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基于数据挖掘算法建立银行风控模型

作者:互联网

Bp神经网络:
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 = './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

混淆矩阵可视化

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

截图

 

 

 

标签:plt,cm,yp,风控,算法,time,数据挖掘,import,model
来源: https://www.cnblogs.com/Mrlee28/p/16074619.html