机器学习实战之信用卡欺诈检测

最新推荐文章于 2025-03-14 15:24:16 发布
最新推荐文章于 2025-03-14 15:24:16 发布
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分类专栏： 机器学习 算法 文章标签： 机器学习实战 信用卡欺诈检测 sklearn
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数据dataset

import pandas as pd
import matplotlib.pyplot as plt
import numpy as np

%matplotlib inline
data = pd.read_csv("creditcard.csv")
data.head()
.dataframe thead tr:only-child th { text-align: right; } .dataframe thead th { text-align: left; } .dataframe tbody tr th { vertical-align: top; }
TimeV1V2V3V4V5V6V7V8V9…V21V22V23V24V25V26V27V28AmountClass
00.0-1.359807-0.0727812.5363471.378155-0.3383210.4623880.2395990.0986980.363787…-0.0183070.277838-0.1104740.0669280.128539-0.1891150.133558-0.021053149.620
10.01.1918570.2661510.1664800.4481540.060018-0.082361-0.0788030.085102-0.255425…-0.225775-0.6386720.101288-0.3398460.1671700.125895-0.0089830.0147242.690
21.0-1.358354-1.3401631.7732090.379780-0.5031981.8004990.7914610.247676-1.514654…0.2479980.7716790.909412-0.689281-0.327642-0.139097-0.055353-0.059752378.660
31.0-0.966272-0.1852261.792993-0.863291-0.0103091.2472030.2376090.377436-1.387024…-0.1083000.005274-0.190321-1.1755750.647376-0.2219290.0627230.061458123.500
42.0-1.1582330.8777371.5487180.403034-0.4071930.0959210.592941-0.2705330.817739…-0.0094310.798278-0.1374580.141267-0.2060100.5022920.2194220.21515369.990

5 rows × 31 columns

count_classes = pd.value_counts(data['Class'], sort = True).sort_index()
count_classes.plot(kind = 'bar')
plt.title("Fraud class histogram")
plt.xlabel("Class")
plt.ylabel("Frequency")
Text(0,0.5,'Frequency')

这里写图片描述

from sklearn.preprocessing import StandardScaler

data['normAmount'] = StandardScaler().fit_transform(data['Amount'].values.reshape(-1, 1))
data = data.drop(['Time','Amount'],axis=1)
data.head()
.dataframe thead tr:only-child th { text-align: right; } .dataframe thead th { text-align: left; } .dataframe tbody tr th { vertical-align: top; }
V1V2V3V4V5V6V7V8V9V10…V21V22V23V24V25V26V27V28ClassnormAmount
0-1.359807-0.0727812.5363471.378155-0.3383210.4623880.2395990.0986980.3637870.090794…-0.0183070.277838-0.1104740.0669280.128539-0.1891150.133558-0.02105300.244964
11.1918570.2661510.1664800.4481540.060018-0.082361-0.0788030.085102-0.255425-0.166974…-0.225775-0.6386720.101288-0.3398460.1671700.125895-0.0089830.0147240-0.342475
2-1.358354-1.3401631.7732090.379780-0.5031981.8004990.7914610.247676-1.5146540.207643…0.2479980.7716790.909412-0.689281-0.327642-0.139097-0.055353-0.05975201.160686
3-0.966272-0.1852261.792993-0.863291-0.0103091.2472030.2376090.377436-1.387024-0.054952…-0.1083000.005274-0.190321-1.1755750.647376-0.2219290.0627230.06145800.140534
4-1.1582330.8777371.5487180.403034-0.4071930.0959210.592941-0.2705330.8177390.753074…-0.0094310.798278-0.1374580.141267-0.2060100.5022920.2194220.2151530-0.073403

5 rows × 30 columns

X = data.loc[:, data.columns != 'Class']
y = data.loc[:, data.columns == 'Class']

# Number of data points in the minority class
number_records_fraud = len(data[data.Class == 1])
fraud_indices = np.array(data[data.Class == 1].index)

