Script Metode Logistic Discriminant Analysis (LDA) dan Principal Component Analysis (PCA)
Chiara Dewi Chatlina
2025-06-07
NAMA : Chiara Dewi Chatlina
NIM : 23031554170
MATA KULIAH : ANALISIS MULTIVARIAT
DOSEN PENGAMPU : Ike Fitriyaningsih, M.Si
NIDN : 0109049001
dataset <- read.csv("D:/SEMESTER 4/ANMUL/archive (9)/train.csv")
head(dataset)## battery_power blue clock_speed dual_sim fc four_g int_memory m_dep mobile_wt
## 1 842 0 2.2 0 1 0 7 0.6 188
## 2 1021 1 0.5 1 0 1 53 0.7 136
## 3 563 1 0.5 1 2 1 41 0.9 145
## 4 615 1 2.5 0 0 0 10 0.8 131
## 5 1821 1 1.2 0 13 1 44 0.6 141
## 6 1859 0 0.5 1 3 0 22 0.7 164
## n_cores pc px_height px_width ram sc_h sc_w talk_time three_g touch_screen
## 1 2 2 20 756 2549 9 7 19 0 0
## 2 3 6 905 1988 2631 17 3 7 1 1
## 3 5 6 1263 1716 2603 11 2 9 1 1
## 4 6 9 1216 1786 2769 16 8 11 1 0
## 5 2 14 1208 1212 1411 8 2 15 1 1
## 6 1 7 1004 1654 1067 17 1 10 1 0
## wifi price_range
## 1 1 1
## 2 0 2
## 3 0 2
## 4 0 2
## 5 0 1
## 6 0 1str(dataset)## 'data.frame': 2000 obs. of 21 variables:
## $ battery_power: int 842 1021 563 615 1821 1859 1821 1954 1445 509 ...
## $ blue : int 0 1 1 1 1 0 0 0 1 1 ...
## $ clock_speed : num 2.2 0.5 0.5 2.5 1.2 0.5 1.7 0.5 0.5 0.6 ...
## $ dual_sim : int 0 1 1 0 0 1 0 1 0 1 ...
## $ fc : int 1 0 2 0 13 3 4 0 0 2 ...
## $ four_g : int 0 1 1 0 1 0 1 0 0 1 ...
## $ int_memory : int 7 53 41 10 44 22 10 24 53 9 ...
## $ m_dep : num 0.6 0.7 0.9 0.8 0.6 0.7 0.8 0.8 0.7 0.1 ...
## $ mobile_wt : int 188 136 145 131 141 164 139 187 174 93 ...
## $ n_cores : int 2 3 5 6 2 1 8 4 7 5 ...
## $ pc : int 2 6 6 9 14 7 10 0 14 15 ...
## $ px_height : int 20 905 1263 1216 1208 1004 381 512 386 1137 ...
## $ px_width : int 756 1988 1716 1786 1212 1654 1018 1149 836 1224 ...
## $ ram : int 2549 2631 2603 2769 1411 1067 3220 700 1099 513 ...
## $ sc_h : int 9 17 11 16 8 17 13 16 17 19 ...
## $ sc_w : int 7 3 2 8 2 1 8 3 1 10 ...
## $ talk_time : int 19 7 9 11 15 10 18 5 20 12 ...
## $ three_g : int 0 1 1 1 1 1 1 1 1 1 ...
## $ touch_screen : int 0 1 1 0 1 0 0 1 0 0 ...
## $ wifi : int 1 0 0 0 0 0 1 1 0 0 ...
## $ price_range : int 1 2 2 2 1 1 3 0 0 0 ...summary(dataset)## battery_power blue clock_speed dual_sim
## Min. : 501.0 Min. :0.000 Min. :0.500 Min. :0.0000
## 1st Qu.: 851.8 1st Qu.:0.000 1st Qu.:0.700 1st Qu.:0.0000
## Median :1226.0 Median :0.000 Median :1.500 Median :1.0000
## Mean :1238.5 Mean :0.495 Mean :1.522 Mean :0.5095
## 3rd Qu.:1615.2 3rd Qu.:1.000 3rd Qu.:2.200 3rd Qu.:1.0000
## Max. :1998.0 Max. :1.000 Max. :3.000 Max. :1.0000
## fc four_g int_memory m_dep
## Min. : 0.000 Min. :0.0000 Min. : 2.00 Min. :0.1000
## 1st Qu.: 1.000 1st Qu.:0.0000 1st Qu.:16.00 1st Qu.:0.2000
## Median : 3.000 Median :1.0000 Median :32.00 Median :0.5000
## Mean : 4.309 Mean :0.5215 Mean :32.05 Mean :0.5018
## 3rd Qu.: 7.000 3rd Qu.:1.0000 3rd Qu.:48.00 3rd Qu.:0.8000
## Max. :19.000 Max. :1.0000 Max. :64.00 Max. :1.0000
## mobile_wt n_cores pc px_height
## Min. : 80.0 Min. :1.000 Min. : 0.000 Min. : 0.0
## 1st Qu.:109.0 1st Qu.:3.000 1st Qu.: 5.000 1st Qu.: 282.8
## Median :141.0 Median :4.000 Median :10.000 Median : 564.0
## Mean :140.2 Mean :4.521 Mean : 9.916 Mean : 645.1
## 3rd Qu.:170.0 3rd Qu.:7.000 3rd Qu.:15.000 3rd Qu.: 947.2
## Max. :200.0 Max. :8.000 Max. :20.000 Max. :1960.0
## px_width ram sc_h sc_w
## Min. : 500.0 Min. : 256 Min. : 5.00 Min. : 0.000
## 1st Qu.: 874.8 1st Qu.:1208 1st Qu.: 9.00 1st Qu.: 2.000
## Median :1247.0 Median :2146 Median :12.00 Median : 5.000
## Mean :1251.5 Mean :2124 Mean :12.31 Mean : 5.767
## 3rd Qu.:1633.0 3rd Qu.:3064 3rd Qu.:16.00 3rd Qu.: 9.000
## Max. :1998.0 Max. :3998 Max. :19.00 Max. :18.000
## talk_time three_g touch_screen wifi
## Min. : 2.00 Min. :0.0000 Min. :0.000 Min. :0.000
## 1st Qu.: 6.00 1st Qu.:1.0000 1st Qu.:0.000 1st Qu.:0.000
## Median :11.00 Median :1.0000 Median :1.000 Median :1.000
## Mean :11.01 Mean :0.7615 Mean :0.503 Mean :0.507
## 3rd Qu.:16.00 3rd Qu.:1.0000 3rd Qu.:1.000 3rd Qu.:1.000
## Max. :20.00 Max. :1.0000 Max. :1.000 Max. :1.000
## price_range
## Min. :0.00
## 1st Qu.:0.75
## Median :1.50
## Mean :1.50
## 3rd Qu.:2.25
## Max. :3.00#Pre-processing
missing_values <- colSums(is.na(dataset))
cat("Missing values per kolom:\n")## Missing values per kolom:print(missing_values)## battery_power blue clock_speed dual_sim fc
## 0 0 0 0 0
## four_g int_memory m_dep mobile_wt n_cores
## 0 0 0 0 0
## pc px_height px_width ram sc_h
## 0 0 0 0 0
## sc_w talk_time three_g touch_screen wifi
## 0 0 0 0 0
## price_range
## 0duplicate_count <- sum(duplicated(dataset))
cat("Jumlah data duplikat:", duplicate_count, "\n")## Jumlah data duplikat: 0numeric_data <- dataset %>% select_if(is.numeric)
cor_matrix <- cor(numeric_data, use = "complete.obs")
ggcorr(numeric_data,label = TRUE,label_size = 3, hjust = 0.95, layout.exp = 3, name = "Correlation") +
ggtitle("Matriks Korelasi Tiap Variabel") +
theme(
plot.title = element_text(size = 16, face = "bold", hjust = 0.5),
legend.title = element_text(size = 12),
legend.text = element_text(size = 10)
)
library(dplyr)
dataset <- dataset %>%
mutate(
blue = as.factor(blue),
dual_sim = as.factor(dual_sim),
four_g = as.factor(four_g),
three_g = as.factor(three_g),
touch_screen = as.factor(touch_screen),
wifi = as.factor(wifi),
price_range = as.character(price_range),
price_range = dplyr::recode(price_range,
"0" = "low cost",
"1" = "medium cost",
"2" = "high cost",
"3" = "very high cost"),
price_range = ordered(price_range,
levels = c("low cost", "medium cost", "high cost", "very high cost"))
