#Praktikum 1 - K-Means
#===Packages
library("tidyverse")
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.4 ✔ readr 2.1.5
## ✔ forcats 1.0.0 ✔ stringr 1.5.1
## ✔ ggplot2 4.0.0 ✔ tibble 3.3.0
## ✔ lubridate 1.9.4 ✔ tidyr 1.3.1
## ✔ purrr 1.1.0
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
library("factoextra")
## Welcome! Want to learn more? See two factoextra-related books at https://goo.gl/ve3WBa
library("DataExplorer")
library("easystats")
## # Attaching packages: easystats 0.7.5 (red = needs update)
## ✔ bayestestR 0.17.0 ✔ correlation 0.8.8
## ✔ datawizard 1.2.0 ✔ effectsize 1.0.1
## ✔ insight 1.4.2 ✔ modelbased 0.13.0
## ✖ performance 0.15.1 ✖ parameters 0.28.1
## ✔ report 0.6.1 ✖ see 0.11.0
##
## Restart the R-Session and update packages with `easystats::easystats_update()`.
library("umap")
library("ggpubr")
##
## Attaching package: 'ggpubr'
##
## The following objects are masked from 'package:datawizard':
##
## mean_sd, median_mad
#===Import Data
df <- read.csv("D:/Kuliah/IPB 2025 Semester 3/Pemodelan Klasifikasi/Praktikum/Praktikum 1/Data/credit card.csv", stringsAsFactors = T)
glimpse(df)
## Rows: 8,950
## Columns: 18
## $ CUST_ID <fct> C10001, C10002, C10003, C10004, C1000…
## $ BALANCE <dbl> 40.90075, 3202.46742, 2495.14886, 166…
## $ BALANCE_FREQUENCY <dbl> 0.818182, 0.909091, 1.000000, 0.63636…
## $ PURCHASES <dbl> 95.40, 0.00, 773.17, 1499.00, 16.00, …
## $ ONEOFF_PURCHASES <dbl> 0.00, 0.00, 773.17, 1499.00, 16.00, 0…
## $ INSTALLMENTS_PURCHASES <dbl> 95.40, 0.00, 0.00, 0.00, 0.00, 1333.2…
## $ CASH_ADVANCE <dbl> 0.0000, 6442.9455, 0.0000, 205.7880, …
## $ PURCHASES_FREQUENCY <dbl> 0.166667, 0.000000, 1.000000, 0.08333…
## $ ONEOFF_PURCHASES_FREQUENCY <dbl> 0.000000, 0.000000, 1.000000, 0.08333…
## $ PURCHASES_INSTALLMENTS_FREQUENCY <dbl> 0.083333, 0.000000, 0.000000, 0.00000…
## $ CASH_ADVANCE_FREQUENCY <dbl> 0.000000, 0.250000, 0.000000, 0.08333…
## $ CASH_ADVANCE_TRX <int> 0, 4, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ PURCHASES_TRX <int> 2, 0, 12, 1, 1, 8, 64, 12, 5, 3, 12, …
## $ CREDIT_LIMIT <dbl> 1000, 7000, 7500, 7500, 1200, 1800, 1…
## $ PAYMENTS <dbl> 201.8021, 4103.0326, 622.0667, 0.0000…
## $ MINIMUM_PAYMENTS <dbl> 139.50979, 1072.34022, 627.28479, NA,…
## $ PRC_FULL_PAYMENT <dbl> 0.000000, 0.222222, 0.000000, 0.00000…
## $ TENURE <int> 12, 12, 12, 12, 12, 12, 12, 12, 12, 1…
#Eksplorasi Data
#Plot
plot_intro(data = df,ggtheme = theme_minimal())
#Histogram
plot_histogram(data = df,
ncol = 2,nrow = 2,
geom_histogram_args = list(fill="steelblue",col="black"),
ggtheme = theme_minimal())
## `stat_bin()` using `bins = 30`. Pick better value `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value `binwidth`.
#KOrelasi
correlation(data = df,method = "spearman",
include_factors = FALSE) %>%
as.matrix() %>%
plot(text=list(size=2))+theme(text = element_text(size = 6),
axis.text.x = element_text(angle = 45,
hjust = 1))
#Data Cleaning
df <- df %>% na.omit()
plot_intro(df)
#Standarisasi Peubah - KMeans pakai Euclidean yang dipengaruhi skala
df_std <- standardize(df)
plot_histogram(data = df_std,
ncol = 2,nrow = 2,
geom_histogram_args = list(fill="steelblue",col="black"),
ggtheme = theme_minimal())
## `stat_bin()` using `bins = 30`. Pick better value `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value `binwidth`.
