阿里云金融风控-贷款违约预测建模
作者：互联网
直接附上代码
# -*- coding: utf-8 -*-
"""
Created on Sat Jan 16 15:18:33 2021
@author: Administrator
"""
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import datetime
import warnings
warnings.filterwarnings('ignore')
#%%导入数据
data_train = pd.read_csv('D:/python_home/阿里云金融风控-贷款违约预测/train.csv')
data_test_a = pd.read_csv('D:/python_home/阿里云金融风控-贷款违约预测/testA.csv')
#%%基本的数据描述
data_train.shape,data_test_a.shape #((800000, 47), (200000, 46))
data_train.columns
'''
Index(['id', 'loanAmnt', 'term', 'interestRate', 'installment', 'grade',
'subGrade', 'employmentTitle', 'employmentLength', 'homeOwnership',
'annualIncome', 'verificationStatus', 'issueDate', 'isDefault',
'purpose', 'postCode', 'regionCode', 'dti', 'delinquency_2years',
'ficoRangeLow', 'ficoRangeHigh', 'openAcc', 'pubRec',
'pubRecBankruptcies', 'revolBal', 'revolUtil', 'totalAcc',
'initialListStatus', 'applicationType', 'earliesCreditLine', 'title',
'policyCode', 'n0', 'n1', 'n2', 'n3', 'n4', 'n5', 'n6', 'n7', 'n8',
'n9', 'n10', 'n11', 'n12', 'n13', 'n14'],
dtype='object')
'''
data_test_a.columns
'''
Index(['id', 'loanAmnt', 'term', 'interestRate', 'installment', 'grade',
'subGrade', 'employmentTitle', 'employmentLength', 'homeOwnership',
'annualIncome', 'verificationStatus', 'issueDate', 'purpose',
'postCode', 'regionCode', 'dti', 'delinquency_2years', 'ficoRangeLow',
'ficoRangeHigh', 'openAcc', 'pubRec', 'pubRecBankruptcies', 'revolBal',
'revolUtil', 'totalAcc', 'initialListStatus', 'applicationType',
'earliesCreditLine', 'title', 'policyCode', 'n0', 'n1', 'n2', 'n3',
'n4', 'n5', 'n6', 'n7', 'n8', 'n9', 'n10', 'n11', 'n12', 'n13', 'n14'],
dtype='object')
'''
#%%看一下变量的取值的个数，用来划分数值型还是类别型
for i in list(data_train.columns):
print(i, data_train[i].nunique())
'''
id 800000
loanAmnt 1540 贷款金额
term 2
interestRate 641
installment 72360
grade 7
subGrade 35
employmentTitle 248683
employmentLength 11
homeOwnership 6
annualIncome 44926
verificationStatus 3
issueDate 139
isDefault 2
purpose 14
postCode 932
regionCode 51
dti 6321
delinquency_2years 30
ficoRangeLow 39
ficoRangeHigh 39
openAcc 75
pubRec 32
pubRecBankruptcies 11
revolBal 71116
revolUtil 1286
totalAcc 134
initialListStatus 2
applicationType 2
earliesCreditLine 720
title 39644
policyCode 1
n0 39
n1 33
n2 50
n3 50
n4 46
n5 65
n6 107
n7 70
n8 102
n9 44
n10 76
n11 5
n12 5
n13 28
n14 31
'''
cate_col = ['term', 'grade',
'subGrade', 'employmentLength', 'homeOwnership',
'verificationStatus', 'isDefault',
'purpose',
'pubRecBankruptcies',
'initialListStatus', 'applicationType',
'policyCode','n11', 'n12']
num_col = [i for i in list(data_train.columns)[1:] if i not in cate_col]
data_train[cate_col].nunique()
#%%类别变量的iv值计算
import pycard as pc
cate_iv_woedf = pc.WoeDf()
clf = pc.NumBin()
for i in cate_col:
cate_iv_woedf.append(pc.cross_woe(data_train[i] ,data_train.isDefault))
cate_iv_woedf.to_excel('tmp11')
cate_use_col = ['term','grade','verificationStatus']
#%%数值型变量的iv值计算
num_col.remove('issueDate')
num_col.remove('earliesCreditLine')
#上面这两个是时间日期的东西，后面再做处理吧
num_iv_woedf = pc.WoeDf()
clf = pc.NumBin()
for i in num_col:
clf.fit(data_train[i] ,data_train.isDefault)
clf.generate_transform_fun()
num_iv_woedf.append(clf.woe_df_)
num_iv_woedf.to_excel('tmp12')
