20200317_利用神经网络预测贷款率

import pandas as pd
#  忽略弹出的warnings
import warnings
warnings.filterwarnings('ignore')

text=pd.read_excel('data/LoanStats_securev1_2019Q4.xlsx')
text.head()

	id	loan_amnt	funded_amnt	funded_amnt_inv	term	int_rate	installment	grade	sub_grade	emp_title	...	num_tl_op_past_12m	pct_tl_nvr_dlq	percent_bc_gt_75	pub_rec_bankruptcies	tot_hi_cred_lim	total_bal_ex_mort	total_bc_limit	total_il_high_credit_limit
0	164027473	20000	20000	20000	36 months	0.1240	668.12	B	B4	NaN	...	2	100.0	50.0	1	60800	42566	5200	40000.0
1	163984413	16500	16500	16500	60 months	0.1033	353.27	B	B1	NaN	...	0	100.0	0.0	0	223390	40913	40500	39890.0
2	164193225	7500	7500	7500	36 months	0.1240	250.55	B	B4	Rn	...	7	54.5	16.7	0	138468	102122	47700	90768.0
3	162948736	19000	19000	18975	36 months	0.0646	581.99	A	A1	Tech Ops Analyst	...	0	100.0	40.0	0	184034	28461	38400	35000.0
4	164161686	10000	10000	10000	36 months	0.2055	374.45	D	D2	Planner	...	2	100.0	16.7	0	639373	161516	24600	172818.0

5 rows × 114 columns

text['loan_status'].value_counts()

Current               122625
Fully Paid              3539
In Grace Period         1079
Late (31-120 days)       509
Late (16-30 days)        304
Charged Off               80
n                          1
Name: loan_status, dtype: int64

#0为已经完成的
def function(x):if 'Current' in x:return 0elif 'Fully Paid' in x:return 0else:return 1
text['loan_status']=text.apply(lambda x:function(x['loan_status']),axis=1)

text['loan_status'].value_counts()

0    126164
1      1973
Name: loan_status, dtype: int64

pos_trainDf = text[text['loan_status'] == 1]
neg_trainDf = text[text['loan_status'] == 0].sample(n=5000, random_state=2018)
text = pd.concat([pos_trainDf, neg_trainDf], axis=0).sample(frac=1.0,random_state=2018)

text.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 6973 entries, 110105 to 92872
Columns: 114 entries, id to total_il_high_credit_limit
dtypes: datetime64[ns](1), float64(36), int64(50), object(27)
memory usage: 6.1+ MB

缺失值查看

check_null = text.isnull().sum(axis=0).sort_values(ascending=False)/float(len(text)) #查看缺失值比例
print(check_null[check_null >0.2]) # 查看缺失比例大于20%的属性。

desc                              0.999857
mths_since_last_record            0.899613
verification_status_joint         0.882977
annual_inc_joint                  0.864334
dti_joint                         0.864334
mths_since_recent_bc_dlq          0.794206
mths_since_last_major_derog       0.771691
mths_since_recent_revol_delinq    0.704145
mths_since_last_delinq            0.551556
dtype: float64

thresh_count = len(text)*0.4 # 设定阀值
data = text.dropna(thresh=thresh_count, axis=1 ) #若某一列数据缺失的数量超过阀值就会被删除

data.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 6973 entries, 110105 to 92872
Columns: 106 entries, id to total_il_high_credit_limit
dtypes: datetime64[ns](1), float64(30), int64(50), object(25)
memory usage: 5.7+ MB

删除无意义的列

sub_grade：与Grade的信息重复

emp_title ：缺失值较多，同时不能反映借款人收入或资产的真实情况

zip_code：地址邮编，邮编显示不全，没有意义

addr_state：申请地址所属州，不能反映借款人的偿债能力

last_credit_pull_d ：LendingClub平台最近一个提供贷款的时间，没有意义

policy_code ：变量信息全为1

pymnt_plan 基本是n

title： title与purpose的信息重复，同时title的分类信息更加离散

next_pymnt_d : 下一个付款时间，没有意义

policy_code : 没有意义

collection_recovery_fee: 全为0，没有意义

earliest_cr_line : 记录的是借款人发生第一笔借款的时间

issue_d ：贷款发行时间，这里提前向模型泄露了信息

last_pymnt_d、collection_recovery_fee、last_pymnt_amnt：预测贷款违约模型是贷款前的风险控制手段，这些贷后信息都会影响我们训练模型的效果，在此将这些信息删除

drop_list = ['sub_grade', 'emp_title',  'title', 'zip_code', 'addr_state', 'mths_since_last_delinq' ,'initial_list_status','title','issue_d','last_pymnt_d','last_pymnt_amnt','next_pymnt_d','last_credit_pull_d','policy_code','collection_recovery_fee', 'earliest_cr_line']
data.drop(drop_list, axis=1, inplace = True)

data.head()

	id	loan_amnt	funded_amnt	funded_amnt_inv	term	int_rate	installment	grade	emp_length	home_ownership	...	num_tl_90g_dpd_24m	num_tl_op_past_12m	pct_tl_nvr_dlq	percent_bc_gt_75	tot_hi_cred_lim	total_bal_ex_mort	total_bc_limit	total_il_high_credit_limit
110105	160041532	5000	5000	5000	36 months	0.1102	163.75	B	NaN	RENT	...	0	0	66.7	100.0	4900	3313	2800	0.0
36272	162224912	9000	9000	9000	36 months	0.0819	282.82	A	10+ years	MORTGAGE	...	1	1	89.7	0.0	443788	77239	45400	84288.0
66408	161687267	15950	15950	15950	36 months	0.1171	527.57	B	10+ years	MORTGAGE	...	0	2	100.0	0.0	233421	115483	62100	90541.0
121280	159844563	5600	5600	5600	36 months	0.1171	185.23	B	10+ years	RENT	...	0	7	100.0	0.0	64866	5486	51000	4166.0
20195	163365512	28000	28000	28000	60 months	0.0819	570.29	A	< 1 year	MORTGAGE	...	0	4	100.0	10.0	115641	66401	33800	75041.0

