KOMPUTASI STATISTIKA
~ Ujian Tengah Semester ~
| Kontak | : \(\downarrow\) |
| yosia.yosia@student.matanauniversity.ac.id | |
| yyosia | |
| RPubs | https://rpubs.com/yosia/ |
Data Set
Kumpulan data akan anda gunakan dalam ujian tengah semester ini adalah data konsumen yang melakukan pinjaman di suatu Bank. Dataset ini memiliki 613 observasi, 13 atribut sebagai berikut:
Tugas 1
Lakukan proses persiapan data dengan R dan Python, dengan beberapa langkah berikut:
Import Data
R
df <- read.csv("loan-train.csv", header=T, na.strings=c("","NA"))
dfPython
import pandas as pd
df = pd.read_csv("loan-train.csv")
df## Loan_ID Gender Married ... Credit_History Property_Area Loan_Status
## 0 LP001002 Male No ... 1.0 Urban Y
## 1 LP001003 Male Yes ... 1.0 Rural N
## 2 LP001005 Male Yes ... 1.0 Urban Y
## 3 LP001006 Male Yes ... 1.0 Urban Y
## 4 LP001008 Male No ... 1.0 Urban Y
## .. ... ... ... ... ... ... ...
## 609 LP002978 Female No ... 1.0 Rural Y
## 610 LP002979 Male Yes ... 1.0 Rural Y
## 611 LP002983 Male Yes ... 1.0 Urban Y
## 612 LP002984 Male Yes ... 1.0 Urban Y
## 613 LP002990 Female No ... 0.0 Semiurban N
##
## [614 rows x 13 columns]Penanganan Data Hilang
R
Kita lihat type data dari setiap coloumn
str(df)## 'data.frame': 614 obs. of 13 variables:
## $ Loan_ID : chr "LP001002" "LP001003" "LP001005" "LP001006" ...
## $ Gender : chr "Male" "Male" "Male" "Male" ...
## $ Married : chr "No" "Yes" "Yes" "Yes" ...
## $ Dependents : chr "0" "1" "0" "0" ...
## $ Education : chr "Graduate" "Graduate" "Graduate" "Not Graduate" ...
## $ Self_Employed : chr "No" "No" "Yes" "No" ...
## $ ApplicantIncome : int 5849 4583 3000 2583 6000 5417 2333 3036 4006 12841 ...
## $ CoapplicantIncome: num 0 1508 0 2358 0 ...
## $ LoanAmount : int NA 128 66 120 141 267 95 158 168 349 ...
## $ Loan_Amount_Term : int 360 360 360 360 360 360 360 360 360 360 ...
## $ Credit_History : int 1 1 1 1 1 1 1 0 1 1 ...
## $ Property_Area : chr "Urban" "Rural" "Urban" "Urban" ...
## $ Loan_Status : chr "Y" "N" "Y" "Y" ...colSums(is.na(df))## 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
## 0Lalu setelah itu kita dapat mereplace data yang hilang tersebut dengan 0
df[is.na(df)] = 0
dfPython
df.isna().sum()## Loan_ID 0
## Gender 13
## Married 3
## Dependents 15
## Education 0
## Self_Employed 32
## ApplicantIncome 0
## CoapplicantIncome 0
## LoanAmount 22
## Loan_Amount_Term 14
## Credit_History 50
## Property_Area 0
## Loan_Status 0
## dtype: int64df['Gender'] = df['Gender'].fillna(df['Gender'].value_counts().index[0])
df['Married'] = df['Married'].fillna(method='ffill')
df['Dependents'] = df['Dependents'].fillna((df['Dependents'].mode()).iloc[0])
df['Self_Employed'] = df['Self_Employed'].fillna(method='bfill')
df['LoanAmount'] = df['LoanAmount'].fillna((df['LoanAmount'].median()))
df['Loan_Amount_Term'] = df['Loan_Amount_Term'].fillna((df['Loan_Amount_Term'].mean()))
df['Credit_History'] = df['Credit_History'].interpolate(limit_direction="both")df.isna().sum()## Loan_ID 0
## Gender 0
## Married 0
## Dependents 0
## Education 0
## Self_Employed 0
## ApplicantIncome 0
## CoapplicantIncome 0
## LoanAmount 0
## Loan_Amount_Term 0
## Credit_History 0
## Property_Area 0
## Loan_Status 0
## dtype: int64Periksa Data Duplikat
R
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionduplicated(df)## [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [13] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [25] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [37] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [49] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [61] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [73] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [85] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [97] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [109] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [121] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [133] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [145] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [157] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [169] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [181] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [193] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [205] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [217] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [229] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [241] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [253] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [265] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [277] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [289] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [301] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [313] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [325] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [337] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [349] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [361] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [373] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [385] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [397] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [409] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [421] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [433] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [445] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [457] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [469] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [481] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [493] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [505] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [517] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [529] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [541] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [553] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [565] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [577] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [589] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [601] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [613] FALSE FALSEPython
df[df.duplicated(keep=False)]## Empty DataFrame
## Columns: [Loan_ID, Gender, Married, Dependents, Education, Self_Employed, ApplicantIncome, CoapplicantIncome, LoanAmount, Loan_Amount_Term, Credit_History, Property_Area, Loan_Status]
## Index: []Tidak ada data yang duplikat maka kita bisa melanjutkan analisis data kita
Pemisahan Data Kategori dan Numerik
R
df_kat = select_if(df, is.character)
df_katdf_num = select_if(df, is.numeric)
df_numPython
import numpy as np
df_num = df.select_dtypes(include=[np.number]) # Number
df_kat = df.select_dtypes(exclude=[np.number]) # CategoricalPenanganan Data Numerik
R
Standarisasi
standarisasi_datar = as.data.frame(lapply(df_num, scale))
standarisasi_datarNormalisasi
normalisasi = function(x){return((x-min(x))/(max(x)-min(x)))}
normalisasi_datar = as.data.frame(lapply(df_num,normalisasi))
normalisasi_datarPenskalaan Robust
robust = function(x){return((x-median(x))/(quantile(x,probs=.75)-quantile(x,probs=.25)))}
robust_r = as.data.frame(lapply(df_num, robust))
robust_rPython
Standarisasi
import seaborn as sns
import matplotlib.pyplot as plt
x = sns.pairplot(df)
plt.show(x)
kita buat standarisasi karena data tidak normal.
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
scaler = scaler.fit(df_num)normalized = scaler.transform(df_num)
for i in range(5):
print(normalized[i])## [ 0.07299082 -0.55448733 -0.21124125 0.27985054 0.44783036]
## [-0.13441195 -0.03873155 -0.21124125 0.27985054 0.44783036]
## [-0.39374734 -0.55448733 -0.94899647 0.27985054 0.44783036]
## [-0.46206247 0.2519796 -0.30643547 0.27985054 0.44783036]
## [ 0.09772844 -0.55448733 -0.05655064 0.27985054 0.44783036]inversed = scaler.inverse_transform(normalized)
for i in range(5):
print(inversed[i])## [5.849e+03 0.000e+00 1.280e+02 3.600e+02 1.000e+00]
## [4.583e+03 1.508e+03 1.280e+02 3.600e+02 1.000e+00]
## [3.0e+03 0.0e+00 6.6e+01 3.6e+02 1.0e+00]
## [2.583e+03 2.358e+03 1.200e+02 3.600e+02 1.000e+00]
## [6.00e+03 0.00e+00 1.41e+02 3.60e+02 1.00e+00]Normalisasi
from sklearn.preprocessing import normalize
normal = normalize(df_num)
normal## array([[9.97873194e-01, 0.00000000e+00, 2.18375396e-02, 6.14180800e-02,
## 1.70605778e-04],
## [9.46934419e-01, 3.11581301e-01, 2.64472192e-02, 7.43828040e-02,
## 2.06618900e-04],
## [9.92640004e-01, 0.00000000e+00, 2.18380801e-02, 1.19116800e-01,
## 3.30880001e-04],
## ...,
## [9.98077833e-01, 2.96752577e-02, 3.12826675e-02, 4.45128865e-02,
## 1.23646907e-04],
## [9.98572068e-01, 0.00000000e+00, 2.46252112e-02, 4.74068237e-02,
## 1.31685622e-04],
## [9.96512102e-01, 0.00000000e+00, 2.89190726e-02, 7.82771889e-02,
## 0.00000000e+00]])Penskalaan Robust
from sklearn.preprocessing import RobustScaler
scaler = RobustScaler()
scaler.fit(df_num)RobustScaler()In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
RobustScaler()
scaler.transform(df_num)## array([[ 0.69802913, -0.51735771, 0. , 0. , 0. ],
## [ 0.26409597, 0.13907933, 0. , 0. , 0. ],
## [-0.27849186, -0.51735771, -0.96124031, 0. , 0. ],
## ...,
## [ 1.45998286, -0.41288497, 1.9379845 , 0. , 0. ],
## [ 1.29237361, -0.51735771, 0.91472868, 0. , 0. ],
## [ 0.26409597, -0.51735771, 0.07751938, 0. , -1. ]])Pengananan Data Pencilan
R
dpen <- df
outliers<-function(x){
Q1 <- quantile (x, probs=.25)
Q3 <- quantile(x, probs=.75)
iqr = Q3-Q1
upper_limit =Q3 + (iqr*1.5)
lower_limit =Q1 -(iqr*1.5)
x > upper_limit | x < lower_limit}
outlier1 <- subset(dpen, outliers (dpen$LoanAmount))
outlier2 <- subset(dpen, outliers (dpen$ApplicantIncome))
outlier3 <- subset(dpen, outliers (dpen$CoapplicantIncome))
dpenlier <- rbind(outlier1, outlier2, outlier3) %>% distinct()
dpenlierdata_aa = dpen %>% anti_join (dpenlier)## Joining, by = c("Loan_ID", "Gender", "Married", "Dependents", "Education",
