Primary Auto Rating Factors
- Driver Safety Record
- Annual Mileage
- Years Licensed (Driving Experience)
These above 3 factors must be used before considering other variables from the following prescribed list of “optional factors”:
knitr::opts_chunk$set(echo = TRUE)
library(tweedie)
library(cplm)
library(statmod)
library(rstan)
library(actuar)
library(fishMod)
library(ggplot2)
library(tidyr)
library(dplyr)
library(tibble)
# library(rbokeh)
library(openxlsx)
library(readr)
library(readxl)
library(knitr)
library(kableExtra)
# library(formattable)
library(RColorBrewer) # display.brewer.all()
# library(plotly)
# library(gapminder)
Disclaimer: This document is written by a Cal Insurance intern and is intended for the other members of the intern team as audience. We include detailed documentation and code in hopes of helping the intern better familiarize with working in R as well as open room for improvement moving forward. The analyses contained herein are for internal use within Cal Insurance only.
Here we’ll perform a pivot on our data. Our given data is very ‘tall’, with repeated entries. We notice that the Vehicle Number
entries are periodic, where we have \[135,000 \text{ total rows of data} = 15,000 \text{ rows} \times 9 \text{ years}.\] Because we’ve sorted by Policy Year
(PY) and Policy Number
, each ‘block’ of 15000 rows corresponds to a year (we confirm this in data validation, but this will be evident in a moment). We’ll break these apart with pivot_wider()
.
# just run once
# rearrange to compare years
# select(tbl, `Vehicle Number`, `Policy Year`, `Marital Status` )
if (names(tbl)[2] != "Policy Year") { print("Error: Make sure tbl has Policy Year as second column!") }
# tblnames <- c()
# generate tibbles
for (i in names(tbl)[- c(1,2) ]) {
assign( paste0("tbl.",i),
select(tbl, `Vehicle Number`, `Policy Year`, i) %>%
pivot_wider(names_from = "Policy Year",
values_from = i,
names_prefix = paste0(i, " PY"))
)
# tblnames <- c(tblnames, paste0("tbl.",i))
print(i) # debugging
}
This gives us a number of tables, one for each original column. A couple of examples is shown below. Notice that each row no longer has the Policy Year
column, and now each row contains data for each year (as opposed to multiple rows before corresponding to the same Vehicle Number
).
Now we’ll stitch these together back into a consolidated database. We’ll generally keep the same order as given in the original .xlsx
file.
for (k in tblcols) {
tbl.wider <- tbl.wider %>% left_join( k )
}
tbl.wider[1:20,] %>% qkable()
Vehicle Number | Policy Number PY2010 | Policy Number PY2011 | Policy Number PY2012 | Policy Number PY2013 | Policy Number PY2014 | Policy Number PY2015 | Policy Number PY2016 | Policy Number PY2017 | Policy Number PY2018 | Marital Status PY2010 | Marital Status PY2011 | Marital Status PY2012 | Marital Status PY2013 | Marital Status PY2014 | Marital Status PY2015 | Marital Status PY2016 | Marital Status PY2017 | Marital Status PY2018 | Driver Age PY2010 | Driver Age PY2011 | Driver Age PY2012 | Driver Age PY2013 | Driver Age PY2014 | Driver Age PY2015 | Driver Age PY2016 | Driver Age PY2017 | Driver Age PY2018 | Driver Age Band PY2010 | Driver Age Band PY2011 | Driver Age Band PY2012 | Driver Age Band PY2013 | Driver Age Band PY2014 | Driver Age Band PY2015 | Driver Age Band PY2016 | Driver Age Band PY2017 | Driver Age Band PY2018 | Annual Mileage PY2010 | Annual Mileage PY2011 | Annual Mileage PY2012 | Annual Mileage PY2013 | Annual Mileage PY2014 | Annual Mileage PY2015 | Annual Mileage PY2016 | Annual Mileage PY2017 | Annual Mileage PY2018 | Territory PY2010 | Territory PY2011 | Territory PY2012 | Territory PY2013 | Territory PY2014 | Territory PY2015 | Territory PY2016 | Territory PY2017 | Territory PY2018 | Credit Score PY2010 | Credit Score PY2011 | Credit Score PY2012 | Credit Score PY2013 | Credit Score PY2014 | Credit Score PY2015 | Credit Score PY2016 | Credit Score PY2017 | Credit Score PY2018 | Car Value PY2010 | Car Value PY2011 | Car Value PY2012 | Car Value PY2013 | Car Value PY2014 | Car Value PY2015 | Car Value PY2016 | Car Value PY2017 | Car Value PY2018 | Car Value Band PY2010 | Car Value Band PY2011 | Car Value Band PY2012 | Car Value Band PY2013 | Car Value Band PY2014 | Car Value Band PY2015 | Car Value Band PY2016 | Car Value Band PY2017 | Car Value Band PY2018 | Liability Limit PY2010 | Liability Limit PY2011 | Liability Limit PY2012 | Liability Limit PY2013 | Liability Limit PY2014 | Liability Limit PY2015 | Liability Limit PY2016 | Liability Limit PY2017 | Liability Limit PY2018 | Physical Damage Deductible PY2010 | Physical Damage Deductible PY2011 | Physical Damage Deductible PY2012 | Physical Damage Deductible PY2013 | Physical Damage Deductible PY2014 | Physical Damage Deductible PY2015 | Physical Damage Deductible PY2016 | Physical Damage Deductible PY2017 | Physical Damage Deductible PY2018 | Reported Losses PY2010 | Reported Losses PY2011 | Reported Losses PY2012 | Reported Losses PY2013 | Reported Losses PY2014 | Reported Losses PY2015 | Reported Losses PY2016 | Reported Losses PY2017 | Reported Losses PY2018 | Late Fees PY2010 | Late Fees PY2011 | Late Fees PY2012 | Late Fees PY2013 | Late Fees PY2014 | Late Fees PY2015 | Late Fees PY2016 | Late Fees PY2017 | Late Fees PY2018 | Conviction Points PY2010 | Conviction Points PY2011 | Conviction Points PY2012 | Conviction Points PY2013 | Conviction Points PY2014 | Conviction Points PY2015 | Conviction Points PY2016 | Conviction Points PY2017 | Conviction Points PY2018 | Base Pure Premium PY2010 | Base Pure Premium PY2011 | Base Pure Premium PY2012 | Base Pure Premium PY2013 | Base Pure Premium PY2014 | Base Pure Premium PY2015 | Base Pure Premium PY2016 | Base Pure Premium PY2017 | Base Pure Premium PY2018 | Marital Status Relativity PY2010 | Marital Status Relativity PY2011 | Marital Status Relativity PY2012 | Marital Status Relativity PY2013 | Marital Status Relativity PY2014 | Marital Status Relativity PY2015 | Marital Status Relativity PY2016 | Marital Status Relativity PY2017 | Marital Status Relativity PY2018 | Driver Age Relativity PY2010 | Driver Age Relativity PY2011 | Driver Age Relativity PY2012 | Driver Age Relativity PY2013 | Driver Age Relativity PY2014 | Driver Age Relativity PY2015 | Driver Age Relativity PY2016 | Driver Age Relativity PY2017 | Driver Age Relativity PY2018 | Annual Mileage Relativity PY2010 | Annual Mileage Relativity PY2011 | Annual Mileage Relativity PY2012 | Annual Mileage Relativity PY2013 | Annual Mileage Relativity PY2014 | Annual Mileage Relativity PY2015 | Annual Mileage Relativity PY2016 | Annual Mileage Relativity PY2017 | Annual Mileage Relativity PY2018 | Car Value Relativity PY2010 | Car Value Relativity PY2011 | Car Value Relativity PY2012 | Car Value Relativity PY2013 | Car Value Relativity PY2014 | Car Value Relativity PY2015 | Car Value Relativity PY2016 | Car Value Relativity PY2017 | Car Value Relativity PY2018 | Liability Limit Relativity PY2010 | Liability Limit Relativity PY2011 | Liability Limit Relativity PY2012 | Liability Limit Relativity PY2013 | Liability Limit Relativity PY2014 | Liability Limit Relativity PY2015 | Liability Limit Relativity PY2016 | Liability Limit Relativity PY2017 | Liability Limit Relativity PY2018 | Physical Damage Deductible Relativity PY2010 | Physical Damage Deductible Relativity PY2011 | Physical Damage Deductible Relativity PY2012 | Physical Damage Deductible Relativity PY2013 | Physical Damage Deductible Relativity PY2014 | Physical Damage Deductible Relativity PY2015 | Physical Damage Deductible Relativity PY2016 | Physical Damage Deductible Relativity PY2017 | Physical Damage Deductible Relativity PY2018 | Multi-Car PY2010 | Multi-Car PY2011 | Multi-Car PY2012 | Multi-Car PY2013 | Multi-Car PY2014 | Multi-Car PY2015 | Multi-Car PY2016 | Multi-Car PY2017 | Multi-Car PY2018 | Multi-Car Relativity PY2010 | Multi-Car Relativity PY2011 | Multi-Car Relativity PY2012 | Multi-Car Relativity PY2013 | Multi-Car Relativity PY2014 | Multi-Car Relativity PY2015 | Multi-Car Relativity PY2016 | Multi-Car Relativity PY2017 | Multi-Car Relativity PY2018 | Accident Points (AP) last 3 years PY2010 | Accident Points (AP) last 3 years PY2011 | Accident Points (AP) last 3 years PY2012 | Accident Points (AP) last 3 years PY2013 | Accident Points (AP) last 3 years PY2014 | Accident Points (AP) last 3 years PY2015 | Accident Points (AP) last 3 years PY2016 | Accident Points (AP) last 3 years PY2017 | Accident Points (AP) last 3 years PY2018 | AP Relativity PY2010 | AP Relativity PY2011 | AP Relativity PY2012 | AP Relativity PY2013 | AP Relativity PY2014 | AP Relativity PY2015 | AP Relativity PY2016 | AP Relativity PY2017 | AP Relativity PY2018 | Conviction Points (CP) last 3 years PY2010 | Conviction Points (CP) last 3 years PY2011 | Conviction Points (CP) last 3 years PY2012 | Conviction Points (CP) last 3 years PY2013 | Conviction Points (CP) last 3 years PY2014 | Conviction Points (CP) last 3 years PY2015 | Conviction Points (CP) last 3 years PY2016 | Conviction Points (CP) last 3 years PY2017 | Conviction Points (CP) last 3 years PY2018 | CP Relativity PY2010 | CP Relativity PY2011 | CP Relativity PY2012 | CP Relativity PY2013 | CP Relativity PY2014 | CP Relativity PY2015 | CP Relativity PY2016 | CP Relativity PY2017 | CP Relativity PY2018 | Driver Experience PY2010 | Driver Experience PY2011 | Driver Experience PY2012 | Driver Experience PY2013 | Driver Experience PY2014 | Driver Experience PY2015 | Driver Experience PY2016 | Driver Experience PY2017 | Driver Experience PY2018 | Driver Experience Band PY2010 | Driver Experience Band PY2011 | Driver Experience Band PY2012 | Driver Experience Band PY2013 | Driver Experience Band PY2014 | Driver Experience Band PY2015 | Driver Experience Band PY2016 | Driver Experience Band PY2017 | Driver Experience Band PY2018 |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
6125 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | Single | Single | Single | Single | Single | Single | Single | Single | Single | 18 | 19 | 20 | 21 | 22 | 23 | 24 | 25 | 26 | 16-19 | 16-19 | 20-24 | 20-24 | 20-24 | 20-24 | 20-24 | 25-29 | 25-29 | 0-7500 | 0-7500 | 0-7500 | 0-7500 | 0-7500 | 0-7500 | 0-7500 | 0-7500 | 0-7500 | 4 | 4 | 4 | 4 | 4 | 4 | 4 | 4 | 4 | High | High | High | High | High | High | High | High | High | 3220 | 3220 | 3220 | 3220 | 3220 | 3220 | 3220 | 3220 | 3220 | 0-10000 | 0-10000 | 0-10000 | 0-10000 | 0-10000 | 0-10000 | 0-10000 | 0-10000 | 0-10000 | 5e+04 | 5e+04 | 5e+04 | 5e+04 | 5e+04 | 5e+04 | 5e+04 | 5e+04 | 5e+04 | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 0 | 0.000 | 0.0000 | 200.0692 | 0.0000 | 0.0000 | 12602.418 | 0.000 | 0.000 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1500 | 1500 | 1500 | 1500 | 1500 | 1500 | 1500 | 1500 | 1500 | 1.2 | 1.2 | 1.2 | 1.2 | 1.2 | 1.2 | 1.2 | 1.2 | 1.2 | 3.0 | 3.0 | 2.0 | 2.0 | 2.0 | 2.0 | 2.0 | 1.5 | 1.5 | 0.7 | 0.7 | 0.7 | 0.7 | 0.7 | 0.7 | 0.7 | 0.7 | 0.7 | 0.720 | 0.720 | 0.720 | 0.720 | 0.720 | 0.720 | 0.720 | 0.720 | 0.720 | 0.60 | 0.60 | 0.60 | 0.60 | 0.60 | 0.60 | 0.60 | 0.60 | 0.60 | 1.080 | 1.080 | 1.080 | 1.080 | 1.080 | 1.080 | 1.080 | 1.080 | 1.080 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 0.9 | 0.9 | 0.9 | 0.9 | 0.9 | 0.9 | 0.9 | 0.9 | 0.9 | -1 | -1 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | -1 | -1 | 1.0 | 1.2 | 1.2 | 1.2 | 1.2 | 1.2 | 1.2 | -1 | -1 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | -1 | -1 | 1.4 | 1.2 | 1.0 | 1.0 | 1.0 | 1.0 | 1 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 0-3 | 0-3 | 4-8 | 4-8 | 4-8 | 4-8 | 4-8 | 9-13 | 9-13 |
10332 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | Married | Married | Married | Married | Married | Married | Married | Married | Married | 45 | 46 | 47 | 48 | 49 | 50 | 51 | 52 | 53 | 40-49 | 40-49 | 40-49 | 40-49 | 40-49 | 50-59 | 50-59 | 50-59 | 50-59 | 7500-10000 | 7500-10000 | 7500-10000 | 7500-10000 | 7500-10000 | 7500-10000 | 7500-10000 | 7500-10000 | 7500-10000 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | High | High | High | High | High | High | High | High | High | 20090 | 20090 | 20090 | 20090 | 20090 | 20090 | 20090 | 20090 | 20090 | 20000-30000 | 20000-30000 | 20000-30000 | 20000-30000 | 20000-30000 | 20000-30000 | 20000-30000 | 20000-30000 | 20000-30000 | 5e+05 | 5e+05 | 5e+05 | 5e+05 | 5e+05 | 5e+05 | 5e+05 | 5e+05 | 5e+05 | 250 | 250 | 250 | 250 | 250 | 250 | 250 | 250 | 250 | 0 | 0.000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.000 | 0.000 | 0.000 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1500 | 1500 | 1500 | 1500 | 1500 | 1500 | 1500 | 1500 | 1500 | 0.8 | 0.8 | 0.8 | 0.8 | 0.8 | 0.8 | 0.8 | 0.8 | 0.8 | 0.9 | 0.9 | 0.9 | 0.9 | 0.9 | 0.8 | 0.8 | 0.8 | 0.8 | 0.9 | 0.9 | 0.9 | 0.9 | 0.9 | 0.9 | 0.9 | 0.9 | 0.9 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.12 | 1.12 | 1.12 | 1.12 | 1.12 | 1.12 | 1.12 | 1.12 | 1.12 | 1.045 | 1.045 | 1.045 | 1.045 | 1.045 | 1.045 | 1.045 | 1.045 | 1.045 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | -1 | -1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | -1 | -1 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | -1 | -1 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | -1 | -1 | 1.0 | 1.0 | 1.2 | 1.2 | 1.2 | 1.0 | 1 | 30 | 31 | 32 | 33 | 34 | 35 | 36 | 37 | 38 | 24-33 | 24-33 | 24-33 | 24-33 | 24-33 | 34-43 | 34-43 | 34-43 | 34-43 |
1965 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | Married | Married | Married | Married | Married | Married | Married | Married | Married | 54 | 55 | 56 | 57 | 58 | 59 | 60 | 61 | 62 | 50-59 | 50-59 | 50-59 | 50-59 | 50-59 | 50-59 | 60-69 | 60-69 | 60-69 | 15000+ | 15000+ | 15000+ | 15000+ | 15000+ | 15000+ | 15000+ | 15000+ | 15000+ | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | Medium | Medium | Medium | Medium | Medium | Medium | Medium | Medium | Medium | 13810 | 13810 | 13810 | 13810 | 13810 | 13810 | 13810 | 13810 | 13810 | 10000-20000 | 10000-20000 | 10000-20000 | 10000-20000 | 10000-20000 | 10000-20000 | 10000-20000 | 10000-20000 | 10000-20000 | 1e+05 | 1e+05 | 1e+05 | 1e+05 | 1e+05 | 1e+05 | 1e+05 | 1e+05 | 1e+05 | 500 | 500 | 500 | 500 | 500 | 500 | 500 | 500 | 500 | 0 | 0.000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.000 | 0.000 | 0.000 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1500 | 1500 | 1500 | 1500 | 1500 | 1500 | 1500 | 1500 | 1500 | 0.8 | 0.8 | 0.8 | 0.8 | 0.8 | 0.8 | 0.8 | 0.8 | 0.8 | 0.8 | 0.8 | 0.8 | 0.8 | 0.8 | 0.8 | 0.8 | 0.8 | 0.8 | 1.2 | 1.2 | 1.2 | 1.2 | 1.2 | 1.2 | 1.2 | 1.2 | 1.2 | 0.880 | 0.880 | 0.880 | 0.880 | 0.880 | 0.880 | 0.880 | 0.880 | 0.880 | 0.88 | 0.88 | 0.88 | 0.88 | 0.88 | 0.88 | 0.88 | 0.88 | 0.88 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 0.9 | 0.9 | 0.9 | 0.9 | 0.9 | 0.9 | 0.9 | 0.9 | 0.9 | -1 | -1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | -1 | -1 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | -1 | -1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | -1 | -1 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1 | 39 | 40 | 41 | 42 | 43 | 44 | 45 | 46 | 47 | 34-43 | 34-43 | 34-43 | 34-43 | 34-43 | 34-43 | 44-53 | 44-53 | 44-53 |
14130 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | Married | Married | Married | Married | Married | Married | Married | Married | Married | 49 | 50 | 51 | 52 | 53 | 54 | 55 | 56 | 57 | 40-49 | 50-59 | 50-59 | 50-59 | 50-59 | 50-59 | 50-59 | 50-59 | 50-59 | 10000-15000 | 10000-15000 | 10000-15000 | 10000-15000 | 10000-15000 | 10000-15000 | 10000-15000 | 10000-15000 | 10000-15000 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | High | High | High | High | High | High | High | High | High | 37060 | 37060 | 37060 | 37060 | 37060 | 37060 | 37060 | 37060 | 37060 | 30000-40000 | 30000-40000 | 30000-40000 | 30000-40000 | 30000-40000 | 30000-40000 | 30000-40000 | 30000-40000 | 30000-40000 | 1e+05 | 1e+05 | 1e+05 | 1e+05 | 1e+05 | 1e+05 | 1e+05 | 1e+05 | 1e+05 | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 0 | 0.000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.000 | 0.000 | 0.000 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1500 | 1500 | 1500 | 1500 | 1500 | 1500 | 1500 | 1500 | 1500 | 0.8 | 0.8 | 0.8 | 0.8 | 0.8 | 0.8 | 0.8 | 0.8 | 0.8 | 0.9 | 0.8 | 0.8 | 0.8 | 0.8 | 0.8 | 0.8 | 0.8 | 0.8 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.045 | 1.045 | 1.045 | 1.045 | 1.045 | 1.045 | 1.045 | 1.045 | 1.045 | 0.88 | 0.88 | 0.88 | 0.88 | 0.88 | 0.88 | 0.88 | 0.88 | 0.88 | 1.080 | 1.080 | 1.080 | 1.080 | 1.080 | 1.080 | 1.080 | 1.080 | 1.080 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | -1 | -1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | -1 | -1 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | -1 | -1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | -1 | -1 | 1.2 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1 | 34 | 35 | 36 | 37 | 38 | 39 | 40 | 41 | 42 | 24-33 | 34-43 | 34-43 | 34-43 | 34-43 | 34-43 | 34-43 | 34-43 | 34-43 |
2976 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | Single | Single | Single | Single | Single | Single | Single | Single | Single | 31 | 32 | 33 | 34 | 35 | 36 | 37 | 38 | 39 | 30-39 | 30-39 | 30-39 | 30-39 | 30-39 | 30-39 | 30-39 | 30-39 | 30-39 | 7500-10000 | 7500-10000 | 7500-10000 | 7500-10000 | 7500-10000 | 7500-10000 | 7500-10000 | 7500-10000 | 7500-10000 | 4 | 4 | 4 | 4 | 4 | 4 | 4 | 4 | 4 | High | High | High | High | High | High | High | High | High | 20860 | 20860 | 20860 | 20860 | 20860 | 20860 | 20860 | 20860 | 20860 | 20000-30000 | 20000-30000 | 20000-30000 | 20000-30000 | 20000-30000 | 20000-30000 | 20000-30000 | 20000-30000 | 20000-30000 | 5e+04 | 5e+04 | 5e+04 | 5e+04 | 5e+04 | 5e+04 | 5e+04 | 5e+04 | 5e+04 | 250 | 250 | 250 | 250 | 250 | 250 | 250 | 250 | 250 | 0 | 0.000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.000 | 0.000 | 0.000 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1500 | 1500 | 1500 | 1500 | 1500 | 1500 | 1500 | 1500 | 1500 | 1.2 | 1.2 | 1.2 | 1.2 | 1.2 | 1.2 | 1.2 | 1.2 | 1.2 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.9 | 0.9 | 0.9 | 0.9 | 0.9 | 0.9 | 0.9 | 0.9 | 0.9 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 0.60 | 0.60 | 0.60 | 0.60 | 0.60 | 0.60 | 0.60 | 0.60 | 0.60 | 1.045 | 1.045 | 1.045 | 1.045 | 1.045 | 1.045 | 1.045 | 1.045 | 1.045 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | -1 | -1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | -1 | -1 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | -1 | -1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | -1 | -1 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1 | 16 | 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | 14-23 | 14-23 | 14-23 | 14-23 | 14-23 | 14-23 | 14-23 | 14-23 | 14-23 |
76 | 4 | 4 | 4 | 4 | 4 | 4 | 4 | 4 | 4 | Married | Married | Married | Married | Married | Married | Married | Married | Married | 53 | 54 | 55 | 56 | 57 | 58 | 59 | 60 | 61 | 50-59 | 50-59 | 50-59 | 50-59 | 50-59 | 50-59 | 50-59 | 60-69 | 60-69 | 15000+ | 15000+ | 15000+ | 15000+ | 15000+ | 15000+ | 15000+ | 15000+ | 15000+ | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | High | High | High | High | High | High | High | High | High | 80850 | 80850 | 80850 | 80850 | 80850 | 80850 | 80850 | 80850 | 80850 | 40000+ | 40000+ | 40000+ | 40000+ | 40000+ | 40000+ | 40000+ | 40000+ | 40000+ | 5e+04 | 5e+04 | 5e+04 | 5e+04 | 5e+04 | 5e+04 | 5e+04 | 5e+04 | 5e+04 | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 0 | 0.000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.000 | 0.000 | 0.000 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1500 | 1500 | 1500 | 1500 | 1500 | 1500 | 1500 | 1500 | 1500 | 0.8 | 0.8 | 0.8 | 0.8 | 0.8 | 0.8 | 0.8 | 0.8 | 0.8 | 0.8 | 0.8 | 0.8 | 0.8 | 0.8 | 0.8 | 0.8 | 0.8 | 0.8 | 1.2 | 1.2 | 1.2 | 1.2 | 1.2 | 1.2 | 1.2 | 1.2 | 1.2 | 1.080 | 1.080 | 1.080 | 1.080 | 1.080 | 1.080 | 1.080 | 1.080 | 1.080 | 0.60 | 0.60 | 0.60 | 0.60 | 0.60 | 0.60 | 0.60 | 0.60 | 0.60 | 1.080 | 1.080 | 1.080 | 1.080 | 1.080 | 1.080 | 1.080 | 1.080 | 1.080 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | -1 | -1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | -1 | -1 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | -1 | -1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | -1 | -1 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1 | 38 | 39 | 40 | 41 | 42 | 43 | 44 | 45 | 46 | 34-43 | 34-43 | 34-43 | 34-43 | 34-43 | 34-43 | 34-43 | 44-53 | 44-53 |
8571 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | Single | Single | Single | Single | Single | Single | Single | Single | Single | 21 | 22 | 23 | 24 | 25 | 26 | 27 | 28 | 29 | 20-24 | 20-24 | 20-24 | 20-24 | 25-29 | 25-29 | 25-29 | 25-29 | 25-29 | 0-7500 | 0-7500 | 0-7500 | 0-7500 | 0-7500 | 0-7500 | 0-7500 | 0-7500 | 0-7500 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | Low | Low | Low | Low | Low | Low | Low | Low | Low | 5610 | 5610 | 5610 | 5610 | 5610 | 5610 | 5610 | 5610 | 5610 | 0-10000 | 0-10000 | 0-10000 | 0-10000 | 0-10000 | 0-10000 | 0-10000 | 0-10000 | 0-10000 | 1e+05 | 1e+05 | 1e+05 | 1e+05 | 1e+05 | 1e+05 | 1e+05 | 1e+05 | 1e+05 | 500 | 500 | 500 | 500 | 500 | 500 | 500 | 500 | 500 | 0 | 0.000 | 0.0000 | 10121.7588 | 0.0000 | 0.0000 | 0.000 | 0.000 | 0.000 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1500 | 1500 | 1500 | 1500 | 1500 | 1500 | 1500 | 1500 | 1500 | 1.2 | 1.2 | 1.2 | 1.2 | 1.2 | 1.2 | 1.2 | 1.2 | 1.2 | 2.0 | 2.0 | 2.0 | 2.0 | 1.5 | 1.5 | 1.5 | 1.5 | 1.5 | 0.7 | 0.7 | 0.7 | 0.7 | 0.7 | 0.7 | 0.7 | 0.7 | 0.7 | 0.720 | 0.720 | 0.720 | 0.720 | 0.720 | 0.720 | 0.720 | 0.720 | 0.720 | 0.88 | 0.88 | 0.88 | 0.88 | 0.88 | 0.88 | 0.88 | 0.88 | 0.88 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | -1 | -1 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | -1 | -1 | 1.0 | 1.2 | 1.2 | 1.2 | 1.0 | 1.0 | 1.0 | -1 | -1 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | -1 | -1 | 1.0 | 1.2 | 1.2 | 1.2 | 1.0 | 1.0 | 1 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 4-8 | 4-8 | 4-8 | 4-8 | 9-13 | 9-13 | 9-13 | 9-13 | 9-13 |
8530 | 6 | 6 | 6 | 6 | 6 | 6 | 6 | 6 | 6 | Single | Single | Single | Single | Single | Single | Single | Single | Single | 28 | 29 | 30 | 31 | 32 | 33 | 34 | 35 | 36 | 25-29 | 25-29 | 30-39 | 30-39 | 30-39 | 30-39 | 30-39 | 30-39 | 30-39 | 7500-10000 | 7500-10000 | 7500-10000 | 7500-10000 | 7500-10000 | 7500-10000 | 7500-10000 | 7500-10000 | 7500-10000 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | High | High | High | High | High | High | High | High | High | 20000 | 20000 | 20000 | 20000 | 20000 | 20000 | 20000 | 20000 | 20000 | 20000-30000 | 20000-30000 | 20000-30000 | 20000-30000 | 20000-30000 | 20000-30000 | 20000-30000 | 20000-30000 | 20000-30000 | 5e+04 | 5e+04 | 5e+04 | 5e+04 | 5e+04 | 5e+04 | 5e+04 | 5e+04 | 5e+04 | 250 | 250 | 250 | 250 | 250 | 250 | 250 | 250 | 250 | 0 | 0.000 | 0.0000 | 28602.5937 | 0.0000 | 0.0000 | 0.000 | 0.000 | 70200.000 | 0 | 2 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 1500 | 1500 | 1500 | 1500 | 1500 | 1500 | 1500 | 1500 | 1500 | 1.2 | 1.2 | 1.2 | 1.2 | 1.2 | 1.2 | 1.2 | 1.2 | 1.2 | 1.5 | 1.5 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.9 | 0.9 | 0.9 | 0.9 | 0.9 | 0.9 | 0.9 | 0.9 | 0.9 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 0.60 | 0.60 | 0.60 | 0.60 | 0.60 | 0.60 | 0.60 | 0.60 | 0.60 | 1.045 | 1.045 | 1.045 | 1.045 | 1.045 | 1.045 | 1.045 | 1.045 | 1.045 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | -1 | -1 | 0 | 1 | 1 | 1 | 0 | 0 | 1 | -1 | -1 | 1.0 | 1.2 | 1.2 | 1.2 | 1.0 | 1.0 | 1.2 | -1 | -1 | 0 | 1 | 1 | 2 | 1 | 1 | 0 | -1 | -1 | 1.0 | 1.2 | 1.2 | 1.4 | 1.2 | 1.2 | 1 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | 21 | 9-13 | 9-13 | 14-23 | 14-23 | 14-23 | 14-23 | 14-23 | 14-23 | 14-23 |
6104 | 7 | 7 | 7 | 7 | 7 | 7 | 7 | 7 | 7 | Single | Single | Single | Single | Single | Single | Single | Single | Single | 33 | 34 | 35 | 36 | 37 | 38 | 39 | 40 | 41 | 30-39 | 30-39 | 30-39 | 30-39 | 30-39 | 30-39 | 30-39 | 40-49 | 40-49 | 0-7500 | 0-7500 | 0-7500 | 0-7500 | 0-7500 | 0-7500 | 0-7500 | 0-7500 | 0-7500 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | High | High | High | High | High | High | High | High | High | 13020 | 13020 | 13020 | 13020 | 13020 | 13020 | 13020 | 13020 | 13020 | 10000-20000 | 10000-20000 | 10000-20000 | 10000-20000 | 10000-20000 | 10000-20000 | 10000-20000 | 10000-20000 | 10000-20000 | 5e+04 | 5e+04 | 5e+04 | 5e+04 | 5e+04 | 5e+04 | 5e+04 | 5e+04 | 5e+04 | 500 | 500 | 500 | 500 | 500 | 500 | 500 | 500 | 500 | 0 | 0.000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.000 | 0.000 | 0.000 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1500 | 1500 | 1500 | 1500 | 1500 | 1500 | 1500 | 1500 | 1500 | 1.2 | 1.2 | 1.2 | 1.2 | 1.2 | 1.2 | 1.2 | 1.2 | 1.2 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.9 | 0.9 | 0.7 | 0.7 | 0.7 | 0.7 | 0.7 | 0.7 | 0.7 | 0.7 | 0.7 | 0.880 | 0.880 | 0.880 | 0.880 | 0.880 | 0.880 | 0.880 | 0.880 | 0.880 | 0.60 | 0.60 | 0.60 | 0.60 | 0.60 | 0.60 | 0.60 | 0.60 | 0.60 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 0.9 | 0.9 | 0.9 | 0.9 | 0.9 | 0.9 | 0.9 | 0.9 | 0.9 | -1 | -1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | -1 | -1 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | -1 | -1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | -1 | -1 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | 25 | 26 | 14-23 | 14-23 | 14-23 | 14-23 | 14-23 | 14-23 | 14-23 | 24-33 | 24-33 |
12468 | 7 | 7 | 7 | 7 | 7 | 7 | 7 | 7 | 7 | Single | Single | Single | Single | Single | Single | Single | Single | Single | 27 | 28 | 29 | 30 | 31 | 32 | 33 | 34 | 35 | 25-29 | 25-29 | 25-29 | 30-39 | 30-39 | 30-39 | 30-39 | 30-39 | 30-39 | 0-7500 | 0-7500 | 0-7500 | 0-7500 | 0-7500 | 0-7500 | 0-7500 | 0-7500 | 0-7500 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | High | High | High | High | High | High | High | High | High | 26350 | 26350 | 26350 | 26350 | 26350 | 26350 | 26350 | 26350 | 26350 | 20000-30000 | 20000-30000 | 20000-30000 | 20000-30000 | 20000-30000 | 20000-30000 | 20000-30000 | 20000-30000 | 20000-30000 | 1e+05 | 1e+05 | 1e+05 | 1e+05 | 1e+05 | 1e+05 | 1e+05 | 1e+05 | 1e+05 | 250 | 250 | 250 | 250 | 250 | 250 | 250 | 250 | 250 | 0 | 0.000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.000 | 0.000 | 0.000 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 1500 | 1500 | 1500 | 1500 | 1500 | 1500 | 1500 | 1500 | 1500 | 1.2 | 1.2 | 1.2 | 1.2 | 1.2 | 1.2 | 1.2 | 1.2 | 1.2 | 1.5 | 1.5 | 1.5 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.7 | 0.7 | 0.7 | 0.7 | 0.7 | 0.7 | 0.7 | 0.7 | 0.7 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 0.88 | 0.88 | 0.88 | 0.88 | 0.88 | 0.88 | 0.88 | 0.88 | 0.88 | 1.045 | 1.045 | 1.045 | 1.045 | 1.045 | 1.045 | 1.045 | 1.045 | 1.045 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | -1 | -1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | -1 | -1 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | -1 | -1 | 1 | 2 | 2 | 2 | 1 | 0 | 0 | -1 | -1 | 1.2 | 1.4 | 1.4 | 1.4 | 1.2 | 1.0 | 1 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | 9-13 | 9-13 | 9-13 | 14-23 | 14-23 | 14-23 | 14-23 | 14-23 | 14-23 |
1547 | 8 | 8 | 8 | 8 | 8 | 8 | 8 | 8 | 8 | Married | Married | Married | Married | Married | Married | Married | Married | Married | 52 | 53 | 54 | 55 | 56 | 57 | 58 | 59 | 60 | 50-59 | 50-59 | 50-59 | 50-59 | 50-59 | 50-59 | 50-59 | 50-59 | 60-69 | 0-7500 | 0-7500 | 0-7500 | 0-7500 | 0-7500 | 0-7500 | 0-7500 | 0-7500 | 0-7500 | 4 | 4 | 4 | 4 | 4 | 4 | 4 | 4 | 4 | Low | Low | Low | Low | Low | Low | Low | Low | Low | 39180 | 39180 | 39180 | 39180 | 39180 | 39180 | 39180 | 39180 | 39180 | 30000-40000 | 30000-40000 | 30000-40000 | 30000-40000 | 30000-40000 | 30000-40000 | 30000-40000 | 30000-40000 | 30000-40000 | 5e+05 | 5e+05 | 5e+05 | 5e+05 | 5e+05 | 5e+05 | 5e+05 | 5e+05 | 5e+05 | 1000 | 1000 | 1000 | 1000 | 1000 | 1000 | 1000 | 1000 | 1000 | 0 | 0.000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.000 | 0.000 | 0.000 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1500 | 1500 | 1500 | 1500 | 1500 | 1500 | 1500 | 1500 | 1500 | 0.8 | 0.8 | 0.8 | 0.8 | 0.8 | 0.8 | 0.8 | 0.8 | 0.8 | 0.8 | 0.8 | 0.8 | 0.8 | 0.8 | 0.8 | 0.8 | 0.8 | 0.8 | 0.7 | 0.7 | 0.7 | 0.7 | 0.7 | 0.7 | 0.7 | 0.7 | 0.7 | 1.045 | 1.045 | 1.045 | 1.045 | 1.045 | 1.045 | 1.045 | 1.045 | 1.045 | 1.12 | 1.12 | 1.12 | 1.12 | 1.12 | 1.12 | 1.12 | 1.12 | 1.12 | 0.935 | 0.935 | 0.935 | 0.935 | 0.935 | 0.935 | 0.935 | 0.935 | 0.935 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | -1 | -1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | -1 | -1 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | -1 | -1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | -1 | -1 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1 | 37 | 38 | 39 | 40 | 41 | 42 | 43 | 44 | 45 | 34-43 | 34-43 | 34-43 | 34-43 | 34-43 | 34-43 | 34-43 | 34-43 | 44-53 |
736 | 9 | 9 | 9 | 9 | 9 | 9 | 9 | 9 | 9 | Married | Married | Married | Married | Married | Married | Married | Married | Married | 49 | 50 | 51 | 52 | 53 | 54 | 55 | 56 | 57 | 40-49 | 50-59 | 50-59 | 50-59 | 50-59 | 50-59 | 50-59 | 50-59 | 50-59 | 10000-15000 | 10000-15000 | 10000-15000 | 10000-15000 | 10000-15000 | 10000-15000 | 10000-15000 | 10000-15000 | 10000-15000 | 4 | 4 | 4 | 4 | 4 | 4 | 4 | 4 | 4 | Medium | Medium | Medium | Medium | Medium | Medium | Medium | Medium | Medium | 30150 | 30150 | 30150 | 30150 | 30150 | 30150 | 30150 | 30150 | 30150 | 30000-40000 | 30000-40000 | 30000-40000 | 30000-40000 | 30000-40000 | 30000-40000 | 30000-40000 | 30000-40000 | 30000-40000 | 1e+05 | 1e+05 | 1e+05 | 1e+05 | 1e+05 | 1e+05 | 1e+05 | 1e+05 | 1e+05 | 500 | 500 | 500 | 500 | 500 | 500 | 500 | 500 | 500 | 0 | 0.000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.000 | 0.000 | 52977.089 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1500 | 1500 | 1500 | 1500 | 1500 | 1500 | 1500 | 1500 | 1500 | 0.8 | 0.8 | 0.8 | 0.8 | 0.8 | 0.8 | 0.8 | 0.8 | 0.8 | 0.9 | 0.8 | 0.8 | 0.8 | 0.8 | 0.8 | 0.8 | 0.8 | 0.8 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.045 | 1.045 | 1.045 | 1.045 | 1.045 | 1.045 | 1.045 | 1.045 | 1.045 | 0.88 | 0.88 | 0.88 | 0.88 | 0.88 | 0.88 | 0.88 | 0.88 | 0.88 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 0.8 | 0.8 | 0.8 | 0.8 | 0.8 | 0.8 | 0.8 | 0.8 | 0.8 | -1 | -1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | -1 | -1 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.2 | -1 | -1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | -1 | -1 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1 | 34 | 35 | 36 | 37 | 38 | 39 | 40 | 41 | 42 | 24-33 | 34-43 | 34-43 | 34-43 | 34-43 | 34-43 | 34-43 | 34-43 | 34-43 |
12236 | 9 | 9 | 9 | 9 | 9 | 9 | 9 | 9 | 9 | Single | Single | Single | Single | Single | Single | Single | Single | Single | 50 | 51 | 52 | 53 | 54 | 55 | 56 | 57 | 58 | 50-59 | 50-59 | 50-59 | 50-59 | 50-59 | 50-59 | 50-59 | 50-59 | 50-59 | 0-7500 | 0-7500 | 0-7500 | 0-7500 | 0-7500 | 0-7500 | 0-7500 | 0-7500 | 0-7500 | 4 | 4 | 4 | 4 | 4 | 4 | 4 | 4 | 4 | Medium | Medium | Medium | Medium | Medium | Medium | Medium | Medium | Medium | 22600 | 22600 | 22600 | 22600 | 22600 | 22600 | 22600 | 22600 | 22600 | 20000-30000 | 20000-30000 | 20000-30000 | 20000-30000 | 20000-30000 | 20000-30000 | 20000-30000 | 20000-30000 | 20000-30000 | 5e+04 | 5e+04 | 5e+04 | 5e+04 | 5e+04 | 5e+04 | 5e+04 | 5e+04 | 5e+04 | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 0 | 0.000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.000 | 0.000 | 0.000 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1500 | 1500 | 1500 | 1500 | 1500 | 1500 | 1500 | 1500 | 1500 | 1.2 | 1.2 | 1.2 | 1.2 | 1.2 | 1.2 | 1.2 | 1.2 | 1.2 | 0.8 | 0.8 | 0.8 | 0.8 | 0.8 | 0.8 | 0.8 | 0.8 | 0.8 | 0.7 | 0.7 | 0.7 | 0.7 | 0.7 | 0.7 | 0.7 | 0.7 | 0.7 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 0.60 | 0.60 | 0.60 | 0.60 | 0.60 | 0.60 | 0.60 | 0.60 | 0.60 | 1.080 | 1.080 | 1.080 | 1.080 | 1.080 | 1.080 | 1.080 | 1.080 | 1.080 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 0.9 | 0.9 | 0.9 | 0.9 | 0.9 | 0.9 | 0.9 | 0.9 | 0.9 | -1 | -1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | -1 | -1 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | -1 | -1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | -1 | -1 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1 | 35 | 36 | 37 | 38 | 39 | 40 | 41 | 42 | 43 | 34-43 | 34-43 | 34-43 | 34-43 | 34-43 | 34-43 | 34-43 | 34-43 | 34-43 |
14398 | 9 | 9 | 9 | 9 | 9 | 9 | 9 | 9 | 9 | Single | Single | Single | Single | Single | Single | Single | Single | Single | 47 | 48 | 49 | 50 | 51 | 52 | 53 | 54 | 55 | 40-49 | 40-49 | 40-49 | 50-59 | 50-59 | 50-59 | 50-59 | 50-59 | 50-59 | 0-7500 | 0-7500 | 0-7500 | 0-7500 | 0-7500 | 0-7500 | 0-7500 | 0-7500 | 0-7500 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | Medium | Medium | Medium | Medium | Medium | Medium | Medium | Medium | Medium | 28950 | 28950 | 28950 | 28950 | 28950 | 28950 | 28950 | 28950 | 28950 | 20000-30000 | 20000-30000 | 20000-30000 | 20000-30000 | 20000-30000 | 20000-30000 | 20000-30000 | 20000-30000 | 20000-30000 | 5e+04 | 5e+04 | 5e+04 | 5e+04 | 5e+04 | 5e+04 | 5e+04 | 5e+04 | 5e+04 | 1000 | 1000 | 1000 | 1000 | 1000 | 1000 | 1000 | 1000 | 1000 | 0 | 0.000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.000 | 0.000 | 0.000 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1500 | 1500 | 1500 | 1500 | 1500 | 1500 | 1500 | 1500 | 1500 | 1.2 | 1.2 | 1.2 | 1.2 | 1.2 | 1.2 | 1.2 | 1.2 | 1.2 | 0.9 | 0.9 | 0.9 | 0.8 | 0.8 | 0.8 | 0.8 | 0.8 | 0.8 | 0.7 | 0.7 | 0.7 | 0.7 | 0.7 | 0.7 | 0.7 | 0.7 | 0.7 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 0.60 | 0.60 | 0.60 | 0.60 | 0.60 | 0.60 | 0.60 | 0.60 | 0.60 | 0.935 | 0.935 | 0.935 | 0.935 | 0.935 | 0.935 | 0.935 | 0.935 | 0.935 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | -1 | -1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | -1 | -1 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | -1 | -1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | -1 | -1 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1 | 32 | 33 | 34 | 35 | 36 | 37 | 38 | 39 | 40 | 24-33 | 24-33 | 24-33 | 34-43 | 34-43 | 34-43 | 34-43 | 34-43 | 34-43 |
6386 | 10 | 10 | 10 | 10 | 10 | 10 | 10 | 10 | 10 | Single | Single | Single | Single | Single | Single | Single | Single | Single | 16 | 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | 16-19 | 16-19 | 16-19 | 16-19 | 20-24 | 20-24 | 20-24 | 20-24 | 20-24 | 7500-10000 | 7500-10000 | 7500-10000 | 7500-10000 | 7500-10000 | 7500-10000 | 7500-10000 | 7500-10000 | 7500-10000 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | Low | Low | Low | Low | Low | Low | Low | Low | Low | 1020 | 1020 | 1020 | 1020 | 1020 | 1020 | 1020 | 1020 | 1020 | 0-10000 | 0-10000 | 0-10000 | 0-10000 | 0-10000 | 0-10000 | 0-10000 | 0-10000 | 0-10000 | 5e+04 | 5e+04 | 5e+04 | 5e+04 | 5e+04 | 5e+04 | 5e+04 | 5e+04 | 5e+04 | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 0 | 2440.102 | 239.7231 | 6483.2473 | 2026.9269 | 220.6801 | 1897.678 | 200.273 | 2659.388 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1500 | 1500 | 1500 | 1500 | 1500 | 1500 | 1500 | 1500 | 1500 | 1.2 | 1.2 | 1.2 | 1.2 | 1.2 | 1.2 | 1.2 | 1.2 | 1.2 | 3.0 | 3.0 | 3.0 | 3.0 | 2.0 | 2.0 | 2.0 | 2.0 | 2.0 | 0.9 | 0.9 | 0.9 | 0.9 | 0.9 | 0.9 | 0.9 | 0.9 | 0.9 | 0.720 | 0.720 | 0.720 | 0.720 | 0.720 | 0.720 | 0.720 | 0.720 | 0.720 | 0.60 | 0.60 | 0.60 | 0.60 | 0.60 | 0.60 | 0.60 | 0.60 | 0.60 | 1.080 | 1.080 | 1.080 | 1.080 | 1.080 | 1.080 | 1.080 | 1.080 | 1.080 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | -1 | -1 | 2 | 3 | 3 | 3 | 3 | 3 | 3 | -1 | -1 | 1.4 | 1.6 | 1.6 | 1.6 | 1.6 | 1.6 | 1.6 | -1 | -1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | -1 | -1 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 0-3 | 0-3 | 0-3 | 0-3 | 4-8 | 4-8 | 4-8 | 4-8 | 4-8 |
5284 | 11 | 11 | 11 | 11 | 11 | 11 | 11 | 11 | 11 | Married | Married | Married | Married | Married | Married | Married | Married | Married | 27 | 28 | 29 | 30 | 31 | 32 | 33 | 34 | 35 | 25-29 | 25-29 | 25-29 | 30-39 | 30-39 | 30-39 | 30-39 | 30-39 | 30-39 | 7500-10000 | 7500-10000 | 7500-10000 | 7500-10000 | 7500-10000 | 7500-10000 | 7500-10000 | 7500-10000 | 7500-10000 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | High | High | High | High | High | High | High | High | High | 1270 | 1270 | 1270 | 1270 | 1270 | 1270 | 1270 | 1270 | 1270 | 0-10000 | 0-10000 | 0-10000 | 0-10000 | 0-10000 | 0-10000 | 0-10000 | 0-10000 | 0-10000 | 1e+05 | 1e+05 | 1e+05 | 1e+05 | 1e+05 | 1e+05 | 1e+05 | 1e+05 | 1e+05 | 250 | 250 | 250 | 250 | 250 | 250 | 250 | 250 | 250 | 0 | 0.000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.000 | 0.000 | 0.000 | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1500 | 1500 | 1500 | 1500 | 1500 | 1500 | 1500 | 1500 | 1500 | 0.8 | 0.8 | 0.8 | 0.8 | 0.8 | 0.8 | 0.8 | 0.8 | 0.8 | 1.5 | 1.5 | 1.5 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.9 | 0.9 | 0.9 | 0.9 | 0.9 | 0.9 | 0.9 | 0.9 | 0.9 | 0.720 | 0.720 | 0.720 | 0.720 | 0.720 | 0.720 | 0.720 | 0.720 | 0.720 | 0.88 | 0.88 | 0.88 | 0.88 | 0.88 | 0.88 | 0.88 | 0.88 | 0.88 | 1.045 | 1.045 | 1.045 | 1.045 | 1.045 | 1.045 | 1.045 | 1.045 | 1.045 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | -1 | -1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | -1 | -1 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | -1 | -1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | -1 | -1 | 1.2 | 1.2 | 1.2 | 1.2 | 1.2 | 1.0 | 1 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | 9-13 | 9-13 | 9-13 | 14-23 | 14-23 | 14-23 | 14-23 | 14-23 | 14-23 |
999 | 12 | 12 | 12 | 12 | 12 | 12 | 12 | 12 | 12 | Married | Married | Married | Married | Married | Married | Married | Married | Married | 65 | 66 | 67 | 68 | 69 | 70 | 71 | 72 | 73 | 60-69 | 60-69 | 60-69 | 60-69 | 60-69 | 70-79 | 70-79 | 70-79 | 70-79 | 10000-15000 | 10000-15000 | 10000-15000 | 10000-15000 | 10000-15000 | 10000-15000 | 10000-15000 | 10000-15000 | 10000-15000 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | Medium | Medium | Medium | Medium | Medium | Medium | Medium | Medium | Medium | 62030 | 62030 | 62030 | 62030 | 62030 | 62030 | 62030 | 62030 | 62030 | 40000+ | 40000+ | 40000+ | 40000+ | 40000+ | 40000+ | 40000+ | 40000+ | 40000+ | 1e+05 | 1e+05 | 1e+05 | 1e+05 | 1e+05 | 1e+05 | 1e+05 | 1e+05 | 1e+05 | 500 | 500 | 500 | 500 | 500 | 500 | 500 | 500 | 500 | 0 | 0.000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 3620.310 | 0.000 | 0.000 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1500 | 1500 | 1500 | 1500 | 1500 | 1500 | 1500 | 1500 | 1500 | 0.8 | 0.8 | 0.8 | 0.8 | 0.8 | 0.8 | 0.8 | 0.8 | 0.8 | 0.8 | 0.8 | 0.8 | 0.8 | 0.8 | 0.8 | 0.8 | 0.8 | 0.8 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.080 | 1.080 | 1.080 | 1.080 | 1.080 | 1.080 | 1.080 | 1.080 | 1.080 | 0.88 | 0.88 | 0.88 | 0.88 | 0.88 | 0.88 | 0.88 | 0.88 | 0.88 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | -1 | -1 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | -1 | -1 | 1.0 | 1.0 | 1.0 | 1.0 | 1.2 | 1.2 | 1.2 | -1 | -1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | -1 | -1 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1 | 50 | 51 | 52 | 53 | 54 | 55 | 56 | 57 | 58 | 44-53 | 44-53 | 44-53 | 44-53 | 44-53 | 54-63 | 54-63 | 54-63 | 54-63 |
1553 | 13 | 13 | 13 | 13 | 13 | 13 | 13 | 13 | 13 | Single | Single | Single | Single | Single | Single | Single | Single | Single | 37 | 38 | 39 | 40 | 41 | 42 | 43 | 44 | 45 | 30-39 | 30-39 | 30-39 | 40-49 | 40-49 | 40-49 | 40-49 | 40-49 | 40-49 | 15000+ | 15000+ | 15000+ | 15000+ | 15000+ | 15000+ | 15000+ | 15000+ | 15000+ | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | High | High | High | High | High | High | High | High | High | 39430 | 39430 | 39430 | 39430 | 39430 | 39430 | 39430 | 39430 | 39430 | 30000-40000 | 30000-40000 | 30000-40000 | 30000-40000 | 30000-40000 | 30000-40000 | 30000-40000 | 30000-40000 | 30000-40000 | 3e+05 | 3e+05 | 3e+05 | 3e+05 | 3e+05 | 3e+05 | 3e+05 | 3e+05 | 3e+05 | 500 | 500 | 500 | 500 | 500 | 500 | 500 | 500 | 500 | 0 | 0.000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.000 | 0.000 | 0.000 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1500 | 1500 | 1500 | 1500 | 1500 | 1500 | 1500 | 1500 | 1500 | 1.2 | 1.2 | 1.2 | 1.2 | 1.2 | 1.2 | 1.2 | 1.2 | 1.2 | 1.0 | 1.0 | 1.0 | 0.9 | 0.9 | 0.9 | 0.9 | 0.9 | 0.9 | 1.2 | 1.2 | 1.2 | 1.2 | 1.2 | 1.2 | 1.2 | 1.2 | 1.2 | 1.045 | 1.045 | 1.045 | 1.045 | 1.045 | 1.045 | 1.045 | 1.045 | 1.045 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | -1 | -1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | -1 | -1 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | -1 | -1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | -1 | -1 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1 | 22 | 23 | 24 | 25 | 26 | 27 | 28 | 29 | 30 | 14-23 | 14-23 | 14-23 | 24-33 | 24-33 | 24-33 | 24-33 | 24-33 | 24-33 |
8480 | 14 | 14 | 14 | 14 | 14 | 14 | 14 | 14 | 14 | Single | Single | Single | Single | Single | Single | Single | Single | Single | 20 | 21 | 22 | 23 | 24 | 25 | 26 | 27 | 28 | 20-24 | 20-24 | 20-24 | 20-24 | 20-24 | 25-29 | 25-29 | 25-29 | 25-29 | 7500-10000 | 7500-10000 | 7500-10000 | 7500-10000 | 7500-10000 | 7500-10000 | 7500-10000 | 7500-10000 | 7500-10000 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | High | High | High | High | High | High | High | High | High | 23570 | 23570 | 23570 | 23570 | 23570 | 23570 | 23570 | 23570 | 23570 | 20000-30000 | 20000-30000 | 20000-30000 | 20000-30000 | 20000-30000 | 20000-30000 | 20000-30000 | 20000-30000 | 20000-30000 | 5e+04 | 5e+04 | 5e+04 | 5e+04 | 5e+04 | 5e+04 | 5e+04 | 5e+04 | 5e+04 | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 0 | 0.000 | 0.0000 | 0.0000 | 236.8179 | 0.0000 | 0.000 | 0.000 | 0.000 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1500 | 1500 | 1500 | 1500 | 1500 | 1500 | 1500 | 1500 | 1500 | 1.2 | 1.2 | 1.2 | 1.2 | 1.2 | 1.2 | 1.2 | 1.2 | 1.2 | 2.0 | 2.0 | 2.0 | 2.0 | 2.0 | 1.5 | 1.5 | 1.5 | 1.5 | 0.9 | 0.9 | 0.9 | 0.9 | 0.9 | 0.9 | 0.9 | 0.9 | 0.9 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 0.60 | 0.60 | 0.60 | 0.60 | 0.60 | 0.60 | 0.60 | 0.60 | 0.60 | 1.080 | 1.080 | 1.080 | 1.080 | 1.080 | 1.080 | 1.080 | 1.080 | 1.080 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 0.9 | 0.9 | 0.9 | 0.9 | 0.9 | 0.9 | 0.9 | 0.9 | 0.9 | -1 | -1 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | -1 | -1 | 1.0 | 1.0 | 1.2 | 1.2 | 1.2 | 1.0 | 1.0 | -1 | -1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | -1 | -1 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 4-8 | 4-8 | 4-8 | 4-8 | 4-8 | 9-13 | 9-13 | 9-13 | 9-13 |
10430 | 14 | 14 | 14 | 14 | 14 | 14 | 14 | 14 | 14 | Single | Single | Single | Single | Single | Single | Single | Single | Single | 51 | 52 | 53 | 54 | 55 | 56 | 57 | 58 | 59 | 50-59 | 50-59 | 50-59 | 50-59 | 50-59 | 50-59 | 50-59 | 50-59 | 50-59 | 10000-15000 | 10000-15000 | 10000-15000 | 10000-15000 | 10000-15000 | 10000-15000 | 10000-15000 | 10000-15000 | 10000-15000 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | High | High | High | High | High | High | High | High | High | 11020 | 11020 | 11020 | 11020 | 11020 | 11020 | 11020 | 11020 | 11020 | 10000-20000 | 10000-20000 | 10000-20000 | 10000-20000 | 10000-20000 | 10000-20000 | 10000-20000 | 10000-20000 | 10000-20000 | 1e+05 | 1e+05 | 1e+05 | 1e+05 | 1e+05 | 1e+05 | 1e+05 | 1e+05 | 1e+05 | 250 | 250 | 250 | 250 | 250 | 250 | 250 | 250 | 250 | 0 | 0.000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.000 | 0.000 | 0.000 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1500 | 1500 | 1500 | 1500 | 1500 | 1500 | 1500 | 1500 | 1500 | 1.2 | 1.2 | 1.2 | 1.2 | 1.2 | 1.2 | 1.2 | 1.2 | 1.2 | 0.8 | 0.8 | 0.8 | 0.8 | 0.8 | 0.8 | 0.8 | 0.8 | 0.8 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.880 | 0.880 | 0.880 | 0.880 | 0.880 | 0.880 | 0.880 | 0.880 | 0.880 | 0.88 | 0.88 | 0.88 | 0.88 | 0.88 | 0.88 | 0.88 | 0.88 | 0.88 | 1.045 | 1.045 | 1.045 | 1.045 | 1.045 | 1.045 | 1.045 | 1.045 | 1.045 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | -1 | -1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | -1 | -1 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | -1 | -1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | -1 | -1 | 1.2 | 1.2 | 1.2 | 1.0 | 1.0 | 1.0 | 1 | 36 | 37 | 38 | 39 | 40 | 41 | 42 | 43 | 44 | 34-43 | 34-43 | 34-43 | 34-43 | 34-43 | 34-43 | 34-43 | 34-43 | 34-43 |
From the display of tbl.wider[1:20,]
, we can see that many columns are redundant. Before we remove any of these columns, this provides critical information for us as actuaries. Because we are trying to create a prospective underwriting guideline, we want to understand the data that go into our models.
