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Mobicom Telecomfinal analysis.R
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Mobicom Telecomfinal analysis.R
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getwd()
tele<-read.csv("telecomfinal.csv", stringsAsFactors = T)
options(scipen = 999)
library(dplyr)
names(tele)
str(tele)
summary(tele)
##---------Creating Data Quality Report(dqr)-----------##
#Extracting Variable names
Variables<-names(tele)
dqr<-as.data.frame(Variables)
rm(Variables)
#Recording Data Type for each Variable
dqr$DataType<-sapply(tele,class)
#No. of Records for each Variable
dqr$No.ofRecords<-nrow(tele)
#Counting No. of Unique Values for each variable
for(i in 1:ncol(tele))
{
dqr$UniqueRecords[i]<-length(unique(tele[,i]))
}
#No.of observations available for each variable and its percentage
dqr$DataAvailable<-colSums(!is.na(tele))
dqr$AvailablePercentage<-round(colMeans(!is.na(tele)),4)
#Total and Percentage of Missing Values for each Variable
dqr$Missing<-colSums(is.na(tele))
dqr$MissingPercentage<-round(colMeans(is.na(tele)),4)
#Minimum, Maximum, Mean, Quantile Values for each Variable
for(i in 1:ncol(tele))
{
dqr$Minimum[i]<-round(ifelse(class(tele[,i])=="integer"|class(tele[,i])=="numeric",min(tele[,i],na.rm=T),0),2)
dqr$Maximum[i]<-round(ifelse(class(tele[,i])=="integer"|class(tele[,i])=="numeric",max(tele[,i],na.rm=T),0),2)
dqr$Mean[i]<-round(ifelse(class(tele[,i])=="integer"|class(tele[,i])=="numeric",mean(tele[,i],na.rm=T),0),2)
dqr$fifthPercentile[i]<-round(ifelse(class(tele[,i])=="integer"|class(tele[,i])=="numeric",quantile(tele[,i],p=0.05,na.rm=T),0),2)
dqr$tenthPercentile[i]<-round(ifelse(class(tele[,i])=="integer"|class(tele[,i])=="numeric",quantile(tele[,i],p=0.10,na.rm=T),0),2)
dqr$twentyfifthPercentile[i]<-round(ifelse(class(tele[,i])=="integer"|class(tele[,i])=="numeric",quantile(tele[,i],p=0.25,na.rm=T),0),2)
dqr$fiftythPercentile[i]<-round(ifelse(class(tele[,i])=="integer"|class(tele[,i])=="numeric",quantile(tele[,i],p=0.50,na.rm=T),0),2)
dqr$seventyfifthPercentile[i]<-round(ifelse(class(tele[,i])=="integer"|class(tele[,i])=="numeric",quantile(tele[,i],p=0.75,na.rm=T),0),2)
dqr$ninetythPercentile[i]<-round(ifelse(class(tele[,i])=="integer"|class(tele[,i])=="numeric",quantile(tele[,i],p=0.90,na.rm=T),0),2)
dqr$ninetyfifthPercentile[i]<-round(ifelse(class(tele[,i])=="integer"|class(tele[,i])=="numeric",quantile(tele[,i],p=0.95,na.rm=T),0),2)
}
str(dqr)
#Exporting Data Quality Report
write.csv(dqr,"Data Quality Report.csv",row.names = T)
#Missing Value treatment of var, "retdays" and Creating Dummy Variable
summary(tele$retdays)
sort(unique(tele$retdays), na.last = F)
tele$retdays_1<-ifelse(is.na(tele$retdays)==TRUE, 0, 1)
str(tele$retdays_1)
summary(tele$retdays_1)
#Ommitting variables with more than 15% missing values and creating a new data set
tele1<-tele[,colMeans(is.na(tele))<=0.15]
#Variable drop_blk_Mean is created by adding vars blck_dat_Mean + BLCK_VCE_MEAN + DROP_DAT_MEAN + DROP_VCE_MEAN
# So omitting Variable blck_dat_Mean
names(tele1)
tele1<-tele1[,-50]
##**********Data Exploration => Profiling (dat-Continuous Variables , datC-Categorical Variables)**********##
##-------- Deciling Continuous Variables Basis Target Variabe Churn---------##
names(tele1)
str(tele1)
# <1>Variable 'mou_Mean'
summary(tele1$mou_Mean)
tele1%>%mutate(dec=ntile(mou_Mean,n=10))%>%count(churn,dec)%>%filter(churn==1)->dat1
dat1$N<-unclass(tele1%>%mutate(dec=ntile(mou_Mean,n=10))%>%count(dec)%>%unname())[[2]]
dat1$churn_perc<-round(dat1$n/dat1$N,2)
dat1$GreaterThan<-unclass(tele1%>%mutate(dec=ntile(mou_Mean,n=10))%>%group_by(dec)%>%summarise(min(mou_Mean)))[[2]]
dat1$LessThan<-unclass(tele1%>%mutate(dec=ntile(mou_Mean,n=10))%>%group_by(dec)%>%summarise(max(mou_Mean)))[[2]]
dat1$varname<-rep("mou_Mean",nrow(dat1))
# <2> Variable "totmrc_Mean"
summary(tele1$totmrc_Mean)
tele1%>%mutate(dec=ntile(totmrc_Mean,n=10))%>%count(churn,dec)%>%filter(churn==1)->dat2
dat2$N<-unclass(tele1%>%mutate(dec=ntile(totmrc_Mean,n=10))%>%count(dec)%>%unname())[[2]]
dat2$churn_perc<-dat2$n/dat2$N
dat2$GreaterThan<-unclass(tele1%>%mutate(dec=ntile(totmrc_Mean,n=10))%>%group_by(dec)%>%summarise(min(totmrc_Mean)))[[2]]
dat2$LessThan<-unclass(tele1%>%mutate(dec=ntile(totmrc_Mean,n=10))%>%group_by(dec)%>%summarise(max(totmrc_Mean)))[[2]]
dat2$varname<-rep("totmrc_Mean",nrow(dat2))
# <3> Variable "rev_Range"
summary(tele1$rev_Range)
tele1%>%mutate(dec=ntile(rev_Range,n=10))%>%count(churn,dec)%>%filter(churn==1)->dat3
dat3$N<-unclass(tele1%>%mutate(dec=ntile(rev_Range,n=10))%>%count(dec)%>%unname())[[2]]
dat3$churn_perc<-dat3$n/dat3$N
dat3$GreaterThan<-unclass(tele1%>%mutate(dec=ntile(rev_Range,n=10))%>%group_by(dec)%>%summarise(min(rev_Range)))[[2]]
