1. 数据来源：Kaggle 信用卡欺诈检测数据集https://www.kaggle.com/datasets/mlg-ulb/creditcardfraud?resource=download；
2. 本文采用 XGBoost、随机森林、KNN、逻辑回归、SVM 决策树解决信用卡欺诈检测问题；

# 导入所需的模块 python 环境中 import pandas as pd import numpy as np import matplotlib.pyplot as plt from termcolor import colored as cl import itertools from sklearn.preprocessing import StandardScaler from sklearn.model_selection import train_test_split from sklearn.tree import DecisionTreeClassifier from sklearn.neighbors import KNeighborsClassifier from sklearn.linear_model import LogisticRegression from sklearn.svm import SVC from sklearn.ensemble import RandomForestClassifier from xgboost import XGBClassifier from sklearn.metrics import confusion_matrix, accuracy_score, f1_score 

• 关于数据： 我们将使用的数据是 Kaggle 信用卡欺诈检测数据集。它包含特征 V1 到 V28，是 PCA 获得的主要成分忽略了建模无用的时间特征。
• 其他特征包括总交易金额"金额"特征和包括交易是否为欺诈案件"类别"特征，类别0识别欺诈，类别1表示正常。

df = pd.read_csv(r'../creditcard.csv') print("Data's columns contain:\n", df.columns) print("Data shape:\n", df.shape) df.drop('Time', axis=1, inplace=True) pd.set_option('display.max_columns', df.shape[1])
print(df.head())
''' Data's columns contain: Index(['Time', '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', 'Class'], dtype='object') Data shape: (284807, 31) V1 V2 V3 V4 V5 V6 V7 \ 0 -1.359807 -0.072781 2.536347 1.378155 -0.338321 0.462388 0.239599 1 1.191857 0.266151 0.166480 0.448154 0.060018 -0.082361 -0.078803 2 -1.358354 -1.340163 1.773209 0.379780 -0.503198 1.800499 0.791461 3 -0.966272 -0.185226 1.792993 -0.863291 -0.010309 1.247203 0.237609 4 -1.158233 0.877737 1.548718 0.403034 -0.407193 0.095921 0.592941 V8 V9 V10 V11 V12 V13 V14 \ 0 0.098698 0.363787 0.090794 -0.551600 -0.617801 -0.991390 -0.311169 1 0.085102 -0.255425 -0.166974 1.612727 1.065235 0.489095 -0.143772 2 0.247676 -1.514654 0.207643 0.624501 0.066084 0.717293 -0.165946 3 0.377436 -1.387024 -0.054952 -0.226487 0.178228 0.507757 -0.287924 4 -0.270533 0.817739 0.753074 -0.822843 0.538196 1.345852 -1.119670 V15 V16 V17 V18 V19 V20 V21 \ 0 1.468177 -0.470401 0.207971 0.025791 0.403993 0.251412 -0.018307 1 0.635558 0.463917 -0.114805 -0.183361 -0.145783 -0.069083 -0.225775 2 2.345865 -2.890083 1.109969 -0.121359 -2.261857 0.524980 0.247998 3 -0.631418 -1.059647 -0.684093 1.965775 -1.232622 -0.208038 -0.108300 4 0.175121 -0.451449 -0.237033 -0.038195 0.803487 0.408542 -0.009431 V22 V23 V24 V25 V26 V27 V28 \ 0 0.277838 -0.110474 0.066928 0.128539 -0.189115 0.133558 -0.021053 1 -0.638672 0.101288 -0.339846 0.167170 0.125895 -0.008983 0.014724 2 0.771679 0.909412 -0.689281 -0.327642 -0.139097 -0.055353 -0.059752 3 0.005274 -0.190321 -1.175575 0.647376 -0.221929 0.062723 0.061458 4 0.798278 -0.137458 0.141267 -0.206010 0.502292 0.219422 0.215153 Amount Class 0 149.62 0 1 2.69 0 2 378.66 0 3 123.50 0 4 69.99 0 '''

