Credit Card Fraud Detection
Tural Naghi
2023-04-26
Credit Card Fraud Detection
First, you’ll need to download the dataset from Kaggle and import it into R. Go to the Kaggle website and navigate to the “Credit Card Fraud Detection” dataset, download and unzip the csv file. https://www.kaggle.com/datasets/mlg-ulb/creditcardfraud?resource=download
Data Import and Exploration
Now let’s import this dataset:
credit <- read.csv("D:\\Tural Nagi\\R\\DataSets\\creditcard.csv", header = TRUE, stringsAsFactors = FALSE)This will load the dataset into a data frame called “credit”. The “header = TRUE” argument indicates that the first row of the CSV file contains column names, and the “stringsAsFactors = FALSE” argument prevents R from converting string variables into factor variables.
Now, let’s explore the dataset using some basic analytics techniques:
# Summary statistics
summary(credit)## 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# Correlation matrix
cor(credit)## Time V1 V2 V3 V4
## Time 1.000000000 1.173963e-01 -1.059333e-02 -4.196182e-01 -1.052602e-01
## V1 0.117396306 1.000000e+00 -6.965284e-17 -5.689257e-16 -2.602863e-16
## V2 -0.010593327 -6.965284e-17 1.000000e+00 5.207402e-17 -1.613213e-16
## V3 -0.419618172 -5.689257e-16 5.207402e-17 1.000000e+00 -2.229734e-16
## V4 -0.105260205 -2.602863e-16 -1.613213e-16 -2.229734e-16 1.000000e+00
## V5 0.173072123 3.146931e-16 1.119124e-16 -6.014871e-16 -1.841492e-15
## V6 -0.063016470 1.492868e-16 3.870545e-16 1.427210e-15 -4.247485e-16
## V7 0.084714375 7.841775e-17 -1.307637e-16 2.297393e-16 -7.423988e-17
## V8 -0.036949435 -5.446145e-17 -2.461255e-17 -7.356493e-17 6.405396e-16
## V9 -0.008660434 3.813198e-17 -1.123192e-16 1.037123e-16 5.956330e-16
## V10 0.030616629 5.323676e-17 -1.342760e-16 2.047704e-16 -1.044370e-16
## V11 -0.247689437 3.002286e-16 3.438822e-16 1.136658e-16 -2.921831e-16
## V12 0.124348068 1.818542e-16 -3.249832e-16 2.105842e-16 -1.939196e-16
## V13 -0.065902024 -4.924522e-17 -3.781395e-17 -3.519517e-17 1.732999e-17
## V14 -0.098756819 3.872776e-16 -3.806711e-16 6.698461e-16 -9.668574e-17
## V15 -0.183453273 -9.135222e-17 6.457046e-17 -6.312482e-17 1.844076e-16
## V16 0.011902868 3.349857e-16 4.068406e-17 5.714038e-16 -4.182935e-17
## V17 -0.073297213 -2.373744e-17 -6.403585e-16 9.221563e-17 -3.727928e-16
## V18 0.090438133 1.468961e-16 2.334236e-16 3.128313e-16 -1.514837e-17
## V19 0.028975303 1.649928e-16 1.202548e-17 3.456581e-16 -2.884334e-16
## V20 -0.050866018 1.432581e-16 8.194049e-17 7.004887e-17 -1.837991e-16
## V21 0.044735726 -9.271675e-17 8.039593e-17 -1.592155e-16 -5.925622e-17
## V22 0.144059055 9.611336e-17 1.701033e-16 -2.257641e-16 2.371879e-16
## V23 0.051142365 1.757891e-16 1.346719e-16 -7.683090e-17 2.000434e-16
## V24 -0.016181868 -5.157132e-17 -1.071030e-16 2.526865e-17 1.606241e-16
## V25 -0.233082791 -2.390623e-16 1.157084e-16 1.145955e-16 6.473123e-16
## V26 -0.041407101 -1.264191e-16 2.620792e-16 -2.164134e-16 -4.040848e-16
## V27 -0.005134591 9.657711e-17 -5.267197e-16 5.247791e-16 -1.059009e-16
## V28 -0.009412688 3.679910e-16 -3.781747e-16 7.328569e-16 -3.463299e-18
## Amount -0.010596373 -2.277087e-01 -5.314089e-01 -2.108805e-01 9.873167e-02
## Class -0.012322571 -1.013473e-01 9.128865e-02 -1.929608e-01 1.334475e-01
## V5 V6 V7 V8 V9
## Time 1.730721e-01 -6.301647e-02 8.471437e-02 -3.694943e-02 -8.660434e-03
## V1 3.146931e-16 1.492868e-16 7.841775e-17 -5.446145e-17 3.813198e-17
## V2 1.119124e-16 3.870545e-16 -1.307637e-16 -2.461255e-17 -1.123192e-16
## V3 -6.014871e-16 1.427210e-15 2.297393e-16 -7.356493e-17 1.037123e-16
## V4 -1.841492e-15 -4.247485e-16 -7.423988e-17 6.405396e-16 5.956330e-16
## V5 1.000000e+00 6.266854e-16 -2.011798e-17 5.160094e-16 4.855550e-16
## V6 6.266854e-16 1.000000e+00 -4.980567e-17 -3.464946e-16 -9.720859e-17
## V7 -2.011798e-17 -4.980567e-17 1.000000e+00 -1.220995e-17 7.581563e-18
## V8 5.160094e-16 -3.464946e-16 -1.220995e-17 1.000000e+00 4.234618e-16
## V9 4.855550e-16 -9.720859e-17 7.581563e-18 4.234618e-16 1.000000e+00
## V10 1.097669e-16 1.367542e-16 3.058215e-16 -1.375841e-18 -2.699398e-16
## V11 7.268487e-16 8.797284e-16 -3.654117e-16 1.371280e-16 3.139639e-16
## V12 3.915888e-16 2.777281e-16 6.628940e-16 3.125071e-17 -1.250532e-15
## V13 -2.925906e-16 -1.586079e-16 -6.222310e-17 -2.956807e-16 9.315873e-16
## V14 2.428524e-16 3.377903e-16 3.323236e-17 -2.671262e-16 9.308315e-16
## V15 1.151762e-16 -1.122062e-16 -3.686690e-17 1.063869e-16 -8.886415e-16
## V16 6.014895e-16 -1.039022e-16 4.924499e-16 1.624649e-16 -4.609106e-16
## V17 4.239453e-16 1.246951e-16 5.445838e-16 -3.623854e-16 7.046948e-16
## V18 4.134664e-16 5.574127e-17 2.001104e-16 -3.325984e-16 1.454444e-16
## V19 -1.192412e-16 8.169762e-17 -7.326312e-17 -3.349560e-16 1.175618e-16
## V20 -1.930386e-16 1.161439e-16 2.160135e-16 1.261930e-16 -3.553584e-16
## V21 -7.207268e-17 -8.657658e-17 9.842322e-18 2.685700e-17 2.371978e-16
## V22 2.278784e-17 -1.153466e-16 -6.600083e-16 2.519185e-17 -1.715969e-16
## V23 1.102508e-16 3.484483e-17 -2.641793e-16 1.918773e-16 -8.975970e-17
## V24 -9.709665e-16 -1.073779e-15 -7.012260e-18 -2.115920e-16 -2.817883e-16
## V25 -1.058767e-16 5.546756e-16 1.861577e-17 -1.568089e-16 2.428573e-16
## V26 3.387285e-16 -2.540491e-16 -7.833061e-16 2.096443e-18 -9.565914e-17
## V27 4.526686e-16 -1.387765e-16 -1.856769e-16 3.203139e-16 -1.730431e-16
## V28 -1.776307e-16 4.321112e-16 8.208381e-17 -5.808313e-16 7.961430e-16
## Amount -3.863563e-01 2.159812e-01 3.973113e-01 -1.030791e-01 -4.424560e-02
## Class -9.497430e-02 -4.364316e-02 -1.872566e-01 1.987512e-02 -9.773269e-02
## V10 V11 V12 V13 V14
## Time 3.061663e-02 -2.476894e-01 1.243481e-01 -6.590202e-02 -9.875682e-02
## V1 5.323676e-17 3.002286e-16 1.818542e-16 -4.924522e-17 3.872776e-16
## V2 -1.342760e-16 3.438822e-16 -3.249832e-16 -3.781395e-17 -3.806711e-16
## V3 2.047704e-16 1.136658e-16 2.105842e-16 -3.519517e-17 6.698461e-16
## V4 -1.044370e-16 -2.921831e-16 -1.939196e-16 1.732999e-17 -9.668574e-17
## V5 1.097669e-16 7.268487e-16 3.915888e-16 -2.925906e-16 2.428524e-16
## V6 1.367542e-16 8.797284e-16 2.777281e-16 -1.586079e-16 3.377903e-16
## V7 3.058215e-16 -3.654117e-16 6.628940e-16 -6.222310e-17 3.323236e-17
## V8 -1.375841e-18 1.371280e-16 3.125071e-17 -2.956807e-16 -2.671262e-16
## V9 -2.699398e-16 3.139639e-16 -1.250532e-15 9.315873e-16 9.308315e-16
## V10 1.000000e+00 -3.362664e-16 8.314966e-16 -4.311178e-16 6.226081e-16
## V11 -3.362664e-16 1.000000e+00 -6.271674e-16 4.003475e-16 -7.695011e-17
## V12 8.314966e-16 -6.271674e-16 1.000000e+00 -2.294537e-14 4.339276e-16
## V13 -4.311178e-16 4.003475e-16 -2.294537e-14 1.000000e+00 1.432712e-15
## V14 6.226081e-16 -7.695011e-17 4.339276e-16 1.432712e-15 1.000000e+00
## V15 4.221862e-16 2.088049e-16 -2.845353e-16 -1.094370e-16 -2.954878e-16
## V16 1.765313e-16 1.680389e-16 4.961492e-16 4.758591e-16 -8.132066e-16
## V17 6.929137e-16 6.731052e-16 -3.581485e-16 7.757847e-17 1.149633e-15
## V18 4.809759e-16 9.846238e-17 -6.057951e-16 2.424779e-16 -2.203375e-16
## V19 2.297130e-17 -1.095230e-15 1.822685e-16 -1.202426e-16 2.346856e-16
## V20 -1.270898e-15 -2.069629e-16 2.525286e-16 3.699481e-17 -2.180906e-17
## V21 1.055033e-15 9.506204e-18 5.754933e-16 1.423086e-16 -2.100761e-16
## V22 -2.589804e-16 1.040834e-17 -6.489571e-17 -4.945052e-17 6.148449e-16
## V23 2.352341e-16 1.282108e-16 2.837502e-16 -6.830450e-16 2.297548e-16
## V24 -8.473482e-17 1.649224e-15 4.385884e-16 -6.517493e-16 3.197605e-17
## V25 -3.467882e-16 -6.049823e-16 -1.158139e-17 -9.572245e-17 -3.550131e-17
## V26 -3.766310e-16 -1.124197e-16 1.755297e-16 -1.371594e-16 -2.415534e-17
## V27 -3.667056e-16 -1.687641e-16 -3.083437e-16 -4.856405e-16 4.264244e-18
## V28 2.289423e-16 -3.465876e-16 7.010326e-16 1.084762e-15 2.404893e-15
## Amount -1.015021e-01 1.039770e-04 -9.541802e-03 5.293409e-03 3.375117e-02
## Class -2.168829e-01 1.548756e-01 -2.605929e-01 -4.569779e-03 -3.025437e-01
## V15 V16 V17 V18 V19
## Time -1.834533e-01 1.190287e-02 -7.329721e-02 9.043813e-02 2.897530e-02
## V1 -9.135222e-17 3.349857e-16 -2.373744e-17 1.468961e-16 1.649928e-16
## V2 6.457046e-17 4.068406e-17 -6.403585e-16 2.334236e-16 1.202548e-17
## V3 -6.312482e-17 5.714038e-16 9.221563e-17 3.128313e-16 3.456581e-16
## V4 1.844076e-16 -4.182935e-17 -3.727928e-16 -1.514837e-17 -2.884334e-16
## V5 1.151762e-16 6.014895e-16 4.239453e-16 4.134664e-16 -1.192412e-16
## V6 -1.122062e-16 -1.039022e-16 1.246951e-16 5.574127e-17 8.169762e-17
## V7 -3.686690e-17 4.924499e-16 5.445838e-16 2.001104e-16 -7.326312e-17
## V8 1.063869e-16 1.624649e-16 -3.623854e-16 -3.325984e-16 -3.349560e-16
## V9 -8.886415e-16 -4.609106e-16 7.046948e-16 1.454444e-16 1.175618e-16
## V10 4.221862e-16 1.765313e-16 6.929137e-16 4.809759e-16 2.297130e-17
## V11 2.088049e-16 1.680389e-16 6.731052e-16 9.846238e-17 -1.095230e-15
## V12 -2.845353e-16 