Assignment1
Moch. Akbar Hanafi
2025-02-18
Load dataset
data <- read.csv("D:\\fourth\\Assignment\\Housing.csv")Encode data kategorikal
Encode data kategorikalEncode data kategori ke numerik agar dapat dihitung covariance dan variance ,eigen, dan korelasi nya
data$mainroad <- as.numeric(factor(data$mainroad))kategori <- names(data)[sapply(data, function(x) is.character(x) || is.factor(x))]for (col in kategori) {data[[col]] <- as.numeric(factor(data[[col]]))}Eigen Values & Vectors
convert df ke matriks
matriks_dataset <- as.matrix(data)karena tidak persegi, kita ubah ke bentuk matriks persegi menggunakan covariance, menggunakan cov()
cov_matriks <- cov(matriks_dataset)hitung eigen nya menggunakan eigen()
eigendata <- eigen(cov_matriks)ambil eigen value nya
eigendata$values## [1] 3.498546e+12 3.356500e+06 7.544051e-01 6.131668e-01 5.145586e-01
## [6] 3.986848e-01 2.210006e-01 1.797849e-01 1.697761e-01 1.343937e-01
## [11] 1.016951e-01 9.768376e-02 4.030909e-02ambil eigen vectornya
eigendata$vectors## [,1] [,2] [,3] [,4] [,5]
## [1,] 9.999998e-01 6.218809e-04 2.096730e-07 2.570731e-07 7.756116e-08
## [2,] 6.218809e-04 -9.999998e-01 -1.038211e-04 4.431336e-05 -2.516273e-05
## [3,] 1.446162e-07 2.127405e-05 -4.487104e-01 -3.223081e-01 -2.030000e-02
## [4,] 1.390319e-07 2.715391e-05 -1.107822e-01 -6.524337e-02 1.387440e-02
## [5,] 1.951224e-07 7.936671e-05 -7.805069e-01 -2.272525e-01 1.373783e-02
## [6,] 5.533946e-08 -2.924396e-05 3.001069e-02 -2.387851e-02 -2.232729e-02
## [7,] 5.230024e-08 -8.267189e-07 3.607960e-02 4.905755e-02 -6.477647e-02
## [8,] 4.775840e-08 1.631643e-05 1.607491e-01 5.917235e-02 -9.153149e-02
## [9,] 1.041965e-08 8.003516e-06 9.632872e-03 -1.152994e-02 6.591862e-03
## [10,] 1.126500e-07 6.132321e-06 -5.447681e-02 -1.329150e-02 -1.326760e-02
## [11,] 1.770643e-07 -8.185750e-05 3.597498e-01 -8.450274e-01 3.622313e-01
## [12,] 7.480868e-08 -1.591647e-05 5.421387e-02 6.285965e-02 -3.634784e-02
## [13,] -1.240385e-07 3.994880e-06 -1.171882e-01 3.397157e-01 9.237868e-01
## [,6] [,7] [,8] [,9] [,10]
## [1,] -2.633319e-08 -1.371272e-07 7.080318e-08 -8.454439e-08 -1.208154e-07
## [2,] 1.682020e-06 8.775871e-06 -3.050618e-05 1.868926e-05 -7.233902e-06
## [3,] 7.698694e-01 -1.173763e-01 2.716325e-01 -4.939608e-02 -4.002304e-02
## [4,] 1.641472e-01 -2.221342e-01 -7.416446e-01 2.682432e-01 5.168497e-01
## [5,] -4.448797e-01 3.102219e-01 -1.429321e-01 -7.870748e-02 -6.032466e-02
## [6,] -1.049995e-01 1.359580e-01 5.434723e-02 -2.019543e-01 3.921092e-02
## [7,] 1.077152e-01 4.313342e-01 -1.876936e-01 1.521216e-01 -2.364411e-01
## [8,] 3.503125e-01 6.576657e-01 -2.695084e-01 9.897206e-02 -1.825636e-01
## [9,] 9.900171e-03 -5.623295e-02 -6.015005e-02 -7.748045e-02 -9.342759e-02
## [10,] -1.065508e-01 1.745289e-01 4.410841e-01 8.066786e-01 2.859041e-01
## [11,] -1.053156e-01 1.023446e-01 -2.680177e-02 -4.404100e-04 1.093070e-02
## [12,] 6.284219e-02 3.926174e-01 2.144755e-01 -4.346353e-01 7.390015e-01
## [13,] 1.029611e-01 7.306351e-02 6.394522e-03 6.858280e-03 -1.190165e-02
## [,11] [,12] [,13]
## [1,] -1.986477e-08 3.130235e-08 3.771858e-08
## [2,] -2.469345e-05 1.472683e-05 -8.265645e-06
## [3,] 8.139692e-02 -5.860742e-02 1.396765e-03
## [4,] 7.464084e-02 -1.098096e-01 -1.536549e-02
## [5,] -1.118744e-01 4.510007e-02 -2.770873e-03
## [6,] 3.179183e-01 -9.061783e-01 -3.183307e-02
## [7,] 7.785477e-01 2.718687e-01 -1.097562e-02
## [8,] -5.146473e-01 -1.625224e-01 -1.499917e-03
## [9,] -2.080439e-02 2.791596e-02 -9.883869e-01
## [10,] -1.019774e-02 -1.025173e-01 -1.312464e-01
## [11,] 3.327004e-03 5.256081e-02 1.094878e-02
## [12,] 4.965499e-02 2.141025e-01 -6.598862e-02
## [13,] 1.426586e-02 -3.310793e-02 -3.106711e-03VARIANCE-COVARIANCE MATRIKS
varcovmatriks <- cov(matriks_dataset)