#===Banyaknya cluster Terbaik
#1. Visualisasi PCA
pca0 <- prcomp(x = df_std %>% select(-CUST_ID))
fviz_pca_ind(pca0,
geom.ind = "point"
)
#2.UMAP
umap0 <- umap(d = df_std %>% select(-CUST_ID))
data_umap <- data.frame(x=umap0$layout[,1],y=umap0$layout[,2])
ggscatter(data_umap, x = "x", y = "y")
#3. Metric WSS (Within Sum of Squared)
set.seed(123)
fviz_nbclust(
x = df_std %>% select(-CUST_ID),
FUNcluster = stats::kmeans,
method = "wss",
iter.max = 100,
k.max = 25
)
#4. Metric Koefisien Silhouette
set.seed(123)
fviz_nbclust(
x = df_std %>% select(-CUST_ID),
FUNcluster = stats::kmeans,
method = "silhouette",
iter.max = 100,
k.max = 25
)
#===Penerapan Metode Terbaik
set.seed(123)
kmean_res <- kmeans(df_std %>% select(-CUST_ID),
centers = 7,
iter.max = 100)
kmean_res$centers
## BALANCE BALANCE_FREQUENCY PURCHASES ONEOFF_PURCHASES
## 1 -0.003679619 0.3932206 -0.34750262 -0.2271971
## 2 -0.379315434 0.3234073 -0.04645383 -0.2350586
## 3 1.664019449 0.3776543 -0.20597786 -0.1526654
## 4 -0.329026156 -0.3213573 -0.28694700 -0.2148390
## 5 1.475135796 0.3780967 7.19735250 6.2895625
## 6 -0.699045017 -2.2196501 -0.29692601 -0.2219300
## 7 0.132238165 0.4120371 0.95907495 0.9119334
## INSTALLMENTS_PURCHASES CASH_ADVANCE PURCHASES_FREQUENCY
## 1 -0.4039690 -0.110923345 -0.8205139
## 2 0.3220250 -0.371230271 0.9743029
## 3 -0.2065262 1.974769039 -0.4576642
## 4 -0.2832352 0.081180989 -0.1712167
## 5 5.4549970 0.008129406 1.0587149
## 6 -0.2940439 -0.312988271 -0.5488391
## 7 0.5915735 -0.306660011 1.0891967
## ONEOFF_PURCHASES_FREQUENCY PURCHASES_INSTALLMENTS_FREQUENCY
## 1 -0.3411377 -0.7615576
## 2 -0.3534162 1.1670026
## 3 -0.1935885 -0.4039784
## 4 -0.2795264 -0.2050887
## 5 1.8115197 1.0250211
## 6 -0.4046639 -0.4639245
## 7 1.8575427 0.5494410
## CASH_ADVANCE_FREQUENCY CASH_ADVANCE_TRX PURCHASES_TRX CREDIT_LIMIT PAYMENTS
## 1 0.07921907 -0.045465272 -0.4688747 -0.3083535 -0.2566096
## 2 -0.48436681 -0.366771145 0.1654523 -0.2835829 -0.2375091
## 3 1.88773119 1.915126019 -0.2426090 1.0142178 0.8036150
## 4 0.32329922 -0.004497674 -0.3841455 -0.5565460 -0.3811768
## 5 -0.32871705 -0.125359753 4.7684590 2.2086159 4.9907591
## 6 -0.51285671 -0.372141174 -0.4155147 -0.1663502 -0.1556026
## 7 -0.40902805 -0.317379255 1.2152026 0.7161350 0.4082754
## MINIMUM_PAYMENTS PRC_FULL_PAYMENT TENURE
## 1 -0.01378486 -0.46497307 0.26551761
## 2 -0.02477574 0.28262149 0.25299785
## 3 0.55639126 -0.41160714 0.06703541
## 4 -0.21865992 0.02140241 -3.24174836
## 5 1.16363215 0.77745281 0.32505012
## 6 -0.29566285 0.41837217 0.21200614
## 7 -0.02754895 0.45113504 0.29948228
table(kmean_res$cluster)
##
## 1 2 3 4 5 6 7
## 2774 2001 871 594 76 1087 1233
#===Validasi Hasil Clustering
sil <- cluster::silhouette(x = kmean_res$cluster,
dist = dist(x =df_std %>% select(-CUST_ID) ,
method = "euclidean"))
fviz_silhouette(sil,print.summary = FALSE)+
scale_color_manual(values = get_palette("jco",k=7))+
# get_palette berasal dari package ggpubr
scale_fill_manual(values = get_palette("jco",k=7))+
theme_minimal()+
theme(legend.position = "top")