## "Self_Employed", "ApplicantIncome", "CoapplicantIncome", "LoanAmount",
## "Loan_Amount_Term", "Credit_History", "Property_Area", "Loan_Status")data_aaPython
import numpy as np
q25, q75 = np.percentile(df['ApplicantIncome'], 25), np.percentile(df['ApplicantIncome'], 75)
iqr = q75-q25
print(f'Percentile ApplicantIncome: 25th={np.round(q25)}, 75th={np.round(q75)}, IQR={np.round(iqr)}')## Percentile ApplicantIncome: 25th=2878.0, 75th=5795.0, IQR=2918.0cut_off = iqr*1.5
lower, upper = q25 - cut_off, q75 + cut_off
outliers = [x for x in df['ApplicantIncome'] if x < lower or x > upper]
print('Data Pencilan ApplicantIncome: %d' % len(outliers))## Data Pencilan ApplicantIncome: 50df.drop(df[ (df['ApplicantIncome'] > upper) | (df['ApplicantIncome'] < lower) ].index , inplace=True)import numpy as np
q25, q75 = np.percentile(df['CoapplicantIncome'], 25), np.percentile(df['CoapplicantIncome'], 75)
iqr = q75-q25
print(f'Percentile CoapplicantIncome: 25th={np.round(q25)}, 75th={np.round(q75)}, IQR={np.round(iqr)}')## Percentile CoapplicantIncome: 25th=0.0, 75th=2337.0, IQR=2337.0cut_off = iqr*1.5
lower, upper = q25 - cut_off, q75 + cut_off
outliers = [x for x in df['CoapplicantIncome'] if x < lower or x > upper]
print('Data Pencilan CoapplicantIncome: %d' % len(outliers))## Data Pencilan CoapplicantIncome: 16df.drop(df[ (df['CoapplicantIncome'] > upper) | (df['CoapplicantIncome'] < lower) ].index , inplace=True)import numpy as np
q25, q75 = np.percentile(df['LoanAmount'], 25), np.percentile(df['LoanAmount'], 75)
iqr = q75-q25
print(f'Percentile LoanAmount: 25th={np.round(q25)}, 75th={np.round(q75)}, IQR={np.round(iqr)}')## Percentile LoanAmount: 25th=100.0, 75th=155.0, IQR=55.0cut_off = iqr*1.5
lower, upper = q25 - cut_off, q75 + cut_off
outliers = [x for x in df['LoanAmount'] if x < lower or x > upper]
print('Data Pencilan LoanAmount: %d' % len(outliers))## Data Pencilan LoanAmount: 28df.drop(df[ (df['LoanAmount'] > upper) | (df['LoanAmount'] < lower) ].index , inplace=True)import numpy as np
q25, q75 = np.percentile(df['Loan_Amount_Term'], 25), np.percentile(df['Loan_Amount_Term'], 75)
iqr = q75-q25
print(f'Percentile Loan_Amount_Term: 25th={np.round(q25)}, 75th={np.round(q75)}, IQR={np.round(iqr)}')## Percentile Loan_Amount_Term: 25th=360.0, 75th=360.0, IQR=0.0cut_off = iqr*1.5
lower, upper = q25 - cut_off, q75 + cut_off
outliers = [x for x in df['Loan_Amount_Term'] if x < lower or x > upper]
print('Data Pencilan Loan_Amount_Term: %d' % len(outliers))## Data Pencilan Loan_Amount_Term: 89df.drop(df[ (df['Loan_Amount_Term'] > upper) | (df['Loan_Amount_Term'] < lower) ].index , inplace=True)import numpy as np
q25, q75 = np.percentile(df['Credit_History'], 25), np.percentile(df['Credit_History'], 75)
iqr = q75-q25
print(f'Percentile ApplicantIncome: 25th={np.round(q25)}, 75th={np.round(q75)}, IQR={np.round(iqr)}')## Percentile ApplicantIncome: 25th=1.0, 75th=1.0, IQR=0.0cut_off = iqr*1.5
lower, upper = q25 - cut_off, q75 + cut_off
outliers = [x for x in df['Credit_History'] if x < lower or x > upper]
print('Data Pencilan Credit_History: %d' % len(outliers))## Data Pencilan Credit_History: 69df.drop(df[ (df['Credit_History'] > upper) | (df['Credit_History'] < lower) ].index , inplace=True)Data yang sudah tidak ada pencilan
df.shape ## (362, 13)Penanganan Data Kategorikal
R
df_kat %>% summarise_all(n_distinct)GenderLabel <-factor(df_kat$Gender, labels=c(0, 1, 2))
MarriedLabel <-factor(df_kat$Married, labels=c(0, 1, 2))
DependentsLabel <-factor (df_kat$Dependents, labels=c(0, 1, 2, 3))
EducationLabel <-factor(df_kat$Education, labels=c(0, 1))
Self_EmployedLabel <-factor(df_kat$Self_Employed, labels=c(0, 1, 2))
Property_AreaLabel <- factor (df_kat$Property_Area, labels=c(0, 1, 2))
Loan_StatusLabel <-factor (df_kat$Loan_Status, labels=c(0, 1))
df_kat_label <- data.frame("ID" = df_kat$Loan_ID, GenderLabel, MarriedLabel, DependentsLabel, EducationLabel, Self_EmployedLabel, Property_AreaLabel, Loan_StatusLabel)
df_kat_labelPython
df_kat.info()## <class 'pandas.core.frame.DataFrame'>
## RangeIndex: 614 entries, 0 to 613
## Data columns (total 8 columns):
## # Column Non-Null Count Dtype
## --- ------ -------------- -----
## 0 Loan_ID 614 non-null object
## 1 Gender 614 non-null object
## 2 Married 614 non-null object
## 3 Dependents 614 non-null object
## 4 Education 614 non-null object
## 5 Self_Employed 614 non-null object
## 6 Property_Area 614 non-null object
## 7 Loan_Status 614 non-null object
## dtypes: object(8)
## memory usage: 38.5+ KBfrom sklearn.preprocessing import LabelEncoder
label = df_kat.apply(LabelEncoder().fit_transform)
label.head()## Loan_ID Gender Married ... Self_Employed Property_Area Loan_Status
## 0 0 1 0 ... 0 2 1
## 1 1 1 1 ... 0 0 0
## 2 2 1 1 ... 1 2 1
## 3 3 1 1 ... 0 2 1
## 4 4 1 0 ... 0 2 1
##
## [5 rows x 8 columns]df_kat1 = df_kat[['Gender', 'Married','Dependents','Education','Self_Employed','Property_Area','Loan_Status']]
df_kat1.head()## Gender Married Dependents ... Self_Employed Property_Area Loan_Status
## 0 Male No 0 ... No Urban Y
## 1 Male Yes 1 ... No Rural N
## 2 Male Yes 0 ... Yes Urban Y
## 3 Male Yes 0 ... No Urban Y
## 4 Male No 0 ... No Urban Y
##
## [5 rows x 7 columns]import pandas as pd
cat_df = df_kat1.copy()
cat_df = pd.get_dummies(cat_df, columns=['Gender'], prefix = ['Gender'])
print(cat_df)## Married Dependents Education ... Loan_Status Gender_Female Gender_Male
## 0 No 0 Graduate ... Y 0 1
## 1 Yes 1 Graduate ... N 0 1
## 2 Yes 0 Graduate ... Y 0 1
## 3 Yes 0 Not Graduate ... Y 0 1
## 4 No 0 Graduate ... Y 0 1
## .. ... ... ... ... ... ... ...
## 609 No 0 Graduate ... Y 1 0
## 610 Yes 3+ Graduate ... Y 0 1
## 611 Yes 1 Graduate ... Y 0 1
## 612 Yes 2 Graduate ... Y 0 1
## 613 No 0 Graduate ... N 1 0
##
## [614 rows x 8 columns]Tugas 2
Lakukan Proses Visualisasi Data dengan menggunakan R dan Python dengan beberapa langkah berikut:
Visualisasi Univariabel
R
Data Kategori
library(dplyr)
library(gridExtra)##
## Attaching package: 'gridExtra'## The following object is masked from 'package:dplyr':
##
## combinelibrary(ggplot2)
plot_gender <- ggplot(data_aa, aes(x = Gender))+geom_bar()
plot_married <- ggplot(data_aa, aes(x = Married))+geom_bar()
plot_dependents <- ggplot(data_aa, aes(x = Dependents))+geom_bar()
plot_education <- ggplot(data_aa, aes(x = Education))+geom_bar()
plot_selfemployed <- ggplot(data_aa, aes(x = Self_Employed))+geom_bar()
plot_loanamountterm <- ggplot(data_aa, aes(x = Loan_Amount_Term))+geom_bar() + theme(axis.text.x = element_text(angle = 45, vjust = 0.5, hjust=0.5))
plot_credithist<- ggplot(data_aa, aes(x = Credit_History))+geom_bar()
plot_proparea<- ggplot(data_aa, aes(x = Property_Area))+geom_bar()
plot_loanstat<- ggplot(data_aa, aes(x = Loan_Status))+geom_bar()
grid.arrange(plot_gender,plot_married,plot_dependents,plot_education,plot_selfemployed,plot_loanamountterm,plot_credithist,plot_proparea,plot_loanstat)
Data Numerik
data_aa$APPCO_TotalIncome <- data_aa$ApplicantIncome + data_aa$CoapplicantIncome
plot_ApIn <- ggplot(data_aa, aes(x = ApplicantIncome))+geom_histogram(bins = 12, colour = "white")
plot_COAp <- ggplot(data_aa, aes(x = APPCO_TotalIncome))+geom_histogram(bins = 12, colour = "white")
plot_LoAm <- ggplot(data_aa, aes(x = LoanAmount))+geom_histogram(bins = 12, colour = "white")
grid.arrange(plot_ApIn,plot_COAp, plot_LoAm)
Python
Data Kategori
df_kat['Gender'].value_counts().plot(kind='bar', figsize=(12, 6), rot=0, color="red")
df_kat['Education'].value_counts().plot(kind='bar', figsize=(7, 6), rot=0)
df_kat['Dependents'].value_counts().plot(kind='bar', figsize=(7, 6), rot=0, color="gold")
df_kat['Married'].value_counts().plot(kind='pie', figsize=(7, 6), rot=0)
df_kat['Property_Area'].value_counts().plot(kind='pie', figsize=(7, 6), rot=0)
Data Numerik
import seaborn as sns
import matplotlib.pyplot as plt
plt.figure(figsize=(8,6))
sns.set_theme(style="darkgrid")
sns.histplot(data=df, x="LoanAmount", bins=20, kde=True)
plt.show()
plt.figure(figsize=(8,6))
sns.set_theme(style="darkgrid")
sns.histplot(data=df, x="LoanAmount", bins=20, kde=True, color="tomato")
plt.show()
plt.figure(figsize=(8,6))
sns.set_theme(style="darkgrid")
sns.histplot(data=df, x="CoapplicantIncome", bins=20, kde=True, color="crimson")
plt.show()
Visualisasi Bivariabel
R
Category vs Category
plot_Gen_Mar <- ggplot(data_aa, aes(x = Gender, fill = Married)) +
theme_minimal() + # use a minimal theme
geom_bar(position = position_dodge(preserve = "single"))
plot_Gen_Edu <- ggplot(data_aa, aes(x = Gender, fill = Education)) +
theme_minimal() + # use a minimal theme
geom_bar(position = position_dodge(preserve = "single"))
plot_Mar_Edu <- ggplot(data_aa, aes(x = Married, fill = Education)) +
theme_minimal() + # use a minimal theme
geom_bar(position = position_dodge(preserve = "single"))
Plot_Edu_ProAre <- ggplot(data_aa, aes(x = Education, fill = Property_Area)) +
theme_minimal() + # use a minimal theme
geom_bar(position = position_dodge(preserve = "single"))
grid.arrange(plot_Gen_Mar,plot_Gen_Edu,plot_Mar_Edu,Plot_Edu_ProAre)
Numerik vs Numerik
plot_LoAm_ACT <- ggplot(data_aa, aes(x = LoanAmount, y = APPCO_TotalIncome )) +
theme_minimal() + # use a minimal theme
geom_line()
plot_ApIn_CoIn <- ggplot(data_aa, aes(x = ApplicantIncome, y = CoapplicantIncome )) +
theme_minimal() + # use a minimal theme
geom_line()
plot_ApIn_LoAm <- ggplot(data_aa, aes(x = ApplicantIncome, y = LoanAmount )) +
theme_minimal() + # use a minimal theme
geom_line()
grid.arrange(plot_LoAm_ACT,plot_ApIn_CoIn,plot_ApIn_LoAm)
Numerik vs Kategorikal
plot_LoAm_LoStat <- ggplot(data_aa,
aes(x = LoanAmount,
fill = Loan_Status)) +
geom_density(alpha = 0.3) +
theme_minimal() +
labs(title = "Loan Amount distribution by Loan Amount Term")
plot_CoIn_Mar <- ggplot(data_aa,
aes(x = CoapplicantIncome,
fill = Married)) +
geom_density(alpha = 0.3) +
theme_minimal() +
labs(title = "Coapplicant Income distribution by Married")
plot_ApIn_Edu <- ggplot(data_aa,
aes(x = ApplicantIncome,
fill = Education)) +
geom_density(alpha = 0.3) +
theme_minimal() +
labs(title = "Applicant Income distribution by Education")
plot_LoAm_ProAre <- ggplot(data_aa,