varcovmatriks## price area bedrooms bathrooms
## price 3.498544e+12 2.175676e+09 5.059464e+05 4.864093e+05
## area 2.175676e+09 4.709512e+06 2.432321e+02 2.113466e+02
## bedrooms 5.059464e+05 2.432321e+02 5.447383e-01 1.386738e-01
## bathrooms 4.864093e+05 2.113466e+02 1.386738e-01 2.524757e-01
## stories 6.826446e+05 1.581294e+02 2.615893e-01 1.421715e-01
## mainroad 1.936075e+05 2.185582e+02 -3.096330e-03 7.427145e-03
## guestroom 1.829747e+05 1.165634e+02 2.276039e-02 2.432879e-02
## basement 1.670850e+05 4.914084e+01 3.429911e-02 2.450081e-02
## hotwaterheating 3.645362e+04 -4.193993e+00 7.116838e-03 7.066244e-03
## airconditioning 3.941112e+05 2.245072e+02 5.514031e-02 4.368929e-02
## parking 6.194673e+05 6.599897e+02 8.856247e-02 7.684161e-02
## prefarea 2.617215e+05 2.161833e+02 2.474703e-02 1.353211e-02
## furnishingstatus -4.339543e+05 -2.832768e+02 -6.925594e-02 -5.492107e-02
## stories mainroad guestroom basement
## price 6.826446e+05 1.936075e+05 1.829747e+05 1.670850e+05
## area 1.581294e+02 2.185582e+02 1.165634e+02 4.914084e+01
## bedrooms 2.615893e-01 -3.096330e-03 2.276039e-02 3.429911e-02
## bathrooms 1.421715e-01 7.427145e-03 2.432879e-02 2.450081e-02
## stories 7.525432e-01 3.680855e-02 1.445966e-02 -7.141797e-02
## mainroad 3.680855e-02 1.215461e-01 1.232461e-02 7.325958e-03
## guestroom 1.445966e-02 1.232461e-02 1.465731e-01 6.802482e-02
## basement -7.141797e-02 7.325958e-03 6.802482e-02 2.280559e-01
## hotwaterheating 3.423502e-03 -8.600917e-04 -8.263627e-04 4.384781e-04
## airconditioning 1.184802e-01 1.709727e-02 2.460874e-02 1.051673e-02
## parking 3.404277e-02 6.140718e-02 1.235834e-02 2.118861e-02
## prefarea 1.635186e-02 2.956692e-02 2.613667e-02 4.621560e-02
## furnishingstatus -6.913451e-02 -4.160146e-02 -3.449137e-02 -4.102469e-02
## hotwaterheating airconditioning parking prefarea
## price 3.645362e+04 3.941112e+05 6.194673e+05 2.617215e+05
## area -4.193993e+00 2.245072e+02 6.599897e+02 2.161833e+02
## bedrooms 7.116838e-03 5.514031e-02 8.856247e-02 2.474703e-02
## bathrooms 7.066244e-03 4.368929e-02 7.684161e-02 1.353211e-02
## stories 3.423502e-03 1.184802e-01 3.404277e-02 1.635186e-02
## mainroad -8.600917e-04 1.709727e-02 6.140718e-02 2.956692e-02
## guestroom -8.263627e-04 2.460874e-02 1.235834e-02 2.613667e-02
## basement 4.384781e-04 1.051673e-02 2.118861e-02 4.621560e-02
## hotwaterheating 4.384781e-02 -1.266527e-02 1.224366e-02 -5.278602e-03
## airconditioning -1.266527e-02 2.163923e-01 6.379520e-02 2.316851e-02
## parking 1.224366e-02 6.379520e-02 7.423300e-01 3.349636e-02
## prefarea -5.278602e-03 2.316851e-02 3.349636e-02 1.800324e-01
## furnishingstatus -5.042499e-03 -5.329533e-02 -1.164632e-01 -3.478818e-02
## furnishingstatus
## price -4.339543e+05
## area -2.832768e+02
## bedrooms -6.925594e-02
## bathrooms -5.492107e-02
## stories -6.913451e-02
## mainroad -4.160146e-02
## guestroom -3.449137e-02
## basement -4.102469e-02
## hotwaterheating -5.042499e-03
## airconditioning -5.329533e-02
## parking -1.164632e-01
## prefarea -3.478818e-02
## furnishingstatus 5.796883e-01CORRELATION MATRIX
cari korelasiinya menggunakan cov2cor() dimana cov2cor menggunakan metode pearson
