married, Education) %>%table()
bicat10
bicat11<- loan.train %>%dplyr::select(Married, Self_Employed) %>%table()
bicat11
bicat12<- loan.train %>%dplyr::select(Married, Loan_Amount_Term) %>%table()
bicat12
bicat13<- loan.train %>%dplyr::select(Married, Credit_History) %>%table()
bicat13
bicat14<- loan.train %>%dplyr::select(Married, Property_Area) %>%table()
bicat14
bicat15<- loan.train %>%dplyr::select(Married, Loan_Status) %>%table()
bicat15

bicat16<- loan.train %>%dplyr::select(Dependents, Education) %>%table()
bicat16
bicat17<- loan.train %>%dplyr::select(Dependents, Self_Employed) %>%table()
bicat17
bicat18<- loan.train %>%dplyr::select(Dependents, Loan_Amount_Term) %>%table()
bicat18
bicat19<- loan.train %>%dplyr::select(Dependents, Credit_History) %>%table()
bicat19
bicat20<- loan.train %>%dplyr::select(Dependents, Property_Area) %>%table()
bicat20
bicat21<- loan.train %>%dplyr::select(Dependents, Loan_Status) %>%table()
bicat21

bicat22<- loan.train %>%dplyr::select(Education, Self_Employed) %>%table()
bicat22
bicat23<- loan.train %>%dplyr::select(Education, Loan_Amount_Term) %>%table()
bicat23
bicat25<- loan.train %>%dplyr::select(Education, Credit_History) %>%table()
bicat25
bicat26<- loan.train %>%dplyr::select(Education, Property_Area) %>%table()
bicat26
bicat27<- loan.train %>%dplyr::select(Education, Loan_Status) %>%table()
bicat27

bicat28<- loan.train %>%dplyr::select(Self_Employed, Loan_Amount_Term) %>%table()
bicat28
bicat29<- loan.train %>%dplyr::select(Self_Employed, Credit_History) %>%table()
bicat29
bicat30<- loan.train %>%dplyr::select(Self_Employed, Property_Area) %>%table()
bicat30
bicat31<- loan.train %>%dplyr::select(Self_Employed, Loan_Status) %>%table()
bicat31

bicat32<- loan.train %>%dplyr::select(Loan_Amount_Term, Credit_History) %>%table()
bicat32
bicat33<- loan.train %>%dplyr::select(Loan_Amount_Term, Property_Area) %>%table()
bicat33
bicat34<- loan.train %>%dplyr::select(Loan_Amount_Term, Loan_Status) %>%table()
bicat34

bicat35<- loan.train %>%dplyr::select(Credit_History, Property_Area) %>%table()
bicat35
bicat36<- loan.train %>%dplyr::select(Credit_History, Loan_Status) %>%table()
bicat36

bicat37<- loan.train %>%dplyr::select(Property_Area, Loan_Status) %>%table()
bicat37
```

### Kategori Multivariat
```{r}
mulcat1 <- loan.train %>%dplyr::select(Gender, Married, Dependents) %>% ftable()
mulcat1
mulcat2 <- loan.train %>%dplyr::select(Gender, Married, Education) %>% ftable()
mulcat2
mulcat3 <- loan.train %>%dplyr::select(Gender, Married, Self_Employed) %>% ftable()
mulcat3
mulcat4 <- loan.train %>%dplyr::select(Gender, Married, Loan_Amount_Term) %>% ftable()
mulcat4
mulcat5 <- loan.train %>%dplyr::select(Gender, Married, Credit_History) %>% ftable()
mulcat5
mulcat6 <- loan.train %>%dplyr::select(Gender, Married, Property_Area) %>% ftable()
mulcat6
mulcat7 <- loan.train %>%dplyr::select(Gender, Married, Loan_Status) %>% ftable()
mulcat7