# k <- 2 # counting index
keep <- c(1,2) # columns to keep
for (i in 3:(length(names(tbl.wider)) ) ) {
compare <- prod( tbl.wider[i-1] == tbl.wider[i] )
if ( is.na(compare) ) { compare <- TRUE }
# print(diff)
if ( !as.logical(compare) ) { # if not exact match
keep <- c(keep, i)
}
}
tbl.cleaned <- tbl.wider[keep]
# View(tbl.cleaned, title = "Cleaned Data Table")
tbl.cleaned[1:20,] %>% qkable()
Vehicle Number | Policy Number PY2010 | Marital Status PY2010 | Driver Age PY2010 | Driver Age PY2011 | Driver Age PY2012 | Driver Age PY2013 | Driver Age PY2014 | Driver Age PY2015 | Driver Age PY2016 | Driver Age PY2017 | Driver Age PY2018 | Driver Age Band PY2010 | Driver Age Band PY2011 | Driver Age Band PY2012 | Driver Age Band PY2013 | Driver Age Band PY2014 | Driver Age Band PY2015 | Driver Age Band PY2016 | Driver Age Band PY2017 | Driver Age Band PY2018 | Annual Mileage PY2010 | Territory PY2010 | Credit Score PY2010 | Car Value PY2010 | Car Value Band PY2010 | Liability Limit PY2010 | Physical Damage Deductible PY2010 | Reported Losses PY2010 | Reported Losses PY2011 | Reported Losses PY2012 | Reported Losses PY2013 | Reported Losses PY2014 | Reported Losses PY2015 | Reported Losses PY2016 | Reported Losses PY2017 | Reported Losses PY2018 | Late Fees PY2010 | Late Fees PY2011 | Late Fees PY2012 | Late Fees PY2013 | Late Fees PY2014 | Conviction Points PY2010 | Conviction Points PY2011 | Conviction Points PY2012 | Conviction Points PY2013 | Conviction Points PY2014 | Conviction Points PY2015 | Conviction Points PY2016 | Base Pure Premium PY2010 | Marital Status Relativity PY2010 | Driver Age Relativity PY2010 | Driver Age Relativity PY2011 | Driver Age Relativity PY2012 | Driver Age Relativity PY2013 | Driver Age Relativity PY2014 | Driver Age Relativity PY2015 | Driver Age Relativity PY2016 | Driver Age Relativity PY2017 | Driver Age Relativity PY2018 | Annual Mileage Relativity PY2010 | Car Value Relativity PY2010 | Liability Limit Relativity PY2010 | Physical Damage Deductible Relativity PY2010 | Multi-Car PY2010 | Multi-Car Relativity PY2010 | Accident Points (AP) last 3 years PY2010 | Accident Points (AP) last 3 years PY2011 | Accident Points (AP) last 3 years PY2012 | Accident Points (AP) last 3 years PY2013 | Accident Points (AP) last 3 years PY2014 | Accident Points (AP) last 3 years PY2015 | Accident Points (AP) last 3 years PY2016 | Accident Points (AP) last 3 years PY2017 | Accident Points (AP) last 3 years PY2018 | AP Relativity PY2010 | AP Relativity PY2011 | AP Relativity PY2012 | AP Relativity PY2013 | AP Relativity PY2014 | AP Relativity PY2015 | AP Relativity PY2016 | AP Relativity PY2017 | AP Relativity PY2018 | Conviction Points (CP) last 3 years PY2013 | Conviction Points (CP) last 3 years PY2014 | Conviction Points (CP) last 3 years PY2015 | Conviction Points (CP) last 3 years PY2016 | Conviction Points (CP) last 3 years PY2017 | Conviction Points (CP) last 3 years PY2018 | CP Relativity PY2010 | CP Relativity PY2012 | CP Relativity PY2013 | CP Relativity PY2014 | CP Relativity PY2015 | CP Relativity PY2016 | CP Relativity PY2017 | CP Relativity PY2018 | Driver Experience PY2010 | Driver Experience PY2011 | Driver Experience PY2012 | Driver Experience PY2013 | Driver Experience PY2014 | Driver Experience PY2015 | Driver Experience PY2016 | Driver Experience PY2017 | Driver Experience PY2018 | Driver Experience Band PY2010 | Driver Experience Band PY2011 | Driver Experience Band PY2012 | Driver Experience Band PY2013 | Driver Experience Band PY2014 | Driver Experience Band PY2015 | Driver Experience Band PY2016 | Driver Experience Band PY2017 | Driver Experience Band PY2018 |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
6125 | 1 | Single | 18 | 19 | 20 | 21 | 22 | 23 | 24 | 25 | 26 | 16-19 | 16-19 | 20-24 | 20-24 | 20-24 | 20-24 | 20-24 | 25-29 | 25-29 | 0-7500 | 4 | High | 3220 | 0-10000 | 5e+04 | 100 | 0 | 0.000 | 0.0000 | 200.0692 | 0.0000 | 0.0000 | 12602.418 | 0.000 | 0.000 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 1500 | 1.2 | 3.0 | 3.0 | 2.0 | 2.0 | 2.0 | 2.0 | 2.0 | 1.5 | 1.5 | 0.7 | 0.720 | 0.60 | 1.080 | 2 | 0.9 | -1 | -1 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | -1 | -1 | 1.0 | 1.2 | 1.2 | 1.2 | 1.2 | 1.2 | 1.2 | 1 | 0 | 0 | 0 | 0 | 0 | -1 | 1.4 | 1.2 | 1.0 | 1.0 | 1.0 | 1.0 | 1 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 0-3 | 0-3 | 4-8 | 4-8 | 4-8 | 4-8 | 4-8 | 9-13 | 9-13 |
10332 | 1 | Married | 45 | 46 | 47 | 48 | 49 | 50 | 51 | 52 | 53 | 40-49 | 40-49 | 40-49 | 40-49 | 40-49 | 50-59 | 50-59 | 50-59 | 50-59 | 7500-10000 | 3 | High | 20090 | 20000-30000 | 5e+05 | 250 | 0 | 0.000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.000 | 0.000 | 0.000 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1500 | 0.8 | 0.9 | 0.9 | 0.9 | 0.9 | 0.9 | 0.8 | 0.8 | 0.8 | 0.8 | 0.9 | 1.000 | 1.12 | 1.045 | 1 | 1.0 | -1 | -1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | -1 | -1 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0 | 1 | 1 | 1 | 0 | 0 | -1 | 1.0 | 1.0 | 1.2 | 1.2 | 1.2 | 1.0 | 1 | 30 | 31 | 32 | 33 | 34 | 35 | 36 | 37 | 38 | 24-33 | 24-33 | 24-33 | 24-33 | 24-33 | 34-43 | 34-43 | 34-43 | 34-43 |
1965 | 2 | Married | 54 | 55 | 56 | 57 | 58 | 59 | 60 | 61 | 62 | 50-59 | 50-59 | 50-59 | 50-59 | 50-59 | 50-59 | 60-69 | 60-69 | 60-69 | 15000+ | 1 | Medium | 13810 | 10000-20000 | 1e+05 | 500 | 0 | 0.000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.000 | 0.000 | 0.000 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1500 | 0.8 | 0.8 | 0.8 | 0.8 | 0.8 | 0.8 | 0.8 | 0.8 | 0.8 | 0.8 | 1.2 | 0.880 | 0.88 | 1.000 | 2 | 0.9 | -1 | -1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | -1 | -1 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0 | 0 | 0 | 0 | 0 | 0 | -1 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1 | 39 | 40 | 41 | 42 | 43 | 44 | 45 | 46 | 47 | 34-43 | 34-43 | 34-43 | 34-43 | 34-43 | 34-43 | 44-53 | 44-53 | 44-53 |
14130 | 2 | Married | 49 | 50 | 51 | 52 | 53 | 54 | 55 | 56 | 57 | 40-49 | 50-59 | 50-59 | 50-59 | 50-59 | 50-59 | 50-59 | 50-59 | 50-59 | 10000-15000 | 1 | High | 37060 | 30000-40000 | 1e+05 | 100 | 0 | 0.000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.000 | 0.000 | 0.000 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1500 | 0.8 | 0.9 | 0.8 | 0.8 | 0.8 | 0.8 | 0.8 | 0.8 | 0.8 | 0.8 | 1.0 | 1.045 | 0.88 | 1.080 | 1 | 1.0 | -1 | -1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | -1 | -1 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0 | 0 | 0 | 0 | 0 | 0 | -1 | 1.2 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1 | 34 | 35 | 36 | 37 | 38 | 39 | 40 | 41 | 42 | 24-33 | 34-43 | 34-43 | 34-43 | 34-43 | 34-43 | 34-43 | 34-43 | 34-43 |
2976 | 3 | Single | 31 | 32 | 33 | 34 | 35 | 36 | 37 | 38 | 39 | 30-39 | 30-39 | 30-39 | 30-39 | 30-39 | 30-39 | 30-39 | 30-39 | 30-39 | 7500-10000 | 4 | High | 20860 | 20000-30000 | 5e+04 | 250 | 0 | 0.000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.000 | 0.000 | 0.000 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1500 | 1.2 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.9 | 1.000 | 0.60 | 1.045 | 1 | 1.0 | -1 | -1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | -1 | -1 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0 | 0 | 0 | 0 | 0 | 0 | -1 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1 | 16 | 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | 14-23 | 14-23 | 14-23 | 14-23 | 14-23 | 14-23 | 14-23 | 14-23 | 14-23 |
76 | 4 | Married | 53 | 54 | 55 | 56 | 57 | 58 | 59 | 60 | 61 | 50-59 | 50-59 | 50-59 | 50-59 | 50-59 | 50-59 | 50-59 | 60-69 | 60-69 | 15000+ | 2 | High | 80850 | 40000+ | 5e+04 | 100 | 0 | 0.000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.000 | 0.000 | 0.000 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1500 | 0.8 | 0.8 | 0.8 | 0.8 | 0.8 | 0.8 | 0.8 | 0.8 | 0.8 | 0.8 | 1.2 | 1.080 | 0.60 | 1.080 | 1 | 1.0 | -1 | -1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | -1 | -1 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0 | 0 | 0 | 0 | 0 | 0 | -1 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1 | 38 | 39 | 40 | 41 | 42 | 43 | 44 | 45 | 46 | 34-43 | 34-43 | 34-43 | 34-43 | 34-43 | 34-43 | 34-43 | 44-53 | 44-53 |
8571 | 5 | Single | 21 | 22 | 23 | 24 | 25 | 26 | 27 | 28 | 29 | 20-24 | 20-24 | 20-24 | 20-24 | 25-29 | 25-29 | 25-29 | 25-29 | 25-29 | 0-7500 | 3 | Low | 5610 | 0-10000 | 1e+05 | 500 | 0 | 0.000 | 0.0000 | 10121.7588 | 0.0000 | 0.0000 | 0.000 | 0.000 | 0.000 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1500 | 1.2 | 2.0 | 2.0 | 2.0 | 2.0 | 1.5 | 1.5 | 1.5 | 1.5 | 1.5 | 0.7 | 0.720 | 0.88 | 1.000 | 1 | 1.0 | -1 | -1 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | -1 | -1 | 1.0 | 1.2 | 1.2 | 1.2 | 1.0 | 1.0 | 1.0 | 1 | 1 | 1 | 0 | 0 | 0 | -1 | 1.0 | 1.2 | 1.2 | 1.2 | 1.0 | 1.0 | 1 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 4-8 | 4-8 | 4-8 | 4-8 | 9-13 | 9-13 | 9-13 | 9-13 | 9-13 |
8530 | 6 | Single | 28 | 29 | 30 | 31 | 32 | 33 | 34 | 35 | 36 | 25-29 | 25-29 | 30-39 | 30-39 | 30-39 | 30-39 | 30-39 | 30-39 | 30-39 | 7500-10000 | 2 | High | 20000 | 20000-30000 | 5e+04 | 250 | 0 | 0.000 | 0.0000 | 28602.5937 | 0.0000 | 0.0000 | 0.000 | 0.000 | 70200.000 | 0 | 2 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1500 | 1.2 | 1.5 | 1.5 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.9 | 1.000 | 0.60 | 1.045 | 1 | 1.0 | -1 | -1 | 0 | 1 | 1 | 1 | 0 | 0 | 1 | -1 | -1 | 1.0 | 1.2 | 1.2 | 1.2 | 1.0 | 1.0 | 1.2 | 1 | 1 | 2 | 1 | 1 | 0 | -1 | 1.0 | 1.2 | 1.2 | 1.4 | 1.2 | 1.2 | 1 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | 21 | 9-13 | 9-13 | 14-23 | 14-23 | 14-23 | 14-23 | 14-23 | 14-23 | 14-23 |
6104 | 7 | Single | 33 | 34 | 35 | 36 | 37 | 38 | 39 | 40 | 41 | 30-39 | 30-39 | 30-39 | 30-39 | 30-39 | 30-39 | 30-39 | 40-49 | 40-49 | 0-7500 | 1 | High | 13020 | 10000-20000 | 5e+04 | 500 | 0 | 0.000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.000 | 0.000 | 0.000 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1500 | 1.2 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.9 | 0.9 | 0.7 | 0.880 | 0.60 | 1.000 | 2 | 0.9 | -1 | -1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | -1 | -1 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0 | 0 | 0 | 0 | 0 | 0 | -1 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | 25 | 26 | 14-23 | 14-23 | 14-23 | 14-23 | 14-23 | 14-23 | 14-23 | 24-33 | 24-33 |
12468 | 7 | Single | 27 | 28 | 29 | 30 | 31 | 32 | 33 | 34 | 35 | 25-29 | 25-29 | 25-29 | 30-39 | 30-39 | 30-39 | 30-39 | 30-39 | 30-39 | 0-7500 | 2 | High | 26350 | 20000-30000 | 1e+05 | 250 | 0 | 0.000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.000 | 0.000 | 0.000 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 1500 | 1.2 | 1.5 | 1.5 | 1.5 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.7 | 1.000 | 0.88 | 1.045 | 1 | 1.0 | -1 | -1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | -1 | -1 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 2 | 2 | 2 | 1 | 0 | 0 | -1 | 1.2 | 1.4 | 1.4 | 1.4 | 1.2 | 1.0 | 1 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | 9-13 | 9-13 | 9-13 | 14-23 | 14-23 | 14-23 | 14-23 | 14-23 | 14-23 |
1547 | 8 | Married | 52 | 53 | 54 | 55 | 56 | 57 | 58 | 59 | 60 | 50-59 | 50-59 | 50-59 | 50-59 | 50-59 | 50-59 | 50-59 | 50-59 | 60-69 | 0-7500 | 4 | Low | 39180 | 30000-40000 | 5e+05 | 1000 | 0 | 0.000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.000 | 0.000 | 0.000 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1500 | 0.8 | 0.8 | 0.8 | 0.8 | 0.8 | 0.8 | 0.8 | 0.8 | 0.8 | 0.8 | 0.7 | 1.045 | 1.12 | 0.935 | 1 | 1.0 | -1 | -1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | -1 | -1 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0 | 0 | 0 | 0 | 0 | 0 | -1 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1 | 37 | 38 | 39 | 40 | 41 | 42 | 43 | 44 | 45 | 34-43 | 34-43 | 34-43 | 34-43 | 34-43 | 34-43 | 34-43 | 34-43 | 44-53 |
736 | 9 | Married | 49 | 50 | 51 | 52 | 53 | 54 | 55 | 56 | 57 | 40-49 | 50-59 | 50-59 | 50-59 | 50-59 | 50-59 | 50-59 | 50-59 | 50-59 | 10000-15000 | 4 | Medium | 30150 | 30000-40000 | 1e+05 | 500 | 0 | 0.000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.000 | 0.000 | 52977.089 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1500 | 0.8 | 0.9 | 0.8 | 0.8 | 0.8 | 0.8 | 0.8 | 0.8 | 0.8 | 0.8 | 1.0 | 1.045 | 0.88 | 1.000 | 3 | 0.8 | -1 | -1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | -1 | -1 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.2 | 0 | 0 | 0 | 0 | 0 | 0 | -1 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1 | 34 | 35 | 36 | 37 | 38 | 39 | 40 | 41 | 42 | 24-33 | 34-43 | 34-43 | 34-43 | 34-43 | 34-43 | 34-43 | 34-43 | 34-43 |
12236 | 9 | Single | 50 | 51 | 52 | 53 | 54 | 55 | 56 | 57 | 58 | 50-59 | 50-59 | 50-59 | 50-59 | 50-59 | 50-59 | 50-59 | 50-59 | 50-59 | 0-7500 | 4 | Medium | 22600 | 20000-30000 | 5e+04 | 100 | 0 | 0.000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.000 | 0.000 | 0.000 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1500 | 1.2 | 0.8 | 0.8 | 0.8 | 0.8 | 0.8 | 0.8 | 0.8 | 0.8 | 0.8 | 0.7 | 1.000 | 0.60 | 1.080 | 2 | 0.9 | -1 | -1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | -1 | -1 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0 | 0 | 0 | 0 | 0 | 0 | -1 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1 | 35 | 36 | 37 | 38 | 39 | 40 | 41 | 42 | 43 | 34-43 | 34-43 | 34-43 | 34-43 | 34-43 | 34-43 | 34-43 | 34-43 | 34-43 |
14398 | 9 | Single | 47 | 48 | 49 | 50 | 51 | 52 | 53 | 54 | 55 | 40-49 | 40-49 | 40-49 | 50-59 | 50-59 | 50-59 | 50-59 | 50-59 | 50-59 | 0-7500 | 3 | Medium | 28950 | 20000-30000 | 5e+04 | 1000 | 0 | 0.000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.000 | 0.000 | 0.000 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1500 | 1.2 | 0.9 | 0.9 | 0.9 | 0.8 | 0.8 | 0.8 | 0.8 | 0.8 | 0.8 | 0.7 | 1.000 | 0.60 | 0.935 | 1 | 1.0 | -1 | -1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | -1 | -1 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0 | 0 | 0 | 0 | 0 | 0 | -1 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1 | 32 | 33 | 34 | 35 | 36 | 37 | 38 | 39 | 40 | 24-33 | 24-33 | 24-33 | 34-43 | 34-43 | 34-43 | 34-43 | 34-43 | 34-43 |
6386 | 10 | Single | 16 | 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | 16-19 | 16-19 | 16-19 | 16-19 | 20-24 | 20-24 | 20-24 | 20-24 | 20-24 | 7500-10000 | 2 | Low | 1020 | 0-10000 | 5e+04 | 100 | 0 | 2440.102 | 239.7231 | 6483.2473 | 2026.9269 | 220.6801 | 1897.678 | 200.273 | 2659.388 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1500 | 1.2 | 3.0 | 3.0 | 3.0 | 3.0 | 2.0 | 2.0 | 2.0 | 2.0 | 2.0 | 0.9 | 0.720 | 0.60 | 1.080 | 1 | 1.0 | -1 | -1 | 2 | 3 | 3 | 3 | 3 | 3 | 3 | -1 | -1 | 1.4 | 1.6 | 1.6 | 1.6 | 1.6 | 1.6 | 1.6 | 0 | 0 | 0 | 0 | 0 | 0 | -1 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 0-3 | 0-3 | 0-3 | 0-3 | 4-8 | 4-8 | 4-8 | 4-8 | 4-8 |
5284 | 11 | Married | 27 | 28 | 29 | 30 | 31 | 32 | 33 | 34 | 35 | 25-29 | 25-29 | 25-29 | 30-39 | 30-39 | 30-39 | 30-39 | 30-39 | 30-39 | 7500-10000 | 3 | High | 1270 | 0-10000 | 1e+05 | 250 | 0 | 0.000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.000 | 0.000 | 0.000 | 0 | 0 | 3 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1500 | 0.8 | 1.5 | 1.5 | 1.5 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.9 | 0.720 | 0.88 | 1.045 | 1 | 1.0 | -1 | -1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | -1 | -1 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1 | 1 | 1 | 1 | 0 | 0 | -1 | 1.2 | 1.2 | 1.2 | 1.2 | 1.2 | 1.0 | 1 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | 9-13 | 9-13 | 9-13 | 14-23 | 14-23 | 14-23 | 14-23 | 14-23 | 14-23 |
999 | 12 | Married | 65 | 66 | 67 | 68 | 69 | 70 | 71 | 72 | 73 | 60-69 | 60-69 | 60-69 | 60-69 | 60-69 | 70-79 | 70-79 | 70-79 | 70-79 | 10000-15000 | 3 | Medium | 62030 | 40000+ | 1e+05 | 500 | 0 | 0.000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 3620.310 | 0.000 | 0.000 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1500 | 0.8 | 0.8 | 0.8 | 0.8 | 0.8 | 0.8 | 0.8 | 0.8 | 0.8 | 0.8 | 1.0 | 1.080 | 0.88 | 1.000 | 1 | 1.0 | -1 | -1 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | -1 | -1 | 1.0 | 1.0 | 1.0 | 1.0 | 1.2 | 1.2 | 1.2 | 0 | 0 | 0 | 0 | 0 | 0 | -1 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1 | 50 | 51 | 52 | 53 | 54 | 55 | 56 | 57 | 58 | 44-53 | 44-53 | 44-53 | 44-53 | 44-53 | 54-63 | 54-63 | 54-63 | 54-63 |
1553 | 13 | Single | 37 | 38 | 39 | 40 | 41 | 42 | 43 | 44 | 45 | 30-39 | 30-39 | 30-39 | 40-49 | 40-49 | 40-49 | 40-49 | 40-49 | 40-49 | 15000+ | 2 | High | 39430 | 30000-40000 | 3e+05 | 500 | 0 | 0.000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.000 | 0.000 | 0.000 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1500 | 1.2 | 1.0 | 1.0 | 1.0 | 0.9 | 0.9 | 0.9 | 0.9 | 0.9 | 0.9 | 1.2 | 1.045 | 1.00 | 1.000 | 1 | 1.0 | -1 | -1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | -1 | -1 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0 | 0 | 0 | 0 | 0 | 0 | -1 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1 | 22 | 23 | 24 | 25 | 26 | 27 | 28 | 29 | 30 | 14-23 | 14-23 | 14-23 | 24-33 | 24-33 | 24-33 | 24-33 | 24-33 | 24-33 |
8480 | 14 | Single | 20 | 21 | 22 | 23 | 24 | 25 | 26 | 27 | 28 | 20-24 | 20-24 | 20-24 | 20-24 | 20-24 | 25-29 | 25-29 | 25-29 | 25-29 | 7500-10000 | 3 | High | 23570 | 20000-30000 | 5e+04 | 100 | 0 | 0.000 | 0.0000 | 0.0000 | 236.8179 | 0.0000 | 0.000 | 0.000 | 0.000 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1500 | 1.2 | 2.0 | 2.0 | 2.0 | 2.0 | 2.0 | 1.5 | 1.5 | 1.5 | 1.5 | 0.9 | 1.000 | 0.60 | 1.080 | 2 | 0.9 | -1 | -1 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | -1 | -1 | 1.0 | 1.0 | 1.2 | 1.2 | 1.2 | 1.0 | 1.0 | 0 | 0 | 0 | 0 | 0 | 0 | -1 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 4-8 | 4-8 | 4-8 | 4-8 | 4-8 | 9-13 | 9-13 | 9-13 | 9-13 |
10430 | 14 | Single | 51 | 52 | 53 | 54 | 55 | 56 | 57 | 58 | 59 | 50-59 | 50-59 | 50-59 | 50-59 | 50-59 | 50-59 | 50-59 | 50-59 | 50-59 | 10000-15000 | 2 | High | 11020 | 10000-20000 | 1e+05 | 250 | 0 | 0.000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.000 | 0.000 | 0.000 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1500 | 1.2 | 0.8 | 0.8 | 0.8 | 0.8 | 0.8 | 0.8 | 0.8 | 0.8 | 0.8 | 1.0 | 0.880 | 0.88 | 1.045 | 1 | 1.0 | -1 | -1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | -1 | -1 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1 | 1 | 0 | 0 | 0 | 0 | -1 | 1.2 | 1.2 | 1.2 | 1.0 | 1.0 | 1.0 | 1 | 36 | 37 | 38 | 39 | 40 | 41 | 42 | 43 | 44 | 34-43 | 34-43 | 34-43 | 34-43 | 34-43 | 34-43 | 34-43 | 34-43 | 34-43 |
We see here in tbl.cleaned
all the unique columns, with all repetitions removed. To emphasize this fact, we keep the ...PY2010
suffix in the column names.
The factors that are constant are listed below, along with the years for which they are repeated:
From this list, we have reason to believe that either the computer system is broken or there was a serious error in the data reporting.
Because our filed relativity is based on metrics rolling over 3 years, for our project we are interested in modeling based on experience from policy years 2013 through 2016. We want to use Conviction Points to drive our GLM, and this variable for years 2017 and 2018 is not credible.
Here we’ll have a better look into our data and form assumptions to help with modeling. While we are certainly interested in how we have historically charged policyholders and their resulting losses, our ratemaking algorithm and underwriting guidelines must be prospective. This is because customers are free to shop around for coverage, and an insurer’s intent to recoup past losses from particular individuals may be useless. The individual can simply switch to a competitor insurer that offers cheaper rates.
Let’s take a look at the distribution of claim severity. For an exhibit, we’ll look at our data for Policy Year 2013.
qloss <- select(tbl.cleaned, `Vehicle Number`, `Reported Losses PY2013`)
pos.loss <- qloss$`Reported Losses PY2013` > 0
This first plot below shows the overall distribution of claim severity, where (as we expect) the bulk (91%) of policyholders file no claims. In fact, 95% of insureds have a reported loss of less than $1,000 in 2013. We call this distribution “zero-inflated,” as there is a very large amount of “zero-severity” claims (no claims filed).
qloss[pos.loss,] %>% ggplot(aes(x = `Reported Losses PY2013`)) + geom_histogram(bins = 15, fill = "lightblue", color = "lightyellow") + ggtitle("Distribution of Claims by Severity")
To have a better look at the tail, we scale the \(x\)-axis by log10()
. This eliminates zero-severity claims from our plot (as \(\log_{10} 0 = - \infty\)), and groups higher severity claims together.
qloss[pos.loss,] %>% ggplot(aes(x = `Reported Losses PY2013`)) + geom_histogram(bins = 15, fill = "lightblue", color = "lightyellow") + scale_x_log10() + ggtitle("Distribution of Claims by Log Severity")
This appears like a typical loss distribution, where we can fit this histogram to a Gamma or Tweedie distribution.
We may want to cap the losses for our model if we expect that the few high severity claims will adversely affect our model.
For our predictive model, we must choose whether to model true reported losses or the incurred losses after adjusting to deductible and liability limits. Although it may appear beneficial to capture the underlying data via the raw, unadjusted reported losses, we must note that there is a substantial likelihood that policyholders with a high deductible do not report a small-cost claim, whether to avoid the hassle or to not adversely affect their record when there is no benefit or payout.
Now this leaves us to expect that there are some low-amount losses that are incurred but will not be reported. Because this is a natural (and often desired as it helps lower adjusting costs) mechanism of the deductible, we choose to model to adjusted loss data. This will naturally put a cap on the high reported losses, capped at the maximum liability limit for the policy.
In our data cleaning, we notice that some filed premiums are high (above $4000), but many of these also incur high losses in our experience period. To evaluate claims by their impact on Cal Insurance’s finances, we want to normalize the losses by first applying the liability limits and deductibles.
n <- length(tbl$`Reported Losses`)
tmp <- matrix(data = NA, nrow = n )
# adjust the losses to deductible and liability limit
for (i in 1:n) {
tmp[i] <- max( min(tbl$`Liability Limit`[i], tbl$`Reported Losses`[i]) - tbl$`Physical Damage Deductible`[i], 0 )
# take minimum of limit and the loss, and
# then take maximum of 0 and the difference to the deductible
}
# View(adj.loss)
loss <- tbl %>% mutate(`Adjusted Loss` = tmp) # append adjusted loss
loss %>% ggplot(aes(x = `Adjusted Loss`)) + geom_histogram(bins = 15, fill = "lightblue", color = "lightyellow") + ggtitle("Log-Distribution of Nonzero Claims by Severity") + scale_y_log10()
loss %>% ggplot(aes(x = `Adjusted Loss`)) + geom_histogram(bins = 15, fill = "lightblue", color = "lightyellow") + scale_x_log10() + ggtitle("Distribution of Nonzero Claims by Log-Severity")
To gain some insight on the data after applying the deductible and liability limits, we’ll look at some distribution plots.
loss %>% ggplot(aes(x = `Credit Score`, y = `Adjusted Loss`)) + geom_bar(aes(fill = `Credit Score`), stat = "summary", fun.y = "mean", show.legend = TRUE) + theme(legend.position = "bottom", axis.text.x = element_blank(), axis.ticks = element_blank()) + ggtitle("Average Adjusted Loss by Credit Score and Marital Status") + facet_grid(. ~ `Marital Status`)
From this above plot, we see that Credit Score
and Marital Status
are indeed very strong indicators of expected loss. This validates the significantly lower premiums charged to married policyholders with high credit scores.
Let’s take a look at the average adjusted loss for Annual Mileage
and Driver Experience Band
, two variables that are heavily influenced by sequential analysis in compliance with California’s Proposition 103.
loss %>% ggplot(aes(x = `Annual Mileage`, y = `Adjusted Loss`)) + geom_bar(aes(fill = `Annual Mileage`), stat = "summary", fun.y = "mean", show.legend = TRUE) + theme(legend.position = "bottom", axis.text.x = element_blank()) + ggtitle("Average Adjusted Loss", subtitle = "by Annual Mileage and Marital Status") + facet_grid(. ~ `Marital Status`)
loss %>% ggplot(aes(x = `Driver Experience Band`, y = `Adjusted Loss`)) + geom_bar(aes(fill = `Driver Experience Band`), stat = "summary", fun.y = "mean", show.legend = TRUE) + theme(legend.position = "bottom", axis.text.x = element_blank(), axis.ticks = element_blank()) + ggtitle("Average Adjusted Loss by Driver Experience Band and Territory") + facet_grid(. ~ `Territory`)
# loss %>% ggplot(aes(x = `Territory`, y = `Adjusted Loss`)) + geom_bar(aes(fill = `Territory`), stat = "identity", show.legend = TRUE) + facet_grid(. ~ `Marital Status`) + theme(legend.position = "bottom") + ggtitle("Total loss by Territory and Marital Status")
loss %>% ggplot(aes(x = `Territory`, y = `Adjusted Loss`)) + geom_bar(aes(fill = `Territory`), stat = "summary", fun.y = "mean", show.legend = TRUE) + facet_grid(. ~ `Marital Status`) + theme(legend.position = "bottom", axis.text.x = element_blank()) + ggtitle("Average Loss by Territory and Marital Status")
The total and average losses by aggregating by Marital Status
and Territory
appears to be very similar. We can check the counts and see that they are very closely uniform across the territories:
loss %>% ggplot(aes(x = `Territory`)) + geom_bar(aes(fill = `Territory`), stat = "count", show.legend = FALSE) + facet_grid(. ~ `Marital Status`) + theme(legend.position = "bottom") + ggtitle("Distribution of Policyholders by Territory and Marital Status")
loss %>% ggplot(aes(x = `Policy Year`, y = `Adjusted Loss`)) + geom_bar(aes(fill = `Policy Year`), stat = "summary", fun.y = "mean", show.legend = TRUE) + facet_grid(. ~ `Marital Status`) + theme(legend.position = "bottom", axis.text.x = element_blank()) + ggtitle("Average loss by Policy Year, Comparing Marital Status")
Without specific data on the loss-related expenses, we take the simplifying assumption that loss related expenses are 20% of the adjusted loss. That is, we assume \[ \begin{align*} \text{Pure Premium} &= \text{Losses} + \text{Loss Related Expenses} \\ &= 1.20 \times \text{Losses}. \end{align*}\] We verify that this assumption is asymptotically sound in that low-severity claims under the deductible have no loss related expenses, while large severity claims may involve attorney fees that scale with the size of the claim. Because we are training on the years 2013-2015, we assume that there has been sufficient time (3~6 years) for losses to develop to ultimate.
# loss %>% ggplot(aes(x = `Driver Experience Band`, y = `Adjusted Loss`)) + geom_histogram(aes(fill = `Driver Experience Band`), stat = "summary", fun.y = "mean", show.legend = TRUE) + facet_grid(. ~ `Marital Status`) + theme(legend.position = "bottom", axis.text.x = element_blank()) + ggtitle("Average loss by Policy Year and Marital Status")
loss <- loss %>% mutate(`Pure Premium` = 1.20 * `Adjusted Loss`)
df.loss <- filter(loss, `Policy Year` >= 2013, `Policy Year` <= 2015 ) %>% select(`Policy Number`, `Vehicle Number`, `Pure Premium`, `Conviction Points (CP) last 3 years`, `Accident Points (AP) last 3 years`, `Annual Mileage`, `Driver Experience Band`, `Marital Status`, `Territory`, `Driver Age`, `Credit Score`, `Late Fees`, `Liability Limit` ) # bring credit score for cross-validation of proxy later
df.loss[1:100,] %>% qkable()
Policy Number | Vehicle Number | Pure Premium | Conviction Points (CP) last 3 years | Accident Points (AP) last 3 years | Annual Mileage | Driver Experience Band | Marital Status | Territory | Driver Age | Credit Score | Late Fees | Liability Limit |
---|---|---|---|---|---|---|---|---|---|---|---|---|
1 | 6125 | 120.0830 | 1 | 1 | 0-7500 | 4-8 | Single | 4 | 21 | High | 0 | 5e+04 |
1 | 10332 | 0.0000 | 0 | 0 | 7500-10000 | 24-33 | Married | 3 | 48 | High | 0 | 5e+05 |
2 | 1965 | 0.0000 | 0 | 0 | 15000+ | 34-43 | Married | 1 | 57 | Medium | 0 | 1e+05 |
2 | 14130 | 0.0000 | 0 | 0 | 10000-15000 | 34-43 | Married | 1 | 52 | High | 0 | 1e+05 |
3 | 2976 | 0.0000 | 0 | 0 | 7500-10000 | 14-23 | Single | 4 | 34 | High | 0 | 5e+04 |
4 | 76 | 0.0000 | 0 | 0 | 15000+ | 34-43 | Married | 2 | 56 | High | 0 | 5e+04 |
5 | 8571 | 11546.1106 | 1 | 1 | 0-7500 | 4-8 | Single | 3 | 24 | Low | 0 | 1e+05 |
6 | 8530 | 34023.1125 | 1 | 1 | 7500-10000 | 14-23 | Single | 2 | 31 | High | 1 | 5e+04 |
7 | 6104 | 0.0000 | 0 | 0 | 0-7500 | 14-23 | Single | 1 | 36 | High | 0 | 5e+04 |
7 | 12468 | 0.0000 | 2 | 0 | 0-7500 | 14-23 | Single | 2 | 30 | High | 0 | 1e+05 |
8 | 1547 | 0.0000 | 0 | 0 | 0-7500 | 34-43 | Married | 4 | 55 | Low | 0 | 5e+05 |
9 | 736 | 0.0000 | 0 | 0 | 10000-15000 | 34-43 | Married | 4 | 52 | Medium | 0 | 1e+05 |
9 | 12236 | 0.0000 | 0 | 0 | 0-7500 | 34-43 | Single | 4 | 53 | Medium | 0 | 5e+04 |
9 | 14398 | 0.0000 | 0 | 0 | 0-7500 | 34-43 | Single | 3 | 50 | Medium | 0 | 5e+04 |
10 | 6386 | 7659.8968 | 0 | 3 | 7500-10000 | 0-3 | Single | 2 | 19 | Low | 0 | 5e+04 |
11 | 5284 | 0.0000 | 1 | 0 | 7500-10000 | 14-23 | Married | 3 | 30 | High | 0 | 1e+05 |
12 | 999 | 0.0000 | 0 | 0 | 10000-15000 | 44-53 | Married | 3 | 68 | Medium | 0 | 1e+05 |
13 | 1553 | 0.0000 | 0 | 0 | 15000+ | 24-33 | Single | 2 | 40 | High | 0 | 3e+05 |
14 | 8480 | 0.0000 | 0 | 0 | 7500-10000 | 4-8 | Single | 3 | 23 | High | 0 | 5e+04 |
14 | 10430 | 0.0000 | 1 | 0 | 10000-15000 | 34-43 | Single | 2 | 54 | High | 0 | 1e+05 |
15 | 5609 | 0.0000 | 2 | 0 | 7500-10000 | 34-43 | Married | 4 | 52 | Low | 0 | 5e+05 |
16 | 5679 | 0.0000 | 0 | 0 | 10000-15000 | 14-23 | Single | 1 | 31 | High | 0 | 3e+05 |
16 | 14610 | 122.8126 | 0 | 1 | 7500-10000 | 34-43 | Married | 1 | 51 | High | 0 | 5e+05 |
17 | 3462 | 0.0000 | 0 | 0 | 0-7500 | 34-43 | Single | 1 | 54 | High | 3 | 3e+05 |
17 | 12553 | 63413.4231 | 0 | 1 | 10000-15000 | 14-23 | Single | 2 | 37 | Low | 0 | 1e+05 |
18 | 5397 | 0.0000 | 0 | 0 | 15000+ | 34-43 | Married | 1 | 54 | High | 2 | 3e+05 |
19 | 2344 | 0.0000 | 0 | 0 | 0-7500 | 24-33 | Single | 3 | 41 | High | 0 | 3e+05 |
20 | 8792 | 0.0000 | 1 | 0 | 0-7500 | 14-23 | Single | 4 | 36 | Medium | 0 | 5e+05 |
21 | 4380 | 0.0000 | 0 | 0 | 10000-15000 | 9-13 | Single | 1 | 27 | Medium | 0 | 3e+05 |
21 | 12703 | 120.0024 | 0 | 1 | 10000-15000 | 4-8 | Single | 3 | 24 | Low | 0 | 5e+04 |
22 | 9614 | 0.0000 | 1 | 0 | 7500-10000 | 24-33 | Single | 3 | 41 | High | 0 | 3e+05 |
23 | 7817 | 0.0000 | 0 | 0 | 7500-10000 | 14-23 | Single | 3 | 34 | Low | 0 | 5e+05 |
24 | 4802 | 0.0000 | 0 | 0 | 10000-15000 | 24-33 | Single | 1 | 41 | Low | 0 | 1e+05 |
24 | 11025 | 0.0000 | 0 | 0 | 7500-10000 | 44-53 | Married | 4 | 67 | High | 0 | 3e+05 |
25 | 2795 | 0.0000 | 0 | 0 | 10000-15000 | 9-13 | Single | 4 | 27 | Low | 1 | 1e+05 |
25 | 11584 | 0.0000 | 1 | 0 | 0-7500 | 4-8 | Single | 1 | 22 | Low | 0 | 1e+05 |
26 | 2611 | 0.0000 | 0 | 0 | 10000-15000 | 14-23 | Single | 3 | 31 | Low | 0 | 5e+04 |
27 | 6005 | 0.0000 | 1 | 0 | 0-7500 | 14-23 | Single | 4 | 31 | Low | 0 | 5e+04 |
28 | 3107 | 0.0000 | 0 | 0 | 0-7500 | 9-13 | Single | 4 | 27 | High | 0 | 1e+05 |
28 | 10304 | 0.0000 | 0 | 0 | 15000+ | 14-23 | Married | 3 | 39 | High | 0 | 5e+04 |
28 | 11879 | 0.0000 | 1 | 0 | 7500-10000 | 44-53 | Married | 1 | 67 | High | 0 | 5e+04 |
29 | 9910 | 0.0000 | 0 | 0 | 10000-15000 | 34-43 | Single | 4 | 54 | Low | 0 | 5e+04 |
30 | 5177 | 0.0000 | 0 | 1 | 0-7500 | 9-13 | Single | 2 | 29 | Low | 0 | 5e+04 |
30 | 14568 | 0.0000 | 0 | 0 | 15000+ | 34-43 | Married | 4 | 59 | High | 0 | 1e+05 |
31 | 9230 | 0.0000 | 0 | 0 | 15000+ | 4-8 | Single | 2 | 22 | High | 0 | 3e+05 |
32 | 1665 | 0.0000 | 1 | 0 | 15000+ | 9-13 | Single | 3 | 29 | Medium | 0 | 1e+05 |
33 | 4652 | 0.0000 | 1 | 0 | 15000+ | 34-43 | Single | 2 | 51 | High | 0 | 5e+04 |
34 | 3222 | 0.0000 | 0 | 0 | 0-7500 | 24-33 | Married | 4 | 41 | High | 0 | 5e+04 |
34 | 10923 | 0.0000 | 2 | 0 | 0-7500 | 34-43 | Single | 3 | 52 | Medium | 0 | 1e+05 |
34 | 12380 | 0.0000 | 0 | 0 | 0-7500 | 9-13 | Single | 2 | 25 | Medium | 0 | 1e+05 |
34 | 13859 | 0.0000 | 1 | 0 | 10000-15000 | 44-53 | Married | 2 | 68 | High | 0 | 1e+05 |
35 | 9666 | 0.0000 | 1 | 0 | 7500-10000 | 24-33 | Married | 4 | 47 | High | 0 | 1e+05 |
36 | 6180 | 0.0000 | 1 | 0 | 0-7500 | 64-73 | Married | 4 | 85 | High | 0 | 5e+05 |
37 | 6273 | 0.0000 | 2 | 0 | 0-7500 | 44-53 | Married | 1 | 69 | Medium | 0 | 1e+05 |
37 | 11273 | 0.0000 | 0 | 0 | 15000+ | 44-53 | Married | 2 | 65 | Medium | 0 | 5e+05 |
38 | 7200 | 0.0000 | 0 | 0 | 15000+ | 4-8 | Single | 2 | 22 | High | 0 | 5e+04 |
39 | 1753 | 0.0000 | 1 | 1 | 7500-10000 | 9-13 | Single | 1 | 26 | Low | 0 | 1e+05 |
40 | 4931 | 0.0000 | 1 | 0 | 7500-10000 | 34-43 | Single | 2 | 56 | Medium | 0 | 1e+05 |
41 | 2560 | 0.0000 | 0 | 0 | 10000-15000 | 9-13 | Single | 2 | 25 | High | 0 | 3e+05 |
42 | 644 | 1331.1593 | 3 | 1 | 7500-10000 | 44-53 | Married | 1 | 63 | Low | 0 | 5e+04 |
43 | 2991 | 0.0000 | 1 | 1 | 7500-10000 | 14-23 | Single | 4 | 30 | High | 0 | 1e+05 |
44 | 9914 | 0.0000 | 0 | 0 | 0-7500 | 9-13 | Single | 3 | 27 | High | 0 | 5e+04 |
44 | 12279 | 0.0000 | 0 | 0 | 7500-10000 | 24-33 | Married | 2 | 47 | Medium | 0 | 1e+05 |
45 | 7013 | 0.0000 | 0 | 0 | 10000-15000 | 24-33 | Married | 1 | 43 | Medium | 0 | 3e+05 |
45 | 10173 | 458.2196 | 0 | 1 | 0-7500 | 9-13 | Single | 1 | 28 | High | 0 | 5e+04 |
45 | 10767 | 0.0000 | 1 | 0 | 0-7500 | 44-53 | Married | 2 | 65 | High | 0 | 5e+04 |
46 | 4662 | 0.0000 | 0 | 0 | 15000+ | 24-33 | Married | 2 | 43 | High | 0 | 5e+04 |
46 | 11794 | 0.0000 | 0 | 1 | 0-7500 | 34-43 | Married | 2 | 56 | Low | 0 | 5e+05 |
46 | 12190 | 0.0000 | 0 | 0 | 15000+ | 44-53 | Married | 2 | 67 | High | 1 | 3e+05 |
47 | 9509 | 0.0000 | 0 | 0 | 0-7500 | 54-63 | Married | 4 | 71 | High | 0 | 5e+04 |
47 | 10134 | 0.0000 | 0 | 0 | 15000+ | 24-33 | Married | 4 | 40 | Medium | 1 | 3e+05 |
48 | 9985 | 0.0000 | 1 | 0 | 15000+ | 24-33 | Married | 2 | 43 | Medium | 0 | 3e+05 |
48 | 11131 | 0.0000 | 1 | 0 | 7500-10000 | 24-33 | Married | 4 | 41 | Medium | 0 | 1e+05 |
48 | 11320 | 0.0000 | 0 | 0 | 15000+ | 34-43 | Married | 3 | 59 | High | 0 | 3e+05 |
48 | 14055 | 0.0000 | 1 | 0 | 10000-15000 | 24-33 | Married | 1 | 47 | High | 0 | 1e+05 |
49 | 7219 | 0.0000 | 0 | 0 | 0-7500 | 14-23 | Married | 1 | 36 | High | 0 | 5e+05 |
50 | 6263 | 0.0000 | 0 | 0 | 0-7500 | 24-33 | Married | 1 | 42 | High | 0 | 1e+05 |
50 | 13926 | 1869.7136 | 0 | 1 | 0-7500 | 14-23 | Single | 2 | 38 | High | 0 | 3e+05 |
51 | 7260 | 0.0000 | 0 | 0 | 15000+ | 44-53 | Married | 2 | 67 | Medium | 0 | 3e+05 |
52 | 6243 | 0.0000 | 1 | 0 | 7500-10000 | 14-23 | Single | 2 | 31 | Medium | 0 | 5e+05 |
53 | 7866 | 0.0000 | 1 | 0 | 0-7500 | 14-23 | Single | 1 | 33 | High | 0 | 5e+04 |
53 | 11892 | 0.0000 | 0 | 0 | 0-7500 | 24-33 | Single | 2 | 40 | Low | 0 | 5e+05 |
54 | 7 | 0.0000 | 0 | 1 | 15000+ | 4-8 | Single | 3 | 24 | High | 0 | 3e+05 |
55 | 3650 | 0.0000 | 0 | 0 | 0-7500 | 4-8 | Single | 2 | 21 | Low | 0 | 1e+05 |
56 | 6369 | 0.0000 | 0 | 0 | 10000-15000 | 44-53 | Married | 1 | 69 | High | 0 | 5e+05 |
57 | 9857 | 0.0000 | 1 | 0 | 10000-15000 | 44-53 | Married | 3 | 67 | Medium | 0 | 5e+05 |
58 | 395 | 0.0000 | 0 | 0 | 10000-15000 | 34-43 | Married | 2 | 56 | Low | 0 | 5e+05 |
59 | 5637 | 0.0000 | 0 | 0 | 10000-15000 | 14-23 | Married | 3 | 39 | Medium | 0 | 3e+05 |
59 | 12228 | 0.0000 | 1 | 0 | 15000+ | 4-8 | Single | 4 | 21 | Low | 0 | 3e+05 |
59 | 14572 | 0.0000 | 0 | 1 | 7500-10000 | 34-43 | Married | 3 | 51 | Medium | 0 | 3e+05 |
60 | 5079 | 0.0000 | 0 | 0 | 0-7500 | 44-53 | Married | 3 | 62 | High | 0 | 3e+05 |
61 | 6131 | 0.0000 | 0 | 1 | 15000+ | 24-33 | Married | 4 | 44 | High | 0 | 5e+04 |
62 | 8742 | 0.0000 | 0 | 1 | 0-7500 | 9-13 | Single | 3 | 27 | High | 0 | 5e+04 |
62 | 12257 | 0.0000 | 0 | 0 | 0-7500 | 34-43 | Single | 1 | 52 | High | 1 | 5e+05 |
62 | 14614 | 0.0000 | 0 | 0 | 10000-15000 | 44-53 | Married | 2 | 67 | Low | 0 | 5e+04 |
63 | 9104 | 0.0000 | 1 | 0 | 7500-10000 | 24-33 | Married | 2 | 40 | High | 0 | 5e+04 |
63 | 14213 | 0.0000 | 0 | 0 | 7500-10000 | 24-33 | Single | 3 | 42 | Low | 1 | 3e+05 |
64 | 7515 | 0.0000 | 0 | 0 | 7500-10000 | 24-33 | Married | 2 | 42 | High | 0 | 1e+05 |
64 | 10697 | 0.0000 | 0 | 0 | 0-7500 | 9-13 | Single | 3 | 25 | Medium | 0 | 5e+05 |
65 | 4023 | 0.0000 | 0 | 0 | 15000+ | 14-23 | Single | 1 | 38 | Medium | 0 | 3e+05 |
As we generally expect, the average loss (severity) is highest for inexperienced and over-experienced drivers (where old age effects come into account). Interestingly, the old age single drivers and young married drivers tend to not have high losses.
df.loss %>%
ggplot(aes(x = `Accident Points (AP) last 3 years`, y = `Pure Premium`)) + geom_bar(aes(fill = `Conviction Points (CP) last 3 years` ), stat = "summary", fun.y = "mean", show.legend = TRUE) + facet_grid(. ~ `Conviction Points (CP) last 3 years`, labeller = label_parsed) + theme(legend.position = "bottom") + ggtitle("Average Pure Premium by 3-Year AP and CP")
filter(df.loss, `Accident Points (AP) last 3 years` == 1, `Conviction Points (CP) last 3 years` == 3) %>% qkable(height = "240px")
Policy Number | Vehicle Number | Pure Premium | Conviction Points (CP) last 3 years | Accident Points (AP) last 3 years | Annual Mileage | Driver Experience Band | Marital Status | Territory | Driver Age | Credit Score | Late Fees | Liability Limit |
---|---|---|---|---|---|---|---|---|---|---|---|---|
42 | 644 | 1331.159 | 3 | 1 | 7500-10000 | 44-53 | Married | 1 | 63 | Low | 0 | 5e+04 |
795 | 11667 | 0.000 | 3 | 1 | 15000+ | 34-43 | Married | 1 | 57 | Medium | 0 | 5e+05 |
968 | 11412 | 46363.502 | 3 | 1 | 10000-15000 | 34-43 | Married | 2 | 52 | Medium | 0 | 5e+05 |
1462 | 6432 | 11873.345 | 3 | 1 | 10000-15000 | 34-43 | Single | 2 | 54 | High | 0 | 1e+05 |
2138 | 14961 | 66620.400 | 3 | 1 | 10000-15000 | 4-8 | Single | 2 | 23 | Medium | 0 | 3e+05 |
6411 | 4113 | 78368.346 | 3 | 1 | 7500-10000 | 24-33 | Single | 3 | 41 | Medium | 0 | 3e+05 |
6411 | 4113 | 0.000 | 3 | 1 | 7500-10000 | 24-33 | Single | 3 | 42 | Medium | 0 | 3e+05 |
6471 | 5023 | 6269.003 | 3 | 1 | 7500-10000 | 34-43 | Married | 2 | 58 | High | 0 | 1e+05 |
Very oddly, in the 2015 data above, we see an anomaly. The combination of 3 conviction points in the last 3 years and precisely 1 accident in the last 3 years appears to have a high average loss severity (and pure premium). Although this is only a sample of 6, this may be an indication to reject this category of drivers with these specifications.
