信用卡反欺诈

古城微笑少年丶 2022-02-27 01:54 256阅读 0赞

### 1、 脱敏后的数据文件 ###

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2NoZW5neXVxaWFuZw_size_16_color_FFFFFF_t_70]![在这里插入图片描述][20190326152105815.png]  
最后一列Class，0为正常，1为欺诈

### 2、程序解读 ###

#### 2.1 读取文件 ####

#!/usr/bin/env python3
    # -*- coding: utf-8 -*-
    
    import pandas as pd
    import numpy as np 
    import matplotlib.pyplot as plt
    from sklearn.utils import shuffle
    import seaborn as sns
    import matplotlib.gridspec as gridspec
    
    #读取csv文件
    data = pd.read_csv('../dataset/creditcard.csv')
    # .iloc：根据标签的所在位置，从0开始计数，选取列
    x_train = np.array(data.iloc[:,0:29])
    y_train = np.array(data.iloc[:,30])

#### 2.2 查看前5行记录 ####

# df.head(n)：查看DataFrame对象的前n行
    print(data.head())

Time        V1        V2        V3        V4        V5        V6        V7  \
    0   0.0 -1.359807 -0.072781  2.536347  1.378155 -0.338321  0.462388  0.239599   
    1   0.0  1.191857  0.266151  0.166480  0.448154  0.060018 -0.082361 -0.078803   
    2   1.0 -1.358354 -1.340163  1.773209  0.379780 -0.503198  1.800499  0.791461   
    3   1.0 -0.966272 -0.185226  1.792993 -0.863291 -0.010309  1.247203  0.237609   
    4   2.0 -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

#### 2.3 统计描述 ####

# 生成描述性统计，总结数据集分布的中心趋势，分散和形状，不包括NaN值。
    print(data.describe())

Time            V1            V2            V3            V4  \
    count  284807.000000  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05   
    mean    94813.859575  1.759072e-12 -8.251146e-13 -9.655448e-13  8.321385e-13   
    std     47488.145955  1.958696e+00  1.651309e+00  1.516255e+00  1.415869e+00   
    min         0.000000 -5.640751e+01 -7.271573e+01 -4.832559e+01 -5.683171e+00   
    25%     54201.500000 -9.203734e-01 -5.985499e-01 -8.903648e-01 -8.486401e-01   
    50%     84692.000000  1.810880e-02  6.548556e-02  1.798463e-01 -1.984653e-02   
    75%    139320.500000  1.315642e+00  8.037239e-01  1.027196e+00  7.433413e-01   
    max    172792.000000  2.454930e+00  2.205773e+01  9.382558e+00  1.687534e+01   
    
                     V5            V6            V7            V8            V9  \
    count  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05   
    mean   1.649983e-13  4.248434e-13 -3.054696e-13  8.777981e-14 -1.179757e-12   
    std    1.380247e+00  1.332271e+00  1.237094e+00  1.194353e+00  1.098632e+00   
    min   -1.137433e+02 -2.616051e+01 -4.355724e+01 -7.321672e+01 -1.343407e+01   
    25%   -6.915971e-01 -7.682956e-01 -5.540759e-01 -2.086297e-01 -6.430976e-01   
    50%   -5.433583e-02 -2.741871e-01  4.010308e-02  2.235804e-02 -5.142873e-02   
    75%    6.119264e-01  3.985649e-01  5.704361e-01  3.273459e-01  5.971390e-01   
    max    3.480167e+01  7.330163e+01  1.205895e+02  2.000721e+01  1.559499e+01   
    
                    V10           V11           V12           V13           V14  \
    count  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05   
    mean   7.092627e-13  1.874974e-12  1.053347e-12  7.127607e-13 -1.474787e-13   
    std    1.088850e+00  1.020713e+00  9.992014e-01  9.952742e-01  9.585956e-01   
    min   -2.458826e+01 -4.797473e+00 -1.868371e+01 -5.791881e+00 -1.921433e+01   
    25%   -5.354257e-01 -7.624942e-01 -4.055715e-01 -6.485393e-01 -4.255740e-01   
    50%   -9.291738e-02 -3.275735e-02  1.400326e-01 -1.356806e-02  5.060132e-02   
    75%    4.539234e-01  7.395934e-01  6.182380e-01  6.625050e-01  4.931498e-01   
    max    2.374514e+01  1.201891e+01  7.848392e+00  7.126883e+00  1.052677e+01   
    