)summary(dataset)## battery_power blue clock_speed dual_sim fc four_g
## Min. : 501.0 0:1010 Min. :0.500 0: 981 Min. : 0.000 0: 957
## 1st Qu.: 851.8 1: 990 1st Qu.:0.700 1:1019 1st Qu.: 1.000 1:1043
## Median :1226.0 Median :1.500 Median : 3.000
## Mean :1238.5 Mean :1.522 Mean : 4.309
## 3rd Qu.:1615.2 3rd Qu.:2.200 3rd Qu.: 7.000
## Max. :1998.0 Max. :3.000 Max. :19.000
## int_memory m_dep mobile_wt n_cores
## Min. : 2.00 Min. :0.1000 Min. : 80.0 Min. :1.000
## 1st Qu.:16.00 1st Qu.:0.2000 1st Qu.:109.0 1st Qu.:3.000
## Median :32.00 Median :0.5000 Median :141.0 Median :4.000
## Mean :32.05 Mean :0.5018 Mean :140.2 Mean :4.521
## 3rd Qu.:48.00 3rd Qu.:0.8000 3rd Qu.:170.0 3rd Qu.:7.000
## Max. :64.00 Max. :1.0000 Max. :200.0 Max. :8.000
## pc px_height px_width ram
## Min. : 0.000 Min. : 0.0 Min. : 500.0 Min. : 256
## 1st Qu.: 5.000 1st Qu.: 282.8 1st Qu.: 874.8 1st Qu.:1208
## Median :10.000 Median : 564.0 Median :1247.0 Median :2146
## Mean : 9.916 Mean : 645.1 Mean :1251.5 Mean :2124
## 3rd Qu.:15.000 3rd Qu.: 947.2 3rd Qu.:1633.0 3rd Qu.:3064
## Max. :20.000 Max. :1960.0 Max. :1998.0 Max. :3998
## sc_h sc_w talk_time three_g touch_screen
## Min. : 5.00 Min. : 0.000 Min. : 2.00 0: 477 0: 994
## 1st Qu.: 9.00 1st Qu.: 2.000 1st Qu.: 6.00 1:1523 1:1006
## Median :12.00 Median : 5.000 Median :11.00
## Mean :12.31 Mean : 5.767 Mean :11.01
## 3rd Qu.:16.00 3rd Qu.: 9.000 3rd Qu.:16.00
## Max. :19.00 Max. :18.000 Max. :20.00
## wifi price_range
## 0: 986 low cost :500
## 1:1014 medium cost :500
## high cost :500
## very high cost:500
##
## str(dataset)## 'data.frame': 2000 obs. of 21 variables:
## $ battery_power: int 842 1021 563 615 1821 1859 1821 1954 1445 509 ...
## $ blue : Factor w/ 2 levels "0","1": 1 2 2 2 2 1 1 1 2 2 ...
## $ clock_speed : num 2.2 0.5 0.5 2.5 1.2 0.5 1.7 0.5 0.5 0.6 ...
## $ dual_sim : Factor w/ 2 levels "0","1": 1 2 2 1 1 2 1 2 1 2 ...
## $ fc : int 1 0 2 0 13 3 4 0 0 2 ...
## $ four_g : Factor w/ 2 levels "0","1": 1 2 2 1 2 1 2 1 1 2 ...
## $ int_memory : int 7 53 41 10 44 22 10 24 53 9 ...
## $ m_dep : num 0.6 0.7 0.9 0.8 0.6 0.7 0.8 0.8 0.7 0.1 ...
## $ mobile_wt : int 188 136 145 131 141 164 139 187 174 93 ...
## $ n_cores : int 2 3 5 6 2 1 8 4 7 5 ...
## $ pc : int 2 6 6 9 14 7 10 0 14 15 ...
## $ px_height : int 20 905 1263 1216 1208 1004 381 512 386 1137 ...
## $ px_width : int 756 1988 1716 1786 1212 1654 1018 1149 836 1224 ...
## $ ram : int 2549 2631 2603 2769 1411 1067 3220 700 1099 513 ...
## $ sc_h : int 9 17 11 16 8 17 13 16 17 19 ...
## $ sc_w : int 7 3 2 8 2 1 8 3 1 10 ...
## $ talk_time : int 19 7 9 11 15 10 18 5 20 12 ...
## $ three_g : Factor w/ 2 levels "0","1": 1 2 2 2 2 2 2 2 2 2 ...
## $ touch_screen : Factor w/ 2 levels "0","1": 1 2 2 1 2 1 1 2 1 1 ...
## $ wifi : Factor w/ 2 levels "0","1": 2 1 1 1 1 1 2 2 1 1 ...
## $ price_range : Ord.factor w/ 4 levels "low cost"<"medium cost"<..: 2 3 3 3 2 2 4 1 1 1 ...ANALISIS STATISTIK DESKRIPTIF
statistik deskriptif untuk variabel numerik
describe(dataset %>% select_if(is.numeric))## vars n mean sd median trimmed mad min max
## battery_power 1 2000 1238.52 439.42 1226.0 1236.25 566.35 501.0 1998
## clock_speed 2 2000 1.52 0.82 1.5 1.48 1.19 0.5 3
## fc 3 2000 4.31 4.34 3.0 3.69 4.45 0.0 19
## int_memory 4 2000 32.05 18.15 32.0 31.88 23.72 2.0 64
## m_dep 5 2000 0.50 0.29 0.5 0.50 0.44 0.1 1
## mobile_wt 6 2000 140.25 35.40 141.0 140.22 45.96 80.0 200
## n_cores 7 2000 4.52 2.29 4.0 4.53 2.97 1.0 8
## pc 8 2000 9.92 6.06 10.0 9.89 7.41 0.0 20
## px_height 9 2000 645.11 443.78 564.0 606.95 471.47 0.0 1960
## px_width 10 2000 1251.52 432.20 1247.0 1250.53 557.46 500.0 1998
## ram 11 2000 2124.21 1084.73 2146.5 2122.93 1382.52 256.0 3998
## sc_h 12 2000 12.31 4.21 12.0 12.37 5.93 5.0 19
## sc_w 13 2000 5.77 4.36 5.0 5.40 4.45 0.0 18
## talk_time 14 2000 11.01 5.46 11.0 11.01 7.41 2.0 20
## range skew kurtosis se
## battery_power 1497.0 0.03 -1.23 9.83
## clock_speed 2.5 0.18 -1.32 0.02
## fc 19.0 1.02 0.27 0.10
## int_memory 62.0 0.06 -1.22 0.41
## m_dep 0.9 0.09 -1.28 0.01
## mobile_wt 120.0 0.01 -1.21 0.79
## n_cores 7.0 0.00 -1.23 0.05
## pc 20.0 0.02 -1.17 0.14
## px_height 1960.0 0.67 -0.32 9.92
## px_width 1498.0 0.01 -1.19 9.66
## ram 3742.0 0.01 -1.19 24.26
## sc_h 14.0 -0.10 -1.19 0.09
## sc_w 18.0 0.63 -0.39 0.10
## talk_time 18.0 0.01 -1.22 0.12dataset %>%
select_if(is.numeric) %>%
gather(key = "Variabel", value = "Nilai") %>%
ggplot(aes(x = Nilai)) +
facet_wrap(~ Variabel, scales = "free", ncol = 3) +
geom_histogram(bins = 10, fill = "steelblue", color = "black") +
theme_minimal()
numerik_vars <- names(dataset)[sapply(dataset, is.numeric)]
for (var in numerik_vars) {
p <- ggplot(dataset, aes(x = price_range, y = dataset[[var]], fill = price_range)) +
geom_boxplot() +
labs(title = paste("Distribusi", var, "berdasarkan Harga")) +
theme_minimal() +
theme(legend.position = "none")
print(p)
}## Warning: Use of `dataset[[var]]` is discouraged.
## ℹ Use `.data[[var]]` instead.
## Warning: Use of `dataset[[var]]` is discouraged.
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## Warning: Use of `dataset[[var]]` is discouraged.
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## Warning: Use of `dataset[[var]]` is discouraged.
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## Warning: Use of `dataset[[var]]` is discouraged.
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## Warning: Use of `dataset[[var]]` is discouraged.
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## Warning: Use of `dataset[[var]]` is discouraged.