# Picking the indices of the normal classes
normal_indices = data[data.Class == 0].index

# Out of the indices we picked, randomly select "x" number (number_records_fraud)
random_normal_indices = np.random.choice(normal_indices, number_records_fraud, replace = False)
random_normal_indices = np.array(random_normal_indices)

# Appending the 2 indices
under_sample_indices = np.concatenate([fraud_indices,random_normal_indices])

# Under sample dataset
under_sample_data = data.iloc[under_sample_indices,:]

X_undersample = under_sample_data.loc[:, under_sample_data.columns != 'Class']
y_undersample = under_sample_data.loc[:, under_sample_data.columns == 'Class']

# Showing ratio
print("Percentage of normal transactions: ", len(under_sample_data[under_sample_data.Class == 0])/len(under_sample_data))
print("Percentage of fraud transactions: ", len(under_sample_data[under_sample_data.Class == 1])/len(under_sample_data))
print("Total number of transactions in resampled data: ", len(under_sample_data))
Percentage of normal transactions:  0.5
Percentage of fraud transactions:  0.5
Total number of transactions in resampled data:  984

from sklearn.model_selection import train_test_split

# Whole dataset
X_train, X_test, y_train, y_test = train_test_split(X,y,test_size = 0.3, random_state = 0)

print("Number transactions train dataset: ", len(X_train))
print("Number transactions test dataset: ", len(X_test))
print("Total number of transactions: ", len(X_train)+len(X_test))

# Undersampled dataset
X_train_undersample, X_test_undersample, y_train_undersample, y_test_undersample = train_test_split(X_undersample
                                                                                                   ,y_undersample
                                                                                                   ,test_size = 0.3
                                                                                                   ,random_state = 0)
print("")
print("Number transactions train dataset: ", len(X_train_undersample))
print("Number transactions test dataset: ", len(X_test_undersample))
print("Total number of transactions: ", len(X_train_undersample)+len(X_test_undersample))
Number transactions train dataset:  199364
Number transactions test dataset:  85443
Total number of transactions:  284807

Number transactions train dataset:  688
Number transactions test dataset:  296
Total number of transactions:  984

#Recall = TP/(TP+FN)
from sklearn.linear_model import LogisticRegression
from sklearn.cross_validation import KFold, cross_val_score
from sklearn.metrics import confusion_matrix,recall_score,classification_report 
def printing_Kfold_scores(x_train_data,y_train_data):
    fold = KFold(len(y_train_data),5,shuffle=False) 

    # Different C parameters
    c_param_range = [0.01,0.1,1,10,100]

    results_table = pd.DataFrame(index = range(len(c_param_range),2), columns = ['C_parameter','Mean recall score'])

    results_table['C_parameter'] = c_param_range

    # the k-fold will give 2 lists: train_indices = indices[0], test_indices = indices[1]
    j = 0
    for c_param in c_param_range:
        print('-------------------------------------------')
        print('C parameter: ', c_param)
        print('-------------------------------------------')
        print('')

        recall_accs = []
        for iteration, indices in enumerate(fold,start=1):

            # Call the logistic regression model with a certain C parameter
            lr = LogisticRegression(C = c_param, penalty = 'l1')

            # Use the training data to fit the model. In this case, we use the portion of the fold to train the model
            # with indices[0]. We then predict on the portion assigned as the 'test cross validation' with indices[1]
            lr.fit(x_train_data.iloc[indices[0],:],y_train_data.iloc[indices[0],:].values.ravel())

            # Predict values using the test indices in the training data
            y_pred_undersample = lr.predict(x_train_data.iloc[indices[1],:].values)

            # Calculate the recall score and append it to a list for recall scores representing the current c_parameter
            recall_acc = recall_score(y_train_data.iloc[indices[1],:].values,y_pred_undersample)
            recall_accs.append(recall_acc)
            print('Iteration ', iteration,': recall score = ', recall_acc)

        # The mean value of those recall scores is the metric we want to save and get hold of.
        results_table.loc[j,'Mean recall score'] = np.mean(recall_accs)
        j += 1
        print('')
        print('Mean recall score ', np.mean(recall_accs))
        print('')

    best_c = results_table.loc[results_table['Mean recall score'].idxmax()]['C_parameter']