## 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.
## ℹ The deprecated feature was likely used in the factoextra package.
## Please report the issue at <https://github.com/kassambara/factoextra/issues>.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
sil %>%
as.data.frame() %>%
mutate(obs=1:nrow(sil)) %>%
relocate(obs) %>%
filter(sil_width<0) %>%
arrange(sil_width)
## obs cluster neighbor sil_width
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## 1123 1485 3 1 -0.0072482113
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## 1125 3050 7 2 -0.0071471038
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## 1130 458 7 1 -0.0063344118
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## 1137 7371 2 1 -0.0056513940
## 1138 2725 3 1 -0.0056408631
## 1139 8350 6 1 -0.0053103130
## 1140 4744 7 2 -0.0052789912
## 1141 4291 7 2 -0.0052367081
## 1142 2998 3 7 -0.0049760480
## 1143 1767 7 2 -0.0045930667
## 1144 7297 2 1 -0.0043064103
## 1145 7425 3 1 -0.0042456346
## 1146 2038 6 1 -0.0041801536
## 1147 3099 6 1 -0.0040236960
## 1148 170 2 1 -0.0039754037
## 1149 766 7 1 -0.0037562487
## 1150 3543 7 2 -0.0037041183
## 1151 1125 7 2 -0.0035544035
## 1152 4172 6 1 -0.0033143970
## 1153 4515 3 7 -0.0032457759
## 1154 2833 7 2 -0.0029752164
## 1155 8528 4 1 -0.0029068558
## 1156 1543 7 6 -0.0028983955
## 1157 5872 6 2 -0.0027986931
## 1158 3406 6 1 -0.0027700981
## 1159 7252 7 4 -0.0027586200
## 1160 3905 7 2 -0.0024185787
## 1161 6501 6 1 -0.0023330639
## 1162 3193 6 1 -0.0022726989
## 1163 6842 2 1 -0.0022357146
## 1164 4101 2 1 -0.0021511920
## 1165 5627 7 2 -0.0018848117
## 1166 2468 6 1 -0.0018542767
## 1167 408 7 1 -0.0018340920
## 1168 345 6 2 -0.0017247923
## 1169 4468 3 1 -0.0015928080
## 1170 3881 2 1 -0.0014960271
## 1171 7620 3 1 -0.0013629686
## 1172 4154 3 1 -0.0009575059
## 1173 482 2 1 -0.0009178279
## 1174 2848 3 1 -0.0008305044
## 1175 2423 7 2 -0.0004238309
## 1176 5343 7 1 -0.0002970147
## 1177 2046 7 1 -0.0001746034
corrected_cluster <- sil %>%
as.data.frame() %>%
mutate(cluster=if_else(sil_width<0,neighbor,cluster))
corrected_cluster %>%
filter(sil_width<0) %>%
arrange(sil_width)
## cluster neighbor sil_width
## 1 1 1 -0.3135004935
## 2 1 1 -0.3122560482
## 3 1 1 -0.3117921649
## 4 1 1 -0.3086938780
## 5 1 1 -0.3068152191
## 6 1 1 -0.2973489201
## 7 1 1 -0.2959118748
## 8 1 1 -0.2957829785
## 9 1 1 -0.2956147089
## 10 1 1 -0.2934967087
## 11 1 1 -0.2894230881
## 12 1 1 -0.2860991163
## 13 1 1 -0.2846156119
## 14 1 1 -0.2843348161
## 15 1 1 -0.2819121200
## 16 7 7 -0.2814070392
## 17 1 1 -0.2800812253
## 18 7 7 -0.2794147055
## 19 1 1 -0.2785872319
## 20 7 7 -0.2780118826
## 21 7 7 -0.2779463958
## 22 1 1 -0.2742544175
## 23 1 1 -0.2726074742
## 