aes(x = ApplicantIncome,
fill = Property_Area)) +
geom_density(alpha = 0.3) +
theme_minimal() +
labs(title = "Applicant Income distribution by Property Area")
grid.arrange(plot_LoAm_LoStat, plot_CoIn_Mar, plot_ApIn_Edu, plot_LoAm_ProAre)
Python
df_line = df[['ApplicantIncome','CoapplicantIncome']]
df_line.plot.line()
Numerik vs Numerik
plt.figure(figsize=(8,6))
sns.set_theme(style="darkgrid")
sns.scatterplot(data=df, y="CoapplicantIncome",x="ApplicantIncome")
plt.show()
Category vs Category
sns.set(rc={'figure.figsize':(11.7,8.27)})
plt.subplot(231)
sns.countplot(x="Gender", hue='Loan_Status', data=df)
plt.subplot(232)
sns.countplot(x="Married", hue='Loan_Status', data=df)
plt.subplot(233)
sns.countplot(x="Education", hue='Loan_Status', data=df)
plt.subplot(234)
sns.countplot(x="Self_Employed", hue='Loan_Status', data=df)
plt.subplot(235)
sns.countplot(x="Dependents", hue='Loan_Status', data=df)
plt.subplot(236)
sns.countplot(x="Property_Area", hue='Loan_Status', data=df)
plt.figure(figsize=(8,6))
sns.set_theme(style = "darkgrid")
sns.violinplot(x = "Property_Area",
y = "LoanAmount",
hue = "Gender",
data=df,
palette="Pastel1")
plt.show()
plt.figure(figsize=(8,6))
sns.set_theme(style = "darkgrid")
sns.boxplot(x = "Dependents",
y = "LoanAmount",
hue = "Married",
data=df)
plt.show()
Visualisasi Multivariabel
R
plot_ApIn_LoAm_ProAre <-ggplot(data_aa, aes(x=ApplicantIncome, y=LoanAmount, shape=Property_Area, colour=Property_Area))+geom_point()
plot_ApIn_LoAm_Edu <-ggplot(data_aa, aes(x=ApplicantIncome, y=LoanAmount, shape=Education, colour=Property_Area))+geom_point()
plot_LoAm_CoIn_SeEm <-ggplot(data_aa, aes(x=LoanAmount, y=APPCO_TotalIncome, shape=Self_Employed, colour=Property_Area))+geom_point()
grid.arrange(plot_ApIn_LoAm_ProAre,plot_ApIn_LoAm_Edu, plot_LoAm_CoIn_SeEm)
Python
bins = np.linspace(df.CoapplicantIncome.min(), df.CoapplicantIncome.max(),12)
graph = sns.FacetGrid(df, col="Gender", hue="Loan_Status", palette="Set2", col_wrap=2)
graph.map(plt.hist, 'CoapplicantIncome', bins=bins, ec="k")
graph.axes[-1].legend()
plt.show()
sns.set_theme(style="darkgrid")
plt.figure(figsize=(8,6))
n = sns.lmplot(x = "ApplicantIncome",
y = "LoanAmount",
data = df,
fit_reg = False,
hue = "Property_Area",
legend = False,
markers=["o","+","x"])
plt.legend(loc="lower right")
plt.show(n)
n = sns.pairplot(df,
kind = "scatter",
hue = "Gender",
markers = ["o","s"],
palette = "Set2")
plt.show(n)
Tugas 3
Lakukan proses analisa data secara deskriptif menggunakan R dan Python dengan beberapa langkah berikut:
Kualitatif Kategori Univariat
R
prop.table(table(data_aa$Gender))##
## 0 Female Male
## 0.01682243 0.18691589 0.79626168prop.table(table(data_aa$Married))##
## 0 No Yes
## 0.005607477 0.349532710 0.644859813prop.table(table(data_aa$Dependents))##
## 0 1 2 3+
## 0.60000000 0.15700935 0.16822430 0.07476636prop.table(table(data_aa$Education))##
## Graduate Not Graduate
## 0.7551402 0.2448598prop.table(table(data_aa$Self_Employed))##
## 0 No Yes
## 0.05420561 0.83177570 0.11401869prop.table(table(data_aa$Loan_Amount_Term))##
## 0 12 36 60 84 120
## 0.026168224 0.001869159 0.003738318 0.003738318 0.007476636 0.005607477
## 180 240 300 360 480
## 0.067289720 0.007476636 0.018691589 0.831775701 0.026168224prop.table(table(data_aa$Credit_History))##
## 0 1
## 0.2261682 0.7738318prop.table(table(data_aa$Property_Area))##
## Rural Semiurban Urban
## 0.2990654 0.3831776 0.3177570prop.table(table(data_aa$Loan_Status))##
## N Y
## 0.3046729 0.6953271Python
from collections import Counter
Counter(df_kat['Gender'])## Counter({'Male': 502, 'Female': 112})c = Counter(df_kat['Gender'])
[(i, c[i] / len(df_kat['Gender']) * 100.0) for i in c]## [('Male', 81.75895765472313), ('Female', 18.241042345276874)]Kualitatif Kategori Bivariat
R
data_aa %>% select(Gender, Married) %>% table() ## Married
## Gender 0 No Yes
## 0 0 2 7
## Female 1 72 27
## Male 2 113 311data_aa %>% select(Gender, Education) %>% table()## Education
## Gender Graduate Not Graduate
## 0 8 1
## Female 82 18
## Male 314 112data_aa %>% select(Gender, Property_Area) %>% table() ## Property_Area
## Gender Rural Semiurban Urban
## 0 3 4 2
## Female 24 49 27
## Male 133 152 141data_aa %>% select(Education, Self_Employed) %>% table()## Self_Employed
## Education 0 No Yes
## Graduate 23 335 46
## Not Graduate 6 110 15data_aa %>% select(Gender, Loan_Amount_Term) %>% table()## Loan_Amount_Term
## Gender 0 12 36 60 84 120 180 240 300 360 480
## 0 0 0 0 0 0 0 1 0 0 8 0
## Female 3 0 1 0 1 0 2 1 1 87 4
## Male 11 1 1 2 3 3 33 3 9 350 10data_aa %>% select(Married, Loan_Amount_Term) %>% table()## Loan_Amount_Term
## Married 0 12 36 60 84 120 180 240 300 360 480
## 0 0 0 0 0 0 0 0 1 0 1 1
## No 5 0 2 1 0 1 7 1 3 159 8
## Yes 9 1 0 1 4 2 29 2 7 285 5Python
cat2 = df_kat[['Gender','Property_Area']]
pd.crosstab(cat2.Gender,cat2.Property_Area)## Property_Area Rural Semiurban Urban
## Gender
## Female 24 55 33
## Male 155 178 169cat2 = df_kat[['Dependents','Education']]
pd.crosstab(cat2.Dependents,cat2.Education)## Education Graduate Not Graduate
## Dependents
## 0 286 74
## 1 81 21
## 2 77 24
## 3+ 36 15cat2 = df_kat[['Property_Area','Loan_Status']]
pd.crosstab(cat2.Property_Area,cat2.Loan_Status)## Loan_Status N Y
## Property_Area
## Rural 69 110
## Semiurban 54 179
## Urban 69 133Kualitatif Kategori Multivariat
R
data_aa %>% select(Gender, Married, Education) %>% ftable()## Education Graduate Not Graduate
## Gender Married
## 0 0 0 0
## No 2 0
## Yes 6 1
## Female 0 1 0
## No 60 12
## Yes 21 6
## Male 0 2 0
## No 82 31
## Yes 230 81data_aa %>% select(Gender, Married, Education,Property_Area,Loan_Amount_Term) %>% ftable()## Loan_Amount_Term 0 12 36 60 84 120 180 240 300 360 480
## Gender Married Education Property_Area
## 0 0 Graduate Rural 0 0 0 0 0 0 0 0 0 0 0
## Semiurban 0 0 0 0 0 0 0 0 0 0 0
## Urban 0 0 0 0 0 0 0 0 0 0 0
## Not Graduate Rural 0 0 0 0 0 0 0 0 0 0 0
## Semiurban 0 0 0 0 0 0 0 0 0 0 0
## Urban 0 0 0 0 0 0 0 0 0 0 0
## No Graduate Rural 0 0 0 0 0 0 0 0 0 0 0
## Semiurban 0 0 0 0 0 0 0 0 0 1 0
## Urban 0 0 0 0 0 0 0 0 0 1 0
## Not Graduate Rural 0 0 0 0 0 0 0 0 0 0 0
## Semiurban 0 0 0 0 0 0 0 0 0 0 0
## Urban 0 0 0 0 0 0 0 0 0 0 0
## Yes Graduate Rural 0 0 0 0 0 0 0 0 0 2 0
## Semiurban 0 0 0 0 0 0 0 0 0 3 0
## Urban 0 0 0 0 0 0 1 0 0 0 0
## Not Graduate Rural 0 0 0 0 0 0 0 0 0 1 0
## Semiurban 0 0 0 0 0 0 0 0 0 0 0
## Urban 0 0 0 0 0 0 0 0 0 0 0
## Female 0 Graduate Rural 0 0 0 0 0 0 0 0 0 0 0
## Semiurban 0 0 0 0 0 0 0 1 0 0 0
## Urban 0 0 0 0 0 0 0 0 0 0 0
## Not Graduate Rural 0 0 0 0 0 0 0 0 0 0 0
## Semiurban 0 0 0 0 0 0 0 0 0 0 0
## Urban 0 0 0 0 0 0 0 0 0 0 0
## No Graduate Rural 0 0 0 0 0 0 0 0 0 13 2
## Semiurban 0 0 1 0 0 0 0 0 1 23 0
## Urban 1 0 0 0 0 0 1 0 0 17 1
## Not Graduate Rural 0 0 0 0 0 0 0 0 0 4 0
## Semiurban 0 0 0 0 0 0 0 0 0 5 0
## Urban 2 0 0 0 0 0 0 0 0 1 0
## Yes Graduate Rural 0 0 0 0 0 0 0 0 0 3 0
## Semiurban 0 0 0 0 1 0 1 0 0 11 1
## Urban 0 0 0 0 0 0 0 0 0 4 0
## Not Graduate Rural 0 0 0 0 0 0 0 0 0 2 0
## Semiurban 0 0 0 0 0 0 0 0 0 4 0
## Urban 0 0 0 0 0 0 0 0 0 0 0
## Male 0 Graduate Rural 0 0 0 0 0 0 0 0 0 0 0
## Semiurban 0 0 0 0 0 0 0 0 0 0 1
## Urban 0 0 0 0 0 0 0 0 0 1 0
## Not Graduate Rural 0 0 0 0 0 0 0 0 0 0 0
## Semiurban 0 0 0 0 0 0 0 0 0 0 0
## Urban 0 0 0 0 0 0 0 0 0 0 0
## No Graduate Rural 1 0 0 0 0 0 0 0 0 26 0
## Semiurban 0 0 0 0 0 0 1 0 1 22 2
## Urban 0 0 0 1 0 0 3 0 0 23 2
## Not Graduate Rural 1 0 0 0 0 0 0 0 1 11 0
## Semiurban 0 0 1 0 0 1 0 0 0 9 0
## Urban 0 0 0 0 0 0 2 1 0 3 1
## Yes Graduate Rural 1 0 0 0 2 0 7 0 1 54 0
## Semiurban 2 0 0 0 0 1 3 1 3 79 1
## Urban 3 1 0 0 1 1 4 1 1 63 0
## Not Graduate Rural 1 0 0 0 0 0 3 0 0 24 0
## Semiurban 1 0 0 0 0 0 2 0 1 18 2
## Urban 1 0 0 1 0 0 8 0 1 17 1Python
cat3 = df_kat[['Gender','Property_Area','Loan_Status']]
pd.crosstab([cat3.Gender,cat3.Property_Area], cat3.Loan_Status, rownames=['Gender', 'Property_Area'], colnames=['Loan_Status'])## Loan_Status N Y
## Gender Property_Area
## Female Rural 10 14
## Semiurban 13 42
## Urban 14 19
## Male Rural 59 96
## Semiurban 41 137
## Urban 55 114cat3 = df_kat[['Married','Education','Loan_Status']]
pd.crosstab([cat3.Married,cat3.Education], cat3.Loan_Status, rownames=['Married', 'Education'], colnames=['Loan_Status'])## Loan_Status N Y
## Married Education
## No Graduate 62 107
## Not Graduate 17 28
## Yes Graduate 78 233
## Not Graduate 35 54Kuantitatif Univariat numerik
R
da_numerik_1 <- select_if(data_aa, is.numeric)
summary(da_numerik_1)## ApplicantIncome CoapplicantIncome LoanAmount Loan_Amount_Term
## Min. : 150 Min. : 0 Min. : 0.0 Min. : 0.0
## 1st Qu.: 2752 1st Qu.: 0 1st Qu.: 96.0 1st Qu.:360.0
## Median : 3598 Median :1260 Median :120.0 Median :360.0
## Mean : 4054 Mean :1323 Mean :121.6 Mean :333.2
## 3rd Qu.: 4891 3rd Qu.:2194 3rd Qu.:151.5 3rd Qu.:360.0
## Max. :10139 Max. :5701 Max. :260.0 Max. :480.0
## Credit_History APPCO_TotalIncome
## Min. :0.0000 Min. : 1442
## 1st Qu.:1.0000 1st Qu.: 3900
## Median :1.0000 Median : 5000
## Mean :0.7738 Mean : 5377
## 3rd Qu.:1.0000 3rd Qu.: 6411
## Max. :1.0000 Max. :13746library(moments)
fungsi <- function(x){
data.frame(var(x),
sd(x),
mad(x),
IQR(x),
skewness(x),
kurtosis(x))
}
sapply(da_numerik_1,fungsi)## ApplicantIncome CoapplicantIncome LoanAmount Loan_Amount_Term
## var.x. 3435005 2019827 2551.245 7266.282
## sd.x. 1853.377 1421.206 50.50985 85.24249
## mad.x. 1504.839 1868.076 38.5476 0
## IQR.x. 2138.5 2194 55.5 0
## skewness.x. 1.139029 0.8435314 0.003766545 -2.467683
## kurtosis.x. 4.208463 3.030757 3.554249 9.183479
## Credit_History APPCO_TotalIncome
## var.x. 0.1753439 4049507
## sd.x. 0.4187409 2012.339
## mad.x. 0 1813.22
## IQR.x. 0 2511
## skewness.x. -1.309106 0.9294353
## kurtosis.x. 2.713758 3.963494Python
Mean
import statistics as st
print("Mean:",st.mean(df.ApplicantIncome) )## Mean: 3987.6546961325967Quartile
df.ApplicantIncome.quantile([0, .25, .5, .75, 1])## 0.00 150.00
## 0.25 2698.25
## 0.50 3600.50
## 0.75 4746.25
## 1.00 10000.00
## Name: ApplicantIncome, dtype: float64Median
print("Median:",st.median(df.ApplicantIncome))## Median: 3600.5Mode
