1 📌 Pendahuluan

Dokumen ini bertujuan untuk melakukan analisis terhadap Credit Card Approvals Dataset menggunakan R. Kita akan menghitung:

  • Eigen Values dan Eigen Vectors
  • Variance-Covariance Matrix
  • Correlation Matrix

1.1 📊 1. Memasukkan Data ke R

library(knitr)  # Untuk membuat output tabel yang lebih rapi

# Membuat dataframe berdasarkan data numerik dari tabel
data <- data.frame(
  Gender = c(1, 0, 0, 1, 1),
  Age = c(30.83, 58.67, 24.50, 27.83, 20.17),
  Debt = c(0.000, 4.460, 0.500, 1.540, 5.625),
  Married = c(1, 1, 1, 1, 1),
  BankCustomer = c(1, 1, 1, 1, 1),
  YearsEmployed = c(1.25, 3.04, 1.50, 3.75, 1.71),
  PriorDefault = c(1, 1, 1, 1, 1),
  Employed = c(1, 1, 0, 1, 0),
  CreditScore = c(1, 6, 0, 5, 0),
  DriversLicense = c(0, 0, 0, 1, 0),
  ZipCode = c(202, 43, 280, 100, 120),
  Income = c(0, 560, 824, 3, 0),
  Approved = c(1, 1, 1, 1, 1)
)

# Menampilkan 5 data pertama
kable(head(data))
Gender Age Debt Married BankCustomer YearsEmployed PriorDefault Employed CreditScore DriversLicense ZipCode Income Approved
1 30.83 0.000 1 1 1.25 1 1 1 0 202 0 1
0 58.67 4.460 1 1 3.04 1 1 6 0 43 560 1
0 24.50 0.500 1 1 1.50 1 0 0 0 280 824 1
1 27.83 1.540 1 1 3.75 1 1 5 1 100 3 1
1 20.17 5.625 1 1 1.71 1 0 0 0 120 0 1

1.2 📈 2. Analisis Matriks

1.2.1 🔹 Eigen Values dan Eigen Vectors

# Menghitung eigen value dan eigen vector dari matriks kovarians
cov_matrix <- cov(data)
eigen_values_vectors <- eigen(cov_matrix)

