基于数据挖掘算法建立银行风控模型

一、BP神经网络算法

import pandas as pd
import numpy as np
#导入划分数据集函数
from sklearn.model_selection import train_test_split
#读取数据
datafile = 'C:/Users/Desktop/86152/data/bankloan.xls'#文件路径
data = pd.read_excel(datafile)
x = data.iloc[:,:8]
y = data.iloc[:,8]
#划分数据集
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=100)
#导入模型和函数
from keras.models import Sequential
from keras.layers import Dense,Dropout
#导入指标
from keras.metrics import BinaryAccuracy
#导入时间库计时
import time
start_time = time.time()
#-------------------------------------------------------#
model = Sequential()
model.add(Dense(input_dim=8,units=800,activation='relu'))#激活函数relu
model.add(Dropout(0.5))#防止过拟合的掉落函数
model.add(Dense(input_dim=800,units=400,activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(input_dim=400,units=1,activation='sigmoid'))
 
model.compile(loss='binary_crossentropy', optimizer='adam',metrics=[BinaryAccuracy()])
model.fit(x_train,y_train,epochs=100,batch_size=128)
loss,binary_accuracy = model.evaluate(x,y,batch_size=128)
#--------------------------------------------------------#
end_time = time.time()
run_time = end_time-start_time#运行时间
 
print('模型运行时间：{}'.format(run_time))
print('模型损失值：{}'.format(loss))
print('模型精度：{}'.format(binary_accuracy))
 
yp = model.predict(x).reshape(len(y))
yp = np.around(yp,0).astype(int) #转换为整型
from cm_plot import *  # 导入自行编写的混淆矩阵可视化函数
 
cm_plot(y,yp).show()  # 显示混淆矩阵可视化结果

cm_plot函数

#-*- coding: utf-8 -*-
def cm_plot(y, yp):
   
  from sklearn.metrics import confusion_matrix #导入混淆矩阵函数
 
  cm = confusion_matrix(y, yp) #混淆矩阵
   
  import matplotlib.pyplot as plt #导入作图库
  plt.matshow(cm, cmap=plt.cm.Greens) #画混淆矩阵图，配色风格使用cm.Greens，更多风格请参考官网。
  plt.colorbar() #颜色标签
   
  for x in range(len(cm)): #数据标签
    for y in range(len(cm)):
      plt.annotate(cm[x,y], xy=(x, y), horizontalalignment='center', verticalalignment='center')
   
  plt.ylabel('True label') #坐标轴标签
  plt.xlabel('Predicted label') #坐标轴标签
  return plt

混淆矩阵

得分:

 

 

 

二、用机器学习相关算法搭建

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132



# -*- coding: utf-8 -*-
"""
Created on Sun Mar 27 19:33:58 2022
 
@author: 86152
"""
import pandas as pd
import time
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier as DTC
from sklearn.ensemble import RandomForestClassifier as RFC
from sklearn import svm
from sklearn import tree
from sklearn.metrics import confusion_matrix
from sklearn.metrics import accuracy_score
from sklearn.metrics import roc_curve, auc
from sklearn.neighbors import KNeighborsClassifier as KNN
#导入plot_roc_curve,roc_curve和roc_auc_score模块
from sklearn.metrics import plot_roc_curve,roc_curve,auc,roc_auc_score
filePath = 'C:/Users/86152/Desktop/data/bankloan.xls'
data = pd.read_excel(filePath)
x = data.iloc[:,:8]
y = data.iloc[:,8]
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=100)
 
#模型
svm_clf = svm.SVC()#支持向量机
dtc_clf = DTC(criterion='entropy')#决策树
rfc_clf = RFC(n_estimators=10)#随机森林
knn_clf = KNN()#K邻近
 
#训练
knn_clf.fit(x_train,y_train)
rfc_clf.fit(x_train,y_train)
dtc_clf.fit(x_train,y_train)
svm_clf.fit(x_train, y_train)
 
 
#ROC曲线比较
fig,ax = plt.subplots(figsize=(12,10))
rfc_roc = plot_roc_curve(estimator=rfc_clf, X=x,
                        y=y, ax=ax, linewidth=1)
svm_roc = plot_roc_curve(estimator=svm_clf, X=x,
                        y=y, ax=ax, linewidth=1)
dtc_roc = plot_roc_curve(estimator=dtc_clf, X=x,
                        y=y, ax=ax, linewidth=1)
knn_roc = plot_roc_curve(estimator=knn_clf, X=x,
                        y=y, ax=ax, linewidth=1)
ax.legend(fontsize=12)
plt.show()
 