mulcat8 <- loan.train %>%dplyr::select(Married, Dependents, Education) %>% ftable()
mulcat8
mulcat9 <- loan.train %>%dplyr::select(Married, Dependents, Self_Employed) %>% ftable()
mulcat9
mulcat11 <- loan.train %>%dplyr::select(Married, Dependents, Loan_Amount_Term) %>% ftable()
mulcat11
mulcat12 <- loan.train %>%dplyr::select(Married, Dependents, Credit_History) %>% ftable()
mulcat12
mulcat13 <- loan.train %>%dplyr::select(Married, Dependents, Property_Area) %>% ftable()
mulcat13
mulcat14 <- loan.train %>%dplyr::select(Married, Dependents, Loan_Status) %>% ftable()
mulcat14

mulcat15 <- loan.train %>%dplyr::select( Dependents, Self_Employed, Loan_Amount_Term) %>% ftable()
mulcat15
mulcat16 <- loan.train %>%dplyr::select(Dependents, Self_Employed, Credit_History) %>% ftable()
mulcat16
mulcat17 <- loan.train %>%dplyr::select(Dependents, Self_Employed, Property_Area) %>% ftable()
mulcat17
mulcat18 <- loan.train %>%dplyr::select(Dependents, Self_Employed, Loan_Status) %>% ftable()
mulcat18
mulcat25 <- loan.train %>%dplyr::select(Dependents, Self_Employed, Married) %>% ftable()
mulcat25
mulcat26 <- loan.train %>%dplyr::select(Dependents, Self_Employed, Gender) %>% ftable()
mulcat26
mulcat27 <- loan.train %>%dplyr::select(Dependents, Self_Employed, Education) %>% ftable()
mulcat27

mulcat19 <- loan.train %>%dplyr::select(Self_Employed, Credit_History, Education) %>% ftable()
mulcat19
mulcat20 <- loan.train %>%dplyr::select(Self_Employed, Credit_History, Property_Area) %>% ftable()
mulcat20
mulcat21 <- loan.train %>%dplyr::select(Self_Employed, Credit_History, Loan_Status) %>% ftable()
mulcat21
mulcat22 <- loan.train %>%dplyr::select(Self_Employed, Credit_History, Married) %>% ftable()
mulcat22
mulcat23 <- loan.train %>%dplyr::select(Self_Employed, Credit_History, Gender) %>% ftable()
mulcat23
mulcat24 <- loan.train %>%dplyr::select(Self_Employed, Credit_History, Loan_Amount_Term) %>% ftable()
mulcat24

mulcat28 <- loan.train %>%dplyr::select(Credit_History, Property_Area, Education) %>% ftable()
mulcat28
mulcat29 <- loan.train %>%dplyr::select(Credit_History, Property_Area, Dependents) %>% ftable()
mulcat29
mulcat30 <- loan.train %>%dplyr::select(Credit_History, Property_Area, Loan_Status) %>% ftable()
mulcat30
mulcat31 <- loan.train %>%dplyr::select(Credit_History, Property_Area, Married) %>% ftable()
mulcat31
mulcat32 <- loan.train %>%dplyr::select(Credit_History, Property_Area, Gender) %>% ftable()
mulcat32
mulcat33 <- loan.train %>%dplyr::select(Credit_History, Property_Area, Loan_Amount_Term) %>% ftable()
mulcat3

```



Memahami distribusi data terhadap status pinjaman (yaitu variabel target) memvisualisasikan variabel kategori dan status pinjaman

```{r}
train.new <- loan.train %>% 
  filter(Loan_Status!="test") %>%
  mutate(Loan_Status=ifelse(Loan_Status=="Y",1,0))

train.new %>% 
  group_by(Loan_Status) %>% 
  summarise(n.count=n()) %>%
  mutate(percent=round(n.count/nrow(train.new)*100,1),
         Loan_Status=as.factor(Loan_Status)) %>%
  ungroup() %>%
  ggplot(aes(x=Loan_Status, y=percent, fill=Loan_Status)) +
  geom_bar(stat="identity")+
  theme_economist_white()
```

```{r}
# buat fungsi untuk memplot banyak variabel kategori dan bagaimana mereka berinteraksi dengan variabel target