print("Mode:",st.mode(df.ApplicantIncome))## Mode: 2500Alternatif Statistic
df.describe()## ApplicantIncome CoapplicantIncome ... Loan_Amount_Term Credit_History
## count 362.000000 362.000000 ... 362.0 362.0
## mean 3987.654696 1319.892597 ... 360.0 1.0
## std 1817.816886 1371.817473 ... 0.0 0.0
## min 150.000000 0.000000 ... 360.0 1.0
## 25% 2698.250000 0.000000 ... 360.0 1.0
## 50% 3600.500000 1339.000000 ... 360.0 1.0
## 75% 4746.250000 2167.750000 ... 360.0 1.0
## max 10000.000000 5625.000000 ... 360.0 1.0
##
## [8 rows x 5 columns]Scale
median=st.median(df.ApplicantIncome)
mad = st.median([abs(number-median) for number in df.ApplicantIncome])
print("Median Absolute Deviation:" ,mad)## Median Absolute Deviation: 1017.5#calculate interquartile range
q3, q1 = np.percentile((df.ApplicantIncome), [75 ,25])
iqr = q3 - q1
#display interquartile range
print("IQR:",iqr)## IQR: 2048.0Skewness
print("Skewness:",df.ApplicantIncome.skew(axis=0))## Skewness: 1.1768488832164874Kurtosis
print("Kurtosis:",df.ApplicantIncome.kurt(axis=0))## Kurtosis: 1.4935966674040806Kuantitatif Bivariat numerik
R
cov(da_numerik_1$ApplicantIncome,da_numerik_1$CoapplicantIncome)## [1] -702662.6cov(da_numerik_1$CoapplicantIncome,da_numerik_1$LoanAmount)## [1] 19171.36cov(da_numerik_1$LoanAmount,da_numerik_1$ApplicantIncome)## [1] 38506.02cov(da_numerik_1$LoanAmount, da_numerik_1$APPCO_TotalIncome)## [1] 57677.38cor(da_numerik_1$ApplicantIncome,da_numerik_1$CoapplicantIncome)## [1] -0.2667633cor(da_numerik_1$CoapplicantIncome,da_numerik_1$LoanAmount)## [1] 0.2670667cor(da_numerik_1$LoanAmount,da_numerik_1$ApplicantIncome)## [1] 0.4113285cor(da_numerik_1$LoanAmount, da_numerik_1$APPCO_TotalIncome)## [1] 0.567451Python
Covariance
print("Covariance:",df.ApplicantIncome.cov(df.LoanAmount))## Covariance: 33566.25470990649Correlation
print("Correlation:",df.ApplicantIncome.corr(df.LoanAmount))## Correlation: 0.46607451449801046import matplotlib.pyplot as plt
correlation_mat = df.corr()## <string>:1: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.mask = np.zeros_like(correlation_mat)
mask[np.triu_indices_from(mask)] = True
with sns.axes_style("white"):
f, ax = plt.subplots(figsize=(7,6))
ax = sns.heatmap(correlation_mat,
mask=mask,annot=True,cmap="YlGnBu")
plt.show()
Z-Score
import scipy.stats as stats
zscores = stats.zscore(df.ApplicantIncome)
print(zscores)## 0 1.025363
## 1 0.327959
## 2 -0.544071
## 3 -0.773785
## 4 1.108544
## ...
## 606 -0.323722
## 607 -0.000361
## 608 -0.416269
## 609 -0.599158
## 612 1.980574
## Name: ApplicantIncome, Length: 362, dtype: float64Kuantitatif Multivariat numerik
R
cov(da_numerik_1)## ApplicantIncome CoapplicantIncome LoanAmount
## ApplicantIncome 3435005.10002 -702662.55117 3.850602e+04
## CoapplicantIncome -702662.55117 2019826.68432 1.917136e+04
## LoanAmount 38506.01963 19171.35832 2.551245e+03
## Loan_Amount_Term -8272.62165 -4656.60781 3.423033e+02
## Credit_History 55.92708 -18.61241 1.349715e-02
## APPCO_TotalIncome 2732342.54885 1317164.13315 5.767738e+04
## Loan_Amount_Term Credit_History APPCO_TotalIncome
## ApplicantIncome -8272.621653 55.92708180 2732342.54885
## CoapplicantIncome -4656.607813 -18.61241094 1317164.13315
## LoanAmount 342.303266 0.01349715 57677.37794
## Loan_Amount_Term 7266.281634 1.72424656 -12929.22947
## Credit_History 1.724247 0.17534390 37.31467
## APPCO_TotalIncome -12929.229466 37.31467086 4049506.68200cor(da_numerik_1)## ApplicantIncome CoapplicantIncome LoanAmount
## ApplicantIncome 1.00000000 -0.26676329 0.4113285245
## CoapplicantIncome -0.26676329 1.00000000 0.2670666904
## LoanAmount 0.41132852 0.26706669 1.0000000000
## Loan_Amount_Term -0.05236286 -0.03843762 0.0795021406
## Credit_History 0.07206313 -0.03127521 0.0006381467
## APPCO_TotalIncome 0.73260587 0.46055530 0.5674509687
## Loan_Amount_Term Credit_History APPCO_TotalIncome
## ApplicantIncome -0.05236286 0.0720631314 0.73260587
## CoapplicantIncome -0.03843762 -0.0312752107 0.46055530
## LoanAmount 0.07950214 0.0006381467 0.56745097
## Loan_Amount_Term 1.00000000 0.0483056487 -0.07537294
## Credit_History 0.04830565 1.0000000000 0.04428261
## APPCO_TotalIncome -0.07537294 0.0442826109 1.00000000Python
Covariance
cof = df.select_dtypes(include=[np.number]) # Number
pd.set_option('display.max_columns', None)
cof.cov()## ApplicantIncome CoapplicantIncome LoanAmount \
## ApplicantIncome 3.304458e+06 -7.126391e+05 33566.254710
## CoapplicantIncome -7.126391e+05 1.881883e+06 16731.448762
## LoanAmount 3.356625e+04 1.673145e+04 1569.620973
## Loan_Amount_Term 0.000000e+00 0.000000e+00 0.000000
## Credit_History 0.000000e+00 0.000000e+00 0.000000
##
## Loan_Amount_Term Credit_History
## ApplicantIncome 0.0 0.0
## CoapplicantIncome 0.0 0.0
## LoanAmount 0.0 0.0
## Loan_Amount_Term 0.0 0.0
## Credit_History 0.0 0.0Covariance
pd.set_option('display.max_columns', None)
cof.corr()## ApplicantIncome CoapplicantIncome LoanAmount \
## ApplicantIncome 1.000000 -0.285774 0.466075
## CoapplicantIncome -0.285774 1.000000 0.307850
## LoanAmount 0.466075 0.307850 1.000000
## Loan_Amount_Term NaN NaN NaN
## Credit_History NaN NaN NaN
##
## Loan_Amount_Term Credit_History
## ApplicantIncome NaN NaN
## CoapplicantIncome NaN NaN
## LoanAmount NaN NaN
## Loan_Amount_Term NaN NaN
## Credit_History NaN NaNEDA dengan cara Malas
R
library(funModeling) ## Loading required package: Hmisc## Loading required package: lattice## Loading required package: survival## Loading required package: Formula##
## Attaching package: 'Hmisc'## The following objects are masked from 'package:dplyr':
##
## src, summarize## The following objects are masked from 'package:base':
##
## format.pval, units## funModeling v.1.9.4 :)
## Examples and tutorials at livebook.datascienceheroes.com
## / Now in Spanish: librovivodecienciadedatos.ailibrary(tidyverse) ## ── Attaching packages
## ───────────────────────────────────────
## tidyverse 1.3.2 ──## ✔ tibble 3.1.8 ✔ purrr 0.3.4
## ✔ tidyr 1.2.0 ✔ stringr 1.4.1
## ✔ readr 2.1.2 ✔ forcats 0.5.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ gridExtra::combine() masks dplyr::combine()
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ✖ Hmisc::src() masks dplyr::src()
## ✖ Hmisc::summarize() masks dplyr::summarize()library(Hmisc)
library(skimr)
eda <- function(data)
{
glimpse(data)
skim(data)
df_status(data)
freq(data)
profiling_num(data)
plot_num(data)
describe(data)
}
eda(df)## Rows: 614
## Columns: 13
## $ Loan_ID <chr> "LP001002", "LP001003", "LP001005", "LP001006", "LP0…
## $ Gender <chr> "Male", "Male", "Male", "Male", "Male", "Male", "Mal…
## $ Married <chr> "No", "Yes", "Yes", "Yes", "No", "Yes", "Yes", "Yes"…
## $ Dependents <chr> "0", "1", "0", "0", "0", "2", "0", "3+", "2", "1", "…
## $ Education <chr> "Graduate", "Graduate", "Graduate", "Not Graduate", …
## $ Self_Employed <chr> "No", "No", "Yes", "No", "No", "Yes", "No", "No", "N…
## $ 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> 0, 128, 66, 120, 141, 267, 95, 158, 168, 349, 70, 10…
## $ Loan_Amount_Term <dbl> 360, 360, 360, 360, 360, 360, 360, 360, 360, 360, 36…
## $ Credit_History <dbl> 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0…
## $ Property_Area <chr> "Urban", "Rural", "Urban", "Urban", "Urban", "Urban"…
## $ Loan_Status <chr> "Y", "N", "Y", "Y", "Y", "Y", "Y", "N", "Y", "N", "Y…
## variable q_zeros p_zeros q_na p_na q_inf p_inf type unique
## 1 Loan_ID 0 0.00 0 0 0 0 character 614
## 2 Gender 13 2.12 0 0 0 0 character 3
## 3 Married 3 0.49 0 0 0 0 character 3
## 4 Dependents 360 58.63 0 0 0 0 character 4
## 5 Education 0 0.00 0 0 0 0 character 2
## 6 Self_Employed 32 5.21 0 0 0 0 character 3
## 7 ApplicantIncome 0 0.00 0 0 0 0 integer 505
## 8 CoapplicantIncome 273 44.46 0 0 0 0 numeric 287
## 9 LoanAmount 22 3.58 0 0 0 0 numeric 204
## 10 Loan_Amount_Term 14 2.28 0 0 0 0 numeric 11
## 11 Credit_History 139 22.64 0 0 0 0 numeric 2
## 12 Property_Area 0 0.00 0 0 0 0 character 3
## 13 Loan_Status 0 0.00 0 0 0 0 character 2## Warning in freq_logic(data = data, input = input[i], plot, na.rm, path_out =
## path_out): Skipping plot for variable 'Loan_ID' (more than 100 categories)## Loan_ID frequency percentage cumulative_perc
## 1 LP001002 1 0.16 0.16
## 2 LP001003 1 0.16 0.32
## 3 LP001005 1 0.16 0.48
## 4 LP001006 1 0.16 0.64
## 5 LP001008 1 0.16 0.80
## 6 LP001011 1 0.16 0.96
## 7 LP001013 1 0.16 1.12
## 8 LP001014 1 0.16 1.28
## 9 LP001018 1 0.16 1.44
## 10 LP001020 1 0.16 1.60
## 11 LP001024 1 0.16 1.76
## 12 LP001027 1 0.16 1.92
## 13 LP001028 1 0.16 2.08
## 14 LP001029 1 0.16 2.24
## 15 LP001030 1 0.16 2.40
## 16 LP001032 1 0.16 2.56
## 17 LP001034 1 0.16 2.72
## 18 LP001036 1 0.16 2.88
## 19 LP001038 1 0.16 3.04
## 20 LP001041 1 0.16 3.20
## 21 LP001043 1 0.16 3.36
## 22 LP001046 1 0.16 3.52
## 23 LP001047 1 0.16 3.68
## 24 LP001050 1 0.16 3.84
## 25 LP001052 1 0.16 4.00
## 26 LP001066 1 0.16 4.16
## 27 LP001068 1 0.16 4.32
## 28 LP001073 1 0.16 4.48
## 29 LP001086 1 0.16 4.64
## 30 LP001087 1 0.16 4.80
## 31 LP001091 1 0.16 4.96
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## 611 LP002979 1 0.16 97.76