dat3$LessThan<-unclass(tele1%>%mutate(dec=ntile(rev_Range,n=10))%>%group_by(dec)%>%summarise(max(rev_Range)))[[2]]
dat3$varname<-rep("rev_Range",nrow(dat3))
# <4> Variable "mou_Range"
summary(tele1$mou_Range)
tele1%>%mutate(dec=ntile(mou_Range,n=10))%>%count(churn,dec)%>%filter(churn==1)->dat4
dat4$N<-unclass(tele1%>%mutate(dec=ntile(mou_Range,n=10))%>%count(dec)%>%unname())[[2]]
dat4$churn_perc<-dat4$n/dat4$N
dat4$GreaterThan<-unclass(tele1%>%mutate(dec=ntile(mou_Range,n=10))%>%group_by(dec)%>%summarise(min(mou_Range)))[[2]]
dat4$LessThan<-unclass(tele1%>%mutate(dec=ntile(mou_Range,n=10))%>%group_by(dec)%>%summarise(max(mou_Range)))[[2]]
dat4$varname<-rep("mou_Range",nrow(dat4))
# <5> Variable "change_mou"
summary(tele1$change_mou)
tele1%>%mutate(dec=ntile(change_mou,n=10))%>%count(churn,dec)%>%filter(churn==1)->dat5
dat5$N<-unclass(tele1%>%mutate(dec=ntile(change_mou,n=10))%>%count(dec)%>%unname())[[2]]
dat5$churn_perc<-dat5$n/dat5$N
dat5$GreaterThan<-unclass(tele1%>%mutate(dec=ntile(change_mou,n=10))%>%group_by(dec)%>%summarise(min(change_mou)))[[2]]
dat5$LessThan<-unclass(tele1%>%mutate(dec=ntile(change_mou,n=10))%>%group_by(dec)%>%summarise(max(change_mou)))[[2]]
dat5$varname<-rep("change_mou",nrow(dat5))
# <6> Variable "drop_blk_Mean"
tele1%>%mutate(dec=ntile(drop_blk_Mean,n=10))%>%count(churn,dec)%>%filter(churn==1)->dat6
dat6$N<-unclass(tele1%>%mutate(dec=ntile(drop_blk_Mean,n=10))%>%count(dec)%>%unname())[[2]]
dat6$churn_perc<-dat6$n/dat6$N
dat6$GreaterThan<-unclass(tele1%>%mutate(dec=ntile(drop_blk_Mean,n=10))%>%group_by(dec)%>%summarise(min(drop_blk_Mean)))[[2]]
dat6$LessThan<-unclass(tele1%>%mutate(dec=ntile(drop_blk_Mean,n=10))%>%group_by(dec)%>%summarise(max(drop_blk_Mean)))[[2]]
dat6$varname<-rep("drop_blk_Mean",nrow(dat6))
# <7> Variable "drop_vce_Range"
summary(tele1$drop_vce_Range)
tele1%>%mutate(dec=ntile(drop_vce_Range,n=10))%>%count(churn,dec)%>%filter(churn==1)->dat7
dat7$N<-unclass(tele1%>%mutate(dec=ntile(drop_vce_Range,n=10))%>%count(dec)%>%unname())[[2]]
dat7$churn_perc<-dat7$n/dat7$N
dat7$GreaterThan<-unclass(tele1%>%mutate(dec=ntile(drop_vce_Range,n=10))%>%group_by(dec)%>%summarise(min(drop_vce_Range)))[[2]]
dat7$LessThan<-unclass(tele1%>%mutate(dec=ntile(drop_vce_Range,n=10))%>%group_by(dec)%>%summarise(max(drop_vce_Range)))[[2]]
dat7$varname<-rep("drop_vce_Range",nrow(dat7))
# <8> Variable "owylis_vce_Range"
summary(tele1$owylis_vce_Range)
tele1%>%mutate(dec=ntile(owylis_vce_Range,n=10))%>%count(churn,dec)%>%filter(churn==1)->dat8
dat8$N<-unclass(tele1%>%mutate(dec=ntile(owylis_vce_Range,n=10))%>%count(dec)%>%unname())[[2]]
dat8$churn_perc<-dat8$n/dat8$N
dat8$GreaterThan<-unclass(tele1%>%mutate(dec=ntile(owylis_vce_Range,n=10))%>%group_by(dec)%>%summarise(min(owylis_vce_Range)))[[2]]
dat8$LessThan<-unclass(tele1%>%mutate(dec=ntile(owylis_vce_Range,n=10))%>%group_by(dec)%>%summarise(max(owylis_vce_Range)))[[2]]
dat8$varname<-rep("owylis_vce_Range",nrow(dat8))
# <9> Variable "mou_opkv_Range"
summary(tele1$mou_opkv_Range)
tele1%>%mutate(dec=ntile(mou_opkv_Range,n=10))%>%count(churn,dec)%>%filter(churn==1)->dat9
dat9$N<-unclass(tele1%>%mutate(dec=ntile(mou_opkv_Range,n=10))%>%count(dec)%>%unname())[[2]]
dat9$churn_perc<-dat9$n/dat9$N
dat9$GreaterThan<-unclass(tele1%>%mutate(dec=ntile(mou_opkv_Range,n=10))%>%group_by(dec)%>%summarise(min(mou_opkv_Range)))[[2]]
dat9$LessThan<-unclass(tele1%>%mutate(dec=ntile(mou_opkv_Range,n=10))%>%group_by(dec)%>%summarise(max(mou_opkv_Range)))[[2]]
dat9$varname<-rep("mou_opkv_Range",nrow(dat9))
# <10> Variable "months"
summary(tele1$months)
tele1%>%mutate(dec=ntile(months,n=10))%>%count(churn,dec)%>%filter(churn==1)->dat10
dat10$N<-unclass(tele1%>%mutate(dec=ntile(months,n=10))%>%count(dec)%>%unname())[[2]]
dat10$churn_perc<-dat10$n/dat10$N
dat10$GreaterThan<-unclass(tele1%>%mutate(dec=ntile(months,n=10))%>%group_by(dec)%>%summarise(min(months)))[[2]]
dat10$LessThan<-unclass(tele1%>%mutate(dec=ntile(months,n=10))%>%group_by(dec)%>%summarise(max(months)))[[2]]
dat10$varname<-rep("months",nrow(dat10))
# <11> Variable "totcalls"
summary(tele1$totcalls)
tele1%>%mutate(dec=ntile(totcalls,n=10))%>%count(churn,dec)%>%filter(churn==1)->dat11
dat11$N<-unclass(tele1%>%mutate(dec=ntile(totcalls,n=10))%>%count(dec)%>%unname())[[2]]
dat11$churn_perc<-dat11$n/dat11$N
dat11$GreaterThan<-unclass(tele1%>%mutate(dec=ntile(totcalls,n=10))%>%group_by(dec)%>%summarise(min(totcalls)))[[2]]
dat11$LessThan<-unclass(tele1%>%mutate(dec=ntile(totcalls,n=10))%>%group_by(dec)%>%summarise(max(totcalls)))[[2]]
dat11$varname<-rep("totcalls",nrow(dat11))
# <12> Variable "eqpdays"
summary(tele1$eqpdays)
#Missing Value Treatment - Since there is just 1 missing observation, will remove the same.
