data <- data.frame(
Gender =c(1, 0, 0, 1, 1),
Age =c(30.83, 58.67, 24.50, 27.83, 20.17),
Dept =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, 0, 0, 1, 0),
CreditStore =c(1, 6, 0, 5, 0),
DriverLicense =c(0, 0, 0, 1, 0),
Citizen =c("ByBIrth", "ByBirth", "ByBirth", "ByBirth", "ByOtherMeans"),
ZipCode =c(202, 43, 280, 100, 120),
Income =c(0, 560, 824, 3, 0),
Approved =c(1, 1, 1, 1, 1),
Industry =c("Industrials", "Materials", "Materials", "Industrials", "Industrials")
)
print(data)
## Gender Age Dept Married BankCustomer YearsEmployed PriorDefault Employed
## 1 1 30.83 0.000 1 1 1.25 1 1
## 2 0 58.67 4.460 1 1 3.04 1 0
## 3 0 24.50 0.500 1 1 1.50 1 0
## 4 1 27.83 1.540 1 1 3.75 1 1
## 5 1 20.17 5.625 1 1 1.71 1 0
## CreditStore DriverLicense Citizen ZipCode Income Approved Industry
## 1 1 0 ByBIrth 202 0 1 Industrials
## 2 6 0 ByBirth 43 560 1 Materials
## 3 0 0 ByBirth 280 824 1 Materials
## 4 5 1 ByBirth 100 3 1 Industrials
## 5 0 0 ByOtherMeans 120 0 1 Industrials
#Memilih kolom numerik
data_numeric <- data[, sapply(data, is.numeric)]
print(data_numeric)
## Gender Age Dept Married BankCustomer YearsEmployed PriorDefault Employed
## 1 1 30.83 0.000 1 1 1.25 1 1
## 2 0 58.67 4.460 1 1 3.04 1 0
## 3 0 24.50 0.500 1 1 1.50 1 0
## 4 1 27.83 1.540 1 1 3.75 1 1
## 5 1 20.17 5.625 1 1 1.71 1 0
## CreditStore DriverLicense ZipCode Income Approved
## 1 1 0 202 0 1
## 2 6 0 43 560 1
## 3 0 0 280 824 1
## 4 5 1 100 3 1
## 5 0 0 120 0 1
#Kovarians matriks
cov_matrix <- cov(data_numeric)
print(cov_matrix)
## Gender Age Dept Married BankCustomer
## Gender 0.3000 -4.592500 -0.027500 0 0
## Age -4.5925 231.361400 9.345663 0 0
## Dept -0.0275 9.345663 6.187675 0 0
## Married 0.0000 0.000000 0.000000 0 0
## BankCustomer 0.0000 0.000000 0.000000 0 0
## YearsEmployed -0.0100 6.999375 0.605225 0 0
## PriorDefault 0.0000 0.000000 0.000000 0 0
## Employed 0.2000 -1.535000 -0.827500 0 0
## CreditStore -0.3000 33.300000 1.340000 0 0
## DriverLicense 0.1000 -1.142500 -0.221250 0 0
## ZipCode -6.2500 -831.032500 -161.461250 0 0
## Income -207.3000 2046.972500 -112.313750 0 0
## Approved 0.0000 0.000000 0.000000 0 0
## YearsEmployed PriorDefault Employed CreditStore DriverLicense
## Gender -0.010000 0 0.2000 -0.30 0.10000
## Age 6.999375 0 -1.5350 33.30 -1.14250
## Dept 0.605225 0 -0.8275 1.34 -0.22125
## Married 0.000000 0 0.0000 0.00 0.00000
## BankCustomer 0.000000 0 0.0000 0.00 0.00000
## YearsEmployed 1.182050 0 0.1250 2.81 0.37500
## PriorDefault 0.000000 0 0.0000 0.00 0.00000
## Employed 0.125000 0 0.3000 0.30 0.15000
## CreditStore 2.810000 0 0.3000 8.30 0.65000
## DriverLicense 0.375000 0 0.1500 0.65 0.20000
## ZipCode -73.207500 0 1.0000 -207.00 -12.25000
## Income -42.775000 0 -137.9500 11.55 -68.60000
## Approved 0.000000 0 0.0000 0.00 0.00000
## ZipCode Income Approved
## Gender -6.2500 -207.3000 0
## Age -831.0325 2046.9725 0
## Dept -161.4613 -112.3137 0
## Married 0.0000 0.0000 0
## BankCustomer 0.0000 0.0000 0
## YearsEmployed -73.2075 -42.7750 0
## PriorDefault 0.0000 0.0000 0
## Employed 1.0000 -137.9500 0
## CreditStore -207.0000 11.5500 0
## DriverLicense -12.2500 -68.6000 0
## ZipCode 8612.0000 12109.2500 0
## Income 12109.2500 151957.8000 0
## Approved 0.0000 0.0000 0
#Eigen Value dan Eigen Vector
eigen_result <- eigen(cov_matrix)
eigen_values <- eigen_result$values
eigen_vectors <- eigen_result$vectors
print("Eigen Values:")
## [1] "Eigen Values:"
print(eigen_values)