## 612 LP002983 1 0.16 97.92
## 613 LP002984 1 0.16 98.08
## 614 LP002990 1 0.16 100.00## Warning: `guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> =
## "none")` instead.## Gender frequency percentage cumulative_perc
## 1 Male 489 79.64 79.64
## 2 Female 112 18.24 97.88
## 3 0 13 2.12 100.00## Warning: `guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> =
## "none")` instead.
## Married frequency percentage cumulative_perc
## 1 Yes 398 64.82 64.82
## 2 No 213 34.69 99.51
## 3 0 3 0.49 100.00## Warning: `guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> =
## "none")` instead.
## 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## Warning: `guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> =
## "none")` instead.
## Education frequency percentage cumulative_perc
## 1 Graduate 480 78.18 78.18
## 2 Not Graduate 134 21.82 100.00## Warning: `guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> =
## "none")` instead.
## Self_Employed frequency percentage cumulative_perc
## 1 No 500 81.43 81.43
## 2 Yes 82 13.36 94.79
## 3 0 32 5.21 100.00## Warning: `guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> =
## "none")` instead.
## 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## Warning: `guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> =
## "none")` instead.
## Loan_Status frequency percentage cumulative_perc
## 1 Y 422 68.73 68.73
## 2 N 192 31.27 100.00## Warning: `guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> =
## "none")` instead.
## data
##
## 13 Variables 614 Observations
## --------------------------------------------------------------------------------
## Loan_ID
## n missing distinct
## 614 0 614
##
## lowest : LP001002 LP001003 LP001005 LP001006 LP001008
## highest: LP002978 LP002979 LP002983 LP002984 LP002990
## --------------------------------------------------------------------------------
## Gender
## n missing distinct
## 614 0 3
##
## Value 0 Female Male
## Frequency 13 112 489
## Proportion 0.021 0.182 0.796
## --------------------------------------------------------------------------------
## Married
## n missing distinct
## 614 0 3
##
## Value 0 No Yes
## Frequency 3 213 398
## Proportion 0.005 0.347 0.648
## --------------------------------------------------------------------------------
## 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
## 614 0 3
##
## Value 0 No Yes
## Frequency 32 500 82
## Proportion 0.052 0.814 0.134
## --------------------------------------------------------------------------------
## 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 1 141.2 84.09 38.6 63.6
## .25 .50 .75 .90 .95
## 98.0 125.0 164.8 229.4 293.4
##
## lowest : 0 9 17 25 26, highest: 500 570 600 650 700
## --------------------------------------------------------------------------------
## Loan_Amount_Term
## n missing distinct Info Mean Gmd .05 .10
## 614 0 11 0.42 334.2 57.12 180 180
## .25 .50 .75 .90 .95
## 360 360 360 360 360
##
## lowest : 0 12 36 60 84, highest: 180 240 300 360 480
##
## Value 0 12 36 60 84 120 180 240 300 360 480
## Frequency 14 1 2 2 4 3 44 4 13 512 15
## Proportion 0.023 0.002 0.003 0.003 0.007 0.005 0.072 0.007 0.021 0.834 0.024
## --------------------------------------------------------------------------------
## Credit_History
## n missing distinct Info Sum Mean Gmd
## 614 0 2 0.525 475 0.7736 0.3508
##
## --------------------------------------------------------------------------------
## 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
## --------------------------------------------------------------------------------Python
Tugas 4
Lakukan pemeriksaan distribusi densitas menggunakan R dan Python pada setiap variabel kuantitatif dengan beberapa bagian sebagai berikut:
Univariat numerik
R
library(ks)
library(MASS)##
## Attaching package: 'MASS'## The following object is masked from 'package:dplyr':
##
## selectfhat<-kde(x=da_numerik_1[,3])
plot_1<-plot(fhat,cont=50,col.cont=4,cont.lwd=2,xlab="LoanAmount",drawpoints=TRUE)
fhat1<-kde(x=da_numerik_1[,2])
plot_2<-plot(fhat,cont=50,col.cont=4,cont.lwd=2,xlab="Coapplicant Income",drawpoints=TRUE)
fhat2<-kde(x=da_numerik_1[,2])
plot_3<-plot(fhat,cont=50,col.cont=4,cont.lwd=2,xlab="AppCoapplicant Income",drawpoints=TRUE)
fhat3<-kde(x=da_numerik_1[,2])
plot_4<-plot(fhat,cont=50,col.cont=4,cont.lwd=2,xlab="Applicant Income",drawpoints=TRUE)
Python
sns.set_theme(style="darkgrid")
fig, axs = plt.subplots(2,2, figsize=(7,7))
sns.kdeplot(data=df, x="ApplicantIncome", color="skyblue", shade=True, ax=axs[0,0])
sns.kdeplot(data=df, x="CoapplicantIncome", color="teal", shade=True, ax=axs[0,1])
sns.kdeplot(data=df, x="LoanAmount", color="gold", shade=True, ax=axs[1,0])
sns.distplot(df['CoapplicantIncome'], hist=True, kde=True,
bins=int(180/5), color = 'darkblue',
hist_kws={'edgecolor':'black'},
kde_kws={'linewidth': 2})
sns.distplot(df['ApplicantIncome'], hist=True, kde=True,
bins=int(180/5), color = 'darkblue',
hist_kws={'edgecolor':'black'},
kde_kws={'linewidth': 2})
sns.distplot(df['LoanAmount'], hist=True, kde=True,
bins=int(180/5), color = 'darkblue',
hist_kws={'edgecolor':'black'},
kde_kws={'linewidth': 2})
Bivariat numerik
R
fhat4<-kde(x=da_numerik_1[,3:4])
plot(fhat4,display="filled.contour",cont=seq(10,90,by=10),lwd=1)
plot(fhat4,display="persp",border=1)
Python
plt.rcParams["figure.figsize"]=(8,6)
sns.set_theme(style="darkgrid")
sns.kdeplot(data = df,
x = "ApplicantIncome",
hue = "Property_Area",
cut = 0,
fill = True,
alpha = 0.4)
plt.show()
plt.rcParams["figure.figsize"]=(8,6)
sns.set_theme(style="darkgrid")
sns.kdeplot(data = df,
x = "LoanAmount",
hue = "Loan_Status",
cut = 0,
fill = True,
alpha = 0.4)
plt.show()
Multivariat numerik
R
fhat5<-kde(x=da_numerik_1[,1:3])
plot(fhat5)
Python
data = df[["ApplicantIncome","CoapplicantIncome","LoanAmount","Loan_Status"]]
n = sns.pairplot(data,
kind = "scatter",
hue = "Loan_Status",
markers = ["o","s"],
palette = "flare")
plt.legend(loc="center right")
plt.show(n)
data = df[["ApplicantIncome","CoapplicantIncome","LoanAmount","Self_Employed"]]
n = sns.pairplot(data,
kind = "scatter",
hue = "Self_Employed",
markers = ["D","s"],
palette = "bright")
plt.legend(loc="center right")
plt.show(n)
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
k = sum(data_aa$Gender == "Female")
n = sum(count(data_aa))
pbar = k/n
SE = sqrt (pbar*(1-pbar)/n); SE## [1] 0.01685443E = qnorm(.975)*SE; E## [1] 0.03303407pbar + c(-E, E)## [1] 0.1538818 0.2199500Python
pd.crosstab(df.Loan_Status, df.Gender)## Gender Female Male
## Loan_Status
## N 16 49
## Y 54 243p_fm = 75/(37+75)
n=37+75
se_female = np.sqrt(p_fm * (1 - p_fm) / n)
print("Standard Error:",se_female)## Standard Error: 0.044443111813668223Confidence Interval
z_score = 1.96
lcb = p_fm - z_score* se_female #lower limit of the CI
ucb = p_fm + z_score* se_female #upper limit of the CI
print("Lower:",lcb)## Lower: 0.5825343579880674print("Upper:",ucb)## Upper: 0.7567513562976468Margin of Error
ME = z_score*se_female
print("Margin of Error:",ME)## Margin of Error: 0.08710849915478971import math
import scipy.stats as ss
P_Gender = df.Gender.value_counts() / len(df)
P_Female = P_Gender.Female
z_critical = ss.norm.ppf(q = 0.975)
margin_error = z_critical * math.sqrt(P_Female *(1 - P_Female)/len(df))
interval = (P_Female - margin_error, P_Female + margin_error)
print("Margin of Error :",margin_error)## Margin of Error : 0.040684190665113print('Interval of Estimation :',interval)## Interval of Estimation : (0.15268597508074336, 0.23405435641096936)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## [1] 384.1459Python
p = 0.5
E = 0.05
z_score**2*p*(1-p)/E**2## 384.1599999999999z_critical = ss.norm.ppf(q = 0.975)
p = 0.5
E = 0.05
sample_needed = int(z_critical**2*p*(1-p)/E**2)
print('ukuran sample yang dibutuhkan :', sample_needed)## ukuran sample yang dibutuhkan : 384Lakukan pembuktian kebenaran assumsi dengan tingakat signifikansi 0.05, jika Bank mengklaim bahwa pinjaman rata-rata konsumen adalah:
- Lebih besar $ 150.
- Lebih kecil $ 150
- Sama dengan $ 150.
R
Lebih besar $ 150
set.seed(100)
Data1 <- sample_n(data_aa,30)
Data1\[ \begin{align*} H_0 &= >= $150 \\ H_1 &= ?? > $150 \\ \end{align*}\\ \]
mu0 = 150
xbar = mean(Data1$LoanAmount)
s = sd(Data1$LoanAmount)
n = sum(count(Data1))
t = (xbar-mu0)/(s/sqrt(n));t## [1] -2.520249alpha = .05
t.alpha = qt(1-alpha, df=n-1)
t.alpha## [1] 1.699127Lebih kecil $ 150
mu0 = 150
xbar = mean(Data1$LoanAmount)
s = sd(Data1$LoanAmount)
n = sum(count(Data1))
t = (xbar-mu0)/(s/sqrt(n));t## [1] -2.520249alpha = .05
t.alpha = qt(1-alpha, df=n-1)
-t.alpha## [1] -1.699127Sama dengan $ 150
mu0 = 150
xbar = mean(Data1$LoanAmount)
s = sd(Data1$LoanAmount)
n = sum(count(Data1))
t = (xbar-mu0)/(s/sqrt(n));t## [1] -2.520249alpha = .05
t.alpha = qt(1-alpha, df=n-1)
t.alpha## [1] 1.699127-t.alpha## [1] -1.699127Python
mu0 = 150
xbar = df['LoanAmount'].mean()
s = df['LoanAmount'].std()
n = len(df)
t = (xbar-mu0)/(s/(n**(1/2)))
print("t hitung",t)## t hitung -12.048412160809425- Lebih besar $ 150.
alpha = 0.05
t_alpha = ss.t.isf(alpha, n - 1)
print("t hitung:",t)## t hitung: -12.048412160809425print("t table 0.5 one tail positive:",t_alpha)## t table 0.5 one tail positive: 1.6490855127686623- Lebih kecil $ 150
alpha = 0.05
t_alpha = ss.t.isf(alpha, n - 1)
print("t hitung",t)## t hitung -12.048412160809425print("t table 0.5 one tail negative:", -t_alpha)## t table 0.5 one tail negative: -1.6490855127686623- Sama dengan $ 150.
alpha = 0.05
t_alpha = ss.t.isf(alpha/2, n-1)
print("t hitung:",t)## t hitung: -12.048412160809425print("t table 0.5 two tail:",-t_alpha, t_alpha)## t table 0.5 two tail: -1.9665570854590666 1.9665570854590666Lakukan pembuktian kebenaran assumsi dengan tingakat signifikansi 0.05, seperti diatas jika diketahui simpangan baku pinjaman adalah $ 85.
R
mu0 = 150
xbar = mean(Data1$LoanAmount)
s = 85
n = sum(count(Data1))
t = (xbar-mu0)/(s/sqrt(n));t## [1] -1.546511alpha = .05
t.alpha = qt(1-alpha, df=n-1)
t.alpha## [1] 1.699127Python
mu0 = 150
xbar = df['LoanAmount'].mean()
s_2 = 85
n = len(df)
t_2 = (xbar-mu0)/(s_2/(n**(1/2)))
print("t hitung:",t_2)## t hitung: -5.6157567343639965- Lebih besar $ 150
alpha = 0.05
t_alpha = ss.t.isf(alpha, n - 1)
print("t hitung:",t_2)## t hitung: -5.6157567343639965print("t table 0.5 one tail positive:",t_alpha)## t table 0.5 one tail positive: 1.6490855127686623- Lebih kecil $ 150
alpha = 0.05
t_alpha = ss.t.isf(alpha, n - 1)
print("t hitung:",t_2)## t hitung: -5.6157567343639965print("t table 0.5 one tail negative:", -t_alpha)## t table 0.5 one tail negative: -1.6490855127686623- Sama dengan $ 150.
alpha = 0.05
t_alpha = ss.t.isf(alpha/2, n - 1)
print("t hitung:",t_2)## t hitung: -5.6157567343639965print("t table 0.5 two tail:",-t_alpha, t_alpha)## t table 0.5 two tail: -1.9665570854590666 1.9665570854590666Referensi
- ref 1
- ref 2
- ref 3
---
title: "KOMPUTASI STATISTIKA"
subtitle: "~ Ujian Tengah Semester ~"
author: "Yosia"
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
    css: "style.css"