index<-which(is.na(tele1$eqpdays))
tele1<-tele1[-index,]
#Deciling basis Variable churn
tele1%>%mutate(dec=ntile(eqpdays,n=10))%>%count(churn,dec)%>%filter(churn==1)->dat12
dat12$N<-unclass(tele1%>%mutate(dec=ntile(eqpdays,n=10))%>%count(dec)%>%unname())[[2]]
dat12$churn_perc<-dat12$n/dat12$N
dat12$GreaterThan<-unclass(tele1%>%mutate(dec=ntile(eqpdays,n=10))%>%group_by(dec)%>%summarise(min(eqpdays)))[[2]]
dat12$LessThan<-unclass(tele1%>%mutate(dec=ntile(eqpdays,n=10))%>%group_by(dec)%>%summarise(max(eqpdays)))[[2]]
dat12$varname<-rep("eqpdays",nrow(dat12))
# <13> Variable "custcare_Mean"===>> ***Getting less than 4 deciles. Omit***
summary(tele1$custcare_Mean)
tele1%>%mutate(dec=ntile(custcare_Mean,n=4))%>%count(churn,dec)%>%filter(churn==1)->dat13
dat13$varname<-rep("custcare_Mean",nrow(dat13))
plot(tele1$churn,tele1$custcare_Mean, col="red")
# <14> Variable "callwait_Mean"
summary(tele1$callwait_Mean)
tele1%>%mutate(dec=ntile(callwait_Mean,n=4))%>%count(churn,dec)%>%filter(churn==1)->dat14
dat14$N<-unclass(tele1%>%mutate(dec=ntile(callwait_Mean,n=4))%>%count(dec)%>%unname())[[2]]
dat14$churn_perc<-dat14$n/dat14$N
dat14$GreaterThan<-unclass(tele1%>%mutate(dec=ntile(callwait_Mean,n=4))%>%group_by(dec)%>%summarise(min(callwait_Mean)))[[2]]
dat14$LessThan<-unclass(tele1%>%mutate(dec=ntile(callwait_Mean,n=4))%>%group_by(dec)%>%summarise(max(callwait_Mean)))[[2]]
dat14$varname<-rep("callwait_Mean",nrow(dat14))
# <15> Variable "iwylis_vce_Mean"
summary(tele1$iwylis_vce_Mean)
tele1%>%mutate(dec=ntile(iwylis_vce_Mean,n=6))%>%count(churn,dec)%>%filter(churn==1)->dat15
dat15$N<-unclass(tele1%>%mutate(dec=ntile(iwylis_vce_Mean,n=6))%>%count(dec)%>%unname())[[2]]
dat15$churn_perc<-dat15$n/dat15$N
dat15$GreaterThan<-unclass(tele1%>%mutate(dec=ntile(iwylis_vce_Mean,n=6))%>%group_by(dec)%>%summarise(min(iwylis_vce_Mean)))[[2]]
dat15$LessThan<-unclass(tele1%>%mutate(dec=ntile(iwylis_vce_Mean,n=6))%>%group_by(dec)%>%summarise(max(iwylis_vce_Mean)))[[2]]
dat15$varname<-rep("iwylis_vce_Mean",nrow(dat15))
# <16> Variable "callwait_Range"===>> ***Getting less than 4 deciles. Omit***
summary(tele1$callwait_Range)
tele1%>%mutate(dec=ntile(callwait_Range,n=4))%>%count(churn,dec)%>%filter(churn==1)->dat16
dat16$varname<-rep("callwait_Range",nrow(dat16))
# <17> Variable "ccrndmou_Range"===>> ***Getting less than 4 deciles. Omit***
summary(tele1$ccrndmou_Range)
tele1%>%mutate(dec=ntile(ccrndmou_Range,n=4))%>%count(churn,dec)%>%filter(churn==1)->dat17
# <18> Variable "adjqty"
summary(tele1$adjqty)
tele1%>%mutate(dec=ntile(adjqty,n=10))%>%count(churn,dec)%>%filter(churn==1)->dat18
dat18$N<-unclass(tele1%>%mutate(dec=ntile(adjqty,n=10))%>%count(dec)%>%unname())[[2]]
dat18$churn_perc<-dat18$n/dat18$N
dat18$GreaterThan<-unclass(tele1%>%mutate(dec=ntile(adjqty,n=10))%>%group_by(dec)%>%summarise(min(adjqty)))[[2]]
dat18$LessThan<-unclass(tele1%>%mutate(dec=ntile(adjqty,n=10))%>%group_by(dec)%>%summarise(max(adjqty)))[[2]]
dat18$varname<-rep("adjqty",nrow(dat18))
# <19> Variable "ovrrev_Mean"
summary(tele1$ovrrev_Mean)
tele1%>%mutate(dec=ntile(ovrrev_Mean,n=4))%>%count(churn,dec)%>%filter(churn==1)->dat19
dat19$N<-unclass(tele1%>%mutate(dec=ntile(ovrrev_Mean,n=4))%>%count(dec)%>%unname())[[2]]
dat19$churn_perc<-dat19$n/dat19$N
dat19$GreaterThan<-unclass(tele1%>%mutate(dec=ntile(ovrrev_Mean,n=4))%>%group_by(dec)%>%summarise(min(ovrrev_Mean)))[[2]]
dat19$LessThan<-unclass(tele1%>%mutate(dec=ntile(ovrrev_Mean,n=4))%>%group_by(dec)%>%summarise(max(ovrrev_Mean)))[[2]]
dat19$varname<-rep("ovrrev_Mean",nrow(dat19))
# <20> Variable "rev_Mean"
summary(tele1$rev_Mean)
tele1%>%mutate(dec=ntile(rev_Mean,n=10))%>%count(churn,dec)%>%filter(churn==1)->dat20
dat20$N<-unclass(tele1%>%mutate(dec=ntile(rev_Mean,n=10))%>%count(dec)%>%unname())[[2]]
dat20$churn_perc<-dat20$n/dat20$N
dat20$GreaterThan<-unclass(tele1%>%mutate(dec=ntile(rev_Mean,n=10))%>%group_by(dec)%>%summarise(min(rev_Mean)))[[2]]
dat20$LessThan<-unclass(tele1%>%mutate(dec=ntile(rev_Mean,n=10))%>%group_by(dec)%>%summarise(max(rev_Mean)))[[2]]
dat20$varname<-rep("rev_Mean",nrow(dat20))
# <21> Variable "ovrmou_Mean"
summary(tele1$ovrmou_Mean)
tele1%>%mutate(dec=ntile(ovrmou_Mean,n=4))%>%count(churn,dec)%>%filter(churn==1)->dat21
dat21$N<-unclass(tele1%>%mutate(dec=ntile(ovrmou_Mean,n=4))%>%count(dec)%>%unname())[[2]]
dat21$churn_perc<-dat21$n/dat21$N
dat21$GreaterThan<-unclass(tele1%>%mutate(dec=ntile(ovrmou_Mean,n=4))%>%group_by(dec)%>%summarise(min(ovrmou_Mean)))[[2]]
dat21$LessThan<-unclass(tele1%>%mutate(dec=ntile(ovrmou_Mean,n=4))%>%group_by(dec)%>%summarise(max(ovrmou_Mean)))[[2]]
dat21$varname<-rep("ovrmou_Mean",nrow(dat21))
# <22> Variable "comp_vce_Mean" ===>> **** Data Transformation then Delete ****
summary(tele1$comp_vce_Mean)
