Tentang data
- Data yang digunakan untuk tutorial dari www.kaggle.com, - Creditcard Fraud Detection.
- Kumpulan data berisi transaksi yang dilakukan dengan kartu kredit oleh pemegang kartu di Eropa.
- Dataset ini menyajikan transaksi yang terjadi dalam dua hari.
- Data terdiri dari 284.807 observasi dan 31 variable.
Dimensi data
dim(df)
## [1] 284807 31
Variable
str(df)
## Classes 'spec_tbl_df', 'tbl_df', 'tbl' and 'data.frame': 284807 obs. of 31 variables:
## $ Time : num 0 0 1 1 2 2 4 7 7 9 ...
## $ V1 : num -1.36 1.192 -1.358 -0.966 -1.158 ...
## $ V2 : num -0.0728 0.2662 -1.3402 -0.1852 0.8777 ...
## $ V3 : num 2.536 0.166 1.773 1.793 1.549 ...
## $ V4 : num 1.378 0.448 0.38 -0.863 0.403 ...
## $ V5 : num -0.3383 0.06 -0.5032 -0.0103 -0.4072 ...
## $ V6 : num 0.4624 -0.0824 1.8005 1.2472 0.0959 ...
## $ V7 : num 0.2396 -0.0788 0.7915 0.2376 0.5929 ...
## $ V8 : num 0.0987 0.0851 0.2477 0.3774 -0.2705 ...
## $ V9 : num 0.364 -0.255 -1.515 -1.387 0.818 ...
## $ V10 : num 0.0908 -0.167 0.2076 -0.055 0.7531 ...
## $ V11 : num -0.552 1.613 0.625 -0.226 -0.823 ...
## $ V12 : num -0.6178 1.0652 0.0661 0.1782 0.5382 ...
## $ V13 : num -0.991 0.489 0.717 0.508 1.346 ...
## $ V14 : num -0.311 -0.144 -0.166 -0.288 -1.12 ...
## $ V15 : num 1.468 0.636 2.346 -0.631 0.175 ...
## $ V16 : num -0.47 0.464 -2.89 -1.06 -0.451 ...
## $ V17 : num 0.208 -0.115 1.11 -0.684 -0.237 ...
## $ V18 : num 0.0258 -0.1834 -0.1214 1.9658 -0.0382 ...
## $ V19 : num 0.404 -0.146 -2.262 -1.233 0.803 ...
## $ V20 : num 0.2514 -0.0691 0.525 -0.208 0.4085 ...
## $ V21 : num -0.01831 -0.22578 0.248 -0.1083 -0.00943 ...
## $ V22 : num 0.27784 -0.63867 0.77168 0.00527 0.79828 ...
## $ V23 : num -0.11 0.101 0.909 -0.19 -0.137 ...
## $ V24 : num 0.0669 -0.3398 -0.6893 -1.1756 0.1413 ...
## $ V25 : num 0.129 0.167 -0.328 0.647 -0.206 ...
## $ V26 : num -0.189 0.126 -0.139 -0.222 0.502 ...
## $ V27 : num 0.13356 -0.00898 -0.05535 0.06272 0.21942 ...
## $ V28 : num -0.0211 0.0147 -0.0598 0.0615 0.2152 ...
## $ Amount: num 149.62 2.69 378.66 123.5 69.99 ...
## $ Class : num 0 0 0 0 0 0 0 0 0 0 ...
## - attr(*, "spec")=
## .. cols(
## .. Time = col_double(),
## .. V1 = col_double(),
## .. V2 = col_double(),
## .. V3 = col_double(),
## .. V4 = col_double(),
## .. V5 = col_double(),
## .. V6 = col_double(),
## .. V7 = col_double(),
## .. V8 = col_double(),
## .. V9 = col_double(),
## .. V10 = col_double(),
## .. V11 = col_double(),
## .. V12 = col_double(),
## .. V13 = col_double(),
## .. V14 = col_double(),
## .. V15 = col_double(),
## .. V16 = col_double(),
## .. V17 = col_double(),
## .. V18 = col_double(),
## .. V19 = col_double(),
## .. V20 = col_double(),
## .. V21 = col_double(),
## .. V22 = col_double(),
## .. V23 = col_double(),
## .. V24 = col_double(),
## .. V25 = col_double(),
## .. V26 = col_double(),
## .. V27 = col_double(),
## .. V28 = col_double(),
## .. Amount = col_double(),
## .. Class = col_double()
## .. )
Cek data yang kosong