---

```{r setup, include=FALSE}
library(reticulate)
library(Rcpp)
use_condaenv("py3_10", required = TRUE)
```


<br>

<img style="float: right; margin: 0px 50px 0px 50px; width:30%" src="poto.jpg"/> 

|
:---- |:----
**Kontak**| **: $\downarrow$**
Email| yosia.yosia@student.matanauniversity.ac.id
Instagram | [yyosia](https://www.instagram.com/yyosia/) 
RPubs  | https://rpubs.com/yosia/ 

***

# Data Set

Kumpulan data akan anda gunakan dalam ujian tengah semester ini adalah data konsumen yang melakukan pinjaman di suatu Bank. Dataset ini memiliki 613 observasi, 13 atribut sebagai berikut: 

<img style="float: center; margin: 0px 0px 0px 0px; width:70%" src="loan_data.png"/> 


# Tugas 1

Lakukan proses persiapan data dengan R dan Python, dengan beberapa langkah berikut:

## Import Data {.tabset .tabset-fade .tabset-pills}

### R 
```{r}
df <- read.csv("loan-train.csv", header=T, na.strings=c("","NA"))
df
```

### Python    

```{python}
import pandas as pd
df = pd.read_csv("loan-train.csv")
df
```


## Penanganan Data Hilang {.tabset .tabset-fade .tabset-pills}

### R

Kita lihat type data dari setiap coloumn
```{r}
str(df)
```

```{r}
colSums(is.na(df))
```
Lalu setelah itu kita dapat mereplace data yang hilang tersebut dengan 0

```{r}
df[is.na(df)] = 0
df
```

### Python

```{python}
df.isna().sum()
```


```{python}
df['Gender'] = df['Gender'].fillna(df['Gender'].value_counts().index[0])
df['Married'] = df['Married'].fillna(method='ffill')
df['Dependents'] = df['Dependents'].fillna((df['Dependents'].mode()).iloc[0])
df['Self_Employed'] = df['Self_Employed'].fillna(method='bfill')
df['LoanAmount'] = df['LoanAmount'].fillna((df['LoanAmount'].median()))
df['Loan_Amount_Term'] = df['Loan_Amount_Term'].fillna((df['Loan_Amount_Term'].mean()))
df['Credit_History'] = df['Credit_History'].interpolate(limit_direction="both")
```

```{python}
df.isna().sum()
```
## Periksa Data Duplikat {.tabset .tabset-fade .tabset-pills}

### R

```{r}
library(dplyr)
duplicated(df)
```

### Python
```{python}
df[df.duplicated(keep=False)]
```

**Tidak ada data yang duplikat maka kita bisa melanjutkan analisis data kita**

## Pemisahan Data Kategori dan Numerik {.tabset .tabset-fade .tabset-pills}

### R
```{r}
df_kat = select_if(df, is.character)
df_kat
```
```{r}
df_num = select_if(df, is.numeric)
df_num
```


### Python
```{python}
import numpy as np
df_num = df.select_dtypes(include=[np.number]) # Number
df_kat = df.select_dtypes(exclude=[np.number]) # Categorical
```


## Penanganan Data Numerik {.tabset .tabset-fade .tabset-pills}

### R

**Standarisasi**

```{r}
standarisasi_datar = as.data.frame(lapply(df_num, scale))
standarisasi_datar
```
**Normalisasi**

```{r}
normalisasi = function(x){return((x-min(x))/(max(x)-min(x)))}

normalisasi_datar = as.data.frame(lapply(df_num,normalisasi))
normalisasi_datar
```

**Penskalaan Robust** 

```{r}
robust = function(x){return((x-median(x))/(quantile(x,probs=.75)-quantile(x,probs=.25)))}

robust_r = as.data.frame(lapply(df_num, robust))
robust_r
```

### Python
**Standarisasi**
```{python}
import seaborn as sns
import matplotlib.pyplot as plt
x = sns.pairplot(df)
plt.show(x)
```

**kita buat standarisasi karena data tidak normal.**

```{python}
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
scaler = scaler.fit(df_num)
```

```{python}
normalized = scaler.transform(df_num)
for i in range(5):
  print(normalized[i])
```

```{python}
inversed = scaler.inverse_transform(normalized)
for i in range(5):
  print(inversed[i])
```
**Normalisasi**
```{python}
from sklearn.preprocessing import normalize
normal = normalize(df_num)
normal
```

**Penskalaan Robust**

```{python}
from sklearn.preprocessing import RobustScaler
scaler = RobustScaler()
scaler.fit(df_num)
```

```{python}
scaler.transform(df_num)
```


## Pengananan Data Pencilan {.tabset .tabset-fade .tabset-pills}

### R
```{r}
dpen <- df
outliers<-function(x){
  Q1 <- quantile (x, probs=.25) 
  Q3 <- quantile(x, probs=.75)
  iqr = Q3-Q1

upper_limit =Q3 + (iqr*1.5) 
lower_limit =Q1 -(iqr*1.5)

x > upper_limit | x < lower_limit}


outlier1 <- subset(dpen, outliers (dpen$LoanAmount))
outlier2 <- subset(dpen, outliers (dpen$ApplicantIncome)) 
outlier3 <- subset(dpen, outliers (dpen$CoapplicantIncome))

dpenlier <- rbind(outlier1, outlier2, outlier3) %>% distinct()

dpenlier
```
```{r}
data_aa = dpen %>% anti_join (dpenlier)
data_aa
```


### Python

```{python}

import numpy as np
q25, q75 = np.percentile(df['ApplicantIncome'], 25), np.percentile(df['ApplicantIncome'], 75)
iqr = q75-q25

print(f'Percentile ApplicantIncome: 25th={np.round(q25)}, 75th={np.round(q75)}, IQR={np.round(iqr)}')

cut_off = iqr*1.5
lower, upper = q25 - cut_off, q75 + cut_off

outliers = [x for x in df['ApplicantIncome'] if x < lower or x > upper]
print('Data Pencilan ApplicantIncome: %d' % len(outliers))

df.drop(df[ (df['ApplicantIncome'] > upper) | (df['ApplicantIncome'] < lower) ].index , inplace=True)
```

```{python}
import numpy as np
q25, q75 = np.percentile(df['CoapplicantIncome'], 25), np.percentile(df['CoapplicantIncome'], 75)
iqr = q75-q25

print(f'Percentile CoapplicantIncome: 25th={np.round(q25)}, 75th={np.round(q75)}, IQR={np.round(iqr)}')

cut_off = iqr*1.5
lower, upper = q25 - cut_off, q75 + cut_off

outliers = [x for x in df['CoapplicantIncome'] if x < lower or x > upper]
print('Data Pencilan CoapplicantIncome: %d' % len(outliers))

df.drop(df[ (df['CoapplicantIncome'] > upper) | (df['CoapplicantIncome'] < lower) ].index , inplace=True)
```

```{python}
import numpy as np
q25, q75 = np.percentile(df['LoanAmount'], 25), np.percentile(df['LoanAmount'], 75)
iqr = q75-q25

print(f'Percentile LoanAmount: 25th={np.round(q25)}, 75th={np.round(q75)}, IQR={np.round(iqr)}')

cut_off = iqr*1.5
lower, upper = q25 - cut_off, q75 + cut_off

outliers = [x for x in df['LoanAmount'] if x < lower or x > upper]
print('Data Pencilan LoanAmount: %d' % len(outliers))

df.drop(df[ (df['LoanAmount'] > upper) | (df['LoanAmount'] < lower) ].index , inplace=True)
```


```{python}
import numpy as np
q25, q75 = np.percentile(df['Loan_Amount_Term'], 25), np.percentile(df['Loan_Amount_Term'], 75)
iqr = q75-q25

print(f'Percentile Loan_Amount_Term: 25th={np.round(q25)}, 75th={np.round(q75)}, IQR={np.round(iqr)}')

cut_off = iqr*1.5
lower, upper = q25 - cut_off, q75 + cut_off

outliers = [x for x in df['Loan_Amount_Term'] if x < lower or x > upper]
print('Data Pencilan Loan_Amount_Term: %d' % len(outliers))

df.drop(df[ (df['Loan_Amount_Term'] > upper) | (df['Loan_Amount_Term'] < lower) ].index , inplace=True)
```

```{python}
import numpy as np
q25, q75 = np.percentile(df['Credit_History'], 25), np.percentile(df['Credit_History'], 75)
iqr = q75-q25

print(f'Percentile ApplicantIncome: 25th={np.round(q25)}, 75th={np.round(q75)}, IQR={np.round(iqr)}')

cut_off = iqr*1.5
lower, upper = q25 - cut_off, q75 + cut_off

outliers = [x for x in df['Credit_History'] if x < lower or x > upper]
print('Data Pencilan Credit_History: %d' % len(outliers))

df.drop(df[ (df['Credit_History'] > upper) | (df['Credit_History'] < lower) ].index , inplace=True)
```

**Data yang sudah tidak ada pencilan**
```{python}
df.shape 
```


## Penanganan Data Kategorikal{.tabset .tabset-fade .tabset-pills}

### R
```{r}
df_kat %>% summarise_all(n_distinct)
```

```{r}
GenderLabel <-factor(df_kat$Gender, labels=c(0, 1, 2))
MarriedLabel <-factor(df_kat$Married, labels=c(0, 1, 2))
DependentsLabel <-factor (df_kat$Dependents, labels=c(0, 1, 2, 3))
EducationLabel <-factor(df_kat$Education, labels=c(0, 1))
Self_EmployedLabel <-factor(df_kat$Self_Employed, labels=c(0, 1, 2))
Property_AreaLabel <- factor (df_kat$Property_Area, labels=c(0, 1, 2))
Loan_StatusLabel <-factor (df_kat$Loan_Status, labels=c(0, 1))

df_kat_label <- data.frame("ID" = df_kat$Loan_ID, GenderLabel, MarriedLabel, DependentsLabel, EducationLabel, Self_EmployedLabel, Property_AreaLabel, Loan_StatusLabel)

df_kat_label
```


### Python

```{python}
df_kat.info()
```

```{python}
from sklearn.preprocessing import LabelEncoder

label = df_kat.apply(LabelEncoder().fit_transform)
label.head()
```

```{python}
df_kat1 = df_kat[['Gender', 'Married','Dependents','Education','Self_Employed','Property_Area','Loan_Status']]
df_kat1.head()
```

```{python}
import pandas as pd

cat_df = df_kat1.copy()
cat_df = pd.get_dummies(cat_df, columns=['Gender'], prefix = ['Gender'])

print(cat_df)
```


# Tugas 2

Lakukan Proses Visualisasi Data dengan menggunakan R dan Python dengan beberapa langkah berikut:

## Visualisasi Univariabel {.tabset .tabset-fade .tabset-pills}

### R
**Data Kategori**
```{r}
library(dplyr)
library(gridExtra)
library(ggplot2)

plot_gender <- ggplot(data_aa, aes(x = Gender))+geom_bar()
plot_married <- ggplot(data_aa, aes(x = Married))+geom_bar()
plot_dependents <- ggplot(data_aa, aes(x = Dependents))+geom_bar()
plot_education <- ggplot(data_aa, aes(x = Education))+geom_bar()
plot_selfemployed <- ggplot(data_aa, aes(x = Self_Employed))+geom_bar()
plot_loanamountterm <- ggplot(data_aa, aes(x = Loan_Amount_Term))+geom_bar() + theme(axis.text.x =  element_text(angle = 45, vjust = 0.5, hjust=0.5))
plot_credithist<- ggplot(data_aa, aes(x = Credit_History))+geom_bar()
plot_proparea<- ggplot(data_aa, aes(x = Property_Area))+geom_bar()
plot_loanstat<- ggplot(data_aa, aes(x = Loan_Status))+geom_bar()


grid.arrange(plot_gender,plot_married,plot_dependents,plot_education,plot_selfemployed,plot_loanamountterm,plot_credithist,plot_proparea,plot_loanstat)
```
**Data Numerik**

```{r}
data_aa$APPCO_TotalIncome <- data_aa$ApplicantIncome + data_aa$CoapplicantIncome

plot_ApIn <- ggplot(data_aa, aes(x = ApplicantIncome))+geom_histogram(bins = 12, colour = "white")
plot_COAp <- ggplot(data_aa, aes(x = APPCO_TotalIncome))+geom_histogram(bins = 12, colour = "white")
plot_LoAm <- ggplot(data_aa, aes(x = LoanAmount))+geom_histogram(bins = 12, colour = "white")

grid.arrange(plot_ApIn,plot_COAp, plot_LoAm)
```