from numpy import *
data_train['loanAmnt_bin'] = pd.cut(data_train.loanAmnt,bins=[-inf, 3512.5, 9012.5, 10012.5, 11987.5, 15012.5, 28012.5, inf])
#interestRate
data_train['interestRate_bin'] = pd.cut(data_train.interestRate,bins=[-inf, 7.885, 9.73, 11.415, 13.175, 15.975, 17.785, 21.985, inf])
#annualIncome
data_train['annualIncome_bin'] = pd.cut(data_train.annualIncome,bins=[-inf, 37001.5996, 45670.5, 60995.5, 70017.5, 86462.0, 100670.5, 160030.0, inf])
#dti,先用均值填充，再分
data_train['dti'] = data_train['dti'].fillna(data_train['dti'].mean())
data_train['dti_bin'] = pd.cut(data_train.dti,bins=[-inf, 10.745, 14.845, 18.255, 21.745, 25.325, 30.195, 33.225, inf])
#ficoRangeLow
data_train['ficoRangeLow_bin'] = pd.cut(data_train.ficoRangeLow,bins=[-inf, 667.5, 682.5, 692.5, 702.5, 717.5, 732.5, 767.5, inf])
#revolUtil，均值填充，再分
data_train['revolUtil'] = data_train['revolUtil'].fillna(data_train['revolUtil'].mean())
data_train['revolUtil_bin'] = pd.cut(data_train.revolUtil,bins=[-inf, 19.75, 29.35, 38.55, 47.95, 56.55, 86.85, inf])
#n14 空值作为一列，
data_train['n14_bin'] = pd.cut(data_train.n14,bins=[-inf, 0.5, 1.5, 2.5, 3.5, 4.5, 6.5, inf])
woe_col = [i for i in ['term','grade','verificationStatus']+list(data_train.columns)[-7:]]
#%%
cate_iv_woedf = pc.WoeDf()
clf = pc.NumBin()
for i in woe_col:
cate_iv_woedf.append(pc.cross_woe(data_train[i] ,data_train.isDefault))
cate_iv_woedf.to_excel('tmp11')
data_train.grade[data_train.grade =='G'] = 'F'
#%%woe转换
pc.obj_info(cate_iv_woedf)
cate_iv_woedf.bin2woe(data_train,woe_col)
model_col = [i for i in ['id', 'isDefault']+list(data_train.columns)[-10:]]
data_train[model_col].isnull().sum()
data_train[model_col].info()
model_data = data_train[model_col]
model_data = model_data.astype(float)
model_data.n14_woe[model_data.n14_woe.isnull()]=0.34984133
#%%建模
import pandas as pd
import matplotlib.pyplot as plt #导入图像库
import matplotlib
import seaborn as sns
import statsmodels.api as sm
from sklearn.metrics import roc_curve, auc
from sklearn.model_selection import train_test_split
X = model_data[['_woe',
'g_woe',
'verificationSt_woe',
'loanAmnt_woe',
'interestRate_woe',
'annualIncome_woe',
'dti_woe',
'ficoRangeLow_woe',
'revolUtil_woe',
'n14_woe']]
Y = model_data['isDefault']
x_train,x_test,y_train,y_test=train_test_split(X,Y,test_size=0.3,random_state=0)
#(10127, 44)
X1=sm.add_constant(x_train) #在X前加上一列常数1，方便做带截距项的回归
logit=sm.Logit(y_train.astype(float),X1.astype(float))
result=logit.fit()
result.summary()
result.params
X3 = sm.add_constant(x_test)
resu = result.predict(X3.astype(float))
fpr, tpr, threshold = roc_curve(y_test, resu)
rocauc = auc(fpr, tpr)
plt.plot(fpr, tpr, 'b', label='AUC = %0.2f' % rocauc)
plt.legend(loc='lower right')
plt.plot([0, 1], [0, 1], 'r--')
plt.xlim([0, 1])
plt.ylim([0, 1])
plt.ylabel('真正率')
plt.xlabel('假正率')
plt.show()
resu_1 = result.predict(X1.astype(float))
fpr, tpr, threshold = roc_curve(y_train, resu_1)
rocauc = auc(fpr, tpr)
plt.plot(fpr, tpr, 'b', label='AUC = %0.2f' % rocauc)
plt.legend(loc='lower right')
plt.plot([0, 1], [0, 1], 'r--')
plt.xlim([0, 1])
plt.ylim([0, 1])
plt.ylabel('真正率')
plt.xlabel('假正率')
plt.show()
标签：plt,违约,建模,风控,train,woe,import,data,col 来源： https://www.cnblogs.com/cgmcoding/p/14287004.html