#模型评价
rfc_yp = rfc_clf.predict(x)
rfc_score = accuracy_score(y, rfc_yp)
svm_yp = svm_clf.predict(x)
svm_score = accuracy_score(y, svm_yp)
dtc_yp = dtc_clf.predict(x)
dtc_score = accuracy_score(y, dtc_yp)
knn_yp = knn_clf.predict(x)
knn_score = accuracy_score(y, knn_yp)
score = {"随机森林得分":rfc_score,"支持向量机得分":svm_score,"决策树得分":dtc_score,"K邻近得分":knn_score}
score = sorted(score.items(),key = lambda score:score[0],reverse=True)
print(pd.DataFrame(score))
 
#中文标签、负号正常显示
plt.rcParams['font.sans-serif'] = ['SimHei']
plt.rcParams['axes.unicode_minus'] = False
 
#绘制混淆矩阵
figure = plt.subplots(figsize=(12,10))
plt.subplot(2,2,1)
plt.title('随机森林')
rfc_cm = confusion_matrix(y, rfc_yp)
heatmap = sns.heatmap(rfc_cm, annot=True, fmt='d')
heatmap.yaxis.set_ticklabels(heatmap.yaxis.get_ticklabels(), rotation=0, ha='right')
heatmap.xaxis.set_ticklabels(heatmap.xaxis.get_ticklabels(), rotation=45, ha='right')
plt.ylabel("true label")
plt.xlabel("predict label")
 
plt.subplot(2,2,2)
plt.title('支持向量机')
svm_cm = confusion_matrix(y, svm_yp)
heatmap = sns.heatmap(svm_cm, annot=True, fmt='d')
heatmap.yaxis.set_ticklabels(heatmap.yaxis.get_ticklabels(), rotation=0, ha='right')
heatmap.xaxis.set_ticklabels(heatmap.xaxis.get_ticklabels(), rotation=45, ha='right')
plt.ylabel("true label")
plt.xlabel("predict label")
 
plt.subplot(2,2,3)
plt.title('决策树')
dtc_cm = confusion_matrix(y, dtc_yp)
heatmap = sns.heatmap(dtc_cm, annot=True, fmt='d')
heatmap.yaxis.set_ticklabels(heatmap.yaxis.get_ticklabels(), rotation=0, ha='right')
heatmap.xaxis.set_ticklabels(heatmap.xaxis.get_ticklabels(), rotation=45, ha='right')
plt.ylabel("true label")
plt.xlabel("predict label")
 
plt.subplot(2,2,4)
plt.title('K邻近')
knn_cm = confusion_matrix(y, knn_yp)
heatmap = sns.heatmap(knn_cm, annot=True, fmt='d')
heatmap.yaxis.set_ticklabels(heatmap.yaxis.get_ticklabels(), rotation=0, ha='right')
heatmap.xaxis.set_ticklabels(heatmap.xaxis.get_ticklabels(), rotation=45, ha='right')
plt.ylabel("true label")
plt.xlabel("predict label")
plt.show()
 
#画出决策树
import pandas as pd
import os
os.environ["PATH"] += os.pathsep + 'D:/软件下载安装/Graphviz/bin'
from sklearn.tree import export_graphviz
x = pd.DataFrame(x)
 
with open(r"C:/Users/86152/Desktop/data/banklodan_tree.dot", 'w') as f:
    export_graphviz(dtc_clf, feature_names = x.columns, out_file = f)
    f.close()  
from IPython.display import Image 
from sklearn import tree
import pydotplus
dot_data = tree.export_graphviz(dtc_clf, out_file=None,  #regr_1 是对应分类器
                         feature_names=x.columns,   #对应特征的名字
                         class_names= ['不违约','违约'],    #对应类别的名字
                         filled=True, rounded=True, 
                         special_characters=True) 
让graphviz显示中文用"MicrosoftYaHei"代替'helvetica'
graph = pydotplus.graph_from_dot_data(dot_data.replace('helvetica',"MicrosoftYaHei")) 
graph.write_png('C:/Users/86152/Desktop/data/banklodan_tree.png')    #保存图像
Image(graph.create_png())

混淆矩阵：

决策树：

 

 

ROC曲线：

 

得分：

 

结论：

决策树和随机森林的效果最好