### Python

**Data Kategori**

```{python}
df_kat['Gender'].value_counts().plot(kind='bar', figsize=(12, 6), rot=0, color="red")
```

```{python}
df_kat['Education'].value_counts().plot(kind='bar', figsize=(7, 6), rot=0)
```

```{python}
df_kat['Dependents'].value_counts().plot(kind='bar', figsize=(7, 6), rot=0, color="gold")
```

```{python}
df_kat['Married'].value_counts().plot(kind='pie', figsize=(7, 6), rot=0)
```

```{python}
df_kat['Property_Area'].value_counts().plot(kind='pie', figsize=(7, 6), rot=0)
```

**Data Numerik**

```{python}
import seaborn as sns
import matplotlib.pyplot as plt

plt.figure(figsize=(8,6))

sns.set_theme(style="darkgrid")

sns.histplot(data=df, x="LoanAmount", bins=20, kde=True)

plt.show()
```

```{python}
plt.figure(figsize=(8,6))

sns.set_theme(style="darkgrid")
sns.histplot(data=df, x="LoanAmount", bins=20, kde=True, color="tomato")

plt.show()
```
```{python}
plt.figure(figsize=(8,6))

sns.set_theme(style="darkgrid")
sns.histplot(data=df, x="CoapplicantIncome", bins=20, kde=True, color="crimson")

plt.show()
```

## Visualisasi Bivariabel {.tabset .tabset-fade .tabset-pills}

### R
**Category vs Category**

```{r}
plot_Gen_Mar <- ggplot(data_aa, aes(x = Gender, fill = Married)) +
             theme_minimal() +                                  # use a minimal theme
             geom_bar(position = position_dodge(preserve = "single"))
plot_Gen_Edu <- ggplot(data_aa, aes(x = Gender, fill = Education)) +
             theme_minimal() +                                  # use a minimal theme
             geom_bar(position = position_dodge(preserve = "single"))
plot_Mar_Edu <- ggplot(data_aa, aes(x = Married, fill = Education)) +
             theme_minimal() +                                  # use a minimal theme
             geom_bar(position = position_dodge(preserve = "single"))
Plot_Edu_ProAre <- ggplot(data_aa, aes(x = Education, fill = Property_Area)) +
              theme_minimal() +                                  # use a minimal theme
              geom_bar(position = position_dodge(preserve = "single"))

grid.arrange(plot_Gen_Mar,plot_Gen_Edu,plot_Mar_Edu,Plot_Edu_ProAre)
```
**Numerik vs Numerik**

```{r}
plot_LoAm_ACT <- ggplot(data_aa, aes(x = LoanAmount, y = APPCO_TotalIncome )) +
               theme_minimal() +                                  # use a minimal theme
               geom_line()

plot_ApIn_CoIn <- ggplot(data_aa, aes(x = ApplicantIncome, y = CoapplicantIncome )) +
               theme_minimal() +                                  # use a minimal theme
               geom_line()

plot_ApIn_LoAm <- ggplot(data_aa, aes(x = ApplicantIncome, y = LoanAmount )) +
               theme_minimal() +                                  # use a minimal theme
               geom_line()


grid.arrange(plot_LoAm_ACT,plot_ApIn_CoIn,plot_ApIn_LoAm)
```
**Numerik vs Kategorikal**

```{r}
plot_LoAm_LoStat <- ggplot(data_aa, 
                      aes(x = LoanAmount, 
                          fill = Loan_Status)) +
                          geom_density(alpha = 0.3) +
                          theme_minimal() +
                          labs(title = "Loan Amount distribution by Loan Amount  Term")

plot_CoIn_Mar <- ggplot(data_aa, 
                      aes(x = CoapplicantIncome, 
                          fill = Married)) +
                          geom_density(alpha = 0.3) +
                          theme_minimal() +
                          labs(title = "Coapplicant Income distribution by Married")

plot_ApIn_Edu <- ggplot(data_aa, 
                      aes(x = ApplicantIncome, 
                          fill = Education)) +
                          geom_density(alpha = 0.3) +
                          theme_minimal() +
                          labs(title = "Applicant Income distribution by Education")
plot_LoAm_ProAre <- ggplot(data_aa, 
                      aes(x = ApplicantIncome, 
                          fill = Property_Area)) +
                          geom_density(alpha = 0.3) +
                          theme_minimal() +
                          labs(title = "Applicant Income distribution by Property Area")



grid.arrange(plot_LoAm_LoStat, plot_CoIn_Mar, plot_ApIn_Edu, plot_LoAm_ProAre)
```

### Python

```{python}
df_line = df[['ApplicantIncome','CoapplicantIncome']]
df_line.plot.line()
```

**Numerik vs Numerik**
```{python}
plt.figure(figsize=(8,6))

sns.set_theme(style="darkgrid")
sns.scatterplot(data=df, y="CoapplicantIncome",x="ApplicantIncome")

plt.show()
```

**Category vs Category**

```{python}
sns.set(rc={'figure.figsize':(11.7,8.27)})
plt.subplot(231)
sns.countplot(x="Gender", hue='Loan_Status', data=df)
plt.subplot(232)
sns.countplot(x="Married", hue='Loan_Status', data=df)
plt.subplot(233)
sns.countplot(x="Education", hue='Loan_Status', data=df)
plt.subplot(234)
sns.countplot(x="Self_Employed", hue='Loan_Status', data=df)
plt.subplot(235)
sns.countplot(x="Dependents", hue='Loan_Status', data=df)
plt.subplot(236)
sns.countplot(x="Property_Area", hue='Loan_Status', data=df)
```


```{python}
plt.figure(figsize=(8,6))

sns.set_theme(style = "darkgrid")
sns.violinplot(x = "Property_Area",
               y = "LoanAmount",
               hue = "Gender",
               data=df,
               palette="Pastel1")

plt.show()
```

```{python}
plt.figure(figsize=(8,6))

sns.set_theme(style = "darkgrid")
sns.boxplot(x = "Dependents",
               y = "LoanAmount",
               hue = "Married",
               data=df)

plt.show()
```


## Visualisasi Multivariabel {.tabset .tabset-fade .tabset-pills}

### R
```{r}
plot_ApIn_LoAm_ProAre <-ggplot(data_aa, aes(x=ApplicantIncome, y=LoanAmount, shape=Property_Area, colour=Property_Area))+geom_point()
plot_ApIn_LoAm_Edu <-ggplot(data_aa, aes(x=ApplicantIncome, y=LoanAmount, shape=Education, colour=Property_Area))+geom_point()
plot_LoAm_CoIn_SeEm <-ggplot(data_aa, aes(x=LoanAmount, y=APPCO_TotalIncome, shape=Self_Employed, colour=Property_Area))+geom_point()

grid.arrange(plot_ApIn_LoAm_ProAre,plot_ApIn_LoAm_Edu, plot_LoAm_CoIn_SeEm)
```


### Python

```{python}
bins = np.linspace(df.CoapplicantIncome.min(), df.CoapplicantIncome.max(),12)
graph = sns.FacetGrid(df, col="Gender", hue="Loan_Status", palette="Set2", col_wrap=2)
graph.map(plt.hist, 'CoapplicantIncome', bins=bins, ec="k")
graph.axes[-1].legend()
plt.show()
```


```{python}
sns.set_theme(style="darkgrid")
plt.figure(figsize=(8,6))

n = sns.lmplot(x = "ApplicantIncome",
           y = "LoanAmount",
           data = df,
           fit_reg = False,
           hue = "Property_Area",
           legend = False,
           markers=["o","+","x"])
plt.legend(loc="lower right")
plt.show(n)
```


```{python}
n = sns.pairplot(df,
        kind = "scatter",
         hue = "Gender",
     markers = ["o","s"],
     palette = "Set2")
plt.show(n)
```

# Tugas 3

Lakukan proses analisa data secara deskriptif menggunakan R dan Python dengan beberapa langkah berikut:

## Kualitatif Kategori Univariat {.tabset .tabset-fade .tabset-pills}

### R

```{r}
prop.table(table(data_aa$Gender))
prop.table(table(data_aa$Married))
prop.table(table(data_aa$Dependents))
prop.table(table(data_aa$Education))
prop.table(table(data_aa$Self_Employed))
prop.table(table(data_aa$Loan_Amount_Term))
prop.table(table(data_aa$Credit_History))
prop.table(table(data_aa$Property_Area))
prop.table(table(data_aa$Loan_Status))
```

### Python

```{python}
from collections import Counter
Counter(df_kat['Gender'])
```


```{python}
c = Counter(df_kat['Gender'])
[(i, c[i] / len(df_kat['Gender']) * 100.0) for i in c]
```

## Kualitatif Kategori Bivariat {.tabset .tabset-fade .tabset-pills}

### R

```{r}
data_aa %>% select(Gender, Married) %>%  table() 
data_aa %>% select(Gender, Education) %>%  table()
data_aa %>% select(Gender, Property_Area) %>%  table() 
data_aa %>% select(Education, Self_Employed) %>%  table()
data_aa %>% select(Gender, Loan_Amount_Term) %>%  table()
data_aa %>% select(Married, Loan_Amount_Term) %>%  table()
```

### Python

```{python}
cat2 = df_kat[['Gender','Property_Area']]
pd.crosstab(cat2.Gender,cat2.Property_Area)
```

```{python}
cat2 = df_kat[['Dependents','Education']]
pd.crosstab(cat2.Dependents,cat2.Education)
```

```{python}
cat2 = df_kat[['Property_Area','Loan_Status']]
pd.crosstab(cat2.Property_Area,cat2.Loan_Status)
```




## Kualitatif Kategori Multivariat {.tabset .tabset-fade .tabset-pills}

### R

```{r}
data_aa %>% select(Gender, Married, Education) %>% ftable()
data_aa %>% select(Gender, Married, Education,Property_Area,Loan_Amount_Term) %>% ftable()
```

### Python

```{python}
cat3 = df_kat[['Gender','Property_Area','Loan_Status']]
pd.crosstab([cat3.Gender,cat3.Property_Area], cat3.Loan_Status, rownames=['Gender', 'Property_Area'], colnames=['Loan_Status'])
```

```{python}
cat3 = df_kat[['Married','Education','Loan_Status']]
pd.crosstab([cat3.Married,cat3.Education], cat3.Loan_Status, rownames=['Married', 'Education'], colnames=['Loan_Status'])
```

## Kuantitatif Univariat numerik {.tabset .tabset-fade .tabset-pills}

### R

```{r}
da_numerik_1 <- select_if(data_aa, is.numeric)
summary(da_numerik_1)
```
```{r}
library(moments)
fungsi <- function(x){
  data.frame(var(x),
       sd(x),
       mad(x),
       IQR(x),
       skewness(x),
       kurtosis(x))
}

sapply(da_numerik_1,fungsi)
```

### Python

**Mean**

```{python}
import statistics as st

print("Mean:",st.mean(df.ApplicantIncome) )
```

**Quartile**

```{python}
df.ApplicantIncome.quantile([0, .25, .5, .75, 1])
```

**Median**

```{python}
print("Median:",st.median(df.ApplicantIncome))
```

**Mode**

```{python}
print("Mode:",st.mode(df.ApplicantIncome))
```

**Alternatif Statistic**

```{python}
df.describe()
```

**Scale**

```{python}
median=st.median(df.ApplicantIncome)
mad = st.median([abs(number-median) for number in df.ApplicantIncome])
print("Median Absolute Deviation:" ,mad)
```

```{python}
#calculate interquartile range 
q3, q1 = np.percentile((df.ApplicantIncome), [75 ,25])
iqr = q3 - q1

#display interquartile range 
print("IQR:",iqr)
```