                    V15           V16           V17           V18           V19  \
    count  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05   
    mean  -5.231430e-13 -2.282231e-13 -6.425412e-13  4.950748e-13  7.057401e-13   
    std    9.153160e-01  8.762529e-01  8.493371e-01  8.381762e-01  8.140405e-01   
    min   -4.498945e+00 -1.412985e+01 -2.516280e+01 -9.498746e+00 -7.213527e+00   
    25%   -5.828843e-01 -4.680368e-01 -4.837483e-01 -4.988498e-01 -4.562989e-01   
    50%    4.807155e-02  6.641332e-02 -6.567575e-02 -3.636312e-03  3.734823e-03   
    75%    6.488208e-01  5.232963e-01  3.996750e-01  5.008067e-01  4.589494e-01   
    max    8.877742e+00  1.731511e+01  9.253526e+00  5.041069e+00  5.591971e+00   
    
                    V20           V21           V22           V23           V24  \
    count  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05   
    mean   1.766109e-12 -3.405785e-13 -5.723165e-13 -9.725860e-13  1.464148e-12   
    std    7.709250e-01  7.345240e-01  7.257016e-01  6.244603e-01  6.056471e-01   
    min   -5.449772e+01 -3.483038e+01 -1.093314e+01 -4.480774e+01 -2.836627e+00   
    25%   -2.117214e-01 -2.283949e-01 -5.423504e-01 -1.618463e-01 -3.545861e-01   
    50%   -6.248109e-02 -2.945017e-02  6.781943e-03 -1.119293e-02  4.097606e-02   
    75%    1.330408e-01  1.863772e-01  5.285536e-01  1.476421e-01  4.395266e-01   
    max    3.942090e+01  2.720284e+01  1.050309e+01  2.252841e+01  4.584549e+00   
    
                    V25           V26           V27           V28         Amount  \
    count  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05  284807.000000   
    mean  -6.987110e-13 -5.617884e-13  3.332082e-12 -3.518875e-12      88.349619   
    std    5.212781e-01  4.822270e-01  4.036325e-01  3.300833e-01     250.120109   
    min   -1.029540e+01 -2.604551e+00 -2.256568e+01 -1.543008e+01       0.000000   
    25%   -3.171451e-01 -3.269839e-01 -7.083953e-02 -5.295979e-02       5.600000   
    50%    1.659350e-02 -5.213911e-02  1.342146e-03  1.124383e-02      22.000000   
    75%    3.507156e-01  2.409522e-01  9.104512e-02  7.827995e-02      77.165000   
    max    7.519589e+00  3.517346e+00  3.161220e+01  3.384781e+01   25691.160000   
    
                   Class  
    count  284807.000000  
    mean        0.001727  
    std         0.041527  
    min         0.000000  
    25%         0.000000  
    50%         0.000000  
    75%         0.000000  
    max         1.000000

#### 2.4 空值统计 ####

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

#### 2.5 维度统计描述 ####

# Time, 时间维度
    print("Fraud")
    # data.Class == 1表示选择Class列值等于1的记录
    print(data.Time[data.Class == 1].describe())
    print()
    print("Normal")
    print(data.Time[data.Class == 0].describe())
    print()

Fraud
    count       492.000000
    mean      80746.806911
    std       47835.365138
    min         406.000000
    25%       41241.500000
    50%       75568.500000
    75%      128483.000000
    max      170348.000000
    Name: Time, dtype: float64
    
    Normal
    count    284315.000000
    mean      94838.202258
    std       47484.015786
    min           0.000000
    25%       54230.000000
    50%       84711.000000
    75%      139333.000000
    max      172792.000000
    Name: Time, dtype: float64