“Unfortunately, Cal Insurance’s pricing model was already filed with the Department of Insurance and cannot be changed for another two years.”
Although this is unfortunate, this affords us with two years to research and develop models to inform a better set of underwriting guidelines.
We will use a Generalized Linear Model (GLM) with target variable Pure Premium (dollars of loss per exposure) for pricing, where exposure is per vehicle insured.
For a response variable \(y\), our generalized linear model is given by
\[\begin{align*} f(y) = c(y, \phi) e^{\left( \frac{y \theta - a (\theta) }{\phi} \right)} , \quad g(\mu) = x' \beta, \end{align*}\] where the second equation is the generalization of the linear model. We take our link \(g(\mu)\) to be \(\log\) to align with our desired multiplicative rating system. Then by considering the average count \(\mu/n\), we have \[ \begin{align*} \log\left( \frac{\mu}{n} \right) = x' \beta \implies \log \mu = \log n + x' \beta \implies \mu = n e^{x' \beta}. \end{align*}\] where \(\log n\) is our offset.
Now the Maximum Likelihood Estimate (MLE) of \(\beta\) and \(\phi\) are solved numerically (computationally as opposed to analytically) via iterative methods and a non-positive definite Hessian matrix to indicate the maximum.
As mentioned earlier in this document,
“Traditional models take the assumption that individual risks are independent. In our present case, we have multi-car policies that introduce a dependence, where marital status or multi-car policy may mitigate the collective risk among the policy’s assets. Further, we do not have information regarding the claims process, and we do not know if a policy year consolidates multiple claims in that given year.”
To not assume that frequency and severity are independent, we use a Tweedie distribution. Recall that a Poisson\((\lambda)\) and Gamma(\(\alpha, \theta\)) distribution has expected values \(\lambda\) and \(\alpha \dot \theta\) respectively. Correspondingly, Tweedie distribution has expected value \[ \mu = \lambda \cdot \alpha \cdot \theta, \] where \(\lambda\) is the expected frequency (modeled via Poisson) and \(\alpha \theta\) is the expected severity (modeled via Gamma). Hence we think of the Tweedie distribution as a Poisson sum (number of) Gamma-severity claims. The power \(p\) is given by \[ p = \frac{\alpha + 2}{\alpha + 1}, \] and the Tweedie dispersion parameter \(\phi\) is given by \[ \phi = \frac{\lambda^{1 - p} (\alpha \theta )^{2 - p}}{2 - p}. \] We have special cases of the normal distribution \((p = 0)\), the Poisson distribution \((p = 1, \phi = 1)\), the Gamma distribution \((p=2)\), and the inverse Gaussian \((p = 3)\).
# X <- select(loss, `Driver Age`, `Marital Status`)
pp <- df.loss$`Pure Premium` # response variable;
x.conviction <- df.loss$`Conviction Points (CP) last 3 years`
x.mileage <- df.loss$`Annual Mileage`
x.experience <- df.loss$`Driver Experience Band`
x.accident <- df.loss$`Accident Points (AP) last 3 years`
x.marital <- df.loss$`Marital Status`
x.territory <- df.loss$Territory
# determine p from gamma glm, given by Glenn Meyers
# https://dsury.com/tweedie-distribution-on-the-generalized-linear-model/
posi <- pp > 0
glm.sev <- glm(pp[posi] ~ x.conviction[posi] + x.mileage[posi] + x.experience[posi] + x.accident[posi] + x.marital[posi] + x.territory[posi],
family = Gamma(link="log"))
tmp <- summary(glm.sev)
phi.sev <- tmp$dispersion
alpha1 <- 1 / phi.sev
p.hat <- (2 + alpha1) / (1 + alpha1)
print(paste0("The p-value given by Gamma fitting is: ",p.hat))
## [1] "The p-value given by Gamma fitting is: 1.7894905804437"
Using the Gamma-fitted GLM parameters, we initialize a log-linked tweedie GLM fit with the same \(\hat p\) parameter.
x.vec <- c(x.conviction, x.mileage, x.experience, x.accident, x.marital, x.territory)
# initial fit using p.hat = 1.7916
glm.init <- glm(pp ~ x.conviction + x.mileage + x.experience + x.accident + x.marital + x.territory, family = tweedie(var.power = p.hat, link.power = 0)) # 0 for log-link
We would like to fine-tune the \(p\) parameter and we do this using the tglm
function, with output suppressed as it normally prints pages of iterations.
# use as start into tglm
glm.fisher <- tglm(pp ~ x.conviction + x.mileage + x.experience + x.accident + x.marital + x.territory, data = df.loss, inits = glm.init$coefficients)
Here we’ll use the value of \(p\) from our last result to drive our glm. We use the coefficients from glm.init$coefficients
to start the iterative optimization process. The details for the Tweedie GLM fit are given by summary(glm.tweedie)
below.
# glm with tweedie family, using p from last result
# p <- glm.fisher$coef[["p"]]
glm.tweedie <- glm(formula = pp ~ x.conviction + x.mileage + x.experience + x.accident + x.marital + x.territory, family = tweedie(var.power = p.hat, link.power = 0), start = glm.init$coefficients, data = df.loss)
summary(glm.tweedie)
##
## Call:
## glm(formula = pp ~ x.conviction + x.mileage + x.experience +
## x.accident + x.marital + x.territory, family = tweedie(var.power = p.hat,
## link.power = 0), data = df.loss, start = glm.init$coefficients)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -8.915 -0.069 -0.069 -0.069 32.651
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.29287 0.24540 -9.343 < 2e-16 ***
## x.conviction.L 0.71134 0.28402 2.504 0.01227 *
## x.conviction.Q 0.40416 0.21764 1.857 0.06332 .
## x.conviction.C 0.09275 0.11873 0.781 0.43472
## x.mileage.L 0.06565 0.03266 2.010 0.04443 *
## x.mileage.Q 0.02614 0.03190 0.819 0.41254
## x.mileage.C -0.05908 0.03127 -1.889 0.05884 .
## x.experience.L -0.15900 0.27703 -0.574 0.56601
## x.experience.Q -0.06377 0.26988 -0.236 0.81320
## x.experience.C -0.74950 0.24005 -3.122 0.00180 **
## x.experience^4 -0.25593 0.19409 -1.319 0.18730
## x.experience^5 -0.47085 0.14853 -3.170 0.00153 **
## x.experience^6 -0.74376 0.11560 -6.434 1.25e-10 ***
## x.experience^7 -0.46117 0.09189 -5.019 5.22e-07 ***
## x.experience^8 -0.20726 0.06825 -3.037 0.00239 **
## x.experience^9 -0.15517 0.05114 -3.034 0.00241 **
## x.accident.L 32.05088 0.56879 56.349 < 2e-16 ***
## x.accident.Q -23.59221 0.42401 -55.640 < 2e-16 ***
## x.accident.C 10.39852 0.19138 54.333 < 2e-16 ***
## x.marital.L -0.18222 0.04278 -4.260 2.05e-05 ***
## x.territory2 -0.04984 0.04165 -1.197 0.23148
## x.territory3 -0.04758 0.04402 -1.081 0.27973
## x.territory4 -0.14142 0.04563 -3.099 0.00194 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Tweedie family taken to be 13.35685)
##
## Null deviance: 2293547 on 44999 degrees of freedom
## Residual deviance: 386865 on 44977 degrees of freedom
## AIC: NA
##
## Number of Fisher Scoring iterations: 11
print(paste("The AIC with this Tweedie fit is: ", AICtweedie(glm.tweedie))) # aic in glm is unsupported for tweedie, so use this value
## [1] "The AIC with this Tweedie fit is: 113072.018195223"
# confint(glm.tweedie) # run if confidence interval is desired; this is cpu and time-intensive
print(paste("The Log-Likelihood is: ",logLiktweedie(glm.tweedie)))
## [1] "The Log-Likelihood is: -56512.0090976116"
We’d like to compare the observed and predicted pure premiums.
pp.hat <- predict(glm.tweedie, type="response")
# compare pure premiums
compare.pp <- df.loss %>% mutate(`Predicted Pure Premium` = floor(pp.hat) )
compare.pp <- compare.pp %>% select(1:3, 14, 4:13)
We can take a look at two sides of the model: (1) the individuals who have higher Pure Premiums than predicted (we want to consider these for rejection via UW guidelines) and (2) those who incur less loss (at least in experience) than predicted. We show these below.
compare.pp %>% filter(`Pure Premium` > `Predicted Pure Premium`) %>% slice(1:40) %>% qkable() # type 1: higher observed than predicted
Policy Number | Vehicle Number | Pure Premium | Predicted Pure Premium | Conviction Points (CP) last 3 years | Accident Points (AP) last 3 years | Annual Mileage | Driver Experience Band | Marital Status | Territory | Driver Age | Credit Score | Late Fees | Liability Limit |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
5 | 8571 | 11546.111 | 8911 | 1 | 1 | 0-7500 | 4-8 | Single | 3 | 24 | Low | 0 | 1e+05 |
6 | 8530 | 34023.112 | 6311 | 1 | 1 | 7500-10000 | 14-23 | Single | 2 | 31 | High | 1 | 5e+04 |
17 | 12553 | 63413.423 | 7057 | 0 | 1 | 10000-15000 | 14-23 | Single | 2 | 37 | Low | 0 | 1e+05 |
71 | 6110 | 45661.666 | 10682 | 0 | 2 | 15000+ | 9-13 | Single | 2 | 27 | Low | 0 | 5e+05 |
73 | 10790 | 24235.851 | 6845 | 1 | 1 | 0-7500 | 9-13 | Single | 3 | 26 | Low | 0 | 3e+05 |
100 | 4979 | 30921.584 | 10707 | 0 | 2 | 15000+ | 9-13 | Single | 3 | 27 | Low | 0 | 5e+04 |
126 | 10770 | 36485.179 | 6519 | 0 | 1 | 7500-10000 | 9-13 | Single | 2 | 28 | Medium | 0 | 5e+05 |
138 | 9746 | 119880.000 | 9924 | 0 | 1 | 15000+ | 24-33 | Single | 2 | 40 | High | 0 | 1e+05 |
144 | 10613 | 167697.445 | 15197 | 1 | 3 | 15000+ | 4-8 | Single | 1 | 24 | Low | 0 | 3e+05 |
155 | 6927 | 61615.862 | 8655 | 0 | 1 | 15000+ | 4-8 | Single | 4 | 22 | Medium | 0 | 5e+05 |
222 | 7400 | 13467.457 | 9351 | 0 | 1 | 0-7500 | 24-33 | Single | 3 | 40 | Low | 0 | 1e+05 |
222 | 12590 | 59700.000 | 14333 | 0 | 3 | 0-7500 | 4-8 | Single | 1 | 20 | Low | 1 | 5e+04 |
253 | 3009 | 195796.493 | 9485 | 0 | 1 | 15000+ | 4-8 | Single | 2 | 23 | Low | 0 | 3e+05 |
342 | 11869 | 102554.379 | 7669 | 0 | 1 | 15000+ | 24-33 | Married | 2 | 48 | High | 1 | 5e+05 |
359 | 12495 | 29606.490 | 13667 | 0 | 3 | 0-7500 | 4-8 | Single | 3 | 23 | Low | 0 | 5e+05 |
366 | 11846 | 150178.152 | 9374 | 0 | 1 | 0-7500 | 4-8 | Single | 1 | 23 | Low | 0 | 3e+05 |
391 | 2093 | 9763.145 | 6439 | 0 | 1 | 10000-15000 | 14-23 | Single | 4 | 33 | Low | 0 | 1e+05 |
429 | 11233 | 36016.321 | 5949 | 0 | 1 | 7500-10000 | 9-13 | Single | 4 | 29 | Medium | 0 | 5e+05 |
459 | 3839 | 119880.000 | 12442 | 0 | 3 | 0-7500 | 4-8 | Single | 4 | 24 | Low | 0 | 1e+05 |
466 | 6452 | 15951.110 | 14503 | 0 | 3 | 15000+ | 4-8 | Single | 2 | 22 | Low | 0 | 5e+04 |
484 | 10925 | 40784.553 | 8460 | 1 | 1 | 7500-10000 | 4-8 | Single | 2 | 24 | Low | 1 | 5e+05 |
542 | 385 | 81434.919 | 9332 | 0 | 1 | 7500-10000 | 24-33 | Single | 1 | 45 | High | 0 | 5e+05 |
569 | 1682 | 102256.288 | 7054 | 1 | 1 | 15000+ | 14-23 | Single | 2 | 37 | Low | 0 | 1e+05 |
569 | 11376 | 135318.705 | 6534 | 0 | 1 | 7500-10000 | 9-13 | Single | 3 | 26 | High | 1 | 3e+05 |
579 | 9501 | 7942.275 | 4937 | 0 | 1 | 0-7500 | 54-63 | Married | 2 | 70 | Medium | 0 | 5e+05 |
591 | 6143 | 10921.049 | 7076 | 0 | 1 | 15000+ | 14-23 | Single | 2 | 37 | High | 0 | 1e+05 |
630 | 2661 | 79833.405 | 8486 | 0 | 1 | 7500-10000 | 4-8 | Single | 2 | 22 | Medium | 0 | 5e+05 |
632 | 266 | 119700.000 | 19067 | 0 | 2 | 10000-15000 | 0-3 | Single | 3 | 19 | High | 3 | 1e+05 |
638 | 12439 | 56539.803 | 11679 | 0 | 2 | 7500-10000 | 34-43 | Married | 1 | 53 | High | 0 | 5e+04 |
653 | 11742 | 9970.465 | 6251 | 0 | 1 | 0-7500 | 9-13 | Single | 4 | 27 | Medium | 0 | 3e+05 |
661 | 10676 | 44546.224 | 7869 | 0 | 1 | 0-7500 | 44-53 | Married | 3 | 65 | Medium | 0 | 5e+04 |
679 | 4235 | 26765.813 | 13681 | 0 | 2 | 7500-10000 | 24-33 | Single | 1 | 49 | Low | 1 | 1e+05 |
685 | 2454 | 27305.808 | 8302 | 1 | 1 | 10000-15000 | 44-53 | Married | 2 | 61 | Medium | 3 | 5e+04 |
719 | 5937 | 21149.783 | 19997 | 0 | 2 | 10000-15000 | 0-3 | Single | 1 | 19 | Medium | 0 | 5e+04 |
793 | 6776 | 198037.554 | 6534 | 0 | 1 | 7500-10000 | 9-13 | Single | 3 | 29 | Low | 0 | 3e+05 |
806 | 3380 | 59880.000 | 19879 | 1 | 3 | 15000+ | 0-3 | Single | 3 | 19 | Low | 0 | 5e+04 |
856 | 8207 | 36108.439 | 6877 | 0 | 1 | 7500-10000 | 24-33 | Married | 3 | 48 | High | 2 | 3e+05 |
881 | 1686 | 15909.689 | 10397 | 0 | 2 | 15000+ | 14-23 | Single | 3 | 30 | High | 0 | 1e+05 |
883 | 11809 | 20614.581 | 8655 | 0 | 1 | 15000+ | 4-8 | Single | 4 | 21 | Low | 0 | 5e+04 |
898 | 10744 | 5508.839 | 4892 | 0 | 1 | 7500-10000 | 14-23 | Married | 2 | 37 | High | 0 | 3e+05 |
compare.pp %>% filter(`Pure Premium` < `Predicted Pure Premium`, `Pure Premium` > 0) %>% slice(1:40) %>% qkable() # type 2 : lower observed than predicted
Policy Number | Vehicle Number | Pure Premium | Predicted Pure Premium | Conviction Points (CP) last 3 years | Accident Points (AP) last 3 years | Annual Mileage | Driver Experience Band | Marital Status | Territory | Driver Age | Credit Score | Late Fees | Liability Limit |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1 | 6125 | 120.083050 | 8112 | 1 | 1 | 0-7500 | 4-8 | Single | 4 | 21 | High | 0 | 5e+04 |
10 | 6386 | 7659.896762 | 17800 | 0 | 3 | 7500-10000 | 0-3 | Single | 2 | 19 | Low | 0 | 5e+04 |
16 | 14610 | 122.812605 | 7966 | 0 | 1 | 7500-10000 | 34-43 | Married | 1 | 51 | High | 0 | 5e+05 |
21 | 12703 | 120.002350 | 9481 | 0 | 1 | 10000-15000 | 4-8 | Single | 3 | 24 | Low | 0 | 5e+04 |
42 | 644 | 1331.159272 | 21258 | 3 | 1 | 7500-10000 | 44-53 | Married | 1 | 63 | Low | 0 | 5e+04 |
45 | 10173 | 458.219589 | 7201 | 0 | 1 | 0-7500 | 9-13 | Single | 1 | 28 | High | 0 | 5e+04 |
50 | 13926 | 1869.713603 | 6653 | 0 | 1 | 0-7500 | 14-23 | Single | 2 | 38 | High | 0 | 3e+05 |
67 | 14663 | 4721.120736 | 12404 | 1 | 3 | 0-7500 | 4-8 | Single | 4 | 23 | Low | 0 | 5e+04 |
72 | 4694 | 3238.283113 | 11111 | 0 | 2 | 7500-10000 | 34-43 | Married | 2 | 50 | Medium | 0 | 5e+04 |
134 | 10731 | 170.466932 | 8879 | 0 | 1 | 7500-10000 | 24-33 | Single | 2 | 41 | Low | 0 | 5e+04 |
160 | 5309 | 5059.700585 | 13868 | 0 | 2 | 10000-15000 | 4-8 | Single | 2 | 20 | Medium | 1 | 1e+05 |
160 | 11547 | 9556.861052 | 13639 | 0 | 3 | 7500-10000 | 4-8 | Single | 1 | 23 | Low | 0 | 1e+05 |
170 | 1998 | 77.453846 | 14571 | 1 | 2 | 15000+ | 4-8 | Single | 1 | 23 | Medium | 0 | 3e+05 |
185 | 176 | 65.444578 | 8252 | 0 | 1 | 0-7500 | 44-53 | Married | 1 | 65 | Low | 0 | 5e+04 |
188 | 8759 | 124.945101 | 6439 | 0 | 1 | 10000-15000 | 14-23 | Single | 4 | 37 | Medium | 0 | 3e+05 |
227 | 13876 | 2374.628316 | 5949 | 0 | 1 | 7500-10000 | 9-13 | Single | 4 | 26 | High | 0 | 1e+05 |
228 | 9054 | 1554.679772 | 12688 | 0 | 2 | 15000+ | 4-8 | Single | 4 | 21 | Low | 0 | 5e+05 |
229 | 9047 | 236.542783 | 6851 | 0 | 1 | 0-7500 | 9-13 | Single | 2 | 27 | Medium | 0 | 3e+05 |
239 | 11550 | 236.838725 | 7212 | 0 | 1 | 7500-10000 | 24-33 | Married | 1 | 45 | High | 0 | 5e+04 |
242 | 4193 | 7927.919848 | 8347 | 0 | 1 | 10000-15000 | 44-53 | Married | 3 | 68 | High | 0 | 5e+04 |
245 | 12361 | 93.473388 | 8372 | 0 | 1 | 0-7500 | 34-43 | Married | 1 | 59 | Medium | 0 | 3e+05 |
255 | 12575 | 195.377965 | 17066 | 0 | 2 | 7500-10000 | 0-3 | Single | 2 | 19 | Low | 0 | 5e+04 |
263 | 11482 | 121.608025 | 10364 | 1 | 2 | 15000+ | 14-23 | Single | 3 | 33 | High | 1 | 1e+05 |
284 | 250 | 156.448694 | 8891 | 1 | 1 | 0-7500 | 4-8 | Single | 2 | 21 | Low | 0 | 1e+05 |
287 | 8863 | 2045.819190 | 7659 | 0 | 1 | 15000+ | 9-13 | Single | 1 | 27 | Medium | 0 | 3e+05 |
295 | 7144 | 182.375381 | 9943 | 0 | 1 | 10000-15000 | 4-8 | Single | 1 | 21 | Low | 0 | 3e+05 |
296 | 6859 | 2929.094250 | 6853 | 0 | 1 | 7500-10000 | 9-13 | Single | 1 | 25 | High | 0 | 1e+05 |
322 | 2562 | 127.074275 | 15112 | 0 | 2 | 7500-10000 | 34-43 | Single | 1 | 53 | Low | 1 | 5e+04 |
362 | 8086 | 1162.610422 | 8605 | 1 | 1 | 10000-15000 | 4-8 | Single | 4 | 23 | High | 0 | 1e+05 |
370 | 7186 | 120.379670 | 11107 | 1 | 3 | 15000+ | 9-13 | Single | 2 | 29 | Low | 0 | 5e+04 |
379 | 4132 | 4.918854 | 10046 | 0 | 2 | 7500-10000 | 9-13 | Single | 1 | 26 | Low | 0 | 1e+05 |
383 | 7407 | 58.551625 | 8853 | 1 | 1 | 10000-15000 | 34-43 | Married | 1 | 56 | High | 0 | 3e+05 |
388 | 12099 | 69.613962 | 11930 | 0 | 2 | 0-7500 | 4-8 | Single | 4 | 24 | Medium | 0 | 1e+05 |
413 | 14921 | 1415.149733 | 8754 | 0 | 1 | 10000-15000 | 44-53 | Married | 1 | 65 | High | 0 | 3e+05 |
422 | 432 | 174.921748 | 8347 | 0 | 1 | 10000-15000 | 44-53 | Married | 3 | 63 | Medium | 0 | 5e+04 |
425 | 2623 | 654.456802 | 13639 | 0 | 3 | 7500-10000 | 4-8 | Single | 1 | 21 | Low | 2 | 1e+05 |
427 | 14101 | 11064.581506 | 14532 | 1 | 2 | 10000-15000 | 4-8 | Single | 1 | 24 | Medium | 0 | 5e+04 |
458 | 8477 | 1800.937360 | 9970 | 0 | 1 | 15000+ | 4-8 | Single | 1 | 20 | Low | 0 | 5e+04 |
463 | 1179 | 6975.193925 | 9507 | 0 | 1 | 15000+ | 4-8 | Single | 3 | 22 | Low | 0 | 1e+05 |
476 | 4442 | 6376.173124 | 7659 | 0 | 1 | 15000+ | 9-13 | Single | 1 | 26 | High | 0 | 5e+04 |
We can see that some rows (individuals) are fit quite closely by our model, whereas some are quite a way off. Perhaps we can get a better fit by capping high severity claims at $100,000. Then predictions of high severities should be interpreted as possibly higher severity.
pp.capped <- df.loss[["Pure Premium"]]
tmp <- pp.capped > 100000
pp.capped[tmp] <- 100000 # assign cap of $100,000 in PP
df.loss <- df.loss %>% mutate(`PP Capped` = pp.capped)
# %>% select(1:2, 11, 3:10)
# glm tweedie capped (glm tw c)
glm.tw.c <- glm(formula = pp.capped ~ x.conviction + x.mileage + x.experience + x.accident + x.marital + x.territory, family = tweedie(var.power = p.hat, link.power = 0), start = glm.init$coefficients, data = df.loss)
summary(glm.tw.c)
##
## Call:
## glm(formula = pp.capped ~ x.conviction + x.mileage + x.experience +
## x.accident + x.marital + x.territory, family = tweedie(var.power = p.hat,
## link.power = 0), data = df.loss, start = glm.init$coefficients)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -8.9497 -0.0694 -0.0694 -0.0694 14.9911
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.34197 0.19260 -12.160 < 2e-16 ***
## x.conviction.L 0.83367 0.22252 3.746 0.000180 ***
## x.conviction.Q 0.49735 0.17073 2.913 0.003580 **
## x.conviction.C 0.22521 0.09381 2.401 0.016369 *
## x.mileage.L 0.03690 0.02603 1.418 0.156295
## x.mileage.Q 0.03138 0.02542 1.234 0.217170
## x.mileage.C -0.09932 0.02493 -3.983 6.8e-05 ***
## x.experience.L -0.26469 0.21897 -1.209 0.226748
## x.experience.Q -0.12543 0.21235 -0.591 0.554738
## x.experience.C -0.58121 0.18888 -3.077 0.002092 **
## x.experience^4 -0.07659 0.15416 -0.497 0.619323
## x.experience^5 -0.24826 0.11989 -2.071 0.038391 *
## x.experience^6 -0.45401 0.09381 -4.840 1.3e-06 ***
## x.experience^7 -0.23625 0.07384 -3.199 0.001378 **
## x.experience^8 -0.07872 0.05444 -1.446 0.148146
## x.experience^9 -0.07951 0.04078 -1.950 0.051213 .
## x.accident.L 32.05443 0.44615 71.848 < 2e-16 ***
## x.accident.Q -23.48979 0.33259 -70.628 < 2e-16 ***
## x.accident.C 10.32694 0.15015 68.777 < 2e-16 ***
## x.marital.L -0.12760 0.03410 -3.742 0.000183 ***
## x.territory2 -0.06745 0.03322 -2.030 0.042313 *
## x.territory3 -0.08151 0.03516 -2.318 0.020435 *
## x.territory4 -0.10372 0.03628 -2.859 0.004257 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Tweedie family taken to be 8.216501)
##
## Null deviance: 2213257 on 44999 degrees of freedom
## Residual deviance: 367501 on 44977 degrees of freedom
## AIC: NA
##
## Number of Fisher Scoring iterations: 11
AICtweedie(glm.tw.c) # aic in glm is unsupported for tweedie, so use this value
## [1] 112712.7
# confint(glm.tweedie) # run if needed
logLiktweedie(glm.tw.c)
## [1] -56332.37
pp.cap.hat <- predict(glm.tw.c, type="response")
# compare pure premiums
compare.pp.capped <- compare.pp %>% mutate(`PP Capped` = floor(pp.capped), `Predicted PP Capped` = floor(pp.cap.hat), `Pure Premium` = floor(`Pure Premium`) ) %>% as_tibble()
compare.pp.capped <- compare.pp.capped %>% select(1:2, 15:16, 3:14)
# compare.pp.capped %>% filter(`Pure Premium` > `Predicted Pure Premium`) %>% slice(1:40) %>% qkable()
# compare.pp.capped %>% filter(`Pure Premium` < `Predicted Pure Premium`, `Pure Premium` > 0) %>% slice(1:40) %>% qkable()
compare.pp.capped %>% filter( abs(`PP Capped` - `Predicted PP Capped`) < 4000, `Pure Premium` > 0) %>% select(`PP Capped`, `Predicted PP Capped`, `Driver Age`, `Annual Mileage`, `Marital Status`, `Territory`, `Credit Score`, `Late Fees`) %>% qkable()
PP Capped | Predicted PP Capped | Driver Age | Annual Mileage | Marital Status | Territory | Credit Score | Late Fees |
---|---|---|---|---|---|---|---|
11546 | 7604 | 24 | 0-7500 | Single | 3 | Low | 0 |
1869 | 5768 | 38 | 0-7500 | Single | 2 | High | 0 |
9556 | 12084 | 23 | 7500-10000 | Single | 1 | Low | 0 |
2374 | 5019 | 26 | 7500-10000 | Single | 4 | High | 0 |
7927 | 6597 | 68 | 10000-15000 | Married | 3 | High | 0 |
2929 | 5567 | 25 | 7500-10000 | Single | 1 | High | 0 |
9763 | 5824 | 33 | 10000-15000 | Single | 4 | Low | 0 |
11064 | 13963 | 24 | 10000-15000 | Single | 1 | Medium | 0 |
6975 | 7077 | 22 | 15000+ | Single | 3 | Low | 0 |
15951 | 12594 | 22 | 15000+ | Single | 2 | Low | 0 |
6376 | 6207 | 26 | 15000+ | Single | 1 | High | 0 |
4023 | 7177 | 21 | 15000+ | Single | 2 | High | 1 |
7942 | 4877 | 70 | 0-7500 | Married | 2 | Medium | 0 |
14146 | 12937 | 20 | 10000-15000 | Single | 3 | Low | 0 |
13229 | 12411 | 21 | 15000+ | Single | 1 | Low | 0 |
9229 | 9131 | 25 | 7500-10000 | Single | 2 | Medium | 0 |
8885 | 9084 | 26 | 7500-10000 | Single | 2 | High | 0 |
7917 | 9105 | 19 | 7500-10000 | Single | 1 | Medium | 0 |
3219 | 5955 | 36 | 10000-15000 | Single | 3 | Medium | 0 |
4633 | 6229 | 35 | 0-7500 | Single | 2 | High | 0 |
6161 | 8752 | 30 | 7500-10000 | Single | 4 | Low | 0 |
5508 | 4341 | 37 | 7500-10000 | Married | 2 | High | 0 |
4976 | 7373 | 21 | 10000-15000 | Single | 3 | Medium | 0 |
7690 | 8208 | 19 | 7500-10000 | Single | 4 | Low | 0 |
5943 | 6422 | 61 | 15000+ | Married | 2 | High | 0 |
5698 | 7999 | 23 | 10000-15000 | Single | 1 | Low | 0 |
8278 | 5562 | 37 | 7500-10000 | Single | 1 | High | 0 |
5642 | 6887 | 24 | 7500-10000 | Single | 1 | Low | 0 |
3380 | 5768 | 38 | 0-7500 | Single | 2 | High | 0 |
8840 | 5797 | 33 | 15000+ | Single | 2 | High | 0 |
7957 | 9180 | 50 | 10000-15000 | Single | 3 | High | 0 |
12897 | 15338 | 19 | 15000+ | Single | 2 | High | 0 |
10656 | 13472 | 20 | 15000+ | Single | 1 | Medium | 0 |
9180 | 7373 | 22 | 10000-15000 | Single | 3 | Medium | 0 |
7513 | 6461 | 32 | 10000-15000 | Single | 1 | Low | 0 |
2481 | 6162 | 60 | 0-7500 | Married | 4 | Medium | 0 |
3471 | 7097 | 58 | 10000-15000 | Married | 3 | High | 0 |
2211 | 5199 | 39 | 7500-10000 | Single | 2 | High | 0 |
2811 | 6170 | 32 | 0-7500 | Single | 1 | High | 0 |
6970 | 6162 | 61 | 0-7500 | Married | 4 | High | 0 |
7503 | 9151 | 19 | 15000+ | Single | 4 | Medium | 0 |
8394 | 6045 | 26 | 10000-15000 | Single | 2 | High | 0 |
5688 | 5824 | 30 | 10000-15000 | Single | 4 | Medium | 0 |
9430 | 6652 | 48 | 10000-15000 | Married | 2 | High | 0 |
6180 | 6201 | 30 | 15000+ | Single | 1 | Low | 0 |
9189 | 5199 | 33 | 7500-10000 | Single | 2 | Medium | 0 |
10002 | 12298 | 48 | 0-7500 | Single | 2 | High | 0 |
2460 | 6348 | 23 | 7500-10000 | Single | 3 | High | 0 |
7445 | 6859 | 48 | 7500-10000 | Single | 2 | High | 0 |
3963 | 7141 | 21 | 0-7500 | Single | 2 | Low | 0 |
5843 | 6348 | 24 | 7500-10000 | Single | 3 | High | 0 |
13963 | 11601 | 22 | 15000+ | Single | 2 | High | 0 |
3477 | 6670 | 27 | 0-7500 | Single | 1 | Medium | 0 |
17985 | 16480 | 19 | 0-7500 | Single | 2 | Medium | 0 |
6426 | 5768 | 34 | 0-7500 | Single | 2 | Medium | 0 |
7582 | 6039 | 36 | 10000-15000 | Single | 2 | High | 0 |
2951 | 6922 | 20 | 15000+ | Single | 4 | Low | 0 |
7460 | 10552 | 25 | 10000-15000 | Single | 2 | Low | 0 |
11482 | 12930 | 21 | 10000-15000 | Single | 1 | Medium | 0 |
9210 | 6704 | 28 | 15000+ | Single | 1 | Low | 0 |
3672 | 5036 | 76 | 10000-15000 | Married | 3 | Low | 0 |
7964 | 6133 | 66 | 7500-10000 | Married | 3 | Medium | 0 |
7722 | 7157 | 66 | 10000-15000 | Married | 1 | High | 0 |
2737 | 6032 | 26 | 7500-10000 | Single | 2 | High | 0 |
5088 | 6688 | 66 | 15000+ | Married | 4 | High | 0 |
6543 | 9634 | 25 | 10000-15000 | Single | 3 | High | 2 |
7870 | 8208 | 19 | 7500-10000 | Single | 4 | Low | 0 |
9732 | 7177 | 22 | 15000+ | Single | 2 | Medium | 0 |
8896 | 6332 | 62 | 15000+ | Married | 3 | High | 0 |
10600 | 7541 | 48 | 15000+ | Single | 3 | High | 0 |
2659 | 6559 | 48 | 10000-15000 | Married | 3 | Medium | 0 |
6709 | 6631 | 25 | 15000+ | Single | 3 | Medium | 0 |
9395 | 11381 | 21 | 0-7500 | Single | 3 | Medium | 0 |
8810 | 7116 | 44 | 10000-15000 | Married | 1 | Low | 0 |
8917 | 5562 | 31 | 0-7500 | Single | 4 | Low | 0 |
7274 | 6012 | 28 | 7500-10000 | Single | 1 | Low | 0 |
7116 | 5999 | 65 | 7500-10000 | Married | 4 | High | 0 |
6978 | 10405 | 21 | 7500-10000 | Single | 2 | Low | 0 |
11663 | 11601 | 21 | 15000+ | Single | 2 | Low | 0 |
9834 | 9634 | 25 | 10000-15000 | Single | 3 | Low | 0 |
7793 | 10453 | 28 | 10000-15000 | Single | 1 | Low | 0 |
5941 | 7141 | 24 | 0-7500 | Single | 2 | Medium | 0 |
4665 | 7116 | 48 | 10000-15000 | Married | 1 | High | 0 |
9971 | 8807 | 57 | 0-7500 | Single | 1 | High | 0 |
5639 | 6952 | 22 | 7500-10000 | Single | 2 | Low | 0 |
5087 | 6835 | 62 | 0-7500 | Married | 1 | Medium | 0 |
6951 | 5415 | 33 | 7500-10000 | Single | 4 | Medium | 0 |
7233 | 7639 | 21 | 0-7500 | Single | 1 | Medium | 2 |
11909 | 11917 | 21 | 10000-15000 | Single | 3 | Medium | 0 |
2134 | 5773 | 27 | 0-7500 | Single | 2 | Medium | 0 |
6135 | 5415 | 34 | 7500-10000 | Single | 4 | Medium | 0 |
7160 | 4772 | 38 | 15000+ | Married | 3 | Medium | 0 |
5613 | 6941 | 58 | 10000-15000 | Married | 4 | Medium | 0 |
8149 | 6385 | 48 | 15000+ | Married | 2 | Medium | 0 |
7278 | 10260 | 21 | 7500-10000 | Single | 3 | Medium | 0 |
6246 | 10033 | 29 | 15000+ | Single | 1 | Low | 0 |
2987 | 6045 | 26 | 10000-15000 | Single | 2 | Medium | 0 |
10444 | 8412 | 29 | 7500-10000 | Single | 2 | Medium | 0 |
2588 | 6039 | 33 | 10000-15000 | Single | 2 | Medium | 0 |
2254 | 6039 | 31 | 10000-15000 | Single | 2 | Medium | 0 |
9430 | 5692 | 25 | 0-7500 | Single | 3 | Low | 0 |
11385 | 7773 | 57 | 10000-15000 | Married | 2 | Medium | 0 |
6268 | 6332 | 62 | 15000+ | Married | 3 | High | 0 |
5538 | 6234 | 28 | 0-7500 | Single | 2 | Medium | 0 |
8709 | 11439 | 23 | 15000+ | Single | 3 | Medium | 0 |
6185 | 6941 | 50 | 10000-15000 | Married | 4 | High | 0 |
7911 | 6663 | 31 | 0-7500 | Single | 1 | Medium | 0 |
3673 | 5955 | 31 | 10000-15000 | Single | 3 | High | 0 |
2479 | 5721 | 28 | 15000+ | Single | 3 | Medium | 0 |
3311 | 7197 | 58 | 10000-15000 | Married | 2 | High | 0 |
9124 | 6461 | 38 | 10000-15000 | Single | 1 | Medium | 2 |
9562 | 12411 | 22 | 15000+ | Single | 1 | Low | 0 |
10894 | 10453 | 26 | 10000-15000 | Single | 1 | Low | 0 |
6103 | 8112 | 29 | 7500-10000 | Single | 4 | Low | 0 |
9051 | 10891 | 29 | 15000+ | Single | 1 | Low | 0 |
10071 | 8259 | 40 | 15000+ | Single | 2 | Low | 0 |
6845 | 5199 | 35 | 7500-10000 | Single | 2 | Medium | 0 |
9180 | 6909 | 52 | 15000+ | Married | 2 | Medium | 0 |
6643 | 6126 | 47 | 0-7500 | Married | 4 | High | 0 |
5520 | 8143 | 40 | 15000+ | Single | 3 | Low | 0 |
14315 | 13410 | 22 | 15000+ | Single | 3 | Low | 0 |
6068 | 6812 | 58 | 15000+ | Married | 3 | Medium | 0 |
3818 | 6234 | 25 | 0-7500 | Single | 2 | Low | 0 |
12904 | 14725 | 19 | 7500-10000 | Single | 3 | High | 0 |
9321 | 6461 | 34 | 10000-15000 | Single | 1 | High | 0 |
8878 | 5542 | 28 | 7500-10000 | Single | 3 | Low | 0 |
14774 | 11542 | 20 | 0-7500 | Single | 2 | High | 0 |
5950 | 8292 | 24 | 15000+ | Single | 1 | Low | 0 |
6151 | 5824 | 34 | 10000-15000 | Single | 4 | Low | 0 |
8210 | 7353 | 56 | 0-7500 | Married | 1 | High | 0 |
9471 | 9709 | 36 | 0-7500 | Single | 4 | Medium | 0 |
8324 | 6422 | 64 | 15000+ | Married | 2 | Low | 0 |
5382 | 3167 | 90 | 10000-15000 | Married | 4 | Medium | 0 |
7291 | 6629 | 53 | 7500-10000 | Married | 1 | High | 0 |
10780 | 9201 | 26 | 0-7500 | Single | 3 | Low | 0 |
3555 | 5802 | 29 | 15000+ | Single | 2 | Low | 0 |
7459 | 7041 | 21 | 0-7500 | Single | 3 | Medium | 0 |
19488 | 17346 | 19 | 10000-15000 | Single | 2 | Low | 0 |
6005 | 6039 | 36 | 10000-15000 | Single | 2 | Low | 0 |
4242 | 5590 | 36 | 15000+ | Single | 4 | Medium | 0 |
9374 | 7375 | 47 | 15000+ | Single | 4 | High | 0 |
8223 | 11764 | 21 | 7500-10000 | Single | 4 | Low | 0 |
12869 | 10405 | 24 | 7500-10000 | Single | 2 | Low | 0 |
7251 | 8314 | 51 | 10000-15000 | Single | 4 | Low | 0 |
3250 | 5942 | 33 | 7500-10000 | Single | 3 | High | 0 |
15066 | 11131 | 22 | 7500-10000 | Single | 1 | Low | 0 |
8756 | 7319 | 52 | 0-7500 | Married | 3 | Medium | 0 |
2839 | 5178 | 39 | 15000+ | Married | 1 | Low | 0 |
5721 | 9322 | 31 | 0-7500 | Single | 2 | Low | 0 |
5649 | 6461 | 32 | 10000-15000 | Single | 1 | Low | 0 |
1827 | 5132 | 27 | 7500-10000 | Single | 3 | Low | 0 |
7221 | 6110 | 58 | 7500-10000 | Married | 3 | High | 0 |
6575 | 6984 | 28 | 10000-15000 | Single | 1 | Low | 0 |
3077 | 6629 | 54 | 7500-10000 | Married | 1 | Medium | 0 |
10546 | 7376 | 41 | 15000+ | Married | 1 | High | 0 |
6374 | 5768 | 38 | 0-7500 | Single | 2 | High | 0 |
6657 | 3024 | 93 | 7500-10000 | Married | 1 | Low | 0 |
13249 | 11656 | 23 | 10000-15000 | Single | 4 | Low | 0 |
9654 | 11656 | 24 | 10000-15000 | Single | 4 | Low | 0 |
5053 | 6170 | 36 | 0-7500 | Single | 1 | Medium | 0 |
11685 | 11828 | 51 | 7500-10000 | Single | 3 | Medium | 0 |
11085 | 13120 | 20 | 10000-15000 | Single | 2 | Low | 0 |
9915 | 6873 | 50 | 0-7500 | Married | 2 | High | 0 |
11231 | 12145 | 21 | 15000+ | Single | 4 | Low | 0 |
9887 | 7195 | 59 | 15000+ | Married | 4 | High | 0 |
6431 | 8574 | 53 | 7500-10000 | Single | 1 | High | 0 |
14612 | 11131 | 20 | 7500-10000 | Single | 1 | Low | 0 |
3592 | 6437 | 24 | 7500-10000 | Single | 2 | High | 0 |
7436 | 5127 | 33 | 7500-10000 | Single | 3 | High | 0 |
4229 | 7225 | 66 | 10000-15000 | Married | 2 | High | 0 |
3345 | 5132 | 27 | 7500-10000 | Single | 3 | Low | 0 |
6300 | 6037 | 32 | 15000+ | Single | 4 | Medium | 0 |
3491 | 5132 | 29 | 7500-10000 | Single | 3 | Medium | 0 |
4432 | 7041 | 22 | 0-7500 | Single | 3 | Low | 0 |
3570 | 7391 | 59 | 15000+ | Married | 1 | Medium | 0 |
2621 | 6264 | 43 | 0-7500 | Married | 3 | Medium | 0 |
5439 | 5955 | 33 | 10000-15000 | Single | 3 | High | 0 |
13863 | 12937 | 20 | 10000-15000 | Single | 3 | Medium | 0 |
5395 | 7338 | 47 | 0-7500 | Single | 4 | Low | 0 |
8599 | 5445 | 36 | 10000-15000 | Married | 2 | High | 0 |
14026 | 12653 | 24 | 10000-15000 | Single | 4 | Low | 0 |
1838 | 5596 | 29 | 15000+ | Single | 4 | High | 0 |
5214 | 7963 | 23 | 10000-15000 | Single | 3 | Low | 0 |
4676 | 5132 | 28 | 7500-10000 | Single | 3 | Low | 0 |
5494 | 5204 | 26 | 7500-10000 | Single | 2 | Low | 0 |
3050 | 5692 | 25 | 0-7500 | Single | 3 | Low | 0 |
9590 | 12587 | 23 | 10000-15000 | Single | 4 | Low | 0 |
17089 | 13776 | 42 | 10000-15000 | Single | 1 | Low | 0 |
7797 | 8936 | 55 | 15000+ | Single | 2 | Low | 0 |
4105 | 6461 | 31 | 10000-15000 | Single | 1 | Medium | 0 |
2650 | 5014 | 39 | 7500-10000 | Single | 4 | Medium | 0 |
10952 | 13410 | 20 | 15000+ | Single | 3 | Low | 0 |
3842 | 6126 | 46 | 7500-10000 | Married | 1 | Medium | 0 |
14867 | 12594 | 21 | 15000+ | Single | 2 | Low | 0 |
4093 | 5199 | 37 | 7500-10000 | Single | 2 | Medium | 0 |
3953 | 5692 | 25 | 0-7500 | Single | 3 | Low | 0 |
5536 | 5802 | 27 | 15000+ | Single | 2 | High | 0 |
1864 | 5019 | 26 | 7500-10000 | Single | 4 | Medium | 0 |
8533 | 12082 | 24 | 15000+ | Single | 4 | Medium | 0 |
8772 | 9534 | 19 | 10000-15000 | Single | 4 | Low | 0 |
12926 | 10995 | 26 | 15000+ | Single | 2 | High | 0 |
7971 | 5961 | 27 | 10000-15000 | Single | 3 | Medium | 0 |
2742 | 6348 | 21 | 7500-10000 | Single | 3 | Low | 0 |
9426 | 9771 | 28 | 10000-15000 | Single | 2 | Medium | 0 |
5546 | 5522 | 41 | 7500-10000 | Married | 4 | High | 0 |
4384 | 7137 | 44 | 15000+ | Married | 4 | Medium | 0 |
3268 | 6348 | 20 | 7500-10000 | Single | 3 | Low | 0 |
4053 | 6812 | 53 | 15000+ | Married | 3 | Low | 0 |
8882 | 9331 | 28 | 0-7500 | Single | 2 | Medium | 0 |
15991 | 15158 | 23 | 10000-15000 | Single | 1 | Low | 0 |
3146 | 5721 | 28 | 15000+ | Single | 3 | Low | 0 |
3228 | 6670 | 27 | 0-7500 | Single | 1 | High | 0 |
10282 | 10181 | 25 | 15000+ | Single | 2 | Low | 0 |
8293 | 6830 | 45 | 15000+ | Married | 1 | Low | 0 |
9050 | 7375 | 47 | 15000+ | Single | 4 | High | 0 |
4401 | 7188 | 30 | 15000+ | Single | 1 | Low | 0 |
10255 | 7177 | 23 | 15000+ | Single | 2 | High | 0 |
2633 | 6157 | 41 | 15000+ | Married | 4 | Medium | 0 |
