UTS
Faza
2024-10-18
UTS Statistika Ekonomi & Industri
Level Risiko Investasi
Memanggil library yang dibutuhkan
library(caret)## Loading required package: ggplot2## Loading required package: latticelibrary(readxl)
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(mice)##
## Attaching package: 'mice'## The following object is masked from 'package:stats':
##
## filter## The following objects are masked from 'package:base':
##
## cbind, rbindlibrary(reshape2)
library(corrplot)## corrplot 0.95 loadedlibrary(ggplot2)
library(VIM)## Loading required package: colorspace## Loading required package: grid## VIM is ready to use.## Suggestions and bug-reports can be submitted at: https://github.com/statistikat/VIM/issues##
## Attaching package: 'VIM'## The following object is masked from 'package:datasets':
##
## sleeplibrary(e1071)Input Data
train_data <- read_excel("C:\\Users\\LENOVO\\OneDrive\\Dokumen\\SMT 3\\Statistika Ekonomi & Industri\\Level Risiko Investasi.xlsx", sheet = "Training")
test_data <- read_excel("C:\\Users\\LENOVO\\OneDrive\\Dokumen\\SMT 3\\Statistika Ekonomi & Industri\\Level Risiko Investasi.xlsx", sheet = "Testing")print(train_data)## # A tibble: 100 × 16
## Country X1 X2 X3 X4 X5 X6 X7 X8 X9 X10
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 AD 17.5 38675. 173. 0.68 1.22 1.79 -2.08 55 -26.5 2.86
## 2 AE 18.2 40105. 104. 1.77 0.870 2.66 -0.725 103. -13.6 353.
## 3 AE-AZ 18.7 76038. 31.0 2.63 1.49 1.85 -1.90 103. -56.2 200.
## 4 AE-RK NA 27883. 24.8 1.29 1.75 2.23 -1.14 103. 24.8 10.1
## 5 AM 14 4251. 89.6 1.44 0.256 4.75 2.33 167. 47.3 12.6
## 6 AO NA 2034. 57.1 22.4 3.34 -0.878 -5.20 34.8 15.4 62.5
## 7 AR 23.3 9203. 43.3 36.7 0.966 -0.237 -3.73 NA -5.01 375.
## 8 AT 18.6 53174. 159. 1.52 0.726 1.88 -0.300 116. 15.4 430.
## 9 AU 15.7 63972. 122. 1.65 1.48 2.45 0.0306 192. 58.0 1359.
## 10 AW 33.5 24643. 92.8 1.22 0.797 2.06 -4.72 80.5 28.1 2.38
## # ℹ 90 more rows
## # ℹ 5 more variables: X11 <dbl>, X12 <dbl>, X13 <dbl>, X14 <dbl>,
## # `Risk Level` <chr>print(test_data)## # A tibble: 17 × 15
## Country X1 X2 X3 X4 X5 X6 X7 X8 X9 X10
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 SE 23.2 60338. 175. 1.62 0.676 2.47 0.353 186. 64.1 538.