#### 2.6 ####

f, (ax1, ax2) = plt.subplots(2, 1, sharex=True, figsize=(12,4))
    
    bins = 50
    
    ax1.hist(data.Time[data.Class == 1], bins = bins)
    ax1.set_title('Fraud')
    
    ax2.hist(data.Time[data.Class == 0], bins = bins)
    ax2.set_title('Normal')
    plt.xlabel('Time (in Seconds)')
    plt.ylabel('Number of Transactions')
    plt.show()

图  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2NoZW5neXVxaWFuZw_size_16_color_FFFFFF_t_70 1]代码

# Amount 金额
    print("Fraud")
    print(data.Amount[data.Class == 1].describe())
    print()
    print("Normal")
    print(data.Amount[data.Class == 0].describe())

Fraud
    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
    
    Normal
    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

f, (ax1, ax2) = plt.subplots(2, 1, sharex=True, figsize=(12,4))
    
    bins = 30
    
    ax1.hist(data.Amount[data.Class == 1], bins = bins)
    ax1.set_title('Fraud')
    
    ax2.hist(data.Amount[data.Class == 0], bins = bins)
    ax2.set_title('Normal')
    
    plt.xlabel('Amount ($)')
    plt.ylabel('Number of Transactions')
    plt.yscale('log')
    plt.show()

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2NoZW5neXVxaWFuZw_size_16_color_FFFFFF_t_70 2]

data['Amount_max_fraud'] = 1
    data.loc[data.Amount <= 2125.87, 'Amount_max_fraud'] = 0
    
    f, (ax1, ax2) = plt.subplots(2, 1, sharex=True, figsize=(12,6))
    
    ax1.scatter(data.Time[data.Class == 1], data.Amount[data.Class == 1])
    ax1.set_title('Fraud')
    
    ax2.scatter(data.Time[data.Class == 0], data.Amount[data.Class == 0])
    ax2.set_title('Normal')
    
    plt.xlabel('Time (in Seconds)')
    plt.ylabel('Amount')
    plt.show()

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2NoZW5neXVxaWFuZw_size_16_color_FFFFFF_t_70 3]

# analysis the anonymized features.
    #Select only the anonymized features.
    v_features = data.iloc[:,1:29].columns
    
    plt.figure(figsize=(12,28*4))
    gs = gridspec.GridSpec(28, 1)
    for i, cn in enumerate(data[v_features]):
        ax = plt.subplot(gs[i])
        sns.distplot(data[cn][data.Class == 1], bins=50)    # 看两者的形状差异
        sns.distplot(data[cn][data.Class == 0], bins=50)
        ax.set_xlabel('')
        ax.set_title('histogram of feature: ' + str(cn))
    plt.show()

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2NoZW5neXVxaWFuZw_size_16_color_FFFFFF_t_70 4]

#Drop all of the features that have very similar distributions between the two types of transactions.
    data = data.drop(['V28','V27','V26','V25','V24','V23','V22','V20','V15','V13','V8'], axis =1)
    
    
    #Based on the plots above, these features are created to identify values where fraudulent transaction are more common.
    data['V1_'] = data.V1.map(lambda x: 1 if x < -3 else 0)
    data['V2_'] = data.V2.map(lambda x: 1 if x > 2.5 else 0)
    data['V3_'] = data.V3.map(lambda x: 1 if x < -4 else 0)
    data['V4_'] = data.V4.map(lambda x: 1 if x > 2.5 else 0)
    data['V5_'] = data.V5.map(lambda x: 1 if x < -4.5 else 0)
    data['V6_'] = data.V6.map(lambda x: 1 if x < -2.5 else 0)
    data['V7_'] = data.V7.map(lambda x: 1 if x < -3 else 0)
    data['V9_'] = data.V9.map(lambda x: 1 if x < -2 else 0)
    data['V10_'] = data.V10.map(lambda x: 1 if x < -2.5 else 0)
    data['V11_'] = data.V11.map(lambda x: 1 if x > 2 else 0)
    data['V12_'] = data.V12.map(lambda x: 1 if x < -2 else 0)
    data['V14_'] = data.V14.map(lambda x: 1 if x < -2.5 else 0)
    data['V16_'] = data.V16.map(lambda x: 1 if x < -2 else 0)
    data['V17_'] = data.V17.map(lambda x: 1 if x < -2 else 0)
    data['V18_'] = data.V18.map(lambda x: 1 if x < -2 else 0)
    data['V19_'] = data.V19.map(lambda x: 1 if x > 1.5 else 0)
    data['V21_'] = data.V21.map(lambda x: 1 if x > 0.6 else 0)
    