9439 | 6184 | 41 | 7500-10000 | Married | 2 | High | 0 |
7457 | 7338 | 48 | 7500-10000 | Single | 1 | Low | 0 |
10416 | 7647 | 43 | 15000+ | Single | 2 | Medium | 0 |
4629 | 6012 | 29 | 0-7500 | Single | 4 | Medium | 0 |
878 | 4186 | 38 | 7500-10000 | Married | 4 | Medium | 0 |
5051 | 6126 | 45 | 7500-10000 | Married | 1 | Low | 0 |
1149 | 4748 | 39 | 0-7500 | Married | 3 | High | 0 |
4756 | 6126 | 46 | 0-7500 | Married | 4 | Medium | 0 |
7685 | 6887 | 24 | 7500-10000 | Single | 1 | Low | 0 |
6579 | 6870 | 63 | 15000+ | Married | 1 | High | 0 |
5882 | 6859 | 46 | 7500-10000 | Single | 2 | Medium | 0 |
9569 | 12594 | 23 | 15000+ | Single | 2 | Low | 0 |
7661 | 5824 | 35 | 10000-15000 | Single | 4 | Low | 0 |
7813 | 7391 | 52 | 15000+ | Married | 1 | High | 0 |
10786 | 8250 | 21 | 0-7500 | Single | 1 | Low | 0 |
1227 | 5199 | 31 | 7500-10000 | Single | 2 | Medium | 0 |
2573 | 5019 | 26 | 7500-10000 | Single | 4 | Medium | 0 |
12762 | 13410 | 20 | 15000+ | Single | 3 | Low | 0 |
7320 | 8990 | 37 | 7500-10000 | Single | 1 | Medium | 0 |
2257 | 5567 | 29 | 7500-10000 | Single | 1 | High | 0 |
6077 | 9988 | 29 | 0-7500 | Single | 3 | Low | 0 |
4386 | 8143 | 42 | 15000+ | Single | 3 | High | 0 |
5310 | 6012 | 29 | 7500-10000 | Single | 1 | Low | 0 |
2106 | 6043 | 28 | 15000+ | Single | 4 | Low | 0 |
6980 | 5204 | 26 | 7500-10000 | Single | 2 | Medium | 0 |
5184 | 6528 | 27 | 10000-15000 | Single | 2 | High | 0 |
15494 | 12348 | 22 | 0-7500 | Single | 1 | Low | 0 |
12350 | 12355 | 20 | 0-7500 | Single | 3 | Low | 0 |
9966 | 12145 | 21 | 15000+ | Single | 4 | Medium | 0 |
1465 | 4397 | 73 | 7500-10000 | Married | 2 | High | 0 |
3287 | 6296 | 46 | 15000+ | Married | 3 | Low | 0 |
8604 | 7478 | 24 | 10000-15000 | Single | 2 | Low | 0 |
2886 | 6196 | 53 | 7500-10000 | Married | 2 | Low | 0 |
16991 | 13120 | 20 | 10000-15000 | Single | 2 | Low | 0 |
3089 | 6207 | 29 | 15000+ | Single | 1 | Low | 0 |
5619 | 4834 | 71 | 15000+ | Married | 3 | High | 0 |
4945 | 6922 | 21 | 15000+ | Single | 4 | Low | 0 |
7105 | 5218 | 70 | 0-7500 | Married | 1 | High | 0 |
9857 | 6348 | 22 | 7500-10000 | Single | 3 | Low | 0 |
13379 | 10033 | 29 | 15000+ | Single | 1 | High | 0 |
6675 | 6039 | 36 | 10000-15000 | Single | 2 | Medium | 0 |
6690 | 6045 | 25 | 10000-15000 | Single | 2 | Low | 0 |
10385 | 13600 | 23 | 15000+ | Single | 2 | Low | 0 |
2821 | 6196 | 54 | 7500-10000 | Married | 2 | High | 0 |
10743 | 8811 | 51 | 15000+ | Single | 3 | High | 0 |
5159 | 6012 | 28 | 7500-10000 | Single | 1 | Low | 0 |
11236 | 12145 | 23 | 15000+ | Single | 4 | Low | 0 |
6308 | 8819 | 41 | 0-7500 | Single | 2 | High | 0 |
10428 | 6692 | 58 | 7500-10000 | Married | 2 | Medium | 0 |
4003 | 5647 | 45 | 7500-10000 | Married | 3 | High | 0 |
14799 | 12870 | 20 | 10000-15000 | Single | 3 | Low | 0 |
7273 | 7647 | 44 | 15000+ | Single | 2 | Low | 0 |
5842 | 6162 | 63 | 0-7500 | Married | 4 | Low | 0 |
4859 | 7141 | 21 | 0-7500 | Single | 2 | Medium | 0 |
16353 | 12594 | 21 | 15000+ | Single | 2 | Medium | 0 |
1017 | 4877 | 74 | 0-7500 | Married | 2 | Low | 0 |
8877 | 5679 | 65 | 7500-10000 | Married | 3 | Medium | 0 |
2700 | 5773 | 25 | 0-7500 | Single | 2 | Low | 0 |
6406 | 7604 | 23 | 0-7500 | Single | 3 | Medium | 0 |
7619 | 6855 | 23 | 7500-10000 | Single | 3 | Low | 0 |
5670 | 6629 | 53 | 7500-10000 | Married | 1 | Low | 0 |
3973 | 7437 | 24 | 7500-10000 | Single | 1 | Low | 0 |
2392 | 5797 | 34 | 15000+ | Single | 2 | Medium | 0 |
6468 | 5955 | 33 | 10000-15000 | Single | 3 | Low | 0 |
5800 | 6389 | 66 | 0-7500 | Married | 2 | High | 0 |
5556 | 6922 | 21 | 15000+ | Single | 4 | High | 0 |
4918 | 6264 | 47 | 0-7500 | Married | 3 | Medium | 0 |
7008 | 6704 | 26 | 15000+ | Single | 1 | Low | 0 |
7005 | 7211 | 24 | 10000-15000 | Single | 4 | Low | 0 |
1759 | 5692 | 25 | 0-7500 | Single | 3 | Low | 0 |
1927 | 5036 | 71 | 10000-15000 | Married | 3 | High | 0 |
10811 | 7211 | 22 | 10000-15000 | Single | 4 | Low | 0 |
6337 | 6928 | 43 | 10000-15000 | Married | 4 | Medium | 0 |
14421 | 11295 | 22 | 7500-10000 | Single | 2 | Low | 0 |
15539 | 12421 | 41 | 10000-15000 | Married | 1 | High | 0 |
7277 | 9370 | 33 | 15000+ | Single | 2 | Low | 0 |
4585 | 5726 | 40 | 7500-10000 | Married | 2 | High | 0 |
5194 | 7177 | 22 | 15000+ | Single | 2 | Low | 0 |
5308 | 6201 | 32 | 15000+ | Single | 1 | Medium | 0 |
2118 | 5687 | 37 | 0-7500 | Single | 3 | Low | 0 |
3327 | 5567 | 26 | 7500-10000 | Single | 1 | Medium | 0 |
7294 | 6173 | 35 | 15000+ | Single | 3 | Medium | 0 |
9857 | 6220 | 63 | 7500-10000 | Married | 2 | High | 0 |
4593 | 8045 | 52 | 10000-15000 | Married | 4 | Medium | 0 |
6727 | 10405 | 23 | 7500-10000 | Single | 2 | Low | 0 |
11651 | 13600 | 23 | 15000+ | Single | 2 | Low | 0 |
12001 | 10552 | 29 | 10000-15000 | Single | 2 | Medium | 0 |
3416 | 5824 | 38 | 10000-15000 | Single | 4 | Low | 0 |
8344 | 6437 | 26 | 10000-15000 | Single | 3 | Low | 0 |
5228 | 6039 | 35 | 10000-15000 | Single | 2 | High | 0 |
1808 | 5773 | 27 | 0-7500 | Single | 2 | Medium | 0 |
7524 | 7077 | 22 | 15000+ | Single | 3 | Medium | 0 |
13726 | 13120 | 22 | 10000-15000 | Single | 2 | Low | 0 |
4416 | 5204 | 28 | 7500-10000 | Single | 2 | Medium | 0 |
9372 | 5590 | 31 | 15000+ | Single | 4 | Low | 0 |
8982 | 11188 | 24 | 15000+ | Single | 4 | Low | 0 |
4291 | 7225 | 63 | 10000-15000 | Married | 2 | High | 0 |
13016 | 12418 | 24 | 15000+ | Single | 3 | Medium | 0 |
3751 | 6173 | 37 | 15000+ | Single | 3 | High | 0 |
18872 | 14877 | 41 | 10000-15000 | Single | 1 | High | 0 |
6119 | 5204 | 25 | 7500-10000 | Single | 2 | High | 0 |
11355 | 10837 | 21 | 7500-10000 | Single | 4 | Low | 0 |
7350 | 7903 | 54 | 7500-10000 | Single | 3 | Medium | 0 |
2164 | 5596 | 26 | 15000+ | Single | 4 | Medium | 0 |
6986 | 6522 | 36 | 10000-15000 | Single | 2 | Medium | 0 |
4642 | 7211 | 21 | 10000-15000 | Single | 4 | Medium | 0 |
9763 | 6266 | 25 | 15000+ | Single | 2 | Low | 0 |
7209 | 6652 | 40 | 10000-15000 | Married | 2 | High | 0 |
4094 | 6452 | 63 | 10000-15000 | Married | 4 | High | 0 |
5810 | 9423 | 29 | 10000-15000 | Single | 4 | Low | 0 |
11915 | 13120 | 24 | 10000-15000 | Single | 2 | Low | 0 |
5499 | 6332 | 66 | 15000+ | Married | 3 | Medium | 0 |
13633 | 15615 | 21 | 15000+ | Single | 1 | Low | 0 |
13632 | 11295 | 21 | 7500-10000 | Single | 2 | Medium | 0 |
3629 | 5721 | 26 | 15000+ | Single | 3 | High | 0 |
2193 | 5955 | 34 | 10000-15000 | Single | 3 | High | 0 |
3026 | 6870 | 61 | 15000+ | Married | 1 | Low | 0 |
12948 | 12493 | 62 | 10000-15000 | Married | 1 | Low | 0 |
2759 | 6196 | 52 | 7500-10000 | Married | 2 | Medium | 0 |
1591 | 5567 | 29 | 0-7500 | Single | 4 | Low | 0 |
11654 | 13472 | 24 | 15000+ | Single | 1 | Low | 0 |
11843 | 11946 | 56 | 15000+ | Married | 1 | Medium | 0 |
9376 | 10770 | 30 | 0-7500 | Single | 1 | Low | 0 |
11373 | 12086 | 21 | 10000-15000 | Single | 2 | Low | 0 |
14037 | 13403 | 22 | 15000+ | Single | 1 | High | 0 |
2304 | 6179 | 27 | 15000+ | Single | 3 | Low | 0 |
14879 | 12348 | 20 | 0-7500 | Single | 1 | Low | 0 |
9020 | 5961 | 25 | 10000-15000 | Single | 3 | High | 0 |
10342 | 6922 | 23 | 15000+ | Single | 4 | High | 0 |
9325 | 12348 | 22 | 0-7500 | Single | 1 | Low | 0 |
8732 | 12653 | 24 | 10000-15000 | Single | 4 | Medium | 0 |
6904 | 6629 | 50 | 7500-10000 | Married | 1 | Medium | 0 |
4275 | 7712 | 24 | 0-7500 | Single | 2 | Medium | 0 |
5877 | 6690 | 64 | 10000-15000 | Married | 2 | Medium | 0 |
9348 | 5768 | 31 | 0-7500 | Single | 2 | Low | 0 |
6906 | 6296 | 47 | 15000+ | Married | 3 | Low | 0 |
2446 | 5415 | 33 | 7500-10000 | Single | 4 | Low | 0 |
8630 | 8484 | 41 | 10000-15000 | Single | 3 | High | 0 |
3684 | 7197 | 51 | 10000-15000 | Married | 2 | Low | 0 |
6572 | 7683 | 44 | 10000-15000 | Single | 4 | Medium | 0 |
8949 | 12587 | 24 | 10000-15000 | Single | 4 | Low | 0 |
3249 | 6629 | 52 | 0-7500 | Married | 4 | Low | 0 |
8030 | 5562 | 35 | 0-7500 | Single | 4 | High | 0 |
220 | 3010 | 102 | 7500-10000 | Married | 3 | Medium | 0 |
5973 | 6162 | 63 | 0-7500 | Married | 4 | High | 0 |
15324 | 12930 | 22 | 10000-15000 | Single | 1 | Low | 0 |
9080 | 5797 | 35 | 15000+ | Single | 2 | High | 0 |
3305 | 6007 | 31 | 0-7500 | Single | 4 | Medium | 0 |
7750 | 5127 | 39 | 7500-10000 | Single | 3 | Medium | 0 |
1102 | 4727 | 71 | 15000+ | Married | 4 | Low | 0 |
12797 | 12418 | 21 | 15000+ | Single | 3 | Medium | 0 |
3634 | 6110 | 50 | 7500-10000 | Married | 3 | Low | 0 |
5527 | 6437 | 28 | 10000-15000 | Single | 3 | Low | 0 |
13142 | 15158 | 21 | 10000-15000 | Single | 1 | Medium | 0 |
3341 | 6290 | 32 | 10000-15000 | Single | 4 | Medium | 0 |
3672 | 7376 | 46 | 15000+ | Married | 1 | Medium | 0 |
1785 | 5615 | 35 | 7500-10000 | Single | 2 | High | 0 |
8479 | 5768 | 34 | 0-7500 | Single | 2 | High | 0 |
8063 | 7077 | 21 | 15000+ | Single | 3 | Low | 0 |
3949 | 6685 | 34 | 0-7500 | Single | 2 | Medium | 0 |
4862 | 6763 | 42 | 7500-10000 | Single | 3 | Medium | 0 |
10170 | 12908 | 54 | 15000+ | Married | 3 | Low | 0 |
4947 | 7116 | 48 | 10000-15000 | Married | 1 | Low | 0 |
5133 | 8412 | 25 | 7500-10000 | Single | 2 | Low | 0 |
9558 | 6763 | 46 | 7500-10000 | Single | 3 | High | 0 |
4223 | 7177 | 23 | 15000+ | Single | 2 | Low | 0 |
6702 | 5797 | 38 | 15000+ | Single | 2 | Medium | 0 |
4094 | 7437 | 21 | 0-7500 | Single | 4 | Low | 0 |
9859 | 10176 | 25 | 10000-15000 | Single | 4 | Low | 0 |
4667 | 4186 | 38 | 7500-10000 | Married | 4 | High | 0 |
11804 | 14320 | 22 | 0-7500 | Single | 3 | Low | 0 |
5493 | 9045 | 27 | 15000+ | Single | 4 | Low | 0 |
5820 | 6461 | 34 | 10000-15000 | Single | 1 | Low | 0 |
4937 | 4335 | 70 | 7500-10000 | Married | 3 | High | 0 |
9703 | 6007 | 33 | 7500-10000 | Single | 1 | Medium | 0 |
6600 | 7678 | 24 | 15000+ | Single | 1 | High | 0 |
11743 | 11439 | 23 | 15000+ | Single | 3 | Low | 0 |
5584 | 6126 | 43 | 0-7500 | Married | 4 | High | 0 |
4653 | 6348 | 22 | 7500-10000 | Single | 3 | Low | 0 |
9714 | 8835 | 41 | 15000+ | Single | 1 | High | 0 |
16316 | 13600 | 20 | 15000+ | Single | 2 | Low | 0 |
10368 | 8118 | 55 | 0-7500 | Single | 3 | Medium | 0 |
11196 | 12354 | 21 | 15000+ | Single | 3 | Medium | 0 |
1790 | 5716 | 32 | 15000+ | Single | 3 | High | 0 |
1800 | 5768 | 34 | 0-7500 | Single | 2 | Low | 0 |
10895 | 8181 | 44 | 15000+ | Single | 1 | Medium | 0 |
10709 | 12594 | 22 | 15000+ | Single | 2 | Low | 0 |
4974 | 6290 | 39 | 10000-15000 | Single | 4 | Medium | 0 |
4029 | 6830 | 47 | 15000+ | Married | 1 | High | 0 |
10428 | 8915 | 76 | 10000-15000 | Married | 2 | High | 0 |
7531 | 5132 | 29 | 7500-10000 | Single | 3 | High | 0 |
8535 | 11601 | 24 | 15000+ | Single | 2 | Low | 0 |
11567 | 9379 | 28 | 15000+ | Single | 2 | High | 0 |
4479 | 6170 | 32 | 0-7500 | Single | 1 | High | 0 |
8848 | 10835 | 25 | 15000+ | Single | 1 | Low | 0 |
6402 | 6207 | 27 | 15000+ | Single | 1 | Medium | 0 |
5857 | 6663 | 30 | 0-7500 | Single | 1 | Low | 0 |
8094 | 8112 | 27 | 7500-10000 | Single | 4 | Medium | 0 |
9812 | 6352 | 41 | 0-7500 | Married | 2 | High | 0 |
9948 | 10068 | 31 | 0-7500 | Single | 2 | Low | 0 |
11010 | 7647 | 49 | 15000+ | Single | 2 | High | 0 |
3445 | 6649 | 47 | 15000+ | Married | 4 | Medium | 0 |
9811 | 6196 | 52 | 7500-10000 | Married | 2 | Low | 0 |
905 | 4644 | 38 | 7500-10000 | Married | 1 | Medium | 0 |
2137 | 5773 | 27 | 0-7500 | Single | 2 | Medium | 0 |
12019 | 10995 | 26 | 15000+ | Single | 2 | Low | 0 |
12845 | 10033 | 29 | 15000+ | Single | 1 | Low | 0 |
6338 | 5721 | 26 | 15000+ | Single | 3 | Low | 0 |
8897 | 6348 | 23 | 7500-10000 | Single | 3 | Medium | 0 |
10050 | 7678 | 24 | 15000+ | Single | 1 | High | 0 |
3582 | 6528 | 28 | 10000-15000 | Single | 2 | Low | 0 |
12548 | 10891 | 26 | 15000+ | Single | 1 | Low | 0 |
5351 | 6654 | 61 | 7500-10000 | Married | 1 | Low | 0 |
7497 | 7382 | 69 | 0-7500 | Married | 1 | Medium | 0 |
7187 | 10181 | 25 | 15000+ | Single | 2 | Low | 0 |
14118 | 10405 | 21 | 7500-10000 | Single | 2 | Low | 0 |
5136 | 6902 | 33 | 10000-15000 | Single | 3 | Medium | 0 |
12974 | 11630 | 22 | 7500-10000 | Single | 4 | High | 0 |
9646 | 12084 | 23 | 7500-10000 | Single | 1 | Low | 0 |
14221 | 14549 | 22 | 15000+ | Single | 1 | Low | 0 |
14379 | 11601 | 24 | 15000+ | Single | 2 | Medium | 0 |
8820 | 6704 | 24 | 7500-10000 | Single | 4 | Medium | 0 |
1778 | 5590 | 39 | 15000+ | Single | 4 | Medium | 0 |
3817 | 7097 | 56 | 10000-15000 | Married | 3 | Medium | 0 |
14548 | 12084 | 21 | 7500-10000 | Single | 1 | Medium | 0 |
2334 | 5537 | 34 | 7500-10000 | Single | 3 | Medium | 0 |
8212 | 6812 | 59 | 15000+ | Married | 3 | High | 0 |
12189 | 10033 | 25 | 15000+ | Single | 1 | High | 0 |
4296 | 6870 | 65 | 15000+ | Married | 1 | Medium | 0 |
9252 | 5562 | 37 | 7500-10000 | Single | 1 | High | 0 |
14413 | 12355 | 21 | 0-7500 | Single | 3 | Low | 0 |
7519 | 10260 | 24 | 7500-10000 | Single | 3 | Low | 0 |
9412 | 10405 | 22 | 7500-10000 | Single | 2 | Low | 0 |
2501 | 5647 | 48 | 7500-10000 | Married | 3 | Low | 0 |
11084 | 11336 | 38 | 10000-15000 | Single | 1 | Low | 0 |
2257 | 5961 | 28 | 10000-15000 | Single | 3 | Medium | 0 |
12892 | 10405 | 24 | 7500-10000 | Single | 2 | Low | 0 |
649 | 4397 | 76 | 7500-10000 | Married | 2 | Medium | 0 |
8976 | 5797 | 34 | 15000+ | Single | 2 | High | 0 |
13626 | 11946 | 53 | 15000+ | Married | 1 | Medium | 0 |
3802 | 6110 | 53 | 7500-10000 | Married | 3 | High | 0 |
3111 | 4877 | 71 | 0-7500 | Married | 2 | High | 0 |
2798 | 5824 | 34 | 10000-15000 | Single | 4 | High | 0 |
4888 | 6629 | 58 | 0-7500 | Married | 4 | Medium | 0 |
7772 | 5721 | 26 | 15000+ | Single | 3 | Low | 0 |
2600 | 5679 | 65 | 7500-10000 | Married | 3 | Low | 0 |
5267 | 8899 | 21 | 15000+ | Single | 1 | Medium | 0 |
7692 | 6615 | 43 | 7500-10000 | Single | 4 | Low | 0 |
1783 | 5542 | 28 | 7500-10000 | Single | 3 | High | 0 |
4416 | 8404 | 30 | 7500-10000 | Single | 2 | High | 0 |
11534 | 8523 | 44 | 10000-15000 | Single | 1 | High | 0 |
7765 | 7391 | 55 | 15000+ | Married | 1 | Low | 0 |
3692 | 7373 | 24 | 10000-15000 | Single | 3 | Low | 0 |
6378 | 10181 | 26 | 15000+ | Single | 2 | Low | 0 |
5379 | 5824 | 31 | 10000-15000 | Single | 4 | Low | 0 |
4542 | 4748 | 70 | 7500-10000 | Married | 2 | High | 0 |
9173 | 7319 | 59 | 0-7500 | Married | 3 | High | 0 |
5982 | 5961 | 29 | 10000-15000 | Single | 3 | Medium | 0 |
7426 | 9634 | 26 | 10000-15000 | Single | 3 | High | 0 |
8728 | 6201 | 32 | 15000+ | Single | 1 | Low | 0 |
8884 | 5797 | 30 | 15000+ | Single | 2 | Low | 0 |
14202 | 11455 | 25 | 10000-15000 | Single | 2 | Medium | 0 |
6189 | 7141 | 21 | 0-7500 | Single | 2 | Low | 0 |
9045 | 5955 | 30 | 10000-15000 | Single | 3 | High | 0 |
8683 | 7373 | 23 | 10000-15000 | Single | 3 | Low | 0 |
8300 | 10024 | 32 | 15000+ | Single | 1 | Low | 0 |
7417 | 9771 | 27 | 10000-15000 | Single | 2 | Low | 0 |
7173 | 5773 | 28 | 0-7500 | Single | 2 | Low | 0 |
10944 | 14036 | 21 | 10000-15000 | Single | 1 | Medium | 0 |
10373 | 6690 | 65 | 10000-15000 | Married | 2 | Medium | 0 |
6808 | 6007 | 31 | 0-7500 | Single | 4 | Low | 0 |
4650 | 6208 | 24 | 7500-10000 | Single | 4 | Medium | 0 |
4610 | 5797 | 34 | 15000+ | Single | 2 | Low | 0 |
5782 | 6179 | 25 | 15000+ | Single | 3 | High | 0 |
2365 | 5716 | 39 | 15000+ | Single | 3 | Low | 0 |
7412 | 11219 | 50 | 10000-15000 | Married | 4 | Medium | 0 |
7523 | 9625 | 34 | 10000-15000 | Single | 3 | Medium | 0 |
9101 | 9625 | 34 | 10000-15000 | Single | 3 | Low | 0 |
2236 | 5596 | 27 | 15000+ | Single | 4 | Medium | 0 |
6011 | 6415 | 46 | 10000-15000 | Married | 4 | Medium | 0 |
15702 | 12411 | 22 | 15000+ | Single | 1 | High | 0 |
4083 | 5687 | 31 | 0-7500 | Single | 3 | High | 0 |
5007 | 6697 | 36 | 15000+ | Single | 1 | Medium | 0 |
8133 | 9973 | 31 | 0-7500 | Single | 1 | Low | 0 |
3950 | 7683 | 44 | 10000-15000 | Single | 4 | High | 0 |
5689 | 5687 | 36 | 0-7500 | Single | 3 | Low | 0 |
12724 | 11080 | 22 | 7500-10000 | Single | 3 | Low | 0 |
2740 | 6045 | 28 | 10000-15000 | Single | 2 | Medium | 0 |
8598 | 10825 | 33 | 15000+ | Single | 1 | Low | 0 |
8428 | 6528 | 27 | 10000-15000 | Single | 2 | Medium | 0 |
8210 | 7373 | 23 | 10000-15000 | Single | 3 | Medium | 0 |
9719 | 7391 | 51 | 15000+ | Married | 1 | High | 0 |
3045 | 5679 | 64 | 7500-10000 | Married | 3 | Low | 0 |
10914 | 13349 | 40 | 15000+ | Single | 2 | High | 0 |
2638 | 6385 | 48 | 15000+ | Married | 2 | Medium | 0 |
5077 | 6037 | 32 | 15000+ | Single | 4 | Medium | 0 |
2370 | 6045 | 26 | 10000-15000 | Single | 2 | Medium | 0 |
8010 | 6777 | 56 | 0-7500 | Married | 3 | High | 0 |
7660 | 6290 | 32 | 10000-15000 | Single | 4 | Low | 0 |
3203 | 6039 | 39 | 10000-15000 | Single | 2 | Medium | 0 |
3209 | 6176 | 25 | 0-7500 | Single | 1 | Low | 0 |
6691 | 9959 | 66 | 7500-10000 | Married | 1 | Low | 0 |
6140 | 4925 | 70 | 10000-15000 | Married | 4 | Medium | 0 |
3014 | 6830 | 48 | 15000+ | Married | 1 | High | 0 |
3638 | 5590 | 33 | 15000+ | Single | 4 | Low | 0 |
16351 | 12594 | 24 | 15000+ | Single | 2 | Low | 0 |
3834 | 6804 | 66 | 0-7500 | Married | 3 | High | 0 |
3914 | 7319 | 56 | 0-7500 | Married | 3 | Medium | 0 |
11763 | 9201 | 28 | 0-7500 | Single | 3 | Low | 0 |
2940 | 6922 | 24 | 15000+ | Single | 4 | Low | 0 |
8316 | 6012 | 29 | 0-7500 | Single | 4 | Medium | 0 |
6692 | 5976 | 52 | 7500-10000 | Married | 4 | High | 0 |
10842 | 7924 | 40 | 7500-10000 | Single | 1 | Low | 0 |
3115 | 5768 | 30 | 0-7500 | Single | 2 | Medium | 0 |
10871 | 12084 | 23 | 7500-10000 | Single | 1 | Low | 0 |
8356 | 6855 | 23 | 7500-10000 | Single | 3 | Low | 0 |
9693 | 6296 | 44 | 15000+ | Married | 3 | High | 0 |
9184 | 8523 | 41 | 10000-15000 | Single | 1 | Low | 0 |
3499 | 5687 | 36 | 0-7500 | Single | 3 | Medium | 0 |
6783 | 6812 | 58 | 15000+ | Married | 3 | High | 0 |
14074 | 10172 | 37 | 15000+ | Single | 2 | Low | 0 |
6535 | 6855 | 24 | 7500-10000 | Single | 3 | Low | 0 |
10026 | 6201 | 34 | 15000+ | Single | 1 | Medium | 0 |
1765 | 5679 | 61 | 7500-10000 | Married | 3 | Low | 0 |
7725 | 5824 | 39 | 10000-15000 | Single | 4 | High | 0 |
2178 | 5716 | 31 | 15000+ | Single | 3 | Low | 0 |
5696 | 6461 | 39 | 10000-15000 | Single | 1 | Low | 0 |
9429 | 11502 | 42 | 10000-15000 | Married | 1 | Low | 0 |
4091 | 5562 | 34 | 0-7500 | Single | 4 | Low | 0 |
3969 | 6900 | 69 | 0-7500 | Married | 2 | Medium | 0 |
8014 | 6045 | 25 | 10000-15000 | Single | 2 | Low | 0 |
7483 | 7939 | 59 | 0-7500 | Single | 4 | Medium | 0 |
4244 | 6045 | 28 | 10000-15000 | Single | 2 | Medium | 0 |
13266 | 13472 | 21 | 15000+ | Single | 1 | Medium | 0 |
9223 | 7700 | 58 | 10000-15000 | Married | 1 | High | 0 |
2391 | 5692 | 25 | 0-7500 | Single | 3 | High | 0 |
7907 | 10542 | 37 | 10000-15000 | Single | 2 | Medium | 0 |
7454 | 10034 | 21 | 7500-10000 | Single | 4 | Low | 0 |
8464 | 12348 | 22 | 0-7500 | Single | 1 | Low | 0 |
1975 | 5687 | 34 | 0-7500 | Single | 3 | Low | 0 |
6386 | 6812 | 50 | 15000+ | Married | 3 | High | 0 |
12317 | 13963 | 21 | 10000-15000 | Single | 1 | Low | 0 |
3039 | 6352 | 44 | 0-7500 | Married | 2 | Low | 0 |
11570 | 9036 | 30 | 15000+ | Single | 4 | Low | 0 |
8099 | 12084 | 21 | 7500-10000 | Single | 1 | Low | 0 |
10095 | 9370 | 39 | 15000+ | Single | 2 | Medium | 0 |
2509 | 6332 | 60 | 15000+ | Married | 3 | Low | 0 |
6298 | 8999 | 25 | 7500-10000 | Single | 1 | Medium | 0 |
6329 | 6796 | 42 | 0-7500 | Married | 1 | Low | 0 |
3807 | 7373 | 22 | 10000-15000 | Single | 3 | Medium | 0 |
6209 | 6629 | 53 | 0-7500 | Married | 4 | High | 0 |
11899 | 14036 | 22 | 10000-15000 | Single | 1 | Low | 0 |
2743 | 5199 | 33 | 7500-10000 | Single | 2 | Medium | 0 |
3757 | 7373 | 24 | 10000-15000 | Single | 3 | Low | 0 |
10482 | 10260 | 21 | 7500-10000 | Single | 3 | High | 0 |
9427 | 8999 | 25 | 7500-10000 | Single | 1 | Medium | 0 |
1655 | 5567 | 29 | 7500-10000 | Single | 1 | High | 0 |
10337 | 9762 | 30 | 10000-15000 | Single | 2 | Medium | 0 |
7408 | 5567 | 27 | 7500-10000 | Single | 1 | Low | 0 |
940 | 4704 | 71 | 0-7500 | Married | 4 | High | 0 |
8243 | 6170 | 30 | 0-7500 | Single | 1 | Low | 0 |
7432 | 6461 | 37 | 10000-15000 | Single | 1 | High | 0 |
2702 | 5199 | 34 | 7500-10000 | Single | 2 | Low | 0 |
8667 | 7856 | 48 | 10000-15000 | Single | 3 | High | 0 |
7791 | 6625 | 31 | 15000+ | Single | 3 | Medium | 0 |
7221 | 10714 | 53 | 0-7500 | Married | 4 | Low | 0 |
2907 | 4644 | 39 | 7500-10000 | Married | 1 | Medium | 0 |
9392 | 9423 | 26 | 10000-15000 | Single | 4 | Low | 0 |
2606 | 5132 | 29 | 7500-10000 | Single | 3 | Medium | 0 |
3590 | 6467 | 25 | 10000-15000 | Single | 1 | Medium | 0 |
4403 | 8159 | 51 | 15000+ | Single | 3 | Medium | 0 |
6538 | 6922 | 21 | 15000+ | Single | 4 | Low | 0 |
10098 | 9322 | 35 | 0-7500 | Single | 2 | High | 0 |
10774 | 7177 | 23 | 15000+ | Single | 2 | Medium | 0 |
3381 | 5802 | 27 | 15000+ | Single | 2 | Medium | 0 |
11485 | 10176 | 26 | 10000-15000 | Single | 4 | Low | 0 |
11882 | 10405 | 22 | 7500-10000 | Single | 2 | Low | 0 |
9709 | 6763 | 40 | 7500-10000 | Single | 3 | Low | 0 |
6409 | 6859 | 49 | 7500-10000 | Single | 2 | Medium | 0 |
2569 | 6296 | 44 | 15000+ | Married | 3 | Medium | 0 |
8462 | 10453 | 29 | 10000-15000 | Single | 1 | Low | 0 |
7573 | 10033 | 29 | 15000+ | Single | 1 | Medium | 0 |
16698 | 12870 | 24 | 10000-15000 | Single | 3 | Medium | 0 |
6486 | 5830 | 29 | 10000-15000 | Single | 4 | Low | 0 |
15437 | 12198 | 22 | 7500-10000 | Single | 2 | Medium | 0 |
6872 | 5014 | 33 | 7500-10000 | Single | 4 | Low | 0 |
1874 | 5768 | 35 | 0-7500 | Single | 2 | Medium | 0 |
8699 | 11131 | 24 | 0-7500 | Single | 4 | High | 0 |
1504 | 5127 | 31 | 7500-10000 | Single | 3 | Medium | 0 |
6370 | 6039 | 32 | 10000-15000 | Single | 2 | Low | 0 |
8319 | 7609 | 40 | 0-7500 | Single | 2 | Medium | 0 |
13508 | 10607 | 29 | 10000-15000 | Single | 2 | High | 0 |
1538 | 5132 | 26 | 7500-10000 | Single | 3 | Low | 0 |
3700 | 7188 | 36 | 15000+ | Single | 1 | Low | 0 |
5977 | 9768 | 25 | 15000+ | Single | 4 | Medium | 0 |
9254 | 5961 | 28 | 10000-15000 | Single | 3 | Medium | 0 |
9214 | 7195 | 56 | 15000+ | Married | 4 | Medium | 0 |
5014 | 6385 | 49 | 15000+ | Married | 2 | High | 0 |
5352 | 6461 | 35 | 10000-15000 | Single | 1 | High | 0 |
7073 | 10039 | 26 | 15000+ | Single | 3 | Low | 0 |
2526 | 6332 | 67 | 15000+ | Married | 3 | High | 0 |
5063 | 5567 | 25 | 7500-10000 | Single | 1 | Medium | 0 |
8764 | 7647 | 48 | 15000+ | Single | 2 | High | 0 |
5952 | 8412 | 28 | 7500-10000 | Single | 2 | Low | 0 |
5699 | 7177 | 24 | 15000+ | Single | 2 | Low | 0 |
1080 | 4682 | 76 | 7500-10000 | Married | 3 | Low | 0 |
2752 | 6260 | 31 | 15000+ | Single | 2 | Low | 0 |
12208 | 8523 | 44 | 10000-15000 | Single | 1 | Medium | 0 |
14639 | 11455 | 27 | 10000-15000 | Single | 2 | Low | 0 |
8560 | 8807 | 50 | 0-7500 | Single | 1 | Medium | 0 |
3766 | 7502 | 44 | 0-7500 | Single | 3 | Low | 0 |
8530 | 8999 | 25 | 7500-10000 | Single | 1 | Medium | 0 |
6281 | 6467 | 27 | 10000-15000 | Single | 1 | High | 0 |
10969 | 13472 | 23 | 15000+ | Single | 1 | Medium | 0 |
8780 | 6909 | 54 | 15000+ | Married | 2 | High | 0 |
1689 | 5596 | 25 | 15000+ | Single | 4 | Medium | 0 |
5111 | 6649 | 47 | 15000+ | Married | 4 | Low | 0 |
7050 | 7751 | 24 | 15000+ | Single | 2 | High | 0 |
3206 | 6110 | 54 | 7500-10000 | Married | 3 | High | 0 |
2973 | 6690 | 65 | 10000-15000 | Married | 2 | High | 0 |
6506 | 6201 | 37 | 15000+ | Single | 1 | Low | 0 |
3838 | 6234 | 29 | 0-7500 | Single | 2 | Low | 0 |
12993 | 9709 | 32 | 0-7500 | Single | 4 | Low | 0 |
3768 | 7478 | 24 | 10000-15000 | Single | 2 | Low | 0 |
5346 | 6704 | 23 | 7500-10000 | Single | 4 | High | 0 |
6260 | 5199 | 38 | 7500-10000 | Single | 2 | Low | 0 |
4677 | 6461 | 32 | 10000-15000 | Single | 1 | Medium | 0 |
5961 | 6007 | 30 | 0-7500 | Single | 4 | Low | 0 |
7622 | 5760 | 64 | 7500-10000 | Married | 2 | Low | 0 |
15558 | 13258 | 21 | 15000+ | Single | 3 | Low | 0 |
10947 | 11439 | 22 | 15000+ | Single | 3 | Low | 0 |
6496 | 7496 | 58 | 10000-15000 | Married | 4 | High | 0 |
11582 | 9978 | 34 | 15000+ | Single | 3 | Medium | 0 |
9343 | 7678 | 23 | 15000+ | Single | 1 | Low | 0 |
7725 | 5590 | 34 | 15000+ | Single | 4 | High | 0 |
5216 | 6201 | 35 | 15000+ | Single | 1 | High | 0 |
5022 | 5900 | 72 | 10000-15000 | Married | 1 | High | 0 |
6551 | 6812 | 54 | 15000+ | Married | 3 | Low | 0 |
6776 | 5687 | 35 | 0-7500 | Single | 3 | Low | 0 |
4578 | 6352 | 46 | 0-7500 | Married | 2 | Medium | 0 |
4545 | 8275 | 54 | 15000+ | Single | 2 | Low | 0 |
4149 | 4335 | 72 | 7500-10000 | Married | 3 | Medium | 0 |
3230 | 5768 | 38 | 0-7500 | Single | 2 | High | 0 |
3311 | 5199 | 33 | 7500-10000 | Single | 2 | Low | 0 |
10488 | 11439 | 24 | 15000+ | Single | 3 | Medium | 0 |
7233 | 7924 | 45 | 7500-10000 | Single | 1 | Low | 0 |
3758 | 5716 | 38 | 15000+ | Single | 3 | High | 0 |
8287 | 5802 | 29 | 15000+ | Single | 2 | Medium | 0 |
7666 | 7353 | 53 | 0-7500 | Married | 1 | Medium | 0 |
7814 | 7400 | 41 | 15000+ | Married | 2 | Medium | 0 |
3027 | 6690 | 60 | 10000-15000 | Married | 2 | Medium | 0 |
9501 | 6835 | 60 | 0-7500 | Married | 1 | Medium | 0 |
This looks like a much better fit. However, we don’t worry about our fit not being perfect–we’re interested in this model generalizing well for future observations and know that we avoid overfitting to data. Our GLM is tuned to minimize the deviation between observed and expected Pure Premiums and so generalizes to other experience data.
Given the low credibility of our data as representative of the entire population of Cal Insurance’s policyholders, we will focus on the interpretation of the GLM. Individuals “below” the GLM have a lower historical loss cost than expected.
The overall goal here is balancing compliance with California’s ratemaking regulations, where our models must be equitable. However, using sequential analysis with the allowed factors can lead us to a weak classification of risk, leading to adverse selection where low-risk individuals prefer competitors with lower rates, and higher-risk individuals (who may have been rejected from competitors’ underwriting guidelines) may seek coverage from Cal Insurance (but incur high losses).
We consider two selections of individuals below: sel1
and sel2
. The subset sel1
contains individuals who historically incur higher losses than average (as predicted by the GLM). This does not necessarily mean these are the individuals we want to reject via underwriting guidelines (if anything, we would like to legally charge them higher premiums), but we can expect to gain a lot of insight here.
The subset sel2
contains individuals who historically in 2013-2015 have significantly lower incurred losses than estimated by the GLM. Although this is not a causal relationship between the combination of factors and fewer incurred losses, there may be interesting insights to gain.
# selections of individuals for analysis
sel1 <- compare.pp.capped %>% filter(`Pure Premium` > 2 * `Predicted Pure Premium`)
sel2 <- compare.pp.capped %>% filter(`Pure Premium` * 2 < `Predicted Pure Premium`, `Pure Premium` > 0)
# compare.pp.capped %>% ggplot(aes(x = compare.pp.capped$`Pure Premium`)) + geom_histogram(bins = 10, fill = "lightblue", color = "lightyellow") + ggtitle("Distribution of Observed Pure Premiums") + scale_x_log10()
# compare.pp.capped %>% ggplot(aes(x = compare.pp.capped$`Predicted PP Capped`)) + geom_histogram(bins = 20, fill = "lightblue", color = "lightyellow") + ggtitle("Distribution of Projected Pure Premiums") + scale_x_log10()
sel2 %>% ggplot(aes(x = sel2$`Pure Premium`)) + geom_histogram(bins = 10, fill = "lightblue", color = "lightyellow") + ggtitle("Distribution of Observed Pure Premiums")
# + scale_x_log10()
sel2 %>% ggplot(aes(x = sel2$`Predicted PP Capped`)) + geom_histogram(bins = 20, fill = "lightblue", color = "lightyellow") + ggtitle("Distribution of Projected Pure Premiums")
compare.pp.capped %>% ggplot(aes(x = `Annual Mileage`, y = `Predicted PP Capped`)) + geom_bar(aes(fill = `Annual Mileage`), stat = "summary", fun.y = "mean", show.legend = TRUE) + facet_grid(. ~ `Driver Experience Band`) + theme(legend.position = "bottom", axis.text.x = element_blank(), axis.ticks.x = element_blank()) + ggtitle("Average Predicted Pure Premium \nby Driver Experience Years")
compare.pp.capped %>% ggplot(aes(x = `Driver Experience Band`, y = `Predicted PP Capped`)) + geom_bar(aes(fill = `Driver Experience Band`), stat = "summary", fun.y = "mean", show.legend = TRUE) + facet_grid(. ~ `Annual Mileage`) + theme(legend.position = "bottom", axis.text.x = element_blank(), axis.ticks.x = element_blank()) + ggtitle("Average Predicted Pure Premium \nby Annual Mileage")
# loss %>% ggplot(aes(x = `Territory`, y = `Adjusted Loss`)) + geom_bar(aes(fill = `Territory`), stat = "identity", show.legend = TRUE) + facet_grid(. ~ `Marital Status`) + theme(legend.position = "bottom") + ggtitle("Total loss by Territory and Marital Status")
The predicted Pure Premiums are generally monotonic as Annual Mileage
increases, with the exception of the third mileage band, where it appears that low-experience drivers with this mileage are expected to incur a relatively high average loss. This may be due to a tradeoff between lower mileage corresponding to less road-time and exposure to risk, versus high mileage (150,000+) corresponding to more skilled drivers, despite fewer years of experience.
Credit Score
compare.pp.capped %>% ggplot(aes(x = `Driver Experience Band`, y = `Predicted PP Capped`)) + geom_bar(aes(fill = `Driver Experience Band`), stat = "summary", fun.y = "mean", show.legend = TRUE) + facet_grid(. ~ `Credit Score`) + theme(legend.position = "bottom", axis.text.x = element_blank(), axis.ticks.x = element_blank()) + ggtitle("Average Predicted Pure Premium by Credit Score")
compare.pp.capped %>% ggplot(aes(x = `Annual Mileage`, y = `Predicted PP Capped`)) + geom_bar(aes(fill = `Annual Mileage`), stat = "summary", fun.y = "mean", show.legend = TRUE) + facet_grid(. ~ `Credit Score`) + theme(legend.position = "bottom", axis.text.x = element_blank(), axis.ticks.x = element_blank()) + ggtitle("Average Predicted Pure Premium by Credit Score")
compare.pp.capped %>% ggplot(aes(x = `Territory`, y = `Predicted PP Capped`)) + geom_bar(aes(fill = `Territory`), stat = "summary", fun.y = "mean", show.legend = TRUE) + facet_grid(. ~ `Credit Score`) + theme(legend.position = "bottom", axis.text.x = element_blank(), axis.ticks.x = element_blank()) + ggtitle("Average Predicted Pure Premium by Credit Score")
The past filed relativities for territory (1.2, 1.1, 0.9, 0.8) appear to be insufficient to distinguish the expected loss from each territory.
# compound poisson from zhang
glm.cp <- cpglm(formula = pp ~ x.conviction + x.mileage + x.experience + x.accident + x.marital + x.territory, link = 0, data = df.loss)
# does not converge; do not use
# brute force "optimize" approach; # NOT USED
start <- c(glm.tweedie$coefficients/2, glm.tweedie$coefficients/2)
# compute negative log likelihood (nll)
nll <- function(param){
# f0, s0 for offset
f0 <- param[1]
f1 <- param[2]
f2 <- param[3]
f3 <- param[4]
f4 <- param[5]
f5 <- param[6]
f6 <- param[7]
s0 <- param[1]
s1 <- param[2]
s2 <- param[3]
s3 <- param[4]
s4 <- param[5]
s5 <- param[6]
s6 <- param[7]
# lambda <- exp(sum( c(f0, f1, f2, f3, f4, f5, f6) * c(1, x.vec) ))
lambda <- exp(f0 + f1*x.conviction + f2*x.mileage + f3*x.experience + f4*x.accident + f5*x.marital + f6*x.territory)
# theta <- exp(sum( c(s0, s1, s2, s3, s4, s5, s6) * c(1, x.vec) ))
theta <- exp(s0 + s1*x.conviction + s2*x.mileage + s3*x.experience + s4*x.accident + s5*x.marital + s6*x.territory)
tau <- alpha1 * theta
p <- (alpha1 + 2) / (alpha1 + 1)
mu <- lambda * tau
print(p) # debug
phi <- abs( lambda^(1-p) * tau^(2-p) / (2-p) )
#log-likelihood
ll <- log(dtweedie(pp, p, mu, phi))
return(-sum(ll))
}
bfmle <- optim(start, nll)
bfmle
Now because we hand-select the factors to include into our GLM (as opposed to randomized subsetting), a \(k\)-fold cross-validation of data would not be effective. We trained our data on the experience years 2013-2015, reasoning that four years experience captures a good amount of the underlying policyholder risk behavior.
