用户贷款风险预测-datacastle竞赛题目

自己是大菜鸟一枚，datacastle比赛题目，用的是Logistic，做出的结果不好，目前只排在200多名。先放在博客上面，项目比较紧张，还得学一些javaweb的东西，就怕以后没时间做了。。。。

# -*- coding: utf-8 -*-
"""
Created on Tue Jan 10 09:54:12 2017
###Datacastle的‘用户贷款风险预测’竞赛题目###
#初步想法是利用逻辑斯蒂回归，特征的选择对结果影响很大，有时间的话多看看特征选择方面的东西
"""
import pandas as pd
from sklearn import preprocessing
from sklearn.cross_validation import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import classification_report

class DataCastle(object):
def __init__(self):
self.name = "<<- User loan forecast match ->>"
self.result = "result.csv"

#读取用户信息表 并返回
def readUserInfo(self):
user_info_train = readData("train/user_info_train.txt")
user_info_test = readData("test/user_info_test.txt")
col_names = ['userid', 'sex', 'occupation', 'education', 'marriage', 'household']
user_info_train.columns = col_names
user_info_test.columns = col_names
user_info = pd.concat([user_info_train, user_info_test])
user_info.index = user_info['userid']
user_info.drop('userid',axis=1,inplace=True)
return user_info

#读取用户银行账单表 对账单数据求和并返回
def readBankDetail(self):
bank_detail_train = readData("train/bank_detail_train.txt")
bank_detail_test = readData("test/bank_detail_test.txt")
col_names = ['userid', 'time_bank', 'tradeType', 'tradeMoney', 'incomeTag']
bank_detail_train.columns = col_names
bank_detail_test.columns = col_names
bank_detail_pre = pd.concat([bank_detail_train,bank_detail_test])
bank_detail = (bank_detail_pre.loc[:,['userid','tradeType', 'tradeMoney']]).groupby(['userid','tradeType']).sum()
bank_detail = bank_detail.unstack()
bank_detail.columns = ['income','outcome']
return bank_detail

#读取用户的浏览历史 对浏览数据求和并返回
def readBrowseHistory(self):
browse_history_train = readData("train/browse_history_train.txt")
browse_history_test = readData("test/browse_history_test.txt")
col_names = ['userid', 'time_browse', 'browseData', 'browseTag']
browse_history_train.columns = col_names
browse_history_test.columns = col_names
browse_history_pre = pd.concat([browse_history_train, browse_history_test])
browse_history = (browse_history_pre.loc[:,['userid','browseData']]).groupby(['userid']).sum()
return browse_history

#读取信用卡账单记录 取均值并返回
def readBillDetail(self):
bill_detail_train = readData("train/bill_detail_train.txt")
bill_detail_test = readData("test/bill_detail_test.txt")
col_names = ['userid', 'time_bill', 'bank_id', 'prior_account', 'prior_repay',
'credit_limit', 'account_balance', 'minimun_repay', 'consume_count',
'account', 'adjust_account', 'circulated_interest', 'avaliable_balance',
'cash_limit', 'repay_state']
bill_detail_train.columns = col_names
bill_detail_test.columns = col_names
bill_detail_pre = pd.concat([bill_detail_train,bill_detail_test])
bill_detail_pre.drop('bank_id',axis=1,inplace=True)
bill_detail = bill_detail_pre.groupby(['userid']).mean()
return bill_detail

#读取用户发放贷款时间 并返回
def readLoanTime(self):
loan_time_train = readData("train/loan_time_train.txt")
loan_time_test = readData("test/loan_time_test.txt")
col_names = ['userid','loanTime']
loan_time_train.columns = col_names
loan_time_test.columns = col_names
loan_time = pd.concat([loan_time_train,loan_time_test])
loan_time.index = loan_time['userid']
loan_time.drop('userid',axis=1,inplace=True)
return loan_time

#读取类别信息
def readTarget(self):
target = readData("train/overdue_train.txt")
target.columns = ['userid', 'label']
target.index = target['userid']
target.drop('userid',axis = 1,inplace = True)
return target

#利用逻辑斯蒂回归
def logisticMethod(self):

user_info = self.readUserInfo()
bank_detail = self.readBankDetail()
bill_detail = self.readBillDetail()
loan_time = self.readLoanTime()
browse_history = self.readBrowseHistory()
target = self.readTarget()

loan_data = user_info.join(bank_detail,how='outer')
loan_data = loan_data.join(bill_detail,how='outer')
loan_data = loan_data.join(browse_history,how='outer')
loan_data = loan_data.join(loan_time,how='outer')
loan_data = loan_data.fillna(0.0)

#对数据进行归一化
datas = loan_data.values
datas = preprocessing.scale(datas)
col_names = list(loan_data.columns)
nums=0
for col in col_names:
loan_data.loc[:,[col]] = datas[:,nums]
nums += 1

#对数据进行划分并且进行训练
train = loan_data.iloc[0: 55596, :]
test = loan_data.iloc[55596:, :]
train_X, test_X, train_y, test_y = train_test_split(train,target,test_size = 0.2,random_state = 0)
train_y = train_y['label']
test_y = test_y['label']
lr_model = LogisticRegression(C = 1.0,penalty = 'l2')
lr_model.fit(train_X, train_y)
#验证集进行预测
pred_test = lr_model.predict(test_X)
#对预测结果进行评估
print classification_report(test_y, pred_test)

#对测试集生成结果并存储为csv格式
pred = lr_model.predict_proba(test)
result = pd.DataFrame(pred)
result.index = test.index
result.columns = ['0', 'probability']
result.drop('0',axis = 1,inplace = True)
print result.head(5)
result.to_csv(self.result)

#数据读取
def readData(filename):
filepath = './'+filename
data = pd.read_csv(filepath,header=None)
return data