⭐ 1. Introduction & Overview¶
It is important that credit card companies are able to recognize fraudulent credit card transactions so that customers are not charged for items that they did not purchase.
🔹 2. Import Libraries & Set Up¶
In [26]:
# General
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from tqdm import tqdm
# Machine Learning
import xgboost as xg
from sklearn.model_selection import train_test_split, GridSearchCV, KFold, cross_val_score
from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score, mean_absolute_error, mean_squared_error, r2_score, root_mean_squared_error, roc_auc_score
from sklearn.linear_model import LinearRegression, Ridge
from sklearn.ensemble import RandomForestClassifier
from sklearn.preprocessing import StandardScaler, LabelEncoder
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout
from tensorflow.keras.optimizers import Adam
from imblearn.over_sampling import SMOTE
# Feature Importance & Explainability
import shap
# Settings
import warnings
warnings.filterwarnings("ignore")
# Set random seed for reproducibility
SEED = 42
np.random.seed(SEED)
print("Libraries loaded. Ready to go!")
Libraries loaded. Ready to go!
🔹 3. Load & Explore Data¶
In [27]:
data = pd.read_csv('creditcard.csv')
In [28]:
data.head()
Out[28]:
| 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
In [29]:
data.describe()
Out[29]:
| 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
In [30]:
data.info
Out[30]:
<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]>
🔹 4. Data Visualization & EDA¶
In [31]:
float_cols = [col for col in data.columns if data[col].dtype == "float64" or data[col].dtype == "int64"]
cols_per_row = 3
num_plots = len(float_cols)
rows = (num_plots // cols_per_row) + (num_plots % cols_per_row > 0)
fig, axes = plt.subplots(rows, cols_per_row, figsize=(15, 5 * rows))
axes = axes.flatten()
for idx, col in enumerate(float_cols):
sns.histplot(data[col], bins=50, kde=True, ax=axes[idx])
axes[idx].set_title(f"Distribution of {col}")
for i in range(idx + 1, len(axes)):
fig.delaxes(axes[i])
plt.tight_layout()
plt.show()
In [32]:
plt.figure(figsize=(6, 4))
sns.countplot(data=data, x='Class')
plt.title('Fraud vs Non-Fraud Transactions')
plt.xlabel('Class (0: Non-Fraud, 1: Fraud)')
plt.ylabel('Count')
plt.show()
In [33]:
plt.figure(figsize=(8, 6))
sns.boxplot(data=data, x='Class', y='Amount')
plt.title('Transaction Amount, fraud vs non-fraud')
plt.xlabel('Class')
plt.ylabel('Transaction Amount')
plt.show()
🔹 5. Feature Engineering¶
In [34]:
print(data.isnull().sum())
Time 0 V1 0 V2 0 V3 0 V4 0 V5 0 V6 0 V7 0 V8 0 V9 0 V10 0 V11 0 V12 0 V13 0 V14 0 V15 0 V16 0 V17 0 V18 0 V19 0 V20 0 V21 0 V22 0 V23 0 V24 0 V25 0 V26 0 V27 0 V28 0 Amount 0 Class 0 dtype: int64
In [35]:
print(data.Class.value_counts())
Class 0 284315 1 492 Name: count, dtype: int64
In [37]:
X = data.drop(columns=['Class'])
y = data['Class']
X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2, random_state=SEED, stratify=y)
from imblearn.over_sampling import SMOTE
smote = SMOTE(sampling_strategy='auto', random_state=SEED)
X_res, y_res = smote.fit_resample(X_train, y_train)
k = 5 # Common choice is 5 or 10 folds
kf = KFold(n_splits=k, shuffle=True, random_state=42)
model = xg.XGBClassifier()
# Perform cross-validation
cv_scores = cross_val_score(model, X_res, y_res, cv=kf, scoring='accuracy')
# Print results
print(f"Cross-validation scores: {cv_scores}")
print(f"Mean CV score: {np.mean(cv_scores):.4f}")
print(f"Standard deviation: {np.std(cv_scores):.4f}")
model.fit(X_res, y_res)
predictions = model.predict(X_val)
val_accuracy = accuracy_score(y_val, predictions)
val_precision = precision_score(y_val, predictions)
val_recall = recall_score(y_val, predictions)
val_f1 = f1_score(y_val, predictions)
val_probs = model.predict_proba(X_val)[:, 1]
val_auc = roc_auc_score(y_val, val_probs)
print("accuracy: ", val_accuracy)
print("precision: ", val_precision)
print("recall: ", val_recall)
print("f1: ", val_f1)
print("auc: ", val_auc)
Cross-validation scores: [0.99989009 0.99982414 0.99989009 0.99982414 0.99990108] Mean CV score: 0.9999 Standard deviation: 0.0000 accuracy: 0.9993504441557529 precision: 0.7904761904761904 recall: 0.8469387755102041 f1: 0.8177339901477833 auc: 0.9831145633548861