## ℹ Use `.data[[var]]` instead.
## Warning: Use of `dataset[[var]]` is discouraged.
## ℹ Use `.data[[var]]` instead.
## Warning: Use of `dataset[[var]]` is discouraged.
## ℹ Use `.data[[var]]` instead.
## Warning: Use of `dataset[[var]]` is discouraged.
## ℹ Use `.data[[var]]` instead.
## Warning: Use of `dataset[[var]]` is discouraged.
## ℹ Use `.data[[var]]` instead.
## Warning: Use of `dataset[[var]]` is discouraged.
## ℹ Use `.data[[var]]` instead.
## Warning: Use of `dataset[[var]]` is discouraged.
## ℹ Use `.data[[var]]` instead.
## Warning: Use of `dataset[[var]]` is discouraged.
## ℹ Use `.data[[var]]` instead.
numeric_data <- dataset %>% select_if(is.numeric)
cor_matrix <- cor(numeric_data, use = "complete.obs")
ggcorr(numeric_data,label = TRUE,label_size = 3, hjust = 0.95, layout.exp = 3, name = "Correlation") +
ggtitle("Matriks Korelasi Tiap Variabel") +
theme(
plot.title = element_text(size = 16, face = "bold", hjust = 0.5),
legend.title = element_text(size = 12),
legend.text = element_text(size = 10)
)
ggplot(dataset, aes(x = price_range, fill = price_range)) +
geom_bar() +
scale_fill_brewer(palette = "Set2") +
labs(
title = "Distribusi Variabel Price Range",
x = "Price Range",
y = "Jumlah Data",
fill = "Price Range"
) +
theme_minimal(base_size = 14) +
theme(legend.position = "right")
dataset %>%
group_by(price_range) %>%
summarise(across(where(is.numeric),
list(mean = ~mean(.x, na.rm = TRUE),
sd = ~sd(.x, na.rm = TRUE)),
.names = "{.col}_{.fn}"))## # A tibble: 4 × 29
## price_range battery_power_mean battery_power_sd clock_speed_mean
## <ord> <dbl> <dbl> <dbl>
## 1 low cost 1117. 411. 1.55
## 2 medium cost 1229. 439. 1.49
## 3 high cost 1228. 453. 1.53
## 4 very high cost 1380. 415. 1.52
## # ℹ 25 more variables: clock_speed_sd <dbl>, fc_mean <dbl>, fc_sd <dbl>,
## # int_memory_mean <dbl>, int_memory_sd <dbl>, m_dep_mean <dbl>,
## # m_dep_sd <dbl>, mobile_wt_mean <dbl>, mobile_wt_sd <dbl>,
## # n_cores_mean <dbl>, n_cores_sd <dbl>, pc_mean <dbl>, pc_sd <dbl>,
## # px_height_mean <dbl>, px_height_sd <dbl>, px_width_mean <dbl>,
## # px_width_sd <dbl>, ram_mean <dbl>, ram_sd <dbl>, sc_h_mean <dbl>,
## # sc_h_sd <dbl>, sc_w_mean <dbl>, sc_w_sd <dbl>, talk_time_mean <dbl>, …dataset## battery_power blue clock_speed dual_sim fc four_g int_memory m_dep
## 1 842 0 2.2 0 1 0 7 0.6
## 2 1021 1 0.5 1 0 1 53 0.7
## 3 563 1 0.5 1 2 1 41 0.9
## 4 615 1 2.5 0 0 0 10 0.8
## 5 1821 1 1.2 0 13 1 44 0.6
## 6 1859 0 0.5 1 3 0 22 0.7
## 7 1821 0 1.7 0 4 1 10 0.8
## 8 1954 0 0.5 1 0 0 24 0.8
## 9 1445 1 0.5 0 0 0 53 0.7
## 10 509 1 0.6 1 2 1 9 0.1
## 11 769 1 2.9 1 0 0 9 0.1
## 12 1520 1 2.2 0 5 1 33 0.5
## 13 1815 0 2.8 0 2 0 33 0.6
## 14 803 1 2.1 0 7 0 17 1.0
## 15 1866 0 0.5 0 13 1 52 0.7
## 16 775 0 1.0 0 3 0 46 0.7
## 17 838 0 0.5 0 1 1 13 0.1
## 18 595 0 0.9 1 7 1 23 0.1
## 19 1131 1 0.5 1 11 0 49 0.6
## 20 682 1 0.5 0 4 0 19 1.0
## 21 772 0 1.1 1 12 0 39 0.8
## 22 1709 1 2.1 0 1 0 13 1.0
## 23 1949 0 2.6 1 4 0 47 0.3
## 24 1602 1 2.8 1 4 1 38 0.7
## 25 503 0 1.2 1 5 1 8 0.4
## 26 961 1 1.4 1 0 1 57 0.6
## 27 519 1 1.6 1 7 1 51 0.3
## 28 956 0 0.5 0 1 1 41 1.0
## 29 1453 0 1.6 1 12 1 52 0.3
## 30 851 0 0.5 0 3 0 21 0.4
## 31 1579 1 0.5 1 0 0 5 0.2
## 32 1568 1 0.5 0 16 0 33 1.0
## 33 1319 1 0.9 0 3 1 41 0.9
## 34 1310 1 2.2 1 0 1 51 0.6
## 35 644 1 2.7 0 0 0 22 0.7
## 36 725 0 1.3 1 16 0 60 0.4
## 37 589 1 2.3 1 1 0 61 0.6
## 38 1725 1 1.6 1 6 1 6 0.5
## 39 790 0 2.0 1 16 1 11 0.3
## 40 560 0 0.5 1 15 0 50 0.3
## 41 1347 0 2.9 0 5 0 44 0.6
## 42 1646 1 2.3 0 8 1 41 0.2
## 43 1253 1 0.5 1 5 1 5 0.2
## 44 1656 0 1.0 0 5 1 34 0.1
## 45 1195 1 2.8 0 1 1 20 0.8
## 46 1514 0 2.9 0 0 0 27 0.2
## 47 1723 1 1.1 1 1 0 42 1.0
## 48 1054 1 1.8 1 3 1 40 0.8
## 49 578 1 2.6 1 2 1 57 0.2
## 50 596 0 2.1 1 9 0 64 0.8
## 51 1547 1 3.0 1 2 1 14 0.7
## 52 1760 0 1.4 1 5 0 63 0.8
## 53 1654 1 1.5 1 0 1 43 0.3
## 54 1457 0 1.9 1 1 1 16 0.3
## 55 1073 1 0.5 1 0 0 51 0.5
## 56 1936 0 2.1 1 10 1 46 0.6
## 57 823 1 2.7 1 13 0 60 0.5
## 58 987 0 1.3 1 0 1 61 0.4
## 59 1757 0 0.5 0 8 0 49 0.5
## 60 1063 0 1.4 1 2 1 48 1.0
## 61 1484 0 3.0 0 3 0 12 0.6
## 62 799 1 2.3 0 1 1 63 0.8
## 63 1156 1 1.2 1 0 1 50 0.8
## 64 1720 0 2.0 0 15 1 55 0.5
## 65 702 0 2.6 1 2 1 9 0.7
## 66 616 0 1.9 1 13 1 44 0.8
## 67 1358 1 0.5 0 11 1 36 0.3
## 68 1866 0 1.4 0 0 0 30 0.5
## 69 1242 0 1.1 1 0 0 10 0.6
## 70 1166 0 1.5 1 0 1 43 0.8
## 71 1448 1 0.5 1 6 1 45 0.8
## 72 1407 1 2.4 0 1 1 22 0.7
## 73 605 0 1.0 0 8 1 9 0.1
## 74 1038 0 1.2 0 3 0 43 0.7
## 75 797 1 2.9 1 4 0 38 0.5
## 76 819 0 0.6 1 8 1 42 0.9
## 77 1114 0 2.8 0 4 1 9 0.4
## 78 1234 1 1.6 1 1 0 33 0.6
## 79 1199 1 2.5 1 15 1 16 0.2
## 80 1103 0 1.0 1 6 0 29 0.7
## 81 1589 1 0.6 1 0 1 58 0.9
## 82 999 0 2.9 1 11 1 64 0.2
## 83 1510 1 0.9 1 2 0 45 0.9
## 84 1008 0 0.8 0 11 0 61 1.0
## 85 1127 1 2.9 1 5 1 57 0.8
## 86 1412 1 2.4 0 5 0 25 0.8
## 87 1496 1 2.0 1 4 0 42 0.5
## 88 1083 1 2.9 1 1 1 64 0.8
## 89 668 0 0.5 1 0 0 3 0.1
## 90 1309 0 1.1 1 0 0 33 0.5
## 91 1724 0 2.0 1 2 1 57 0.5
## 92 1977 1 2.0 1 7 1 54 1.0
## 93 885 0 2.3 1 0 1 15 0.4
## 94 879 1 2.5 0 11 1 14 0.7
## 95 1322 0 1.7 1 6 0 7 0.8
## 96 1137 1 1.0 0 18 0 7 1.0
## 97 1355 0 2.3 0 10 1 23 0.2
## 98 1665 1 0.5 0 3 1 60 0.2
## 99 657 0 2.5 0 0 0 37 0.7
## 100 593 0 0.5 0 6 0 31 0.4
## 101 1883 1 0.5 0 3 1 10 0.3
## 102 1732 1 1.1 0 4 1 12 0.8
## 103 543 0 0.5 0 0 0 57 0.7
## 104 1939 1 2.8 1 0 1 55 0.6
## 105 553 0 2.4 1 1 1 8 0.5
## 106 832 0 0.7 1 2 1 39 0.7
## 