any(is.na(df))
## [1] FALSE
summary(df)
## Time V1 V2 V3
## Min. : 0 Min. :-56.40751 Min. :-72.71573 Min. :-48.3256
## 1st Qu.: 54202 1st Qu.: -0.92037 1st Qu.: -0.59855 1st Qu.: -0.8904
## Median : 84692 Median : 0.01811 Median : 0.06549 Median : 0.1799
## Mean : 94814 Mean : 0.00000 Mean : 0.00000 Mean : 0.0000
## 3rd Qu.:139321 3rd Qu.: 1.31564 3rd Qu.: 0.80372 3rd Qu.: 1.0272
## Max. :172792 Max. : 2.45493 Max. : 22.05773 Max. : 9.3826
## V4 V5 V6 V7
## Min. :-5.68317 Min. :-113.74331 Min. :-26.1605 Min. :-43.5572
## 1st Qu.:-0.84864 1st Qu.: -0.69160 1st Qu.: -0.7683 1st Qu.: -0.5541
## Median :-0.01985 Median : -0.05434 Median : -0.2742 Median : 0.0401
## Mean : 0.00000 Mean : 0.00000 Mean : 0.0000 Mean : 0.0000
## 3rd Qu.: 0.74334 3rd Qu.: 0.61193 3rd Qu.: 0.3986 3rd Qu.: 0.5704
## Max. :16.87534 Max. : 34.80167 Max. : 73.3016 Max. :120.5895
## V8 V9 V10 V11
## Min. :-73.21672 Min. :-13.43407 Min. :-24.58826 Min. :-4.79747
## 1st Qu.: -0.20863 1st Qu.: -0.64310 1st Qu.: -0.53543 1st Qu.:-0.76249
## Median : 0.02236 Median : -0.05143 Median : -0.09292 Median :-0.03276
## Mean : 0.00000 Mean : 0.00000 Mean : 0.00000 Mean : 0.00000
## 3rd Qu.: 0.32735 3rd Qu.: 0.59714 3rd Qu.: 0.45392 3rd Qu.: 0.73959
## Max. : 20.00721 Max. : 15.59500 Max. : 23.74514 Max. :12.01891
## V12 V13 V14 V15
## Min. :-18.6837 Min. :-5.79188 Min. :-19.2143 Min. :-4.49894
## 1st Qu.: -0.4056 1st Qu.:-0.64854 1st Qu.: -0.4256 1st Qu.:-0.58288
## Median : 0.1400 Median :-0.01357 Median : 0.0506 Median : 0.04807
## Mean : 0.0000 Mean : 0.00000 Mean : 0.0000 Mean : 0.00000
## 3rd Qu.: 0.6182 3rd Qu.: 0.66251 3rd Qu.: 0.4931 3rd Qu.: 0.64882
## Max. : 7.8484 Max. : 7.12688 Max. : 10.5268 Max. : 8.87774
## V16 V17 V18
## Min. :-14.12985 Min. :-25.16280 Min. :-9.498746
## 1st Qu.: -0.46804 1st Qu.: -0.48375 1st Qu.:-0.498850
## Median : 0.06641 Median : -0.06568 Median :-0.003636
## Mean : 0.00000 Mean : 0.00000 Mean : 0.000000
## 3rd Qu.: 0.52330 3rd Qu.: 0.39968 3rd Qu.: 0.500807
## Max. : 17.31511 Max. : 9.25353 Max. : 5.041069
## V19 V20 V21
## Min. :-7.213527 Min. :-54.49772 Min. :-34.83038
## 1st Qu.:-0.456299 1st Qu.: -0.21172 1st Qu.: -0.22839
## Median : 0.003735 Median : -0.06248 Median : -0.02945
## Mean : 0.000000 Mean : 0.00000 Mean : 0.00000
## 3rd Qu.: 0.458949 3rd Qu.: 0.13304 3rd Qu.: 0.18638
## Max. : 5.591971 Max. : 39.42090 Max. : 27.20284
## V22 V23 V24
## Min. :-10.933144 Min. :-44.80774 Min. :-2.83663
## 