From our exhibits above and GLM, we want to essentially reject drivers with the following specifications, given in the table below. Of course, in the process, we can expect to unfortunately reject individuals who are falsely projected to incur high losses.
rej <- compare.pp.capped %>% filter(`Predicted Pure Premium` > 12000)
rej %>% qkable() # reject these
Policy Number | Vehicle Number | PP Capped | Predicted PP Capped | Pure Premium | Predicted Pure Premium | Conviction Points (CP) last 3 years | Accident Points (AP) last 3 years | Annual Mileage | Driver Experience Band | Marital Status | Territory | Driver Age | Credit Score | Late Fees | Liability Limit |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
10 | 6386 | 7659 | 14934 | 7659 | 17800 | 0 | 3 | 7500-10000 | 0-3 | Single | 2 | 19 | Low | 0 | 5e+04 |
42 | 644 | 1331 | 20855 | 1331 | 21258 | 3 | 1 | 7500-10000 | 44-53 | Married | 1 | 63 | Low | 0 | 5e+04 |
67 | 14663 | 4721 | 13049 | 4721 | 12404 | 1 | 3 | 0-7500 | 4-8 | Single | 4 | 23 | Low | 0 | 5e+04 |
144 | 10613 | 100000 | 14549 | 167697 | 15197 | 1 | 3 | 15000+ | 4-8 | Single | 1 | 24 | Low | 0 | 3e+05 |
160 | 5309 | 5059 | 12086 | 5059 | 13868 | 0 | 2 | 10000-15000 | 4-8 | Single | 2 | 20 | Medium | 1 | 1e+05 |
160 | 11547 | 9556 | 12084 | 9556 | 13639 | 0 | 3 | 7500-10000 | 4-8 | Single | 1 | 23 | Low | 0 | 1e+05 |
170 | 1998 | 77 | 13403 | 77 | 14571 | 1 | 2 | 15000+ | 4-8 | Single | 1 | 23 | Medium | 0 | 3e+05 |
222 | 12590 | 59700 | 13404 | 59700 | 14333 | 0 | 3 | 0-7500 | 4-8 | Single | 1 | 20 | Low | 1 | 5e+04 |
228 | 9054 | 1554 | 11188 | 1554 | 12688 | 0 | 2 | 15000+ | 4-8 | Single | 4 | 21 | Low | 0 | 5e+05 |
255 | 12575 | 195 | 13757 | 195 | 17066 | 0 | 2 | 7500-10000 | 0-3 | Single | 2 | 19 | Low | 0 | 5e+04 |
311 | 2430 | 0 | 11131 | 0 | 13077 | 0 | 2 | 7500-10000 | 4-8 | Single | 1 | 20 | Low | 0 | 1e+05 |
322 | 2562 | 127 | 12833 | 127 | 15112 | 0 | 2 | 7500-10000 | 34-43 | Single | 1 | 53 | Low | 1 | 5e+04 |
359 | 12495 | 29606 | 12355 | 29606 | 13667 | 0 | 3 | 0-7500 | 4-8 | Single | 3 | 23 | Low | 0 | 5e+05 |
425 | 2623 | 654 | 12084 | 654 | 13639 | 0 | 3 | 7500-10000 | 4-8 | Single | 1 | 21 | Low | 2 | 1e+05 |
427 | 14101 | 11064 | 13963 | 11064 | 14532 | 1 | 2 | 10000-15000 | 4-8 | Single | 1 | 24 | Medium | 0 | 5e+04 |
459 | 3839 | 100000 | 12083 | 119880 | 12442 | 0 | 3 | 0-7500 | 4-8 | Single | 4 | 24 | Low | 0 | 1e+05 |
466 | 6452 | 15951 | 12594 | 15951 | 14503 | 0 | 3 | 15000+ | 4-8 | Single | 2 | 22 | Low | 0 | 5e+04 |
507 | 5816 | 0 | 11131 | 0 | 13077 | 0 | 2 | 7500-10000 | 4-8 | Single | 1 | 22 | Low | 0 | 5e+04 |
549 | 2165 | 0 | 10196 | 0 | 12196 | 1 | 1 | 0-7500 | 0-3 | Single | 2 | 19 | Medium | 0 | 3e+05 |
580 | 9359 | 0 | 9202 | 0 | 15957 | 1 | 1 | 0-7500 | 64-73 | Married | 2 | 86 | Medium | 0 | 3e+05 |
581 | 12571 | 120 | 14512 | 120 | 14579 | 1 | 2 | 10000-15000 | 34-43 | Single | 4 | 57 | Low | 0 | 5e+04 |
584 | 14769 | 14146 | 12937 | 14146 | 14497 | 0 | 3 | 10000-15000 | 4-8 | Single | 3 | 20 | Low | 0 | 1e+05 |
601 | 9459 | 0 | 10430 | 0 | 12155 | 2 | 2 | 7500-10000 | 9-13 | Single | 1 | 25 | Low | 0 | 5e+04 |
601 | 11555 | 189 | 11295 | 189 | 12976 | 0 | 3 | 7500-10000 | 4-8 | Single | 2 | 24 | Low | 0 | 5e+04 |
608 | 6741 | 13229 | 12411 | 13229 | 14616 | 0 | 2 | 15000+ | 4-8 | Single | 1 | 21 | Low | 0 | 1e+05 |
622 | 12734 | 0 | 9441 | 0 | 12234 | 0 | 1 | 0-7500 | 0-3 | Single | 2 | 19 | Low | 1 | 5e+05 |
632 | 266 | 100000 | 15756 | 119700 | 19067 | 0 | 2 | 10000-15000 | 0-3 | Single | 3 | 19 | High | 3 | 1e+05 |
660 | 13300 | 4070 | 13472 | 4070 | 15244 | 0 | 3 | 15000+ | 4-8 | Single | 1 | 21 | Low | 0 | 3e+05 |
663 | 3618 | 120 | 12145 | 120 | 13234 | 0 | 3 | 15000+ | 4-8 | Single | 4 | 24 | Low | 0 | 3e+05 |
674 | 7410 | 9920 | 33070 | 9920 | 28841 | 3 | 2 | 10000-15000 | 9-13 | Single | 2 | 29 | Low | 0 | 5e+04 |
679 | 4235 | 26765 | 11860 | 26765 | 13681 | 0 | 2 | 7500-10000 | 24-33 | Single | 1 | 49 | Low | 1 | 1e+05 |
696 | 8004 | 0 | 11188 | 0 | 12688 | 0 | 2 | 15000+ | 4-8 | Single | 4 | 21 | Low | 0 | 5e+04 |
719 | 5937 | 21149 | 17094 | 21149 | 19997 | 0 | 2 | 10000-15000 | 0-3 | Single | 1 | 19 | Medium | 0 | 5e+04 |
756 | 8498 | 1043 | 13116 | 1043 | 13193 | 1 | 3 | 15000+ | 4-8 | Single | 4 | 24 | Low | 0 | 5e+04 |
788 | 9747 | 7917 | 9105 | 7917 | 12236 | 0 | 1 | 7500-10000 | 0-3 | Single | 1 | 19 | Medium | 0 | 3e+05 |
795 | 11667 | 0 | 25014 | 0 | 24104 | 3 | 1 | 15000+ | 34-43 | Married | 1 | 57 | Medium | 0 | 5e+05 |
806 | 3380 | 59880 | 17730 | 59880 | 19879 | 1 | 3 | 15000+ | 0-3 | Single | 3 | 19 | Low | 0 | 5e+04 |
830 | 9367 | 0 | 9886 | 0 | 12977 | 0 | 1 | 10000-15000 | 0-3 | Single | 2 | 19 | Low | 1 | 3e+05 |
852 | 1798 | 5629 | 12028 | 5629 | 12965 | 1 | 3 | 7500-10000 | 4-8 | Single | 3 | 23 | Medium | 0 | 5e+04 |
908 | 5522 | 35374 | 12411 | 35374 | 14616 | 0 | 2 | 15000+ | 4-8 | Single | 1 | 23 | High | 0 | 3e+05 |
959 | 5589 | 120 | 12411 | 120 | 14616 | 0 | 2 | 15000+ | 4-8 | Single | 1 | 21 | Medium | 0 | 1e+05 |
960 | 9085 | 4507 | 15756 | 4507 | 19067 | 0 | 2 | 10000-15000 | 0-3 | Single | 3 | 19 | Medium | 0 | 1e+05 |
962 | 5826 | 386 | 12930 | 386 | 14577 | 0 | 2 | 10000-15000 | 4-8 | Single | 1 | 20 | Low | 0 | 5e+04 |
968 | 11412 | 46363 | 24360 | 46363 | 22872 | 3 | 1 | 10000-15000 | 34-43 | Married | 2 | 52 | Medium | 0 | 5e+05 |
992 | 5507 | 2236 | 17730 | 2236 | 19879 | 1 | 3 | 15000+ | 0-3 | Single | 3 | 19 | Medium | 1 | 5e+04 |
1013 | 10079 | 21778 | 12348 | 21778 | 13742 | 0 | 2 | 0-7500 | 4-8 | Single | 1 | 21 | Low | 0 | 5e+04 |
1059 | 1423 | 134 | 12411 | 134 | 14616 | 0 | 2 | 15000+ | 4-8 | Single | 1 | 22 | Medium | 0 | 1e+05 |
1095 | 10470 | 134 | 16250 | 134 | 17920 | 1 | 2 | 0-7500 | 0-3 | Single | 3 | 19 | Low | 0 | 5e+04 |
1116 | 2778 | 0 | 10932 | 0 | 13045 | 0 | 2 | 7500-10000 | 24-33 | Single | 3 | 43 | Low | 0 | 3e+05 |
1121 | 12043 | 17451 | 11086 | 17451 | 13016 | 0 | 2 | 7500-10000 | 24-33 | Single | 2 | 41 | High | 0 | 1e+05 |
1123 | 13141 | 120 | 14036 | 120 | 15204 | 0 | 3 | 10000-15000 | 4-8 | Single | 1 | 21 | Low | 0 | 5e+04 |
1162 | 12613 | 497 | 11422 | 497 | 13598 | 1 | 1 | 10000-15000 | 0-3 | Single | 1 | 19 | Low | 0 | 5e+04 |
1167 | 75 | 72567 | 12145 | 72567 | 13234 | 0 | 3 | 15000+ | 4-8 | Single | 4 | 20 | Medium | 0 | 3e+05 |
1176 | 12198 | 347 | 13757 | 347 | 17066 | 0 | 2 | 7500-10000 | 0-3 | Single | 2 | 19 | Low | 0 | 1e+05 |
1195 | 5610 | 0 | 11080 | 0 | 12430 | 1 | 2 | 7500-10000 | 4-8 | Single | 3 | 21 | Low | 0 | 5e+05 |
1196 | 4300 | 427 | 8923 | 427 | 16979 | 0 | 1 | 10000-15000 | 64-73 | Married | 2 | 85 | Medium | 0 | 3e+05 |
1214 | 5681 | 142 | 13049 | 142 | 13597 | 1 | 3 | 7500-10000 | 4-8 | Single | 1 | 20 | Low | 0 | 5e+04 |
1215 | 5923 | 1237 | 12145 | 1237 | 13234 | 0 | 3 | 15000+ | 4-8 | Single | 4 | 24 | Low | 3 | 5e+04 |
1267 | 7221 | 0 | 12361 | 0 | 14548 | 0 | 2 | 15000+ | 24-33 | Single | 2 | 47 | High | 0 | 3e+05 |
1277 | 2649 | 448 | 18733 | 448 | 19781 | 1 | 3 | 10000-15000 | 0-3 | Single | 2 | 19 | Low | 0 | 5e+04 |
1312 | 764 | 0 | 10405 | 0 | 12441 | 0 | 2 | 7500-10000 | 4-8 | Single | 2 | 22 | Low | 0 | 1e+05 |
1317 | 8409 | 0 | 10260 | 0 | 12469 | 0 | 2 | 7500-10000 | 4-8 | Single | 3 | 22 | Low | 0 | 5e+04 |
1332 | 2437 | 56584 | 10260 | 56584 | 12469 | 0 | 2 | 7500-10000 | 4-8 | Single | 3 | 21 | Low | 0 | 1e+05 |
1334 | 3004 | 4407 | 12021 | 4407 | 13036 | 1 | 2 | 7500-10000 | 4-8 | Single | 1 | 24 | High | 0 | 3e+05 |
1375 | 4114 | 0 | 9357 | 0 | 13041 | 0 | 1 | 15000+ | 0-3 | Single | 3 | 19 | Low | 0 | 5e+05 |
1430 | 4182 | 0 | 9441 | 0 | 12234 | 0 | 1 | 0-7500 | 0-3 | Single | 2 | 19 | Low | 0 | 1e+05 |
1442 | 196 | 11 | 16418 | 11 | 19940 | 0 | 3 | 15000+ | 0-3 | Single | 3 | 19 | High | 0 | 1e+05 |
1462 | 6432 | 11873 | 29178 | 11873 | 29595 | 3 | 1 | 10000-15000 | 34-43 | Single | 2 | 54 | High | 0 | 1e+05 |
1464 | 2561 | 0 | 11237 | 0 | 12402 | 1 | 2 | 7500-10000 | 4-8 | Single | 2 | 24 | Medium | 0 | 1e+05 |
1543 | 13719 | 519 | 11917 | 519 | 13900 | 0 | 2 | 10000-15000 | 4-8 | Single | 3 | 22 | Low | 0 | 3e+05 |
1672 | 8125 | 0 | 13335 | 0 | 13699 | 1 | 2 | 0-7500 | 4-8 | Single | 1 | 21 | Low | 0 | 1e+05 |
1678 | 13775 | 12897 | 15338 | 12897 | 19075 | 0 | 2 | 15000+ | 0-3 | Single | 2 | 19 | High | 0 | 1e+05 |
1694 | 7524 | 0 | 11656 | 0 | 12655 | 0 | 2 | 10000-15000 | 4-8 | Single | 4 | 21 | Low | 0 | 1e+05 |
1716 | 13708 | 100000 | 11131 | 182708 | 13077 | 0 | 2 | 7500-10000 | 4-8 | Single | 1 | 24 | Low | 1 | 3e+05 |
1721 | 11192 | 2011 | 17257 | 2011 | 18966 | 1 | 2 | 10000-15000 | 0-3 | Single | 2 | 19 | Low | 0 | 5e+04 |
1739 | 10845 | 86590 | 10100 | 86590 | 12859 | 0 | 1 | 0-7500 | 0-3 | Single | 1 | 19 | Low | 0 | 5e+05 |
1765 | 5484 | 34592 | 13963 | 34592 | 14532 | 1 | 2 | 10000-15000 | 4-8 | Single | 1 | 22 | Low | 0 | 3e+05 |
1807 | 11300 | 10656 | 13472 | 10656 | 15244 | 0 | 3 | 15000+ | 4-8 | Single | 1 | 20 | Medium | 0 | 5e+04 |
1829 | 13317 | 86946 | 15979 | 86946 | 19024 | 0 | 2 | 10000-15000 | 0-3 | Single | 2 | 19 | Low | 0 | 1e+05 |
1843 | 9209 | 313 | 16057 | 313 | 18154 | 0 | 3 | 15000+ | 0-3 | Single | 4 | 19 | Low | 0 | 3e+05 |
1867 | 8501 | 100000 | 9844 | 159187 | 16772 | 1 | 1 | 0-7500 | 64-73 | Married | 1 | 84 | Low | 0 | 5e+05 |
1895 | 12963 | 0 | 8445 | 0 | 17062 | 0 | 1 | 15000+ | 64-73 | Married | 3 | 85 | High | 0 | 5e+04 |
2008 | 14190 | 0 | 12529 | 0 | 13862 | 1 | 2 | 15000+ | 4-8 | Single | 2 | 23 | High | 0 | 1e+05 |
2046 | 1256 | 69141 | 11188 | 69141 | 12688 | 0 | 2 | 15000+ | 4-8 | Single | 4 | 24 | Low | 0 | 1e+05 |
2050 | 8006 | 3811 | 13600 | 3811 | 14458 | 1 | 3 | 15000+ | 4-8 | Single | 2 | 22 | Low | 2 | 3e+05 |
2069 | 7881 | 120 | 12084 | 120 | 13639 | 0 | 3 | 7500-10000 | 4-8 | Single | 1 | 20 | Low | 0 | 5e+04 |
2075 | 4150 | 1203 | 12594 | 1203 | 14503 | 0 | 3 | 15000+ | 4-8 | Single | 2 | 24 | Low | 0 | 5e+04 |
2138 | 14961 | 66620 | 25309 | 66620 | 25609 | 3 | 1 | 10000-15000 | 4-8 | Single | 2 | 23 | Medium | 0 | 3e+05 |
2195 | 4739 | 978 | 11601 | 978 | 13905 | 0 | 2 | 15000+ | 4-8 | Single | 2 | 24 | Low | 0 | 1e+05 |
2209 | 1479 | 57116 | 13120 | 57116 | 14465 | 0 | 3 | 10000-15000 | 4-8 | Single | 2 | 20 | Low | 0 | 5e+04 |
2221 | 510 | 4897 | 12411 | 4897 | 14616 | 0 | 2 | 15000+ | 4-8 | Single | 1 | 23 | Low | 0 | 1e+05 |
2256 | 652 | 10002 | 12298 | 10002 | 13678 | 0 | 2 | 0-7500 | 24-33 | Single | 2 | 48 | High | 0 | 5e+04 |
2342 | 13622 | 35384 | 12145 | 35384 | 13234 | 0 | 3 | 15000+ | 4-8 | Single | 4 | 22 | Low | 0 | 3e+05 |
2352 | 4571 | 0 | 7408 | 0 | 13898 | 0 | 1 | 7500-10000 | 64-73 | Married | 4 | 85 | Low | 0 | 5e+04 |
2377 | 11124 | 0 | 10527 | 0 | 12967 | 1 | 1 | 10000-15000 | 0-3 | Single | 3 | 19 | Low | 0 | 5e+04 |
2385 | 1659 | 6418 | 13404 | 6418 | 14333 | 0 | 3 | 0-7500 | 4-8 | Single | 1 | 21 | Low | 1 | 5e+04 |
2391 | 7753 | 0 | 9105 | 0 | 12236 | 0 | 1 | 7500-10000 | 0-3 | Single | 1 | 19 | High | 0 | 1e+05 |
2419 | 1040 | 20636 | 13120 | 20636 | 14465 | 0 | 3 | 10000-15000 | 4-8 | Single | 2 | 20 | Low | 0 | 1e+05 |
2430 | 8616 | 0 | 8217 | 0 | 16009 | 0 | 1 | 7500-10000 | 64-73 | Married | 1 | 85 | Low | 0 | 1e+05 |
2439 | 9308 | 0 | 9441 | 0 | 12234 | 0 | 1 | 0-7500 | 0-3 | Single | 2 | 19 | Low | 0 | 1e+05 |
2514 | 2666 | 100000 | 11131 | 161465 | 13077 | 0 | 2 | 7500-10000 | 4-8 | Single | 1 | 24 | High | 0 | 3e+05 |
2534 | 597 | 13963 | 11601 | 13963 | 13905 | 0 | 2 | 15000+ | 4-8 | Single | 2 | 22 | High | 0 | 5e+04 |
2569 | 5574 | 17985 | 16480 | 17985 | 17879 | 1 | 2 | 0-7500 | 0-3 | Single | 2 | 19 | Medium | 0 | 1e+05 |
2580 | 8202 | 0 | 12086 | 0 | 13868 | 0 | 2 | 10000-15000 | 4-8 | Single | 2 | 22 | Low | 1 | 5e+04 |
2607 | 11909 | 83351 | 15979 | 83351 | 19024 | 0 | 2 | 10000-15000 | 0-3 | Single | 2 | 19 | Medium | 0 | 3e+05 |
2663 | 1983 | 55344 | 15158 | 55344 | 15157 | 1 | 3 | 10000-15000 | 4-8 | Single | 1 | 21 | Medium | 0 | 5e+04 |
2672 | 5138 | 11482 | 12930 | 11482 | 14577 | 0 | 2 | 10000-15000 | 4-8 | Single | 1 | 21 | Medium | 0 | 5e+04 |
2695 | 9013 | 1186 | 18557 | 1186 | 20856 | 0 | 3 | 10000-15000 | 0-3 | Single | 1 | 19 | Medium | 0 | 1e+05 |
2703 | 2156 | 7828 | 17346 | 7828 | 19842 | 0 | 3 | 10000-15000 | 0-3 | Single | 2 | 19 | Low | 0 | 5e+05 |
2742 | 9117 | 0 | 10607 | 0 | 14366 | 2 | 1 | 15000+ | 0-3 | Single | 4 | 19 | Medium | 0 | 5e+04 |
2747 | 5175 | 259 | 10576 | 259 | 13640 | 0 | 1 | 10000-15000 | 0-3 | Single | 1 | 19 | Medium | 1 | 1e+05 |
2750 | 11370 | 8657 | 16408 | 8657 | 20050 | 0 | 2 | 15000+ | 0-3 | Single | 1 | 19 | Medium | 0 | 1e+05 |
2781 | 12112 | 125 | 12188 | 125 | 14581 | 0 | 2 | 15000+ | 24-33 | Single | 3 | 48 | Low | 0 | 1e+05 |
2794 | 12111 | 919 | 12529 | 919 | 13862 | 1 | 2 | 15000+ | 4-8 | Single | 2 | 20 | Medium | 0 | 1e+05 |
2799 | 10404 | 1619 | 11131 | 1619 | 13077 | 0 | 2 | 7500-10000 | 4-8 | Single | 1 | 22 | Low | 0 | 5e+04 |
2805 | 8348 | 4499 | 14169 | 4499 | 14420 | 1 | 3 | 10000-15000 | 4-8 | Single | 2 | 20 | Low | 0 | 5e+04 |
2813 | 14580 | 48898 | 13120 | 48898 | 14465 | 0 | 3 | 10000-15000 | 4-8 | Single | 2 | 20 | High | 0 | 1e+05 |
2835 | 500 | 1572 | 12145 | 1572 | 13234 | 0 | 3 | 15000+ | 4-8 | Single | 4 | 21 | Low | 0 | 1e+05 |
2837 | 7811 | 5739 | 12930 | 5739 | 14577 | 0 | 2 | 10000-15000 | 4-8 | Single | 1 | 23 | Low | 0 | 5e+05 |
2880 | 2538 | 61905 | 15893 | 61905 | 17883 | 1 | 2 | 7500-10000 | 0-3 | Single | 1 | 19 | Low | 0 | 3e+05 |
2883 | 8321 | 0 | 11917 | 0 | 13900 | 0 | 2 | 10000-15000 | 4-8 | Single | 3 | 21 | Low | 0 | 3e+05 |
2893 | 13736 | 0 | 11656 | 0 | 12655 | 0 | 2 | 10000-15000 | 4-8 | Single | 4 | 22 | Low | 0 | 1e+05 |
2934 | 4897 | 32317 | 12418 | 32317 | 14536 | 0 | 3 | 15000+ | 4-8 | Single | 3 | 22 | Low | 1 | 5e+05 |
2937 | 9728 | 0 | 12930 | 0 | 14577 | 0 | 2 | 10000-15000 | 4-8 | Single | 1 | 22 | Low | 0 | 1e+05 |
2946 | 270 | 2195 | 12198 | 2195 | 12936 | 1 | 3 | 7500-10000 | 4-8 | Single | 2 | 24 | Low | 0 | 1e+05 |
2951 | 8297 | 123 | 11381 | 123 | 13103 | 0 | 2 | 0-7500 | 4-8 | Single | 3 | 20 | Medium | 0 | 5e+04 |
3039 | 8516 | 0 | 11860 | 0 | 13681 | 0 | 2 | 7500-10000 | 24-33 | Single | 1 | 40 | Low | 0 | 1e+05 |
3067 | 117 | 120 | 15382 | 120 | 15745 | 1 | 3 | 0-7500 | 34-43 | Single | 3 | 50 | Medium | 0 | 5e+04 |
3220 | 11693 | 120 | 13049 | 120 | 13597 | 1 | 3 | 7500-10000 | 4-8 | Single | 1 | 21 | Medium | 0 | 5e+04 |
3259 | 2938 | 9395 | 11381 | 9395 | 13103 | 0 | 2 | 0-7500 | 4-8 | Single | 3 | 21 | Medium | 0 | 1e+05 |
3260 | 12920 | 0 | 13052 | 0 | 13826 | 1 | 2 | 10000-15000 | 4-8 | Single | 2 | 24 | Medium | 0 | 5e+04 |
3271 | 10132 | 2473 | 15756 | 2473 | 19067 | 0 | 2 | 10000-15000 | 0-3 | Single | 3 | 19 | Low | 0 | 5e+05 |
3314 | 6202 | 5752 | 14549 | 5752 | 15197 | 1 | 3 | 15000+ | 4-8 | Single | 1 | 24 | Medium | 0 | 5e+04 |
3351 | 11006 | 1468 | 12930 | 1468 | 14577 | 0 | 2 | 10000-15000 | 4-8 | Single | 1 | 21 | Low | 0 | 1e+05 |
3359 | 5342 | 0 | 16250 | 0 | 17920 | 1 | 2 | 0-7500 | 0-3 | Single | 3 | 19 | Medium | 0 | 1e+05 |
3361 | 13104 | 100000 | 13052 | 158223 | 13826 | 1 | 2 | 10000-15000 | 4-8 | Single | 2 | 23 | Low | 1 | 5e+05 |
3399 | 11887 | 1396 | 13565 | 1396 | 17105 | 0 | 2 | 7500-10000 | 0-3 | Single | 3 | 19 | Medium | 0 | 3e+05 |
3408 | 14848 | 0 | 15893 | 0 | 17883 | 1 | 2 | 7500-10000 | 0-3 | Single | 1 | 19 | Low | 0 | 5e+05 |
3452 | 11557 | 0 | 11131 | 0 | 13077 | 0 | 2 | 7500-10000 | 4-8 | Single | 1 | 20 | Low | 0 | 3e+05 |
3459 | 439 | 332 | 14169 | 332 | 14420 | 1 | 3 | 10000-15000 | 4-8 | Single | 2 | 22 | Low | 0 | 5e+04 |
3478 | 10667 | 0 | 11618 | 0 | 13193 | 2 | 2 | 15000+ | 14-23 | Single | 1 | 31 | Low | 1 | 5e+04 |
3493 | 10012 | 218 | 12937 | 218 | 14497 | 0 | 3 | 10000-15000 | 4-8 | Single | 3 | 23 | Low | 0 | 5e+04 |
3504 | 1133 | 0 | 10100 | 0 | 12859 | 0 | 1 | 0-7500 | 0-3 | Single | 1 | 19 | Low | 0 | 1e+05 |
3508 | 9009 | 5926 | 14036 | 5926 | 15204 | 0 | 3 | 10000-15000 | 4-8 | Single | 1 | 22 | Low | 0 | 3e+05 |
3530 | 14261 | 1595 | 12198 | 1595 | 12936 | 1 | 3 | 7500-10000 | 4-8 | Single | 2 | 23 | Low | 0 | 1e+05 |
3552 | 4206 | 6978 | 10405 | 6978 | 12441 | 0 | 2 | 7500-10000 | 4-8 | Single | 2 | 21 | Low | 0 | 5e+04 |
3553 | 13983 | 11663 | 11601 | 11663 | 13905 | 0 | 2 | 15000+ | 4-8 | Single | 2 | 21 | Low | 0 | 3e+05 |
3556 | 8072 | 6107 | 12937 | 6107 | 14497 | 0 | 3 | 10000-15000 | 4-8 | Single | 3 | 20 | Low | 0 | 5e+04 |
3571 | 4321 | 37345 | 14476 | 37345 | 14289 | 1 | 3 | 0-7500 | 4-8 | Single | 1 | 24 | Low | 0 | 5e+04 |
3578 | 8492 | 168 | 12291 | 168 | 13063 | 1 | 2 | 0-7500 | 4-8 | Single | 3 | 24 | Low | 0 | 1e+05 |
3599 | 13195 | 0 | 8874 | 0 | 14560 | 1 | 1 | 0-7500 | 64-73 | Married | 4 | 82 | Medium | 0 | 5e+05 |
3611 | 4939 | 3765 | 11917 | 3765 | 13900 | 0 | 2 | 10000-15000 | 4-8 | Single | 3 | 20 | High | 0 | 5e+04 |
3662 | 6891 | 28895 | 11237 | 28895 | 12402 | 1 | 2 | 7500-10000 | 4-8 | Single | 2 | 20 | Medium | 0 | 5e+04 |
3682 | 13487 | 0 | 10100 | 0 | 12859 | 0 | 1 | 0-7500 | 0-3 | Single | 1 | 19 | High | 0 | 1e+05 |
3723 | 5129 | 0 | 8564 | 0 | 17024 | 0 | 1 | 15000+ | 64-73 | Married | 2 | 87 | Medium | 2 | 1e+05 |
3748 | 6740 | 0 | 9886 | 0 | 12977 | 0 | 1 | 10000-15000 | 0-3 | Single | 2 | 19 | Medium | 0 | 5e+05 |
3753 | 6383 | 0 | 12082 | 0 | 12649 | 1 | 2 | 15000+ | 4-8 | Single | 4 | 20 | Medium | 0 | 1e+05 |
3780 | 1427 | 1292 | 12198 | 1292 | 12936 | 1 | 3 | 7500-10000 | 4-8 | Single | 2 | 21 | Low | 0 | 5e+04 |
3884 | 6450 | 0 | 11860 | 0 | 13681 | 0 | 2 | 7500-10000 | 24-33 | Single | 1 | 41 | Low | 0 | 5e+04 |
3907 | 7522 | 159 | 11601 | 159 | 13905 | 0 | 2 | 15000+ | 4-8 | Single | 2 | 24 | Low | 1 | 5e+04 |
3928 | 11576 | 71121 | 13757 | 71121 | 17066 | 0 | 2 | 7500-10000 | 0-3 | Single | 2 | 19 | Low | 0 | 3e+05 |
4000 | 9093 | 425 | 10405 | 425 | 12441 | 0 | 2 | 7500-10000 | 4-8 | Single | 2 | 22 | Low | 0 | 5e+04 |
4117 | 1272 | 6650 | 12418 | 6650 | 14536 | 0 | 3 | 15000+ | 4-8 | Single | 3 | 22 | Low | 0 | 5e+04 |
4125 | 2457 | 0 | 9441 | 0 | 12234 | 0 | 1 | 0-7500 | 0-3 | Single | 2 | 19 | Low | 0 | 5e+05 |
4125 | 10958 | 0 | 8402 | 0 | 16042 | 0 | 1 | 0-7500 | 64-73 | Married | 3 | 86 | Medium | 0 | 5e+04 |
4167 | 369 | 0 | 13776 | 0 | 15251 | 0 | 2 | 10000-15000 | 24-33 | Single | 1 | 43 | Low | 0 | 5e+04 |
4168 | 8803 | 34433 | 13600 | 34433 | 14458 | 1 | 3 | 15000+ | 4-8 | Single | 2 | 22 | Low | 0 | 3e+05 |
4174 | 6845 | 667 | 11080 | 667 | 12430 | 1 | 2 | 7500-10000 | 4-8 | Single | 3 | 23 | High | 0 | 5e+04 |
4183 | 11341 | 0 | 9886 | 0 | 12977 | 0 | 1 | 10000-15000 | 0-3 | Single | 2 | 19 | Medium | 0 | 1e+05 |
4229 | 898 | 1765 | 13472 | 1765 | 15244 | 0 | 3 | 15000+ | 4-8 | Single | 1 | 21 | Low | 0 | 1e+05 |
4231 | 5690 | 11909 | 11917 | 11909 | 13900 | 0 | 2 | 10000-15000 | 4-8 | Single | 3 | 21 | Medium | 0 | 5e+04 |
4297 | 4617 | 120 | 12084 | 120 | 13639 | 0 | 3 | 7500-10000 | 4-8 | Single | 1 | 20 | Medium | 1 | 5e+04 |
4336 | 5452 | 956 | 12348 | 956 | 13742 | 0 | 2 | 0-7500 | 4-8 | Single | 1 | 23 | Low | 1 | 5e+04 |
4383 | 6201 | 66169 | 13472 | 66169 | 15244 | 0 | 3 | 15000+ | 4-8 | Single | 1 | 20 | Low | 0 | 5e+05 |
4401 | 5179 | 538 | 17722 | 538 | 19662 | 0 | 3 | 0-7500 | 0-3 | Single | 1 | 19 | Low | 0 | 5e+04 |
4472 | 5801 | 0 | 11237 | 0 | 12402 | 1 | 2 | 7500-10000 | 4-8 | Single | 2 | 24 | High | 0 | 5e+04 |
4483 | 9338 | 218 | 12021 | 218 | 13036 | 1 | 2 | 7500-10000 | 4-8 | Single | 1 | 21 | Low | 0 | 1e+05 |
4508 | 1073 | 1122 | 9309 | 1122 | 12261 | 0 | 1 | 0-7500 | 0-3 | Single | 3 | 19 | Medium | 0 | 1e+05 |
4508 | 14369 | 7278 | 10260 | 7278 | 12469 | 0 | 2 | 7500-10000 | 4-8 | Single | 3 | 21 | Medium | 0 | 3e+05 |
4541 | 8541 | 5090 | 14717 | 5090 | 16365 | 0 | 2 | 0-7500 | 0-3 | Single | 4 | 19 | Medium | 0 | 1e+05 |
4605 | 6611 | 1846 | 12348 | 1846 | 13742 | 0 | 2 | 0-7500 | 4-8 | Single | 1 | 22 | High | 0 | 5e+04 |
4639 | 1239 | 59700 | 12465 | 59700 | 13033 | 1 | 2 | 0-7500 | 4-8 | Single | 2 | 20 | Low | 0 | 5e+04 |
4689 | 10598 | 198 | 11381 | 198 | 13103 | 0 | 2 | 0-7500 | 4-8 | Single | 3 | 20 | Medium | 0 | 3e+05 |
4715 | 14591 | 4315 | 16728 | 4315 | 18106 | 0 | 3 | 10000-15000 | 0-3 | Single | 4 | 19 | Low | 0 | 1e+05 |
4754 | 7724 | 4044 | 9886 | 4044 | 12977 | 0 | 1 | 10000-15000 | 0-3 | Single | 2 | 19 | High | 0 | 5e+05 |
4764 | 9322 | 52649 | 12084 | 52649 | 13639 | 0 | 3 | 7500-10000 | 4-8 | Single | 1 | 24 | Low | 0 | 3e+05 |
4786 | 5468 | 19316 | 12465 | 19316 | 13033 | 1 | 2 | 0-7500 | 4-8 | Single | 2 | 24 | Low | 0 | 5e+04 |
4790 | 8389 | 1336 | 12529 | 1336 | 13862 | 1 | 2 | 15000+ | 4-8 | Single | 2 | 24 | Low | 0 | 5e+05 |
4794 | 228 | 88420 | 12086 | 88420 | 13868 | 0 | 2 | 10000-15000 | 4-8 | Single | 2 | 21 | Medium | 0 | 1e+05 |
4841 | 14397 | 120 | 12418 | 120 | 14536 | 0 | 3 | 15000+ | 4-8 | Single | 3 | 21 | Low | 0 | 5e+04 |
4873 | 11193 | 354 | 13813 | 354 | 16818 | 2 | 2 | 10000-15000 | 4-8 | Single | 3 | 23 | Low | 0 | 5e+04 |
4882 | 13981 | 1062 | 10405 | 1062 | 12441 | 0 | 2 | 7500-10000 | 4-8 | Single | 2 | 23 | Low | 1 | 1e+05 |
4889 | 6305 | 120 | 14169 | 120 | 14420 | 1 | 3 | 10000-15000 | 4-8 | Single | 2 | 21 | Low | 0 | 5e+04 |
4936 | 10081 | 0 | 8217 | 0 | 16009 | 0 | 1 | 7500-10000 | 64-73 | Married | 1 | 85 | Medium | 0 | 3e+05 |
4960 | 419 | 286 | 13258 | 286 | 16863 | 2 | 2 | 15000+ | 4-8 | Single | 3 | 23 | Low | 0 | 3e+05 |
4964 | 12979 | 104 | 11188 | 104 | 12688 | 0 | 2 | 15000+ | 4-8 | Single | 4 | 23 | Low | 0 | 1e+05 |
4965 | 9470 | 0 | 12529 | 0 | 13862 | 1 | 2 | 15000+ | 4-8 | Single | 2 | 23 | High | 0 | 1e+05 |
4972 | 6998 | 312 | 10260 | 312 | 12469 | 0 | 2 | 7500-10000 | 4-8 | Single | 3 | 23 | Low | 0 | 5e+04 |
5005 | 14321 | 2416 | 12930 | 2416 | 14577 | 0 | 2 | 10000-15000 | 4-8 | Single | 1 | 23 | Medium | 0 | 3e+05 |
5055 | 9891 | 192 | 13472 | 192 | 15244 | 0 | 3 | 15000+ | 4-8 | Single | 1 | 20 | Medium | 0 | 5e+04 |
5100 | 5607 | 7114 | 12348 | 7114 | 13742 | 0 | 2 | 0-7500 | 4-8 | Single | 1 | 22 | High | 0 | 3e+05 |
5103 | 10672 | 523 | 11917 | 523 | 13900 | 0 | 2 | 10000-15000 | 4-8 | Single | 3 | 20 | Medium | 0 | 5e+04 |
5110 | 4923 | 8709 | 11439 | 8709 | 13937 | 0 | 2 | 15000+ | 4-8 | Single | 3 | 23 | Medium | 0 | 5e+04 |
5111 | 4085 | 2789 | 11188 | 2789 | 12688 | 0 | 2 | 15000+ | 4-8 | Single | 4 | 23 | Low | 0 | 1e+05 |
5124 | 1262 | 155 | 13713 | 155 | 14497 | 1 | 2 | 10000-15000 | 24-33 | Single | 3 | 42 | Low | 0 | 5e+04 |
5146 | 7526 | 0 | 11973 | 0 | 12976 | 1 | 2 | 7500-10000 | 24-33 | Single | 2 | 44 | High | 0 | 5e+04 |
5148 | 8114 | 52811 | 9879 | 52811 | 12588 | 2 | 1 | 10000-15000 | 24-33 | Single | 1 | 40 | High | 0 | 1e+05 |
5193 | 14919 | 568 | 13664 | 568 | 13158 | 1 | 3 | 10000-15000 | 4-8 | Single | 4 | 21 | Low | 0 | 5e+05 |
5248 | 2755 | 0 | 11131 | 0 | 13077 | 0 | 2 | 7500-10000 | 4-8 | Single | 1 | 24 | Medium | 0 | 1e+05 |
5248 | 12586 | 0 | 10527 | 0 | 12967 | 1 | 1 | 10000-15000 | 0-3 | Single | 3 | 19 | High | 0 | 1e+05 |
5269 | 4392 | 28970 | 12901 | 28970 | 13013 | 1 | 2 | 15000+ | 34-43 | Married | 1 | 58 | High | 0 | 3e+05 |
5277 | 6221 | 24807 | 12084 | 24807 | 13639 | 0 | 3 | 7500-10000 | 4-8 | Single | 1 | 22 | Low | 0 | 3e+05 |
5310 | 892 | 50138 | 16325 | 50138 | 18851 | 0 | 2 | 0-7500 | 0-3 | Single | 1 | 19 | High | 0 | 3e+05 |
5410 | 10087 | 121 | 11946 | 121 | 13053 | 0 | 2 | 15000+ | 34-43 | Married | 1 | 57 | Medium | 0 | 5e+04 |
5415 | 11472 | 46 | 11131 | 46 | 13077 | 0 | 2 | 7500-10000 | 4-8 | Single | 1 | 21 | Low | 0 | 1e+05 |
5462 | 9445 | 29569 | 17812 | 29569 | 20912 | 0 | 3 | 15000+ | 0-3 | Single | 1 | 19 | Medium | 0 | 5e+04 |
5476 | 3788 | 1124 | 10576 | 1124 | 13640 | 0 | 1 | 10000-15000 | 0-3 | Single | 1 | 19 | High | 0 | 1e+05 |
5497 | 5881 | 9562 | 12411 | 9562 | 14616 | 0 | 2 | 15000+ | 4-8 | Single | 1 | 22 | Low | 0 | 5e+04 |
5552 | 1695 | 49555 | 11325 | 49555 | 12891 | 2 | 2 | 10000-15000 | 9-13 | Single | 2 | 28 | Low | 1 | 1e+05 |
5574 | 8878 | 46 | 12594 | 46 | 14503 | 0 | 3 | 15000+ | 4-8 | Single | 2 | 22 | Low | 0 | 1e+05 |
5593 | 12713 | 0 | 10576 | 0 | 13640 | 0 | 1 | 10000-15000 | 0-3 | Single | 1 | 19 | High | 0 | 3e+05 |
5595 | 12742 | 2388 | 13267 | 2388 | 15573 | 0 | 2 | 7500-10000 | 0-3 | Single | 4 | 19 | Low | 0 | 1e+05 |
5646 | 10191 | 100000 | 8564 | 213238 | 17024 | 0 | 1 | 15000+ | 64-73 | Married | 2 | 86 | High | 0 | 3e+05 |
5675 | 3865 | 6629 | 11542 | 6629 | 13074 | 0 | 2 | 0-7500 | 4-8 | Single | 2 | 23 | Medium | 0 | 5e+04 |
5712 | 6203 | 2047 | 13963 | 2047 | 14532 | 1 | 2 | 10000-15000 | 4-8 | Single | 1 | 23 | Low | 0 | 3e+05 |
5717 | 11564 | 123 | 12594 | 123 | 14503 | 0 | 3 | 15000+ | 4-8 | Single | 2 | 20 | Low | 0 | 5e+04 |
5723 | 12765 | 63774 | 12529 | 63774 | 13862 | 1 | 2 | 15000+ | 4-8 | Single | 2 | 22 | Low | 0 | 1e+05 |
5787 | 8587 | 51839 | 10870 | 51839 | 12926 | 2 | 2 | 15000+ | 9-13 | Single | 2 | 29 | Medium | 0 | 5e+05 |
5790 | 13476 | 1480 | 13120 | 1480 | 14465 | 0 | 3 | 10000-15000 | 4-8 | Single | 2 | 23 | Low | 0 | 5e+04 |
5823 | 12546 | 0 | 11422 | 0 | 13598 | 1 | 1 | 10000-15000 | 0-3 | Single | 1 | 19 | Low | 0 | 1e+05 |
5834 | 2271 | 100000 | 17094 | 161363 | 19997 | 0 | 2 | 10000-15000 | 0-3 | Single | 1 | 19 | Low | 0 | 5e+05 |
5838 | 13063 | 5850 | 14549 | 5850 | 15197 | 1 | 3 | 15000+ | 4-8 | Single | 1 | 20 | Low | 1 | 5e+04 |
5843 | 3376 | 391 | 11439 | 391 | 13937 | 0 | 2 | 15000+ | 4-8 | Single | 3 | 22 | Low | 0 | 1e+05 |
5849 | 2876 | 0 | 10405 | 0 | 12441 | 0 | 2 | 7500-10000 | 4-8 | Single | 2 | 24 | Low | 0 | 5e+05 |
5856 | 6237 | 30591 | 17104 | 30591 | 19887 | 0 | 3 | 10000-15000 | 0-3 | Single | 3 | 19 | Low | 2 | 1e+05 |
5864 | 727 | 26479 | 11295 | 26479 | 12976 | 0 | 3 | 7500-10000 | 4-8 | Single | 2 | 20 | Medium | 0 | 1e+05 |
5890 | 7990 | 221 | 11917 | 221 | 13900 | 0 | 2 | 10000-15000 | 4-8 | Single | 3 | 24 | Low | 0 | 3e+05 |
5908 | 12542 | 35636 | 14717 | 35636 | 17938 | 0 | 2 | 7500-10000 | 0-3 | Single | 1 | 19 | Low | 0 | 3e+05 |
5953 | 11819 | 5587 | 14717 | 5587 | 17938 | 0 | 2 | 7500-10000 | 0-3 | Single | 1 | 19 | Low | 0 | 3e+05 |
6005 | 7451 | 6739 | 13472 | 6739 | 15244 | 0 | 3 | 15000+ | 4-8 | Single | 1 | 23 | Low | 0 | 1e+05 |
6005 | 13460 | 49502 | 12594 | 49502 | 14503 | 0 | 3 | 15000+ | 4-8 | Single | 2 | 24 | Low | 0 | 5e+04 |
6011 | 10541 | 2764 | 11946 | 2764 | 13053 | 0 | 2 | 15000+ | 34-43 | Married | 1 | 55 | Medium | 0 | 5e+04 |
6015 | 13687 | 15467 | 11188 | 15467 | 12688 | 0 | 2 | 15000+ | 4-8 | Single | 4 | 23 | Low | 0 | 1e+05 |
6041 | 1992 | 29149 | 11131 | 29149 | 13077 | 0 | 2 | 7500-10000 | 4-8 | Single | 1 | 23 | Medium | 0 | 5e+05 |
6052 | 14654 | 0 | 12878 | 0 | 14509 | 0 | 2 | 10000-15000 | 24-33 | Single | 2 | 40 | Low | 0 | 5e+04 |
6055 | 13899 | 121 | 14549 | 121 | 15197 | 1 | 3 | 15000+ | 4-8 | Single | 1 | 21 | Low | 0 | 5e+05 |
6062 | 2351 | 71310 | 15976 | 71310 | 18710 | 0 | 3 | 7500-10000 | 0-3 | Single | 1 | 19 | Low | 0 | 3e+05 |
6087 | 13494 | 30654 | 13403 | 30654 | 14571 | 1 | 2 | 15000+ | 4-8 | Single | 1 | 21 | Low | 0 | 1e+05 |
6098 | 4698 | 42055 | 13403 | 42055 | 14571 | 1 | 2 | 15000+ | 4-8 | Single | 1 | 21 | High | 0 | 3e+05 |
6120 | 8243 | 14315 | 13410 | 14315 | 14491 | 1 | 3 | 15000+ | 4-8 | Single | 3 | 22 | Low | 0 | 5e+04 |
6141 | 7120 | 0 | 10100 | 0 | 12859 | 0 | 1 | 0-7500 | 0-3 | Single | 1 | 19 | Low | 0 | 5e+04 |
6162 | 11748 | 2919 | 15338 | 2919 | 19075 | 0 | 2 | 15000+ | 0-3 | Single | 2 | 19 | Medium | 0 | 1e+05 |
6169 | 12711 | 1791 | 12930 | 1791 | 14577 | 0 | 2 | 10000-15000 | 4-8 | Single | 1 | 20 | Low | 0 | 3e+05 |
6176 | 295 | 130 | 13472 | 130 | 15244 | 0 | 3 | 15000+ | 4-8 | Single | 1 | 21 | Low | 0 | 5e+04 |
6196 | 12821 | 2958 | 17104 | 2958 | 19887 | 0 | 3 | 10000-15000 | 0-3 | Single | 3 | 19 | Low | 0 | 5e+04 |
6217 | 12059 | 0 | 10260 | 0 | 12469 | 0 | 2 | 7500-10000 | 4-8 | Single | 3 | 20 | High | 0 | 5e+04 |
6238 | 5600 | 12904 | 14725 | 12904 | 17840 | 0 | 3 | 7500-10000 | 0-3 | Single | 3 | 19 | High | 0 | 5e+04 |
6242 | 9168 | 70826 | 12021 | 70826 | 13036 | 1 | 2 | 7500-10000 | 4-8 | Single | 1 | 22 | Low | 0 | 3e+05 |
6272 | 13758 | 120 | 9357 | 120 | 13041 | 0 | 1 | 15000+ | 0-3 | Single | 3 | 19 | High | 0 | 5e+04 |
6278 | 5967 | 121 | 11917 | 121 | 13900 | 0 | 2 | 10000-15000 | 4-8 | Single | 3 | 20 | Medium | 0 | 1e+05 |
6280 | 14265 | 5540 | 11656 | 5540 | 12655 | 0 | 2 | 10000-15000 | 4-8 | Single | 4 | 23 | Medium | 0 | 5e+04 |
6288 | 7510 | 0 | 9309 | 0 | 12261 | 0 | 1 | 0-7500 | 0-3 | Single | 3 | 19 | Medium | 0 | 1e+05 |
6297 | 9079 | 11 | 13739 | 11 | 16063 | 0 | 2 | 10000-15000 | 34-43 | Single | 3 | 51 | High | 0 | 3e+05 |
6304 | 12219 | 14774 | 11542 | 14774 | 13074 | 0 | 2 | 0-7500 | 4-8 | Single | 2 | 20 | High | 0 | 5e+04 |
6337 | 11570 | 0 | 9441 | 0 | 12234 | 0 | 1 | 0-7500 | 0-3 | Single | 2 | 19 | High | 0 | 5e+04 |
6411 | 4113 | 78368 | 22891 | 78368 | 24089 | 3 | 1 | 7500-10000 | 24-33 | Single | 3 | 41 | Medium | 0 | 3e+05 |
6432 | 10368 | 51087 | 11131 | 51087 | 13077 | 0 | 2 | 7500-10000 | 4-8 | Single | 1 | 22 | Low | 0 | 5e+05 |
6434 | 585 | 0 | 16408 | 0 | 20050 | 0 | 2 | 15000+ | 0-3 | Single | 1 | 19 | Medium | 0 | 1e+05 |
6465 | 10441 | 32231 | 15976 | 32231 | 18710 | 0 | 3 | 7500-10000 | 0-3 | Single | 1 | 19 | Medium | 0 | 5e+04 |
6496 | 4681 | 0 | 11601 | 0 | 13905 | 0 | 2 | 15000+ | 4-8 | Single | 2 | 24 | Medium | 0 | 3e+05 |
6540 | 791 | 24447 | 11656 | 24447 | 12655 | 0 | 2 | 10000-15000 | 4-8 | Single | 4 | 23 | Low | 0 | 5e+04 |
6592 | 11802 | 23526 | 17722 | 23526 | 19662 | 0 | 3 | 0-7500 | 0-3 | Single | 1 | 19 | Low | 0 | 5e+04 |
6613 | 10270 | 211 | 8217 | 211 | 14605 | 0 | 1 | 0-7500 | 64-73 | Married | 4 | 85 | High | 0 | 5e+04 |
6657 | 7155 | 120 | 11542 | 120 | 13074 | 0 | 2 | 0-7500 | 4-8 | Single | 2 | 20 | Low | 0 | 5e+05 |
6686 | 6166 | 0 | 13162 | 0 | 14536 | 1 | 2 | 15000+ | 24-33 | Single | 3 | 40 | Medium | 0 | 5e+05 |
6693 | 424 | 19488 | 17346 | 19488 | 19842 | 0 | 3 | 10000-15000 | 0-3 | Single | 2 | 19 | Low | 0 | 5e+04 |
6700 | 7941 | 0 | 12348 | 0 | 13742 | 0 | 2 | 0-7500 | 4-8 | Single | 1 | 20 | Medium | 0 | 5e+04 |
6727 | 9705 | 969 | 17094 | 969 | 19997 | 0 | 2 | 10000-15000 | 0-3 | Single | 1 | 19 | Low | 0 | 1e+05 |
6733 | 10312 | 22448 | 12465 | 22448 | 13033 | 1 | 2 | 0-7500 | 4-8 | Single | 2 | 21 | Low | 0 | 5e+05 |
6745 | 4117 | 17711 | 12529 | 17711 | 13862 | 1 | 2 | 15000+ | 4-8 | Single | 2 | 23 | Low | 0 | 5e+04 |
6746 | 6257 | 1974 | 12653 | 1974 | 13199 | 0 | 3 | 10000-15000 | 4-8 | Single | 4 | 24 | Low | 0 | 5e+04 |
6750 | 219 | 12869 | 10405 | 12869 | 12441 | 0 | 2 | 7500-10000 | 4-8 | Single | 2 | 24 | Low | 0 | 5e+05 |
6803 | 831 | 0 | 11080 | 0 | 12430 | 1 | 2 | 7500-10000 | 4-8 | Single | 3 | 20 | Low | 0 | 1e+05 |