    print('每个单一属性的欺诈记录与整车记录的差异统计：')
    print(data.describe())
    print(data.sum())

每个单一属性的欺诈记录与整车记录的差异统计：
                    Time            V1            V2            V3            V4  \
    count  284807.000000  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05   
    mean    94813.859575  1.759072e-12 -8.251146e-13 -9.655448e-13  8.321385e-13   
    std     47488.145955  1.958696e+00  1.651309e+00  1.516255e+00  1.415869e+00   
    min         0.000000 -5.640751e+01 -7.271573e+01 -4.832559e+01 -5.683171e+00   
    25%     54201.500000 -9.203734e-01 -5.985499e-01 -8.903648e-01 -8.486401e-01   
    50%     84692.000000  1.810880e-02  6.548556e-02  1.798463e-01 -1.984653e-02   
    75%    139320.500000  1.315642e+00  8.037239e-01  1.027196e+00  7.433413e-01   
    max    172792.000000  2.454930e+00  2.205773e+01  9.382558e+00  1.687534e+01   
    
                     V5            V6            V7            V9           V10  \
    count  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05   
    mean   1.649983e-13  4.248434e-13 -3.054696e-13 -1.179757e-12  7.092627e-13   
    std    1.380247e+00  1.332271e+00  1.237094e+00  1.098632e+00  1.088850e+00   
    min   -1.137433e+02 -2.616051e+01 -4.355724e+01 -1.343407e+01 -2.458826e+01   
    25%   -6.915971e-01 -7.682956e-01 -5.540759e-01 -6.430976e-01 -5.354257e-01   
    50%   -5.433583e-02 -2.741871e-01  4.010308e-02 -5.142873e-02 -9.291738e-02   
    75%    6.119264e-01  3.985649e-01  5.704361e-01  5.971390e-01  4.539234e-01   
    max    3.480167e+01  7.330163e+01  1.205895e+02  1.559499e+01  2.374514e+01   
    
                    V11           V12           V14           V16           V17  \
    count  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05   
    mean   1.874974e-12  1.053347e-12 -1.474787e-13 -2.282231e-13 -6.425412e-13   
    std    1.020713e+00  9.992014e-01  9.585956e-01  8.762529e-01  8.493371e-01   
    min   -4.797473e+00 -1.868371e+01 -1.921433e+01 -1.412985e+01 -2.516280e+01   
    25%   -7.624942e-01 -4.055715e-01 -4.255740e-01 -4.680368e-01 -4.837483e-01   
    50%   -3.275735e-02  1.400326e-01  5.060132e-02  6.641332e-02 -6.567575e-02   
    75%    7.395934e-01  6.182380e-01  4.931498e-01  5.232963e-01  3.996750e-01   
    max    1.201891e+01  7.848392e+00  1.052677e+01  1.731511e+01  9.253526e+00   
    
                    V18           V19           V21         Amount          Class  \
    count  2.848070e+05  2.848070e+05  2.848070e+05  284807.000000  284807.000000   
    mean   4.950748e-13  7.057401e-13 -3.405785e-13      88.349619       0.001727   
    std    8.381762e-01  8.140405e-01  7.345240e-01     250.120109       0.041527   
    min   -9.498746e+00 -7.213527e+00 -3.483038e+01       0.000000       0.000000   
    25%   -4.988498e-01 -4.562989e-01 -2.283949e-01       5.600000       0.000000   
    50%   -3.636312e-03  3.734823e-03 -2.945017e-02      22.000000       0.000000   
    75%    5.008067e-01  4.589494e-01  1.863772e-01      77.165000       0.000000   
    max    5.041069e+00  5.591971e+00  2.720284e+01   25691.160000       1.000000   
    