4.961492e-16 -3.581485e-16 -6.057951e-16 1.822685e-16
## V13 -1.094370e-16 4.758591e-16 7.757847e-17 2.424779e-16 -1.202426e-16
## V14 -2.954878e-16 -8.132066e-16 1.149633e-15 -2.203375e-16 2.346856e-16
## V15 1.000000e+00 9.896690e-16 -5.770624e-16 6.815925e-16 -1.439421e-15
## V16 9.896690e-16 1.000000e+00 1.676170e-15 -2.711204e-15 1.119911e-15
## V17 -5.770624e-16 1.676170e-15 1.000000e+00 -5.244170e-15 3.767476e-16
## V18 6.815925e-16 -2.711204e-15 -5.244170e-15 1.000000e+00 -2.674692e-15
## V19 -1.439421e-15 1.119911e-15 3.767476e-16 -2.674692e-15 1.000000e+00
## V20 1.754788e-16 3.468227e-16 -8.851568e-16 -3.714489e-16 2.875816e-16
## V21 5.272469e-17 -4.003975e-16 -9.524938e-16 -1.207426e-15 5.810910e-16
## V22 -3.438947e-16 2.544008e-16 -3.249489e-16 -5.371814e-16 -1.007031e-15
## V23 9.564145e-17 7.052180e-16 4.373451e-16 -2.962968e-16 6.691001e-16
## V24 -4.483148e-16 -3.522772e-16 -1.631683e-16 -1.808092e-16 -8.718833e-17
## V25 2.180887e-16 -3.331055e-16 7.892950e-17 -2.498278e-16 8.223861e-16
## V26 1.018833e-16 -4.660470e-16 2.542018e-16 2.920778e-16 5.501523e-16
## V27 -1.248456e-15 8.110078e-16 6.945843e-16 2.268477e-16 -1.545547e-16
## V28 -1.121411e-15 7.028481e-16 -8.344534e-17 8.010596e-16 -1.361453e-15
## Amount -2.985848e-03 -3.909527e-03 7.309042e-03 3.565034e-02 -5.615079e-02
## Class -4.223402e-03 -1.965389e-01 -3.264811e-01 -1.114853e-01 3.478301e-02
## V20 V21 V22 V23 V24
## Time -5.086602e-02 4.473573e-02 1.440591e-01 5.114236e-02 -1.618187e-02
## V1 1.432581e-16 -9.271675e-17 9.611336e-17 1.757891e-16 -5.157132e-17
## V2 8.194049e-17 8.039593e-17 1.701033e-16 1.346719e-16 -1.071030e-16
## V3 7.004887e-17 -1.592155e-16 -2.257641e-16 -7.683090e-17 2.526865e-17
## V4 -1.837991e-16 -5.925622e-17 2.371879e-16 2.000434e-16 1.606241e-16
## V5 -1.930386e-16 -7.207268e-17 2.278784e-17 1.102508e-16 -9.709665e-16
## V6 1.161439e-16 -8.657658e-17 -1.153466e-16 3.484483e-17 -1.073779e-15
## V7 2.160135e-16 9.842322e-18 -6.600083e-16 -2.641793e-16 -7.012260e-18
## V8 1.261930e-16 2.685700e-17 2.519185e-17 1.918773e-16 -2.115920e-16
## V9 -3.553584e-16 2.371978e-16 -1.715969e-16 -8.975970e-17 -2.817883e-16
## V10 -1.270898e-15 1.055033e-15 -2.589804e-16 2.352341e-16 -8.473482e-17
## V11 -2.069629e-16 9.506204e-18 1.040834e-17 1.282108e-16 1.649224e-15
## V12 2.525286e-16 5.754933e-16 -6.489571e-17 2.837502e-16 4.385884e-16
## V13 3.699481e-17 1.423086e-16 -4.945052e-17 -6.830450e-16 -6.517493e-16
## V14 -2.180906e-17 -2.100761e-16 6.148449e-16 2.297548e-16 3.197605e-17
## V15 1.754788e-16 5.272469e-17 -3.438947e-16 9.564145e-17 -4.483148e-16
## V16 3.468227e-16 -4.003975e-16 2.544008e-16 7.052180e-16 -3.522772e-16
## V17 -8.851568e-16 -9.524938e-16 -3.249489e-16 4.373451e-16 -1.631683e-16
## V18 -3.714489e-16 -1.207426e-15 -5.371814e-16 -2.962968e-16 -1.808092e-16
## V19 2.875816e-16 5.810910e-16 -1.007031e-15 6.691001e-16 -8.718833e-17
## V20 1.000000e+00 -1.172015e-15 