24 1 1 -0.2713671433
## 25 1 1 -0.2712283873
## 26 1 1 -0.2698462102
## 27 1 1 -0.2689035676
## 28 1 1 -0.2666269984
## 29 7 7 -0.2645072082
## 30 1 1 -0.2641957726
## 31 1 1 -0.2614277771
## 32 1 1 -0.2602853169
## 33 1 1 -0.2587458954
## 34 1 1 -0.2586797936
## 35 1 1 -0.2581537529
## 36 1 1 -0.2581308581
## 37 1 1 -0.2574551660
## 38 7 7 -0.2569446442
## 39 1 1 -0.2560504809
## 40 1 1 -0.2534589022
## 41 1 1 -0.2534126913
## 42 1 1 -0.2529903398
## 43 1 1 -0.2529738846
## 44 1 1 -0.2511065824
## 45 1 1 -0.2496883577
## 46 7 7 -0.2496718580
## 47 1 1 -0.2495063656
## 48 1 1 -0.2470178557
## 49 1 1 -0.2469597194
## 50 1 1 -0.2464614720
## 51 1 1 -0.2447451145
## 52 1 1 -0.2441569020
## 53 7 7 -0.2437410508
## 54 1 1 -0.2429654220
## 55 1 1 -0.2429351927
## 56 1 1 -0.2421960489
## 57 1 1 -0.2416835330
## 58 1 1 -0.2416239678
## 59 1 1 -0.2404755888
## 60 1 1 -0.2380292320
## 61 1 1 -0.2378014516
## 62 1 1 -0.2373371474
## 63 1 1 -0.2373237703
## 64 1 1 -0.2364536165
## 65 1 1 -0.2364304233
## 66 2 2 -0.2360893674
## 67 1 1 -0.2356515680
## 68 1 1 -0.2354583389
## 69 7 7 -0.2353210221
## 70 1 1 -0.2344998530
## 71 1 1 -0.2341991936
## 72 1 1 -0.2338536624
## 73 1 1 -0.2331639152
## 74 1 1 -0.2326114419
## 75 1 1 -0.2310130804
## 76 1 1 -0.2304386031
## 77 1 1 -0.2297258413
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## 79 7 7 -0.2269807125
## 80 7 7 -0.2269665811
## 81 1 1 -0.2265296405
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## 83 2 2 -0.2257805711
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## 85 1 1 -0.2251995631
## 86 1 1 -0.2251304161
## 87 1 1 -0.2247610157
## 88 1 1 -0.2236371460
## 89 1 1 -0.2233088450
## 90 1 1 -0.2229488485
## 91 2 2 -0.2225089828
## 92 1 1 -0.2221381943
## 93 1 1 -0.2213142443
## 94 1 1 -0.2200477050
## 95 1 1 -0.2190423804
## 96 1 1 -0.2186190345
## 97 1 1 -0.2175686282
## 98 1 1 -0.2166225171
## 99 1 1 -0.2155669099
## 100 1 1 -0.2153552585
## 101 1 1 -0.2147043241
## 102 2 2 -0.2145502704
## 103 1 1 -0.2140194309
## 104 1 1 -0.2138329655
## 105 1 1 -0.2138216417
## 106 7 7 -0.2116652183
## 107 7 7 -0.2112859953
## 108 1 1 -0.2112495960
## 109 1 1 -0.2110173426
## 110 1 1 -0.2109469891
## 111 1 1 -0.2108146378
## 112 1 1 -0.2106647712
## 113 2 2 -0.2101422969
## 114 7 7 -0.2100419597
## 115 2 2 -0.2090088461
## 116 1 1 -0.2085306723
## 117 1 1 -0.2083357000
## 118 1 1 -0.2082544874
## 119 2 2 -0.2081491643
## 120 7 7 -0.2071455019
## 121 1 1 -0.2071229304
## 122 1 1 -0.2066538933
## 123 1 1 -0.2065661603
## 124 1 1 -0.2063841567
## 125 1 1 -0.2060994254
## 126 1 1 -0.2055496730
## 127 1 1 -0.2054249437
## 128 1 1 -0.2052798485
## 129 1 1 -0.2052787020
## 130 1 1 -0.2052738211
## 131 2 2 -0.2052246770
## 132 1 1 -0.2051958287