# Menampilkan eigen value
eigen_values_vectors$values
##  [1]  1.529994e+05  7.738334e+03  7.511641e+01  4.764847e+00  1.428925e-15
##  [6]  7.068017e-16  2.907859e-16  1.352967e-16  0.000000e+00 -1.114266e-29
## [11] -7.400993e-16 -8.157149e-14 -6.285216e-13
# Menampilkan eigen vector
eigen_values_vectors$vectors
##                [,1]          [,2]          [,3]          [,4]          [,5]
##  [1,]  1.353862e-03  1.453049e-03  5.913574e-03  1.118217e-02  0.000000e+00
##  [2,] -1.289706e-02 -1.315606e-01 -9.782888e-01 -1.341870e-01 -2.727247e-05
##  [3,]  8.188103e-04 -1.960516e-02  1.279117e-01 -6.284314e-01 -1.098151e-03
##  [4,]  0.000000e+00 -6.938894e-18 -3.330669e-16  2.248202e-15  2.587289e-01
##  [5,]  0.000000e+00  0.000000e+00  0.000000e+00  0.000000e+00 -9.528872e-01
##  [6,]  3.179447e-04 -9.024824e-03  1.839390e-02  3.274493e-01  9.139571e-02
##  [7,]  0.000000e+00  0.000000e+00  0.000000e+00  0.000000e+00 -8.491124e-14
##  [8,]  4.517900e-04 -2.638214e-03 -4.506051e-02  1.144286e-01  3.247698e-02
##  [9,]  3.495244e-05 -2.713567e-02 -8.389376e-02  6.595999e-01 -1.878131e-02
## [10,]  4.535469e-04 -8.272893e-04  1.328613e-02  1.774107e-01 -1.237069e-01
## [11,] -8.349349e-02  9.873755e-01 -1.281363e-01 -8.616975e-03  2.739021e-04
## [12,] -9.964233e-01 -8.105205e-02  2.350098e-02  2.217912e-03 -3.657910e-05
## [13,]  0.000000e+00  0.000000e+00  0.000000e+00  0.000000e+00  0.000000e+00
##                [,6]          [,7]          [,8] [,9]         [,10]
##  [1,]  0.000000e+00  0.000000e+00  0.000000e+00    0  0.000000e+00
##  [2,]  5.242768e-04  3.652528e-03  4.002876e-05    0  7.806320e-16
##  [3,]  6.507362e-03 -1.160480e-02 -1.051641e-02    0  1.828475e-15
##  [4,]  8.176764e-01  5.054585e-01  8.839162e-02    0  3.327417e-14
##  [5,]  2.375928e-01  7.257514e-02  1.739289e-01    0  1.787417e-14
##  [6,] -3.248708e-02 -5.330671e-02  5.908822e-01    0 -2.690056e-13
##  [7,] -2.235712e-14  2.197478e-13 -4.236195e-13    0 -1.000000e+00
##  [8,] -4.105833e-01  5.240154e-01  5.016899e-01    0 -9.106015e-14
##  [9,]  1.683120e-01 -2.452511e-01 -2.425388e-01    0  4.666045e-14
## [10,] -2.773791e-01  6.336318e-01 -5.496955e-01    0  3.887802e-13
## [11,]  3.150517e-03 -4.969492e-03 -5.722782e-04    0 -9.436491e-16
## [12,] -5.823117e-04  8.599930e-04  1.960920e-04    0  1.219460e-16
## [13,]  0.000000e+00  0.000000e+00  0.000000e+00    1  0.000000e+00
##               [,11]         [,12]        [,13]
##  [1,]  9.999180e-01  0.000000e+00  0.000000000
##  [2,]  7.494924e-03  4.914855e-02 -0.070604555
##  [3,]  6.298706e-03 -2.589795e-01 -0.721753890
##  [4,] -2.341877e-17 -3.280482e-02  0.009334565
##  [5,]  0.000000e+00 -5.777257e-03  0.001963798
##  [6,] -3.757992e-03  5.438403e-01 -0.484962653
##  [7,]  0.000000e+00  0.000000e+00  0.000000000
##  [8,] -1.009952e-03 -5.336627e-01  0.064446586
##  [9,] -6.840824e-03 -4.991498e-01 -0.400376380
## [10,] -2.061986e-03  3.156319e-01 -0.269858023
## [11,] -4.676045e-04 -8.508584e-03 -0.038955782
## [12,]  1.303123e-03 -7.828034e-05  0.003322587
## [13,]  0.000000e+00  0.000000e+00  0.000000000