PlotSimple <- function(dataframe,x,y){
  aaa <- enquo(x)
  bbb <- enquo(y)
  dataframe %>%
    filter(!is.na(!! aaa), !is.na(!! bbb))  %>%
    group_by(!! aaa,!! bbb) %>%
    summarise(n=n())%>%
    mutate(percent=n/nrow(dataframe)) %>%
    ggplot(aes_(fill=aaa, y=~percent, x=bbb)) +
    geom_bar(position="dodge", stat="identity") +
    theme_economist_white()
}

xvars <- list(as.name("Married"),
              as.name("Credit_History"),
              as.name("Gender"),
              as.name("Education"),
              as.name("Self_Employed"),
              as.name("Property_Area"))

cat.data <- loan.train%>% dplyr::select_if(is.factor)

all_plots<-lapply (xvars, PlotSimple, dataframe=cat.data, y =Loan_Status)
cowplot::plot_grid(plotlist = all_plots)
```

60% dari klien memiliki pinjaman mereka disetujui. Demikian pula, 60% klien yang memiliki riwayat kredit kemungkinan besar akan menyetujui pinjaman mereka. Ini merupakan indikasi sejarah kredit dan persetujuan pinjaman memiliki beberapa korelasi. 29,2% Pemohon yang tinggal di area properti semi-perkotaan cenderung menyetujui pinjaman mereka. Menikah cenderung memiliki status pinjaman disetujui

## Kuantitatif
### Univariat numerik

**Mean**
```{r}
mean(loan.train$ApplicantIncome)
mean(loan.train$CoapplicantIncome)
mean(loan.train$LoanAmount)
```

**Quantile**
```{r}
quantile(loan.train$ApplicantIncome)
quantile(loan.train$CoapplicantIncome)
quantile(loan.train$LoanAmount)
```

**Median**
```{r}
median(loan.train$ApplicantIncome)
median(loan.train$CoapplicantIncome)
median(loan.train$LoanAmount)
```


**Mode**
```{r}
mode(loan.train$ApplicantIncome)
mode(loan.train$CoapplicantIncome)
mode(loan.train$LoanAmount)
```

```{r}
loantrain <- loan.train%>% dplyr::select_if(is.numeric)
summary(loantrain)
```
**Var**
```{r}
var(loan.train$ApplicantIncome)
var(loan.train$CoapplicantIncome)
var(loan.train$LoanAmount)
```

**standar deviation**
```{r}
sd(loan.train$ApplicantIncome)
sd(loan.train$CoapplicantIncome)
sd(loan.train$LoanAmount)
```

**Media Absolute Deviation**
```{r}
mad(loan.train$ApplicantIncome)
mad(loan.train$CoapplicantIncome)
mad(loan.train$LoanAmount)
```

**IQR**
```{r}
IQR(loan.train$ApplicantIncome)
IQR(loan.train$CoapplicantIncome)
IQR(loan.train$LoanAmount)
```

**Skewness**
```{r}
library(e1071)   
skewness(loan.train$ApplicantIncome)
skewness(loan.train$CoapplicantIncome)
skewness(loan.train$LoanAmount)
```

**Kurtosis**
```{r}
kurtosis(loan.train$ApplicantIncome)
kurtosis(loan.train$CoapplicantIncome)
kurtosis(loan.train$LoanAmount)
```
### Bivariat numerik
Z-score
```{r}
cov(loan.train$ApplicantIncome,loan.train$CoapplicantIncome)
cov(loan.train$ApplicantIncome,loan.train$LoanAmount)
cov(loan.train$CoapplicantIncome,loan.train$LoanAmount)

cor(loan.train$ApplicantIncome,loan.train$CoapplicantIncome)
cor(loan.train$ApplicantIncome,loan.train$LoanAmount)
cor(loan.train$CoapplicantIncome,loan.train$LoanAmount)

zscore_applicantincome=(loan.train$ApplicantIncome-mean(loan.train$ApplicantIncome))/sd(loan.train$ApplicantIncome)
zscore_coapplicantincome=(loan.train$CoapplicantIncome-mean(loan.train$CoapplicantIncome))/sd(loan.train$CoapplicantIncome)
zscore_LoanAmount=(loan.train$LoanAmount-mean(loan.train$LoanAmount))/sd(loan.train$LoanAmount)
```

### Multivariat numerik

```{r}
cov(loantrain)
cor(loantrain)
```


```{r}
train.new <- train.new %>%
  mutate(Loan_Status=as.factor(Loan_Status))

varlist <- c("ApplicantIncome", "CoapplicantIncome", "LoanAmount")

