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分类变量.R
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分类变量.R
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#率变为分类变量
info_good$sv_due_rate <-ifelse(is.na(info_good$sv_due_rate)==TRUE,0,1)
info_good$sx_eff_rate <-ifelse(is.na(info_good$sx_eff_rate)==TRUE,0,1)
info_good$sx_kp_rate <-ifelse(is.na(info_good$sx_kp_rate)==TRUE,0,1)
info_good$th_due_rate <-ifelse(is.na(info_good$th_due_rate)==TRUE,0,1)
info_good$th_eff_rate <-ifelse(is.na(info_good$th_eff_rate)==TRUE,0,1)
info_good$th_kp_rate <-ifelse(is.na(info_good$th_kp_rate)==TRUE,0,1)
info_good$days_last_p <- ifelse(is.na(info_good$days_last_p)==TRUE,0,1)
#------------------有联系的组--------------
#DPD为4-35之间的
condition<- info_good$dpd %in% c(4:25)
data_4 <- info_good[condition,]
data_connected <- data_4[data_4$sort<500,] #发生了联系的
data_no <- data_4[data_4$sort == 500,] #没有发生联系
#------------------------
###data_1为联系了的,bad组为没有联系的
###其中data_1只有14204条记录 对照组有465450条记录
bad <- bad_space[sample(nrow(bad_space),nrow(data_1)),] #生成一个对等的没有入选的BAD组
#-------------------------------DPD>4和联系组中------------------------------------
#connected 有13415个，no有425299 其比例约为 0.03的情况，催收员大概只是浏览了3%的案件
unique(data_connected$sort)
max(data_connected$sort) #最大联系了265个人
tail((data_connected))
#---------------------------将自变量作为分类变量------------------------------------
data_connected$prd <-with(data_connected,
ifelse(prd %in% c('精英贷','精英贷（银行合作）'),1,
ifelse(prd %in% c('新薪贷','薪薪贷（银行合作）'),2,
ifelse(prd %in% c('新薪宜楼贷'),3,
ifelse(prd %in% c('助业贷'),4,
ifelse(prd %in% c('助业宜楼贷'),5,6
))))))
data_connected$last_activitive[data_connected$last_activitive=='预催收']=1
data_connected$last_activitive[data_connected$last_activitive=='手工电催']=2
data_connected$last_activitive[data_connected$last_activitive=='反欺诈调查']=3
data_connected$last_activitive[is.na(data_connected$last_activitive)]=4
data_connected$last_code <-with(data_connected,
ifelse(last_code %in% c('承诺还款(PTP)','暂时经济困难但有还款意愿','客户提示已还款（自动外呼）','愿意代偿'),1,
ifelse(last_code %in% c('协商跟进','无人应答(NoAnswer)','查找','他人转告','语音信箱(AnsweringMachine)','非本人直系亲属留言','要求回拨(Callback)'),2,
ifelse(last_code %in% c('无法接通','非本人接听,留言(WPC，LeftMsg)','占线(Busy)','非本人接听,留言(WPC，LeftMsg)','停机','非本人号码(Wrong#)'),3,
ifelse(last_code %in% c('空坏号(Bad#)','拒接','其他','拒绝还款(RTP)'),4,5
)))))
for(i in 4:35){
a <- data_connected$bal_percent[data_connected$dpd==i]
print(mean(a,na.rm = T))
sd(a,na.rm = T)
}
for(i in 4:35){
a <- data_connected$bal_percent[data_connected$dpd==i]
mean(a,na.rm = T)
print(sd(a,na.rm = T))
}
data_connected$tim <- with(data_connected, #和账期相关性较强按照账期画分出来
ifelse(tim<200,0,
ifelse(tim<300,1,2)))
data_connected$due_amount <- with(data_connected, #不同账期的分案并不相同
ifelse(due_amount<20000,0,
ifelse(due_amount<40000,1,2)))
data_connected$return_to_loan_term <- with(data_connected, #对于距离的划分
ifelse(return_to_loan_term <0.4,0,1))
data_connected$bal_percent <-with(data_connected, #分类中比较平均
ifelse(bal_percent>0.0055,1,0))
#-------------------------加载年龄age_sex和maxrepaydates-----------------------------
age_sex <- read.csv('age_sex.csv',stringsAsFactors = F)
maxrepay <- read.csv('maxrepaydate.csv',stringsAsFactors = F)
#连接数据
data_1 <- sqldf('select a.*,
b.age0,
sex
from data_connected as a
left join age_sex as b
on a.customer_code = b.customer_code')
data_1 <- sqldf('select a.*,
b.lastpay_tonow
from data_1 as a
left join maxrepay as b
on a.contractno = b.contractno and
a.enter_date2 = b.enter_date2')
data_1$sex <- with(data_1,
ifelse(sex =='男',1,
ifelse(sex == '女',0,2)))