107 1661 0 1.9 1 5 0 23 1.0
## 108 1657 1 2.4 0 3 0 42 0.1
## 109 1135 1 2.0 1 0 0 46 0.3
## 110 1775 1 1.5 0 3 0 41 0.1
## 111 783 0 1.8 1 0 1 43 1.0
## 112 617 1 2.3 0 0 0 32 0.5
## 113 867 0 1.4 1 0 1 4 0.7
## 114 1310 1 1.1 1 13 0 7 1.0
## 115 1804 0 0.5 0 9 1 22 0.6
## 116 1900 1 2.3 0 1 1 18 0.2
## 117 909 1 1.4 0 0 0 53 0.3
## 118 1084 1 2.6 0 3 1 40 0.9
## 119 1308 0 1.3 1 8 0 46 0.9
## 120 1778 0 0.5 0 0 0 15 0.5
## 121 1701 1 1.6 1 3 0 10 0.3
## 122 772 1 2.4 1 1 1 10 0.5
## 123 825 1 0.5 1 7 0 52 0.9
## 124 1379 0 1.1 0 5 1 36 0.8
## 125 1166 0 2.0 0 3 1 25 0.8
## 126 1659 0 2.8 0 5 0 16 0.6
## 127 826 1 2.4 1 0 1 58 0.3
## 128 1564 0 2.3 0 4 1 25 0.5
## 129 1957 0 0.9 1 11 0 31 0.6
## 130 1571 0 1.4 0 0 1 19 0.1
## 131 1414 0 2.0 1 2 0 54 0.8
## 132 1790 0 2.5 1 9 0 2 0.1
## 133 645 0 0.5 0 1 0 41 1.0
## 134 668 0 2.9 1 7 0 30 0.2
## 135 1652 1 1.1 1 1 0 36 0.7
## 136 1213 1 0.5 0 0 0 16 0.5
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## 1542 823 0 1.0 1 9 1 18 0.2
## 1543 1908 0 0.5 0 9 1 30 1.0
## 1544 790 0 0.5 1 7 1 44 0.1
## 1545 1330 1 1.8 1 1 0 40 0.5
## 1546 1660 0 0.5 0 2 1 15 0.7
## 1547 1776 0 2.2 0 2 0 20 0.9
## 1548 1611 0 0.5 1 4 1 27 0.9
## 1549 1410 1 0.5 1 0 1 15 0.3
## 1550 1772 1 1.6 0 17 1 45 0.5
## 1551 1280 1 1.3 0 0 1 39 1.0
## 1552 1712 1 0.9 1 2 0 23 1.0
## 1553 1562 1 0.7 0 0 0 55 0.1
## 1554 891 0 1.9 1 1 1 18 0.1
## 1555 1957 0 1.2 1 18 1 36 0.8
## 1556 1110 0 0.6 0 1 0 42 0.9
## 1557 875 1 1.4 1 1 1 43 0.6
## 1558 1211 1 1.2 0 0 0 28 0.3
## 1559 769 0 0.5 1 12 1 54 0.2
## 1560 671 0 2.9 0 7 1 2 0.1
## 1561 1872 0 0.5 0 5 1 26 0.5
## 1562 1076 0 2.3 1 0 1 14 0.7
## 1563 1325 0 1.9 0 2 0 50 0.1
## 1564 911 1 2.2 1 1 1 28 0.5
## 1565 1273 1 0.7 0 1 0 23 0.6
## 1566 1062 0 0.5 0 3 1 39 0.9
## 1567 1317 0 1.5 0 6 0 12 0.1
## 1568 940 0 2.9 0 0 0 16 0.7
## 1569 729 1 2.0 0 0 0 14 0.6
## 1570 1494 0 2.4 0 6 1 3 0.9
## 1571 1546 1 1.6 1 2 1 27 0.7
## 1572 1253 0 0.7 0 0 1 34 0.9
## 1573 895 0 0.5 1 6 0 16 0.3
## 1574 793 1 1.9 1 0 1 43 0.3
## 1575 1628 1 1.8 1 11 0 56 0.1
## 1576 625 0 2.7 1 2 0 42 0.8
## 1577 1110 0 1.5 0 2 1 12 0.3
## 1578 999 0 1.7 0 6 0 49 0.6
## 1579 1856 0 0.5 1 0 1 24 0.1
## 1580 1715 0 2.6 1 9 1 15 0.6
## 1581 1897 1 1.9 0 2 1 54 0.3
## 1582 1202 1 2.4 1 0 0 19 0.1
## 1583 1171 1 1.7 1 2 0 19 0.3
## 1584 964 0 2.0 0 0 1 22 0.7
## 1585 1973 1 2.4 1 6 0 46 0.1
## 1586 1992 1 0.6 1 11 0 2 0.1
## 1587 546 1 2.6 1 5 0 26 1.0
## 1588 1093 0 0.5 0 1 1 31 0.6
## 1589 1880 1 2.0 0 11 0 35 0.1
## 1590 1765 0 1.1 0 3 0 11 0.7
## 1591 1640 0 0.5 1 1 1 36 0.9
## 1592 1830 0 0.5 0 0 0 7 0.9
## 1593 826 0 0.6 0 3 0 62 0.7
## 1594 1864 0 2.2 0 0 1 7 0.1
## 1595 586 1 0.6 0 16 1 42 0.3
## 1596 1206 0 3.0 1 10 1 30 0.6
## 1597 832 1 0.5 1 1 1 5 0.1
## 1598 848 1 2.8 0 1 0 8 0.1
## 1599 1851 0 2.9 0 0 0 53 0.7
## 1600 1166 1 0.5 0 7 0 59 0.5
## 1601 1776 1 2.1 1 10 0 35 0.1
## 1602 1254 1 1.2 0 2 1 27 0.2
## 1603 1494 0 1.6 1 4 1 24 0.2
## 1604 984 1 2.2 1 0 1 39 0.5
## 1605 1179 1 0.8 0 1 0 5 0.9
## 1606 513 0 1.9 0 8 0 27 0.4
## 1607 557 0 0.5 1 3 0 17 0.2
## 1608 744 0 1.7 1 0 1 33 0.5
## 1609 1129 1 0.8 1 9 1 21 0.6
## 1610 1148 0 2.3 1 0 1 58 0.3
## 1611 1793 0 2.7 0 12 1 44 0.7
## 1612 1310 0 2.3 1 3 0 15 0.2
## 1613 1604 0 1.2 0 1 0 6 0.5
## 1614 1676 0 1.6 0 2 0 37 0.2
## 1615 1001 0 2.8 1 1 0 40 0.2
## 1616 1086 0 2.7 0 0 1 24 0.1
## 1617 1986 1 0.5 1 7 1 39 0.1
## 1618 634 1 0.8 0 5 0 41 0.2
## 1619 502 0 0.8 0 7 0 52 1.0
## 1620 1641 0 1.1 0 6 0 48 0.7
## 1621 623 0 1.4 1 0 1 15 0.2
## 1622 962 1 1.1 1 3 0 17 0.1
## 1623 1057 1 2.7 0 3 1 41 0.1
## 1624 1862 0 1.5 0 1 0 62 1.0
## 1625 555 1 3.0 1 5 1 38 0.8
## 1626 1000 0 2.7 0 1 1 58 1.0
## 1627 841 0 0.5 0 6 1 11 0.3
## 1628 865 0 2.9 0 5 0 16 0.1
## 1629 999 0 2.6 1 11 1 56 1.0
## 1630 1232 0 1.2 0 12 0 30 0.9
## 1631 1194 0 2.3 0 6 1 34 0.2
## 1632 612 1 0.5 1 1 1 20 0.8
## 1633 1362 1 2.9 0 0 0 47 0.5
## 1634 1469 0 1.7 1 11 1 44 0.5
## 1635 986 1 0.8 0 13 1 28 0.8
## 1636 1843 0 2.8 0 1 1 62 0.8
## 1637 1561 0 0.5 1 10 1 4 0.5
## 1638 1695 1 0.5 1 2 1 47 0.5
## 1639 667 0 2.6 1 3 0 8 0.8
## 1640 1768 1 2.0 0 0 1 2 0.8
## 1641 1269 1 2.8 1 16 0 41 1.0
## 1642 1109 1 2.8 1 6 0 57 0.9
## 1643 936 0 0.5 1 14 1 14 0.1
## 1644 1713 1 2.0 0 9 1 5 0.7
## 1645 1175 1 1.7 1 1 1 45 0.6
## 1646 1940 1 1.0 1 0 1 5 0.2
## 1647 855 1 0.5 0 2 1 59 0.5
## 1648 771 1 0.5 1 0 1 33 0.1
## 1649 645 0 2.5 0 10 1 63 1.0
## 1650 1307 0 1.8 0 2 0 7 0.6
## 1651 1948 0 2.9 1 4 0 5 0.2
## 1652 1509 0 0.6 1 5 0 50 0.3
## 1653 626 1 2.8 1 3 1 11 0.8
## 1654 1190 0 2.0 1 0 0 40 0.2
## 1655 1727 1 2.1 1 10 0 57 0.3
## 1656 1670 1 3.0 0 14 0 62 0.5
## 1657 1150 1 0.5 1 4 1 27 1.0
## 1658 1208 0 2.9 0 5 1 16 1.0
## 1659 1812 1 1.3 1 4 1 42 1.0
## 1660 770 1 1.5 1 6 0 61 0.2
## 1661 1559 1 1.3 1 10 1 10 0.3
## 1662 1902 0 0.5 0 10 0 13 0.2
## 1663 1751 0 2.5 0 1 0 4 0.6
## 1664 1416 1 1.8 1 0 0 20 0.1
## 1665 1288 0 0.5 1 3 1 61 0.2
## 1666 1003 1 2.9 0 16 1 16 0.6
## 1667 1715 1 0.5 1 1 1 18 0.1
## 1668 1967 0 0.5 0 0 1 46 0.6
## 1669 962 0 1.0 1 14 0 2 0.9
## 1670 1487 0 1.5 0 5 1 42 0.8
## 1671 1569 1 1.6 1 4 1 54 0.9
## 1672 1146 0 3.0 0 5 1 57 0.4
## 1673 1379 1 1.5 0 6 1 9 0.8
## 1674 1504 0 1.9 1 7 1 25 0.3
## 1675 1945 0 1.2 0 3 0 26 0.7
## 1676 1630 1 2.8 1 0 1 32 0.9
## 1677 1125 1 2.7 0 10 0 23 0.4
## 1678 614 1 0.7 0 4 1 51 0.1
## 1679 854 0 1.0 0 5 1 17 0.9
## 1680 1470 1 1.7 1 6 0 45 0.1
## 1681 1826 1 0.5 1 5 1 49 0.6
## 1682 807 1 0.5 1 0 0 37 0.2
