用我兒子的話說，有一天啊,小烏龜遇見小兔子………

    有一天，我在網上看到這樣一片文章，決策書做貸款決策分析。

import pandas as pd
df = pd.read_csv('loans.csv')

#print(df.head())

X = df.drop('safe_loans', axis=1)


y = df.safe_loans


#change categorical

from sklearn.preprocessing import LabelEncoder
from collections import defaultdict
d = defaultdict(LabelEncoder)
X_trans = X.apply(lambda
 
 x: d[x.name].fit_transform(x))
X_trans.head()

#X_trans.to_excel('X_trans.xls') 

#random take train and test

from sklearn.cross_validation import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X_trans, y, random_state=1)
#call decision tree
from sklearn import tree
clf = tree.DecisionTreeClassifier(max_depth=8
 
)
clf = clf.fit(X_train, y_train)

test_rec = X_test.iloc[1,:]
clf.predict([test_rec])

y_test.iloc[1]
from sklearn.metrics import accuracy_score
print(accuracy_score(y_test, clf.predict(X_test)))

    這篇文章寫的非常好，從中學到好多，但是計算的正確率不太高，8層的決策樹正確率才能達到0.645

0.645480347467

    我用神經網路重新做了計算

#!/usr/bin/env python3
 

# -*- coding: utf-8 -*-
"""
Created on Thu Aug 17 21:14:08 2017

@author: luogan
"""

#read data
import pandas as pd
df = pd.read_csv('loans.csv')

#print(df.head())

#X = df.drop('safe_loans', axis=1)

X = df.drop(['safe_loans' ],axis=1)
y = df.safe_loans


#change categorical

from sklearn.preprocessing import LabelEncoder
from collections import defaultdict
d = defaultdict(LabelEncoder)
X_trans = X.apply(lambda x: d[x.name].fit_transform(x))
X_trans.head()

#X_trans.to_excel('X_trans.xls') 
##############
data_train=X_trans
data_max = data_train.max()
data_min = data_train.min()
data_mean = data_train.mean()
#
# data_std = data_train.std()
X_train1 = (data_train-data_max)/(data_max-data_min)


y=0.5*(y+1)
#random take train and test

from sklearn.cross_validation import train_test_split
x_train, x_test, y_train, y_test = train_test_split(X_train1, y, random_state=1)



#x_train.to_excel('xx_trans.xls') 

#y_train.to_excel('y_trans.xls') 




#call decision tree
#from sklearn import tree
#clf = tree.DecisionTreeClassifier(max_depth=10)
#clf = clf.fit(X_train, y_train)

from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Activation

model = Sequential() #建立模型
model.add(Dense(input_dim = 12, output_dim = 48)) #新增輸入層、隱藏層的連線
model.add(Activation('tanh')) #以Relu函式為啟用函式

model.add(Dense(input_dim = 48, output_dim = 48)) #新增隱藏層、隱藏層的連線
model.add(Activation('relu')) #以Relu函式為啟用函式
model.add(Dropout(0.2))

model.add(Dense(input_dim = 48, output_dim = 36)) #新增隱藏層、隱藏層的連線
model.add(Activation('relu')) #以Relu函式為啟用函式
model.add(Dropout(0.2))
model.add(Dense(input_dim = 36, output_dim = 36)) #新增隱藏層、隱藏層的連線
model.add(Activation('relu')) #以Relu函式為啟用函式

model.add(Dense(input_dim = 36, output_dim = 12)) #新增隱藏層、隱藏層的連線
model.add(Activation('relu')) #以Relu函式為啟用函式
model.add(Dense(input_dim = 12, output_dim = 12)) #新增隱藏層、隱藏層的連線
model.add(Activation('relu')) #以Relu函式為啟用函式


model.add(Dense(input_dim = 12, output_dim = 1)) #新增隱藏層、輸出層的連線
model.add(Activation('sigmoid')) #以sigmoid函式為啟用函式
#編譯模型，損失函式為binary_crossentropy，用adam法求解
model.compile(loss='mean_squared_error', optimizer='adam')
model.fit(x_train.values, y_train.values, nb_epoch = 70, batch_size = 2000) #訓練模型

r = pd.DataFrame(model.predict_classes(x_test.values))
'''
r = pd.DataFrame(model.predict(x_test.values))
rr=r.values
tr=rr.flatten()

for i in range(tr.shape[0]):
    if tr[i]>0.5:
        tr[i]=1
    else:

        tr[i]=0
'''        
from sklearn.metrics import accuracy_score
print(accuracy_score(y_test, r))        

0.650640749978

    我對神經網路進行了各種優化，正確率一直上不去，計算結果還不如我的神經網路股票預測程式碼

    我不高興，非常不高興，65%的正確率是我無法容忍的，讓我鬱悶的是，我一直深愛的神經網路居然也這麼無力
不能這樣下去，下回我們將採用傳說中的xgboost試一下

提取碼      jc5x