Credit card fraud
Machine Learning • June 8, 2025
sklearn,
python,
pandas
Typically, credit card fraud detection relies on machine learning models trained on historical transaction data. These models examine various transaction features—such as amount, frequency, location, and merchant details—to detect patterns associated with fraudulent behavior. Common techniques used in fraud detection include anomaly detection, supervised learning with labeled data, and clustering methods.
Python Notebook Preview
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set()
%matplotlib inline
df = pd.read_csv(r'C:\Users\User\OneDrive\Documents\Data Analysis\data\creditcard.csv')
print(df.shape)
df.head()
(284807, 31)
| Time | V1 | V2 | V3 | V4 | V5 | V6 | V7 | V8 | V9 | ... | V21 | V22 | V23 | V24 | V25 | V26 | V27 | V28 | Amount | Class | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0.0 | -1.359807 | -0.072781 | 2.536347 | 1.378155 | -0.338321 | 0.462388 | 0.239599 | 0.098698 | 0.363787 | ... | -0.018307 | 0.277838 | -0.110474 | 0.066928 | 0.128539 | -0.189115 | 0.133558 | -0.021053 | 149.62 | 0 |
| 1 | 0.0 | 1.191857 | 0.266151 | 0.166480 | 0.448154 | 0.060018 | -0.082361 | -0.078803 | 0.085102 | -0.255425 | ... | -0.225775 | -0.638672 | 0.101288 | -0.339846 | 0.167170 | 0.125895 | -0.008983 | 0.014724 | 2.69 | 0 |
| 2 | 1.0 | -1.358354 | -1.340163 | 1.773209 | 0.379780 | -0.503198 | 1.800499 | 0.791461 | 0.247676 | -1.514654 | ... | 0.247998 | 0.771679 | 0.909412 | -0.689281 | -0.327642 | -0.139097 | -0.055353 | -0.059752 | 378.66 | 0 |
| 3 | 1.0 | -0.966272 | -0.185226 | 1.792993 | -0.863291 | -0.010309 | 1.247203 | 0.237609 | 0.377436 | -1.387024 | ... | -0.108300 | 0.005274 | -0.190321 | -1.175575 | 0.647376 | -0.221929 | 0.062723 | 0.061458 | 123.50 | 0 |
| 4 | 2.0 | -1.158233 | 0.877737 | 1.548718 | 0.403034 | -0.407193 | 0.095921 | 0.592941 | -0.270533 | 0.817739 | ... | -0.009431 | 0.798278 | -0.137458 | 0.141267 | -0.206010 | 0.502292 | 0.219422 | 0.215153 | 69.99 | 0 |
5 rows × 31 columns
df.info
<bound method DataFrame.info of Time V1 V2 V3 V4 V5 \
0 0.0 -1.359807 -0.072781 2.536347 1.378155 -0.338321
1 0.0 1.191857 0.266151 0.166480 0.448154 0.060018
2 1.0 -1.358354 -1.340163 1.773209 0.379780 -0.503198
3 1.0 -0.966272 -0.185226 1.792993 -0.863291 -0.010309
4 2.0 -1.158233 0.877737 1.548718 0.403034 -0.407193
... ... ... ... ... ... ...
284802 172786.0 -11.881118 10.071785 -9.834783 -2.066656 -5.364473
284803 172787.0 -0.732789 -0.055080 2.035030 -0.738589 0.868229
284804 172788.0 1.919565 -0.301254 -3.249640 -0.557828 2.630515
284805 172788.0 -0.240440 0.530483 0.702510 0.689799 -0.377961
284806 172792.0 -0.533413 -0.189733 0.703337 -0.506271 -0.012546
V6 V7 V8 V9 ... V21 V22 \
0 0.462388 0.239599 0.098698 0.363787 ... -0.018307 0.277838
1 -0.082361 -0.078803 0.085102 -0.255425 ... -0.225775 -0.638672
2 1.800499 0.791461 0.247676 -1.514654 ... 0.247998 0.771679
3 1.247203 0.237609 0.377436 -1.387024 ... -0.108300 0.005274
4 0.095921 0.592941 -0.270533 0.817739 ... -0.009431 0.798278
... ... ... ... ... ... ... ...
284802 -2.606837 -4.918215 7.305334 1.914428 ... 0.213454 0.111864
284803 1.058415 0.024330 0.294869 0.584800 ... 0.214205 0.924384
284804 3.031260 -0.296827 0.708417 0.432454 ... 0.232045 0.578229
284805 0.623708 -0.686180 0.679145 0.392087 ... 0.265245 0.800049
284806 -0.649617 1.577006 -0.414650 0.486180 ... 0.261057 0.643078
V23 V24 V25 V26 V27 V28 Amount \
0 -0.110474 0.066928 0.128539 -0.189115 0.133558 -0.021053 149.62
1 0.101288 -0.339846 0.167170 0.125895 -0.008983 0.014724 2.69
2 0.909412 -0.689281 -0.327642 -0.139097 -0.055353 -0.059752 378.66
3 -0.190321 -1.175575 0.647376 -0.221929 0.062723 0.061458 123.50
4 -0.137458 0.141267 -0.206010 0.502292 0.219422 0.215153 69.99
... ... ... ... ... ... ... ...