## 2 SG 16.8 62433. 410. 0.105 0.907 2.78 0.291 94.0 -201. 340.
## 3 SI 18.3 28684. 103. 0.844 0.0746 3.55 1.93 72.3 16.2 52.8
## 4 SK 19.7 21043. 103. 1.17 0.0734 3.22 1.23 112. 33.4 103.
## 5 SM 11.9 49356. 60.2 0.896 0.586 1.75 -1.13 88.6 -145. 1.49
## 6 SV NA 3989. 65.6 0.394 0.504 2.45 -0.125 88.9 27.3 24.6
## 7 TH 19.8 7451. 33.2 0.345 0.315 3.44 1.28 100. -42.6 502.
## 8 TN 12.9 3617. 85.3 5.56 1.12 1.61 -1.50 134. 64.5 39.2
## 9 TR 18 8653. 51.7 11.7 1.48 4.16 1.81 117. 28.6 720.
## 10 TW 14.1 31854. 48.5 0.724 0.102 2.54 2.77 71.1 -189. 668.
## 11 UA 22 3955. 104. 19.2 -0.391 0.34 1.89 72.3 -5.47 156.
## 12 UG 21.6 787. 42.3 4.29 3.66 5.74 0.421 69.5 21.9 33.5
## 13 US 16.3 69325. 104. 1.55 0.626 2.46 0.487 NA 47.7 20935
## 14 UY 17.0 15968. 73.0 8.00 0.359 0.821 -0.717 49.1 -16.2 53.6
## 15 UZ 18.4 1873. 30.0 12.3 1.61 5.84 3.07 NA -45.2 57.7
## 16 VN 12.1 3887. 34.5 2.80 0.851 6.95 5.28 86.6 7.40 352.
## 17 ZA 16.6 6405. 50.8 4.98 1.48 0.789 -2.32 107. 15.0 302.
## # ℹ 4 more variables: X11 <dbl>, X12 <dbl>, X13 <dbl>, X14 <dbl>str(train_data)## tibble [100 × 16] (S3: tbl_df/tbl/data.frame)