tele1%>%mutate(dec=ntile(comp_vce_Mean,n=10))%>%count(churn,dec)%>%filter(churn==1)->dat22
dat22$N<-unclass(tele1%>%mutate(dec=ntile(comp_vce_Mean,n=10))%>%count(dec)%>%unname())[[2]]
dat22$churn_perc<-dat22$n/dat22$N
dat22$GreaterThan<-unclass(tele1%>%mutate(dec=ntile(comp_vce_Mean,n=10))%>%group_by(dec)%>%summarise(min(comp_vce_Mean)))[[2]]
dat22$LessThan<-unclass(tele1%>%mutate(dec=ntile(comp_vce_Mean,n=10))%>%group_by(dec)%>%summarise(max(comp_vce_Mean)))[[2]]
dat22$varname<-rep("comp_vce_Mean",nrow(dat22))
# <23> Variable "plcd_vce_Mean" ===>> **** Data Transformation then Delete ****
summary(tele1$plcd_vce_Mean)
tele1%>%mutate(dec=ntile(plcd_vce_Mean,n=10))%>%count(churn,dec)%>%filter(churn==1)->dat23
dat23$N<-unclass(tele1%>%mutate(dec=ntile(plcd_vce_Mean,n=10))%>%count(dec)%>%unname())[[2]]
dat23$churn_perc<-dat23$n/dat23$N
dat23$GreaterThan<-unclass(tele1%>%mutate(dec=ntile(plcd_vce_Mean,n=10))%>%group_by(dec)%>%summarise(min(plcd_vce_Mean)))[[2]]
dat23$LessThan<-unclass(tele1%>%mutate(dec=ntile(plcd_vce_Mean,n=10))%>%group_by(dec)%>%summarise(max(plcd_vce_Mean)))[[2]]
dat23$varname<-rep("plcd_vce_Mean",nrow(dat23))
# <24> Variable "avg3mou"
summary(tele1$avg3mou)
tele1%>%mutate(dec=ntile(avg3mou,n=10))%>%count(churn,dec)%>%filter(churn==1)->dat24
dat24$N<-unclass(tele1%>%mutate(dec=ntile(avg3mou,n=10))%>%count(dec)%>%unname())[[2]]
dat24$churn_perc<-dat24$n/dat24$N
dat24$GreaterThan<-unclass(tele1%>%mutate(dec=ntile(avg3mou,n=10))%>%group_by(dec)%>%summarise(min(avg3mou)))[[2]]
dat24$LessThan<-unclass(tele1%>%mutate(dec=ntile(avg3mou,n=10))%>%group_by(dec)%>%summarise(max(avg3mou)))[[2]]
dat24$varname<-rep("avg3mou",nrow(dat24))
# <25> Variable "avgmou"
summary(tele1$avgmou)
tele1%>%mutate(dec=ntile(avgmou,n=10))%>%count(churn,dec)%>%filter(churn==1)->dat25
dat25$N<-unclass(tele1%>%mutate(dec=ntile(avgmou,n=10))%>%count(dec)%>%unname())[[2]]
dat25$churn_perc<-dat25$n/dat25$N
dat25$GreaterThan<-unclass(tele1%>%mutate(dec=ntile(avgmou,n=10))%>%group_by(dec)%>%summarise(min(avgmou)))[[2]]
dat25$LessThan<-unclass(tele1%>%mutate(dec=ntile(avgmou,n=10))%>%group_by(dec)%>%summarise(max(avgmou)))[[2]]
dat25$varname<-rep("avgmou",nrow(dat25))
# <26> Variable "avg3qty"
summary(tele1$avg3qty)
tele1%>%mutate(dec=ntile(avg3qty,n=10))%>%count(churn,dec)%>%filter(churn==1)->dat26
dat26$N<-unclass(tele1%>%mutate(dec=ntile(avg3qty,n=10))%>%count(dec)%>%unname())[[2]]
dat26$churn_perc<-dat26$n/dat26$N
dat26$GreaterThan<-unclass(tele1%>%mutate(dec=ntile(avg3qty,n=10))%>%group_by(dec)%>%summarise(min(avg3qty)))[[2]]
dat26$LessThan<-unclass(tele1%>%mutate(dec=ntile(avg3qty,n=10))%>%group_by(dec)%>%summarise(max(avg3qty)))[[2]]
dat26$varname<-rep("avg3qty",nrow(dat26))
# <27> Variable "avgqty"
summary(tele1$avgqty)
tele1%>%mutate(dec=ntile(avgqty,n=10))%>%count(churn,dec)%>%filter(churn==1)->dat27
dat27$N<-unclass(tele1%>%mutate(dec=ntile(avgqty,n=10))%>%count(dec)%>%unname())[[2]]
dat27$churn_perc<-dat27$n/dat27$N
dat27$GreaterThan<-unclass(tele1%>%mutate(dec=ntile(avgqty,n=10))%>%group_by(dec)%>%summarise(min(avgqty)))[[2]]
dat27$LessThan<-unclass(tele1%>%mutate(dec=ntile(avgqty,n=10))%>%group_by(dec)%>%summarise(max(avgqty)))[[2]]
dat27$varname<-rep("avgqty",nrow(dat27))
# <28> Variable "avg6mou"
summary(tele1$avg6mou)
tele1%>%mutate(dec=ntile(avg6mou,n=10))%>%count(churn,dec)%>%filter(churn==1)->dat28
dat28$N<-unclass(tele1%>%mutate(dec=ntile(avg6mou,n=10))%>%count(dec)%>%unname())[[2]]
dat28$churn_perc<-dat28$n/dat28$N
dat28$GreaterThan<-unclass(tele1%>%mutate(dec=ntile(avg6mou,n=10))%>%group_by(dec)%>%summarise(min(avg6mou)))[[2]]
dat28$LessThan<-unclass(tele1%>%mutate(dec=ntile(avg6mou,n=10))%>%group_by(dec)%>%summarise(max(avg6mou)))[[2]]
dat28$varname<-rep("avg6mou",nrow(dat28))
# <29> Variable "avg6qty"
summary(tele1$avg6qty)
tele1%>%mutate(dec=ntile(avg6qty,n=10))%>%count(churn,dec)%>%filter(churn==1)->dat29
dat29$N<-unclass(tele1%>%mutate(dec=ntile(avg6qty,n=10))%>%count(dec)%>%unname())[[2]]
dat29$churn_perc<-dat29$n/dat29$N
dat29$GreaterThan<-unclass(tele1%>%mutate(dec=ntile(avg6qty,n=10))%>%group_by(dec)%>%summarise(min(avg6qty)))[[2]]
dat29$LessThan<-unclass(tele1%>%mutate(dec=ntile(avg6qty,n=10))%>%group_by(dec)%>%summarise(max(avg6qty)))[[2]]
dat29$varname<-rep("avg6qty",nrow(dat29))
# <30> Variable "age1" =====>>****Use As Factor****
summary(tele1$age1)
tele1%>%mutate(dec=ntile(age1,n=6))%>%count(churn,dec)%>%filter(churn==1)->dat30
dat30$N<-unclass(tele1%>%mutate(dec=ntile(age1,n=6))%>%count(dec)%>%unname())[[2]]
dat30$churn_perc<-dat30$n/dat30$N
dat30$GreaterThan<-unclass(tele1%>%mutate(dec=ntile(age1,n=6))%>%group_by(dec)%>%summarise(min(age1)))[[2]]
