银行分控模型的建立

一、神经网络

1、代码

import pandas as pd
from keras.models import Sequential
from keras.layers.core import Dense, Activation
import numpy as np
# 参数初始化
inputfile = 'data/bankloan.xls'
data = pd.read_excel(inputfile)
x_test = data.iloc[:,:8].values
y_test = data.iloc[:,8].values
model = Sequential()  # 建立模型
model.add(Dense(input_dim = 8, units = 8))
model.add(Activation('relu'))  # 用relu函数作为激活函数，能够大幅提供准确度
model.add(Dense(input_dim = 8, units = 1))
model.add(Activation('sigmoid'))  # 由于是0-1输出，用sigmoid函数作为激活函数
model.compile(loss = 'mean_squared_error', optimizer = 'adam')
# 编译模型。由于我们做的是二元分类，所以我们指定损失函数为binary_crossentropy，以及模式为binary
# 另外常见的损失函数还有mean_squared_error、categorical_crossentropy等，请阅读帮助文件。
# 求解方法我们指定用adam，还有sgd、rmsprop等可选
model.fit(x_test, y_test, epochs = 1000, batch_size = 10)
predict_x=model.predict(x_test)
classes_x=np.argmax(predict_x,axis=1)
yp = classes_x.reshape(len(y_test))

def cm_plot(y, yp):
  from sklearn.metrics import confusion_matrix
  cm = confusion_matrix(y, yp)
  import matplotlib.pyplot as plt
  plt.matshow(cm, cmap=plt.cm.Greens)
  plt.colorbar()
  for x in range(len(cm)):
    for y in range(len(cm)):
      plt.annotate(cm[x,y], xy=(x, y), horizontalalignment='center', verticalalignment='center')
  plt.ylabel('True label')
  plt.xlabel('Predicted label')
  return plt
cm_plot(y_test,yp).show()# 显示混淆矩阵可视化结果
score  = model.evaluate(x_test,y_test,batch_size=128)  # 模型评估
print(score)

2、结果

 

二、SVM支持向量机

1、代码

import pandas as pd
import numpy as np
from sklearn import svm
from sklearn.metrics import accuracy_score
from sklearn.metrics import confusion_matrix
from matplotlib import pyplot as plt
import seaborn as sns
from sklearn.model_selection import train_test_split
data_load = "D:/aaa/xiamgmu/bankloan.xls"
data = pd.read_excel(data_load)
data.describe()
data.columns
data.index
## 转为np 数据切割
X = np.array(data.iloc[:,0:-1])
y = np.array(data.iloc[:,-1])
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=1, train_size=0.8, test_size=0.2, shuffle=True)
svm = svm.SVC()
svm.fit(X_test,y_test)
y_pred = svm.predict(X_test)
accuracy_score(y_test, y_pred)
print(accuracy_score(y_test, y_pred))
cm = confusion_matrix(y_test, y_pred)
heatmap = sns.heatmap(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()

2、结果

 

三、ID3决策树建立模型

1、代码

import pandas as pd
# 参数初始化
#导入数据
filename = 'D:/aaa/xiamgmu/bankloan.xls'
#filename = 'data/bankloan.xls'
data = pd.read_excel(filename)  # 导入数据

x = data.iloc[:,:8].astype(int)
y = data.iloc[:,8].astype(int)

import os
os.environ["PATH"] += os.pathsep + 'D:/aaa/xiamgmu/graphviz-3.0.0/graphviz-3.0.0/bin/'

from sklearn.tree import DecisionTreeClassifier as DTC
dtc = DTC(criterion='entropy')  # 建立决策树模型，基于信息熵
dtc.fit(x, y)  # 训练模型

# 导入相关函数，可视化决策树。
# 导出的结果是一个dot文件，需要安装Graphviz才能将它转换为pdf或png等格式。
from sklearn.tree import export_graphviz
x = pd.DataFrame(x)

"""
string1 = '''
edge [fontname="NSimSun"];
node [ fontname="NSimSun" size="15,15"];
{
'''
string2 = '}'
"""

with open("tree.dot", 'w') as f:
    export_graphviz(dtc, 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, out_file=None,  #regr_1 是对应分类器
                         feature_names=data.columns[:8],   #对应特征的名字
                         class_names=data.columns[8],    #对应类别的名字
                         filled=True, rounded=True,
                         special_characters=True)

dot_data = dot_data.replace('helvetica 14', 'MicrosoftYaHei 14') #修改字体
graph = pydotplus.graph_from_dot_data(dot_data)
graph.write_png('D:/aaa/xiamgmu/banktree.png')    #保存图像
Image(graph.create_png())

import matplotlib.pyplot as plt
img = plt.imread('D:/aaa/xiamgmu/banktree.png')
fig = plt.figure('show picture')
plt.imshow(img)

2、结果