5 rows × 91 columns

分类变量

objectColumns = data.select_dtypes(include=["object"]).columns
data[objectColumns].isnull().sum().sort_values(ascending=False)

emp_length             651
application_type         1
url                      1
total_acc                0
delinq_2yrs              0
purpose                  0
pymnt_plan               0
verification_status      0
annual_inc               0
home_ownership           0
grade                    0
term                     0
dtype: int64

# data['int_rate'] = data['int_rate'].str.rstrip('%').astype('float')
# data['revol_util'] = data['revol_util'].str.rstrip('%').astype('float')
# data['annual_inc'] = data['annual_inc'].str.replace(",","").astype('float')

import numpy as np
objectColumns = data.select_dtypes(include=["object"]).columns # 筛选数据类型为object的数据
data[objectColumns] = data[objectColumns].fillna("Unknown") #以分类“Unknown”填充缺失值

import missingno as msno
import matplotlib as mpl
mpl.rcParams['font.sans-serif']=[u'simHei']
mpl.rcParams['axes.unicode_minus']=False
%matplotlib inline
msno.bar(data[objectColumns]) #可视化

<matplotlib.axes._subplots.AxesSubplot at 0x238b1e15a58>

mapping_dict = {"emp_length": {"10+ years": 10,"9 years": 9,"8 years": 8,"7 years": 7,"6 years": 6,"5 years": 5,"4 years": 4,"3 years": 3,"2 years": 2,"1 year": 1,"< 1 year": 0,"n/a": 0},"grade":{"A": 1,"B": 2,"C": 3,"D": 4,"E": 5,"F": 6,"G": 7}
}
data = data.replace(mapping_dict) #变量映射

数值类型缺失值

data.select_dtypes(include=[np.number]).isnull().sum().sort_values(ascending=False)

il_util                  1011
mths_since_recent_inq     773
mo_sin_old_il_acct        234
mths_since_rcnt_il        234
bc_util                   116...
total_cu_tl                 0
inq_fi                      0
total_rev_hi_lim            0
total_bc_limit              0
id                          0
Length: 80, dtype: int64

numColumns = data.select_dtypes(include=[np.number]).columns
msno.matrix(data[numColumns]) #缺失值可视化

<matplotlib.axes._subplots.AxesSubplot at 0x238d13c17f0>

data.isnull().sum().sum()
mean_cols=data.mean()
data= data.fillna(mean_cols)

目标变量

y=data['int_rate']
x=data.drop(['int_rate'],axis=1)
#使用pandas库将类别变量编码
x=pd.get_dummies(x)

特征工程

#数据进行分割（训练数据和测试数据）
from sklearn.model_selection  import train_test_split#测试集和训练集
x_train1, x_test1, y_train1, y_test1 = train_test_split(x, y, train_size=0.8, random_state=14)

x_train, x_test, y_train, y_test = x_train1, x_test1, y_train1, y_test1
print ("训练数据集样本数目：%d, 测试数据集样本数目：%d" % (x_train.shape[0], x_test.shape[0]))
y_train = y_train.astype(np.int)
y_test = y_test.astype(np.int)

训练数据集样本数目：5578, 测试数据集样本数目：1395

#标准化
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler().fit(x_train)
x_train = pd.DataFrame(scaler.transform(x_train))
x_test = pd.DataFrame(scaler.transform(x_test))

x_train.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 5578 entries, 0 to 5577
Columns: 8267 entries, 0 to 8266
dtypes: float64(8267)
memory usage: 351.8 MB

#降维
from sklearn.decomposition import PCA
pca = PCA(n_components=2)
x_train = pca.fit_transform(x_train)
x_test = pca.transform(x_test)
x_train.shape
print(pca.explained_variance_ratio_)

[0.00212175 0.00138494]

y_train.shape

(5578,)

神经网络

import keras
from keras.models import Sequential
from keras.layers import Dense
classifier = Sequential()

Using Theano backend.
WARNING (theano.configdefaults): g++ not available, if using conda: `conda install m2w64-toolchain`
D:\sofewore\anaconda\lib\site-packages\theano\configdefaults.py:560: UserWarning: DeprecationWarning: there is no c++ compiler.This is deprecated and with Theano 0.11 a c++ compiler will be mandatorywarnings.warn("DeprecationWarning: there is no c++ compiler."
WARNING (theano.configdefaults): g++ not detected ! Theano will be unable to execute optimized C-implementations (for both CPU and GPU) and will default to Python implementations. Performance will be severely degraded. To remove this warning, set Theano flags cxx to an empty string.
WARNING (theano.tensor.blas): Using NumPy C-API based implementation for BLAS functions.

model = Sequential()
model.add(Dense(32, activation='relu', input_dim=2))
model.add(Dense(1, activation='relu'))
model.compile(optimizer='rmsprop',loss='mse',metrics=['accuracy'])
model.fit(x_train, y_train, batch_size =1000,epochs=1)
y_true=model.predict(x_test)

Epoch 1/1
5578/5578 [==============================] - 13s 2ms/step - loss: 0.2186 - accuracy: 1.0000