6811 | 1602 | 164 | 16650 | 164 | 19895 | 0 | 3 | 15000+ | 0-3 | Single | 2 | 19 | Low | 0 | 5e+04 |
6846 | 11697 | 52914 | 12411 | 52914 | 14616 | 0 | 2 | 15000+ | 4-8 | Single | 1 | 23 | Low | 0 | 5e+04 |
6881 | 14165 | 309 | 10380 | 309 | 12241 | 0 | 2 | 15000+ | 44-53 | Married | 2 | 65 | High | 0 | 3e+05 |
6892 | 7452 | 15066 | 11131 | 15066 | 13077 | 0 | 2 | 7500-10000 | 4-8 | Single | 1 | 22 | Low | 0 | 3e+05 |
6900 | 14871 | 25951 | 15260 | 25951 | 17934 | 0 | 2 | 0-7500 | 0-3 | Single | 2 | 19 | Low | 0 | 5e+05 |
6902 | 12548 | 4021 | 13404 | 4021 | 14333 | 0 | 3 | 0-7500 | 4-8 | Single | 1 | 24 | Medium | 0 | 5e+04 |
6933 | 1131 | 205 | 15615 | 205 | 18445 | 2 | 3 | 15000+ | 4-8 | Single | 1 | 24 | Low | 0 | 1e+05 |
6935 | 9680 | 140 | 17104 | 140 | 19887 | 0 | 3 | 10000-15000 | 0-3 | Single | 3 | 19 | Low | 0 | 5e+04 |
6946 | 619 | 3619 | 12348 | 3619 | 13742 | 0 | 2 | 0-7500 | 4-8 | Single | 1 | 23 | Low | 0 | 1e+05 |
6962 | 9846 | 294 | 12411 | 294 | 14616 | 0 | 2 | 15000+ | 4-8 | Single | 1 | 24 | Low | 0 | 5e+04 |
6978 | 11740 | 0 | 8899 | 0 | 12063 | 2 | 1 | 15000+ | 4-8 | Single | 1 | 20 | Low | 0 | 5e+05 |
6985 | 11258 | 161 | 11131 | 161 | 13077 | 0 | 2 | 7500-10000 | 4-8 | Single | 1 | 24 | Low | 0 | 5e+04 |
6997 | 14301 | 1156 | 12145 | 1156 | 13234 | 0 | 3 | 15000+ | 4-8 | Single | 4 | 23 | Low | 0 | 5e+04 |
7017 | 9996 | 1000 | 12418 | 1000 | 14536 | 0 | 3 | 15000+ | 4-8 | Single | 3 | 20 | Medium | 0 | 3e+05 |
7046 | 1861 | 3373 | 14036 | 3373 | 15204 | 0 | 3 | 10000-15000 | 4-8 | Single | 1 | 20 | Medium | 0 | 1e+05 |
7050 | 2568 | 129 | 11917 | 129 | 13900 | 0 | 2 | 10000-15000 | 4-8 | Single | 3 | 24 | Low | 0 | 1e+05 |
7064 | 2000 | 92081 | 17720 | 92081 | 19988 | 1 | 2 | 15000+ | 0-3 | Single | 1 | 19 | Low | 0 | 5e+05 |
7072 | 12291 | 0 | 8521 | 0 | 16006 | 0 | 1 | 0-7500 | 64-73 | Married | 2 | 86 | Medium | 0 | 5e+04 |
7090 | 8087 | 40238 | 12411 | 40238 | 14616 | 0 | 2 | 15000+ | 4-8 | Single | 1 | 23 | Low | 0 | 3e+05 |
7099 | 3962 | 197 | 12348 | 197 | 13742 | 0 | 2 | 0-7500 | 4-8 | Single | 1 | 24 | Low | 0 | 5e+04 |
7121 | 12430 | 0 | 11131 | 0 | 13077 | 0 | 2 | 7500-10000 | 4-8 | Single | 1 | 22 | Low | 0 | 5e+04 |
7130 | 12123 | 4936 | 10908 | 4936 | 12819 | 1 | 1 | 0-7500 | 0-3 | Single | 1 | 19 | Low | 0 | 5e+04 |
7140 | 10714 | 22940 | 11188 | 22940 | 12688 | 0 | 2 | 15000+ | 4-8 | Single | 4 | 20 | Low | 0 | 3e+05 |
7179 | 4028 | 2541 | 11601 | 2541 | 13905 | 0 | 2 | 15000+ | 4-8 | Single | 2 | 22 | Low | 0 | 5e+04 |
7181 | 7580 | 13249 | 11656 | 13249 | 12655 | 0 | 2 | 10000-15000 | 4-8 | Single | 4 | 23 | Low | 0 | 1e+05 |
7189 | 3126 | 33126 | 12021 | 33126 | 13036 | 1 | 2 | 7500-10000 | 4-8 | Single | 1 | 24 | Low | 0 | 1e+05 |
7191 | 4509 | 9654 | 11656 | 9654 | 12655 | 0 | 2 | 10000-15000 | 4-8 | Single | 4 | 24 | Low | 0 | 1e+05 |
7208 | 14150 | 5554 | 17104 | 5554 | 19887 | 0 | 3 | 10000-15000 | 0-3 | Single | 3 | 19 | Low | 0 | 5e+04 |
7218 | 14459 | 0 | 10405 | 0 | 12441 | 0 | 2 | 7500-10000 | 4-8 | Single | 2 | 20 | Low | 0 | 1e+05 |
7229 | 9803 | 3986 | 11576 | 3986 | 12704 | 2 | 3 | 0-7500 | 9-13 | Single | 3 | 26 | Medium | 0 | 5e+04 |
7250 | 315 | 495 | 9545 | 495 | 17846 | 0 | 1 | 10000-15000 | 64-73 | Married | 1 | 87 | Low | 0 | 5e+04 |
7251 | 7356 | 1746 | 17057 | 1746 | 21705 | 2 | 2 | 7500-10000 | 0-3 | Single | 1 | 19 | Medium | 0 | 1e+05 |
7252 | 5256 | 11685 | 11828 | 11685 | 14410 | 0 | 2 | 7500-10000 | 34-43 | Single | 3 | 51 | Medium | 0 | 3e+05 |
7312 | 9446 | 681 | 12354 | 681 | 13894 | 1 | 2 | 15000+ | 4-8 | Single | 3 | 23 | Low | 0 | 3e+05 |
7316 | 5127 | 5619 | 12021 | 5619 | 13036 | 1 | 2 | 7500-10000 | 4-8 | Single | 1 | 21 | Medium | 0 | 1e+05 |
7362 | 2346 | 0 | 14235 | 0 | 15881 | 0 | 2 | 0-7500 | 34-43 | Single | 1 | 50 | High | 0 | 5e+04 |
7363 | 12882 | 306 | 14549 | 306 | 15197 | 1 | 3 | 15000+ | 4-8 | Single | 1 | 24 | Low | 0 | 5e+04 |
7377 | 11567 | 11085 | 13120 | 11085 | 14465 | 0 | 3 | 10000-15000 | 4-8 | Single | 2 | 20 | Low | 0 | 1e+05 |
7379 | 14373 | 3222 | 12145 | 3222 | 13234 | 0 | 3 | 15000+ | 4-8 | Single | 4 | 20 | Low | 0 | 5e+04 |
7391 | 4191 | 437 | 11601 | 437 | 13905 | 0 | 2 | 15000+ | 4-8 | Single | 2 | 22 | Medium | 0 | 5e+05 |
7415 | 4511 | 2075 | 13403 | 2075 | 14571 | 1 | 2 | 15000+ | 4-8 | Single | 1 | 21 | High | 0 | 5e+04 |
7425 | 8870 | 241 | 17812 | 241 | 20912 | 0 | 3 | 15000+ | 0-3 | Single | 1 | 19 | Low | 0 | 5e+04 |
7431 | 9742 | 65 | 14036 | 65 | 15204 | 0 | 3 | 10000-15000 | 4-8 | Single | 1 | 20 | Low | 0 | 3e+05 |
7445 | 12519 | 179 | 11295 | 179 | 12976 | 0 | 3 | 7500-10000 | 4-8 | Single | 2 | 20 | Low | 0 | 5e+04 |
7452 | 4558 | 0 | 12411 | 0 | 14616 | 0 | 2 | 15000+ | 4-8 | Single | 1 | 21 | High | 0 | 1e+05 |
7485 | 6122 | 0 | 11629 | 0 | 13586 | 2 | 2 | 15000+ | 9-13 | Single | 1 | 29 | High | 0 | 5e+04 |
7493 | 3007 | 1550 | 13971 | 1550 | 14453 | 1 | 3 | 10000-15000 | 4-8 | Single | 3 | 22 | Medium | 0 | 1e+05 |
7503 | 7825 | 0 | 13281 | 0 | 13636 | 1 | 2 | 0-7500 | 24-33 | Single | 2 | 44 | High | 0 | 5e+04 |
7511 | 9964 | 1506 | 11188 | 1506 | 12688 | 0 | 2 | 15000+ | 4-8 | Single | 4 | 21 | Low | 0 | 1e+05 |
7587 | 10272 | 11231 | 12145 | 11231 | 13234 | 0 | 3 | 15000+ | 4-8 | Single | 4 | 21 | Low | 0 | 5e+04 |
7600 | 2381 | 102 | 12587 | 102 | 12616 | 1 | 2 | 10000-15000 | 4-8 | Single | 4 | 23 | Medium | 0 | 1e+05 |
7633 | 14750 | 120 | 11891 | 120 | 12408 | 1 | 2 | 15000+ | 34-43 | Married | 3 | 53 | Low | 0 | 5e+04 |
7677 | 8694 | 0 | 11656 | 0 | 12655 | 0 | 2 | 10000-15000 | 4-8 | Single | 4 | 22 | High | 0 | 1e+05 |
7694 | 732 | 0 | 12529 | 0 | 13862 | 1 | 2 | 15000+ | 4-8 | Single | 2 | 20 | Low | 0 | 5e+04 |
7699 | 7523 | 0 | 9441 | 0 | 12234 | 0 | 1 | 0-7500 | 0-3 | Single | 2 | 19 | Low | 0 | 1e+05 |
7729 | 4147 | 0 | 7574 | 0 | 15265 | 0 | 1 | 7500-10000 | 64-73 | Married | 3 | 80 | High | 0 | 5e+05 |
7756 | 3638 | 1590 | 11080 | 1590 | 12430 | 1 | 2 | 7500-10000 | 4-8 | Single | 3 | 23 | Low | 0 | 1e+05 |
7765 | 2392 | 330 | 12348 | 330 | 13742 | 0 | 2 | 0-7500 | 4-8 | Single | 1 | 22 | Low | 0 | 1e+05 |
7788 | 3302 | 14612 | 11131 | 14612 | 13077 | 0 | 2 | 7500-10000 | 4-8 | Single | 1 | 20 | Low | 0 | 1e+05 |
7789 | 14978 | 142 | 12411 | 142 | 14616 | 0 | 2 | 15000+ | 4-8 | Single | 1 | 20 | High | 0 | 3e+05 |
7804 | 1275 | 5496 | 10196 | 5496 | 12196 | 1 | 1 | 0-7500 | 0-3 | Single | 2 | 19 | Low | 0 | 1e+05 |
7817 | 8502 | 49772 | 14169 | 49772 | 14420 | 1 | 3 | 10000-15000 | 4-8 | Single | 2 | 24 | Low | 0 | 5e+05 |
7862 | 9438 | 0 | 12419 | 0 | 13240 | 0 | 2 | 10000-15000 | 24-33 | Single | 4 | 41 | Medium | 0 | 5e+04 |
7988 | 13134 | 3143 | 12354 | 3143 | 13894 | 1 | 2 | 15000+ | 4-8 | Single | 3 | 22 | Medium | 0 | 1e+05 |
8007 | 5492 | 244 | 14934 | 244 | 17800 | 0 | 3 | 7500-10000 | 0-3 | Single | 2 | 19 | Low | 0 | 5e+04 |
8015 | 8116 | 80 | 18066 | 80 | 18050 | 1 | 3 | 10000-15000 | 0-3 | Single | 4 | 19 | Low | 0 | 3e+05 |
8041 | 177 | 2589 | 8899 | 2589 | 12063 | 2 | 1 | 15000+ | 4-8 | Single | 1 | 24 | High | 0 | 1e+05 |
8042 | 12568 | 100000 | 12082 | 118833 | 12649 | 1 | 2 | 15000+ | 4-8 | Single | 4 | 23 | Medium | 0 | 3e+05 |
8048 | 14144 | 605 | 10260 | 605 | 12469 | 0 | 2 | 7500-10000 | 4-8 | Single | 3 | 21 | Low | 0 | 1e+05 |
8063 | 3226 | 45361 | 13342 | 45361 | 13625 | 1 | 3 | 0-7500 | 4-8 | Single | 3 | 21 | Low | 0 | 3e+05 |
8066 | 8446 | 1644 | 13404 | 1644 | 14333 | 0 | 3 | 0-7500 | 4-8 | Single | 1 | 20 | Low | 0 | 5e+04 |
8075 | 7377 | 4900 | 12086 | 4900 | 13868 | 0 | 2 | 10000-15000 | 4-8 | Single | 2 | 23 | Low | 0 | 5e+05 |
8105 | 4633 | 7753 | 12930 | 7753 | 14577 | 0 | 2 | 10000-15000 | 4-8 | Single | 1 | 23 | Low | 0 | 3e+05 |
8140 | 10640 | 13863 | 12937 | 13863 | 14497 | 0 | 3 | 10000-15000 | 4-8 | Single | 3 | 20 | Medium | 0 | 1e+05 |
8182 | 2954 | 3817 | 16650 | 3817 | 19895 | 0 | 3 | 15000+ | 0-3 | Single | 2 | 19 | Low | 0 | 1e+05 |
8252 | 14926 | 1360 | 12291 | 1360 | 13063 | 1 | 2 | 0-7500 | 4-8 | Single | 3 | 20 | Medium | 0 | 5e+04 |
8255 | 13794 | 100000 | 15979 | 352088 | 19024 | 0 | 2 | 10000-15000 | 0-3 | Single | 2 | 19 | Medium | 0 | 3e+05 |
8293 | 520 | 0 | 9105 | 0 | 12236 | 0 | 1 | 7500-10000 | 0-3 | Single | 1 | 19 | Medium | 0 | 5e+05 |
8294 | 9700 | 2188 | 11439 | 2188 | 13937 | 0 | 2 | 15000+ | 4-8 | Single | 3 | 24 | Low | 0 | 5e+04 |
8304 | 1298 | 45353 | 11439 | 45353 | 13937 | 0 | 2 | 15000+ | 4-8 | Single | 3 | 22 | Low | 0 | 5e+04 |
8317 | 1261 | 14026 | 12653 | 14026 | 13199 | 0 | 3 | 10000-15000 | 4-8 | Single | 4 | 24 | Low | 0 | 5e+04 |
8325 | 1291 | 132 | 12901 | 132 | 14435 | 2 | 2 | 0-7500 | 4-8 | Single | 4 | 21 | Low | 0 | 3e+05 |
8384 | 6393 | 0 | 9162 | 0 | 17894 | 0 | 1 | 15000+ | 64-73 | Married | 1 | 86 | High | 0 | 5e+04 |
8388 | 3406 | 9590 | 12587 | 9590 | 12616 | 1 | 2 | 10000-15000 | 4-8 | Single | 4 | 23 | Low | 0 | 1e+05 |
8390 | 9658 | 17089 | 13776 | 17089 | 15251 | 0 | 2 | 10000-15000 | 24-33 | Single | 1 | 42 | Low | 0 | 3e+05 |
8440 | 8052 | 791 | 12594 | 791 | 14503 | 0 | 3 | 15000+ | 4-8 | Single | 2 | 24 | Low | 0 | 3e+05 |
8476 | 4203 | 48 | 11917 | 48 | 13900 | 0 | 2 | 10000-15000 | 4-8 | Single | 3 | 21 | Low | 0 | 5e+04 |
8484 | 11792 | 0 | 10405 | 0 | 12441 | 0 | 2 | 7500-10000 | 4-8 | Single | 2 | 20 | Low | 0 | 3e+05 |
8502 | 156 | 41558 | 15338 | 41558 | 19075 | 0 | 2 | 15000+ | 0-3 | Single | 2 | 19 | Low | 0 | 1e+05 |
8554 | 12913 | 0 | 13258 | 0 | 16863 | 2 | 2 | 15000+ | 4-8 | Single | 3 | 20 | Medium | 0 | 5e+04 |
8600 | 5979 | 0 | 13509 | 0 | 15312 | 2 | 2 | 10000-15000 | 4-8 | Single | 4 | 20 | High | 0 | 5e+04 |
8610 | 14702 | 7258 | 12418 | 7258 | 14536 | 0 | 3 | 15000+ | 4-8 | Single | 3 | 24 | Medium | 0 | 5e+04 |
8633 | 4980 | 10952 | 13410 | 10952 | 14491 | 1 | 3 | 15000+ | 4-8 | Single | 3 | 20 | Low | 0 | 1e+05 |
8641 | 12114 | 48747 | 8798 | 48747 | 17017 | 0 | 1 | 10000-15000 | 64-73 | Married | 3 | 85 | High | 0 | 5e+04 |
8690 | 12685 | 125 | 12445 | 125 | 13019 | 0 | 2 | 10000-15000 | 34-43 | Married | 1 | 54 | Low | 0 | 5e+04 |
8692 | 3259 | 2648 | 11131 | 2648 | 13077 | 0 | 2 | 7500-10000 | 4-8 | Single | 1 | 23 | Medium | 0 | 5e+05 |
8705 | 3188 | 4983 | 13600 | 4983 | 14458 | 1 | 3 | 15000+ | 4-8 | Single | 2 | 20 | Low | 0 | 1e+05 |
8709 | 3620 | 8208 | 16333 | 8208 | 19059 | 1 | 2 | 15000+ | 0-3 | Single | 3 | 19 | Low | 0 | 3e+05 |
8748 | 1877 | 0 | 9879 | 0 | 12588 | 2 | 1 | 10000-15000 | 24-33 | Single | 1 | 43 | Medium | 0 | 5e+05 |
8757 | 11135 | 14867 | 12594 | 14867 | 14503 | 0 | 3 | 15000+ | 4-8 | Single | 2 | 21 | Low | 0 | 5e+04 |
8766 | 14451 | 43312 | 12411 | 43312 | 14616 | 0 | 2 | 15000+ | 4-8 | Single | 1 | 21 | Medium | 0 | 1e+05 |
8781 | 5907 | 1710 | 12084 | 1710 | 13639 | 0 | 3 | 7500-10000 | 4-8 | Single | 1 | 22 | Medium | 0 | 1e+05 |
8811 | 8022 | 0 | 10553 | 0 | 14806 | 2 | 1 | 7500-10000 | 0-3 | Single | 1 | 19 | High | 0 | 5e+05 |
8817 | 4261 | 31555 | 10260 | 31555 | 12469 | 0 | 2 | 7500-10000 | 4-8 | Single | 3 | 23 | Low | 0 | 5e+04 |
8837 | 3954 | 126 | 11295 | 126 | 12976 | 0 | 3 | 7500-10000 | 4-8 | Single | 2 | 20 | Medium | 0 | 1e+05 |
8845 | 2482 | 823 | 16250 | 823 | 17920 | 1 | 2 | 0-7500 | 0-3 | Single | 3 | 19 | Medium | 0 | 3e+05 |
8872 | 11047 | 168 | 16728 | 168 | 18106 | 0 | 3 | 10000-15000 | 0-3 | Single | 4 | 19 | Medium | 0 | 5e+04 |
8902 | 5190 | 0 | 10815 | 0 | 12153 | 2 | 2 | 0-7500 | 9-13 | Single | 2 | 27 | Low | 0 | 5e+04 |
8932 | 2899 | 3105 | 12084 | 3105 | 13639 | 0 | 3 | 7500-10000 | 4-8 | Single | 1 | 24 | Low | 0 | 3e+05 |
8944 | 14946 | 100000 | 12021 | 122399 | 13036 | 1 | 2 | 7500-10000 | 4-8 | Single | 1 | 24 | Low | 0 | 5e+05 |
8948 | 3371 | 155 | 9879 | 155 | 12588 | 2 | 1 | 10000-15000 | 24-33 | Single | 1 | 41 | High | 0 | 5e+04 |
9039 | 7910 | 138 | 11917 | 138 | 13900 | 0 | 2 | 10000-15000 | 4-8 | Single | 3 | 20 | Low | 0 | 5e+04 |
9045 | 14057 | 4260 | 15756 | 4260 | 19067 | 0 | 2 | 10000-15000 | 0-3 | Single | 3 | 19 | Low | 0 | 1e+05 |
9071 | 6868 | 640 | 12083 | 640 | 12442 | 0 | 3 | 0-7500 | 4-8 | Single | 4 | 20 | Low | 0 | 5e+04 |
9090 | 1308 | 100000 | 10963 | 117192 | 13635 | 1 | 1 | 15000+ | 0-3 | Single | 1 | 19 | Medium | 0 | 3e+05 |
9111 | 11778 | 129 | 14549 | 129 | 15197 | 1 | 3 | 15000+ | 4-8 | Single | 1 | 20 | Low | 0 | 1e+05 |
9133 | 11486 | 0 | 16333 | 0 | 19059 | 1 | 2 | 15000+ | 0-3 | Single | 3 | 19 | Low | 0 | 1e+05 |
9135 | 859 | 81268 | 11439 | 81268 | 13937 | 0 | 2 | 15000+ | 4-8 | Single | 3 | 20 | Low | 0 | 1e+05 |
9181 | 12917 | 4282 | 12901 | 4282 | 13013 | 1 | 2 | 15000+ | 34-43 | Married | 1 | 59 | Medium | 0 | 5e+05 |
9191 | 8868 | 51386 | 13403 | 51386 | 14571 | 1 | 2 | 15000+ | 4-8 | Single | 1 | 24 | Low | 0 | 5e+05 |
9241 | 1134 | 0 | 9886 | 0 | 12977 | 0 | 1 | 10000-15000 | 0-3 | Single | 2 | 19 | High | 0 | 5e+04 |
9245 | 2267 | 8533 | 12082 | 8533 | 12649 | 1 | 2 | 15000+ | 4-8 | Single | 4 | 24 | Medium | 0 | 5e+04 |
9274 | 4293 | 386 | 12465 | 386 | 13033 | 1 | 2 | 0-7500 | 4-8 | Single | 2 | 21 | Medium | 0 | 5e+04 |
9279 | 11598 | 777 | 15976 | 777 | 18710 | 0 | 3 | 7500-10000 | 0-3 | Single | 1 | 19 | Low | 0 | 1e+05 |
9292 | 12383 | 41523 | 12411 | 41523 | 14616 | 0 | 2 | 15000+ | 4-8 | Single | 1 | 21 | Low | 0 | 5e+05 |
9347 | 3168 | 56744 | 11131 | 56744 | 13077 | 0 | 2 | 7500-10000 | 4-8 | Single | 1 | 21 | High | 0 | 5e+04 |
9392 | 8751 | 120 | 11885 | 120 | 12273 | 0 | 2 | 0-7500 | 34-43 | Married | 1 | 52 | Low | 0 | 5e+04 |
9415 | 6070 | 100000 | 13049 | 154284 | 13597 | 1 | 3 | 7500-10000 | 4-8 | Single | 1 | 23 | Medium | 0 | 5e+05 |
9423 | 1987 | 419 | 12411 | 419 | 14616 | 0 | 2 | 15000+ | 4-8 | Single | 1 | 20 | High | 0 | 1e+05 |
9441 | 9025 | 3652 | 11542 | 3652 | 13074 | 0 | 2 | 0-7500 | 4-8 | Single | 2 | 21 | Medium | 0 | 5e+04 |
9476 | 12148 | 0 | 9441 | 0 | 12234 | 0 | 1 | 0-7500 | 0-3 | Single | 2 | 19 | Medium | 0 | 5e+05 |
9580 | 11269 | 365 | 12348 | 365 | 13742 | 0 | 2 | 0-7500 | 4-8 | Single | 1 | 24 | Low | 0 | 5e+05 |
9590 | 4692 | 898 | 10105 | 898 | 13001 | 1 | 1 | 15000+ | 0-3 | Single | 3 | 19 | Low | 0 | 3e+05 |
9594 | 12399 | 95 | 15047 | 95 | 17975 | 0 | 2 | 0-7500 | 0-3 | Single | 3 | 19 | Low | 0 | 1e+05 |
9619 | 11591 | 2336 | 10405 | 2336 | 12441 | 0 | 2 | 7500-10000 | 4-8 | Single | 2 | 24 | Medium | 0 | 5e+04 |
9689 | 742 | 6675 | 11237 | 6675 | 12402 | 1 | 2 | 7500-10000 | 4-8 | Single | 2 | 20 | Medium | 0 | 3e+05 |
9715 | 12673 | 43871 | 11656 | 43871 | 12655 | 0 | 2 | 10000-15000 | 4-8 | Single | 4 | 20 | Low | 0 | 3e+05 |
9742 | 5064 | 15991 | 15158 | 15991 | 15157 | 1 | 3 | 10000-15000 | 4-8 | Single | 1 | 23 | Low | 0 | 5e+04 |
9760 | 1681 | 1317 | 13472 | 1317 | 15244 | 0 | 3 | 15000+ | 4-8 | Single | 1 | 20 | Low | 0 | 3e+05 |
9774 | 5269 | 55828 | 13963 | 55828 | 14532 | 1 | 2 | 10000-15000 | 4-8 | Single | 1 | 22 | Low | 0 | 1e+05 |
9792 | 5157 | 1282 | 12529 | 1282 | 13862 | 1 | 2 | 15000+ | 4-8 | Single | 2 | 24 | Medium | 0 | 1e+05 |
9807 | 13788 | 0 | 9357 | 0 | 13041 | 0 | 1 | 15000+ | 0-3 | Single | 3 | 19 | High | 0 | 5e+05 |
9812 | 10772 | 49534 | 16334 | 49534 | 18748 | 0 | 3 | 0-7500 | 0-3 | Single | 3 | 19 | Medium | 0 | 5e+05 |
9827 | 10045 | 18154 | 13120 | 18154 | 14465 | 0 | 3 | 10000-15000 | 4-8 | Single | 2 | 20 | Medium | 0 | 5e+04 |
9863 | 9165 | 135 | 11131 | 135 | 13077 | 0 | 2 | 7500-10000 | 4-8 | Single | 1 | 23 | Low | 0 | 5e+04 |
9868 | 12337 | 14 | 10718 | 14 | 12955 | 2 | 2 | 15000+ | 9-13 | Single | 3 | 27 | Low | 0 | 5e+04 |
9921 | 11951 | 6566 | 11917 | 6566 | 13900 | 0 | 2 | 10000-15000 | 4-8 | Single | 3 | 21 | Low | 0 | 1e+05 |
9940 | 5132 | 2776 | 13472 | 2776 | 15244 | 0 | 3 | 15000+ | 4-8 | Single | 1 | 22 | Low | 0 | 5e+04 |
9944 | 13860 | 0 | 11048 | 0 | 12097 | 0 | 2 | 0-7500 | 44-53 | Married | 1 | 64 | Medium | 0 | 3e+05 |
9991 | 8572 | 0 | 12086 | 0 | 13868 | 0 | 2 | 10000-15000 | 4-8 | Single | 2 | 20 | High | 0 | 5e+04 |
10 | 6386 | 2312 | 11295 | 2312 | 12976 | 0 | 3 | 7500-10000 | 4-8 | Single | 2 | 20 | Low | 0 | 5e+04 |
155 | 6927 | 2199 | 12082 | 2199 | 12649 | 1 | 2 | 15000+ | 4-8 | Single | 4 | 23 | Medium | 0 | 5e+05 |
160 | 5309 | 0 | 12086 | 0 | 13868 | 0 | 2 | 10000-15000 | 4-8 | Single | 2 | 21 | Medium | 0 | 1e+05 |
160 | 11547 | 46106 | 12084 | 46106 | 13639 | 0 | 3 | 7500-10000 | 4-8 | Single | 1 | 24 | Low | 0 | 1e+05 |
170 | 1998 | 3381 | 14549 | 3381 | 15197 | 1 | 3 | 15000+ | 4-8 | Single | 1 | 24 | Medium | 0 | 3e+05 |
199 | 8392 | 62945 | 13776 | 62945 | 15251 | 0 | 2 | 10000-15000 | 24-33 | Single | 1 | 45 | Low | 0 | 5e+05 |
222 | 12590 | 43549 | 13404 | 43549 | 14333 | 0 | 3 | 0-7500 | 4-8 | Single | 1 | 21 | Low | 0 | 5e+04 |
228 | 9054 | 8137 | 12145 | 8137 | 13234 | 0 | 3 | 15000+ | 4-8 | Single | 4 | 22 | Low | 0 | 5e+05 |
234 | 13829 | 48829 | 13052 | 48829 | 13826 | 1 | 2 | 10000-15000 | 4-8 | Single | 2 | 21 | Low | 0 | 5e+04 |
253 | 3009 | 2115 | 11601 | 2115 | 13905 | 0 | 2 | 15000+ | 4-8 | Single | 2 | 24 | Low | 0 | 3e+05 |
311 | 2430 | 24703 | 11131 | 24703 | 13077 | 0 | 2 | 7500-10000 | 4-8 | Single | 1 | 21 | Low | 0 | 1e+05 |
322 | 2562 | 0 | 12833 | 0 | 15112 | 0 | 2 | 7500-10000 | 34-43 | Single | 1 | 54 | Low | 0 | 5e+04 |
359 | 12495 | 0 | 11381 | 0 | 13103 | 0 | 2 | 0-7500 | 4-8 | Single | 3 | 24 | Low | 0 | 5e+05 |
425 | 2623 | 36750 | 12084 | 36750 | 13639 | 0 | 3 | 7500-10000 | 4-8 | Single | 1 | 22 | Low | 0 | 1e+05 |
458 | 8477 | 4458 | 13403 | 4458 | 14571 | 1 | 2 | 15000+ | 4-8 | Single | 1 | 21 | Low | 0 | 5e+04 |
466 | 6452 | 9569 | 12594 | 9569 | 14503 | 0 | 3 | 15000+ | 4-8 | Single | 2 | 23 | Low | 0 | 5e+04 |
507 | 5816 | 37896 | 11131 | 37896 | 13077 | 0 | 2 | 7500-10000 | 4-8 | Single | 1 | 23 | Low | 0 | 5e+04 |
549 | 11096 | 94 | 11601 | 94 | 13905 | 0 | 2 | 15000+ | 4-8 | Single | 2 | 23 | Medium | 0 | 5e+04 |
567 | 5976 | 20883 | 11601 | 20883 | 13905 | 0 | 2 | 15000+ | 4-8 | Single | 2 | 22 | High | 0 | 1e+05 |
580 | 9359 | 0 | 9202 | 0 | 15957 | 1 | 1 | 0-7500 | 64-73 | Married | 2 | 87 | Medium | 0 | 3e+05 |
581 | 12571 | 0 | 13438 | 0 | 14624 | 0 | 2 | 10000-15000 | 34-43 | Single | 4 | 58 | Low | 0 | 5e+04 |
584 | 14769 | 0 | 11917 | 0 | 13900 | 0 | 2 | 10000-15000 | 4-8 | Single | 3 | 21 | Low | 0 | 1e+05 |
601 | 9459 | 106 | 10430 | 106 | 12155 | 2 | 2 | 7500-10000 | 9-13 | Single | 1 | 26 | Low | 0 | 5e+04 |
608 | 6741 | 3241 | 13472 | 3241 | 15244 | 0 | 3 | 15000+ | 4-8 | Single | 1 | 22 | Low | 0 | 1e+05 |
660 | 13300 | 0 | 12411 | 0 | 14616 | 0 | 2 | 15000+ | 4-8 | Single | 1 | 22 | Low | 0 | 3e+05 |
696 | 8004 | 3909 | 11188 | 3909 | 12688 | 0 | 2 | 15000+ | 4-8 | Single | 4 | 22 | Low | 0 | 5e+04 |
719 | 5937 | 0 | 12930 | 0 | 14577 | 0 | 2 | 10000-15000 | 4-8 | Single | 1 | 20 | Medium | 0 | 5e+04 |
788 | 9747 | 4987 | 11131 | 4987 | 13077 | 0 | 2 | 7500-10000 | 4-8 | Single | 1 | 20 | Medium | 0 | 3e+05 |
806 | 3380 | 12762 | 13410 | 12762 | 14491 | 1 | 3 | 15000+ | 4-8 | Single | 3 | 20 | Low | 0 | 5e+04 |
808 | 14403 | 404 | 11188 | 404 | 12688 | 0 | 2 | 15000+ | 4-8 | Single | 4 | 20 | Low | 0 | 3e+05 |
852 | 1798 | 0 | 11080 | 0 | 12430 | 1 | 2 | 7500-10000 | 4-8 | Single | 3 | 24 | Medium | 0 | 5e+04 |
883 | 11809 | 151 | 11188 | 151 | 12688 | 0 | 2 | 15000+ | 4-8 | Single | 4 | 22 | Low | 0 | 5e+04 |
908 | 5522 | 0 | 12411 | 0 | 14616 | 0 | 2 | 15000+ | 4-8 | Single | 1 | 24 | High | 0 | 3e+05 |
959 | 5589 | 44420 | 14549 | 44420 | 15197 | 1 | 3 | 15000+ | 4-8 | Single | 1 | 22 | Medium | 0 | 1e+05 |
960 | 9085 | 0 | 12870 | 0 | 13857 | 1 | 2 | 10000-15000 | 4-8 | Single | 3 | 20 | Medium | 0 | 1e+05 |
992 | 5507 | 869 | 12418 | 869 | 14536 | 0 | 3 | 15000+ | 4-8 | Single | 3 | 20 | Medium | 0 | 5e+04 |
1013 | 10079 | 15494 | 12348 | 15494 | 13742 | 0 | 2 | 0-7500 | 4-8 | Single | 1 | 22 | Low | 0 | 5e+04 |
1059 | 1423 | 0 | 12411 | 0 | 14616 | 0 | 2 | 15000+ | 4-8 | Single | 1 | 23 | Medium | 0 | 1e+05 |
1095 | 10470 | 12350 | 12355 | 12350 | 13667 | 0 | 3 | 0-7500 | 4-8 | Single | 3 | 20 | Low | 0 | 5e+04 |
1121 | 12043 | 1206 | 11086 | 1206 | 13016 | 0 | 2 | 7500-10000 | 24-33 | Single | 2 | 42 | High | 0 | 1e+05 |
1123 | 13141 | 128 | 14036 | 128 | 15204 | 0 | 3 | 10000-15000 | 4-8 | Single | 1 | 22 | Low | 0 | 5e+04 |
1162 | 12613 | 535 | 12930 | 535 | 14577 | 0 | 2 | 10000-15000 | 4-8 | Single | 1 | 20 | Low | 0 | 5e+04 |
1167 | 75 | 9966 | 12145 | 9966 | 13234 | 0 | 3 | 15000+ | 4-8 | Single | 4 | 21 | Medium | 0 | 3e+05 |
1176 | 12198 | 2766 | 11295 | 2766 | 12976 | 0 | 3 | 7500-10000 | 4-8 | Single | 2 | 20 | Low | 0 | 1e+05 |
1196 | 4300 | 0 | 8923 | 0 | 16979 | 0 | 1 | 10000-15000 | 64-73 | Married | 2 | 86 | Medium | 0 | 3e+05 |
1214 | 5681 | 120 | 13049 | 120 | 13597 | 1 | 3 | 7500-10000 | 4-8 | Single | 1 | 21 | Low | 0 | 5e+04 |
1249 | 3203 | 120 | 11920 | 120 | 13275 | 0 | 2 | 15000+ | 24-33 | Single | 4 | 40 | Medium | 0 | 5e+04 |
1256 | 486 | 219 | 13907 | 219 | 14465 | 1 | 2 | 10000-15000 | 24-33 | Single | 2 | 45 | Medium | 0 | 3e+05 |
1277 | 2649 | 4915 | 15207 | 4915 | 17502 | 2 | 3 | 10000-15000 | 4-8 | Single | 2 | 20 | Low | 0 | 5e+04 |
1312 | 764 | 1375 | 10405 | 1375 | 12441 | 0 | 2 | 7500-10000 | 4-8 | Single | 2 | 23 | Low | 0 | 1e+05 |
1350 | 4734 | 5837 | 10405 | 5837 | 12441 | 0 | 2 | 7500-10000 | 4-8 | Single | 2 | 22 | Medium | 0 | 3e+05 |
1379 | 11919 | 16582 | 11601 | 16582 | 13905 | 0 | 2 | 15000+ | 4-8 | Single | 2 | 23 | Low | 0 | 3e+05 |
1442 | 196 | 0 | 11439 | 0 | 13937 | 0 | 2 | 15000+ | 4-8 | Single | 3 | 20 | High | 0 | 1e+05 |
1462 | 6432 | 0 | 9992 | 0 | 13228 | 2 | 1 | 10000-15000 | 34-43 | Single | 2 | 55 | High | 0 | 1e+05 |
1493 | 9315 | 1592 | 11860 | 1592 | 12481 | 0 | 2 | 0-7500 | 24-33 | Single | 4 | 46 | Low | 0 | 1e+05 |
1517 | 10042 | 26476 | 11381 | 26476 | 13103 | 0 | 2 | 0-7500 | 4-8 | Single | 3 | 21 | High | 0 | 1e+05 |
1543 | 13719 | 25510 | 11917 | 25510 | 13900 | 0 | 2 | 10000-15000 | 4-8 | Single | 3 | 23 | Low | 0 | 3e+05 |
1672 | 8125 | 25870 | 13335 | 25870 | 13699 | 1 | 2 | 0-7500 | 4-8 | Single | 1 | 22 | Low | 0 | 1e+05 |
1678 | 13775 | 0 | 11601 | 0 | 13905 | 0 | 2 | 15000+ | 4-8 | Single | 2 | 20 | High | 0 | 1e+05 |
1710 | 11822 | 56022 | 11080 | 56022 | 12430 | 1 | 2 | 7500-10000 | 4-8 | Single | 3 | 21 | Low | 0 | 1e+05 |
1721 | 11192 | 120 | 12086 | 120 | 13868 | 0 | 2 | 10000-15000 | 4-8 | Single | 2 | 20 | Low | 0 | 5e+04 |
1765 | 5484 | 3898 | 13963 | 3898 | 14532 | 1 | 2 | 10000-15000 | 4-8 | Single | 1 | 23 | Low | 0 | 3e+05 |
1807 | 11300 | 123 | 13472 | 123 | 15244 | 0 | 3 | 15000+ | 4-8 | Single | 1 | 21 | Medium | 0 | 5e+04 |
1829 | 13317 | 16991 | 13120 | 16991 | 14465 | 0 | 3 | 10000-15000 | 4-8 | Single | 2 | 20 | Low | 0 | 1e+05 |
1843 | 9209 | 5921 | 12145 | 5921 | 13234 | 0 | 3 | 15000+ | 4-8 | Single | 4 | 20 | Low | 0 | 3e+05 |
1867 | 8501 | 0 | 9844 | 0 | 16772 | 1 | 1 | 0-7500 | 64-73 | Married | 1 | 85 | Low | 0 | 5e+05 |
1895 | 12963 | 16068 | 8445 | 16068 | 17062 | 0 | 1 | 15000+ | 64-73 | Married | 3 | 86 | High | 0 | 5e+04 |
1975 | 13502 | 92530 | 11917 | 92530 | 13900 | 0 | 2 | 10000-15000 | 4-8 | Single | 3 | 21 | Medium | 0 | 1e+05 |
1993 | 2844 | 52 | 11439 | 52 | 13937 | 0 | 2 | 15000+ | 4-8 | Single | 3 | 21 | Low | 0 | 1e+05 |
2008 | 14190 | 3450 | 11601 | 3450 | 13905 | 0 | 2 | 15000+ | 4-8 | Single | 2 | 24 | High | 0 | 1e+05 |
2050 | 8006 | 10385 | 13600 | 10385 | 14458 | 1 | 3 | 15000+ | 4-8 | Single | 2 | 23 | Low | 0 | 3e+05 |
2069 | 7881 | 120 | 13049 | 120 | 13597 | 1 | 3 | 7500-10000 | 4-8 | Single | 1 | 21 | Low | 0 | 5e+04 |
2084 | 1109 | 4228 | 11188 | 4228 | 12688 | 0 | 2 | 15000+ | 4-8 | Single | 4 | 20 | Medium | 0 | 5e+05 |
2119 | 7205 | 720 | 11381 | 720 | 13103 | 0 | 2 | 0-7500 | 4-8 | Single | 3 | 23 | High | 0 | 3e+05 |
2209 | 1479 | 1087 | 13120 | 1087 | 14465 | 0 | 3 | 10000-15000 | 4-8 | Single | 2 | 21 | Low | 0 | 5e+04 |
2221 | 510 | 89074 | 13472 | 89074 | 15244 | 0 | 3 | 15000+ | 4-8 | Single | 1 | 24 | Low | 0 | 1e+05 |
2342 | 13622 | 11236 | 12145 | 11236 | 13234 | 0 | 3 | 15000+ | 4-8 | Single | 4 | 23 | Low | 0 | 3e+05 |
2352 | 4571 | 0 | 7408 | 0 | 13898 | 0 | 1 | 7500-10000 | 64-73 | Married | 4 | 86 | Low | 0 | 5e+04 |
2357 | 2824 | 53393 | 12086 | 53393 | 13868 | 0 | 2 | 10000-15000 | 4-8 | Single | 2 | 24 | Medium | 0 | 5e+05 |
2377 | 11124 | 14799 | 12870 | 14799 | 13857 | 1 | 2 | 10000-15000 | 4-8 | Single | 3 | 20 | Low | 0 | 5e+04 |
2385 | 1659 | 120 | 13404 | 120 | 14333 | 0 | 3 | 0-7500 | 4-8 | Single | 1 | 22 | Low | 0 | 5e+04 |
2419 | 1040 | 0 | 12086 | 0 | 13868 | 0 | 2 | 10000-15000 | 4-8 | Single | 2 | 21 | Low | 0 | 1e+05 |
2441 | 4993 | 4861 | 11946 | 4861 | 13053 | 0 | 2 | 15000+ | 34-43 | Married | 1 | 57 | High | 0 | 3e+05 |
2496 | 929 | 1682 | 12348 | 1682 | 13742 | 0 | 2 | 0-7500 | 4-8 | Single | 1 | 24 | Low | 0 | 1e+05 |
2534 | 597 | 122 | 12594 | 122 | 14503 | 0 | 3 | 15000+ | 4-8 | Single | 2 | 23 | High | 0 | 5e+04 |
2569 | 5574 | 0 | 12465 | 0 | 13033 | 1 | 2 | 0-7500 | 4-8 | Single | 2 | 20 | Medium | 0 | 1e+05 |
2580 | 8202 | 4801 | 12086 | 4801 | 13868 | 0 | 2 | 10000-15000 | 4-8 | Single | 2 | 23 | Low | 0 | 5e+04 |
2590 | 2820 | 8 | 11188 | 8 | 12688 | 0 | 2 | 15000+ | 4-8 | Single | 4 | 22 | Medium | 0 | 5e+04 |
2607 | 11909 | 20050 | 13120 | 20050 | 14465 | 0 | 3 | 10000-15000 | 4-8 | Single | 2 | 20 | Medium | 0 | 3e+05 |
2663 | 1983 | 9415 | 14036 | 9415 | 15204 | 0 | 3 | 10000-15000 | 4-8 | Single | 1 | 22 | Medium | 0 | 5e+04 |
2672 | 5138 | 179 | 12930 | 179 | 14577 | 0 | 2 | 10000-15000 | 4-8 | Single | 1 | 22 | Medium | 0 | 5e+04 |
2695 | 9013 | 26491 | 14036 | 26491 | 15204 | 0 | 3 | 10000-15000 | 4-8 | Single | 1 | 20 | Medium | 0 | 1e+05 |
2703 | 2156 | 231 | 13120 | 231 | 14465 | 0 | 3 | 10000-15000 | 4-8 | Single | 2 | 20 | Low | 0 | 5e+05 |
2722 | 8029 | 703 | 14008 | 703 | 16780 | 2 | 2 | 10000-15000 | 4-8 | Single | 2 | 24 | High | 0 | 5e+04 |
2750 | 11370 | 0 | 12411 | 0 | 14616 | 0 | 2 | 15000+ | 4-8 | Single | 1 | 20 | Medium | 0 | 1e+05 |
2781 | 12112 | 0 | 12188 | 0 | 14581 | 0 | 2 | 15000+ | 24-33 | Single | 3 | 49 | Low | 0 | 1e+05 |
2794 | 12111 | 16353 | 12594 | 16353 | 14503 | 0 | 3 | 15000+ | 4-8 | Single | 2 | 21 | Medium | 0 | 1e+05 |
2805 | 8348 | 27246 | 14169 | 27246 | 14420 | 1 | 3 | 10000-15000 | 4-8 | Single | 2 | 21 | Low | 0 | 5e+04 |
2813 | 14580 | 385 | 13120 | 385 | 14465 | 0 | 3 | 10000-15000 | 4-8 | Single | 2 | 21 | High | 0 | 1e+05 |
2835 | 500 | 231 | 12145 | 231 | 13234 | 0 | 3 | 15000+ | 4-8 | Single | 4 | 22 | Low | 0 | 1e+05 |
2837 | 7811 | 0 | 12930 | 0 | 14577 | 0 | 2 | 10000-15000 | 4-8 | Single | 1 | 24 | Low | 0 | 5e+05 |
2880 | 2538 | 0 | 12021 | 0 | 13036 | 1 | 2 | 7500-10000 | 4-8 | Single | 1 | 20 | Low | 0 | 3e+05 |
2883 | 8321 | 1377 | 11917 | 1377 | 13900 | 0 | 2 | 10000-15000 | 4-8 | Single | 3 | 22 | Low | 0 | 3e+05 |
2893 | 13736 | 100000 | 11656 | 119700 | 12655 | 0 | 2 | 10000-15000 | 4-8 | Single | 4 | 23 | Low | 0 | 1e+05 |
2934 | 4897 | 2380 | 13410 | 2380 | 14491 | 1 | 3 | 15000+ | 4-8 | Single | 3 | 23 | Low | 0 | 5e+05 |
2937 | 9728 | 8501 | 12930 | 8501 | 14577 | 0 | 2 | 10000-15000 | 4-8 | Single | 1 | 23 | Low | 0 | 1e+05 |
3034 | 6403 | 44 | 11188 | 44 | 12688 | 0 | 2 | 15000+ | 4-8 | Single | 4 | 24 | Medium | 0 | 1e+05 |
3051 | 1403 | 3583 | 11601 | 3583 | 13905 | 0 | 2 | 15000+ | 4-8 | Single | 2 | 23 | Medium | 0 | 5e+04 |
3067 | 117 | 4368 | 14244 | 4368 | 15794 | 0 | 3 | 0-7500 | 34-43 | Single | 3 | 51 | Medium | 0 | 5e+04 |
3220 | 11693 | 253 | 13049 | 253 | 13597 | 1 | 3 | 7500-10000 | 4-8 | Single | 1 | 22 | Medium | 0 | 5e+04 |
3259 | 2938 | 0 | 12291 | 0 | 13063 | 1 | 2 | 0-7500 | 4-8 | Single | 3 | 22 | Medium | 0 | 1e+05 |
3286 | 2782 | 19106 | 12291 | 19106 | 13063 | 1 | 2 | 0-7500 | 4-8 | Single | 3 | 21 | Low | 0 | 1e+05 |
3351 | 11006 | 6216 | 12930 | 6216 | 14577 | 0 | 2 | 10000-15000 | 4-8 | Single | 1 | 22 | Low | 0 | 1e+05 |
3361 | 13104 | 0 | 13052 | 0 | 13826 | 1 | 2 | 10000-15000 | 4-8 | Single | 2 | 24 | Low | 0 | 5e+05 |
3385 | 3461 | 462 | 12411 | 462 | 14616 | 0 | 2 | 15000+ | 4-8 | Single | 1 | 23 | Medium | 0 | 1e+05 |
3399 | 11887 | 120 | 11138 | 120 | 13005 | 0 | 3 | 7500-10000 | 4-8 | Single | 3 | 20 | Medium | 0 | 3e+05 |
3408 | 14848 | 2087 | 12021 | 2087 | 13036 | 1 | 2 | 7500-10000 | 4-8 | Single | 1 | 20 | Low | 0 | 5e+05 |
3449 | 13635 | 120 | 10405 | 120 | 12441 | 0 | 2 | 7500-10000 | 4-8 | Single | 2 | 24 | High | 0 | 3e+05 |
3459 | 439 | 635 | 13120 | 635 | 14465 | 0 | 3 | 10000-15000 | 4-8 | Single | 2 | 23 | Low | 0 | 5e+04 |
3493 | 10012 | 120 | 12937 | 120 | 14497 | 0 | 3 | 10000-15000 | 4-8 | Single | 3 | 24 | Low | 0 | 5e+04 |
3508 | 9009 | 409 | 14036 | 409 | 15204 | 0 | 3 | 10000-15000 | 4-8 | Single | 1 | 23 | Low | 0 | 3e+05 |
3519 | 2179 | 2440 | 11542 | 2440 | 13074 | 0 | 2 | 0-7500 | 4-8 | Single | 2 | 21 | Medium | 0 | 1e+05 |
3530 | 14261 | 0 | 11237 | 0 | 12402 | 1 | 2 | 7500-10000 | 4-8 | Single | 2 | 24 | Low | 0 | 1e+05 |
3552 | 4206 | 14421 | 11295 | 14421 | 12976 | 0 | 3 | 7500-10000 | 4-8 | Single | 2 | 22 | Low | 0 | 5e+04 |
3553 | 13983 | 3530 | 12594 | 3530 | 14503 | 0 | 3 | 15000+ | 4-8 | Single | 2 | 22 | Low | 0 | 3e+05 |
3556 | 8072 | 120 | 12937 | 120 | 14497 | 0 | 3 | 10000-15000 | 4-8 | Single | 3 | 21 | Low | 0 | 5e+04 |
3599 | 13195 | 0 | 8874 | 0 | 14560 | 1 | 1 | 0-7500 | 64-73 | Married | 4 | 83 | Medium | 0 | 5e+05 |
3640 | 2890 | 16987 | 9844 | 16987 | 16772 | 1 | 1 | 0-7500 | 64-73 | Married | 1 | 86 | High | 0 | 5e+05 |
3675 | 5708 | 175 | 14950 | 175 | 17742 | 2 | 2 | 15000+ | 34-43 | Single | 4 | 58 | Low | 0 | 5e+04 |
3748 | 6740 | 7095 | 12086 | 7095 | 13868 | 0 | 2 | 10000-15000 | 4-8 | Single | 2 | 20 | Medium | 0 | 5e+05 |
3753 | 6383 | 38374 | 11188 | 38374 | 12688 | 0 | 2 | 15000+ | 4-8 | Single | 4 | 21 | Medium | 0 | 1e+05 |
3780 | 1427 | 5088 | 12198 | 5088 | 12936 | 1 | 3 | 7500-10000 | 4-8 | Single | 2 | 22 | Low | 0 | 5e+04 |
3928 | 11576 | 0 | 10405 | 0 | 12441 | 0 | 2 | 7500-10000 | 4-8 | Single | 2 | 20 | Low | 0 | 3e+05 |
4025 | 9790 | 44359 | 12411 | 44359 | 14616 | 0 | 2 | 15000+ | 4-8 | Single | 1 | 23 | Medium | 0 | 1e+05 |
4030 | 2931 | 6727 | 10405 | 6727 | 12441 | 0 | 2 | 7500-10000 | 4-8 | Single | 2 | 23 | Low | 0 | 5e+04 |
4117 | 1272 | 120 | 12418 | 120 | 14536 | 0 | 3 | 15000+ | 4-8 | Single | 3 | 23 | Low | 0 | 5e+04 |
4125 | 10958 | 2844 | 8402 | 2844 | 16042 | 0 | 1 | 0-7500 | 64-73 | Married | 3 | 87 | Medium | 0 | 5e+04 |
4168 | 8803 | 11651 | 13600 | 11651 | 14458 | 1 | 3 | 15000+ | 4-8 | Single | 2 | 23 | Low | 0 | 3e+05 |
4174 | 6845 | 33709 | 11138 | 33709 | 13005 | 0 | 3 | 7500-10000 | 4-8 | Single | 3 | 24 | High | 0 | 5e+04 |
4183 | 11163 | 182 | 13335 | 182 | 13699 | 1 | 2 | 0-7500 | 4-8 | Single | 1 | 22 | Medium | 0 | 1e+05 |
