银行分控模型的建立

 

#神经网络预测
import pandas as pd  
import numpy as np
# 参数初始化
filename=r'data\bankloan.xls' 
data=pd.read_excel(filename)
x=data.iloc[:,:8].values
y=data.iloc[:,8].values # 获取二分类数据
from keras.models import Sequential
from keras.layers.core import Dense, Activation

model = Sequential()  # 建立模型
model.add(Dense(input_dim = 8, units = 10))
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 = 18, 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 = 'binary_crossentropy', 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, y, epochs = 1000, batch_size = 10)  # 训练模型，学习一千次

predict_x=model.predict(x)
classes_x=np.argmax(predict_x,axis=1)

score  = model.evaluate(x,y,batch_size=10)  # 模型评估
print(score)

from cm_plot import * #导入自行编写的混淆矩阵可视化函数
cm_plot(y,classes_x).show() #显示混淆矩阵可视化结果

运行结果如下：

 

 

import pandas as pd

filename ='bankloan.xls'
data = pd.read_excel(filename) # 导入数据

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

from sklearn.tree import DecisionTreeClassifier as DTC
dtc = DTC(criterion='entropy') 
dtc.fit(x, y) 

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, 
feature_names=data.columns[:8], 
class_names=data.columns[8], 
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('banktree.png') #保存图像
Image(graph.create_png())

import matplotlib.pyplot as plt
img = plt.imread('banktree.png')
fig = plt.figure('show picture')
plt.imshow(img)

　　

运行代码结果如下：