## $ Country : chr [1:100] "AD" "AE" "AE-AZ" "AE-RK" ...
## $ X1 : num [1:100] 17.5 18.2 18.7 NA 14 ...
## $ X2 : num [1:100] 38675 40105 76038 27883 4251 ...
## $ X3 : num [1:100] 172.8 103.5 31 24.8 89.6 ...
## $ X4 : num [1:100] 0.68 1.77 2.63 1.29 1.44 ...
## $ X5 : num [1:100] 1.221 0.87 1.489 1.753 0.256 ...
## $ X6 : num [1:100] 1.79 2.66 1.85 2.23 4.75 ...
## $ X7 : num [1:100] -2.084 -0.725 -1.901 -1.135 2.332 ...
## $ X8 : num [1:100] 55 103 103 103 167 ...
## $ X9 : num [1:100] -26.5 -13.6 -56.2 24.8 47.3 ...
## $ X10 : num [1:100] 2.86 352.91 199.93 10.11 12.65 ...
## $ X11 : num [1:100] 8 8.15 8.15 NA 6.6 ...
## $ X12 : num [1:100] 23.1 24.9 20.4 21.7 19.4 ...
## $ X13 : num [1:100] 26.9 32.5 31 17.3 15.1 ...
## $ X14 : num [1:100] 3 2.45 NA NA 18.5 ...
## $ Risk Level: chr [1:100] "low" "low" "low" "low" ...str(test_data)## tibble [17 × 15] (S3: tbl_df/tbl/data.frame)
## $ Country: chr [1:17] "SE" "SG" "SI" "SK" ...
## $ X1 : num [1:17] 23.2 16.8 18.3 19.7 11.9 ...
## $ X2 : num [1:17] 60338 62433 28684 21043 49356 ...
## $ X3 : num [1:17] 175.4 409.7 103.1 102.7 60.2 ...
## $ X4 : num [1:17] 1.62 0.105 0.844 1.174 0.896 ...
## $ X5 : num [1:17] 0.6755 0.9068 0.0746 0.0734 0.5865 ...
## $ X6 : num [1:17] 2.47 2.78 3.55 3.22 1.75 ...
## $ X7 : num [1:17] 0.353 0.291 1.93 1.232 -1.134 ...
## $ X8 : num [1:17] 185.6 94 72.3 111.8 88.6 ...
## $ X9 : num [1:17] 64.1 -201 16.2 33.4 -145.4 ...
## $ X10 : num [1:17] 537.61 339.99 52.76 102.57 1.49 ...
## $ X11 : num [1:17] 0.5 1.31 3.02 2.53 63.5 ...
## $ X12 : num [1:17] 25.1 26.8 19.9 22.8 17.8 ...
## $ X13 : num [1:17] 28 47.3 25.8 21 23.2 ...
## $ X14 : num [1:17] 8.6 3 5 7 7.3 9 2 17 13.2 3.7 ...summary(train_data)## Country X1 X2 X3
## Length:100 Min. : 4.20 Min. : 434.5 Min. : 13.63
## Class :character 1st Qu.:15.93 1st Qu.: 4265.9 1st Qu.: 42.96
## Mode :character Median :18.58 Median : 11659.1 Median : 70.42
## Mean :18.97 Mean : 22641.6 Mean : 191.94
## 3rd Qu.:21.80 3rd Qu.: 34815.2 3rd Qu.: 130.63
## Max. :47.50 Max. :124340.4 Max. :6908.35
## NA's :12
## X4 X5 X6 X7
## Min. :-0.151 Min. :-0.8862 Min. :-5.135 Min. :-9.84530
## 1st Qu.: 0.869 1st Qu.: 0.4419 1st Qu.: 1.765 1st Qu.:-1.18720
## Median : 1.700 Median : 1.1402 Median : 2.984 Median : 0.07155
## Mean : 3.263 Mean : 1.2019 Mean : 3.076 Mean : 0.10804
## 3rd Qu.: 3.939 3rd Qu.: 1.9502 3rd Qu.: 4.305 3rd Qu.: 1.94108
## Max. :36.703 Max. : 4.4021 Max. :10.076 Max. : 6.07120