PlotFast <- function(varName) {

train.new %>% 
group_by_("Loan_Status") %>% 
dplyr::select_("Loan_Status",varName) %>% 
ggplot(aes_string("Loan_Status",varName,fill="Loan_Status")) + 
    geom_boxplot() +
    theme_economist_white()

}

all_plot_cont<-lapply(varlist,PlotFast)
cowplot::plot_grid(plotlist = all_plot_cont, ncol=3)
```
```{r}
rm(train.new)
```

Sulit untuk melihat pola khusus di antara variabel kontinu saat ini. Ini mungkin berarti bahwa kasus yang disetujui dan tidak disetujui memiliki jumlah pinjaman yang sama, pendapatan pemohon/pemohon.

## EDA dengan cara Malas 

```{r}
library(funModeling) 
library(tidyverse) 
library(Hmisc)
library(skimr)
basic_eda <- function(loan.train)
{
  glimpse(loan.train)
  skim(loan.train)
  df_status(loan.train)
  freq(loan.train) 
  profiling_num(loan.train)
  plot_num(loan.train)
  describe(loan.train)
}
basic_eda(loan.train)
```


# Tugas 4 

Lakukan pemeriksaan distribusi densitas  menggunakan R dan Python pada setiap variabel kuantitatif dengan beberapa bagian sebagai berikut:

## Univariat numerik

**Applicant Income**
```{r}
ggplot(loan.train, aes(x = ApplicantIncome)) +
  geom_density()
```

**Coapplicant Income**
```{r}
ggplot(loan.train, aes(x = CoapplicantIncome)) +
  geom_density()
```


**Loan Amount**
```{r}
ggplot(loan.train, aes(x = LoanAmount)) +
  geom_density()
```


## Bivariat numerik

**Applicant Income vs Coapplicant Income**

```{r}
p1 <- ggplot(loan.train, aes(x = ApplicantIncome, y = CoapplicantIncome)) +
  geom_point(alpha = .5) +
  geom_density_2d()
ggplotly(p1)
```

**Coapplicant Income vs LoanAmount**

```{r}
p2 <-ggplot(loan.train, aes(x = CoapplicantIncome, y = LoanAmount)) +
  geom_point(alpha = .5) +
  geom_density_2d()
ggplotly(p2)
```
**ApplicantIncome vs LoanAmount**

```{r}
p3 <- ggplot(loan.train, aes(x = ApplicantIncome, y = LoanAmount)) +
  geom_point(alpha = .5) +
  geom_density_2d()
ggplotly(p3)
```

## Multivariat numerik

```{r}
library(GGally)
ggpairs(loantrain)
```


# Tugas 5

Lakukan proses pengujian Hipotesis  menggunakan R dan Python pada setiap variabel kuantitatif dengan beberapa bagian sebagai berikut:

## Hitunglah margin of error dan estimasi interval untuk proporsi peminjam bejenis kelamin perempuan dalam pada tingkat kepercayaan 95%. 

```{r}
library(MASS)                                           
k = sum(loan.train$Gender == "Female")
n = length(loan.train$Gender)
pbar = k/n                                             
SE = sqrt(pbar*(1-pbar)/n); SE   
E = qnorm(0.975)*SE; E
pbar + c(-E, E)
```
Pada tingkat kepercayaan 95%, antara 15,2% dan 21,3% peminjam bejenis kelamin perempuan, dan margin of error adalah 3,05%.

## 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%.