## [1] 1.529995e+05 7.738299e+03 7.501731e+01 4.805864e+00 4.037435e-17
## [6] 7.944076e-30 0.000000e+00 -2.688251e-16 -5.629193e-16 -1.843004e-15
## [11] -1.925470e-15 -1.215216e-13 -2.992215e-13
print("Eigen Vectors:")
## [1] "Eigen Vectors:"
print(eigen_vectors)
## [,1] [,2] [,3] [,4] [,5]
## [1,] 1.353862e-03 1.453043e-03 5.918653e-03 1.112572e-02 0.000000e+00
## [2,] -1.289707e-02 -1.315593e-01 -9.789674e-01 -1.322619e-01 -2.387329e-04
## [3,] 8.188055e-04 -1.960562e-02 1.279405e-01 -6.259008e-01 -2.839654e-03
## [4,] 1.355253e-20 -6.938894e-18 1.110223e-16 -6.938894e-17 -3.130916e-02
## [5,] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 4.904476e-01
## [6,] 3.179442e-04 -9.024765e-03 1.843188e-02 3.260297e-01 -4.365799e-01
## [7,] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 -1.664282e-12
## [8,] 8.979953e-04 1.599538e-03 -2.667001e-02 1.467268e-01 -3.626692e-01
## [9,] 3.495043e-05 -2.713538e-02 -8.389858e-02 6.569034e-01 1.202893e-01
## [10,] 4.535470e-04 -8.272546e-04 1.330957e-02 1.766350e-01 6.495168e-01
## [11,] -8.349333e-02 9.873782e-01 -1.280608e-01 -8.934813e-03 3.429872e-04
## [12,] -9.964231e-01 -8.104951e-02 2.349989e-02 2.301082e-03 -1.942709e-04
## [13,] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
## [,6] [,7] [,8] [,9] [,10] [,11]
## [1,] 0.000000e+00 0 0.000000e+00 0.000000e+00 0.999918618 0.000000e+00
## [2,] 3.826392e-16 0 -4.308543e-04 -9.377325e-04 0.007474909 5.897488e-04
## [3,] 4.368016e-15 0 -1.320765e-02 -1.793913e-02 0.006234250 1.319211e-02
## [4,] 5.305223e-14 0 1.898361e-01 4.871097e-01 0.000000000 8.518837e-01
## [5,] -8.233721e-13 0 7.325549e-01 -4.574660e-01 0.000000000 1.163630e-01
## [6,] 7.262236e-13 0 2.835177e-01 -3.256251e-02 -0.003724028 -6.104740e-02
## [7,] -1.000000e+00 0 -1.059916e-15 1.238593e-14 0.000000000 -5.440093e-15
## [8,] 5.924166e-13 0 -2.774154e-01 -7.152283e-01 -0.001478251 4.575999e-01
## [9,] -1.974611e-13 0 4.770644e-02 1.818601e-01 -0.006773129 -1.098495e-01
## [10,] -1.082466e-12 0 -5.172239e-01 -8.609839e-02 -0.002043546 1.880781e-01
## [11,] -4.927008e-16 0 3.541792e-03 5.213148e-03 -0.000464348 -3.755040e-03
## [12,] 3.059082e-16 0 -6.953539e-04 -1.127208e-03 0.001302204 7.925272e-04
## [13,] 0.000000e+00 1 0.000000e+00 0.000000e+00 0.000000000 0.000000e+00
## [,12] [,13]
## [1,] 0.0000000000 0.0000000000
## [2,] -0.0017192150 -0.0812099920
## [3,] -0.6022334674 -0.4775898218
## [4,] 0.0004225671 -0.0003623297
## [5,] -0.0028846332 0.0008601727
## [6,] 0.2512599773 -0.7445579416
## [7,] 0.0000000000 0.0000000000
## [8,] -0.1967804235 0.0979511527
## [9,] -0.7062277155 -0.0037057302
## [10,] 0.1893841249 -0.4478533205
## [11,] -0.0286664475 -0.0275303208
## [12,] 0.0018936819 0.0026122398
## [13,] 0.0000000000 0.0000000000
#Variance dan Covariance Matrix
variances <- apply(data_numeric, 2, var)
print("Variance:")
## [1] "Variance:"
print(variances)
## Gender Age Dept Married BankCustomer
## 3.000000e-01 2.313614e+02 6.187675e+00 0.000000e+00 0.000000e+00
## YearsEmployed PriorDefault Employed CreditStore DriverLicense
## 1.182050e+00 0.000000e+00 3.000000e-01 8.300000e+00 2.000000e-01
## ZipCode Income Approved
## 8.612000e+03 1.519578e+05 0.000000e+00
print("Covariance Matrix:")
## [1] "Covariance Matrix:"
print(cov_matrix)
