神經網路建模

import pandas as pd


inputfile = r'C:/Users/Administrator/Desktop/data/bankloan.xls'
outputfile = 'C:/Users/Administrator/Desktop/data_type.xls' 
k = 3 
iteration = 500 
data = pd.read_excel(inputfile, index_col = '年齡') 
data_zs = 1.0*(data - data.mean())/data.std() 


if __name__ == '__main__':
    from
 
 sklearn.cluster import KMeans
    model = KMeans(n_clusters = k, n_jobs = 4, max_iter = iteration) 
    model.fit(data_zs) 

   
    r1 = pd.Series(model.labels_).value_counts() 
    r2 = pd.DataFrame(model.cluster_centers_) 
    r = pd.concat([r2, r1], axis = 1)
    r.columns = list(data.columns) + [u'
 
類別數目'] 
    print(r)

  
    r = pd.concat([data, pd.Series(model.labels_, index = data.index)], axis = 1) 
    r.columns = list(data.columns) + [u'聚類類別'] 
    r.to_excel(outputfile) 
   
    from sklearn.manifold import TSNE

    tsne = TSNE()
    tsne.fit_transform(data_zs) 
    tsne = pd.DataFrame(tsne.embedding_, index = data_zs.index) 

    
 
import matplotlib.pyplot as plt
    plt.rcParams['font.sans-serif'] = ['SimHei'] 
    plt.rcParams['axes.unicode_minus'] = False 

    
    d = tsne[r[u'聚類類別'] == 0]
    plt.plot(d[0], d[1], 'r.')
    d = tsne[r[u'聚類類別'] == 1]
    plt.plot(d[0], d[1], 'go')
    d = tsne[r[u'聚類類別'] == 2]
    plt.plot(d[0], d[1], 'b*')
    plt.show()

 結果：

 銀行分控模型的建立

'''神經網路測試'''
import pandas as pd
from keras.models import Sequential
from keras.layers.core import Dense, Activation
import numpy as np

# 引數初始化
inputfile = 'C:/Users/Administrator/Desktop/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'))  
model.add(Dense(input_dim = 8, units = 1))
model.add(Activation('sigmoid'))  函式

model.compile(loss = 'mean_squared_error', optimizer = 'adam')


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()

score  = model.evaluate(x_test,y_test,batch_size=128) 
print(score)

結果：