corr_matrik <- cov2cor(varcovmatriks)
corr_matrik## price area bedrooms bathrooms stories
## price 1.00000000 0.535997346 0.36649403 0.51754534 0.42071237
## area 0.53599735 1.000000000 0.15185849 0.19381953 0.08399605
## bedrooms 0.36649403 0.151858486 1.00000000 0.37393024 0.40856424
## bathrooms 0.51754534 0.193819531 0.37393024 1.00000000 0.32616471
## stories 0.42071237 0.083996051 0.40856424 0.32616471 1.00000000
## mainroad 0.29689849 0.288874114 -0.01203324 0.04239762 0.12170613
## guestroom 0.25551729 0.140296590 0.08054870 0.12646884 0.04353767
## basement 0.18705660 0.047416989 0.09731242 0.10210571 -0.17239362
## hotwaterheating 0.09307284 -0.009229236 0.04604889 0.06715910 0.01884651
## airconditioning 0.45295408 0.222393104 0.16060326 0.18691503 0.29360200
## parking 0.38439365 0.352980481 0.13926990 0.17749582 0.04554709
## prefarea 0.32977705 0.234778798 0.07902306 0.06347174 0.04442487
## furnishingstatus -0.30472146 -0.171445361 -0.12324400 -0.14355950 -0.10467233
## mainroad guestroom basement hotwaterheating
## price 0.29689849 0.25551729 0.187056598 0.093072844
## area 0.28887411 0.14029659 0.047416989 -0.009229236
## bedrooms -0.01203324 0.08054870 0.097312424 0.046048887
## bathrooms 0.04239762 0.12646884 0.102105706 0.067159096
## stories 0.12170613 0.04353767 -0.172393617 0.018846511
## mainroad 1.00000000 0.09233692 0.044002081 -0.011781490
## guestroom 0.09233692 1.00000000 0.372065708 -0.010307884
## basement 0.04400208 0.37206571 1.000000000 0.004384836
## hotwaterheating -0.01178149 -0.01030788 0.004384836 1.000000000
## airconditioning 0.10542300 0.13817877 0.047341189 -0.130022833
## parking 0.20443255 0.03746575 0.051497175 0.067863888
## prefarea 0.19987578 0.16089694 0.228082853 -0.059411382
## furnishingstatus -0.15672586 -0.11832757 -0.112830732 -0.031628204
## airconditioning parking prefarea furnishingstatus
## price 0.45295408 0.38439365 0.32977705 -0.3047215
## area 0.22239310 0.35298048 0.23477880 -0.1714454
## bedrooms 0.16060326 0.13926990 0.07902306 -0.1232440
## bathrooms 0.18691503 0.17749582 0.06347174 -0.1435595
## stories 0.29360200 0.04554709 0.04442487 -0.1046723
## mainroad 0.10542300 0.20443255 0.19987578 -0.1567259
## guestroom 0.13817877 0.03746575 0.16089694 -0.1183276
## basement 0.04734119 0.05149718 0.22808285 -0.1128307
## hotwaterheating -0.13002283 0.06786389 -0.05941138 -0.0316282
## airconditioning 1.00000000 0.15917268 0.11738210 -0.1504773
## parking 0.15917268 1.00000000 0.09162706 -0.1775386
## prefarea 0.11738210 0.09162706 1.00000000 -0.1076860
## furnishingstatus -0.15047729 -0.17753861 -0.10768597 1.0000000install.packages("corrplot",repos = "https://cran.rstudio.com/")## package 'corrplot' successfully unpacked and MD5 sums checked
##
## The downloaded binary packages are in
## C:\Users\mocha\AppData\Local\Temp\RtmpyykK6f\downloaded_packageslibrary(corrplot)## corrplot 0.95 loadedcorrplot(corr_matrik, method = "color")