           Amount_max_fraud            V1_            V2_            V3_  \
    count     284807.000000  284807.000000  284807.000000  284807.000000   
    mean           0.002117       0.047042       0.024771       0.009838   
    std            0.045965       0.211730       0.155427       0.098699   
    min            0.000000       0.000000       0.000000       0.000000   
    25%            0.000000       0.000000       0.000000       0.000000   
    50%            0.000000       0.000000       0.000000       0.000000   
    75%            0.000000       0.000000       0.000000       0.000000   
    max            1.000000       1.000000       1.000000       1.000000   
    
                     V4_            V5_            V6_            V7_  \
    count  284807.000000  284807.000000  284807.000000  284807.000000   
    mean        0.052794       0.004579       0.006274       0.010059   
    std         0.223622       0.067510       0.078963       0.099791   
    min         0.000000       0.000000       0.000000       0.000000   
    25%         0.000000       0.000000       0.000000       0.000000   
    50%         0.000000       0.000000       0.000000       0.000000   
    75%         0.000000       0.000000       0.000000       0.000000   
    max         1.000000       1.000000       1.000000       1.000000   
    
                     V9_           V10_           V11_           V12_  \
    count  284807.000000  284807.000000  284807.000000  284807.000000   
    mean        0.031530       0.005049       0.018244       0.048408   
    std         0.174746       0.070877       0.133833       0.214628   
    min         0.000000       0.000000       0.000000       0.000000   
    25%         0.000000       0.000000       0.000000       0.000000   
    50%         0.000000       0.000000       0.000000       0.000000   
    75%         0.000000       0.000000       0.000000       0.000000   
    max         1.000000       1.000000       1.000000       1.000000   
    
                    V14_           V16_           V17_           V18_  \
    count  284807.000000  284807.000000  284807.000000  284807.000000   
    mean        0.013697       0.021165       0.002173       0.013943   
    std         0.116230       0.143935       0.046569       0.117254   
    min         0.000000       0.000000       0.000000       0.000000   
    25%         0.000000       0.000000       0.000000       0.000000   
    50%         0.000000       0.000000       0.000000       0.000000   
    75%         0.000000       0.000000       0.000000       0.000000   
    max         1.000000       1.000000       1.000000       1.000000   
    
                    V19_           V21_  
    count  284807.000000  284807.000000  
    mean        0.032952       0.041958  
    std         0.178512       0.200494  
    min         0.000000       0.000000  
    25%         0.000000       0.000000  
    50%         0.000000       0.000000  
    75%         0.000000       0.000000  
    max         1.000000       1.000000  
    Time                2.700365e+10
    V1                  5.009022e-07
    V2                 -2.350312e-07
    V3                 -2.744665e-07
    V4                  2.368500e-07
    V5                  4.533991e-08
    V6                  1.209676e-07
    V7                 -8.687127e-08
    V9                 -3.359903e-07
    V10                 2.020664e-07
    V11                 5.340173e-07
    V12                 3.000407e-07
    V14                -4.247506e-08
    V16                -6.495627e-08
    V17                -1.830887e-07
    V18                 1.412354e-07
    V19                 2.010940e-07
    V21                -9.702072e-08
    Amount              2.516259e+07
    Class               4.920000e+02
    Amount_max_fraud    6.030000e+02
    V1_                 1.339800e+04
    V2_                 7.055000e+03
    V3_                 2.802000e+03
    V4_                 1.503600e+04
    V5_                 1.304000e+03
    V6_                 1.787000e+03
    V7_                 2.865000e+03
    V9_                 8.980000e+03
    V10_                1.438000e+03
    V11_                5.196000e+03
    V12_                1.378700e+04
    V14_                3.901000e+03
    V16_                6.028000e+03
    V17_                6.190000e+02
    V18_                3.971000e+03
    V19_                9.385000e+03
    V21_                1.195000e+04
    dtype: float64