## 1683 1996 1 2.8 1 0 1 7 0.1
## 1684 1083 1 2.0 0 3 0 41 0.7
## 1685 1035 0 0.5 1 14 1 49 0.3
## 1686 1521 1 1.6 0 5 1 62 0.3
## 1687 1314 1 2.8 1 13 1 5 1.0
## 1688 535 0 1.9 0 5 0 24 0.2
## 1689 673 1 1.9 0 13 1 35 0.4
## 1690 1219 0 1.6 1 5 0 4 0.3
## 1691 1606 1 0.5 1 0 1 33 0.9
## 1692 1603 1 0.5 1 1 1 17 0.5
## 1693 1958 0 0.5 0 6 1 52 0.4
## 1694 695 0 0.5 0 18 1 12 0.6
## 1695 1441 0 0.6 1 0 1 53 0.5
## 1696 1470 0 2.2 1 4 1 33 0.4
## 1697 1890 0 1.1 0 4 1 48 0.5
## 1698 1152 1 2.1 1 0 1 26 0.3
## 1699 1619 1 0.9 0 3 0 20 0.9
## 1700 1002 1 1.8 0 1 0 45 0.7
## 1701 742 1 0.5 0 0 1 36 0.7
## 1702 1306 1 2.1 1 2 1 33 0.4
## 1703 1424 1 2.9 1 0 0 20 0.6
## 1704 942 0 1.4 1 8 0 27 0.5
## 1705 1934 1 2.7 0 8 1 2 0.1
## 1706 1290 1 1.4 1 19 1 35 0.3
## 1707 930 0 2.1 1 0 0 54 0.9
## 1708 1699 1 0.5 0 16 0 21 0.1
## 1709 1849 1 0.5 0 1 0 29 0.5
## 1710 1922 0 0.5 0 15 1 42 0.6
## 1711 548 0 1.0 1 0 1 56 0.4
## 1712 916 1 0.5 0 0 1 47 0.2
## 1713 1783 1 0.5 0 8 0 14 0.6
## 1714 1083 1 1.4 1 2 1 49 0.8
## 1715 1698 0 2.8 0 10 1 7 0.8
## 1716 1969 0 0.8 1 5 1 7 0.2
## 1717 869 0 1.9 0 0 0 2 0.6
## 1718 1337 1 2.7 1 0 0 5 0.7
## 1719 637 1 0.8 1 2 0 28 0.6
## 1720 833 1 0.6 1 0 1 27 0.3
## 1721 1248 0 2.4 0 6 1 9 0.4
## 1722 1174 1 0.9 0 7 0 30 1.0
## 1723 1831 1 2.3 0 3 1 44 0.2
## 1724 1002 0 2.6 1 7 0 27 0.1
## 1725 1010 1 2.0 1 2 1 8 0.9
## 1726 1117 1 0.5 1 2 0 21 0.1
## 1727 990 1 2.7 1 3 0 15 0.9
## 1728 1564 0 1.4 0 5 1 26 0.1
## 1729 1065 0 2.8 1 0 1 21 0.1
## 1730 1982 0 1.1 0 12 0 24 0.1
## 1731 1607 0 1.1 1 6 1 20 1.0
## 1732 1066 0 3.0 1 6 1 5 0.5
## 1733 681 0 0.5 1 12 1 17 0.9
## 1734 1163 1 2.1 1 0 1 19 0.9
## 1735 1285 0 0.8 1 4 1 11 0.8
## 1736 753 1 0.5 0 11 1 48 0.1
## 1737 1779 1 2.6 1 2 0 5 0.8
## 1738 987 1 1.9 0 4 1 52 0.5
## 1739 511 0 0.9 1 15 1 24 0.6
## 1740 1044 0 1.8 0 4 1 12 0.7
## 1741 1855 0 0.5 0 5 0 32 0.6
## 1742 946 1 1.4 0 9 0 26 0.1
## 1743 657 1 1.9 1 2 0 21 0.1
## 1744 1673 1 1.6 0 2 0 42 0.5
## 1745 731 1 0.5 1 0 1 28 0.5
## 1746 926 1 0.8 0 6 1 59 0.8
## 1747 1261 0 0.5 0 3 1 52 0.3
## 1748 618 0 1.1 0 11 1 45 0.2
## 1749 936 1 2.3 0 5 1 57 0.2
## 1750 1485 1 0.9 1 0 1 28 0.8
## 1751 1339 1 0.5 1 10 1 21 0.5
## 1752 508 1 1.3 0 1 0 50 0.7
## 1753 1938 1 0.5 1 5 0 44 0.5
## 1754 995 0 1.3 1 1 0 10 0.1
## 1755 1086 1 1.7 1 0 1 43 0.2
## 1756 1322 0 2.3 1 1 1 35 0.1
## 1757 1864 0 1.9 1 2 0 57 0.1
## 1758 1944 1 0.5 0 1 1 26 0.5
## 1759 581 0 0.6 0 5 1 56 0.2
## 1760 1020 1 0.7 1 3 1 27 0.7
## 1761 1936 0 1.3 0 2 0 44 0.1
## 1762 1329 1 1.7 0 5 1 23 0.6
## 1763 808 1 0.5 1 3 0 46 0.5
## 1764 994 0 1.2 1 4 0 40 0.9
## 1765 1811 1 2.0 0 3 1 22 0.2
## 1766 765 1 2.5 0 3 0 44 0.3
## 1767 993 1 2.3 1 0 0 16 0.4
## 1768 1136 1 1.6 0 15 0 32 0.5
## 1769 719 1 0.5 1 0 1 23 0.4
## 1770 733 1 0.6 1 0 0 63 0.3
## 1771 1946 0 2.7 1 8 0 8 0.3
## 1772 1230 1 1.6 0 0 1 48 0.7
## 1773 1577 0 1.0 1 0 1 64 0.1
## 1774 1514 1 1.4 0 3 1 12 0.6
## 1775 579 1 2.5 0 3 0 7 1.0
## 1776 574 1 3.0 0 1 1 30 0.9
## 1777 1072 1 2.2 1 6 0 19 0.4
## 1778 843 0 1.3 1 1 0 16 0.9
## 1779 1492 1 1.5 0 5 0 11 0.9
## 1780 1807 1 2.9 0 0 0 16 0.2
## 1781 909 1 0.7 1 7 0 47 0.1
## 1782 1876 0 1.3 1 9 1 64 1.0
## 1783 1436 1 2.4 1 3 1 23 0.3
## 1784 1251 0 0.5 1 10 1 29 0.7
## 1785 636 0 0.5 0 8 1 28 0.9
## 1786 1354 1 0.7 1 3 1 26 0.5
## 1787 930 1 0.5 1 3 1 34 0.5
## 1788 1830 1 1.0 0 7 1 47 0.3
## 1789 1424 0 0.5 1 16 1 23 0.6
## 1790 583 1 0.9 1 1 0 51 0.9
## 1791 648 1 0.5 1 9 1 54 0.4
## 1792 1203 1 0.5 1 0 1 11 0.9
## 1793 1777 0 2.2 1 0 0 15 0.2
## 1794 1299 1 2.8 1 0 0 64 0.5
## 1795 1494 0 1.9 0 2 1 47 0.4
## 1796 527 0 0.7 1 4 0 53 0.9
## 1797 793 1 2.8 1 1 1 64 0.4
## 1798 873 1 0.5 1 6 0 8 0.6
## 1799 722 0 1.1 0 12 0 12 1.0
## 1800 1250 1 1.5 0 14 1 20 0.4
## 1801 1358 0 1.7 0 0 0 60 0.6
## 1802 1035 1 1.9 1 8 0 11 1.0
## 1803 1289 0 0.8 1 3 1 16 0.9
## 1804 1445 0 2.2 1 2 0 37 0.4
## 1805 1702 1 2.3 1 12 1 47 0.6
## 1806 534 0 0.5 1 4 0 2 0.2
## 1807 628 1 1.8 0 5 0 60 0.2
## 1808 713 0 1.4 1 4 0 53 0.8
## 1809 1207 0 0.5 0 0 0 58 0.3
## 1810 683 1 2.1 0 7 0 30 0.1
## 1811 594 1 2.6 0 1 0 27 0.1
## 1812 537 1 2.0 0 1 1 55 0.3
## 1813 1565 1 0.5 0 0 0 38 0.1
## 1814 1052 0 0.9 0 1 0 6 0.8
## 1815 713 0 1.6 0 4 1 63 0.7
## 1816 1766 1 0.5 1 1 0 16 0.6
## 1817 584 1 1.7 1 1 1 19 0.4
## 1818 1541 1 1.5 1 6 1 5 0.5
## 1819 837 0 2.4 1 1 0 51 0.2
## 1820 935 1 0.5 1 3 1 58 0.1
## 1821 665 1 0.7 0 0 1 31 0.1
## 1822 680 1 2.4 0 13 1 34 0.4
## 1823 877 0 1.5 1 0 1 17 0.1
## 1824 1493 0 1.5 1 13 0 53 0.5
## 1825 1762 0 0.6 0 3 1 26 0.1
## 1826 842 1 1.8 1 4 0 57 1.0
## 1827 925 1 2.1 0 9 0 56 0.2
## 1828 1692 0 2.1 0 4 1 2 0.9
## 1829 1576 1 2.2 0 8 0 5 0.7
## 1830 1065 0 1.7 1 4 1 48 0.3
## 1831 958 1 1.6 0 9 1 60 0.4
## 1832 623 1 0.8 0 6 1 26 0.3
## 1833 897 0 1.4 1 1 0 17 0.4
## 1834 1110 0 1.5 0 4 1 41 0.1
## 1835 616 1 2.2 0 3 1 21 0.8
## 1836 1680 1 1.2 1 6 1 37 0.7
## 1837 1715 0 1.3 0 0 1 12 0.3
## 1838 983 0 0.6 1 0 1 45 0.1
## 1839 720 1 0.9 1 12 1 14 0.8
## 1840 1660 0 0.6 0 2 1 24 0.8
## 1841 1564 0 1.5 1 1 0 42 0.7
## 1842 592 0 0.8 1 1 0 52 0.9
## 1843 969 0 1.8 1 2 1 44 0.6
## 1844 1356 1 0.5 1 0 0 35 0.8
## 1845 504 1 1.0 0 8 0 14 0.5
## 1846 1546 1 0.5 1 3 1 6 0.2
## 1847 689 0 2.9 1 3 1 7 0.8
## 1848 1685 0 0.6 0 14 0 16 0.6
## 1849 1792 1 1.0 0 3 1 62 0.3
## 1850 1786 0 1.2 0 11 0 61 0.1
## 1851 1944 0 0.6 1 9 1 59 0.2
## 1852 1077 1 2.5 0 13 1 49 0.6
## 1853 648 1 1.8 0 1 1 60 0.1
## 1854 1702 1 0.5 1 8 0 2 0.6
## 1855 