1st Qu.: -0.542350 1st Qu.: -0.16185 1st Qu.:-0.35459
## Median : 0.006782 Median : -0.01119 Median : 0.04098
## Mean : 0.000000 Mean : 0.00000 Mean : 0.00000
## 3rd Qu.: 0.528554 3rd Qu.: 0.14764 3rd Qu.: 0.43953
## Max. : 10.503090 Max. : 22.52841 Max. : 4.58455
## V25 V26 V27
## Min. :-10.29540 Min. :-2.60455 Min. :-22.565679
## 1st Qu.: -0.31715 1st Qu.:-0.32698 1st Qu.: -0.070840
## Median : 0.01659 Median :-0.05214 Median : 0.001342
## Mean : 0.00000 Mean : 0.00000 Mean : 0.000000
## 3rd Qu.: 0.35072 3rd Qu.: 0.24095 3rd Qu.: 0.091045
## Max. : 7.51959 Max. : 3.51735 Max. : 31.612198
## V28 Amount Class
## Min. :-15.43008 Min. : 0.00 Min. :0.000000
## 1st Qu.: -0.05296 1st Qu.: 5.60 1st Qu.:0.000000
## Median : 0.01124 Median : 22.00 Median :0.000000
## Mean : 0.00000 Mean : 88.35 Mean :0.001728
## 3rd Qu.: 0.07828 3rd Qu.: 77.17 3rd Qu.:0.000000
## Max. : 33.84781 Max. :25691.16 Max. :1.000000
Konversi variabel Class ke factor
df$Class <- as.factor(df$Class)
Splitting data and training and testing
sample <- sample.split(df$Class, SplitRatio = 0.7)
train <- subset(df, sample == T)
test <- subset(df, sample == F)
Model logistic
model <- glm(Class ~ . , family = binomial(link='logit'),data = train)
Prediksi model
predicted.values <- predict(model,newdata=test,type='response')
Cek Akurasi model
results <- ifelse(predicted.values > 0.5,1,0)
# artinya jika hasil prediksi > 0.5 adalah 1(fraud), 0(normal)
c <- table(test$Class,results)#membuat table matrik untuk akurasi
c
## results
## 0 1
## 0 85285 10
## 1 48 100
Hasil akurasi
library(caret)#regressing and accuracy
## Warning: package 'caret' was built under R version 3.5.3
## Loading required package: lattice
## Loading required package: ggplot2
## Warning: package 'ggplot2' was built under R version 3.5.3
confusionMatrix(c)
## Confusion Matrix and Statistics
##
## results
## 0 1
## 0 85285 10
## 1 48 100
##
## Accuracy : 0.9993
## 95% CI : (0.9991, 0.9995)
## No Information Rate : 0.9987
## P-Value [Acc > NIR] : 3.690e-08
##
## Kappa : 0.7749
##
## Mcnemar's Test P-Value : 1.184e-06
##
## Sensitivity : 0.9994
## Specificity : 0.9091
## Pos Pred Value : 0.9999
## Neg Pred Value : 0.6757
## Prevalence : 0.9987
## Detection Rate : 0.9982
## Detection Prevalence : 0.9983
## Balanced Accuracy : 0.9543
##
## 'Positive' Class : 0
##
Print hasil akurasi model
paste("Accuracy this model:0.9992")
## [1] "Accuracy this model:0.9992"
Visualisasi
p <- ggplot(df,aes(x = df$Class, fill="red")) +
geom_bar(position = "dodge", alpha = 0.5, col ="black") +
scale_x_discrete( name = "Is it Fraud?") +
scale_y_continuous() +
ggtitle("Fraud Case Classes, 1=Fraud,0=Normal") +
theme(plot.title = element_text(hjust = 0.5))
p
