python课程设计作业—贷款发放预测

1.应用调研

​ 贷款业务是银行最基本、最主要的资产业务，是银行获得利润的主要来源，也是一项风险性较大的资产。其风险性在于如果被贷款人没有偿还贷款的能力，那么银行就会产生坏账，造成亏损。因此在银行业务中常常需要做很多是否发放贷款的调研。本课程设计旨在利用python课堂上学习到的numpy和pandas知识对网络上收集到的数据进行数据清洗，对清洗好的数据进行逻辑回归来预测是否发放贷款。

2.代码分析

2.1数据预处理

import pandas as pd

loans_2007 = pd.read_csv('./LoanStats3a.csv', skiprows=1,low_memory=False)

half_count = len(loans_2007) / 2

loans_2007 = loans_2007.dropna(thresh=half_count, axis=1)

loans_2007 = loans_2007.drop(['desc', 'url'],axis=1)

loans_2007.to_csv('./loans_2007.csv', index=False)

对数据进行初步的清洗，把数据量减少为之开始的一半。drop掉desc和url一些无关的信息，把初步清洗的数据保存为loans_2007.csv

import pandas as pd
loans_2007 = pd.read_csv("loans_2007.csv")
#loans_2007.drop_duplicates()
print(loans_2007.iloc[0])
print(loans_2007.shape[1])

​ 打印观察数据，看看还有什么是无关项

id                                1077501
member_id                      1.2966e+06
loan_amnt                            5000
funded_amnt                          5000
funded_amnt_inv                      4975
term                            36 months
int_rate                           10.65%
installment                        162.87
grade                                   B
sub_grade                              B2
emp_title                             NaN
emp_length                      10+ years
home_ownership                       RENT
annual_inc                          24000
verification_status              Verified
issue_d                          Dec-2011
loan_status                    Fully Paid
pymnt_plan                              n
purpose                       credit_card
title                            Computer
zip_code                            860xx
addr_state                             AZ
dti                                 27.65
delinq_2yrs                             0
earliest_cr_line                 Jan-1985
inq_last_6mths                          1
open_acc                                3
pub_rec                                 0
revol_bal                           13648
revol_util                          83.7%
total_acc                               9
initial_list_status                     f
out_prncp                               0
out_prncp_inv                           0
total_pymnt                       5863.16
total_pymnt_inv                   5833.84
total_rec_prncp                      5000
total_rec_int                      863.16
total_rec_late_fee                      0
recoveries                              0
collection_recovery_fee                 0
last_pymnt_d                     Jan-2015
last_pymnt_amnt                    171.62
last_credit_pull_d               Nov-2016
collections_12_mths_ex_med              0
policy_code                             1
application_type               INDIVIDUAL
acc_now_delinq                          0
chargeoff_within_12_mths                0
delinq_amnt                             0
pub_rec_bankruptcies                    0
tax_liens                               0
Name: 0, dtype: object
52

​ 以上是打印的信息，可以看出来，参数太多，如果我们直接拿52个特征去训练可能导致过拟合所以我们需要进一步的对特征值进行选择

loans_2007 = loans_2007.drop(["id", "member_id", "funded_amnt", "funded_amnt_inv", /"grade", "sub_grade", "emp_title", "issue_d"], axis=1)
loans_2007 = loans_2007.drop(["zip_code", "out_prncp", "out_prncp_inv", "total_pymnt", /"total_pymnt_inv", "total_rec_prncp"], axis=1)

​ 这里是drop掉一些表示身份信息的id，以及一些压缩的编码，显然这些数字对于我们的训练是没有作用的。

print(loans_2007.iloc[0])
print(loans_2007.shape[1])

​ 再次打印观察一下，结果如下：

loan_amnt                            5000
term                            36 months
int_rate                           10.65%
installment                        162.87
emp_length                      10+ years
home_ownership                       RENT
annual_inc                          24000
verification_status              Verified
loan_status                    Fully Paid
pymnt_plan                              n
purpose                       credit_card
title                            Computer
addr_state                             AZ
dti                                 27.65
delinq_2yrs                             0
earliest_cr_line                 Jan-1985
inq_last_6mths                          1
open_acc                                3
pub_rec                                 0
revol_bal                           13648
revol_util                          83.7%
total_acc                               9
initial_list_status                     f
last_credit_pull_d               Nov-2016
collections_12_mths_ex_med              0
policy_code                             1
application_type               INDIVIDUAL
acc_now_delinq                          0
chargeoff_within_12_mths                0
delinq_amnt                             0
pub_rec_bankruptcies                    0
tax_liens                               0
Name: 0, dtype: object
32

​ 我们可以看到特征值已经变成32个了，基本上不能再去缩减特征值的数量了，这个时候我们需要去确定训练的目标值。显然，是否贷款就是我们的目标值，我们吧贷款状态打印出来看一下。

print(loans_2007['loan_status'].value_counts())#贷款状态

​ 结果如下：

Fully Paid                                             33902
Charged Off                                             5658
Does