4220 | 4119 | 59700 | 10260 | 59700 | 12469 | 0 | 2 | 7500-10000 | 4-8 | Single | 3 | 24 | Low | 0 | 5e+04 |
4229 | 898 | 32740 | 13472 | 32740 | 15244 | 0 | 3 | 15000+ | 4-8 | Single | 1 | 22 | Low | 0 | 1e+05 |
4297 | 4617 | 59880 | 13049 | 59880 | 13597 | 1 | 3 | 7500-10000 | 4-8 | Single | 1 | 21 | Medium | 0 | 5e+04 |
4336 | 5452 | 120 | 13404 | 120 | 14333 | 0 | 3 | 0-7500 | 4-8 | Single | 1 | 24 | Low | 0 | 5e+04 |
4348 | 9455 | 428 | 12348 | 428 | 13742 | 0 | 2 | 0-7500 | 4-8 | Single | 1 | 21 | Medium | 0 | 3e+05 |
4383 | 6201 | 79416 | 13472 | 79416 | 15244 | 0 | 3 | 15000+ | 4-8 | Single | 1 | 21 | Low | 0 | 5e+05 |
4401 | 5179 | 5450 | 13404 | 5450 | 14333 | 0 | 3 | 0-7500 | 4-8 | Single | 1 | 20 | Low | 0 | 5e+04 |
4483 | 9338 | 50468 | 11131 | 50468 | 13077 | 0 | 2 | 7500-10000 | 4-8 | Single | 1 | 22 | Low | 0 | 1e+05 |
4527 | 316 | 19757 | 10235 | 19757 | 12269 | 0 | 2 | 15000+ | 44-53 | Married | 3 | 66 | Low | 0 | 5e+05 |
4605 | 6611 | 0 | 12348 | 0 | 13742 | 0 | 2 | 0-7500 | 4-8 | Single | 1 | 23 | High | 0 | 5e+04 |
4639 | 1239 | 1637 | 12465 | 1637 | 13033 | 1 | 2 | 0-7500 | 4-8 | Single | 2 | 21 | Low | 0 | 5e+04 |
4689 | 10598 | 72 | 12355 | 72 | 13667 | 0 | 3 | 0-7500 | 4-8 | Single | 3 | 21 | Medium | 0 | 3e+05 |
4715 | 14591 | 2825 | 12653 | 2825 | 13199 | 0 | 3 | 10000-15000 | 4-8 | Single | 4 | 20 | Low | 0 | 1e+05 |
4754 | 7724 | 23597 | 12086 | 23597 | 13868 | 0 | 2 | 10000-15000 | 4-8 | Single | 2 | 20 | High | 0 | 5e+05 |
4803 | 34 | 3018 | 12411 | 3018 | 14616 | 0 | 2 | 15000+ | 4-8 | Single | 1 | 21 | Low | 0 | 1e+05 |
4805 | 13452 | 3664 | 13403 | 3664 | 14571 | 1 | 2 | 15000+ | 4-8 | Single | 1 | 21 | Medium | 0 | 3e+05 |
4841 | 14397 | 3500 | 12418 | 3500 | 14536 | 0 | 3 | 15000+ | 4-8 | Single | 3 | 22 | Low | 0 | 5e+04 |
4873 | 11193 | 55397 | 13971 | 55397 | 14453 | 1 | 3 | 10000-15000 | 4-8 | Single | 3 | 24 | Low | 0 | 5e+04 |
4888 | 3901 | 56974 | 11601 | 56974 | 13905 | 0 | 2 | 15000+ | 4-8 | Single | 2 | 22 | Low | 0 | 5e+05 |
4889 | 6305 | 13726 | 13120 | 13726 | 14465 | 0 | 3 | 10000-15000 | 4-8 | Single | 2 | 22 | Low | 0 | 5e+04 |
4936 | 10081 | 450 | 13282 | 450 | 23469 | 0 | 2 | 7500-10000 | 64-73 | Married | 1 | 86 | Medium | 0 | 3e+05 |
4960 | 419 | 994 | 13258 | 994 | 16863 | 2 | 2 | 15000+ | 4-8 | Single | 3 | 24 | Low | 0 | 3e+05 |
4964 | 12979 | 8982 | 11188 | 8982 | 12688 | 0 | 2 | 15000+ | 4-8 | Single | 4 | 24 | Low | 0 | 1e+05 |
4972 | 6998 | 165 | 11138 | 165 | 13005 | 0 | 3 | 7500-10000 | 4-8 | Single | 3 | 24 | Low | 0 | 5e+04 |
5055 | 9891 | 514 | 13472 | 514 | 15244 | 0 | 3 | 15000+ | 4-8 | Single | 1 | 21 | Medium | 0 | 5e+04 |
5100 | 5607 | 0 | 12348 | 0 | 13742 | 0 | 2 | 0-7500 | 4-8 | Single | 1 | 23 | High | 0 | 3e+05 |
5103 | 10672 | 0 | 11917 | 0 | 13900 | 0 | 2 | 10000-15000 | 4-8 | Single | 3 | 21 | Medium | 0 | 5e+04 |
5110 | 4923 | 13016 | 12418 | 13016 | 14536 | 0 | 3 | 15000+ | 4-8 | Single | 3 | 24 | Medium | 0 | 5e+04 |
5111 | 4085 | 143 | 11188 | 143 | 12688 | 0 | 2 | 15000+ | 4-8 | Single | 4 | 24 | Low | 0 | 1e+05 |
5148 | 8114 | 18872 | 14877 | 18872 | 15204 | 1 | 2 | 10000-15000 | 24-33 | Single | 1 | 41 | High | 0 | 1e+05 |
5174 | 11316 | 1735 | 13335 | 1735 | 13699 | 1 | 2 | 0-7500 | 4-8 | Single | 1 | 24 | Medium | 0 | 3e+05 |
5193 | 14919 | 0 | 11656 | 0 | 12655 | 0 | 2 | 10000-15000 | 4-8 | Single | 4 | 22 | Low | 0 | 5e+05 |
5248 | 12586 | 40700 | 12870 | 40700 | 13857 | 1 | 2 | 10000-15000 | 4-8 | Single | 3 | 20 | High | 0 | 1e+05 |
5269 | 4392 | 0 | 12901 | 0 | 13013 | 1 | 2 | 15000+ | 34-43 | Married | 1 | 59 | High | 0 | 3e+05 |
5277 | 6221 | 0 | 11131 | 0 | 13077 | 0 | 2 | 7500-10000 | 4-8 | Single | 1 | 23 | Low | 0 | 3e+05 |
5282 | 7766 | 2752 | 11633 | 2752 | 12386 | 0 | 2 | 10000-15000 | 34-43 | Married | 2 | 54 | Low | 0 | 3e+05 |
5310 | 892 | 158 | 13335 | 158 | 13699 | 1 | 2 | 0-7500 | 4-8 | Single | 1 | 20 | High | 0 | 3e+05 |
5312 | 14595 | 3272 | 11633 | 3272 | 12386 | 0 | 2 | 10000-15000 | 34-43 | Married | 2 | 59 | High | 0 | 5e+04 |
5410 | 10087 | 120 | 11946 | 120 | 13053 | 0 | 2 | 15000+ | 34-43 | Married | 1 | 58 | Medium | 0 | 5e+04 |
5459 | 14343 | 181 | 13713 | 181 | 14497 | 1 | 2 | 10000-15000 | 24-33 | Single | 3 | 44 | Medium | 0 | 5e+04 |
5462 | 9445 | 277 | 13472 | 277 | 15244 | 0 | 3 | 15000+ | 4-8 | Single | 1 | 20 | Medium | 0 | 5e+04 |
5476 | 3788 | 71194 | 12930 | 71194 | 14577 | 0 | 2 | 10000-15000 | 4-8 | Single | 1 | 20 | High | 0 | 1e+05 |
5497 | 5881 | 410 | 12411 | 410 | 14616 | 0 | 2 | 15000+ | 4-8 | Single | 1 | 23 | Low | 0 | 5e+04 |
5574 | 8878 | 0 | 11601 | 0 | 13905 | 0 | 2 | 15000+ | 4-8 | Single | 2 | 23 | Low | 0 | 1e+05 |
5646 | 10191 | 0 | 8564 | 0 | 17024 | 0 | 1 | 15000+ | 64-73 | Married | 2 | 87 | High | 0 | 3e+05 |
5675 | 3865 | 1454 | 12530 | 1454 | 13636 | 0 | 3 | 0-7500 | 4-8 | Single | 2 | 24 | Medium | 0 | 5e+04 |
5712 | 6203 | 156 | 15158 | 156 | 15157 | 1 | 3 | 10000-15000 | 4-8 | Single | 1 | 24 | Low | 0 | 3e+05 |
5717 | 11564 | 5120 | 12594 | 5120 | 14503 | 0 | 3 | 15000+ | 4-8 | Single | 2 | 21 | Low | 0 | 5e+04 |
5723 | 12765 | 1801 | 11601 | 1801 | 13905 | 0 | 2 | 15000+ | 4-8 | Single | 2 | 23 | Low | 0 | 1e+05 |
5760 | 10937 | 31079 | 10405 | 31079 | 12441 | 0 | 2 | 7500-10000 | 4-8 | Single | 2 | 23 | Medium | 0 | 5e+04 |
5790 | 13476 | 11915 | 13120 | 11915 | 14465 | 0 | 3 | 10000-15000 | 4-8 | Single | 2 | 24 | Low | 0 | 5e+04 |
5838 | 13063 | 13633 | 15615 | 13633 | 18445 | 2 | 3 | 15000+ | 4-8 | Single | 1 | 21 | Low | 0 | 5e+04 |
5843 | 3376 | 120 | 12418 | 120 | 14536 | 0 | 3 | 15000+ | 4-8 | Single | 3 | 23 | Low | 0 | 1e+05 |
5856 | 6237 | 1121 | 12937 | 1121 | 14497 | 0 | 3 | 10000-15000 | 4-8 | Single | 3 | 20 | Low | 0 | 1e+05 |
5864 | 727 | 13632 | 11295 | 13632 | 12976 | 0 | 3 | 7500-10000 | 4-8 | Single | 2 | 21 | Medium | 0 | 1e+05 |
5877 | 14762 | 0 | 11920 | 0 | 13275 | 0 | 2 | 15000+ | 24-33 | Single | 4 | 40 | Low | 0 | 3e+05 |
5908 | 12542 | 738 | 12084 | 738 | 13639 | 0 | 3 | 7500-10000 | 4-8 | Single | 1 | 20 | Low | 0 | 3e+05 |
5953 | 11819 | 608 | 12084 | 608 | 13639 | 0 | 3 | 7500-10000 | 4-8 | Single | 1 | 20 | Low | 0 | 3e+05 |
5962 | 4740 | 1488 | 13776 | 1488 | 15251 | 0 | 2 | 10000-15000 | 24-33 | Single | 1 | 40 | High | 0 | 1e+05 |
5973 | 4367 | 12948 | 12493 | 12948 | 12793 | 1 | 2 | 10000-15000 | 44-53 | Married | 1 | 62 | Low | 0 | 3e+05 |
6005 | 7451 | 11654 | 13472 | 11654 | 15244 | 0 | 3 | 15000+ | 4-8 | Single | 1 | 24 | Low | 0 | 1e+05 |
6011 | 10541 | 11843 | 11946 | 11843 | 13053 | 0 | 2 | 15000+ | 34-43 | Married | 1 | 56 | Medium | 0 | 5e+04 |
6015 | 13687 | 206 | 11188 | 206 | 12688 | 0 | 2 | 15000+ | 4-8 | Single | 4 | 24 | Low | 0 | 1e+05 |
6026 | 27 | 100000 | 11656 | 167332 | 12655 | 0 | 2 | 10000-15000 | 4-8 | Single | 4 | 23 | Low | 0 | 5e+05 |
6040 | 11038 | 11373 | 12086 | 11373 | 13868 | 0 | 2 | 10000-15000 | 4-8 | Single | 2 | 21 | Low | 0 | 5e+05 |
6041 | 1992 | 0 | 11131 | 0 | 13077 | 0 | 2 | 7500-10000 | 4-8 | Single | 1 | 24 | Medium | 0 | 5e+05 |
6055 | 13899 | 0 | 12411 | 0 | 14616 | 0 | 2 | 15000+ | 4-8 | Single | 1 | 22 | Low | 0 | 5e+05 |
6062 | 2351 | 63735 | 12084 | 63735 | 13639 | 0 | 3 | 7500-10000 | 4-8 | Single | 1 | 20 | Low | 0 | 3e+05 |
6087 | 13494 | 120 | 12411 | 120 | 14616 | 0 | 2 | 15000+ | 4-8 | Single | 1 | 22 | Low | 0 | 1e+05 |
6098 | 4698 | 14037 | 13403 | 14037 | 14571 | 1 | 2 | 15000+ | 4-8 | Single | 1 | 22 | High | 0 | 3e+05 |
6120 | 8243 | 123 | 13410 | 123 | 14491 | 1 | 3 | 15000+ | 4-8 | Single | 3 | 23 | Low | 0 | 5e+04 |
6141 | 7120 | 14879 | 12348 | 14879 | 13742 | 0 | 2 | 0-7500 | 4-8 | Single | 1 | 20 | Low | 0 | 5e+04 |
6169 | 12711 | 24974 | 14036 | 24974 | 15204 | 0 | 3 | 10000-15000 | 4-8 | Single | 1 | 21 | Low | 0 | 3e+05 |
6176 | 295 | 3854 | 13472 | 3854 | 15244 | 0 | 3 | 15000+ | 4-8 | Single | 1 | 22 | Low | 0 | 5e+04 |
6181 | 3781 | 5433 | 11656 | 5433 | 12655 | 0 | 2 | 10000-15000 | 4-8 | Single | 4 | 23 | Low | 0 | 5e+04 |
6196 | 12821 | 0 | 11917 | 0 | 13900 | 0 | 2 | 10000-15000 | 4-8 | Single | 3 | 20 | Low | 0 | 5e+04 |
6217 | 12059 | 3998 | 10260 | 3998 | 12469 | 0 | 2 | 7500-10000 | 4-8 | Single | 3 | 21 | High | 0 | 5e+04 |
6238 | 5600 | 0 | 10260 | 0 | 12469 | 0 | 2 | 7500-10000 | 4-8 | Single | 3 | 20 | High | 0 | 5e+04 |
6242 | 9168 | 0 | 12021 | 0 | 13036 | 1 | 2 | 7500-10000 | 4-8 | Single | 1 | 23 | Low | 0 | 3e+05 |
6272 | 13758 | 156 | 11439 | 156 | 13937 | 0 | 2 | 15000+ | 4-8 | Single | 3 | 20 | High | 0 | 5e+04 |
6280 | 3058 | 9325 | 12348 | 9325 | 13742 | 0 | 2 | 0-7500 | 4-8 | Single | 1 | 22 | Low | 0 | 5e+04 |
6280 | 14265 | 8732 | 12653 | 8732 | 13199 | 0 | 3 | 10000-15000 | 4-8 | Single | 4 | 24 | Medium | 0 | 5e+04 |
6297 | 9079 | 0 | 13739 | 0 | 16063 | 0 | 2 | 10000-15000 | 34-43 | Single | 3 | 52 | High | 0 | 3e+05 |
6304 | 12219 | 0 | 11542 | 0 | 13074 | 0 | 2 | 0-7500 | 4-8 | Single | 2 | 21 | High | 0 | 5e+04 |
6411 | 4113 | 0 | 22891 | 0 | 24089 | 3 | 1 | 7500-10000 | 24-33 | Single | 3 | 42 | Medium | 0 | 3e+05 |
6432 | 608 | 0 | 13156 | 0 | 14377 | 0 | 2 | 0-7500 | 24-33 | Single | 1 | 40 | High | 0 | 5e+04 |
6432 | 10368 | 51741 | 11131 | 51741 | 13077 | 0 | 2 | 7500-10000 | 4-8 | Single | 1 | 23 | Low | 0 | 5e+05 |
6434 | 585 | 100000 | 12411 | 119400 | 14616 | 0 | 2 | 15000+ | 4-8 | Single | 1 | 20 | Medium | 0 | 1e+05 |
6465 | 10441 | 137 | 12084 | 137 | 13639 | 0 | 3 | 7500-10000 | 4-8 | Single | 1 | 20 | Medium | 0 | 5e+04 |
6471 | 5023 | 6269 | 20972 | 6269 | 20517 | 3 | 1 | 7500-10000 | 34-43 | Married | 2 | 58 | High | 0 | 1e+05 |
6473 | 3842 | 30340 | 10814 | 30340 | 12209 | 0 | 2 | 10000-15000 | 44-53 | Married | 2 | 64 | High | 0 | 3e+05 |
6479 | 7618 | 0 | 8899 | 0 | 12063 | 2 | 1 | 15000+ | 4-8 | Single | 1 | 21 | Low | 0 | 1e+05 |
6592 | 11802 | 145 | 13404 | 145 | 14333 | 0 | 3 | 0-7500 | 4-8 | Single | 1 | 20 | Low | 0 | 5e+04 |
6613 | 10270 | 29560 | 13282 | 29560 | 21411 | 0 | 2 | 0-7500 | 64-73 | Married | 4 | 86 | High | 0 | 5e+04 |
6646 | 10277 | 36167 | 11656 | 36167 | 12655 | 0 | 2 | 10000-15000 | 4-8 | Single | 4 | 21 | Low | 0 | 3e+05 |
6657 | 7155 | 41774 | 11542 | 41774 | 13074 | 0 | 2 | 0-7500 | 4-8 | Single | 2 | 21 | Low | 0 | 5e+05 |
6673 | 6644 | 8949 | 12587 | 8949 | 12616 | 1 | 2 | 10000-15000 | 4-8 | Single | 4 | 24 | Low | 0 | 5e+04 |
6674 | 3671 | 120 | 11656 | 120 | 12655 | 0 | 2 | 10000-15000 | 4-8 | Single | 4 | 23 | Medium | 0 | 3e+05 |
6693 | 424 | 153 | 13120 | 153 | 14465 | 0 | 3 | 10000-15000 | 4-8 | Single | 2 | 20 | Low | 0 | 5e+04 |
6695 | 5673 | 47776 | 11569 | 47776 | 12833 | 0 | 2 | 10000-15000 | 44-53 | Married | 1 | 63 | Medium | 0 | 5e+04 |
6701 | 12334 | 3750 | 11601 | 3750 | 13905 | 0 | 2 | 15000+ | 4-8 | Single | 2 | 22 | High | 0 | 1e+05 |
6733 | 10312 | 0 | 11542 | 0 | 13074 | 0 | 2 | 0-7500 | 4-8 | Single | 2 | 22 | Low | 0 | 5e+05 |
6745 | 4117 | 7290 | 14597 | 7290 | 17548 | 2 | 3 | 15000+ | 4-8 | Single | 2 | 24 | Low | 0 | 5e+04 |
6780 | 11585 | 3583 | 9162 | 3583 | 17894 | 0 | 1 | 15000+ | 64-73 | Married | 1 | 85 | High | 0 | 5e+05 |
6803 | 831 | 145 | 10260 | 145 | 12469 | 0 | 2 | 7500-10000 | 4-8 | Single | 3 | 21 | Low | 0 | 1e+05 |
6806 | 11267 | 2412 | 10260 | 2412 | 12469 | 0 | 2 | 7500-10000 | 4-8 | Single | 3 | 24 | Medium | 0 | 5e+05 |
6811 | 1602 | 1670 | 12594 | 1670 | 14503 | 0 | 3 | 15000+ | 4-8 | Single | 2 | 20 | Low | 0 | 5e+04 |
6846 | 11697 | 120 | 13472 | 120 | 15244 | 0 | 3 | 15000+ | 4-8 | Single | 1 | 24 | Low | 0 | 5e+04 |
6873 | 2217 | 15324 | 12930 | 15324 | 14577 | 0 | 2 | 10000-15000 | 4-8 | Single | 1 | 22 | Low | 0 | 3e+05 |
6915 | 6676 | 854 | 11656 | 854 | 12655 | 0 | 2 | 10000-15000 | 4-8 | Single | 4 | 24 | Low | 0 | 1e+05 |
6933 | 1131 | 125 | 12624 | 125 | 14170 | 2 | 3 | 15000+ | 9-13 | Single | 1 | 25 | Low | 0 | 1e+05 |
6935 | 9680 | 120 | 12937 | 120 | 14497 | 0 | 3 | 10000-15000 | 4-8 | Single | 3 | 20 | Low | 0 | 5e+04 |
6946 | 619 | 0 | 12348 | 0 | 13742 | 0 | 2 | 0-7500 | 4-8 | Single | 1 | 24 | Low | 0 | 1e+05 |
6978 | 11740 | 0 | 8899 | 0 | 12063 | 2 | 1 | 15000+ | 4-8 | Single | 1 | 21 | Low | 0 | 5e+05 |
6997 | 14301 | 120 | 12145 | 120 | 13234 | 0 | 3 | 15000+ | 4-8 | Single | 4 | 24 | Low | 0 | 5e+04 |
7017 | 9996 | 12797 | 12418 | 12797 | 14536 | 0 | 3 | 15000+ | 4-8 | Single | 3 | 21 | Medium | 0 | 3e+05 |
7020 | 11449 | 0 | 12361 | 0 | 14548 | 0 | 2 | 15000+ | 24-33 | Single | 2 | 40 | Low | 0 | 5e+04 |
7046 | 1861 | 13142 | 15158 | 13142 | 15157 | 1 | 3 | 10000-15000 | 4-8 | Single | 1 | 21 | Medium | 0 | 1e+05 |
7064 | 2000 | 0 | 13403 | 0 | 14571 | 1 | 2 | 15000+ | 4-8 | Single | 1 | 20 | Low | 0 | 5e+05 |
7085 | 13053 | 23535 | 9894 | 23535 | 17839 | 1 | 1 | 15000+ | 64-73 | Married | 1 | 85 | High | 0 | 1e+05 |
7090 | 8087 | 41304 | 12411 | 41304 | 14616 | 0 | 2 | 15000+ | 4-8 | Single | 1 | 24 | Low | 0 | 3e+05 |
7102 | 10550 | 37772 | 12115 | 37772 | 13550 | 2 | 2 | 10000-15000 | 9-13 | Single | 1 | 29 | Low | 0 | 5e+04 |
7121 | 12430 | 15658 | 11131 | 15658 | 13077 | 0 | 2 | 7500-10000 | 4-8 | Single | 1 | 23 | Low | 0 | 5e+04 |
7130 | 12123 | 58454 | 13335 | 58454 | 13699 | 1 | 2 | 0-7500 | 4-8 | Single | 1 | 20 | Low | 0 | 5e+04 |
7140 | 10714 | 25497 | 13116 | 25497 | 13193 | 1 | 3 | 15000+ | 4-8 | Single | 4 | 21 | Low | 0 | 3e+05 |
7208 | 14150 | 0 | 11917 | 0 | 13900 | 0 | 2 | 10000-15000 | 4-8 | Single | 3 | 20 | Low | 0 | 5e+04 |
7218 | 14459 | 2028 | 10405 | 2028 | 12441 | 0 | 2 | 7500-10000 | 4-8 | Single | 2 | 21 | Low | 0 | 1e+05 |
7226 | 7972 | 40380 | 14877 | 40380 | 15204 | 1 | 2 | 10000-15000 | 24-33 | Single | 1 | 40 | High | 0 | 5e+05 |
7229 | 9803 | 120 | 11576 | 120 | 12704 | 2 | 3 | 0-7500 | 9-13 | Single | 3 | 27 | Medium | 0 | 5e+04 |
7250 | 315 | 300 | 15428 | 300 | 26162 | 0 | 2 | 10000-15000 | 64-73 | Married | 1 | 88 | Low | 0 | 5e+04 |
7251 | 7356 | 294 | 12021 | 294 | 13036 | 1 | 2 | 7500-10000 | 4-8 | Single | 1 | 20 | Medium | 0 | 1e+05 |
7312 | 9446 | 0 | 12354 | 0 | 13894 | 1 | 2 | 15000+ | 4-8 | Single | 3 | 24 | Low | 0 | 3e+05 |
7313 | 5776 | 19780 | 11601 | 19780 | 13905 | 0 | 2 | 15000+ | 4-8 | Single | 2 | 21 | Low | 0 | 3e+05 |
7316 | 5127 | 836 | 12021 | 836 | 13036 | 1 | 2 | 7500-10000 | 4-8 | Single | 1 | 22 | Medium | 0 | 1e+05 |
7376 | 14513 | 6010 | 12354 | 6010 | 13894 | 1 | 2 | 15000+ | 4-8 | Single | 3 | 24 | Medium | 0 | 1e+05 |
7377 | 11567 | 510 | 13120 | 510 | 14465 | 0 | 3 | 10000-15000 | 4-8 | Single | 2 | 21 | Low | 0 | 1e+05 |
7379 | 14373 | 120 | 13116 | 120 | 13193 | 1 | 3 | 15000+ | 4-8 | Single | 4 | 21 | Low | 0 | 5e+04 |
7391 | 4191 | 0 | 12529 | 0 | 13862 | 1 | 2 | 15000+ | 4-8 | Single | 2 | 23 | Medium | 0 | 5e+05 |
7392 | 13590 | 260 | 12104 | 260 | 13158 | 2 | 2 | 10000-15000 | 14-23 | Single | 1 | 30 | Low | 0 | 3e+05 |
7415 | 4511 | 4398 | 14549 | 4398 | 15197 | 1 | 3 | 15000+ | 4-8 | Single | 1 | 22 | High | 0 | 5e+04 |
7425 | 8870 | 385 | 13472 | 385 | 15244 | 0 | 3 | 15000+ | 4-8 | Single | 1 | 20 | Low | 0 | 5e+04 |
7431 | 9742 | 100000 | 14036 | 164434 | 15204 | 0 | 3 | 10000-15000 | 4-8 | Single | 1 | 21 | Low | 0 | 3e+05 |
7445 | 12519 | 44924 | 11295 | 44924 | 12976 | 0 | 3 | 7500-10000 | 4-8 | Single | 2 | 21 | Low | 0 | 5e+04 |
7493 | 3007 | 0 | 12870 | 0 | 13857 | 1 | 2 | 10000-15000 | 4-8 | Single | 3 | 23 | Medium | 0 | 1e+05 |
7503 | 10922 | 6677 | 12411 | 6677 | 14616 | 0 | 2 | 15000+ | 4-8 | Single | 1 | 24 | Low | 0 | 1e+05 |
7511 | 9964 | 40612 | 13116 | 40612 | 13193 | 1 | 3 | 15000+ | 4-8 | Single | 4 | 22 | Low | 0 | 1e+05 |
7532 | 7854 | 6277 | 11237 | 6277 | 12402 | 1 | 2 | 7500-10000 | 4-8 | Single | 2 | 21 | Medium | 0 | 5e+04 |
7587 | 10272 | 3311 | 12145 | 3311 | 13234 | 0 | 3 | 15000+ | 4-8 | Single | 4 | 22 | Low | 0 | 5e+04 |
7600 | 2381 | 0 | 11656 | 0 | 12655 | 0 | 2 | 10000-15000 | 4-8 | Single | 4 | 24 | Medium | 0 | 1e+05 |
7633 | 14750 | 10170 | 12908 | 10170 | 12942 | 1 | 3 | 15000+ | 34-43 | Married | 3 | 54 | Low | 0 | 5e+04 |
7729 | 4147 | 0 | 7574 | 0 | 15265 | 0 | 1 | 7500-10000 | 64-73 | Married | 3 | 81 | High | 0 | 5e+05 |
7749 | 5343 | 1013 | 11439 | 1013 | 13937 | 0 | 2 | 15000+ | 4-8 | Single | 3 | 21 | Low | 0 | 3e+05 |
7788 | 3302 | 20015 | 12084 | 20015 | 13639 | 0 | 3 | 7500-10000 | 4-8 | Single | 1 | 21 | Low | 0 | 1e+05 |
7893 | 12456 | 18150 | 12126 | 18150 | 13709 | 0 | 2 | 0-7500 | 24-33 | Single | 3 | 43 | Low | 0 | 1e+05 |
7988 | 13134 | 119 | 13410 | 119 | 14491 | 1 | 3 | 15000+ | 4-8 | Single | 3 | 23 | Medium | 0 | 1e+05 |
8007 | 5492 | 164 | 11295 | 164 | 12976 | 0 | 3 | 7500-10000 | 4-8 | Single | 2 | 20 | Low | 0 | 5e+04 |
8015 | 8116 | 0 | 12587 | 0 | 12616 | 1 | 2 | 10000-15000 | 4-8 | Single | 4 | 20 | Low | 0 | 3e+05 |
8048 | 14144 | 26539 | 11138 | 26539 | 13005 | 0 | 3 | 7500-10000 | 4-8 | Single | 3 | 22 | Low | 0 | 1e+05 |
8063 | 3226 | 11804 | 14320 | 11804 | 16536 | 2 | 3 | 0-7500 | 4-8 | Single | 3 | 22 | Low | 0 | 3e+05 |
8066 | 8446 | 1086 | 13404 | 1086 | 14333 | 0 | 3 | 0-7500 | 4-8 | Single | 1 | 21 | Low | 0 | 5e+04 |
8075 | 7377 | 47675 | 13120 | 47675 | 14465 | 0 | 3 | 10000-15000 | 4-8 | Single | 2 | 24 | Low | 0 | 5e+05 |
8105 | 4633 | 6111 | 14036 | 6111 | 15204 | 0 | 3 | 10000-15000 | 4-8 | Single | 1 | 24 | Low | 0 | 3e+05 |
8140 | 10640 | 3095 | 12937 | 3095 | 14497 | 0 | 3 | 10000-15000 | 4-8 | Single | 3 | 21 | Medium | 0 | 1e+05 |
8182 | 2954 | 293 | 12594 | 293 | 14503 | 0 | 3 | 15000+ | 4-8 | Single | 2 | 20 | Low | 0 | 1e+05 |
8226 | 3875 | 554 | 11381 | 554 | 13103 | 0 | 2 | 0-7500 | 4-8 | Single | 3 | 21 | Medium | 0 | 5e+05 |
8252 | 14926 | 5653 | 12291 | 5653 | 13063 | 1 | 2 | 0-7500 | 4-8 | Single | 3 | 21 | Medium | 0 | 5e+04 |
8255 | 13794 | 0 | 12086 | 0 | 13868 | 0 | 2 | 10000-15000 | 4-8 | Single | 2 | 20 | Medium | 0 | 3e+05 |
8281 | 3307 | 1551 | 13776 | 1551 | 15251 | 0 | 2 | 10000-15000 | 24-33 | Single | 1 | 40 | High | 0 | 5e+04 |
8299 | 9763 | 5756 | 11080 | 5756 | 12430 | 1 | 2 | 7500-10000 | 4-8 | Single | 3 | 22 | Low | 0 | 3e+05 |
8304 | 1298 | 11743 | 11439 | 11743 | 13937 | 0 | 2 | 15000+ | 4-8 | Single | 3 | 23 | Low | 0 | 5e+04 |
8323 | 10552 | 59880 | 12419 | 59880 | 13240 | 0 | 2 | 10000-15000 | 24-33 | Single | 4 | 44 | High | 0 | 5e+04 |
8341 | 7558 | 17663 | 12870 | 17663 | 13857 | 1 | 2 | 10000-15000 | 4-8 | Single | 3 | 24 | Low | 0 | 3e+05 |
8388 | 3406 | 7986 | 12587 | 7986 | 12616 | 1 | 2 | 10000-15000 | 4-8 | Single | 4 | 24 | Low | 0 | 1e+05 |
8415 | 10493 | 96853 | 11188 | 96853 | 12688 | 0 | 2 | 15000+ | 4-8 | Single | 4 | 20 | Medium | 0 | 3e+05 |
8431 | 13816 | 1031 | 11601 | 1031 | 13905 | 0 | 2 | 15000+ | 4-8 | Single | 2 | 21 | Medium | 0 | 3e+05 |
8502 | 156 | 16316 | 13600 | 16316 | 14458 | 1 | 3 | 15000+ | 4-8 | Single | 2 | 20 | Low | 0 | 1e+05 |
8507 | 7027 | 368 | 9272 | 368 | 12031 | 2 | 1 | 10000-15000 | 4-8 | Single | 1 | 21 | High | 0 | 5e+04 |
8523 | 13199 | 6223 | 11920 | 6223 | 13275 | 0 | 2 | 15000+ | 24-33 | Single | 4 | 49 | Low | 0 | 1e+05 |
8554 | 12913 | 11196 | 12354 | 11196 | 13894 | 1 | 2 | 15000+ | 4-8 | Single | 3 | 21 | Medium | 0 | 5e+04 |
8633 | 4980 | 27810 | 13410 | 27810 | 14491 | 1 | 3 | 15000+ | 4-8 | Single | 3 | 21 | Low | 0 | 1e+05 |
8641 | 12114 | 0 | 8798 | 0 | 17017 | 0 | 1 | 10000-15000 | 64-73 | Married | 3 | 86 | High | 0 | 5e+04 |
8667 | 14086 | 129 | 11086 | 129 | 13016 | 0 | 2 | 7500-10000 | 24-33 | Single | 2 | 41 | High | 0 | 5e+04 |
8680 | 13647 | 18398 | 10405 | 18398 | 12441 | 0 | 2 | 7500-10000 | 4-8 | Single | 2 | 23 | Medium | 0 | 5e+05 |
8690 | 12685 | 0 | 13440 | 0 | 12979 | 1 | 2 | 10000-15000 | 34-43 | Married | 1 | 55 | Low | 0 | 5e+04 |
8692 | 3259 | 0 | 11131 | 0 | 13077 | 0 | 2 | 7500-10000 | 4-8 | Single | 1 | 24 | Medium | 0 | 5e+05 |
8705 | 3188 | 26437 | 12594 | 26437 | 14503 | 0 | 3 | 15000+ | 4-8 | Single | 2 | 21 | Low | 0 | 1e+05 |
8709 | 3620 | 100000 | 11439 | 359400 | 13937 | 0 | 2 | 15000+ | 4-8 | Single | 3 | 20 | Low | 0 | 3e+05 |
8720 | 2533 | 2205 | 11601 | 2205 | 13905 | 0 | 2 | 15000+ | 4-8 | Single | 2 | 21 | Medium | 0 | 5e+04 |
8757 | 11135 | 10709 | 12594 | 10709 | 14503 | 0 | 3 | 15000+ | 4-8 | Single | 2 | 22 | Low | 0 | 5e+04 |
8766 | 14451 | 5900 | 12411 | 5900 | 14616 | 0 | 2 | 15000+ | 4-8 | Single | 1 | 22 | Medium | 0 | 1e+05 |
8781 | 5907 | 7957 | 12084 | 7957 | 13639 | 0 | 3 | 7500-10000 | 4-8 | Single | 1 | 23 | Medium | 0 | 1e+05 |
8791 | 14537 | 24544 | 12411 | 24544 | 14616 | 0 | 2 | 15000+ | 4-8 | Single | 1 | 22 | High | 0 | 1e+05 |
8837 | 3954 | 0 | 10405 | 0 | 12441 | 0 | 2 | 7500-10000 | 4-8 | Single | 2 | 21 | Medium | 0 | 1e+05 |
8845 | 2482 | 0 | 12291 | 0 | 13063 | 1 | 2 | 0-7500 | 4-8 | Single | 3 | 20 | Medium | 0 | 3e+05 |
8872 | 11047 | 0 | 11656 | 0 | 12655 | 0 | 2 | 10000-15000 | 4-8 | Single | 4 | 20 | Medium | 0 | 5e+04 |
8948 | 3371 | 123 | 15967 | 123 | 18453 | 2 | 2 | 10000-15000 | 24-33 | Single | 1 | 42 | High | 0 | 5e+04 |
8990 | 8504 | 2564 | 12354 | 2564 | 13894 | 1 | 2 | 15000+ | 4-8 | Single | 3 | 23 | High | 0 | 1e+05 |
9039 | 7910 | 2688 | 12937 | 2688 | 14497 | 0 | 3 | 10000-15000 | 4-8 | Single | 3 | 21 | Low | 0 | 5e+04 |
9043 | 855 | 1678 | 11946 | 1678 | 13053 | 0 | 2 | 15000+ | 34-43 | Married | 1 | 56 | High | 0 | 5e+05 |
9045 | 14057 | 451 | 12937 | 451 | 14497 | 0 | 3 | 10000-15000 | 4-8 | Single | 3 | 20 | Low | 0 | 1e+05 |
9071 | 6868 | 54168 | 12083 | 54168 | 12442 | 0 | 3 | 0-7500 | 4-8 | Single | 4 | 21 | Low | 0 | 5e+04 |
9111 | 11778 | 100000 | 14549 | 101391 | 15197 | 1 | 3 | 15000+ | 4-8 | Single | 1 | 21 | Low | 0 | 1e+05 |
9117 | 13004 | 8535 | 11601 | 8535 | 13905 | 0 | 2 | 15000+ | 4-8 | Single | 2 | 24 | Low | 0 | 5e+04 |
9133 | 11486 | 6779 | 13258 | 6779 | 16863 | 2 | 2 | 15000+ | 4-8 | Single | 3 | 20 | Low | 0 | 1e+05 |
9135 | 859 | 0 | 11439 | 0 | 13937 | 0 | 2 | 15000+ | 4-8 | Single | 3 | 21 | Low | 0 | 1e+05 |
9181 | 12917 | 0 | 11104 | 0 | 12867 | 0 | 2 | 15000+ | 44-53 | Married | 1 | 60 | Medium | 0 | 5e+05 |
9209 | 2525 | 5103 | 10405 | 5103 | 12441 | 0 | 2 | 7500-10000 | 4-8 | Single | 2 | 23 | Medium | 0 | 5e+04 |
9274 | 4293 | 159 | 13378 | 159 | 15819 | 2 | 2 | 0-7500 | 4-8 | Single | 2 | 22 | Medium | 0 | 5e+04 |
9279 | 11598 | 1355 | 12084 | 1355 | 13639 | 0 | 3 | 7500-10000 | 4-8 | Single | 1 | 20 | Low | 0 | 1e+05 |
9347 | 3168 | 249 | 11131 | 249 | 13077 | 0 | 2 | 7500-10000 | 4-8 | Single | 1 | 22 | High | 0 | 5e+04 |
9392 | 8751 | 0 | 11885 | 0 | 12273 | 0 | 2 | 0-7500 | 34-43 | Married | 1 | 53 | Low | 0 | 5e+04 |
9415 | 6070 | 0 | 12901 | 0 | 15822 | 2 | 2 | 7500-10000 | 4-8 | Single | 1 | 24 | Medium | 0 | 5e+05 |
9423 | 1987 | 89 | 13472 | 89 | 15244 | 0 | 3 | 15000+ | 4-8 | Single | 1 | 21 | High | 0 | 1e+05 |
9517 | 13016 | 100000 | 8217 | 281761 | 14605 | 0 | 1 | 0-7500 | 64-73 | Married | 4 | 87 | High | 0 | 3e+05 |
9551 | 5435 | 26666 | 10405 | 26666 | 12441 | 0 | 2 | 7500-10000 | 4-8 | Single | 2 | 20 | Low | 0 | 3e+05 |
9553 | 1865 | 54466 | 11086 | 54466 | 13016 | 0 | 2 | 7500-10000 | 24-33 | Single | 2 | 49 | Medium | 0 | 5e+04 |
9556 | 5209 | 15396 | 11080 | 15396 | 12430 | 1 | 2 | 7500-10000 | 4-8 | Single | 3 | 21 | Low | 0 | 5e+04 |
9568 | 13220 | 120 | 11629 | 120 | 13586 | 2 | 2 | 15000+ | 9-13 | Single | 1 | 27 | Medium | 0 | 5e+04 |
9590 | 4692 | 7233 | 12354 | 7233 | 13894 | 1 | 2 | 15000+ | 4-8 | Single | 3 | 20 | Low | 0 | 3e+05 |
9664 | 653 | 721 | 12411 | 721 | 14616 | 0 | 2 | 15000+ | 4-8 | Single | 1 | 22 | Low | 0 | 1e+05 |
9689 | 742 | 58510 | 11237 | 58510 | 12402 | 1 | 2 | 7500-10000 | 4-8 | Single | 2 | 21 | Medium | 0 | 3e+05 |
9695 | 2939 | 53 | 11439 | 53 | 13937 | 0 | 2 | 15000+ | 4-8 | Single | 3 | 23 | Medium | 0 | 1e+05 |
9715 | 12673 | 0 | 11656 | 0 | 12655 | 0 | 2 | 10000-15000 | 4-8 | Single | 4 | 21 | Low | 0 | 3e+05 |
9742 | 5064 | 473 | 14036 | 473 | 15204 | 0 | 3 | 10000-15000 | 4-8 | Single | 1 | 24 | Low | 0 | 5e+04 |
9760 | 1681 | 4311 | 13472 | 4311 | 15244 | 0 | 3 | 15000+ | 4-8 | Single | 1 | 21 | Low | 0 | 3e+05 |
9774 | 5269 | 0 | 12930 | 0 | 14577 | 0 | 2 | 10000-15000 | 4-8 | Single | 1 | 23 | Low | 0 | 1e+05 |
9801 | 12022 | 0 | 10260 | 0 | 13941 | 2 | 1 | 15000+ | 34-43 | Single | 1 | 56 | Medium | 0 | 5e+05 |
9812 | 10772 | 0 | 11381 | 0 | 13103 | 0 | 2 | 0-7500 | 4-8 | Single | 3 | 20 | Medium | 0 | 5e+05 |
9827 | 10045 | 0 | 12086 | 0 | 13868 | 0 | 2 | 10000-15000 | 4-8 | Single | 2 | 21 | Medium | 0 | 5e+04 |
9863 | 9165 | 546 | 12084 | 546 | 13639 | 0 | 3 | 7500-10000 | 4-8 | Single | 1 | 24 | Low | 0 | 5e+04 |
9921 | 11951 | 0 | 11917 | 0 | 13900 | 0 | 2 | 10000-15000 | 4-8 | Single | 3 | 22 | Low | 0 | 1e+05 |
9940 | 5132 | 132 | 13472 | 132 | 15244 | 0 | 3 | 15000+ | 4-8 | Single | 1 | 23 | Low | 0 | 5e+04 |
9991 | 8572 | 602 | 13052 | 602 | 13826 | 1 | 2 | 10000-15000 | 4-8 | Single | 2 | 21 | High | 0 | 5e+04 |
10 | 6386 | 144 | 11295 | 144 | 12976 | 0 | 3 | 7500-10000 | 4-8 | Single | 2 | 21 | Low | 0 | 5e+04 |
155 | 6927 | 0 | 12082 | 0 | 12649 | 1 | 2 | 15000+ | 4-8 | Single | 4 | 24 | Medium | 0 | 5e+05 |
160 | 5309 | 48816 | 13052 | 48816 | 13826 | 1 | 2 | 10000-15000 | 4-8 | Single | 2 | 22 | Medium | 0 | 1e+05 |
222 | 12590 | 35708 | 13404 | 35708 | 14333 | 0 | 3 | 0-7500 | 4-8 | Single | 1 | 22 | Low | 0 | 5e+04 |
228 | 9054 | 64189 | 12145 | 64189 | 13234 | 0 | 3 | 15000+ | 4-8 | Single | 4 | 23 | Low | 0 | 5e+05 |
255 | 12575 | 14118 | 10405 | 14118 | 12441 | 0 | 2 | 7500-10000 | 4-8 | Single | 2 | 21 | Low | 0 | 5e+04 |
281 | 14736 | 3395 | 10380 | 3395 | 12241 | 0 | 2 | 15000+ | 44-53 | Married | 2 | 69 | Low | 0 | 1e+05 |
295 | 7144 | 20287 | 13963 | 20287 | 14532 | 1 | 2 | 10000-15000 | 4-8 | Single | 1 | 23 | Low | 0 | 3e+05 |
311 | 2430 | 59647 | 11131 | 59647 | 13077 | 0 | 2 | 7500-10000 | 4-8 | Single | 1 | 22 | Low | 0 | 1e+05 |
351 | 6943 | 3288 | 12086 | 3288 | 13868 | 0 | 2 | 10000-15000 | 4-8 | Single | 2 | 23 | Low | 0 | 1e+05 |
404 | 5153 | 12974 | 11630 | 12974 | 13736 | 2 | 2 | 7500-10000 | 4-8 | Single | 4 | 22 | High | 0 | 5e+05 |
425 | 2623 | 9646 | 12084 | 9646 | 13639 | 0 | 3 | 7500-10000 | 4-8 | Single | 1 | 23 | Low | 0 | 1e+05 |
458 | 8477 | 14221 | 14549 | 14221 | 15197 | 1 | 3 | 15000+ | 4-8 | Single | 1 | 22 | Low | 0 | 5e+04 |
466 | 6452 | 54836 | 12594 | 54836 | 14503 | 0 | 3 | 15000+ | 4-8 | Single | 2 | 24 | Low | 0 | 5e+04 |
507 | 5816 | 232 | 11131 | 232 | 13077 | 0 | 2 | 7500-10000 | 4-8 | Single | 1 | 24 | Low | 0 | 5e+04 |
549 | 11096 | 14379 | 11601 | 14379 | 13905 | 0 | 2 | 15000+ | 4-8 | Single | 2 | 24 | Medium | 0 | 5e+04 |
567 | 5976 | 520 | 12594 | 520 | 14503 | 0 | 3 | 15000+ | 4-8 | Single | 2 | 23 | High | 0 | 1e+05 |
584 | 14769 | 6321 | 11917 | 6321 | 13900 | 0 | 2 | 10000-15000 | 4-8 | Single | 3 | 22 | Low | 0 | 1e+05 |
608 | 6741 | 39523 | 14549 | 39523 | 15197 | 1 | 3 | 15000+ | 4-8 | Single | 1 | 23 | Low | 0 | 1e+05 |
660 | 13300 | 26883 | 12411 | 26883 | 14616 | 0 | 2 | 15000+ | 4-8 | Single | 1 | 23 | Low | 0 | 3e+05 |
696 | 8004 | 3068 | 11188 | 3068 | 12688 | 0 | 2 | 15000+ | 4-8 | Single | 4 | 23 | Low | 0 | 5e+04 |
788 | 9747 | 14548 | 12084 | 14548 | 13639 | 0 | 3 | 7500-10000 | 4-8 | Single | 1 | 21 | Medium | 0 | 3e+05 |
806 | 3380 | 0 | 11439 | 0 | 13937 | 0 | 2 | 15000+ | 4-8 | Single | 3 | 21 | Low | 0 | 5e+04 |
808 | 14403 | 0 | 11188 | 0 | 12688 | 0 | 2 | 15000+ | 4-8 | Single | 4 | 21 | Low | 0 | 3e+05 |
883 | 11809 | 0 | 11188 | 0 | 12688 | 0 | 2 | 15000+ | 4-8 | Single | 4 | 23 | Low | 0 | 5e+04 |
959 | 5589 | 47104 | 14549 | 47104 | 15197 | 1 | 3 | 15000+ | 4-8 | Single | 1 | 23 | Medium | 0 | 1e+05 |
962 | 5826 | 24727 | 13963 | 24727 | 14532 | 1 | 2 | 10000-15000 | 4-8 | Single | 1 | 22 | Low | 0 | 5e+04 |
992 | 5507 | 7111 | 12418 | 7111 | 14536 | 0 | 3 | 15000+ | 4-8 | Single | 3 | 21 | Medium | 0 | 5e+04 |
1013 | 10079 | 0 | 12348 | 0 | 13742 | 0 | 2 | 0-7500 | 4-8 | Single | 1 | 23 | Low | 0 | 5e+04 |
1026 | 7959 | 1256 | 11542 | 1256 | 13074 | 0 | 2 | 0-7500 | 4-8 | Single | 2 | 22 | Low | 0 | 1e+05 |
1059 | 1423 | 24342 | 12411 | 24342 | 14616 | 0 | 2 | 15000+ | 4-8 | Single | 1 | 24 | Medium | 0 | 1e+05 |
1095 | 10470 | 14413 | 12355 | 14413 | 13667 | 0 | 3 | 0-7500 | 4-8 | Single | 3 | 21 | Low | 0 | 5e+04 |
1112 | 2718 | 7519 | 10260 | 7519 | 12469 | 0 | 2 | 7500-10000 | 4-8 | Single | 3 | 24 | Low | 0 | 3e+05 |
1121 | 12043 | 0 | 11086 | 0 | 13016 | 0 | 2 | 7500-10000 | 24-33 | Single | 2 | 43 | High | 0 | 1e+05 |
1123 | 13141 | 160 | 14036 | 160 | 15204 | 0 | 3 | 10000-15000 | 4-8 | Single | 1 | 23 | Low | 0 | 5e+04 |
1138 | 9841 | 9412 | 10405 | 9412 | 12441 | 0 | 2 | 7500-10000 | 4-8 | Single | 2 | 22 | Low | 0 | 5e+04 |
1162 | 12613 | 653 | 14036 | 653 | 15204 | 0 | 3 | 10000-15000 | 4-8 | Single | 1 | 21 | Low | 0 | 5e+04 |
1167 | 75 | 0 | 11188 | 0 | 12688 | 0 | 2 | 15000+ | 4-8 | Single | 4 | 22 | Medium | 0 | 3e+05 |
1176 | 12198 | 128 | 11295 | 128 | 12976 | 0 | 3 | 7500-10000 | 4-8 | Single | 2 | 21 | Low | 0 | 1e+05 |
1183 | 5892 | 0 | 12419 | 0 | 13240 | 0 | 2 | 10000-15000 | 24-33 | Single | 4 | 40 | High | 0 | 1e+05 |
1196 | 4300 | 0 | 8923 | 0 | 16979 | 0 | 1 | 10000-15000 | 64-73 | Married | 2 | 87 | Medium | 0 | 3e+05 |
1210 | 5908 | 120 | 13151 | 120 | 14133 | 2 | 3 | 10000-15000 | 9-13 | Single | 1 | 28 | Low | 0 | 5e+04 |
1214 | 5681 | 527 | 13049 | 527 | 13597 | 1 | 3 | 7500-10000 | 4-8 | Single | 1 | 22 | Low | 0 | 5e+04 |
1249 | 3203 | 0 | 11920 | 0 | 13275 | 0 | 2 | 15000+ | 24-33 | Single | 4 | 41 | Medium | 0 | 5e+04 |
1277 | 2649 | 9079 | 14169 | 9079 | 14420 | 1 | 3 | 10000-15000 | 4-8 | Single | 2 | 21 | Low | 0 | 5e+04 |
1312 | 764 | 12892 | 10405 | 12892 | 12441 | 0 | 2 | 7500-10000 | 4-8 | Single | 2 | 24 | Low | 0 | 1e+05 |
1332 | 2437 | 377 | 11080 | 377 | 12430 | 1 | 2 | 7500-10000 | 4-8 | Single | 3 | 23 | Low | 0 | 1e+05 |
1345 | 10136 | 13626 | 11946 | 13626 | 13053 | 0 | 2 | 15000+ | 34-43 | Married | 1 | 53 | Medium | 0 | 5e+05 |
1379 | 11919 | 21263 | 11601 | 21263 | 13905 | 0 | 2 | 15000+ | 4-8 | Single | 2 | 24 | Low | 0 | 3e+05 |
1382 | 4031 | 0 | 9879 | 0 | 12588 | 2 | 1 | 10000-15000 | 24-33 | Single | 1 | 48 | Medium | 0 | 1e+05 |
1397 | 13504 | 71395 | 11656 | 71395 | 12655 | 0 | 2 | 10000-15000 | 4-8 | Single | 4 | 23 | Medium | 0 | 5e+05 |
1408 | 321 | 26492 | 11656 | 26492 | 12655 | 0 | 2 | 10000-15000 | 4-8 | Single | 4 | 22 | Low | 0 | 5e+04 |
1517 | 10042 | 0 | 11381 | 0 | 13103 | 0 | 2 | 0-7500 | 4-8 | Single | 3 | 22 | High | 0 | 1e+05 |
1543 | 13719 | 184 | 12937 | 184 | 14497 | 0 | 3 | 10000-15000 | 4-8 | Single | 3 | 24 | Low | 0 | 3e+05 |
1574 | 422 | 5267 | 8899 | 5267 | 12063 | 2 | 1 | 15000+ | 4-8 | Single | 1 | 21 | Medium | 0 | 5e+05 |
1678 | 13775 | 212 | 11601 | 212 | 13905 | 0 | 2 | 15000+ | 4-8 | Single | 2 | 21 | High | 0 | 1e+05 |