    # Finally, we can check which C parameter is the best amongst the chosen.
    print('*********************************************************************************')
    print('Best model to choose from cross validation is with C parameter = ', best_c)
    print('*********************************************************************************')

    return best_c
best_c = printing_Kfold_scores(X_train_undersample,y_train_undersample)
-------------------------------------------
C parameter:  0.01
-------------------------------------------

Iteration  1 : recall score =  0.931506849315
Iteration  2 : recall score =  0.917808219178
Iteration  3 : recall score =  1.0
Iteration  4 : recall score =  0.972972972973
Iteration  5 : recall score =  0.954545454545

Mean recall score  0.955366699202

-------------------------------------------
C parameter:  0.1
-------------------------------------------

Iteration  1 : recall score =  0.849315068493
Iteration  2 : recall score =  0.86301369863
Iteration  3 : recall score =  0.949152542373
Iteration  4 : recall score =  0.932432432432
Iteration  5 : recall score =  0.878787878788

Mean recall score  0.894540324143

-------------------------------------------
C parameter:  1
-------------------------------------------

Iteration  1 : recall score =  0.849315068493
Iteration  2 : recall score =  0.876712328767
Iteration  3 : recall score =  0.966101694915
Iteration  4 : recall score =  0.932432432432
Iteration  5 : recall score =  0.893939393939

Mean recall score  0.903700183709

-------------------------------------------
C parameter:  10
-------------------------------------------

Iteration  1 : recall score =  0.849315068493
Iteration  2 : recall score =  0.86301369863
Iteration  3 : recall score =  0.966101694915
Iteration  4 : recall score =  0.932432432432
Iteration  5 : recall score =  0.924242424242

Mean recall score  0.907021063743

-------------------------------------------
C parameter:  100
-------------------------------------------

Iteration  1 : recall score =  0.849315068493
Iteration  2 : recall score =  0.86301369863
Iteration  3 : recall score =  0.966101694915
Iteration  4 : recall score =  0.932432432432
Iteration  5 : recall score =  0.924242424242

Mean recall score  0.907021063743

*********************************************************************************
Best model to choose from cross validation is with C parameter =  0.01
*********************************************************************************

def plot_confusion_matrix(cm, classes,
                          title='Confusion matrix',
                          cmap=plt.cm.Blues):
    """
    This function prints and plots the confusion matrix.
    """
    plt.imshow(cm, interpolation='nearest', cmap=cmap)
    plt.title(title)
    plt.colorbar()
    tick_marks = np.arange(len(classes))
    plt.xticks(tick_marks, classes, rotation=0)
    plt.yticks(tick_marks, classes)

    thresh = cm.max() / 2.
    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
        plt.text(j, i, cm[i, j],
                 horizontalalignment="center",
                 color="white" if cm[i, j] > thresh else "black")

    plt.tight_layout()
    plt.ylabel('True label')
    plt.xlabel('Predicted label')
import itertools
lr = LogisticRegression(C = best_c, penalty = 'l1')
lr.fit(X_train_undersample,y_train_undersample.values.ravel())
y_pred_undersample = lr.predict(X_test_undersample.values)

# Compute confusion matrix
cnf_matrix = confusion_matrix(y_test_undersample,y_pred_undersample)
np.set_printoptions(precision=2)

print("Recall metric in the testing dataset: ", cnf_matrix[1,1]/(cnf_matrix[1,0]+cnf_matrix[1,1]))

# Plot non-normalized confusion matrix
class_names = [0,1]
plt.figure()
plot_confusion_matrix(cnf_matrix
                      , classes=class_names
                      , title='Confusion matrix')
plt.show()
Recall metric in the testing dataset:  0.931972789116

这里写图片描述

lr = LogisticRegression(C = best_c, penalty = 'l1')
lr.fit(X_train_undersample,y_train_undersample.values.ravel())
y_pred = lr.predict(X_test.values)

