Python 利用神经网络模型和SVM模型预测bankloan.xls

部分bankloan数据如下：

1.利用神经网络模型预测

import pandas as pd
import numpy as np
from keras.models import Sequential
from keras.layers import Dense,Dropout
# 参数初始化
inputfile = r'C:\Users\22977\Desktop\Study\pythonData\data\data\bankloan.xls'
data = pd.read_excel(inputfile)  # 导入数据

x = data.iloc[:,:8]
y = data.iloc[:,8]
model = Sequential()
model.add(Dense(64,input_dim=8,activation='relu'))
# Drop防止过拟合的数据处理方式
model.add(Dropout(0.5))
model.add(Dense(64,activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(1,activation='sigmoid'))
# 编译模型，定义损失函数，优化函数，绩效评估函数
model.compile(loss='mean_squared_error',optimizer='rmsprop',metrics=['accuracy'])
# 导入数据进行训练
model.fit(x,y,epochs=100,batch_size=128)
# 模型评估
loss,accuracy = model.evaluate(x,y,batch_size=128)
print("loss:{0},accuracy:{1}".format(loss,accuracy))

yp = model.predict(x)  # 分类预测
yp=np.argmax(yp,axis=1)
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
cm_plot(y,yp).show()  # 显示混淆矩阵可视化结果

结果如下：

 混淆矩阵如下：

 2.利用SVM预测

import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt 
from sklearn.model_selection import train_test_split
from sklearn import svm
from sklearn.metrics import accuracy_score
from sklearn.metrics import confusion_matrix
#参数初始化
filename = r'C:\Users\22977\Desktop\Study\pythonData\data\data\bankloan.xls'
data = pd.read_excel(filename)
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)
svm_clf = svm.SVC()
svm_clf.fit(x_train, y_train)
#svm得分
yp = svm_clf.predict(x)
score = accuracy_score(y, yp)
print("SVM得分：",score)
#混淆矩阵
plt.title('SVM')
cm = confusion_matrix(y, yp)
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")

结果如下：

混淆矩阵如下：