dat30$LessThan<-unclass(tele1%>%mutate(dec=ntile(age1,n=6))%>%group_by(dec)%>%summarise(max(age1)))[[2]]
dat30$varname<-rep("age1",nrow(dat30))
# <31> Variable "age2"===>> ***Getting less than 4 deciles. Use As Factor***
summary(tele1$age2)
tele1%>%mutate(dec=ntile(age2,n=4))%>%count(churn,dec)%>%filter(churn==1)->dat31
dat31$varname<-rep("age2",nrow(dat31))
# <32> Variable "models" ===>> ***Getting less than 4 deciles. Factor Variable***
summary(tele1$models)
tele1%>%mutate(dec=ntile(models,n=4))%>%count(churn,dec)%>%filter(churn==1)->dat32
dat32$varname<-rep("models",nrow(dat32))
# <33> Variable "hnd_price" =====>> ****** Use as Factor Variable*****
summary(tele1$hnd_price)
tele1%>%mutate(dec=ntile(hnd_price,n=10))%>%count(churn,dec)%>%filter(churn==1)->dat33
dat33$N<-unclass(tele1%>%mutate(dec=ntile(hnd_price,n=10))%>%count(dec)%>%unname())[[2]]
dat33$churn_perc<-dat33$n/dat33$N
dat33$GreaterThan<-unclass(tele1%>%mutate(dec=ntile(hnd_price,n=10))%>%group_by(dec)%>%summarise(min(hnd_price)))[[2]]
dat33$LessThan<-unclass(tele1%>%mutate(dec=ntile(hnd_price,n=10))%>%group_by(dec)%>%summarise(max(hnd_price)))[[2]]
dat33$varname<-rep("hnd_price",nrow(dat33))
# <34> Variable "actvsubs" ===>> ***Factor Variable***
summary(tele1$actvsubs)
tele1%>%mutate(dec=ntile(actvsubs,n=4))%>%count(churn,dec)%>%filter(churn==1)->dat34
dat34$varname<-rep("actvsubs",nrow(dat34))
# <35> Variable "uniqsubs" ===>> ***Factor Variable***
summary(tele1$uniqsubs)
tele1%>%mutate(dec=ntile(uniqsubs,n=4))%>%count(churn,dec)%>%filter(churn==1)->dat35
dat35$varname<-rep("uniqsubs",nrow(dat35))
# <36> Variable "forgntvl" ===>> ***Factor Variable***
summary(tele1$forgntvl)
tele1%>%mutate(dec=ntile(forgntvl,n=4))%>%count(churn,dec)%>%filter(churn==1)->dat36
dat36$varname<-rep("forgntvl",nrow(dat36))
# <37> Variable "opk_dat_Mean" ===>> ***Omit***
summary(tele1$opk_dat_Mean)
tele1%>%mutate(dec=ntile(opk_dat_Mean,n=4))%>%count(churn,dec)%>%filter(churn==1)->dat37
dat37$varname<-rep("opk_dat_Mean",nrow(dat37))
# <38> Variable "mtrcycle" ===>> ***Factor variable***
summary(tele1$mtrcycle)
tele1%>%mutate(dec=ntile(mtrcycle,n=4))%>%count(churn,dec)%>%filter(churn==1)->dat38
dat38$varname<-rep("mtrcycle",nrow(dat38))
# <39> Variable "truck" ===>> ***Factor variable***
summary(tele1$truck)
tele1%>%mutate(dec=ntile(truck,n=4))%>%count(churn,dec)%>%filter(churn==1)->dat39
dat39$varname<-rep("truck",nrow(dat39))
# <40> Variable "roam_Mean" ===>> ***Getting less than 4 deciles. So Omit***
summary(tele1$roam_Mean)
tele1%>%mutate(dec=ntile(roam_Mean,n=4))%>%count(churn,dec)%>%filter(churn==1)->dat40
dat40$varname<-rep("roam_Mean",nrow(dat40))
# <41> Variable "recv_sms_Mean" ===>> ***Getting less than 4 deciles. So Omit***
summary(tele1$recv_sms_Mean)
tele1%>%mutate(dec=ntile(recv_sms_Mean,n=4))%>%count(churn,dec)%>%filter(churn==1)->dat41
dat41$varname<-rep("recv_sms_Mean",nrow(dat41))
# <42> Variable "mou_pead_Mean" ===>> ***Getting less than 4 deciles. So Omit***
summary(tele1$mou_pead_Mean)
tele1%>%mutate(dec=ntile(mou_pead_Mean,n=4))%>%count(churn,dec)%>%filter(churn==1)->dat42
dat42$varname<-rep("mou_pead_Mean",nrow(dat42))
# <43> Variable "da_Mean"
summary(tele1$da_Mean)
tele1%>%mutate(dec=ntile(da_Mean,n=4))%>%count(churn,dec)%>%filter(churn==1)->dat43
dat43$N<-unclass(tele1%>%mutate(dec=ntile(da_Mean,n=4))%>%count(dec)%>%unname())[[2]]
dat43$churn_perc<-dat43$n/dat43$N
dat43$GreaterThan<-unclass(tele1%>%mutate(dec=ntile(da_Mean,n=4))%>%group_by(dec)%>%summarise(min(da_Mean)))[[2]]
dat43$LessThan<-unclass(tele1%>%mutate(dec=ntile(da_Mean,n=4))%>%group_by(dec)%>%summarise(max(da_Mean)))[[2]]
dat43$varname<-rep("da_Mean",nrow(dat43))
# <44> Variable "da_Range"
summary(tele1$da_Range)
tele1%>%mutate(dec=ntile(da_Range,n=4))%>%count(churn,dec)%>%filter(churn==1)->dat44
dat44$N<-unclass(tele1%>%mutate(dec=ntile(da_Range,n=4))%>%count(dec)%>%unname())[[2]]
dat44$churn_perc<-dat44$n/dat44$N
dat44$GreaterThan<-unclass(tele1%>%mutate(dec=ntile(da_Range,n=4))%>%group_by(dec)%>%summarise(min(da_Range)))[[2]]
dat44$LessThan<-unclass(tele1%>%mutate(dec=ntile(da_Range,n=4))%>%group_by(dec)%>%summarise(max(da_Range)))[[2]]
dat44$varname<-rep("da_Range",nrow(dat44))
# <45> Variable "datovr_Mean" ===>> ***Getting less than 4 deciles. So Omit***
summary(tele1$datovr_Mean)
tele1%>%mutate(dec=ntile(datovr_Mean,n=4))%>%count(churn,dec)%>%filter(churn==1)->dat45
dat45$varname<-rep("datovr_Mean",nrow(dat45))
# <46> Variable "datovr_Range" ===>> ***Getting less than 4 deciles. So Omit***
summary(tele1$datovr_Range)
tele1%>%mutate(dec=ntile(datovr_Range,n=4))%>%count(churn,dec)%>%filter(churn==1)->dat46
dat46$varname<-rep("datovr_Range",nrow(dat46))