1571 0 2.9 0 1 0 30 0.9
## 1856 856 1 1.1 1 14 1 19 0.7
## 1857 1786 0 2.6 0 6 0 14 1.0
## 1858 914 1 0.7 0 1 1 60 0.9
## 1859 1615 1 2.8 0 3 0 9 0.9
## 1860 649 1 1.4 0 4 1 19 0.8
## 1861 1646 0 2.5 0 3 1 25 0.6
## 1862 1189 1 1.9 0 5 0 7 0.5
## 1863 1043 0 2.7 0 3 0 29 0.2
## 1864 1920 0 0.6 1 3 1 19 0.5
## 1865 1220 1 2.8 0 1 0 54 0.7
## 1866 1748 1 0.5 1 11 1 53 0.5
## 1867 907 1 2.6 1 7 0 44 0.2
## 1868 1193 1 2.3 0 7 0 20 1.0
## 1869 1766 1 0.5 1 1 1 22 0.8
## 1870 1278 1 1.0 0 7 1 24 0.8
## 1871 1234 0 1.7 1 8 0 17 0.9
## 1872 1961 1 1.5 0 10 0 55 0.5
## 1873 1745 0 2.9 0 0 1 3 0.9
## 1874 1442 0 0.6 1 1 0 27 0.4
## 1875 1083 1 2.9 1 1 1 39 1.0
## 1876 868 1 0.9 0 5 0 5 0.4
## 1877 864 0 1.6 1 0 1 53 0.3
## 1878 1142 1 1.2 0 0 0 2 0.6
## 1879 608 1 0.5 1 0 0 64 0.9
## 1880 983 0 1.8 0 0 1 53 0.9
## 1881 1720 0 1.6 0 18 1 2 0.8
## 1882 1802 1 1.2 1 8 0 27 0.5
## 1883 591 0 2.1 1 18 1 16 0.5
## 1884 1205 1 2.7 0 5 0 10 0.1
## 1885 1369 1 1.7 1 3 0 28 0.6
## 1886 713 0 0.9 1 0 0 42 0.1
## 1887 1082 0 2.3 1 11 0 2 1.0
## 1888 1719 1 2.1 1 0 0 52 0.1
## 1889 1544 0 2.4 0 18 1 12 0.1
## 1890 1090 1 2.3 0 1 1 8 0.6
## 1891 1991 0 2.8 0 3 1 15 0.1
## 1892 1367 0 2.9 0 4 0 56 0.8
## 1893 1179 1 0.5 0 7 1 32 0.3
## 1894 721 1 2.7 1 1 1 15 0.2
## 1895 1549 1 1.8 1 0 0 62 0.7
## 1896 1349 1 2.1 1 3 0 46 0.9
## 1897 1799 0 1.0 0 1 1 30 0.6
## 1898 1075 0 0.5 1 2 0 11 0.2
## 1899 684 1 0.9 1 3 1 63 1.0
## 1900 1837 0 0.5 0 9 0 36 0.9
## 1901 1619 1 1.9 0 2 1 21 0.4
## 1902 965 1 1.4 0 1 0 13 0.9
## 1903 730 0 1.5 0 9 0 9 0.7
## 1904 1361 1 1.4 0 1 0 47 1.0
## 1905 1494 0 1.6 0 3 1 45 0.8
## 1906 1027 1 0.8 0 7 0 18 1.0
## 1907 1807 0 0.8 1 0 1 38 0.2
## 1908 709 0 2.4 1 1 0 11 0.1
## 1909 689 0 1.4 0 2 0 45 0.5
## 1910 955 1 0.5 0 2 0 2 0.6
## 1911 1872 1 1.9 0 5 0 35 0.6
## 1912 1259 0 1.2 1 0 0 13 0.5
## 1913 695 0 1.2 0 1 1 41 0.4
## 1914 1969 0 1.2 1 9 0 63 0.2
## 1915 759 0 2.5 0 3 1 39 0.3
## 1916 936 0 1.0 0 1 0 18 0.2
## 1917 1176 1 2.1 0 2 1 62 0.5
## 1918 1802 0 2.7 1 0 1 10 0.6
## 1919 772 0 1.0 1 0 1 23 0.6
## 1920 1318 1 2.4 0 1 1 45 0.1
## 1921 591 1 0.5 1 1 1 16 0.9
## 1922 569 1 2.5 1 0 0 41 0.3
## 1923 764 0 1.0 1 9 1 45 0.5
## 1924 1056 1 2.1 1 8 1 37 0.7
## 1925 1982 1 1.6 0 2 1 12 0.2
## 1926 1830 1 0.5 0 5 0 14 0.8
## 1927 1122 0 0.5 0 0 1 40 0.3
## 1928 1023 1 0.7 0 2 1 63 0.7
## 1929 1283 1 2.4 1 8 1 33 1.0
## 1930 1602 0 1.6 1 0 1 35 0.1
## 1931 732 1 1.3 0 5 1 32 0.4
## 1932 1854 0 0.9 1 0 1 33 0.4
## 1933 718 1 1.6 1 1 1 30 0.4
## 1934 897 1 2.0 0 3 1 2 0.6
## 1935 1405 0 1.7 0 0 0 8 0.3
## 1936 798 0 0.6 1 6 1 21 0.5
## 1937 1035 1 2.7 0 1 0 40 0.2
## 1938 1396 1 0.6 0 0 0 37 0.7
## 1939 1749 1 0.5 1 7 1 49 0.8
## 1940 1588 1 2.5 0 12 1 4 0.3
## 1941 1600 0 0.5 0 1 1 16 0.3
## 1942 1522 0 2.2 0 4 0 21 0.7
## 1943 1279 1 2.5 0 5 1 7 0.5
## 1944 719 0 2.9 0 7 0 6 0.7
## 1945 544 1 0.5 1 2 1 7 0.2
## 1946 1007 1 2.9 1 5 1 21 0.8
## 1947 590 1 1.2 1 0 0 32 0.4
## 1948 1788 0 0.5 0 0 1 6 0.2
## 1949 748 0 1.4 0 0 0 25 1.0
## 1950 757 1 0.9 1 4 0 64 0.5
## 1951 1561 1 1.6 1 1 1 27 0.8
## 1952 1327 0 2.8 0 10 0 23 0.5
## 1953 797 1 1.5 1 4 0 18 0.6
## 1954 727 1 2.5 1 0 1 64 0.2
## 1955 686 1 2.7 1 0 0 36 0.1
## 1956 1515 1 2.1 1 4 1 24 0.9
## 1957 555 1 0.5 1 2 0 17 0.2
## 1958 1589 0 0.6 0 0 0 5 0.8
## 1959 1976 1 2.6 1 6 0 45 1.0
## 1960 1884 0 1.0 1 3 0 24 0.5
## 1961 1063 0 2.0 0 0 0 27 0.2
## 1962 672 1 0.6 1 13 0 61 0.1
## 1963 635 1 0.5 0 9 0 16 1.0
## 1964 1906 1 1.9 1 3 0 55 0.2
## 1965 1753 0 0.7 0 4 0 40 0.6
## 1966 659 0 1.4 1 2 1 3 0.1
## 1967 1583 0 1.2 1 0 0 14 0.2
## 1968 1783 0 2.9 1 2 0 42 0.7
## 1969 1667 0 1.0 1 0 1 21 0.9
## 1970 640 0 1.3 1 5 1 6 0.6
## 1971 1913 1 1.8 0 0 0 29 0.6
## 1972 538 0 1.1 1 0 1 25 0.3
## 1973 1191 0 0.8 0 6 1 46 0.8
## 1974 816 0 3.0 1 2 0 9 0.1
## 1975 915 1 0.5 1 9 1 33 0.3
## 1976 1157 1 0.8 0 7 0 27 0.1
## 1977 1201 1 0.5 0 2 0 10 1.0
## 1978 1379 0 1.1 1 1 1 18 0.2
## 1979 1483 1 2.2 0 3 1 53 0.7
## 1980 1614 0 1.2 0 1 1 9 0.1
## 1981 930 1 1.0 1 4 1 4 0.9
## 1982 1454 0 2.6 0 8 0 6 0.4
## 1983 1784 0 1.6 0 4 0 41 0.4
## 1984 1262 0 1.8 1 12 0 34 0.1
## 1985 797 0 2.2 1 0 0 37 0.9
## 1986 1829 1 2.1 0 8 0 59 0.1
## 1987 1139 1 0.9 1 6 1 58 0.5
## 1988 618 1 1.0 0 9 1 13 0.1
## 1989 1547 1 2.9 0 2 0 57 0.4
## 1990 586 0 2.8 0 2 0 15 0.2
## 1991 1617 1 2.4 0 8 1 36 0.8
## 1992 1882 0 2.0 0 11 1 44 0.8
## 1993 674 1 2.9 1 1 0 21 0.2
## 1994 1467 1 0.5 0 0 0 18 0.6
## 1995 858 0 2.2 0 1 0 50 0.1
## 1996 794 1 0.5 1 0 1 2 0.8
## 1997 1965 1 2.6 1 0 0 39 0.2
## 1998 1911 0 0.9 1 1 1 36 0.7
## 1999 1512 0 0.9 0 4 1 46 0.1
## 2000 510 1 2.0 1 5 1 45 0.9
## mobile_wt n_cores pc px_height px_width ram sc_h sc_w talk_time three_g
## 1 188 2 2 20 756 2549 9 7 19 0
## 2 136 3 6 905 1988 2631 17 3 7 1
## 3 145 5 6 1263 1716 2603 11 2 9 1
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## 1755 1 0 high cost