## 133 2 2 -0.2048318391
## 134 1 1 -0.2046308719
## 135 7 7 -0.2042198540
## 136 1 1 -0.2038906686
## 137 2 2 -0.2030462582
## 138 1 1 -0.2002831002
## 139 1 1 -0.1990803182
## 140 1 1 -0.1988824921
## 141 1 1 -0.1979751391
## 142 1 1 -0.1969548533
## 143 1 1 -0.1969517956
## 144 1 1 -0.1968886367
## 145 1 1 -0.1967693994
## 146 1 1 -0.1961227852
## 147 1 1 -0.1957415760
## 148 2 2 -0.1955453736
## 149 1 1 -0.1954043731
## 150 2 2 -0.1950532015
## 151 1 1 -0.1947045824
## 152 1 1 -0.1946829224
## 153 7 7 -0.1943260143
## 154 2 2 -0.1935226810
## 155 1 1 -0.1934327892
## 156 1 1 -0.1931384746
## 157 1 1 -0.1928072392
## 158 1 1 -0.1918421719
## 159 1 1 -0.1914270169
## 160 2 2 -0.1913885085
## 161 2 2 -0.1899148183
## 162 7 7 -0.1891690446
## 163 2 2 -0.1883597706
## 164 2 2 -0.1878366198
## 165 2 2 -0.1877373034
## 166 2 2 -0.1861079359
## 167 2 2 -0.1859822510
## 168 1 1 -0.1855673354
## 169 1 1 -0.1855245848
## 170 2 2 -0.1853726474
## 171 2 2 -0.1848635909
## 172 1 1 -0.1842779671
## 173 2 2 -0.1805901159
## 174 1 1 -0.1804155810
## 175 2 2 -0.1803234170
## 176 1 1 -0.1798386844
## 177 1 1 -0.1787309490
## 178 1 1 -0.1781634054
## 179 2 2 -0.1780600480
## 180 1 1 -0.1779571893
## 181 1 1 -0.1776722215
## 182 2 2 -0.1775170893
## 183 1 1 -0.1767424309
## 184 1 1 -0.1759008413
## 185 1 1 -0.1757086949
## 186 1 1 -0.1755385095
## 187 7 7 -0.1746754913
## 188 1 1 -0.1746530401
## 189 1 1 -0.1742044892
## 190 2 2 -0.1741885084
## 191 1 1 -0.1740247213
## 192 2 2 -0.1730562058
## 193 2 2 -0.1729578579
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#===Interpretasi hasil clustering
#1. Menggunakan center
df |>
select(-CUST_ID) %>%
mutate(cluster=corrected_cluster$cluster) %>%
group_by(cluster) %>%
summarise(across(everything(),mean))
## # A tibble: 7 × 18
## cluster BALANCE BALANCE_FREQUENCY PURCHASES ONEOFF_PURCHASES
## <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1 1819. 0.970 325. 260.
## 2 2 935. 0.962 1128. 345.
## 3 3 6041. 0.980 697. 420.
## 4 4 1001. 0.834 416. 254.
## 5 5 5470. 0.959 24597. 17745.
## 6 6 128. 0.420 393. 244.
## 7 7 2234. 0.989 4139. 2883.
## # ℹ 13 more variables: INSTALLMENTS_PURCHASES <dbl>, CASH_ADVANCE <dbl>,
## # PURCHASES_FREQUENCY <dbl>, ONEOFF_PURCHASES_FREQUENCY <dbl>,
## # PURCHASES_INSTALLMENTS_FREQUENCY <dbl>, CASH_ADVANCE_FREQUENCY <dbl>,
## # CASH_ADVANCE_TRX <dbl>, PURCHASES_TRX <dbl>, CREDIT_LIMIT <dbl>,
## # PAYMENTS <dbl>, MINIMUM_PAYMENTS <dbl>, PRC_FULL_PAYMENT <dbl>,
## # TENURE <dbl>
#2. Menggunakan visualisasi
data_umap_labeled <- data_umap %>%
mutate(cluster=as.factor(corrected_cluster$cluster))
ggscatter(data_umap_labeled, x = "x",
y = "y",
color = "cluster",
palette = "jco",
title = "Cluster Plot with 7-Means")