# <47> Variable "drop_dat_Mean" ===>> ***Getting less than 4 deciles.
#And almost 95% data has values 0. So Omit***
summary(tele1$drop_dat_Mean)
tele1%>%mutate(dec=ntile(drop_dat_Mean,n=4))%>%count(churn,dec)%>%filter(churn==1)->dat47
dat47$varname<-rep("drop_dat_Mean",nrow(dat47))
# <48> Variable "drop_vce_Mean"
summary(tele1$drop_vce_Mean)
tele1%>%mutate(dec=ntile(drop_vce_Mean,n=10))%>%count(churn,dec)%>%filter(churn==1)->dat48
dat48$N<-unclass(tele1%>%mutate(dec=ntile(drop_vce_Mean,n=10))%>%count(dec)%>%unname())[[2]]
dat48$churn_perc<-dat48$n/dat48$N
dat48$GreaterThan<-unclass(tele1%>%mutate(dec=ntile(drop_vce_Mean,n=10))%>%group_by(dec)%>%summarise(min(drop_vce_Mean)))[[2]]
dat48$LessThan<-unclass(tele1%>%mutate(dec=ntile(drop_vce_Mean,n=10))%>%group_by(dec)%>%summarise(max(drop_vce_Mean)))[[2]]
dat48$varname<-rep("drop_vce_Mean",nrow(dat48))
# <49> Variable "adjmou"
summary(tele1$adjmou)
tele1%>%mutate(dec=ntile(adjmou,n=10))%>%count(churn,dec)%>%filter(churn==1)->dat49
dat49$N<-unclass(tele1%>%mutate(dec=ntile(adjmou,n=10))%>%count(dec)%>%unname())[[2]]
dat49$churn_perc<-dat49$n/dat49$N
dat49$GreaterThan<-unclass(tele1%>%mutate(dec=ntile(adjmou,n=10))%>%group_by(dec)%>%summarise(min(adjmou)))[[2]]
dat49$LessThan<-unclass(tele1%>%mutate(dec=ntile(adjmou,n=10))%>%group_by(dec)%>%summarise(max(adjmou)))[[2]]
dat49$varname<-rep("adjmou",nrow(dat49))
# <50> Variable "totrev"
summary(tele1$totrev)
tele1%>%mutate(dec=ntile(totrev,n=10))%>%count(churn,dec)%>%filter(churn==1)->dat50
dat50$N<-unclass(tele1%>%mutate(dec=ntile(totrev,n=10))%>%count(dec)%>%unname())[[2]]
dat50$churn_perc<-dat50$n/dat50$N
dat50$GreaterThan<-unclass(tele1%>%mutate(dec=ntile(totrev,n=10))%>%group_by(dec)%>%summarise(min(totrev)))[[2]]
dat50$LessThan<-unclass(tele1%>%mutate(dec=ntile(totrev,n=10))%>%group_by(dec)%>%summarise(max(totrev)))[[2]]
dat50$varname<-rep("totrev",nrow(dat50))
# <51> Variable "adjrev"
summary(tele1$adjrev)
tele1%>%mutate(dec=ntile(adjrev,n=10))%>%count(churn,dec)%>%filter(churn==1)->dat51
dat51$N<-unclass(tele1%>%mutate(dec=ntile(adjrev,n=10))%>%count(dec)%>%unname())[[2]]
dat51$churn_perc<-dat51$n/dat51$N
dat51$GreaterThan<-unclass(tele1%>%mutate(dec=ntile(adjrev,n=10))%>%group_by(dec)%>%summarise(min(adjrev)))[[2]]
dat51$LessThan<-unclass(tele1%>%mutate(dec=ntile(adjrev,n=10))%>%group_by(dec)%>%summarise(max(adjrev)))[[2]]
dat51$varname<-rep("adjrev",nrow(dat51))
# <52> Variable "avgrev"
summary(tele1$avgrev)
tele1%>%mutate(dec=ntile(avgrev,n=10))%>%count(churn,dec)%>%filter(churn==1)->dat52
dat52$N<-unclass(tele1%>%mutate(dec=ntile(avgrev,n=10))%>%count(dec)%>%unname())[[2]]
dat52$churn_perc<-dat52$n/dat52$N
dat52$GreaterThan<-unclass(tele1%>%mutate(dec=ntile(avgrev,n=10))%>%group_by(dec)%>%summarise(min(avgrev)))[[2]]
dat52$LessThan<-unclass(tele1%>%mutate(dec=ntile(avgrev,n=10))%>%group_by(dec)%>%summarise(max(avgrev)))[[2]]
dat52$varname<-rep("avgrev",nrow(dat52))
# <53> Variable "comp_dat_Mean" ===>> ***Data Transformation then omit***
summary(tele1$comp_dat_Mean)
tele1%>%mutate(dec=ntile(comp_dat_Mean,n=4))%>%count(churn,dec)%>%filter(churn==1)->dat53
dat53$varname<-rep("comp_dat_Mean",nrow(dat53))
# <54> Variable "plcd_dat_Mean" ====>> ****Data Transformation then omit****
summary(tele1$plcd_dat_Mean)
tele1%>%mutate(dec=ntile(plcd_dat_Mean,n=4))%>%count(churn,dec)%>%filter(churn==1)->dat54
## **** Data Transformation. Creating Dummy Variables **** ##
#<55> Create Dummy Variable plcd_Atempt_Mean and Deciling
tele1$plcd_attempt_Mean<-tele1$plcd_vce_Mean+tele1$plcd_dat_Mean
summary(tele1$plcd_attempt_Mean)
tele1%>%mutate(dec=ntile(plcd_attempt_Mean,n=10))%>%count(churn,dec)%>%filter(churn==1)->dat55
dat55$N<-unclass(tele1%>%mutate(dec=ntile(plcd_attempt_Mean,n=10))%>%count(dec)%>%unname())[[2]]
dat55$churn_perc<-dat55$n/dat55$N
dat55$GreaterThan<-unclass(tele1%>%mutate(dec=ntile(plcd_attempt_Mean,n=10))%>%group_by(dec)%>%summarise(min(plcd_attempt_Mean)))[[2]]
dat55$LessThan<-unclass(tele1%>%mutate(dec=ntile(plcd_attempt_Mean,n=10))%>%group_by(dec)%>%summarise(max(plcd_attempt_Mean)))[[2]]
dat55$varname<-rep("plcd_attempt_Mean",nrow(dat55))
#<56> Create Dummy Variable complete_Mean and Deciling
tele1$complete_Mean<-tele1$comp_vce_Mean+tele1$comp_dat_Mean
summary(tele1$complete_Mean)
tele1%>%mutate(dec=ntile(complete_Mean,n=10))%>%count(churn,dec)%>%filter(churn==1)->dat56
dat56$N<-unclass(tele1%>%mutate(dec=ntile(complete_Mean,n=10))%>%count(dec)%>%unname())[[2]]
dat56$churn_perc<-dat56$n/dat56$N
dat56$GreaterThan<-unclass(tele1%>%mutate(dec=ntile(complete_Mean,n=10))%>%group_by(dec)%>%summarise(min(complete_Mean)))[[2]]
dat56$LessThan<-unclass(tele1%>%mutate(dec=ntile(complete_Mean,n=10))%>%group_by(dec)%>%summarise(max(complete_Mean)))[[2]]
dat56$varname<-rep("complete_Mean",nrow(dat56))
#Adding all appropriate dat1 to dat54 objects to create a dat object
dat<-rbind(dat1,dat2,dat3,dat4,dat5,dat6,dat7,dat8,dat9,dat10,dat11,dat12,dat14,dat15,dat18,dat19,
dat20,dat21,dat22,dat23,dat24,dat25,dat26,dat27,dat28,dat29,dat30,dat33,dat43,dat44,
dat48,dat49,dat50,dat51,dat52,dat55,dat56)
#Exporting Deciled variables
write.csv(dat,"Deciled Usable Continuous variables.csv",row.names = F)
#Removing Variables that could not be deciled as will come insignificant in the model
#Also omitting transformed Vars "comp_vce_Mean" ,"comp_dat_Mean", "plcd_vce_Mean", plcd_dat_Mean
names(tele1)
tele1<-tele1[,-c(13,16,17,22,23,45,48:50,56:58,65,66)]