#Create a new feature for normal (non-fraudulent) transactions.
    data.loc[data.Class == 0, 'Normal'] = 1
    data.loc[data.Class == 1, 'Normal'] = 0
    
    #Rename 'Class' to 'Fraud'.
    data = data.rename(columns={'Class': 'Fraud'})
    
    
    #492 fraudulent transactions, 284,315 normal transactions.
    #0.172% of transactions were fraud. 
    print('欺诈记录的占比：')
    print(data.Normal.value_counts())
    print()
    print(data.Fraud.value_counts())
    
    pd.set_option("display.max_columns",101)
    print(data.head())

欺诈记录的占比：
    1.0    284315
    0.0       492
    Name: Normal, dtype: int64
    
    0    284315
    1       492
    Name: Fraud, dtype: int64
       Time        V1        V2        V3        V4        V5        V6        V7  \
    0   0.0 -1.359807 -0.072781  2.536347  1.378155 -0.338321  0.462388  0.239599   
    1   0.0  1.191857  0.266151  0.166480  0.448154  0.060018 -0.082361 -0.078803   
    2   1.0 -1.358354 -1.340163  1.773209  0.379780 -0.503198  1.800499  0.791461   
    3   1.0 -0.966272 -0.185226  1.792993 -0.863291 -0.010309  1.247203  0.237609   
    4   2.0 -1.158233  0.877737  1.548718  0.403034 -0.407193  0.095921  0.592941   
    
             V9       V10       V11       V12       V14       V16       V17  \
    0  0.363787  0.090794 -0.551600 -0.617801 -0.311169 -0.470401  0.207971   
    1 -0.255425 -0.166974  1.612727  1.065235 -0.143772  0.463917 -0.114805   
    2 -1.514654  0.207643  0.624501  0.066084 -0.165946 -2.890083  1.109969   
    3 -1.387024 -0.054952 -0.226487  0.178228 -0.287924 -1.059647 -0.684093   
    4  0.817739  0.753074 -0.822843  0.538196 -1.119670 -0.451449 -0.237033   
    
            V18       V19       V21  Amount  Fraud  Amount_max_fraud  V1_  V2_  \
    0  0.025791  0.403993 -0.018307  149.62      0                 0    0    0   
    1 -0.183361 -0.145783 -0.225775    2.69      0                 0    0    0   
    2 -0.121359 -2.261857  0.247998  378.66      0                 0    0    0   
    3  1.965775 -1.232622 -0.108300  123.50      0                 0    0    0   
    4 -0.038195  0.803487 -0.009431   69.99      0                 0    0    0   
    
       V3_  V4_  V5_  V6_  V7_  V9_  V10_  V11_  V12_  V14_  V16_  V17_  V18_  \
    0    0    0    0    0    0    0     0     0     0     0     0     0     0   
    1    0    0    0    0    0    0     0     0     0     0     0     0     0   
    2    0    0    0    0    0    0     0     0     0     0     1     0     0   
    3    0    0    0    0    0    0     0     0     0     0     0     0     0   
    4    0    0    0    0    0    0     0     0     0     0     0     0     0   
    
       V19_  V21_  Normal  
    0     0     0     1.0  
    1     0     0     1.0  
    2     0     0     1.0  
    3     0     0     1.0  
    4     0     0     1.0

#Create dataframes of only Fraud and Normal transactions.
    Fraud = data[data.Fraud == 1]
    Normal = data[data.Normal == 1]
    
    
    # Set X_train equal to 80% of the fraudulent transactions.
    X_train = Fraud.sample(frac=0.8)
    count_Frauds = len(X_train)
    
    # Add 80% of the normal transactions to X_train.
    X_train = pd.concat([X_train, Normal.sample(frac = 0.8)], axis = 0)
    
    # X_test contains all the transaction not in X_train.
    X_test = data.loc[~data.index.isin(X_train.index)]
    
    
    #Shuffle the dataframes so that the training is done in a random order.
    X_train = shuffle(X_train)
    X_test = shuffle(X_test)
    