9.587679e-16 1.100574e-16 1.617068e-16
## V21 -1.172015e-15 1.000000e+00 3.489827e-15 6.459116e-16 1.391805e-16
## V22 9.587679e-16 3.489827e-15 1.000000e+00 2.998995e-16 3.180808e-17
## V23 1.100574e-16 6.459116e-16 2.998995e-16 1.000000e+00 6.662704e-17
## V24 1.617068e-16 1.391805e-16 3.180808e-17 6.662704e-17 1.000000e+00
## V25 -4.976490e-18 -1.058544e-16 -9.676148e-16 -7.284999e-16 1.240324e-15
## V26 -3.499391e-16 -4.803701e-16 -3.920807e-17 1.279253e-15 1.863838e-16
## V27 -9.887404e-16 -1.398538e-15 1.635775e-16 4.325298e-16 -3.050278e-16
## V28 -2.264586e-16 2.025134e-16 -5.377144e-16 1.367329e-15 -2.770212e-16
## Amount 3.394034e-01 1.059989e-01 -6.480065e-02 -1.126326e-01 5.146217e-03
## Class 2.009032e-02 4.041338e-02 8.053175e-04 -2.685156e-03 -7.220907e-03
## V25 V26 V27 V28 Amount
## Time -2.330828e-01 -4.140710e-02 -5.134591e-03 -9.412688e-03 -0.010596373
## V1 -2.390623e-16 -1.264191e-16 9.657711e-17 3.679910e-16 -0.227708653
## V2 1.157084e-16 2.620792e-16 -5.267197e-16 -3.781747e-16 -0.531408939
## V3 1.145955e-16 -2.164134e-16 5.247791e-16 7.328569e-16 -0.210880475
## V4 6.473123e-16 -4.040848e-16 -1.059009e-16 -3.463299e-18 0.098731666
## V5 -1.058767e-16 3.387285e-16 4.526686e-16 -1.776307e-16 -0.386356256
## V6 5.546756e-16 -2.540491e-16 -1.387765e-16 4.321112e-16 0.215981180
## V7 1.861577e-17 -7.833061e-16 -1.856769e-16 8.208381e-17 0.397311278
## V8 -1.568089e-16 2.096443e-18 3.203139e-16 -5.808313e-16 -0.103079096
## V9 2.428573e-16 -9.565914e-17 -1.730431e-16 7.961430e-16 -0.044245602
## V10 -3.467882e-16 -3.766310e-16 -3.667056e-16 2.289423e-16 -0.101502141
## V11 -6.049823e-16 -1.124197e-16 -1.687641e-16 -3.465876e-16 0.000103977
## V12 -1.158139e-17 1.755297e-16 -3.083437e-16 7.010326e-16 -0.009541802
## V13 -9.572245e-17 -1.371594e-16 -4.856405e-16 1.084762e-15 0.005293409
## V14 -3.550131e-17 -2.415534e-17 4.264244e-18 2.404893e-15 0.033751172
## V15 2.180887e-16 1.018833e-16 -1.248456e-15 -1.121411e-15 -0.002985848
## V16 -3.331055e-16 -4.660470e-16 8.110078e-16 7.028481e-16 -0.003909527
## V17 7.892950e-17 2.542018e-16 6.945843e-16 -8.344534e-17 0.007309042
## V18 -2.498278e-16 2.920778e-16 2.268477e-16 8.010596e-16 0.035650341
## V19 8.223861e-16 5.501523e-16 -1.545547e-16 -1.361453e-15 -0.056150787
## V20 -4.976490e-18 -3.499391e-16 -9.887404e-16 -2.264586e-16 0.339403405
## V21 -1.058544e-16 -4.803701e-16 -1.398538e-15 2.025134e-16 0.105998928
## V22 -9.676148e-16 -3.920807e-17 1.635775e-16 -5.377144e-16 -0.064800646
## V23 -7.284999e-16 1.279253e-15 4.325298e-16 1.367329e-15 -0.112632554
## V24 1.240324e-15 1.863838e-16 -3.050278e-16 -2.770212e-16 0.005146217
## V25 1.000000e+00 2.435465e-15 -5.961657e-16 3.734279e-16 -0.047836863
## V26 2.435465e-15 1.000000e+00 -2.851245e-16 -2.952380e-16 -0.003208037
## V27 -5.961657e-16 -2.851245e-16 1.000000e+00 3.001876e-17 0.028825463
## V28 3.734279e-16 -2.952380e-16 3.001876e-17 1.000000e+00 0.010258216
## Amount -4.783686e-02 -3.208037e-03 2.882546e-02 1.025822e-02 1.000000000
## Class 3.307706e-03 4.455398e-03 1.757973e-02 9.536041e-03 0.005631753
## Class
## Time -0.0123225709
## V1 -0.1013472986
## V2 0.0912886503
## V3 -0.1929608271
## V4 0.1334474862
## V5 -0.0949742990
## V6 -0.0436431607
## V7 -0.1872565915
## V8 0.0198751239
## V9 -0.0977326861
## V10 -0.2168829436
## V11 0.1548756447
## V12 -0.2605929249
## V13 -0.0045697788
## V14 -0.3025436958
## V15 -0.0042234023
## V16 -0.1965389403
## V17 -0.3264810672
## V18 -0.1114852539
## V19 0.0347830130
## V20 0.0200903242
## V21 0.0404133806
## V22 0.0008053175
## V23 -0.0026851557
## V24 -0.0072209067
## V25 0.0033077056
## V26 0.0044553975
## V27 0.0175797282
## V28 0.0095360409
## Amount 0.0056317530
## Class 1.0000000000# Convert Class column to factor
credit$Class <- as.factor(credit$Class)
# Scatterplot matrix
library(GGally)## Warning: package 'GGally' was built under R version 4.2.3## Loading required package: ggplot2## Registered S3 method overwritten by 'GGally':
## method from
## +.gg ggplot2ggpairs(credit[, c(1, 5, 6, 9, 10, 17, 30, 31)], aes(color = Class))## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
# Boxplot of transaction amounts by class
library(ggplot2)
ggplot(credit, aes(x = Class, y = Amount)) + geom_boxplot()
These commands will give you a summary of the dataset, a correlation matrix, a scatterplot matrix, and a boxplot of transaction amounts by class. You can use these techniques to gain insights into the dataset and to identify variables that are associated with fraudulent transactions.
Credit Scoring Model
Next, you can use machine learning techniques to build a credit scoring model that can predict the likelihood of fraudulent transactions. Here is an example code for building a logistic regression model:
# Split the dataset into training and testing sets
library(caret)## Warning: package 'caret' was built under R version 4.2.3## Loading required package: latticeset.seed(123)
trainIndex <- createDataPartition(credit$Class, p = 0.8, list = FALSE)
training <- credit[trainIndex, ]
testing <- credit[-trainIndex, ]
# Build a logistic regression model
model <- glm(Class ~ ., data = training, family = binomial)
# Make predictions on the testing set
predictions <- predict(model, newdata = testing, type = "response")
# Evaluate the performance of the model
library(ROCR)## Warning: package 'ROCR' was built under R version 4.2.3prediction_obj <- prediction(predictions, testing$Class)
performance_obj <- performance(prediction_obj, "tpr", "fpr")
auc <- performance(prediction_obj, measure = "auc")@y.values[[1]]
plot(performance_obj)
These commands will split the dataset into training and testing sets, build a logistic regression model using the training set, make predictions on the testing set, and evaluate the performance of the model using the area under the curve (AUC) of the receiver operating characteristic (ROC) curve.