284802 1.014480 -0.509348 1.436807 0.250034 0.943651 0.823731 0.77
284803 0.012463 -1.016226 -0.606624 -0.395255 0.068472 -0.053527 24.79
284804 -0.037501 0.640134 0.265745 -0.087371 0.004455 -0.026561 67.88
284805 -0.163298 0.123205 -0.569159 0.546668 0.108821 0.104533 10.00
284806 0.376777 0.008797 -0.473649 -0.818267 -0.002415 0.013649 217.00
Class
0 0
1 0
2 0
3 0
4 0
... ...
284802 0
284803 0
284804 0
284805 0
284806 0
[284807 rows x 31 columns]>
df.describe()
| Time | V1 | V2 | V3 | V4 | V5 | V6 | V7 | V8 | V9 | ... | V21 | V22 | V23 | V24 | V25 | V26 | V27 | V28 | Amount | Class | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 284807.000000 | 2.848070e+05 | 2.848070e+05 | 2.848070e+05 | 2.848070e+05 | 2.848070e+05 | 2.848070e+05 | 2.848070e+05 | 2.848070e+05 | 2.848070e+05 | ... | 2.848070e+05 | 2.848070e+05 | 2.848070e+05 | 2.848070e+05 | 2.848070e+05 | 2.848070e+05 | 2.848070e+05 | 2.848070e+05 | 284807.000000 | 284807.000000 |
| mean | 94813.859575 | 1.168375e-15 | 3.416908e-16 | -1.379537e-15 | 2.074095e-15 | 9.604066e-16 | 1.487313e-15 | -5.556467e-16 | 1.213481e-16 | -2.406331e-15 | ... | 1.654067e-16 | -3.568593e-16 | 2.578648e-16 | 4.473266e-15 | 5.340915e-16 | 1.683437e-15 | -3.660091e-16 | -1.227390e-16 | 88.349619 | 0.001727 |
| std | 47488.145955 | 1.958696e+00 | 1.651309e+00 | 1.516255e+00 | 1.415869e+00 | 1.380247e+00 | 1.332271e+00 | 1.237094e+00 | 1.194353e+00 | 1.098632e+00 | ... | 7.345240e-01 | 7.257016e-01 | 6.244603e-01 | 6.056471e-01 | 5.212781e-01 | 4.822270e-01 | 4.036325e-01 | 3.300833e-01 | 250.120109 | 0.041527 |
| min | 0.000000 | -5.640751e+01 | -7.271573e+01 | -4.832559e+01 | -5.683171e+00 | -1.137433e+02 | -2.616051e+01 | -4.355724e+01 | -7.321672e+01 | -1.343407e+01 | ... | -3.483038e+01 | -1.093314e+01 | -4.480774e+01 | -2.836627e+00 | -1.029540e+01 | -2.604551e+00 | -2.256568e+01 | -1.543008e+01 | 0.000000 | 0.000000 |
| 25% | 54201.500000 | -9.203734e-01 | -5.985499e-01 | -8.903648e-01 | -8.486401e-01 | -6.915971e-01 | -7.682956e-01 | -5.540759e-01 | -2.086297e-01 | -6.430976e-01 | ... | -2.283949e-01 | -5.423504e-01 | -1.618463e-01 | -3.545861e-01 | -3.171451e-01 | -3.269839e-01 | -7.083953e-02 | -5.295979e-02 | 5.600000 | 0.000000 |
| 50% | 84692.000000 | 1.810880e-02 | 6.548556e-02 | 1.798463e-01 | -1.984653e-02 | -5.433583e-02 | -2.741871e-01 | 4.010308e-02 | 2.235804e-02 | -5.142873e-02 | ... | -2.945017e-02 | 6.781943e-03 | -1.119293e-02 | 4.097606e-02 | 1.659350e-02 | -5.213911e-02 | 1.342146e-03 | 1.124383e-02 | 22.000000 | 0.000000 |
| 75% | 139320.500000 | 1.315642e+00 | 8.037239e-01 | 1.027196e+00 | 7.433413e-01 | 6.119264e-01 | 3.985649e-01 | 5.704361e-01 | 3.273459e-01 | 5.971390e-01 | ... | 1.863772e-01 | 5.285536e-01 | 1.476421e-01 | 4.395266e-01 | 3.507156e-01 | 2.409522e-01 | 9.104512e-02 | 7.827995e-02 | 77.165000 | 0.000000 |
| max | 172792.000000 | 2.454930e+00 | 2.205773e+01 | 9.382558e+00 | 1.687534e+01 | 3.480167e+01 | 7.330163e+01 | 1.205895e+02 | 2.000721e+01 | 1.559499e+01 | ... | 2.720284e+01 | 1.050309e+01 | 2.252841e+01 | 4.584549e+00 | 7.519589e+00 | 3.517346e+00 | 3.161220e+01 | 3.384781e+01 | 25691.160000 | 1.000000 |
8 rows × 31 columns
class_names = {0:'Not Fraud', 1:'Fraud'}
print(df.Class.value_counts().rename(index = class_names))
Class Not Fraud 284315 Fraud 492 Name: count, dtype: int64
fig = plt.figure(figsize = (15, 12))
plt.subplot(5, 6, 1) ; plt.plot(df.V1) ; plt.subplot(5, 6, 15) ; plt.plot(df.V15)
plt.subplot(5, 6, 2) ; plt.plot(df.V2) ; plt.subplot(5, 6, 16) ; plt.plot(df.V16)