## 1756 0 0 very high cost
## 1757 0 1 very high cost
## 1758 0 1 high cost
## 1759 1 1 low cost
## 1760 0 1 very high cost
## 1761 0 1 medium cost
## 1762 1 0 medium cost
## 1763 0 1 low cost
## 1764 0 0 high cost
## 1765 0 1 high cost
## 1766 1 1 high cost
## 1767 0 1 low cost
## 1768 1 1 very high cost
## 1769 1 1 very high cost
## 1770 0 1 low cost
## 1771 0 0 very high cost
## 1772 1 1 high cost
## 1773 1 1 very high cost
## 1774 0 1 low cost
## 1775 0 1 high cost
## 1776 0 0 low cost
## 1777 1 0 high cost
## 1778 0 1 medium cost
## 1779 1 1 very high cost
## 1780 1 0 very high cost
## 1781 1 0 low cost
## 1782 1 1 very high cost
## 1783 0 0 high cost
## 1784 0 0 low cost
## 1785 0 0 high cost
## 1786 1 0 medium cost
## 1787 0 1 high cost
## 1788 0 0 very high cost
## 1789 1 1 medium cost
## 1790 1 0 medium cost
## 1791 0 0 very high cost
## 1792 1 0 low cost
## 1793 1 0 medium cost
## 1794 0 1 medium cost
## 1795 0 0 very high cost
## 1796 0 0 low cost
## 1797 1 0 medium cost
## 1798 0 1 low cost
## 1799 0 0 medium cost
## 1800 1 1 high cost
## 1801 0 0 low cost
## 1802 1 0 medium cost
## 1803 1 0 very high cost
## 1804 1 1 very high cost
## 1805 0 1 medium cost
## 1806 1 1 high cost
## 1807 1 0 medium cost
## 1808 0 1 low cost
## 1809 1 0 low cost
## 1810 0 0 medium cost
## 1811 1 1 low cost
## 1812 1 1 medium cost
## 1813 1 1 low cost
## 1814 0 1 low cost
## 1815 0 0 medium cost
## 1816 0 1 very high cost
## 1817 1 1 very high cost
## 1818 1 1 medium cost
## 1819 0 1 low cost
## 1820 0 1 high cost
## 1821 0 0 low cost
## 1822 1 1 very high cost
## 1823 1 1 medium cost
## 1824 1 1 high cost
## 1825 1 0 high cost
## 1826 1 0 medium cost
## 1827 0 0 high cost
## 1828 1 1 very high cost
## 1829 1 1 very high cost
## 1830 0 0 low cost
## 1831 0 1 low cost
## 1832 0 0 medium cost
## 1833 0 1 high cost
## 1834 0 1 high cost
## 1835 0 0 high cost
## 1836 1 0 medium cost
## 1837 1 1 high cost
## 1838 0 1 medium cost
## 1839 1 0 low cost
## 1840 1 1 high cost
## 1841 1 0 very high cost
## 1842 0 1 low cost
## 1843 0 1 low cost
## 1844 1 1 very high cost
## 1845 1 1 low cost
## 1846 0 0 very high cost
## 1847 0 0 low cost
## 1848 1 1 low cost
## 1849 0 1 very high cost
## 1850 1 0 very high cost
## 1851 0 1 low cost
## 1852 0 1 very high cost
## 1853 0 0 low cost
## 1854 1 1 low cost
## 1855 1 1 low cost
## 1856 0 1 low cost
## 1857 0 0 very high cost
## 1858 0 0 very high cost
## 1859 1 0 high cost
## 1860 1 0 low cost
## 1861 1 0 low cost
## 1862 1 0 low cost
## 1863 0 0 low cost
## 1864 1 0 very high cost
## 1865 0 0 low cost
## 1866 0 0 high cost
## 1867 0 0 very high cost
## 1868 0 0 high cost
## 1869 0 0 very high cost
## 1870 1 1 very high cost
## 1871 0 1 high cost
## 1872 1 1 very high cost
## 1873 1 0 medium cost
## 1874 0 0 low cost
## 1875 0 1 high cost
## 1876 0 1 low cost
## 1877 1 0 low cost
## 1878 1 0 medium cost
## 1879 1 1 medium cost
## 1880 1 1 medium cost
## 1881 0 1 high cost
## 1882 1 0 high cost
## 1883 1 1 low cost
## 1884 1 1 low cost
## 1885 0 0 very high cost
## 1886 0 1 low cost
## 1887 1 0 medium cost
## 1888 1 1 high cost
## 1889 0 1 low cost
## 1890 0 1 very high cost
## 1891 1 1 medium cost
## 1892 1 0 low cost
## 1893 0 0 low cost
## 1894 1 1 very high cost
## 1895 0 1 low cost
## 1896 0 1 medium cost
## 1897 1 0 low cost
## 1898 0 0 very high cost
## 1899 1 1 very high cost
## 1900 0 1 low cost
## 1901 0 0 medium cost
## 1902 0 0 high cost
## 1903 0 1 high cost
## 1904 0 1 high cost
## 1905 0 0 very high cost
## 1906 0 1 low cost
## 1907 0 0 medium cost
## 1908 0 1 very high cost
## 1909 0 0 very high cost
## 1910 1 0 medium cost
## 1911 0 0 very high cost
## 1912 0 1 low cost
## 1913 1 0 medium cost
## 1914 1 0 low cost
## 1915 0 0 low cost
## 1916 1 1 high cost
## 1917 0 0 medium cost
## 1918 1 0 low cost
## 1919 0 0 high cost
## 1920 0 1 low cost
## 1921 1 0 very high cost
## 1922 0 0 medium cost
## 1923 0 0 low cost
## 1924 1 1 high cost
## 1925 1 1 medium cost
## 1926 1 1 high cost
## 1927 1 0 medium cost
## 1928 1 1 very high cost
## 1929 1 0 low cost
## 1930 0 0 high cost
## 1931 0 1 very high cost
## 1932 0 1 medium cost
## 1933 1 1 medium cost
## 1934 1 0 medium cost
## 1935 1 0 high cost
## 1936 0 0 medium cost
## 1937 1 1 very high cost
## 1938 0 1 high cost
## 1939 0 1 high cost
## 1940 0 0 high cost
## 1941 1 0 high cost
## 1942 0 1 very high cost
## 1943 0 0 medium cost
## 1944 0 0 low cost
## 1945 1 0 low cost
## 1946 1 0 low cost
## 1947 1 0 low cost
## 1948 1 0 high cost
## 1949 1 1 medium cost
## 1950 1 0 high cost
## 1951 1 0 medium cost
## 1952 1 1 low cost
## 1953 1 1 medium cost
## 1954 1 0 very high cost
## 1955 0 0 low cost
## 1956 0 0 very high cost
## 1957 1 1 low cost
## 1958 1 1 very high cost
## 1959 0 0 very high cost
## 1960 1 1 medium cost
## 1961 0 0 high cost
## 1962 0 1 low cost
## 1963 0 0 high cost
## 1964 0 1 medium cost
## 1965 1 1 medium cost
## 1966 0 1 low cost
## 1967 0 1 high cost
## 1968 1 0 medium cost
## 1969 0 0 low cost
## 1970 1 0 high cost
## 1971 1 0 high cost
## 1972 1 1 medium cost
## 1973 0 0 low cost
## 1974 0 1 very high cost
## 1975 1 0 medium cost
## 1976 0 1 very high cost
## 1977 1 1 low cost
## 1978 1 0 high cost
## 1979 0 0 medium cost
## 1980 0 0 medium cost
## 1981 1 1 medium cost
## 1982 0 1 medium cost
## 1983 1 0 high cost
## 1984 1 1 high cost
## 1985 0 0 medium cost
## 1986 1 1 very high cost
## 1987 0 0 medium cost
## 1988 1 0 low cost
## 1989 1 1 medium cost
## 1990 0 0 medium cost
## 1991 0 0 low cost
## 1992 1 0 very high cost
## 1993 1 1 low cost
## 1994 1 1 very high cost
## 1995 1 0 very high cost
## 1996 1 0 low cost
## 1997 1 1 high cost
## 1998 1 0 very high cost
## 1999 1 1 low cost
## 2000 1 1 very high costmemastikan price_range adalah factor ordinal
dataset$price_range <- factor(dataset$price_range, ordered = TRUE)mengambil variabel numerik saja untuk PCA
num_vars <- dataset %>% select_if(is.numeric)standarisasi
num_scaled <- scale(num_vars)#Train-Test Split
set.seed(123)