    #Add our target features to y_train and y_test.
    y_train = X_train.Fraud
    y_train = pd.concat([y_train, X_train.Normal], axis=1)
    
    y_test = X_test.Fraud
    y_test = pd.concat([y_test, X_test.Normal], axis=1)
    
    #Drop target features from X_train and X_test.
    X_train = X_train.drop(['Fraud','Normal'], axis = 1)
    X_test = X_test.drop(['Fraud','Normal'], axis = 1)
    
    #Check to ensure all of the training/testing dataframes are of the correct length
    print()
    print('切割[学习、校验]处理后的记录数量：')
    print(len(X_train))
    print(len(y_train))
    print(len(X_test))
    print(len(y_test))

切割[学习、校验]处理后的记录数量：
    227846
    227846
    56961
    56961

'''
    Due to the imbalance in the data, ratio will act as an equal weighting system for our model. 
    By dividing the number of transactions by those that are fraudulent, ratio will equal the value that when multiplied
    by the number of fraudulent transactions will equal the number of normal transaction. 
    Simply put: # of fraud * ratio = # of normal
    '''
    ratio = len(X_train)/count_Frauds 
    print()
    print('数据的占比:', ratio)
    
    y_train.Fraud *= ratio
    y_test.Fraud  *= ratio
    print('训练数据的数量:\n', y_train.Fraud)
    print('测试数据的数量:\n', y_train.Fraud)

数据的占比: 578.2893401015228
    训练数据的数量:
     22023     0.0
    185560    0.0
    112703    0.0
    165996    0.0
    245243    0.0
    238885    0.0
    39966     0.0
    112043    0.0
    171013    0.0
    255567    0.0
    283619    0.0
    203942    0.0
    82908     0.0
    245906    0.0
    225464    0.0
    13679     0.0
    107609    0.0
    140858    0.0
    156028    0.0
    158914    0.0
    72341     0.0
    208184    0.0
    111027    0.0
    217998    0.0
    229747    0.0
    281186    0.0
    259994    0.0
    112170    0.0
    204651    0.0
    184758    0.0
    
    118430    0.0
    15155     0.0
    28982     0.0
    193685    0.0
    209645    0.0
    201038    0.0
    226108    0.0
    219122    0.0
    266437    0.0
    45419     0.0
    99879     0.0
    167812    0.0
    117954    0.0
    20935     0.0
    238062    0.0
    13355     0.0
    71356     0.0
    54123     0.0
    95958     0.0
    280240    0.0
    271372    0.0
    259493    0.0
    149400    0.0
    231110    0.0
    30784     0.0
    186483    0.0
    74528     0.0
    187912    0.0
    17719     0.0
    42839     0.0
    Name: Fraud, Length: 227846, dtype: float64
    测试数据的数量:
     22023     0.0
    185560    0.0
    112703    0.0
    165996    0.0
    245243    0.0
    238885    0.0
    39966     0.0
    112043    0.0
    171013    0.0
    255567    0.0
    283619    0.0
    203942    0.0
    82908     0.0
    245906    0.0
    225464    0.0
    13679     0.0
    107609    0.0
    140858    0.0
    156028    0.0
    158914    0.0
    72341     0.0
    208184    0.0
    111027    0.0
    217998    0.0
    229747    0.0
    281186    0.0
    259994    0.0
    112170    0.0
    204651    0.0
    184758    0.0
    
    118430    0.0
    15155     0.0
    28982     0.0
    193685    0.0
    209645    0.0
    201038    0.0
    226108    0.0
    219122    0.0
    266437    0.0
    45419     0.0
    99879     0.0
    167812    0.0
    117954    0.0
    20935     0.0
    238062    0.0
    13355     0.0
    71356     0.0
    54123     0.0
    95958     0.0
    280240    0.0
    271372    0.0
    259493    0.0
    149400    0.0
    231110    0.0
    30784     0.0
    186483    0.0
    74528     0.0
    187912    0.0
    17719     0.0
    42839     0.0

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