**Skewness**

```{python}
print("Skewness:",df.ApplicantIncome.skew(axis=0))
```

**Kurtosis**

```{python}
print("Kurtosis:",df.ApplicantIncome.kurt(axis=0))
```

## Kuantitatif Bivariat numerik {.tabset .tabset-fade .tabset-pills}

### R

```{r}
cov(da_numerik_1$ApplicantIncome,da_numerik_1$CoapplicantIncome)
cov(da_numerik_1$CoapplicantIncome,da_numerik_1$LoanAmount)
cov(da_numerik_1$LoanAmount,da_numerik_1$ApplicantIncome)
cov(da_numerik_1$LoanAmount, da_numerik_1$APPCO_TotalIncome)
cor(da_numerik_1$ApplicantIncome,da_numerik_1$CoapplicantIncome)
cor(da_numerik_1$CoapplicantIncome,da_numerik_1$LoanAmount)
cor(da_numerik_1$LoanAmount,da_numerik_1$ApplicantIncome)
cor(da_numerik_1$LoanAmount, da_numerik_1$APPCO_TotalIncome)
```

### Python

**Covariance**

```{python}
print("Covariance:",df.ApplicantIncome.cov(df.LoanAmount))
```

**Correlation**

```{python}
print("Correlation:",df.ApplicantIncome.corr(df.LoanAmount))
```

```{python}
import matplotlib.pyplot as plt
correlation_mat = df.corr()
mask = np.zeros_like(correlation_mat)
mask[np.triu_indices_from(mask)] = True
with sns.axes_style("white"):
    f, ax = plt.subplots(figsize=(7,6))
    ax = sns.heatmap(correlation_mat,
mask=mask,annot=True,cmap="YlGnBu")
plt.show()
```


**Z-Score**

```{python}
import scipy.stats as stats
zscores = stats.zscore(df.ApplicantIncome)
print(zscores)
```

## Kuantitatif Multivariat numerik {.tabset .tabset-fade .tabset-pills}
 
### R

```{r}
cov(da_numerik_1)
```
```{r}
cor(da_numerik_1)
```

### Python

**Covariance**

```{python}
cof = df.select_dtypes(include=[np.number]) # Number
pd.set_option('display.max_columns', None)
cof.cov()
```

**Covariance**

```{python}
pd.set_option('display.max_columns', None)
cof.corr()
```

## EDA dengan cara Malas {.tabset .tabset-fade .tabset-pills}

### R

```{r}
library(funModeling) 
library(tidyverse) 
library(Hmisc)
library(skimr)
eda <- function(data)
{
  glimpse(data)
  skim(data)
  df_status(data)
  freq(data) 
  profiling_num(data)
  plot_num(data)
  describe(data)
}

eda(df)
```

### Python

[EDA Lazy Way](https://colab.research.google.com/drive/1w-MsEhVHLQXBVvZrKR1XgQ1yU-MJwO1f?usp=sharing)

# Tugas 4 

Lakukan pemeriksaan distribusi densitas  menggunakan R dan Python pada setiap variabel kuantitatif dengan beberapa bagian sebagai berikut:

## Univariat numerik {.tabset .tabset-fade .tabset-pills}

### R
```{r}
library(ks)
library(MASS)
fhat<-kde(x=da_numerik_1[,3])
plot_1<-plot(fhat,cont=50,col.cont=4,cont.lwd=2,xlab="LoanAmount",drawpoints=TRUE)
```

```{r}
fhat1<-kde(x=da_numerik_1[,2])
plot_2<-plot(fhat,cont=50,col.cont=4,cont.lwd=2,xlab="Coapplicant Income",drawpoints=TRUE)
```

```{r}
fhat2<-kde(x=da_numerik_1[,2])
plot_3<-plot(fhat,cont=50,col.cont=4,cont.lwd=2,xlab="AppCoapplicant Income",drawpoints=TRUE)
```
```{r}
fhat3<-kde(x=da_numerik_1[,2])
plot_4<-plot(fhat,cont=50,col.cont=4,cont.lwd=2,xlab="Applicant Income",drawpoints=TRUE)
```


### Python

```{python}
sns.set_theme(style="darkgrid")
fig, axs = plt.subplots(2,2, figsize=(7,7))

sns.kdeplot(data=df, x="ApplicantIncome", color="skyblue", shade=True, ax=axs[0,0])
sns.kdeplot(data=df, x="CoapplicantIncome", color="teal", shade=True, ax=axs[0,1])
sns.kdeplot(data=df, x="LoanAmount", color="gold", shade=True, ax=axs[1,0])
```


```{python}
sns.distplot(df['CoapplicantIncome'], hist=True, kde=True, 
             bins=int(180/5), color = 'darkblue', 
             hist_kws={'edgecolor':'black'},
             kde_kws={'linewidth': 2})
```


```{python}
sns.distplot(df['ApplicantIncome'], hist=True, kde=True, 
             bins=int(180/5), color = 'darkblue', 
             hist_kws={'edgecolor':'black'},
             kde_kws={'linewidth': 2})
```


```{python}
sns.distplot(df['LoanAmount'], hist=True, kde=True, 
             bins=int(180/5), color = 'darkblue', 
             hist_kws={'edgecolor':'black'},
             kde_kws={'linewidth': 2})
```

## Bivariat numerik {.tabset .tabset-fade .tabset-pills}

### R
```{r}
fhat4<-kde(x=da_numerik_1[,3:4])
plot(fhat4,display="filled.contour",cont=seq(10,90,by=10),lwd=1)
```

```{r}
plot(fhat4,display="persp",border=1)
```


### Python
```{python}
plt.rcParams["figure.figsize"]=(8,6)

sns.set_theme(style="darkgrid")

sns.kdeplot(data = df,
            x    = "ApplicantIncome",
            hue = "Property_Area",
            cut = 0,
            fill = True,
            alpha = 0.4)

plt.show()
```

```{python}
plt.rcParams["figure.figsize"]=(8,6)

sns.set_theme(style="darkgrid")

sns.kdeplot(data = df,
            x    = "LoanAmount",
            hue = "Loan_Status",
            cut = 0,
            fill = True,
            alpha = 0.4)

plt.show()
```

## Multivariat numerik {.tabset .tabset-fade .tabset-pills}

### R
```{r}
fhat5<-kde(x=da_numerik_1[,1:3])
plot(fhat5)
```


### Python
```{python}
data = df[["ApplicantIncome","CoapplicantIncome","LoanAmount","Loan_Status"]]
n = sns.pairplot(data,
        kind = "scatter",
         hue = "Loan_Status",
     markers = ["o","s"],
     palette = "flare")

plt.legend(loc="center right")
plt.show(n)
```

```{python}
data = df[["ApplicantIncome","CoapplicantIncome","LoanAmount","Self_Employed"]]
n = sns.pairplot(data,
        kind = "scatter",
         hue = "Self_Employed",
     markers = ["D","s"],
     palette = "bright")

plt.legend(loc="center right")
plt.show(n)
```


# 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%. {.tabset .tabset-fade .tabset-pills}

### R
```{r}
k = sum(data_aa$Gender == "Female")
n = sum(count(data_aa))
pbar = k/n 
SE = sqrt (pbar*(1-pbar)/n); SE
```

```{r}
E = qnorm(.975)*SE; E
```

```{r}
pbar + c(-E, E)
```


### Python

```{python}
pd.crosstab(df.Loan_Status, df.Gender)
```

```{python}
p_fm = 75/(37+75)
n=37+75
se_female = np.sqrt(p_fm * (1 - p_fm) / n)
print("Standard Error:",se_female)
```

**Confidence Interval**

```{python}
z_score = 1.96
lcb = p_fm - z_score* se_female #lower limit of the CI
ucb = p_fm + z_score* se_female #upper limit of the CI
print("Lower:",lcb)
print("Upper:",ucb)
```

**Margin of Error**

```{python}
ME = z_score*se_female
print("Margin of Error:",ME)
```

```{python}
import math
import scipy.stats as ss
P_Gender = df.Gender.value_counts() / len(df)
P_Female = P_Gender.Female
z_critical = ss.norm.ppf(q = 0.975)

margin_error = z_critical * math.sqrt(P_Female *(1 - P_Female)/len(df))
interval = (P_Female - margin_error, P_Female + margin_error)

print("Margin of Error :",margin_error)
print('Interval of Estimation :',interval)
```

## 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%. {.tabset .tabset-fade .tabset-pills}

### R
```{r}
zstar = qnorm(.975)
p = 0.5
E = 0.05
zstar^2*p*(1-p)/E^2
```


### Python

```{python}
p = 0.5
E = 0.05
z_score**2*p*(1-p)/E**2
```

```{python}
z_critical = ss.norm.ppf(q = 0.975)
p = 0.5
E = 0.05
sample_needed = int(z_critical**2*p*(1-p)/E**2)

print('ukuran sample yang dibutuhkan :', sample_needed)
```

## Lakukan pembuktian kebenaran assumsi dengan tingakat signifikansi 0.05, jika Bank mengklaim bahwa pinjaman rata-rata konsumen adalah: {.tabset .tabset-fade .tabset-pills}

  * Lebih besar $ 150. 
  * Lebih kecil $ 150
  * Sama dengan $ 150.
  
### R

**Lebih besar $ 150**
```{r}
set.seed(100)
Data1 <- sample_n(data_aa,30)
Data1
```

$$
\begin{align*}
H_0 &= >= $150 \\
H_1 &= ?? > $150 \\
\end{align*}\\
$$


```{r}
mu0 = 150
xbar = mean(Data1$LoanAmount)
s = sd(Data1$LoanAmount)
n = sum(count(Data1))
t = (xbar-mu0)/(s/sqrt(n));t
```

```{r}
alpha = .05
t.alpha = qt(1-alpha, df=n-1)
t.alpha
```

**Lebih kecil $ 150**

```{r}
mu0 = 150
xbar = mean(Data1$LoanAmount)
s = sd(Data1$LoanAmount)
n = sum(count(Data1))
t = (xbar-mu0)/(s/sqrt(n));t
```

```{r}
alpha = .05
t.alpha = qt(1-alpha, df=n-1)
-t.alpha
```

**Sama dengan $ 150**

```{r}
mu0 = 150
xbar = mean(Data1$LoanAmount)
s = sd(Data1$LoanAmount)
n = sum(count(Data1))
t = (xbar-mu0)/(s/sqrt(n));t
```

```{r}
alpha = .05
t.alpha = qt(1-alpha, df=n-1)
t.alpha
```

```{r}
-t.alpha
```

### Python

```{python}
mu0 = 150
xbar = df['LoanAmount'].mean()
s = df['LoanAmount'].std()
n = len(df)
t = (xbar-mu0)/(s/(n**(1/2)))

print("t hitung",t)
```

* Lebih besar $ 150.

```{python}
alpha = 0.05
t_alpha = ss.t.isf(alpha, n - 1)

print("t hitung:",t)
print("t table 0.5 one tail positive:",t_alpha)
```

* Lebih kecil $ 150

```{python}
alpha = 0.05
t_alpha = ss.t.isf(alpha, n - 1)  

print("t hitung",t)
print("t table 0.5 one tail negative:", -t_alpha)
```

* Sama dengan $ 150.

```{python}
alpha = 0.05
t_alpha = ss.t.isf(alpha/2, n-1)  

print("t hitung:",t)
print("t table 0.5 two tail:",-t_alpha, t_alpha)
```


## Lakukan pembuktian kebenaran assumsi dengan tingakat signifikansi 0.05, seperti diatas jika diketahui simpangan baku pinjaman adalah $ 85. {.tabset .tabset-fade .tabset-pills}

### R
```{r}
mu0 = 150
xbar = mean(Data1$LoanAmount)
s = 85
n = sum(count(Data1))
t = (xbar-mu0)/(s/sqrt(n));t
```

```{r}
alpha = .05
t.alpha = qt(1-alpha, df=n-1)
t.alpha
```


### Python


```{python}
mu0 = 150
xbar = df['LoanAmount'].mean()
s_2 = 85
n = len(df)
t_2 = (xbar-mu0)/(s_2/(n**(1/2)))

print("t hitung:",t_2)
```

* Lebih besar $ 150

```{python}
alpha = 0.05
t_alpha = ss.t.isf(alpha, n - 1) 

print("t hitung:",t_2)
print("t table 0.5 one tail positive:",t_alpha)
```

* Lebih kecil $ 150

```{python}
alpha = 0.05
t_alpha = ss.t.isf(alpha, n - 1)

print("t hitung:",t_2)
print("t table 0.5 one tail negative:", -t_alpha)
```

* Sama dengan $ 150.

```{python}
alpha = 0.05
t_alpha = ss.t.isf(alpha/2, n - 1) 

print("t hitung:",t_2)
print("t table 0.5 two tail:",-t_alpha, t_alpha)
```


# Referensi

1.   ref 1
2.   ref 2
3.   ref 3