# Compute confusion matrix
cnf_matrix = confusion_matrix(y_test,y_pred)
np.set_printoptions(precision=2)

print("Recall metric in the testing dataset: ", cnf_matrix[1,1]/(cnf_matrix[1,0]+cnf_matrix[1,1]))

# Plot non-normalized confusion matrix
class_names = [0,1]
plt.figure()
plot_confusion_matrix(cnf_matrix
                      , classes=class_names
                      , title='Confusion matrix')
plt.show()
Recall metric in the testing dataset:  0.91156462585

这里写图片描述

best_c = printing_Kfold_scores(X_train,y_train)
-------------------------------------------
C parameter:  0.01
-------------------------------------------

Iteration  1 : recall score =  0.492537313433
Iteration  2 : recall score =  0.602739726027
Iteration  3 : recall score =  0.683333333333
Iteration  4 : recall score =  0.569230769231
Iteration  5 : recall score =  0.45

Mean recall score  0.559568228405

-------------------------------------------
C parameter:  0.1
-------------------------------------------

Iteration  1 : recall score =  0.567164179104
Iteration  2 : recall score =  0.616438356164
Iteration  3 : recall score =  0.683333333333
Iteration  4 : recall score =  0.584615384615
Iteration  5 : recall score =  0.525

Mean recall score  0.595310250644

-------------------------------------------
C parameter:  1
-------------------------------------------

Iteration  1 : recall score =  0.55223880597
Iteration  2 : recall score =  0.616438356164
Iteration  3 : recall score =  0.716666666667
Iteration  4 : recall score =  0.615384615385
Iteration  5 : recall score =  0.5625

Mean recall score  0.612645688837

-------------------------------------------
C parameter:  10
-------------------------------------------

Iteration  1 : recall score =  0.55223880597
Iteration  2 : recall score =  0.616438356164
Iteration  3 : recall score =  0.733333333333
Iteration  4 : recall score =  0.615384615385
Iteration  5 : recall score =  0.575

Mean recall score  0.61847902217

-------------------------------------------
C parameter:  100
-------------------------------------------

Iteration  1 : recall score =  0.55223880597
Iteration  2 : recall score =  0.616438356164
Iteration  3 : recall score =  0.733333333333
Iteration  4 : recall score =  0.615384615385
Iteration  5 : recall score =  0.575

Mean recall score  0.61847902217

*********************************************************************************
Best model to choose from cross validation is with C parameter =  10.0
*********************************************************************************

lr = LogisticRegression(C = best_c, penalty = 'l1')
lr.fit(X_train,y_train.values.ravel())
y_pred_undersample = lr.predict(X_test.values)

# Compute confusion matrix
cnf_matrix = confusion_matrix(y_test,y_pred_undersample)
np.set_printoptions(precision=2)

print("Recall metric in the testing dataset: ", cnf_matrix[1,1]/(cnf_matrix[1,0]+cnf_matrix[1,1]))

# Plot non-normalized confusion matrix
class_names = [0,1]
plt.figure()
plot_confusion_matrix(cnf_matrix
                      , classes=class_names
                      , title='Confusion matrix')
plt.show()
Recall metric in the testing dataset:  0.619047619048

png

lr = LogisticRegression(C = 0.01, penalty = 'l1')
lr.fit(X_train_undersample,y_train_undersample.values.ravel())
y_pred_undersample_proba = lr.predict_proba(X_test_undersample.values)

thresholds = [0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9]

plt.figure(figsize=(10,10))

j = 1
for i in thresholds:
    y_test_predictions_high_recall = y_pred_undersample_proba[:,1] > i

    plt.subplot(3,3,j)
    j += 1

    # Compute confusion matrix
    cnf_matrix = confusion_matrix(y_test_undersample,y_test_predictions_high_recall)
    np.set_printoptions(precision=2)

    print("Recall metric in the testing dataset: ", cnf_matrix[1,1]/(cnf_matrix[1,0]+cnf_matrix[1,1]))