names(tele1)
##-------- Categorical Variables---------##
##-----Event rate for each level in a categorical variable-----##
# <19> Variable "crclscod" =====>>> **** Some Levels show less than 5% churn rate. So Omit as will come insignificant ****
summary(tele1$crclscod)
tele1%>%count(churn,levels=crclscod)%>%filter(churn==1)->datC19
datC19$N<-unclass(tele1%>%filter(crclscod%in%datC19$levels)%>%count(crclscod))[[2]]
datC19$ChurnPerc<-datC19$n/datC19$N
datC19$Var.Name<-rep("crclscod",nrow(datC19))
# <20> Variable "asl_flag"
summary(tele1$asl_flag)
tele1%>%count(churn,levels=asl_flag)%>%filter(churn==1)->datC20
datC20$N<-unclass(tele1%>%filter(asl_flag%in%datC20$levels)%>%count(asl_flag))[[2]]
datC20$ChurnPerc<-datC20$n/datC20$N
datC20$Var.Name<-rep("asl_flag",nrow(datC20))
# <21> Variable "prizm_social_one"
summary(tele1$prizm_social_one)
tele1%>%count(churn,levels=prizm_social_one)%>%filter(churn==1)->datC21
datC21$N<-unclass(tele1%>%filter(prizm_social_one%in%datC21$levels)%>%count(prizm_social_one))[[2]]
datC21$ChurnPerc<-datC21$n/datC21$N
datC21$Var.Name<-rep("prizm_social_one",nrow(datC21))
# <22> Variable "area"
summary(tele1$area)
tele1%>%count(churn,levels=area)%>%filter(churn==1)->datC22
datC22$N<-unclass(tele1%>%filter(area%in%datC22$levels)%>%count(area))[[2]]
datC22$ChurnPerc<-datC22$n/datC22$N
datC22$Var.Name<-rep("area",nrow(datC22))
# <23> Variable "refurb_new"
summary(tele1$refurb_new)
tele1%>%count(churn,levels=refurb_new)%>%filter(churn==1)->datC23
datC23$N<-unclass(tele1%>%filter(refurb_new%in%datC23$levels)%>%count(refurb_new))[[2]]
datC23$ChurnPerc<-datC23$n/datC23$N
datC23$Var.Name<-rep("refurb_new",nrow(datC23))
# <24> Variable "hnd_webcap"
summary(tele1$hnd_webcap)
tele1%>%count(churn,levels=hnd_webcap)%>%filter(churn==1)->datC24
datC24$N<-unclass(tele1%>%filter(hnd_webcap%in%datC24$levels)%>%count(hnd_webcap))[[2]]
datC24$ChurnPerc<-datC24$n/datC24$N
datC24$Var.Name<-rep("hnd_webcap",nrow(datC24))
# <25> Variable "marital"
summary(tele1$marital)
tele1%>%count(churn,levels=marital)%>%filter(churn==1)->datC25
datC25$N<-unclass(tele1%>%filter(marital%in%datC25$levels)%>%count(marital))[[2]]
datC25$ChurnPerc<-datC25$n/datC25$N
datC25$Var.Name<-rep("marital",nrow(datC25))
# <26> Variable "ethnic"
summary(tele1$ethnic)
tele1%>%count(churn,levels=ethnic)%>%filter(churn==1)->datC26
datC26$N<-unclass(tele1%>%filter(ethnic%in%datC26$levels)%>%count(ethnic))[[2]]
datC26$ChurnPerc<-datC26$n/datC26$N
datC26$Var.Name<-rep("ethnic",nrow(datC26))
# <27> Variable "car_buy"
summary(tele1$car_buy)
tele1%>%count(churn,levels=car_buy)%>%filter(churn==1)->datC27
datC27$N<-unclass(tele1%>%filter(car_buy%in%datC27$levels)%>%count(car_buy))[[2]]
datC27$ChurnPerc<-datC27$n/datC27$N
datC27$Var.Name<-rep("car_buy",nrow(datC27))
# <28> Variable "csa" ===>>> **** Some Levels show less than 5% churn rate. So Omit as will come insignificant ****
summary(tele1$csa)
tele1%>%count(churn,levels=csa)%>%filter(churn==1)->datC28
datC28$N<-unclass(tele1%>%filter(csa%in%datC28$levels)%>%count(csa))[[2]]
datC28$ChurnPerc<-datC28$n/datC28$N
datC28$Var.Name<-rep("csa",nrow(datC28))
# <29> Variable "retdays_1"
summary(tele1$retdays_1)
tele1$retdays_1<-as.factor(tele1$retdays_1)
tele1%>%count(churn,levels=retdays_1)%>%filter(churn==1)->datC29
datC29$N<-unclass(tele1%>%filter(retdays_1%in%datC29$levels)%>%count(retdays_1))[[2]]
datC29$ChurnPerc<-datC29$n/datC29$N
datC29$Var.Name<-rep("retdays_1",nrow(datC29))
# Use VArs as Factor => age2, models, actvsubs, uniqsubs, forgntvl, mtrcycle, truck,
# <31> Variable "age2"
summary(tele1$age2)
tele1%>%count(churn,levels=age2)%>%filter(churn==1)->datC31
datC31$N<-unclass(tele1%>%filter(age2%in%datC31$levels)%>%count(age2))[[2]]
datC31$ChurnPerc<-datC31$n/datC31$N
datC31$Var.Name<-rep("age2",nrow(datC31))
# <32> Variable "models"
summary(tele1$models)
tele1%>%count(churn,levels=models)%>%filter(churn==1)->datC32
datC32$N<-unclass(tele1%>%filter(models%in%datC32$levels)%>%count(models))[[2]]
datC32$ChurnPerc<-datC32$n/datC32$N
datC32$Var.Name<-rep("models",nrow(datC32))
# <34> Variable "actvsubs"
summary(tele1$actvsubs)
tele1%>%count(churn,levels=actvsubs)%>%filter(churn==1)->datC34
datC34$N<-unclass(tele1%>%filter(actvsubs%in%datC34$levels)%>%count(actvsubs))[[2]]
datC34$ChurnPerc<-datC34$n/datC34$N
datC34$Var.Name<-rep("actvsubs",nrow(datC34))
# <35> Variable "uniqsubs"
summary(tele1$uniqsubs)
tele1%>%count(churn,levels=uniqsubs)%>%filter(churn==1)->datC35
datC35$N<-unclass(tele1%>%filter(uniqsubs%in%datC35$levels)%>%count(uniqsubs))[[2]]
datC35$ChurnPerc<-datC35$n/datC35$N
datC35$Var.Name<-rep("uniqsubs",nrow(datC35))
# <36> Variable "forgntvl"
summary(tele1$forgntvl)
tele1%>%count(churn,levels=forgntvl)%>%filter(churn==1)->datC36
datC36$N<-unclass(tele1%>%filter(forgntvl%in%datC36$levels)%>%count(forgntvl))[[2]]
datC36$ChurnPerc<-datC36$n/datC36$N
datC36$Var.Name<-rep("forgntvl",nrow(datC36))
# <37> Variable "mtrcycle"
summary(tele1$mtrcycle)
tele1%>%count(churn,levels=mtrcycle)%>%filter(churn==1)->datC37
datC37$N<-unclass(tele1%>%filter(mtrcycle%in%datC37$levels)%>%count(mtrcycle))[[2]]
datC37$ChurnPerc<-datC37$n/datC37$N
datC37$Var.Name<-rep("mtrcycle",nrow(datC37))
# <38> Variable "Truck"
summary(tele1$truck)
tele1%>%count(churn,levels=truck)%>%filter(churn==1)->datC38
datC38$N<-unclass(tele1%>%filter(truck%in%datC38$levels)%>%count(truck))[[2]]
datC38$ChurnPerc<-datC38$n/datC38$N
datC38$Var.Name<-rep("Truck",nrow(datC38))