##
## X8 X9 X10 X11
## Min. : 34.82 Min. :-1955.72 Min. : 1.171 Min. : 0.3357
## 1st Qu.: 76.95 1st Qu.: -14.11 1st Qu.: 32.813 1st Qu.: 1.9250
## Median : 90.19 Median : 12.67 Median : 106.872 Median : 3.9000
## Mean : 99.94 Mean : -13.58 Mean : 582.318 Mean : 5.5346
## 3rd Qu.:113.39 3rd Qu.: 36.67 3rd Qu.: 366.370 3rd Qu.: 7.9500
## Max. :359.14 Max. : 456.49 Max. :14866.703 Max. :26.9780
## NA's :7 NA's :17
## X12 X13 X14 Risk Level
## Min. :12.67 Min. :10.95 Min. : 0.120 Length:100
## 1st Qu.:20.79 1st Qu.:19.06 1st Qu.: 4.818 Class :character
## Median :23.40 Median :24.28 Median : 6.800 Mode :character
## Mean :24.96 Mean :24.48 Mean : 8.441
## 3rd Qu.:28.38 3rd Qu.:29.36 3rd Qu.:10.500
## Max. :46.83 Max. :55.09 Max. :24.650
## NA's :11summary(test_data)## Country X1 X2 X3
## Length:17 Min. :11.90 Min. : 786.9 Min. : 30.05
## Class :character 1st Qu.:15.76 1st Qu.: 3955.1 1st Qu.: 48.51
## Mode :character Median :17.52 Median : 8653.0 Median : 65.56
## Mean :17.42 Mean :22330.4 Mean : 92.59
## 3rd Qu.:19.70 3rd Qu.:31854.3 3rd Qu.:103.06
## Max. :23.20 Max. :69324.7 Max. :409.70
## NA's :1
## X4 X5 X6 X7
## Min. : 0.1051 Min. :-0.3906 Min. :0.340 Min. :-2.3230
## 1st Qu.: 0.8435 1st Qu.: 0.3153 1st Qu.:1.754 1st Qu.:-0.1248
## Median : 1.6200 Median : 0.6255 Median :2.539 Median : 0.4867
## Mean : 4.4949 Mean : 0.8249 Mean :2.994 Mean : 0.8826
## 3rd Qu.: 5.5560 3rd Qu.: 1.1173 3rd Qu.:3.553 3rd Qu.: 1.8906
## Max. :19.1730 Max. : 3.6551 Max. :6.946 Max. : 5.2762
##
## X8 X9 X10 X11
## Min. : 49.06 Min. :-200.98 Min. : 1.491 Min. : 0.500
## 1st Qu.: 72.28 1st Qu.: -42.56 1st Qu.: 52.762 1st Qu.: 1.571
## Median : 88.89 Median : 15.04 Median : 155.582 Median : 2.530
## Mean : 96.54 Mean : -18.76 Mean : 1463.386 Mean :11.258
## 3rd Qu.:109.52 3rd Qu.: 28.57 3rd Qu.: 501.644 3rd Qu.: 3.336
## Max. :185.64 Max. : 64.46 Max. :20935.000 Max. :63.500
## NA's :2 NA's :4
## X12 X13 X14
## Min. :16.45 Min. : 8.882 Min. : 2.000
## 1st Qu.:17.79 1st Qu.:17.208 1st Qu.: 4.675
## Median :22.03 Median :23.211 Median : 7.150
## Mean :21.91 Mean :23.693 Mean : 8.963
## 3rd Qu.:24.86 3rd Qu.:27.953 3rd Qu.: 9.700
## Max. :31.60 Max. :47.254 Max. :33.700
## NA's :1Pengolahan data
colnames(train_data) <- make.names(colnames(train_data))
colnames(test_data) <- make.names(colnames(test_data))
train_data$Risk.Level <- as.factor(train_data$Risk.Level)
str(train_data)## tibble [100 × 16] (S3: tbl_df/tbl/data.frame)