    # Plot non-normalized confusion matrix
    class_names = [0,1]
    plot_confusion_matrix(cnf_matrix
                          , classes=class_names
                          , title='Threshold >= %s'%i) 
Recall metric in the testing dataset:  1.0
Recall metric in the testing dataset:  1.0
Recall metric in the testing dataset:  1.0
Recall metric in the testing dataset:  0.972789115646
Recall metric in the testing dataset:  0.931972789116
Recall metric in the testing dataset:  0.877551020408
Recall metric in the testing dataset:  0.816326530612
Recall metric in the testing dataset:  0.761904761905
Recall metric in the testing dataset:  0.591836734694

这里写图片描述

import pandas as pd
from imblearn.over_sampling import SMOTE
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import confusion_matrix
from sklearn.model_selection import train_test_split
credit_cards=pd.read_csv('creditcard.csv')

columns=credit_cards.columns
# The labels are in the last column ('Class'). Simply remove it to obtain features columns
features_columns=columns.delete(len(columns)-1)

features=credit_cards[features_columns]
labels=credit_cards['Class']
features_train, features_test, labels_train, labels_test = train_test_split(features, 
                                                                            labels, 
                                                                            test_size=0.2, 
                                                                            random_state=0)
oversampler=SMOTE(random_state=0)
os_features,os_labels=oversampler.fit_sample(features_train,labels_train)
len(os_labels[os_labels==1])
227454

os_features = pd.DataFrame(os_features)
os_labels = pd.DataFrame(os_labels)
best_c = printing_Kfold_scores(os_features,os_labels)
-------------------------------------------
C parameter:  0.01
-------------------------------------------

Iteration  1 : recall score =  0.890322580645
Iteration  2 : recall score =  0.894736842105
Iteration  3 : recall score =  0.968861347792
Iteration  4 : recall score =  0.956045767798
Iteration  5 : recall score =  0.958463855091

Mean recall score  0.933686078686

-------------------------------------------
C parameter:  0.1
-------------------------------------------

Iteration  1 : recall score =  0.890322580645
Iteration  2 : recall score =  0.894736842105
Iteration  3 : recall score =  0.969635941131
Iteration  4 : recall score =  0.96014552489
Iteration  5 : recall score =  0.96051922929

Mean recall score  0.935072023612

-------------------------------------------
C parameter:  1
-------------------------------------------

Iteration  1 : recall score =  0.890322580645
Iteration  2 : recall score =  0.894736842105
Iteration  3 : recall score =  0.970543321899
Iteration  4 : recall score =  0.960288411866
Iteration  5 : recall score =  0.960750046713

Mean recall score  0.935328240646

-------------------------------------------
C parameter:  10
-------------------------------------------

Iteration  1 : recall score =  0.890322580645
Iteration  2 : recall score =  0.894736842105
Iteration  3 : recall score =  0.97065397809
Iteration  4 : recall score =  0.959057385608
Iteration  5 : recall score =  0.959859750937

Mean recall score  0.934926107477

-------------------------------------------
C parameter:  100
-------------------------------------------

Iteration  1 : recall score =  0.890322580645
Iteration  2 : recall score =  0.894736842105
Iteration  3 : recall score =  0.970476928184
Iteration  4 : recall score =  0.957551576703
Iteration  5 : recall score =  0.959145316055

Mean recall score  0.934446648738

*********************************************************************************
Best model to choose from cross validation is with C parameter =  1.0
*********************************************************************************

lr = LogisticRegression(C = best_c, penalty = 'l1')
lr.fit(os_features,os_labels.values.ravel())
y_pred = lr.predict(features_test.values)

# Compute confusion matrix
cnf_matrix = confusion_matrix(labels_test,y_pred)
np.set_printoptions(precision=2)

print("Recall metric in the testing dataset: ", cnf_matrix[1,1]/(cnf_matrix[1,0]+cnf_matrix[1,1]))

# Plot non-normalized confusion matrix
class_names = [0,1]
plt.figure()
plot_confusion_matrix(cnf_matrix
                      , classes=class_names
                      , title='Confusion matrix')
plt.show()
Recall metric in the testing dataset:  0.910891089109