# <39> Variable "hnd_price"
summary(tele1$hnd_price)
tele1%>%count(churn,levels=hnd_price)%>%filter(churn==1)->datC39
datC39$N<-unclass(tele1%>%filter(hnd_price%in%datC39$levels)%>%count(hnd_price))[[2]]
datC39$ChurnPerc<-datC39$n/datC39$N
datC39$Var.Name<-rep("hnd_price",nrow(datC39))
datC39
#Adding datC19 to datC38 objects to create a datC object
datC_1<-rbind(datC19,datC20,datC21,datC22,datC23,datC24,datC25,datC26,datC27,
datC28,datC29)
datC_2<-rbind(datC31,datC32,datC34,datC35,datC36,datC37,datC38)
#Exporting Deciled variables
write.csv(datC_1,"Event Rate - Categorical variables1.csv",row.names = F)
write.csv(datC_2,"Event Rate - Categorical variables2.csv",row.names = F)
#Removing Variables with levels less than 5% churn rate as will come insignificant
names(tele1)
tele1<-tele1[,-c(25,44)]
names(tele1)
#*********Data Preparation*********#
#-----Outlier Treatment----#
#-----Continuous Variables------#
#Method <II> ----- Box Plot Method==>> Used
names(tele1)
summary(tele1)
str(tele1)
#Factor Variables=> asl_flag, prizm_social_one, area, refurb_new, hnd_webcap,
#marital, ethnic, age1, age2, models, hnd_price, actvsubs, "uniqsubs",
#"forgntvl", "mtrcycle", "truck", churn, car_buy, Customer_ID, retdays_1
str(tele1)
list<-names(tele1)
list
# Removing Categorical Variables
list<-list[-c(25:42,50,51)]
list
# Outlier Plots
par(mfrow=c(3,11))
for(i in 1:length(list))
{
boxplot(tele1[,list[i]],main=list[i])
}
for(i in 1:length(list))
{
plot(tele1[,list[i]],main=list[i])
}
# Outlier Treatment
for(i in 1:length(list))
{
x<-boxplot(tele1[,list[i]],main=list[i])
out<-x$out
index<-which(tele1[,list[i]]%in% x$out)
tele1[index,list[i]]<-mean(tele1[,list[i]],na.rm = T)
rm(x)
rm(out)
}
# Checking After Treatment
for(i in 1:length(list))
{
boxplot(tele1[,list[i]],main=list[i])
}
for(i in 1:length(list))
{
plot(tele1[,list[i]],main=list[i])
}
dev.off()
#-----Missing Value Treatment -------#
summary(tele1)
names(tele1)
# Factor Variables=> crclscod, asl_flag, prizm_social_one, area, refurb_new, hnd_webcap, marital, ethnic, "age1",
# "age2", "models", "hnd_price","actvsubs", "uniqsubs", "forgntvl", "mtrcycle", "truck", car_buy, csa retdays_1
# Deleting Missing Values
index1<-which(is.na(tele1[,c(1:5)]))
tele1<-tele1[-index1,]
summary(tele1)
index2<-which(is.na(tele1$change_mou))
tele1<-tele1[-index2,]
index4<-which(is.na(tele1$area))
tele1<-tele1[-index4,]
index5<-which(is.na(tele1$marital))
tele1<-tele1[-index5,]
summary(tele1)
# Mean Imputation
tele1$avg6mou[is.na(tele1$avg6mou)]<-mean(tele1$avg6mou,na.rm = T)
tele1$avg6qty[is.na(tele1$avg6qty)]<-mean(tele1$avg6qty,na.rm = T)
tele1$hnd_price[is.na(tele1$hnd_price)]<-mean(tele1$hnd_price,na.rm = T)
summary(tele1)
# Creating seperate category "Missing" for Factor Variables
#Variable prizm_social_one
tele1$prizm_social_one_1<-ifelse(is.na(tele1$prizm_social_one),"Missing",as.factor(tele1$prizm_social_one))
str(tele1$prizm_social_one_1)
tele1$prizm_social_one_1<-as.factor(tele1$prizm_social_one_1)
summary(tele1$prizm_social_one)
summary(tele1$prizm_social_one_1)
tele1$prizm_social_one_1<-factor(tele1$prizm_social_one_1,labels =c("C","R","S","T","U","Missing"))
summary(tele1$prizm_social_one_1)
names(tele1)
tele1<-tele1[,-26]
summary(tele1)
#Variable hnd_webcap
tele1$hnd_webcap_1<-ifelse(is.na(tele1$hnd_webcap),"Missing",as.factor(tele1$hnd_webcap))
str(tele1$hnd_webcap_1)
tele1$hnd_webcap_1<-as.factor(tele1$hnd_webcap_1)
summary(tele1$hnd_webcap)
summary(tele1$hnd_webcap_1)
tele1$hnd_webcap_1<-factor(tele1$hnd_webcap_1,labels =c("UNKW","WC","WCMB","Missing"))
summary(tele1$hnd_webcap_1)
names(tele1)
tele1<-tele1[,-28]
summary(tele1)
#Checking Churn Rate in the data after Imputations
table(tele$churn)/nrow(tele)
table(tele1$churn)/nrow(tele1)
# Convert to Factor and Create Dummy Variables =>
#age1, age2, models, hnd_price, actvsubs,uniqsubs, forgntvl, mtrcycle, truck, Customer ID, Churn
str(tele1$age1)
tele1$age1_1<-ifelse(tele1$age1==0,"Default",ifelse(tele1$age1<=30,"Young",
ifelse(tele1$age1>30 & tele1$age1<=55,"Mid Age","Old")))
str(tele1$age1_1)
tele1$age1_1<-as.factor(tele1$age1_1)
summary(tele1$age1_1)
names(tele1)
tele1<-tele1[,-30]
summary(tele1)
str(tele1$age2)
tele1$age2_1<-ifelse(tele1$age2==0,"Default",ifelse(tele1$age2<=30,"Young",
ifelse(tele1$age2>30 & tele1$age2<=55,"Mid Age","Old")))
str(tele1$age2_1)
tele1$age2_1<-as.factor(tele1$age2_1)
summary(tele1$age2_1)
names(tele1)
tele1<-tele1[,-30]
summary(tele1)
str(tele1$models)
summary(tele1$models)
tele1$models<-as.factor(tele1$models)
summary(tele1$models)
str(tele1$hnd_price)
summary(tele1$hnd_price)
tele1$hnd_price<-as.factor(tele1$hnd_price)
summary(tele1$hnd_price)
str(tele1$actvsubs)
summary(tele1$actvsubs)
tele1$actvsubs<-as.factor(tele1$actvsubs)
summary(tele1$actvsubs)
str(tele1$uniqsubs)
summary(tele1$uniqsubs)
tele1$uniqsubs<-as.factor(tele1$uniqsubs)
summary(tele1$uniqsubs)
str(tele1$forgntvl)
summary(tele1$forgntvl)
tele1$forgntvl<-as.factor(tele1$forgntvl)
summary(tele1$forgntvl)
str(tele1$mtrcycle)
summary(tele1$mtrcycle)
tele1$mtrcycle<-as.factor(tele1$mtrcycle)
summary(tele1$mtrcycle)
str(tele1$truck)
summary(tele1$truck)
tele1$truck<-as.factor(tele1$truck)
summary(tele1$truck)
### ********** Logistic Regression Model Building ********** ###
# Splitting into Test and Training Samples
set.seed(200)
index<-sample(nrow(tele1),0.70*nrow(tele1),replace=F)
train<-tele1[index,]
test<-tele1[-index,]
#Checking Churn Rate