cases = len(df)
nonfraud_cases = df[df.Class == 0]  # 非欺诈
fraud_cases = df[df.Class == 1]  # 欺诈
fraud_percentage = round(len(nonfraud_cases) / cases * 100, 2)
print(cl('CASE COUNT', attrs=['bold']))
print(cl('-' * 40, attrs=['bold']))
print(cl('Total number of cases are {}'.format(cases), attrs=['bold']))
print(cl('Number of Non-fraud cases are {}'.format(len(nonfraud_cases)), attrs=['bold']))
print(cl('Number of fraud cases are {}'.format(len(fraud_cases)), attrs=['bold']))
print(cl('Percentage of fraud cases is {}%'.format(fraud_percentage), attrs=['bold']))
print(cl('-' * 40, attrs=['bold']))
print(cl('CASE AMOUNT STATISTICS', attrs=['bold']))
print(cl('-' * 40, attrs=['bold']))
print(cl('NON-FRAUD CASE AMOUNT STATS', attrs=['bold']))
print(nonfraud_cases.Amount.describe())
print(cl('-' * 40, attrs=['bold']))
print(cl('FRAUD CASE AMOUNT STATS', attrs=['bold']))
print(fraud_cases.Amount.describe())
print(cl('-' * 40, attrs=['bold']))
# 通过查看，‘Amount’金额变化较大，需对其进行标准化
sc = StandardScaler()
amount = df.Amount.values
df.Amount = sc.fit_transform(amount.reshape(-1, 1))
print(cl(df.Amount.head(10), attrs=['bold']))
# 特征选择和数据集拆分
x = df.drop('Class', axis=1).values
y = df.Class.values
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=0)
''' CASE COUNT ---------------------------------------- Total number of cases are 284807 Number of Non-fraud cases are 284315 Number of fraud cases are 492 Percentage of fraud cases is 99.83% ---------------------------------------- CASE AMOUNT STATISTICS ---------------------------------------- NON-FRAUD CASE AMOUNT STATS count 284315.000000 mean 88.291022 std 250.105092 min 0.000000 25% 5.650000 50% 22.000000 75% 77.050000 max 25691.160000 Name: Amount, dtype: float64 ---------------------------------------- FRAUD CASE AMOUNT STATS count 492.000000 mean 122.211321 std 256.683288 min 0.000000 25% 1.000000 50% 9.250000 75% 105.890000 max 2125.870000 Name: Amount, dtype: float64 ---------------------------------------- 0 0.244964 1 -0.342475 2 1.160686 3 0.140534 4 -0.073403 5 -0.338556 6 -0.333279 7 -0.190107 8 0.019392 9 -0.338516 Name: Amount, dtype: float64'''

1. Decision Tree

tree_model = DecisionTreeClassifier(max_depth=4, criterion='entropy').fit(x_train, y_train)
tree_yhat = tree_model.predict(x_test)

2. K-Nearest Neighbors

knn_model = KNeighborsClassifier(n_neighbors=5).fit(x_train, y_train)
knn_yhat = knn_model.predict(x_test)

3. Logistic Regression

lr_model = LogisticRegression().fit(x_train, y_train)
lr_yhat = lr_model.predict(x_test)

4. SVM

svm_model = SVC().fit(x_train, y_train)
svm_yhat = svm_model.predict(x_test)

5. Random Forest Tree

rf_model = RandomForestClassifier(max_depth=4).fit(x_train, y_train)
rf_yhat = rf_model.predict(x_test)

6. XGBoost

xgb_model = XGBClassifier(max_depth=4).fit(x_train, y_train)
xgb_yhat = xgb_model.predict(x_test)

1. 准确率

print(cl('-' * 40, attrs=['bold']))
print(cl('ACCURACY SCORE', attrs=['bold']))
print(cl('Accuracy score of the Decision Tree model is {}'.format(round(accuracy_score(y_test, tree_yhat), 4)),
         attrs=['bold']))
print(cl('Accuracy score of the knn model is {}'.format(round(accuracy_score(y_test, knn_yhat), 4)), attrs=['bold']))
print(cl('Accuracy score of the Logistic Regression model is {}'.format(round(accuracy_score(y_test, lr_yhat), 4)),
         attrs=['bold']))
print(cl('Accuracy score of the SVM model is {}'.format(round(accuracy_score(y_test, svm_yhat), 4)), attrs=['bold']))
print(cl('Accuracy score of the Random Forest model is {}'.format(round(accuracy_score(y_test, rf_yhat), 4)),
         attrs=['bold']))
print(
    cl('Accuracy score of the XGBoost model is {}'.format(round(accuracy_score(y_test, xgb_yhat), 4)), attrs=['bold']))
''' ACCURACY SCORE Accuracy score of the Decision Tree model is 0.9994 Accuracy score of the knn model is 0.9995 Accuracy score of the Logistic Regression model is 0.9992 Accuracy score of the SVM model is 0.9993 Accuracy score of the Random Forest model is 0.9993 Accuracy score of the XGBoost model is 0.9995 '''

2. F1值

print(cl('-' * 40, attrs=['bold']))
print(cl('F1 SCORE', attrs=['bold']))
print(cl('F1 score of the Decision Tree model is {}'.format(round(f1_score(y_test, tree_yhat), 4)), attrs=['bold']))
print(cl('F1 score of the knn model is {}'.format(round(f1_score(y_test, knn_yhat), 4)), attrs=['bold']))
print(cl('F1 score of the Logistic Regression model is {}'.format(round(f1_score(y_test, lr_yhat), 4)), attrs=['bold']))
print(cl('F1 score of the SVM model is {}'.format(round(f1_score(y_test, svm_yhat), 4)), attrs=['bold']))
print(cl('F1 score of the Random Forest model is {}'.format(round(f1_score(y_test, rf_yhat), 4)), attrs=['bold']))
print(cl('F1 score of the XGBoost model is {}'.format(round(f1_score(y_test, xgb_yhat), 4)), attrs=['bold']))
''' F1 SCORE F1 score of the Decision Tree model is 0.8105 F1 score of the knn model is 0.8571 F1 score of the Logistic Regression model is 0.7356 F1 score of the SVM model is 0.7771 F1 score of the Random Forest model is 0.7657 F1 score of the XGBoost model is 0.8449 '''