train_index <- createDataPartition(dataset$price_range, p = 0.8, list = FALSE)
train_data <- dataset[train_index, ]
test_data <- dataset[-train_index, ]#PCA
train_num <- train_data %>% select_if(is.numeric)
train_scaled <- scale(train_num)
pca_model <- prcomp(train_scaled, center = TRUE, scale. = TRUE)summary(pca_model)## Importance of components:
## PC1 PC2 PC3 PC4 PC5 PC6 PC7
## Standard deviation 1.2948 1.2465 1.2084 1.03104 1.01890 1.01806 1.00530
## Proportion of Variance 0.1197 0.1110 0.1043 0.07593 0.07415 0.07403 0.07219
## Cumulative Proportion 0.1197 0.2307 0.3350 0.41097 0.48512 0.55916 0.63134
## PC8 PC9 PC10 PC11 PC12 PC13 PC14
## Standard deviation 0.99505 0.98416 0.98057 0.94678 0.70256 0.70026 0.60054
## Proportion of Variance 0.07072 0.06918 0.06868 0.06403 0.03526 0.03503 0.02576
## Cumulative Proportion 0.70207 0.77125 0.83993 0.90396 0.93921 0.97424 1.00000mengambil 10 komponen karena sudah menjelaskan 84% variasi data
train_pca <- as.data.frame(pca_model$x[, 1:10])
train_pca$price_range <- train_data$price_rangeTransform test set menggunakan PCA yang sama
test_num <- test_data %>% select_if(is.numeric)
# standardisasi
test_scaled <- scale(test_num,
center = attr(train_scaled, "scaled:center"),
scale = attr(train_scaled, "scaled:scale"))
test_pca_data <- as.data.frame(predict(pca_model, newdata = test_scaled)[, 1:10])
label_mapping <- c("low cost", "medium cost", "high cost", "very high cost")
test_pca_data$price_range <- factor(test_data$price_range,
levels = label_mapping,
ordered = TRUE)Uji Asumsi
library(MVN)## Warning: package 'MVN' was built under R version 4.4.3levels <- unique(train_pca$price_range)
for (lvl in levels) {
cat("Uji normalitas multivariat untuk kelas:", lvl, "\n")
class_data <- train_pca[train_pca$price_range == lvl, 1:10]
result <- mvn(data = class_data, mvnTest = "mardia")
print(result$multivariateNormality)
cat("\n")
}## Uji normalitas multivariat untuk kelas: high cost
## Test Statistic p value Result
## 1 Mardia Skewness 214.414059101043 0.593567015735671 YES
## 2 Mardia Kurtosis -4.98500278175753 6.1960921460269e-07 NO
## 3 MVN <NA> <NA> NO
##
## Uji normalitas multivariat untuk kelas: medium cost
## Test Statistic p value Result
## 1 Mardia Skewness 226.555253324814 0.366488072087657 YES
## 2 Mardia Kurtosis -5.93464234229651 2.94486746099665e-09 NO
## 3 MVN <NA> <NA> NO
##
## Uji normalitas multivariat untuk kelas: low cost
## Test Statistic p value Result
## 1 Mardia Skewness 243.171996564766 0.135745026806791 YES
## 2 Mardia Kurtosis -5.66158370066041 1.49982319896935e-08 NO
## 3 MVN <NA> <NA> NO
##
## Uji normalitas multivariat untuk kelas: very high cost
## Test Statistic p value Result
## 1 Mardia Skewness 267.249523888146 0.016171912476074 NO
## 2 Mardia Kurtosis -6.45735680270698 1.06547437539461e-10 NO
## 3 MVN <NA> <NA> NOlibrary(biotools)## Warning: package 'biotools' was built under R version 4.4.3## ---
## biotools version 4.3# hanya data numerik dan label target
X <- train_pca[, -which(names(train_pca) == "price_range")]
Y <- as.factor(train_pca$price_range)
boxM(X, Y)##
## Box's M-test for Homogeneity of Covariance Matrices
##
## data: X
## Chi-Sq (approx.) = 296.26, df = 165, p-value = 1.57e-09library(MASS)
model_lda <- lda(price_range ~ ., data = train_pca)
pred <- predict(model_lda, newdata = test_pca_data)$classlabel_mapping <- c("low cost", "medium cost", "high cost", "very high cost")
pred_label <- factor(pred, levels = label_mapping, ordered = TRUE)#Evaluasi
conf <- confusionMatrix(pred, test_pca_data$price_range)
print(conf)## Confusion Matrix and Statistics
##
## Reference
## Prediction low cost medium cost high cost very high cost
## low cost 90 7 0 0
## medium cost 10 78 19 0
## high cost 0 15 69 10
## very high cost 0 0 12 90
##
## Overall Statistics
##
## Accuracy : 0.8175
## 95% CI : (0.7761, 0.8541)
## No Information Rate : 0.25
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.7567
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: low cost Class: medium cost Class: high cost
## Sensitivity 0.9000 0.7800 0.6900
## Specificity 0.9767 0.9033 0.9167
## Pos Pred Value 0.9278 0.7290 0.7340
## Neg Pred Value 0.9670 0.9249 0.8987
## Prevalence 0.2500 0.2500 0.2500
## Detection Rate 0.2250 0.1950 0.1725
## Detection Prevalence 0.2425 0.2675 0.2350
## Balanced Accuracy 0.9383 0.8417 0.8033
## Class: very high cost
## Sensitivity 0.9000
## Specificity 0.9600
## Pos Pred Value 0.8824
## Neg Pred Value 0.9664
## Prevalence 0.2500
## Detection Rate 0.2250
## Detection Prevalence 0.2550
## Balanced Accuracy 0.9300Cohen’s Kappa
kappa_val <- kappa2(data.frame(pred, test_pca_data$price_range), "unweighted")
print(kappa_val)## Cohen's Kappa for 2 Raters (Weights: unweighted)
##
## Subjects = 400
## Raters = 2
## Kappa = 0.757
##
## z = 26.2
## p-value = 0#MAE kategori
mae_cat <- mean(abs(as.numeric(pred) - as.numeric(test_pca_data$price_range)))
cat("MAE Kategori:", round(mae_cat, 3), "\n")## MAE Kategori: 0.182label_levels <- levels(train_pca$price_range)
pred_train <- predict(model_lda, newdata = train_pca)$class
pred_train <- factor(pred_train, levels = label_levels, ordered = TRUE)
conf_train <- confusionMatrix(pred_train, train_pca$price_range)
cat("Akurasi Data Train:", round(conf_train$overall["Accuracy"], 4), "\n")## Akurasi Data Train: 0.8531conf_test <- confusionMatrix(pred, test_pca_data$price_range)
cat("Akurasi Data Test :", round(conf_test$overall["Accuracy"], 4), "\n")## Akurasi Data Test : 0.8175