## Gender Age Dept Married BankCustomer
## Gender 0.3000 -4.592500 -0.027500 0 0
## Age -4.5925 231.361400 9.345663 0 0
## Dept -0.0275 9.345663 6.187675 0 0
## Married 0.0000 0.000000 0.000000 0 0
## BankCustomer 0.0000 0.000000 0.000000 0 0
## YearsEmployed -0.0100 6.999375 0.605225 0 0
## PriorDefault 0.0000 0.000000 0.000000 0 0
## Employed 0.2000 -1.535000 -0.827500 0 0
## CreditStore -0.3000 33.300000 1.340000 0 0
## DriverLicense 0.1000 -1.142500 -0.221250 0 0
## ZipCode -6.2500 -831.032500 -161.461250 0 0
## Income -207.3000 2046.972500 -112.313750 0 0
## Approved 0.0000 0.000000 0.000000 0 0
## YearsEmployed PriorDefault Employed CreditStore DriverLicense
## Gender -0.010000 0 0.2000 -0.30 0.10000
## Age 6.999375 0 -1.5350 33.30 -1.14250
## Dept 0.605225 0 -0.8275 1.34 -0.22125
## Married 0.000000 0 0.0000 0.00 0.00000
## BankCustomer 0.000000 0 0.0000 0.00 0.00000
## YearsEmployed 1.182050 0 0.1250 2.81 0.37500
## PriorDefault 0.000000 0 0.0000 0.00 0.00000
## Employed 0.125000 0 0.3000 0.30 0.15000
## CreditStore 2.810000 0 0.3000 8.30 0.65000
## DriverLicense 0.375000 0 0.1500 0.65 0.20000
## ZipCode -73.207500 0 1.0000 -207.00 -12.25000
## Income -42.775000 0 -137.9500 11.55 -68.60000
## Approved 0.000000 0 0.0000 0.00 0.00000
## ZipCode Income Approved
## Gender -6.2500 -207.3000 0
## Age -831.0325 2046.9725 0
## Dept -161.4613 -112.3137 0
## Married 0.0000 0.0000 0
## BankCustomer 0.0000 0.0000 0
## YearsEmployed -73.2075 -42.7750 0
## PriorDefault 0.0000 0.0000 0
## Employed 1.0000 -137.9500 0
## CreditStore -207.0000 11.5500 0
## DriverLicense -12.2500 -68.6000 0
## ZipCode 8612.0000 12109.2500 0
## Income 12109.2500 151957.8000 0
## Approved 0.0000 0.0000 0
#Correlation Matrix
cor_matrix <- cor(data_numeric)
## Warning in cor(data_numeric): the standard deviation is zero
print("Correlation Matrix:")
## [1] "Correlation Matrix:"
print(cor_matrix)
## Gender Age Dept Married BankCustomer
## Gender 1.00000000 -0.5512430 -0.02018405 NA NA
## Age -0.55124300 1.0000000 0.24700224 NA NA
## Dept -0.02018405 0.2470022 1.00000000 NA NA
## Married NA NA NA 1 NA
## BankCustomer NA NA NA NA 1
## YearsEmployed -0.01679274 0.4232489 0.22378717 NA NA
## PriorDefault NA NA NA NA NA
## Employed 0.66666667 -0.1842478 -0.60735642 NA NA
## CreditStore -0.19011728 0.7599058 0.18698295 NA NA
## DriverLicense 0.40824829 -0.1679561 -0.19888615 NA NA
## ZipCode -0.12296100 -0.5887359 -0.69944336 NA NA
## Income -0.97090598 0.3452272 -0.11582643 NA NA
## Approved NA NA NA NA NA
## YearsEmployed PriorDefault Employed CreditStore DriverLicense
## Gender -0.01679274 NA 0.66666667 -0.19011728 0.4082483
## Age 0.42324895 NA -0.18424780 0.75990576 -0.1679561
## Dept 0.22378717 NA -0.60735642 0.18698295 -0.1988861
## Married NA NA NA NA NA
## BankCustomer NA NA NA NA NA
## YearsEmployed 1.00000000 NA 0.20990919 0.89711754 0.7712556
## PriorDefault NA 1 NA NA NA
## Employed 0.20990919 NA 1.00000000 0.19011728 0.6123724
## CreditStore 0.89711754 NA 0.19011728 1.00000000 0.5044978
## DriverLicense 0.77125563 NA 0.61237244 0.50449784 1.0000000
## ZipCode -0.72558057 NA 0.01967376 -0.77424657 -0.2951679
## Income -0.10092775 NA -0.64609976 0.01028446 -0.3935026
## Approved NA NA NA NA NA
## ZipCode Income Approved
## Gender -0.12296100 -0.97090598 NA
## Age -0.58873594 0.34522724 NA
## Dept -0.69944336 -0.11582643 NA
## Married NA NA NA
## BankCustomer NA NA NA
## YearsEmployed -0.72558057 -0.10092775 NA
## PriorDefault NA NA NA
## Employed 0.01967376 -0.64609976 NA
## CreditStore -0.77424657 0.01028446 NA
## DriverLicense -0.29516787 -0.39350261 NA
## ZipCode 1.00000000 0.33473701 NA
## Income 0.33473701 1.00000000 NA
## Approved NA NA 1