plt.subplot(5, 6, 3) ; plt.plot(df.V3) ; plt.subplot(5, 6, 17) ; plt.plot(df.V17)
plt.subplot(5, 6, 4) ; plt.plot(df.V4) ; plt.subplot(5, 6, 18) ; plt.plot(df.V18)
plt.subplot(5, 6, 5) ; plt.plot(df.V5) ; plt.subplot(5, 6, 19) ; plt.plot(df.V19)
plt.subplot(5, 6, 6) ; plt.plot(df.V6) ; plt.subplot(5, 6, 20) ; plt.plot(df.V20)
plt.subplot(5, 6, 7) ; plt.plot(df.V7) ; plt.subplot(5, 6, 21) ; plt.plot(df.V21)
plt.subplot(5, 6, 8) ; plt.plot(df.V8) ; plt.subplot(5, 6, 22) ; plt.plot(df.V22)
plt.subplot(5, 6, 9) ; plt.plot(df.V9) ; plt.subplot(5, 6, 23) ; plt.plot(df.V23)
plt.subplot(5, 6, 10) ; plt.plot(df.V10) ; plt.subplot(5, 6, 24) ; plt.plot(df.V24)
plt.subplot(5, 6, 11) ; plt.plot(df.V11) ; plt.subplot(5, 6, 25) ; plt.plot(df.V25)
plt.subplot(5, 6, 12) ; plt.plot(df.V12) ; plt.subplot(5, 6, 26) ; plt.plot(df.V26)
plt.subplot(5, 6, 13) ; plt.plot(df.V13) ; plt.subplot(5, 6, 27) ; plt.plot(df.V27)
plt.subplot(5, 6, 14) ; plt.plot(df.V14) ; plt.subplot(5, 6, 28) ; plt.plot(df.V28)
plt.subplot(5, 6, 29) ; plt.plot(df.Amount)
plt.show()
from sklearn.model_selection import train_test_split
feature_names = df.iloc[:, 1:30].columns
target = df.iloc[:1, 30: ].columns
print(feature_names)
print(target)
Index(['V1', 'V2', 'V3', 'V4', 'V5', 'V6', 'V7', 'V8', 'V9', 'V10', 'V11',
'V12', 'V13', 'V14', 'V15', 'V16', 'V17', 'V18', 'V19', 'V20', 'V21',
'V22', 'V23', 'V24', 'V25', 'V26', 'V27', 'V28', 'Amount'],
dtype='object')
Index(['Class'], dtype='object')
data_features = df[feature_names]
data_target = df[target]
X_train, X_test, y_train, y_test = train_test_split(data_features, data_target, train_size=0.70, test_size=0.30, random_state=1)
print("Length of X_train is: {X_train}".format(X_train = len(X_train)))
print("Length of X_test is: {X_test}".format(X_test = len(X_test)))
print("Length of y_train is: {y_train}".format(y_train = len(y_train)))
print("Length of y_test is: {y_test}".format(y_test = len(y_test)))
Length of X_train is: 199364 Length of X_test is: 85443 Length of y_train is: 199364 Length of y_test is: 85443
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import confusion_matrix
model = LogisticRegression()
model.fit(X_train, y_train.values.ravel())
c:\ProgramData\anaconda3\Lib\site-packages\sklearn\linear_model\_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.
Increase the number of iterations (max_iter) or scale the data as shown in:
https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
n_iter_i = _check_optimize_result(
LogisticRegression()In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
LogisticRegression()
pred = model.predict(X_test)
class_names = ['not_fraud', 'fraud']
matrix = confusion_matrix(y_test, pred)
# Create pandas dataframe
dataframe = pd.DataFrame(matrix, index=class_names, columns=class_names)
# Create heatmap
sns.heatmap(dataframe, annot=True, cbar=None, cmap="Blues", fmt = 'g')
plt.title("Confusion Matrix"), plt.tight_layout()
plt.ylabel("True Class"), plt.xlabel("Predicted Class")
plt.show()
from sklearn.metrics import f1_score, recall_score
f1_score = round(f1_score(y_test, pred), 2)
recall_score = round(recall_score(y_test, pred), 2)
print("Sensitivity/Recall for Logistic Regression Model 1 : {recall_score}".format(recall_score = recall_score))
print("F1 Score for Logistic Regression Model 1 : {f1_score}".format(f1_score = f1_score))
Sensitivity/Recall for Logistic Regression Model 1 : 0.54 F1 Score for Logistic Regression Model 1 : 0.66
Project Details
- Type: Machine Learning
- Date: June 8, 2025
-
Technologies:
sklearn, python, pandas