```{r}
zstar = qnorm(.975)                                    
p = 0.5                                                
E = 0.05                                               
zstar^2*p*(1-p)/E^2     
```

## Lakukan pembuktian kebenaran assumsi dengan tingakat signifikansi 0.05, jika Bank mengklaim bahwa pinjaman rata-rata konsumen adalah:

### Lebih besar $ 150. 
```{r}
mu0 = 150
xbar = mean(loan.train$LoanAmount)
s = sd(loan.train$LoanAmount)
n = length(loan.train$LoanAmount)
t = (xbar-mu0)/(s/sqrt(n)) ; t
```
```{r}
alpha = 0.05
t.alpha = qt(1-alpha, df=n-1)
-t.alpha
```
Karena $\mu_0 \ge \mu$, dalam hal ini kita harus fokus pada nilai kritis left tail. Di sini, ditemukan bahwa statistik uji -1.057899 lebih besar dari nilai kritis -1.644854. Akibatnya, pada tingkat signifikansi 0,05, kami menolak klaim bahwa rata-rata pinjaman konsumen lebih dari 150 dolar. 

### Lebih kecil $ 150
```{r}
mu0 = 150
xbar = mean(loan.train$LoanAmount)
s = sd(loan.train$LoanAmount)
n = length(loan.train$LoanAmount)
t = (xbar-mu0)/(s/sqrt(n)) ; t
```

```{r}
alpha = 0.05
t.alpha = qt(1-alpha, df=n-1)
t.alpha
```
Nilai statistiknya -1.058 lebih kecil dari nilai kritis yaitu 1.645. maka pada tingkat signifikan 0.05, kita menerima bahwa rata-rata pinjaman konsumen kurang dari 150 dolar. 

### Sama dengan $ 150.

```{r}
mu0 = 150
xbar = mean(loan.train$LoanAmount)
s = sd(loan.train$LoanAmount)
n = length(loan.train$LoanAmount)
t = (xbar-mu0)/(s/sqrt(n)) ; t
```

```{r}
alpha = .05                                           
t.half.alpha = qt(1-alpha/2, df=n-1)                        
c(-t.half.alpha, t.half.alpha)                             
```
Statistik uji -1.057899 terletak di antara nilai kritis -1,96 dan 1,96. Oleh karena itu, pada tingkat signifikansi 0,05, kita tidak menolak hipotesis nol bahwa rata-rata penguin tidak jauh berbeda dari 150.

## Lakukan pembuktian kebenaran assumsi dengan tingakat signifikansi 0.05, seperti diatas jika diketahui simpangan baku pinjaman adalah $ 85. 

### Lebih besar $ 150. 
```{r}
mu0 = 150
xbar = mean(loan.train$LoanAmount)
sigma = 85 
n = length(loan.train$LoanAmount)
z = (xbar-mu0)/(sigma/sqrt(n)) ; z
```

```{r}
alpha = 0.05
z.alpha = qnorm(1-alpha)
-z.alpha
```
Karena $\mu_0 \ge \mu$, dalam hal ini kita harus fokus pada nilai kritis left tail. Di sini, ditemukan bahwa statistik uji -1.045 lebih besar dari nilai kritis -1.644854. Akibatnya, pada tingkat signifikansi 0,05, kami menolak klaim bahwa rata-rata pinjaman konsumen lebih dari 150 dolar. 

### Lebih kecil $ 150
```{r}
mu0 = 150
xbar = mean(loan.train$LoanAmount)
sigma = 85
n = length(loan.train$LoanAmount)
z = (xbar-mu0)/(sigma/sqrt(n)) ; z
```

```{r}
alpha = 0.05
z.alpha = qnorm(1-alpha)
z.alpha
```
Nilai statistiknya -1.046 lebih kecil dari nilai kritis yaitu 1.645. maka pada tingkat signifikan 0.05, kita menerima bahwa rata-rata pinjaman konsumen kurang dari 150 dolar. 

### Sama dengan $ 150.

```{r}
mu0 = 150
xbar = mean(loan.train$LoanAmount)
sigma = 85
n = length(loan.train$LoanAmount)
z = (xbar-mu0)/(sigma/sqrt(n)) ; z
```

```{r}
alpha = .05                                           
z.half.alpha = qnorm(1-alpha/2)                        
c(-z.half.alpha, z.half.alpha)                             
```
Statistik uji -1.046 terletak di antara nilai kritis -1,96 dan 1,96. Oleh karena itu, pada tingkat signifikansi 0,05, kita tidak menolak hipotesis nol bahwa rata-rata penguin tidak jauh berbeda dari 150.

# Referensi

1.   https://bookdown.org/BaktiSiregar/data-science-for-beginners-part-2/3-Hypothesis-Testing.html#one-tailed-population-mean-and-unknown-standard-deviation
2.   ref 2
3.   ref 3