这里写图片描述

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文章目录API模型参数penaltydualtolCfit_interceptclass_weightrandom_statesolvermax_iterverbosewarm_startn_jobs模型对象模型方法mnist数据集iris数据集 关于逻辑回归的完整参数，可以参考：https://scikitlearn.com.cn/0.21.3/2/#1111-logistic API 参考：https://zhuanlan.zhihu.com/p/39780207 import numpy as np
机器学习实战：基于逻辑回归模型的信用卡欺诈检测
12-22
某银行为提升信用卡反欺诈检测能力，提供了脱敏后的一份个人交易记录。考虑数据本身的隐私性，数据提供之初已经进行了类似PCA的处理，并得到了若干数据特征。在不需要做额外特征提取工作的情况下，本项目意在通过...
机器学习案例实战之信用卡欺诈检测（逻辑回归）
huahuaxiaoshao的博客
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机器学习案例实战之信用卡欺诈检测 1.实战案例背景：这是一份个人交易记录，因为其中涉及到了隐私的内容，进行了类似PCA的处理，我们的数据已经把特征数据提取出来了，接下来，通过逻辑回归进行检测。 2.拿到数据千万不要忙着去建立模型，一定要先观察数据 import pandas as pd import numpy as np import matplotlib.pyplot as plt %matp...
案例实战信用卡欺诈检测数据集
06-13
这个数据集来源于"人工智能学习笔记——案例实战信用卡欺诈检测"的博客，旨在通过机器学习技术来识别潜在的欺诈交易，保护用户免受经济损失。这个任务对于金融机构至关重要，因为欺诈行为可能导致巨大的财务损失，并...
卷积神经网络的信用卡欺诈检测
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卷积神经网络的信用卡欺诈检测
Sklearn逻辑回归
2301_81975613的博客
03-13 714
逻辑回归是一种广泛用于分类问题的机器学习算法。在Python中，你可以使用Sklearn库（scikit-learn）来方便地实现逻辑回归。下面是一个简单的例子，展示了如何使用Sklearn进行逻辑回归。这个例子中，我们使用了鸢尾花（Iris）数据集，这是一个常用于机器学习入门的数据集，包含了150个样本，每个样本有4个特征，对应于鸢尾花的萼片和花瓣的长度和宽度。数据集被分为三个类别。
sklearn之逻辑回归
weixin_45580742的博客
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逻辑回归的损失函数 无正则项 添加正则项 sklearn.linear_model.LogisticRegression (penalty=’l2’, dual=False, tol=0.0001, C=1.0,fit_intercept=True, intercept_scaling=1, class_weight=None, random_state=None, solver=’warn’...
sklearn逻辑回归
qq_26645205的博客
05-23 407
逻辑回归自己的理解（明天把实例看完在写） 1.对机器学习的认识 引用大牛的观点： 机器学习算法没有所谓的优劣，也没有绝对的高性能，只有在特定场景、数据和特征下更适合的机器学习算法。 2.机器学习应用方法： 应用机器学习，千万不要一上来就试图做到完美，先做一个基本的model出来，再进行后续的分析步骤，一步步提高。所谓后续步骤可能包括『分析model现在的状态(欠/过拟合)，分析我们使用的
sklearn-逻辑回归
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sklearn中的逻辑回归
momokofly的博客
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1 概述 1.1 名为“回归”的分类器 逻辑回归是一种名为“回归”的线性分类器，它的本质是由线性回归变化而来的，一种广泛使用于分类问题中的广义回归算法。要理解逻辑回归从何而来，得先理解线性回归。线性回归是机器学习中最简单的回归算法，写作一个几乎人人熟悉的方程： z=a0+a1x1+a2x2+...+anxnz=a_0+a_1x_1+a_2x_2+...+a_nx_nz=a0​+a1​x1​+a2​x2​+...+an​xn​ 其中，aaa被统称为模型的参数，其中a0a_0a0​被称为截距（intercept
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