Loading Data
library(readr)
housing <- read_csv("D:/Sains Data smt 4/Analisis Multivariat/Housing.csv")
housing_df <- as.data.frame(housing)
str(housing_df)
## 'data.frame': 545 obs. of 13 variables:
## $ price : num 13300000 12250000 12250000 12215000 11410000 ...
## $ area : num 7420 8960 9960 7500 7420 7500 8580 16200 8100 5750 ...
## $ bedrooms : num 4 4 3 4 4 3 4 5 4 3 ...
## $ bathrooms : num 2 4 2 2 1 3 3 3 1 2 ...
## $ stories : num 3 4 2 2 2 1 4 2 2 4 ...
## $ mainroad : chr "yes" "yes" "yes" "yes" ...
## $ guestroom : chr "no" "no" "no" "no" ...
## $ basement : chr "no" "no" "yes" "yes" ...
## $ hotwaterheating : chr "no" "no" "no" "no" ...
## $ airconditioning : chr "yes" "yes" "no" "yes" ...
## $ parking : num 2 3 2 3 2 2 2 0 2 1 ...
## $ prefarea : chr "yes" "no" "yes" "yes" ...
## $ furnishingstatus: chr "furnished" "furnished" "semi-furnished" "furnished" ...
Mengambil Data Numerik
housing_num <- housing_df[, c("price", "area", "bedrooms", "bathrooms", "stories", "parking")]
head(housing_num)
## price area bedrooms bathrooms stories parking
## 1 13300000 7420 4 2 3 2
## 2 12250000 8960 4 4 4 3
## 3 12250000 9960 3 2 2 2
## 4 12215000 7500 4 2 2 3
## 5 11410000 7420 4 1 2 2
## 6 10850000 7500 3 3 1 2
Mencari Varians dan Covarians Setiap Fitur
varians <- apply(housing_num, 2, var)
print(varians)
## price area bedrooms bathrooms stories parking
## 3.498544e+12 4.709512e+06 5.447383e-01 2.524757e-01 7.525432e-01 7.423300e-01
cov_matrix <- cov(housing_num)
print(cov_matrix)
## price area bedrooms bathrooms stories
## price 3.498544e+12 2.175676e+09 5.059464e+05 4.864093e+05 6.826446e+05
## area 2.175676e+09 4.709512e+06 2.432321e+02 2.113466e+02 1.581294e+02
## bedrooms 5.059464e+05 2.432321e+02 5.447383e-01 1.386738e-01 2.615893e-01
## bathrooms 4.864093e+05 2.113466e+02 1.386738e-01 2.524757e-01 1.421715e-01
## stories 6.826446e+05 1.581294e+02 2.615893e-01 1.421715e-01 7.525432e-01
## parking 6.194673e+05 6.599897e+02 8.856247e-02 7.684161e-02 3.404277e-02
## parking
## price 6.194673e+05
## area 6.599897e+02
## bedrooms 8.856247e-02
## bathrooms 7.684161e-02
## stories 3.404277e-02
## parking 7.423300e-01
Mencari Korelasi Matriks Setiap Fitur
cor_matrix <- cor(housing_num)
print(cor_matrix)
## price area bedrooms bathrooms stories parking
## price 1.0000000 0.53599735 0.3664940 0.5175453 0.42071237 0.38439365
## area 0.5359973 1.00000000 0.1518585 0.1938195 0.08399605 0.35298048
## bedrooms 0.3664940 0.15185849 1.0000000 0.3739302 0.40856424 0.13926990
## bathrooms 0.5175453 0.19381953 0.3739302 1.0000000 0.32616471 0.17749582
## stories 0.4207124 0.08399605 0.4085642 0.3261647 1.00000000 0.04554709
## parking 0.3843936 0.35298048 0.1392699 0.1774958 0.04554709 1.00000000
Mencari Eigen Value & Eigen Vektor berdasarkan Korelasi
Matriks
eigen_result <- eigen(cor_matrix)
print(eigen_result$values)
## [1] 2.5561051 1.2171486 0.6771415 0.6566698 0.5908395 0.3020955
print(eigen_result$vectors)
## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] -0.5395439 0.1203486 0.24279104 0.04162299 -0.14135501 0.78342034
## [2,] -0.3685384 0.5178529 0.51575760 -0.22455991 0.31542421 -0.42436114
## [3,] -0.3915181 -0.3822393 -0.38949056 -0.24634216 0.69703170 0.04864311
## [4,] -0.4322131 -0.2116685 0.01788812 0.81460710 -0.03855905 -0.32093105
## [5,] -0.3682862 -0.4917297 0.12475034 -0.46684594 -0.55146176 -0.29146070
## [6,] -0.3119977 0.5335130 -0.71236303 -0.07500705 -0.29845646 -0.12592842