1.2.2 🔹 Variance-Covariance Matrix

kable(cov_matrix)
Gender Age Debt Married BankCustomer YearsEmployed PriorDefault Employed CreditScore DriversLicense ZipCode Income Approved
Gender 0.3000 -4.592500 -0.027500 0 0 -0.010000 0 0.05000 -0.30 0.10000 -6.2500 -207.3000 0
Age -4.5925 231.361400 9.345662 0 0 6.999375 0 5.03250 33.30 -1.14250 -831.0325 2046.9725 0
Debt -0.0275 9.345662 6.187675 0 0 0.605225 0 -0.31875 1.34 -0.22125 -161.4613 -112.3137 0
Married 0.0000 0.000000 0.000000 0 0 0.000000 0 0.00000 0.00 0.00000 0.0000 0.0000 0
BankCustomer 0.0000 0.000000 0.000000 0 0 0.000000 0 0.00000 0.00 0.00000 0.0000 0.0000 0
YearsEmployed -0.0100 6.999375 0.605225 0 0 1.182050 0 0.32250 2.81 0.37500 -73.2075 -42.7750 0
PriorDefault 0.0000 0.000000 0.000000 0 0 0.000000 0 0.00000 0.00 0.00000 0.0000 0.0000 0
Employed 0.0500 5.032500 -0.318750 0 0 0.322500 0 0.30000 1.20 0.10000 -25.5000 -67.3000 0
CreditScore -0.3000 33.300000 1.340000 0 0 2.810000 0 1.20000 8.30 0.65000 -207.0000 11.5500 0
DriversLicense 0.1000 -1.142500 -0.221250 0 0 0.375000 0 0.10000 0.65 0.20000 -12.2500 -68.6000 0
ZipCode -6.2500 -831.032500 -161.461250 0 0 -73.207500 0 -25.50000 -207.00 -12.25000 8612.0000 12109.2500 0
Income -207.3000 2046.972500 -112.313750 0 0 -42.775000 0 -67.30000 11.55 -68.60000 12109.2500 151957.8000 0
Approved 0.0000 0.000000 0.000000 0 0 0.000000 0 0.00000 0.00 0.00000 0.0000 0.0000 0

1.2.3 🔹 Correlation Matrix

cor_matrix <- cor(data)
## Warning in cor(data): the standard deviation is zero
kable(cor_matrix)
Gender Age Debt Married BankCustomer YearsEmployed PriorDefault Employed CreditScore DriversLicense ZipCode Income Approved
Gender 1.0000000 -0.5512430 -0.0201841 NA NA -0.0167927 NA 0.1666667 -0.1901173 0.4082483 -0.1229610 -0.9709060 NA
Age -0.5512430 1.0000000 0.2470022 NA NA 0.4232489 NA 0.6040567 0.7599058 -0.1679561 -0.5887359 0.3452272 NA
Debt -0.0201841 0.2470022 1.0000000 NA NA 0.2237872 NA -0.2339515 0.1869829 -0.1988861 -0.6994434 -0.1158264 NA
Married NA NA NA 1 NA NA NA NA NA NA NA NA NA
BankCustomer NA NA NA NA 1 NA NA NA NA NA NA NA NA
YearsEmployed -0.0167927 0.4232489 0.2237872 NA NA 1.0000000 NA 0.5415657 0.8971175 0.7712556 -0.7255806 -0.1009278 NA
PriorDefault NA NA NA NA NA NA 1 NA NA NA NA NA NA
Employed 0.1666667 0.6040567 -0.2339515 NA NA 0.5415657 NA 1.0000000 0.7604691 0.4082483 -0.5016809 -0.3152049 NA
CreditScore -0.1901173 0.7599058 0.1869829 NA NA 0.8971175 NA 0.7604691 1.0000000 0.5044978 -0.7742466 0.0102845 NA
DriversLicense 0.4082483 -0.1679561 -0.1988861 NA NA 0.7712556 NA 0.4082483 0.5044978 1.0000000 -0.2951679 -0.3935026 NA
ZipCode -0.1229610 -0.5887359 -0.6994434 NA NA -0.7255806 NA -0.5016809 -0.7742466 -0.2951679 1.0000000 0.3347370 NA
Income -0.9709060 0.3452272 -0.1158264 NA NA -0.1009278 NA -0.3152049 0.0102845 -0.3935026 0.3347370 1.0000000 NA
Approved NA NA NA NA NA NA NA NA NA NA NA NA 1

1.3 🔗 Publikasi ke RPubs

1.3.1 Langkah-langkah:

  1. Knit ke HTML:
    • Klik tombol Knit di RStudio
    • Pilih Knit to HTML
    • Simpan file dengan nama yang sesuai, misal analisis_kredit.html
  2. Upload ke RPubs:
    • Klik Publish → Pilih RPubs
    • Login atau buat akun RPubs
    • Beri judul dan deskripsi
    • Klik Publish
  3. Membuat tautan
    • Setelah dipublikasikan, salin tautan dan bagikan