一、神经网络算法：

 1 import pandas as pd  2 from keras.models import Sequential 3 from keras.layers.core import Dense, Activation 4 import numpy as np 5 # 参数初始化 6 inputfile = 'C:/Users/76319/Desktop/bankloan.xls' 7 data = pd.read_excel(inputfile) 8 x_test = data.iloc[:,:8].values 9 y_test = data.iloc[:,8].values10 inputfile = 'C:/Users/76319/Desktop/bankloan.xls'11 data = pd.read_excel(inputfile)12 x_test = data.iloc[:,:8].values13 y_test = data.iloc[:,8].values14 15 model = Sequential()  # 建立模型16 model.add(Dense(input_dim = 8, units = 8))17 model.add(Activation('relu'))  # 用relu函数作为激活函数，能够大幅提供准确度18 model.add(Dense(input_dim = 8, units = 1))19 model.add(Activation('sigmoid'))  # 由于是0-1输出，用sigmoid函数作为激活函数20 model.compile(loss = 'mean_squared_error', optimizer = 'adam')21 # 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binary22 # 另外常见的损失函数还有mean_squared_error、categorical_crossentropy等，请阅读帮助文件。23 # 求解方法我们指定用adam，还有sgd、rmsprop等可选24 model.fit(x_test, y_test, epochs = 1000, batch_size = 10)25 predict_x=model.predict(x_test)26 classes_x=np.argmax(predict_x,axis=1)27 yp = classes_x.reshape(len(y_test))28 29 def cm_plot(y, yp):30   from sklearn.metrics import confusion_matrix31   cm = confusion_matrix(y, yp)32   import matplotlib.pyplot as plt33   plt.matshow(cm, cmap=plt.cm.Greens)34   plt.colorbar()35   for x in range(len(cm)):36     for y in range(len(cm)):37       plt.annotate(cm[x,y], xy=(x, y), horizontalalignment='center', verticalalignment='center')38   plt.ylabel('True label')39   plt.xlabel('Predicted label')40   return plt41 cm_plot(y_test,yp).show()# 显示混淆矩阵可视化结果42 score  = model.evaluate(x_test,y_test,batch_size=128)  # 模型评估43 print(score)

结果以及混淆矩阵可视化如下：

二、然后我们使用逻辑回归模型进行分析和预测：

import pandas as pdinputfile = 'C:/Users/76319/Desktop/bankloan.xls'data = pd.read_excel(inputfile)print (data.head())X = data.drop(columns='违约')y = data['违约']from sklearn.model_selection import train_test_splitfrom sklearn.linear_model import LogisticRegressionX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=1)model = LogisticRegression()model.fit(X_train, y_train)y_pred = model.predict(X_test)print(y_pred)from sklearn.metrics import accuracy_scorescore = accuracy_score(y_pred, y_test)print(score)def cm_plot(y, y_pred):  from sklearn.metrics import confusion_matrix #导入混淆矩阵函数  cm = confusion_matrix(y, y_pred) #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 pltcm_plot(y_test, y_pred).show()

结果如下：

综上所述得出，两种算法模型总体上跑出来的准确率还是不错的，但是神经网络准确性更高一点。