Untitled
Layyinatul Qolbiyah
2025-03-02
str(data_csv)## 'data.frame': 545 obs. of 13 variables:
## $ price : int 13300000 12250000 12250000 12215000 11410000 10850000 10150000 10150000 9870000 9800000 ...
## $ area : int 7420 8960 9960 7500 7420 7500 8580 16200 8100 5750 ...
## $ bedrooms : int 4 4 3 4 4 3 4 5 4 3 ...
## $ bathrooms : int 2 4 2 2 1 3 3 3 1 2 ...
## $ stories : int 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 : int 2 3 2 3 2 2 2 0 2 1 ...
## $ prefarea : chr "yes" "no" "yes" "yes" ...
## $ furnishingstatus: chr "furnished" "furnished" "semi-furnished" "furnished" ...numerik_data <- data_csv[sapply(data_csv, is.numeric)]
(numerik_data)## 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
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## 545 1750000 3850 3 1 2 0knitr::opts_chunk$set(echo = TRUE)#install.packages("ggplot2")
#install.packages("gridExtra")
# Load library
library(ggplot2)
library(gridExtra)
# Menentukan variabel numerik
numerik_data <- c("price", "area", "bedrooms", "bathrooms", "stories", "parking")
# Membuat daftar plot
plot_list <- list()
for (col in numerik_data) {
p <- ggplot(data_csv, aes_string(x = col)) +
geom_histogram(fill = "green", color = "blue", bins = 30, alpha = 0.7) +
ggtitle(paste("Histogram of", col)) +
theme_minimal()
plot_list[[col]] <- p
}## Warning: `aes_string()` was deprecated in ggplot2 3.0.0.
## ℹ Please use tidy evaluation idioms with `aes()`.
## ℹ See also `vignette("ggplot2-in-packages")` for more information.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.# Menampilkan semua plot dalam satu gambar
do.call(grid.arrange, c(plot_list, ncol = 2))
numerik_kolom <- c("price", "area", "bedrooms", "bathrooms", "stories", "parking")# Memilih kolom numerik dari dataset
numerik_data <- data_csv[, numerik_kolom]
# Memastikan dalam bentuk data frame
numerik_data <- as.data.frame(numerik_data)
# Menghitung kovarian matriks
cov_matrix <- cov(numerik_data)
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-01cor_matrix <- cor(numerik_data)
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#install.packages("reshape2")
library(ggplot2)
library(reshape2)
cor_melted <- melt(cor_matrix)
ggplot(data = cor_melted, aes(x = Var1, y = Var2, fill = value)) +
geom_tile(color = "white") +
geom_text(aes(label = round(value, 2)), size = 4, fontface = "bold") +
scale_fill_gradientn(colors = c("white", "green", "blue", "red"),
values = scales::rescale(c(-1, 0, 0.5, 1)),
limits = c(-1, 1),
name = "Correlation") +
theme_minimal() +
xlab("") + ylab("") +
theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
ggtitle("Heatmap Korelasi Antar Variabel")
# Menghitung Eigen value dengan Rumus |A - λI| = 0
I <- diag(ncol(cov_matrix))
lambda_values <- eigen(cov_matrix)$values
print(lambda_values)## [1] 3.498546e+12 3.356500e+06 7.345801e-01 5.958731e-01 3.626262e-01
## [6] 1.677009e-01# Menghitung Eigen vector dengan (A - λI)v = 0
eigenvectors <- eigen(cov_matrix)$vectors
print(eigenvectors)## [,1] [,2] [,3] [,4] [,5]
## [1,] 9.999998e-01 6.218809e-04 2.350022e-07 2.360030e-07 -1.896406e-08
## [2,] 6.218809e-04 -9.999998e-01 -1.068146e-04 4.693651e-05 1.227917e-05
## [3,] 1.446162e-07 2.127405e-05 -4.831235e-01 -3.524835e-01 -7.742078e-01
## [4,] 1.390319e-07 2.715391e-05 -1.156247e-01 -7.418855e-02 -1.559105e-01
## [5,] 1.951224e-07 7.936672e-05 -7.760615e-01 -2.230034e-01 5.897316e-01
## [6,] 1.770643e-07 -8.185750e-05 3.885243e-01 -9.058261e-01 1.688515e-01
## [,6]
## [1,] 1.167426e-07
## [2,] -2.065080e-05
## [3,] 2.072422e-01
## [4,] -9.781712e-01
## [5,] 1.465063e-02
## [6,] -4.137110e-03# Mengambil Eigen Value terbesar
max_eigen_index <- which.max(lambda_values)
max_eigenvectors <- eigenvectors[, max_eigen_index]
print(lambda_values[max_eigen_index])## [1] 3.498546e+12# Eigen vector maximal
print(max_eigenvectors)## [1] 9.999998e-01 6.218809e-04 1.446162e-07 1.390319e-07 1.951224e-07
## [6] 1.770643e-07# Model regresi
model <- lm(price ~ area + bedrooms + bathrooms + stories + parking, data = numerik_data)
summary(model)##
## Call:
## lm(formula = price ~ area + bedrooms + bathrooms + stories +
## parking, data = numerik_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3396744 -731825 -64056 601486 5651126
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -145734.5 246634.5 -0.591 0.5548
## area 331.1 26.6 12.448 < 2e-16 ***
## bedrooms 167809.8 82932.7 2.023 0.0435 *
## bathrooms 1133740.2 118828.3 9.541 < 2e-16 ***
## stories 547939.8 68894.5 7.953 1.07e-14 ***
## parking 377596.3 66804.1 5.652 2.57e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1244000 on 539 degrees of freedom
## Multiple R-squared: 0.5616, Adjusted R-squared: 0.5575
## F-statistic: 138.1 on 5 and 539 DF, p-value: < 2.2e-16# Menghitung RMSE
predicted_prices <- predict(model, numerik_data)
actual_prices <- numerik_data$price
rmse <- sqrt(mean((predicted_prices - actual_prices)^2))
print(paste("RMSE:", rmse))## [1] "RMSE: 1237339.30781674"# Menampilkan prediksi harga rumah tertinggi
numerik_data$predicted_price <- predicted_prices
max_price_data <- numerik_data[which.max(numerik_data$predicted_price), ]# Visualisasi
library(ggplot2)
ggplot(numerik_data, aes(x = area, y = predicted_price)) +
geom_point(color = "blue", alpha = 0.5) +
geom_point(data = max_price_data, aes(x = area, y = predicted_price), color = "yellow", size = 3) +
ggtitle("Prediksi Price Tertinggi") +
xlab("Luas Area") + ylab("Price Prediksi") + theme_minimal()
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