## $ Country : chr [1:100] "AD" "AE" "AE-AZ" "AE-RK" ...
## $ X1 : num [1:100] 17.5 18.2 18.7 NA 14 ...
## $ X2 : num [1:100] 38675 40105 76038 27883 4251 ...
## $ X3 : num [1:100] 172.8 103.5 31 24.8 89.6 ...
## $ X4 : num [1:100] 0.68 1.77 2.63 1.29 1.44 ...
## $ X5 : num [1:100] 1.221 0.87 1.489 1.753 0.256 ...
## $ X6 : num [1:100] 1.79 2.66 1.85 2.23 4.75 ...
## $ X7 : num [1:100] -2.084 -0.725 -1.901 -1.135 2.332 ...
## $ X8 : num [1:100] 55 103 103 103 167 ...
## $ X9 : num [1:100] -26.5 -13.6 -56.2 24.8 47.3 ...
## $ X10 : num [1:100] 2.86 352.91 199.93 10.11 12.65 ...
## $ X11 : num [1:100] 8 8.15 8.15 NA 6.6 ...
## $ X12 : num [1:100] 23.1 24.9 20.4 21.7 19.4 ...
## $ X13 : num [1:100] 26.9 32.5 31 17.3 15.1 ...
## $ X14 : num [1:100] 3 2.45 NA NA 18.5 ...
## $ Risk.Level: Factor w/ 2 levels "high","low": 2 2 2 2 1 1 1 2 2 1 ...Mengetahui jumlah missing value pada setiap kolom
colSums(is.na(train_data))## Country X1 X2 X3 X4 X5 X6
## 0 12 0 0 0 0 0
## X7 X8 X9 X10 X11 X12 X13
## 0 7 0 0 17 0 0
## X14 Risk.Level
## 11 0colSums(is.na(test_data))## Country X1 X2 X3 X4 X5 X6 X7 X8 X9
## 0 1 0 0 0 0 0 0 2 0
## X10 X11 X12 X13 X14
## 0 4 0 0 1aggr(train_data)
##### Imputasi nilai missing menggunakan metode PMM (Predictive Mean
Matching)
train_data_imp <- mice(train_data, m = 10, method = "pmm", seed = 123)##
## iter imp variable
## 1 1 X1 X8 X11 X14
## 1 2 X1 X8 X11 X14
## 1 3 X1 X8 X11 X14
## 1 4 X1 X8 X11 X14
## 1 5 X1 X8 X11 X14
## 1 6 X1 X8 X11 X14
## 1 7 X1 X8 X11 X14
## 1 8 X1 X8 X11 X14
## 1 9 X1 X8 X11 X14
## 1 10 X1 X8 X11 X14
## 2 1 X1 X8 X11 X14
## 2 2 X1 X8 X11 X14
## 2 3 X1 X8 X11 X14
## 2 4 X1 X8 X11 X14
## 2 5 X1 X8 X11 X14
## 2 6 X1 X8 X11 X14
## 2 7 X1 X8 X11 X14
## 2 8 X1 X8 X11 X14
## 2 9 X1 X8 X11 X14
## 2 10 X1 X8 X11 X14
## 3 1 X1 X8 X11 X14
## 3 2 X1 X8 X11 X14
## 3 3 X1 X8 X11 X14
## 3 4 X1 X8 X11 X14
## 3 5 X1 X8 X11 X14
## 3 6 X1 X8 X11 X14
## 3 7 X1 X8 X11 X14
## 3 8 X1 X8 X11 X14
## 3 9 X1 X8 X11 X14
## 3 10 X1 X8 X11 X14
## 4 1 X1 X8 X11 X14
## 4 2 X1 X8 X11 X14
## 4 3 X1 X8 X11 X14
## 4 4 X1 X8 X11 X14
## 4 5 X1 X8 X11 X14
## 4 6 X1 X8 X11 X14
## 4 7 X1 X8 X11 X14
## 4 8 X1 X8 X11 X14
## 4 9 X1 X8 X11 X14
## 4 10 X1 X8 X11 X14
## 5 1 X1 X8 X11 X14
## 5 2 X1 X8 X11 X14
## 5 3 X1 X8 X11 X14
## 5 4 X1 X8 X11 X14
## 5 5 X1 X8 X11 X14
## 5 6 X1 X8 X11 X14
## 5 7 X1 X8 X11 X14
## 5 8 X1 X8 X11 X14
## 5 9 X1 X8 X11 X14
## 5 10 X1 X8 X11 X14## Warning: Number of logged events: 1train_data_comp <- complete(train_data_imp, action = 10)aggr(train_data_comp)
aggr(test_data)
test_data_imp <- mice(test_data)##
## iter imp variable
## 1 1 X1 X8 X11 X14
## 1 2 X1 X8 X11 X14
## 1 3 X1 X8 X11 X14
## 1 4 X1 X8 X11 X14
## 1 5 X1 X8 X11 X14
## 2 1 X1 X8 X11 X14
## 2 2 X1 X8 X11 X14
## 2 3 X1 X8 X11 X14
## 2 4 X1 X8 X11 X14
## 2 5 X1 X8 X11 X14
## 3 1 X1 X8 X11 X14
## 3 2 X1 X8 X11 X14
## 3 3 X1 X8 X11 X14
## 3 4 X1 X8 X11 X14
## 3 5 X1 X8 X11 X14
## 4 1 X1 X8 X11 X14
## 4 2 X1 X8 X11 X14
## 4 3 X1 X8 X11 X14
## 4 4 X1 X8 X11 X14
## 4 5 X1 X8 X11 X14
## 5 1 X1 X8 X11 X14
## 5 2 X1 X8 X11 X14
## 5 3 X1 X8 X11 X14
## 5 4 X1 X8 X11 X14
## 5 5 X1 X8 X11 X14## Warning: Number of logged events: 31test_data_comp <- complete(test_data_imp)aggr(test_data_comp)
train_data_comp$Risk.Level <- as.factor(train_data_comp$Risk.Level)set.seed(123)
train_index <- createDataPartition(train_data_comp$Risk.Level, p = 0.8, list = FALSE)
train_data <- train_data_comp[train_index,]
test_data <- train_data_comp[-train_index,]
str(train_data)## 'data.frame': 81 obs. of 16 variables:
## $ Country : chr "AE" "AE-AZ" "AE-RK" "AR" ...
## $ X1 : num 18.2 18.7 22.7 23.3 18.6 ...
## $ X2 : num 40105 76038 27883 9203 53174 ...
## $ X3 : num 103.5 31 24.8 43.3 159.4 ...
## $ X4 : num 1.77 2.63 1.29 36.7 1.52 ...
## $ X5 : num 0.87 1.489 1.753 0.966 0.726 ...
## $ X6 : num 2.659 1.85 2.232 -0.237 1.88 ...
## $ X7 : num -0.725 -1.901 -1.135 -3.73 -0.3 ...
## $ X8 : num 102.5 102.5 102.5 50.1 116.4 ...
## $ X9 : num -13.6 -56.24 24.79 -5.01 15.37 ...
## $ X10 : num 352.9 199.9 10.1 375.2 430 ...
## $ X11 : num 8.15 8.15 6.2 10.6 2.02 ...
## $ X12 : num 24.9 20.4 21.7 16.7 24.8 ...
## $ X13 : num 32.5 31 17.3 13.8 26.9 ...
## $ X14 : num 2.45 4.9 7.5 11.05 6 ...
## $ Risk.Level: Factor w/ 2 levels "high","low": 2 2 2 1 2 2 1 1 2 2 ...str(test_data)## 'data.frame': 19 obs. of 16 variables:
## $ Country : chr "AD" "AM" "AO" "BD" ...
## $ X1 : num 17.5 14 16 4.2 19.3 ...
## $ X2 : num 38675 4251 2034 2324 89771 ...
## $ X3 : num 172.8 89.6 57.1 19.7 275.6 ...
## $ X4 : num 0.68 1.44 22.35646 5.812 0.00116 ...
## $ X5 : num 1.221 0.256 3.342 1.057 0.84 ...
## $ X6 : num 1.786 4.748 -0.878 7.39 1.885 ...
## $ X7 : num -2.084 2.332 -5.203 6.071 0.173 ...
## $ X8 : num 55 166.8 34.8 78.4 82.5 ...
## $ X9 : num -26.52 47.27 15.45 4.92 -152.44 ...
## $ X10 : num 2.86 12.65 62.49 347.15 749.02 ...
## $ X11 : num 8 6.6 10.3 7.7 0.75 ...
## $ X12 : num 23.1 19.4 31.1 32.7 24.4 ...
## $ X13 : num 26.9 15.1 20.6 32.2 32.8 ...
## $ X14 : num 3 18.5 10.5 5 3.17 ...
## $ Risk.Level: Factor w/ 2 levels "high","low": 2 1 1 1 2 1 2 1 2 2 ...train_data <- train_data[, -which(names(train_data) == "Country")]
test_data <- test_data[, -which(names(test_data) == "Country")]dim(train_data_comp)## [1] 100 16dim(test_data_comp)## [1] 17 15Buat model SVM linear
svm_model <- svm(Risk.Level ~ ., data = train_data, kernel = "linear")
# menggunakan library 'caret, 'e1071' dan 'ggplot2' untuuk membuat model SVM linear
# ini dikonfigurasi untuk memprediksi 'Risk.Level' berdasarkan semua fitur ('.') dalam ('train_data')Prediksi menggunakan model SVM
svm_pred <- predict(svm_model, newdata = test_data)Menghitung akurasi prediksi vs label sebenarnya
accuracy_svm <- sum(svm_pred == test_data$Risk.Level) / nrow(test_data)
print(paste("SVM Akurasi:", accuracy_svm))## [1] "SVM Akurasi: 0.947368421052632"# akurasi model dihitung dengan menghitung persentase prediksi yang benar ('svm_pred') dibandingkan dengan label aktual ('test_data$Risk.Level')Konfirmasi matrix confusio
confusionMatrix <- confusionMatrix(as.factor(svm_pred), as.factor(test_data$Risk.Level))
print(confusionMatrix)## Confusion Matrix and Statistics
##
## Reference
## Prediction high low
## high 10 1
## low 0 8
##
## Accuracy : 0.9474
## 95% CI : (0.7397, 0.9987)
## No Information Rate : 0.5263
## P-Value [Acc > NIR] : 9.149e-05
##
## Kappa : 0.8939
##
## Mcnemar's Test P-Value : 1
##
## Sensitivity : 1.0000
## Specificity : 0.8889
## Pos Pred Value : 0.9091
## Neg Pred Value : 1.0000
## Prevalence : 0.5263
## Detection Rate : 0.5263
## Detection Prevalence : 0.5789
## Balanced Accuracy : 0.9444
##
## 'Positive' Class : high
## # script juga membuat matrix confusio untuk melihat presisi model SVMConvert konfirmasi matrix ke data frame
confusion_data <- as.data.frame(confusionMatrix$table)
library(ggplot2)Sample data frame
df <- data.frame(
x = rep(1:5, each=5),
y = rep(1:5, times=5),
Freq = sample(1:100, 25, replace=TRUE)
)Membuat plot
ggplot(df, aes(x = x, y = y, fill = Freq)) +
geom_tile(color = "black") +
scale_fill_gradient(low = "white", high = "purple") +
theme_minimal()
Berdasarkan plot diatas, diketahui : x = nilai akurasi y = nilai prediksi Freq = frekuensi atau intensitas
keterangan : 1. skala warna ~ Low = “white” menunjukkan nilai frekuensi rendah dimana intensitas warna putih menunjukkan sedikit atau tidak ada frekuensi ~ High = “purple” menunjukkan nilai frekuensi yang tinggi dimana dengan intensitas warna ungu menunjukkan banyaknya frekuensi 2. interpretasi visual ~ area dengan warna putih menunjukkan kombinasi nilai aktual dan prediksi yang memiliki frekuensi rendah, yang berarti model mungkin kurang akurat pada titik tersebut ~ dan sebaliknya, area dengan warna ungu menunjukkan kombinasi nilai aktual dan prediksi yang memiliki frekuensi tinggi, menunjukkan bahwa model berhasil memprediksi dengan baik pada titik tersebut
- penjelasan keseluruhan :
- berdasarkan akurasi model = dengan menggunakan model SVM linear, kita dapat menentukan bahwa akurasinya mencapai % seperti yang ditunjukkan oleh variabel ‘accuracy_svm’. hal ini memberikan gambaran mengenai kemampuan model dalam membedakan antara tingkat rasio investasi yang benar dan salah.
- berdasarkan konfirmasi matrix = confusion matrix disediakan sebagai tambahan informasi tentang performa model. dimana nilainya akan membantu dalam evaluasi presisi spesifikitas sensitivitas serta kinerja overall model secara menyeluruh.
- berdasarkan penggunaan visualisasi = meskipun tidak ada penggunaan langsung visualisasi dalam contoh kode diatas namun teknik seperti membuat tile plots atau scatterplots bisa sangat berguna jika ingin melihat hubungan antara fitur fitur yang digunakan dalam model SVM.
kesimpulan : Dalam keseluruhan hasil proses analisis ini, kita berhasil mengolah data yang kompleks hingga dapat digunakan dalam pembuatan model prediktif yang efektif. Model SVM telah terbukti memiliki potensi besar dalam mendeteksi tingkat risiko investasi dengan akurasi yang cukup baik. namun perlu dilakukan evaluasi lebih lanjut guna meningkatkan performa hasil.