1710 | 11822 | 32882 | 10260 | 32882 | 12469 | 0 | 2 | 7500-10000 | 4-8 | Single | 3 | 22 | Low | 0 | 1e+05 |
1721 | 11192 | 30064 | 13120 | 30064 | 14465 | 0 | 3 | 10000-15000 | 4-8 | Single | 2 | 21 | Low | 0 | 5e+04 |
1739 | 10845 | 33391 | 12348 | 33391 | 13742 | 0 | 2 | 0-7500 | 4-8 | Single | 1 | 21 | Low | 0 | 5e+05 |
1765 | 5484 | 0 | 13963 | 0 | 14532 | 1 | 2 | 10000-15000 | 4-8 | Single | 1 | 24 | Low | 0 | 3e+05 |
1807 | 11300 | 274 | 13472 | 274 | 15244 | 0 | 3 | 15000+ | 4-8 | Single | 1 | 22 | Medium | 0 | 5e+04 |
1829 | 13317 | 439 | 13120 | 439 | 14465 | 0 | 3 | 10000-15000 | 4-8 | Single | 2 | 21 | Low | 0 | 1e+05 |
1843 | 9209 | 0 | 11188 | 0 | 12688 | 0 | 2 | 15000+ | 4-8 | Single | 4 | 21 | Low | 0 | 3e+05 |
1867 | 8501 | 0 | 9116 | 0 | 16824 | 0 | 1 | 0-7500 | 64-73 | Married | 1 | 86 | Low | 0 | 5e+05 |
1895 | 12963 | 0 | 8445 | 0 | 17062 | 0 | 1 | 15000+ | 64-73 | Married | 3 | 87 | High | 0 | 5e+04 |
1960 | 4676 | 23152 | 11188 | 23152 | 12688 | 0 | 2 | 15000+ | 4-8 | Single | 4 | 22 | Low | 0 | 3e+05 |
1975 | 13502 | 0 | 11917 | 0 | 13900 | 0 | 2 | 10000-15000 | 4-8 | Single | 3 | 22 | Medium | 0 | 1e+05 |
1980 | 14693 | 1412 | 12021 | 1412 | 13036 | 1 | 2 | 7500-10000 | 4-8 | Single | 1 | 24 | Low | 0 | 5e+05 |
1993 | 2844 | 6564 | 11439 | 6564 | 13937 | 0 | 2 | 15000+ | 4-8 | Single | 3 | 22 | Low | 0 | 1e+05 |
2050 | 8006 | 176 | 12594 | 176 | 14503 | 0 | 3 | 15000+ | 4-8 | Single | 2 | 24 | Low | 0 | 3e+05 |
2069 | 7881 | 628 | 13049 | 628 | 13597 | 1 | 3 | 7500-10000 | 4-8 | Single | 1 | 22 | Low | 0 | 5e+04 |
2084 | 1109 | 863 | 12145 | 863 | 13234 | 0 | 3 | 15000+ | 4-8 | Single | 4 | 21 | Medium | 0 | 5e+05 |
2119 | 7205 | 3481 | 11381 | 3481 | 13103 | 0 | 2 | 0-7500 | 4-8 | Single | 3 | 24 | High | 0 | 3e+05 |
2209 | 1479 | 20599 | 14169 | 20599 | 14420 | 1 | 3 | 10000-15000 | 4-8 | Single | 2 | 22 | Low | 0 | 5e+04 |
2288 | 2024 | 0 | 10860 | 0 | 12551 | 2 | 2 | 15000+ | 14-23 | Single | 2 | 34 | Medium | 0 | 3e+05 |
2342 | 13622 | 0 | 11188 | 0 | 12688 | 0 | 2 | 15000+ | 4-8 | Single | 4 | 24 | Low | 0 | 3e+05 |
2377 | 11124 | 7050 | 12870 | 7050 | 13857 | 1 | 2 | 10000-15000 | 4-8 | Single | 3 | 21 | Low | 0 | 5e+04 |
2385 | 1659 | 301 | 13404 | 301 | 14333 | 0 | 3 | 0-7500 | 4-8 | Single | 1 | 23 | Low | 0 | 5e+04 |
2419 | 1040 | 6631 | 12086 | 6631 | 13868 | 0 | 2 | 10000-15000 | 4-8 | Single | 2 | 22 | Low | 0 | 1e+05 |
2489 | 8496 | 561 | 11542 | 561 | 13074 | 0 | 2 | 0-7500 | 4-8 | Single | 2 | 23 | Low | 0 | 1e+05 |
2534 | 597 | 0 | 11601 | 0 | 13905 | 0 | 2 | 15000+ | 4-8 | Single | 2 | 24 | High | 0 | 5e+04 |
2574 | 654 | 5809 | 12445 | 5809 | 13019 | 0 | 2 | 10000-15000 | 34-43 | Married | 1 | 50 | Low | 0 | 5e+04 |
2580 | 8202 | 59400 | 12086 | 59400 | 13868 | 0 | 2 | 10000-15000 | 4-8 | Single | 2 | 24 | Low | 0 | 5e+04 |
2607 | 11909 | 20493 | 13120 | 20493 | 14465 | 0 | 3 | 10000-15000 | 4-8 | Single | 2 | 21 | Medium | 0 | 3e+05 |
2631 | 7350 | 28504 | 12361 | 28504 | 14548 | 0 | 2 | 15000+ | 24-33 | Single | 2 | 45 | Low | 0 | 3e+05 |
2646 | 5436 | 850 | 12082 | 850 | 12649 | 1 | 2 | 15000+ | 4-8 | Single | 4 | 22 | Low | 0 | 1e+05 |
2663 | 1983 | 132 | 14036 | 132 | 15204 | 0 | 3 | 10000-15000 | 4-8 | Single | 1 | 23 | Medium | 0 | 5e+04 |
2672 | 5138 | 24187 | 14036 | 24187 | 15204 | 0 | 3 | 10000-15000 | 4-8 | Single | 1 | 23 | Medium | 0 | 5e+04 |
2695 | 9013 | 10944 | 14036 | 10944 | 15204 | 0 | 3 | 10000-15000 | 4-8 | Single | 1 | 21 | Medium | 0 | 1e+05 |
2703 | 2156 | 6189 | 13120 | 6189 | 14465 | 0 | 3 | 10000-15000 | 4-8 | Single | 2 | 21 | Low | 0 | 5e+05 |
2768 | 4832 | 991 | 13412 | 991 | 13199 | 1 | 2 | 10000-15000 | 24-33 | Single | 4 | 46 | High | 0 | 5e+04 |
2781 | 12112 | 680 | 13188 | 680 | 16106 | 0 | 2 | 15000+ | 34-43 | Single | 3 | 50 | Low | 0 | 1e+05 |
2794 | 12111 | 23202 | 12594 | 23202 | 14503 | 0 | 3 | 15000+ | 4-8 | Single | 2 | 22 | Medium | 0 | 1e+05 |
2799 | 10404 | 15489 | 11131 | 15489 | 13077 | 0 | 2 | 7500-10000 | 4-8 | Single | 1 | 24 | Low | 0 | 5e+04 |
2805 | 8348 | 20068 | 13120 | 20068 | 14465 | 0 | 3 | 10000-15000 | 4-8 | Single | 2 | 22 | Low | 0 | 5e+04 |
2813 | 14580 | 0 | 12086 | 0 | 13868 | 0 | 2 | 10000-15000 | 4-8 | Single | 2 | 22 | High | 0 | 1e+05 |
2835 | 500 | 2736 | 12145 | 2736 | 13234 | 0 | 3 | 15000+ | 4-8 | Single | 4 | 23 | Low | 0 | 1e+05 |
2883 | 8321 | 6657 | 11917 | 6657 | 13900 | 0 | 2 | 10000-15000 | 4-8 | Single | 3 | 23 | Low | 0 | 3e+05 |
2893 | 13736 | 4561 | 11656 | 4561 | 12655 | 0 | 2 | 10000-15000 | 4-8 | Single | 4 | 24 | Low | 0 | 1e+05 |
2901 | 1838 | 15707 | 11131 | 15707 | 13077 | 0 | 2 | 7500-10000 | 4-8 | Single | 1 | 24 | High | 0 | 5e+05 |
2934 | 4897 | 4318 | 13410 | 4318 | 14491 | 1 | 3 | 15000+ | 4-8 | Single | 3 | 24 | Low | 0 | 5e+05 |
2937 | 9728 | 57 | 12930 | 57 | 14577 | 0 | 2 | 10000-15000 | 4-8 | Single | 1 | 24 | Low | 0 | 1e+05 |
3000 | 5593 | 120 | 10932 | 120 | 13045 | 0 | 2 | 7500-10000 | 24-33 | Single | 3 | 42 | High | 0 | 5e+05 |
3051 | 1403 | 0 | 11601 | 0 | 13905 | 0 | 2 | 15000+ | 4-8 | Single | 2 | 24 | Medium | 0 | 5e+04 |
3067 | 117 | 0 | 13121 | 0 | 15143 | 0 | 2 | 0-7500 | 34-43 | Single | 3 | 52 | Medium | 0 | 5e+04 |
3071 | 12164 | 491 | 11542 | 491 | 13074 | 0 | 2 | 0-7500 | 4-8 | Single | 2 | 24 | High | 0 | 1e+05 |
3185 | 13724 | 78941 | 13776 | 78941 | 15251 | 0 | 2 | 10000-15000 | 24-33 | Single | 1 | 46 | Low | 0 | 1e+05 |
3220 | 11693 | 192 | 13049 | 192 | 13597 | 1 | 3 | 7500-10000 | 4-8 | Single | 1 | 23 | Medium | 0 | 5e+04 |
3224 | 14739 | 21707 | 12870 | 21707 | 13857 | 1 | 2 | 10000-15000 | 4-8 | Single | 3 | 24 | Low | 0 | 3e+05 |
3241 | 5821 | 1665 | 12122 | 1665 | 12953 | 0 | 3 | 15000+ | 34-43 | Married | 2 | 50 | Low | 0 | 5e+04 |
3330 | 11520 | 100000 | 10405 | 180341 | 12441 | 0 | 2 | 7500-10000 | 4-8 | Single | 2 | 24 | Medium | 0 | 5e+05 |
3331 | 14154 | 15702 | 12411 | 15702 | 14616 | 0 | 2 | 15000+ | 4-8 | Single | 1 | 22 | High | 0 | 5e+04 |
3351 | 11006 | 3459 | 14036 | 3459 | 15204 | 0 | 3 | 10000-15000 | 4-8 | Single | 1 | 23 | Low | 0 | 1e+05 |
3385 | 3461 | 1020 | 12411 | 1020 | 14616 | 0 | 2 | 15000+ | 4-8 | Single | 1 | 24 | Medium | 0 | 1e+05 |
3399 | 11887 | 972 | 11138 | 972 | 13005 | 0 | 3 | 7500-10000 | 4-8 | Single | 3 | 21 | Medium | 0 | 3e+05 |
3429 | 2036 | 0 | 9879 | 0 | 12588 | 2 | 1 | 10000-15000 | 24-33 | Single | 1 | 40 | Low | 0 | 1e+05 |
3451 | 5076 | 12724 | 11080 | 12724 | 12430 | 1 | 2 | 7500-10000 | 4-8 | Single | 3 | 22 | Low | 0 | 3e+05 |
3459 | 439 | 8315 | 14169 | 8315 | 14420 | 1 | 3 | 10000-15000 | 4-8 | Single | 2 | 24 | Low | 0 | 5e+04 |
3506 | 6342 | 608 | 12348 | 608 | 13742 | 0 | 2 | 0-7500 | 4-8 | Single | 1 | 23 | Low | 0 | 5e+04 |
3508 | 9009 | 404 | 14036 | 404 | 15204 | 0 | 3 | 10000-15000 | 4-8 | Single | 1 | 24 | Low | 0 | 3e+05 |
3519 | 2179 | 0 | 11542 | 0 | 13074 | 0 | 2 | 0-7500 | 4-8 | Single | 2 | 22 | Medium | 0 | 1e+05 |
3552 | 4206 | 30161 | 11295 | 30161 | 12976 | 0 | 3 | 7500-10000 | 4-8 | Single | 2 | 23 | Low | 0 | 5e+04 |
3553 | 13983 | 31115 | 12594 | 31115 | 14503 | 0 | 3 | 15000+ | 4-8 | Single | 2 | 23 | Low | 0 | 3e+05 |
3556 | 8072 | 121 | 12937 | 121 | 14497 | 0 | 3 | 10000-15000 | 4-8 | Single | 3 | 22 | Low | 0 | 5e+04 |
3611 | 4939 | 1415 | 11917 | 1415 | 13900 | 0 | 2 | 10000-15000 | 4-8 | Single | 3 | 22 | High | 0 | 5e+04 |
3640 | 2890 | 0 | 9844 | 0 | 16772 | 1 | 1 | 0-7500 | 64-73 | Married | 1 | 87 | High | 0 | 5e+05 |
3675 | 5708 | 0 | 9249 | 0 | 12102 | 2 | 1 | 15000+ | 34-43 | Single | 4 | 59 | Low | 0 | 5e+04 |
3685 | 8113 | 10914 | 13349 | 10914 | 14503 | 1 | 2 | 15000+ | 24-33 | Single | 2 | 40 | High | 0 | 5e+04 |
3730 | 8463 | 36 | 11601 | 36 | 13905 | 0 | 2 | 15000+ | 4-8 | Single | 2 | 23 | Low | 0 | 1e+05 |
3748 | 6740 | 30574 | 12086 | 30574 | 13868 | 0 | 2 | 10000-15000 | 4-8 | Single | 2 | 21 | Medium | 0 | 5e+05 |
3780 | 1427 | 1218 | 12198 | 1218 | 12936 | 1 | 3 | 7500-10000 | 4-8 | Single | 2 | 23 | Low | 0 | 5e+04 |
3891 | 11428 | 65846 | 11439 | 65846 | 13937 | 0 | 2 | 15000+ | 4-8 | Single | 3 | 22 | Low | 0 | 3e+05 |
3928 | 11576 | 4420 | 10405 | 4420 | 12441 | 0 | 2 | 7500-10000 | 4-8 | Single | 2 | 21 | Low | 0 | 3e+05 |
4014 | 8284 | 3455 | 15373 | 3455 | 15832 | 1 | 2 | 0-7500 | 34-43 | Single | 1 | 59 | High | 0 | 5e+04 |
4025 | 9790 | 3716 | 12411 | 3716 | 14616 | 0 | 2 | 15000+ | 4-8 | Single | 1 | 24 | Medium | 0 | 1e+05 |
4030 | 2931 | 122 | 11295 | 122 | 12976 | 0 | 3 | 7500-10000 | 4-8 | Single | 2 | 24 | Low | 0 | 5e+04 |
4117 | 1272 | 6272 | 12418 | 6272 | 14536 | 0 | 3 | 15000+ | 4-8 | Single | 3 | 24 | Low | 0 | 5e+04 |
4125 | 10958 | 0 | 8402 | 0 | 16042 | 0 | 1 | 0-7500 | 64-73 | Married | 3 | 88 | Medium | 0 | 5e+04 |
4126 | 3352 | 261 | 8798 | 261 | 17017 | 0 | 1 | 10000-15000 | 64-73 | Married | 3 | 88 | Low | 0 | 3e+05 |
4168 | 8803 | 16351 | 12594 | 16351 | 14503 | 0 | 3 | 15000+ | 4-8 | Single | 2 | 24 | Low | 0 | 3e+05 |
4183 | 11163 | 299 | 14476 | 299 | 14289 | 1 | 3 | 0-7500 | 4-8 | Single | 1 | 23 | Medium | 0 | 1e+05 |
4229 | 898 | 63435 | 13472 | 63435 | 15244 | 0 | 3 | 15000+ | 4-8 | Single | 1 | 23 | Low | 0 | 1e+05 |
4297 | 4617 | 2600 | 13049 | 2600 | 13597 | 1 | 3 | 7500-10000 | 4-8 | Single | 1 | 22 | Medium | 0 | 5e+04 |
4383 | 6201 | 20 | 13472 | 20 | 15244 | 0 | 3 | 15000+ | 4-8 | Single | 1 | 22 | Low | 0 | 5e+05 |
4401 | 5179 | 30796 | 13404 | 30796 | 14333 | 0 | 3 | 0-7500 | 4-8 | Single | 1 | 21 | Low | 0 | 5e+04 |
4483 | 9338 | 10871 | 12084 | 10871 | 13639 | 0 | 3 | 7500-10000 | 4-8 | Single | 1 | 23 | Low | 0 | 1e+05 |
4500 | 7182 | 120 | 11917 | 120 | 13900 | 0 | 2 | 10000-15000 | 4-8 | Single | 3 | 24 | Low | 0 | 5e+04 |
4605 | 6611 | 150 | 12348 | 150 | 13742 | 0 | 2 | 0-7500 | 4-8 | Single | 1 | 24 | High | 0 | 5e+04 |
4639 | 1239 | 53 | 13531 | 53 | 13594 | 1 | 3 | 0-7500 | 4-8 | Single | 2 | 22 | Low | 0 | 5e+04 |
4663 | 7489 | 44 | 11860 | 44 | 12481 | 0 | 2 | 0-7500 | 24-33 | Single | 4 | 46 | High | 0 | 5e+04 |
4689 | 10598 | 0 | 11381 | 0 | 13103 | 0 | 2 | 0-7500 | 4-8 | Single | 3 | 22 | Medium | 0 | 3e+05 |
4715 | 14591 | 120 | 12653 | 120 | 13199 | 0 | 3 | 10000-15000 | 4-8 | Single | 4 | 21 | Low | 0 | 1e+05 |
4718 | 2647 | 21810 | 11917 | 21810 | 13900 | 0 | 2 | 10000-15000 | 4-8 | Single | 3 | 23 | Low | 0 | 1e+05 |
4754 | 7724 | 0 | 12086 | 0 | 13868 | 0 | 2 | 10000-15000 | 4-8 | Single | 2 | 21 | High | 0 | 5e+05 |
4773 | 659 | 19 | 12348 | 19 | 13742 | 0 | 2 | 0-7500 | 4-8 | Single | 1 | 23 | High | 0 | 5e+05 |
4794 | 228 | 1623 | 12086 | 1623 | 13868 | 0 | 2 | 10000-15000 | 4-8 | Single | 2 | 23 | Medium | 0 | 1e+05 |
4803 | 34 | 0 | 12411 | 0 | 14616 | 0 | 2 | 15000+ | 4-8 | Single | 1 | 22 | Low | 0 | 1e+05 |
4805 | 13452 | 1250 | 12411 | 1250 | 14616 | 0 | 2 | 15000+ | 4-8 | Single | 1 | 22 | Medium | 0 | 3e+05 |
4841 | 14397 | 1132 | 12418 | 1132 | 14536 | 0 | 3 | 15000+ | 4-8 | Single | 3 | 23 | Low | 0 | 5e+04 |
4888 | 3901 | 890 | 12594 | 890 | 14503 | 0 | 3 | 15000+ | 4-8 | Single | 2 | 23 | Low | 0 | 5e+05 |
4889 | 6305 | 21585 | 13120 | 21585 | 14465 | 0 | 3 | 10000-15000 | 4-8 | Single | 2 | 23 | Low | 0 | 5e+04 |
4936 | 10081 | 5780 | 13282 | 5780 | 23469 | 0 | 2 | 7500-10000 | 64-73 | Married | 1 | 87 | Medium | 0 | 3e+05 |
5055 | 9891 | 1658 | 13472 | 1658 | 15244 | 0 | 3 | 15000+ | 4-8 | Single | 1 | 22 | Medium | 0 | 5e+04 |
5100 | 5607 | 100000 | 12348 | 122443 | 13742 | 0 | 2 | 0-7500 | 4-8 | Single | 1 | 24 | High | 0 | 3e+05 |
5103 | 10672 | 5144 | 11917 | 5144 | 13900 | 0 | 2 | 10000-15000 | 4-8 | Single | 3 | 22 | Medium | 0 | 5e+04 |
5148 | 8114 | 0 | 14877 | 0 | 15204 | 1 | 2 | 10000-15000 | 24-33 | Single | 1 | 42 | High | 0 | 1e+05 |
5248 | 12586 | 84521 | 12870 | 84521 | 13857 | 1 | 2 | 10000-15000 | 4-8 | Single | 3 | 21 | High | 0 | 1e+05 |
5269 | 4392 | 368 | 11104 | 368 | 12867 | 0 | 2 | 15000+ | 44-53 | Married | 1 | 60 | High | 0 | 3e+05 |
5310 | 892 | 3445 | 15536 | 3445 | 17342 | 2 | 3 | 0-7500 | 4-8 | Single | 1 | 21 | High | 0 | 3e+05 |
5312 | 14595 | 0 | 10814 | 0 | 12209 | 0 | 2 | 10000-15000 | 44-53 | Married | 2 | 60 | High | 0 | 5e+04 |
5410 | 10087 | 0 | 11946 | 0 | 13053 | 0 | 2 | 15000+ | 34-43 | Married | 1 | 59 | Medium | 0 | 5e+04 |
5459 | 14343 | 0 | 13713 | 0 | 14497 | 1 | 2 | 10000-15000 | 24-33 | Single | 3 | 45 | Medium | 0 | 5e+04 |
5462 | 9445 | 13266 | 13472 | 13266 | 15244 | 0 | 3 | 15000+ | 4-8 | Single | 1 | 21 | Medium | 0 | 5e+04 |
5476 | 3788 | 0 | 12930 | 0 | 14577 | 0 | 2 | 10000-15000 | 4-8 | Single | 1 | 21 | High | 0 | 1e+05 |
5497 | 5881 | 0 | 12411 | 0 | 14616 | 0 | 2 | 15000+ | 4-8 | Single | 1 | 24 | Low | 0 | 5e+04 |
5574 | 8878 | 1241 | 11601 | 1241 | 13905 | 0 | 2 | 15000+ | 4-8 | Single | 2 | 24 | Low | 0 | 1e+05 |
5646 | 10191 | 0 | 8564 | 0 | 17024 | 0 | 1 | 15000+ | 64-73 | Married | 2 | 88 | High | 0 | 3e+05 |
5683 | 11257 | 8464 | 12348 | 8464 | 13742 | 0 | 2 | 0-7500 | 4-8 | Single | 1 | 22 | Low | 0 | 3e+05 |
5717 | 11564 | 2299 | 12594 | 2299 | 14503 | 0 | 3 | 15000+ | 4-8 | Single | 2 | 22 | Low | 0 | 5e+04 |
5723 | 12765 | 5849 | 12594 | 5849 | 14503 | 0 | 3 | 15000+ | 4-8 | Single | 2 | 24 | Low | 0 | 1e+05 |
5760 | 10937 | 261 | 11295 | 261 | 12976 | 0 | 3 | 7500-10000 | 4-8 | Single | 2 | 24 | Medium | 0 | 5e+04 |
5815 | 10290 | 16131 | 11166 | 16131 | 12419 | 0 | 2 | 15000+ | 34-43 | Married | 2 | 54 | Medium | 0 | 3e+05 |
5834 | 2271 | 12317 | 13963 | 12317 | 14532 | 1 | 2 | 10000-15000 | 4-8 | Single | 1 | 21 | Low | 0 | 5e+05 |
5838 | 13063 | 196 | 14549 | 196 | 15197 | 1 | 3 | 15000+ | 4-8 | Single | 1 | 22 | Low | 0 | 5e+04 |
5843 | 3376 | 1632 | 12418 | 1632 | 14536 | 0 | 3 | 15000+ | 4-8 | Single | 3 | 24 | Low | 0 | 1e+05 |
5856 | 6237 | 0 | 11917 | 0 | 13900 | 0 | 2 | 10000-15000 | 4-8 | Single | 3 | 21 | Low | 0 | 1e+05 |
5864 | 727 | 0 | 10405 | 0 | 12441 | 0 | 2 | 7500-10000 | 4-8 | Single | 2 | 22 | Medium | 0 | 1e+05 |
5908 | 12542 | 1059 | 12084 | 1059 | 13639 | 0 | 3 | 7500-10000 | 4-8 | Single | 1 | 21 | Low | 0 | 3e+05 |
5953 | 11819 | 8099 | 12084 | 8099 | 13639 | 0 | 3 | 7500-10000 | 4-8 | Single | 1 | 21 | Low | 0 | 3e+05 |
5962 | 4740 | 0 | 13776 | 0 | 15251 | 0 | 2 | 10000-15000 | 24-33 | Single | 1 | 41 | High | 0 | 1e+05 |
5972 | 14241 | 2504 | 12878 | 2504 | 14509 | 0 | 2 | 10000-15000 | 24-33 | Single | 2 | 40 | Low | 0 | 5e+04 |
5973 | 4367 | 54405 | 12493 | 54405 | 12793 | 1 | 2 | 10000-15000 | 44-53 | Married | 1 | 63 | Low | 0 | 3e+05 |
6011 | 10541 | 0 | 11946 | 0 | 13053 | 0 | 2 | 15000+ | 34-43 | Married | 1 | 57 | Medium | 0 | 5e+04 |
6026 | 27 | 7227 | 11656 | 7227 | 12655 | 0 | 2 | 10000-15000 | 4-8 | Single | 4 | 24 | Low | 0 | 5e+05 |
6040 | 10838 | 127 | 12411 | 127 | 14616 | 0 | 2 | 15000+ | 4-8 | Single | 1 | 24 | High | 0 | 3e+05 |
6040 | 11038 | 19692 | 12086 | 19692 | 13868 | 0 | 2 | 10000-15000 | 4-8 | Single | 2 | 22 | Low | 0 | 5e+05 |
6055 | 13899 | 54894 | 12411 | 54894 | 14616 | 0 | 2 | 15000+ | 4-8 | Single | 1 | 23 | Low | 0 | 5e+05 |
6062 | 2351 | 0 | 11131 | 0 | 13077 | 0 | 2 | 7500-10000 | 4-8 | Single | 1 | 21 | Low | 0 | 3e+05 |
6087 | 13494 | 359 | 13472 | 359 | 15244 | 0 | 3 | 15000+ | 4-8 | Single | 1 | 23 | Low | 0 | 1e+05 |
6098 | 4698 | 0 | 13403 | 0 | 14571 | 1 | 2 | 15000+ | 4-8 | Single | 1 | 23 | High | 0 | 3e+05 |
6120 | 8243 | 288 | 13410 | 288 | 14491 | 1 | 3 | 15000+ | 4-8 | Single | 3 | 24 | Low | 0 | 5e+04 |
6146 | 3243 | 34271 | 12587 | 34271 | 12616 | 1 | 2 | 10000-15000 | 4-8 | Single | 4 | 22 | Medium | 0 | 3e+05 |
6169 | 12711 | 11899 | 14036 | 11899 | 15204 | 0 | 3 | 10000-15000 | 4-8 | Single | 1 | 22 | Low | 0 | 3e+05 |
6176 | 295 | 1678 | 13472 | 1678 | 15244 | 0 | 3 | 15000+ | 4-8 | Single | 1 | 23 | Low | 0 | 5e+04 |
6181 | 3781 | 4546 | 12653 | 4546 | 13199 | 0 | 3 | 10000-15000 | 4-8 | Single | 4 | 24 | Low | 0 | 5e+04 |
6196 | 12821 | 4531 | 11917 | 4531 | 13900 | 0 | 2 | 10000-15000 | 4-8 | Single | 3 | 21 | Low | 0 | 5e+04 |
6238 | 5600 | 10482 | 10260 | 10482 | 12469 | 0 | 2 | 7500-10000 | 4-8 | Single | 3 | 21 | High | 0 | 5e+04 |
6267 | 161 | 19862 | 12873 | 19862 | 13234 | 1 | 2 | 15000+ | 24-33 | Single | 4 | 41 | High | 0 | 1e+05 |
6272 | 13758 | 0 | 11439 | 0 | 13937 | 0 | 2 | 15000+ | 4-8 | Single | 3 | 21 | High | 0 | 5e+04 |
6280 | 3058 | 0 | 12348 | 0 | 13742 | 0 | 2 | 0-7500 | 4-8 | Single | 1 | 23 | Low | 0 | 5e+04 |
6432 | 10368 | 0 | 11131 | 0 | 13077 | 0 | 2 | 7500-10000 | 4-8 | Single | 1 | 24 | Low | 0 | 5e+05 |
6434 | 585 | 3595 | 12411 | 3595 | 14616 | 0 | 2 | 15000+ | 4-8 | Single | 1 | 21 | Medium | 0 | 1e+05 |
6465 | 10441 | 3984 | 12084 | 3984 | 13639 | 0 | 3 | 7500-10000 | 4-8 | Single | 1 | 21 | Medium | 0 | 5e+04 |
6473 | 3842 | 0 | 10814 | 0 | 12209 | 0 | 2 | 10000-15000 | 44-53 | Married | 2 | 65 | High | 0 | 3e+05 |
6590 | 8663 | 1466 | 13713 | 1466 | 14497 | 1 | 2 | 10000-15000 | 24-33 | Single | 3 | 42 | High | 0 | 1e+05 |
6592 | 11802 | 597 | 13404 | 597 | 14333 | 0 | 3 | 0-7500 | 4-8 | Single | 1 | 21 | Low | 0 | 5e+04 |
6613 | 10270 | 0 | 13282 | 0 | 21411 | 0 | 2 | 0-7500 | 64-73 | Married | 4 | 87 | High | 0 | 5e+04 |
6657 | 7155 | 0 | 11542 | 0 | 13074 | 0 | 2 | 0-7500 | 4-8 | Single | 2 | 22 | Low | 0 | 5e+05 |
6674 | 3671 | 0 | 11656 | 0 | 12655 | 0 | 2 | 10000-15000 | 4-8 | Single | 4 | 24 | Medium | 0 | 3e+05 |
6693 | 424 | 8035 | 13120 | 8035 | 14465 | 0 | 3 | 10000-15000 | 4-8 | Single | 2 | 21 | Low | 0 | 5e+04 |
6695 | 5673 | 0 | 11569 | 0 | 12833 | 0 | 2 | 10000-15000 | 44-53 | Married | 1 | 64 | Medium | 0 | 5e+04 |
6701 | 12334 | 4516 | 12594 | 4516 | 14503 | 0 | 3 | 15000+ | 4-8 | Single | 2 | 23 | High | 0 | 1e+05 |
6745 | 4117 | 16770 | 11800 | 16770 | 13481 | 2 | 3 | 15000+ | 9-13 | Single | 2 | 25 | Low | 0 | 5e+04 |
6780 | 11585 | 0 | 9162 | 0 | 17894 | 0 | 1 | 15000+ | 64-73 | Married | 1 | 86 | High | 0 | 5e+05 |
6811 | 1602 | 3061 | 12594 | 3061 | 14503 | 0 | 3 | 15000+ | 4-8 | Single | 2 | 21 | Low | 0 | 5e+04 |
6819 | 10415 | 41 | 10260 | 41 | 12469 | 0 | 2 | 7500-10000 | 4-8 | Single | 3 | 23 | Low | 0 | 1e+05 |
6873 | 2217 | 0 | 12930 | 0 | 14577 | 0 | 2 | 10000-15000 | 4-8 | Single | 1 | 23 | Low | 0 | 3e+05 |
6892 | 7452 | 6609 | 11131 | 6609 | 13077 | 0 | 2 | 7500-10000 | 4-8 | Single | 1 | 24 | Low | 0 | 3e+05 |
6900 | 14871 | 3223 | 11542 | 3223 | 13074 | 0 | 2 | 0-7500 | 4-8 | Single | 2 | 21 | Low | 0 | 5e+05 |
6911 | 9979 | 4240 | 12878 | 4240 | 14509 | 0 | 2 | 10000-15000 | 24-33 | Single | 2 | 48 | Medium | 0 | 5e+04 |
6935 | 9680 | 186 | 12937 | 186 | 14497 | 0 | 3 | 10000-15000 | 4-8 | Single | 3 | 21 | Low | 0 | 5e+04 |
7010 | 7116 | 6574 | 11188 | 6574 | 12688 | 0 | 2 | 15000+ | 4-8 | Single | 4 | 21 | Low | 0 | 1e+05 |
7013 | 6490 | 728 | 8920 | 728 | 15486 | 1 | 1 | 15000+ | 64-73 | Married | 4 | 88 | High | 0 | 5e+04 |
7017 | 9996 | 0 | 11439 | 0 | 13937 | 0 | 2 | 15000+ | 4-8 | Single | 3 | 22 | Medium | 0 | 3e+05 |
7046 | 1861 | 1043 | 15158 | 1043 | 15157 | 1 | 3 | 10000-15000 | 4-8 | Single | 1 | 22 | Medium | 0 | 1e+05 |
7085 | 13053 | 0 | 9894 | 0 | 17839 | 1 | 1 | 15000+ | 64-73 | Married | 1 | 86 | High | 0 | 1e+05 |
7121 | 12430 | 963 | 11131 | 963 | 13077 | 0 | 2 | 7500-10000 | 4-8 | Single | 1 | 24 | Low | 0 | 5e+04 |
7130 | 12123 | 0 | 13335 | 0 | 13699 | 1 | 2 | 0-7500 | 4-8 | Single | 1 | 21 | Low | 0 | 5e+04 |
7140 | 10714 | 19523 | 13116 | 19523 | 13193 | 1 | 3 | 15000+ | 4-8 | Single | 4 | 22 | Low | 0 | 3e+05 |
7208 | 14150 | 1270 | 11917 | 1270 | 13900 | 0 | 2 | 10000-15000 | 4-8 | Single | 3 | 21 | Low | 0 | 5e+04 |
7218 | 14459 | 11882 | 10405 | 11882 | 12441 | 0 | 2 | 7500-10000 | 4-8 | Single | 2 | 22 | Low | 0 | 1e+05 |
7226 | 7972 | 0 | 13776 | 0 | 15251 | 0 | 2 | 10000-15000 | 24-33 | Single | 1 | 41 | High | 0 | 5e+05 |
7250 | 315 | 147 | 16748 | 147 | 27287 | 0 | 3 | 10000-15000 | 64-73 | Married | 1 | 89 | Low | 0 | 5e+04 |
7251 | 7356 | 0 | 12021 | 0 | 13036 | 1 | 2 | 7500-10000 | 4-8 | Single | 1 | 21 | Medium | 0 | 1e+05 |
7316 | 5127 | 2212 | 12084 | 2212 | 13639 | 0 | 3 | 7500-10000 | 4-8 | Single | 1 | 23 | Medium | 0 | 1e+05 |
7377 | 11567 | 120 | 13120 | 120 | 14465 | 0 | 3 | 10000-15000 | 4-8 | Single | 2 | 22 | Low | 0 | 1e+05 |
7379 | 14373 | 139 | 13116 | 139 | 13193 | 1 | 3 | 15000+ | 4-8 | Single | 4 | 22 | Low | 0 | 5e+04 |
7392 | 13590 | 0 | 12104 | 0 | 13158 | 2 | 2 | 10000-15000 | 14-23 | Single | 1 | 31 | Low | 0 | 3e+05 |
7415 | 4511 | 120 | 13472 | 120 | 15244 | 0 | 3 | 15000+ | 4-8 | Single | 1 | 23 | High | 0 | 5e+04 |
7425 | 8870 | 449 | 13472 | 449 | 15244 | 0 | 3 | 15000+ | 4-8 | Single | 1 | 21 | Low | 0 | 5e+04 |
7431 | 9742 | 303 | 14036 | 303 | 15204 | 0 | 3 | 10000-15000 | 4-8 | Single | 1 | 22 | Low | 0 | 3e+05 |
7445 | 12519 | 155 | 11295 | 155 | 12976 | 0 | 3 | 7500-10000 | 4-8 | Single | 2 | 22 | Low | 0 | 5e+04 |
7493 | 3007 | 16698 | 12870 | 16698 | 13857 | 1 | 2 | 10000-15000 | 4-8 | Single | 3 | 24 | Medium | 0 | 1e+05 |
7511 | 9964 | 158 | 13116 | 158 | 13193 | 1 | 3 | 15000+ | 4-8 | Single | 4 | 23 | Low | 0 | 1e+05 |
7532 | 7854 | 15437 | 12198 | 15437 | 12936 | 1 | 3 | 7500-10000 | 4-8 | Single | 2 | 22 | Medium | 0 | 5e+04 |
7587 | 10272 | 178 | 12145 | 178 | 13234 | 0 | 3 | 15000+ | 4-8 | Single | 4 | 23 | Low | 0 | 5e+04 |
7633 | 14750 | 0 | 11891 | 0 | 12408 | 1 | 2 | 15000+ | 34-43 | Married | 3 | 55 | Low | 0 | 5e+04 |
7749 | 5343 | 18254 | 11439 | 18254 | 13937 | 0 | 2 | 15000+ | 4-8 | Single | 3 | 22 | Low | 0 | 3e+05 |
7765 | 2392 | 6992 | 12348 | 6992 | 13742 | 0 | 2 | 0-7500 | 4-8 | Single | 1 | 24 | Low | 0 | 1e+05 |
7788 | 3302 | 273 | 12084 | 273 | 13639 | 0 | 3 | 7500-10000 | 4-8 | Single | 1 | 22 | Low | 0 | 1e+05 |
7789 | 8099 | 563 | 13963 | 563 | 14532 | 1 | 2 | 10000-15000 | 4-8 | Single | 1 | 23 | High | 0 | 1e+05 |
7893 | 12456 | 0 | 12126 | 0 | 13709 | 0 | 2 | 0-7500 | 24-33 | Single | 3 | 44 | Low | 0 | 1e+05 |
7903 | 8529 | 100000 | 11946 | 139641 | 13053 | 0 | 2 | 15000+ | 34-43 | Married | 1 | 54 | Medium | 0 | 5e+05 |
7953 | 1523 | 147 | 10380 | 147 | 12241 | 0 | 2 | 15000+ | 44-53 | Married | 2 | 67 | High | 0 | 1e+05 |
7983 | 13700 | 177 | 11601 | 177 | 13905 | 0 | 2 | 15000+ | 4-8 | Single | 2 | 24 | Low | 0 | 3e+05 |
7988 | 13134 | 8093 | 12418 | 8093 | 14536 | 0 | 3 | 15000+ | 4-8 | Single | 3 | 24 | Medium | 0 | 1e+05 |
7994 | 12617 | 100000 | 11381 | 157621 | 13103 | 0 | 2 | 0-7500 | 4-8 | Single | 3 | 24 | Low | 0 | 3e+05 |
8007 | 5492 | 7148 | 11295 | 7148 | 12976 | 0 | 3 | 7500-10000 | 4-8 | Single | 2 | 21 | Low | 0 | 5e+04 |
8015 | 8116 | 131 | 12587 | 131 | 12616 | 1 | 2 | 10000-15000 | 4-8 | Single | 4 | 21 | Low | 0 | 3e+05 |
8048 | 14144 | 50012 | 11138 | 50012 | 13005 | 0 | 3 | 7500-10000 | 4-8 | Single | 3 | 23 | Low | 0 | 1e+05 |
8063 | 3226 | 820 | 14320 | 820 | 16536 | 2 | 3 | 0-7500 | 4-8 | Single | 3 | 23 | Low | 0 | 3e+05 |
8066 | 8446 | 1903 | 13404 | 1903 | 14333 | 0 | 3 | 0-7500 | 4-8 | Single | 1 | 22 | Low | 0 | 5e+04 |
8140 | 10640 | 2998 | 12937 | 2998 | 14497 | 0 | 3 | 10000-15000 | 4-8 | Single | 3 | 22 | Medium | 0 | 1e+05 |
8182 | 2954 | 1871 | 12594 | 1871 | 14503 | 0 | 3 | 15000+ | 4-8 | Single | 2 | 21 | Low | 0 | 1e+05 |
8226 | 3875 | 0 | 11381 | 0 | 13103 | 0 | 2 | 0-7500 | 4-8 | Single | 3 | 22 | Medium | 0 | 5e+05 |
8252 | 14926 | 0 | 11381 | 0 | 13103 | 0 | 2 | 0-7500 | 4-8 | Single | 3 | 22 | Medium | 0 | 5e+04 |
8255 | 13794 | 22811 | 12086 | 22811 | 13868 | 0 | 2 | 10000-15000 | 4-8 | Single | 2 | 21 | Medium | 0 | 3e+05 |
8281 | 3307 | 0 | 13776 | 0 | 15251 | 0 | 2 | 10000-15000 | 24-33 | Single | 1 | 41 | High | 0 | 5e+04 |
8299 | 9763 | 0 | 10260 | 0 | 12469 | 0 | 2 | 7500-10000 | 4-8 | Single | 3 | 23 | Low | 0 | 3e+05 |
8304 | 1298 | 0 | 11439 | 0 | 13937 | 0 | 2 | 15000+ | 4-8 | Single | 3 | 24 | Low | 0 | 5e+04 |
8413 | 13192 | 15415 | 10260 | 15415 | 12469 | 0 | 2 | 7500-10000 | 4-8 | Single | 3 | 23 | Low | 0 | 1e+05 |
8415 | 10493 | 39364 | 11188 | 39364 | 12688 | 0 | 2 | 15000+ | 4-8 | Single | 4 | 21 | Medium | 0 | 3e+05 |
8431 | 13816 | 4001 | 11601 | 4001 | 13905 | 0 | 2 | 15000+ | 4-8 | Single | 2 | 22 | Medium | 0 | 3e+05 |
8476 | 4203 | 499 | 11917 | 499 | 13900 | 0 | 2 | 10000-15000 | 4-8 | Single | 3 | 23 | Low | 0 | 5e+04 |
8502 | 156 | 69095 | 13600 | 69095 | 14458 | 1 | 3 | 15000+ | 4-8 | Single | 2 | 21 | Low | 0 | 1e+05 |
8507 | 7027 | 0 | 9272 | 0 | 12031 | 2 | 1 | 10000-15000 | 4-8 | Single | 1 | 22 | High | 0 | 5e+04 |
8523 | 13199 | 0 | 12898 | 0 | 14663 | 0 | 2 | 15000+ | 34-43 | Single | 4 | 50 | Low | 0 | 1e+05 |
8545 | 8659 | 120 | 11601 | 120 | 13905 | 0 | 2 | 15000+ | 4-8 | Single | 2 | 23 | Medium | 0 | 1e+05 |
8554 | 12913 | 120 | 11439 | 120 | 13937 | 0 | 2 | 15000+ | 4-8 | Single | 3 | 22 | Medium | 0 | 5e+04 |
8633 | 4980 | 0 | 11439 | 0 | 13937 | 0 | 2 | 15000+ | 4-8 | Single | 3 | 22 | Low | 0 | 1e+05 |
8641 | 12114 | 0 | 8798 | 0 | 17017 | 0 | 1 | 10000-15000 | 64-73 | Married | 3 | 87 | High | 0 | 5e+04 |
8655 | 14469 | 52013 | 10260 | 52013 | 12469 | 0 | 2 | 7500-10000 | 4-8 | Single | 3 | 23 | Low | 0 | 3e+05 |
8680 | 13647 | 0 | 10405 | 0 | 12441 | 0 | 2 | 7500-10000 | 4-8 | Single | 2 | 24 | Medium | 0 | 5e+05 |
8705 | 3188 | 0 | 11601 | 0 | 13905 | 0 | 2 | 15000+ | 4-8 | Single | 2 | 22 | Low | 0 | 1e+05 |
8709 | 3620 | 632 | 12418 | 632 | 14536 | 0 | 3 | 15000+ | 4-8 | Single | 3 | 21 | Low | 0 | 3e+05 |
8710 | 4917 | 2991 | 13335 | 2991 | 13699 | 1 | 2 | 0-7500 | 4-8 | Single | 1 | 23 | Low | 0 | 5e+05 |
8720 | 2533 | 4352 | 11601 | 4352 | 13905 | 0 | 2 | 15000+ | 4-8 | Single | 2 | 22 | Medium | 0 | 5e+04 |
8757 | 11135 | 22341 | 12594 | 22341 | 14503 | 0 | 3 | 15000+ | 4-8 | Single | 2 | 23 | Low | 0 | 5e+04 |
8766 | 14451 | 10969 | 13472 | 10969 | 15244 | 0 | 3 | 15000+ | 4-8 | Single | 1 | 23 | Medium | 0 | 1e+05 |
8781 | 5907 | 464 | 12084 | 464 | 13639 | 0 | 3 | 7500-10000 | 4-8 | Single | 1 | 24 | Medium | 0 | 1e+05 |
8784 | 9822 | 1788 | 13403 | 1788 | 14571 | 1 | 2 | 15000+ | 4-8 | Single | 1 | 23 | Medium | 0 | 3e+05 |
8837 | 3954 | 120 | 10405 | 120 | 12441 | 0 | 2 | 7500-10000 | 4-8 | Single | 2 | 22 | Medium | 0 | 1e+05 |
8872 | 11047 | 124 | 11656 | 124 | 12655 | 0 | 2 | 10000-15000 | 4-8 | Single | 4 | 21 | Medium | 0 | 5e+04 |
8948 | 3371 | 0 | 14877 | 0 | 15204 | 1 | 2 | 10000-15000 | 24-33 | Single | 1 | 43 | High | 0 | 5e+04 |
8949 | 7890 | 746 | 12411 | 746 | 14616 | 0 | 2 | 15000+ | 4-8 | Single | 1 | 23 | Low | 0 | 1e+05 |
9039 | 7910 | 4443 | 12937 | 4443 | 14497 | 0 | 3 | 10000-15000 | 4-8 | Single | 3 | 22 | Low | 0 | 5e+04 |
9043 | 10199 | 6948 | 12930 | 6948 | 14577 | 0 | 2 | 10000-15000 | 4-8 | Single | 1 | 24 | Medium | 0 | 5e+04 |
9045 | 14057 | 17615 | 12937 | 17615 | 14497 | 0 | 3 | 10000-15000 | 4-8 | Single | 3 | 21 | Low | 0 | 1e+05 |
9057 | 13656 | 35 | 13776 | 35 | 15251 | 0 | 2 | 10000-15000 | 24-33 | Single | 1 | 40 | Medium | 0 | 1e+05 |
9071 | 6868 | 125 | 12083 | 125 | 12442 | 0 | 3 | 0-7500 | 4-8 | Single | 4 | 22 | Low | 0 | 5e+04 |
9090 | 1308 | 49448 | 12411 | 49448 | 14616 | 0 | 2 | 15000+ | 4-8 | Single | 1 | 21 | Medium | 0 | 3e+05 |
9111 | 11778 | 19111 | 14549 | 19111 | 15197 | 1 | 3 | 15000+ | 4-8 | Single | 1 | 22 | Low | 0 | 1e+05 |
9123 | 7099 | 2581 | 11917 | 2581 | 13900 | 0 | 2 | 10000-15000 | 4-8 | Single | 3 | 22 | Low | 0 | 5e+05 |
9133 | 11486 | 15558 | 13258 | 15558 | 16863 | 2 | 2 | 15000+ | 4-8 | Single | 3 | 21 | Low | 0 | 1e+05 |
9135 | 859 | 10947 | 11439 | 10947 | 13937 | 0 | 2 | 15000+ | 4-8 | Single | 3 | 22 | Low | 0 | 1e+05 |
9209 | 2525 | 638 | 11295 | 638 | 12976 | 0 | 3 | 7500-10000 | 4-8 | Single | 2 | 24 | Medium | 0 | 5e+04 |
9274 | 4293 | 0 | 12465 | 0 | 13033 | 1 | 2 | 0-7500 | 4-8 | Single | 2 | 23 | Medium | 0 | 5e+04 |
9279 | 11598 | 20 | 12084 | 20 | 13639 | 0 | 3 | 7500-10000 | 4-8 | Single | 1 | 21 | Low | 0 | 1e+05 |
9292 | 12383 | 1908 | 12411 | 1908 | 14616 | 0 | 2 | 15000+ | 4-8 | Single | 1 | 23 | Low | 0 | 5e+05 |
9347 | 3168 | 0 | 11131 | 0 | 13077 | 0 | 2 | 7500-10000 | 4-8 | Single | 1 | 23 | High | 0 | 5e+04 |
9359 | 10523 | 120 | 11086 | 120 | 13016 | 0 | 2 | 7500-10000 | 24-33 | Single | 2 | 40 | Low | 0 | 5e+04 |
9380 | 5074 | 55069 | 11104 | 55069 | 12867 | 0 | 2 | 15000+ | 44-53 | Married | 1 | 66 | Low | 0 | 5e+04 |
9423 | 1987 | 0 | 12411 | 0 | 14616 | 0 | 2 | 15000+ | 4-8 | Single | 1 | 22 | High | 0 | 1e+05 |
9517 | 13016 | 0 | 8217 | 0 | 14605 | 0 | 1 | 0-7500 | 64-73 | Married | 4 | 88 | High | 0 | 3e+05 |
9551 | 5435 | 0 | 10405 | 0 | 12441 | 0 | 2 | 7500-10000 | 4-8 | Single | 2 | 21 | Low | 0 | 3e+05 |
9553 | 1865 | 0 | 11996 | 0 | 14377 | 0 | 2 | 7500-10000 | 34-43 | Single | 2 | 50 | Medium | 0 | 5e+04 |
9556 | 5209 | 20791 | 12028 | 20791 | 12965 | 1 | 3 | 7500-10000 | 4-8 | Single | 3 | 22 | Low | 0 | 5e+04 |
9590 | 4692 | 0 | 12354 | 0 | 13894 | 1 | 2 | 15000+ | 4-8 | Single | 3 | 21 | Low | 0 | 3e+05 |
9669 | 8981 | 6800 | 12059 | 6800 | 12380 | 1 | 2 | 15000+ | 34-43 | Married | 2 | 50 | Medium | 0 | 3e+05 |
9689 | 742 | 23092 | 11295 | 23092 | 12976 | 0 | 3 | 7500-10000 | 4-8 | Single | 2 | 22 | Medium | 0 | 3e+05 |
9695 | 2939 | 10488 | 11439 | 10488 | 13937 | 0 | 2 | 15000+ | 4-8 | Single | 3 | 24 | Medium | 0 | 1e+05 |
9716 | 4729 | 411 | 12086 | 411 | 13868 | 0 | 2 | 10000-15000 | 4-8 | Single | 2 | 23 | Medium | 0 | 5e+04 |
9760 | 1681 | 0 | 12411 | 0 | 14616 | 0 | 2 | 15000+ | 4-8 | Single | 1 | 22 | Low | 0 | 3e+05 |
9801 | 12022 | 1163 | 10260 | 1163 | 13941 | 2 | 1 | 15000+ | 34-43 | Single | 1 | 57 | Medium | 0 | 5e+05 |
9827 | 10045 | 37061 | 12086 | 37061 | 13868 | 0 | 2 | 10000-15000 | 4-8 | Single | 2 | 22 | Medium | 0 | 5e+04 |
9838 | 11554 | 49220 | 10260 | 49220 | 12469 | 0 | 2 | 7500-10000 | 4-8 | Single | 3 | 24 | Medium | 0 | 3e+05 |
9940 | 5132 | 8836 | 13472 | 8836 | 15244 | 0 | 3 | 15000+ | 4-8 | Single | 1 | 24 | Low | 0 | 5e+04 |
Let’s visualize our rejection regions in order to fine-tune exactly who we need to reject.
compare.pp.capped %>% filter(`Driver Age` < 25) %>% ggplot(aes(x = `Annual Mileage`, y = `Predicted Pure Premium`)) + geom_bar(aes(fill = `Annual Mileage`), stat = "summary", fun.y = "mean", show.legend = TRUE) + facet_grid(. ~ `Marital Status`) + theme(legend.position = "bottom", axis.text.x = element_blank(), axis.ticks.x = element_blank()) + ggtitle("Total Rejection: Average Projected Loss by Annual Mileage")
rej %>% filter(`Driver Age` < 25) %>% ggplot(aes(x = `Annual Mileage`, y = `Predicted Pure Premium`)) + geom_bar(aes(fill = `Annual Mileage`), stat = "summary", fun.y = "mean", show.legend = TRUE) + facet_grid(. ~ `Marital Status`) + theme(legend.position = "bottom", axis.text.x = element_blank(), axis.ticks.x = element_blank()) + ggtitle("Desired Rejection: Average Projected Loss by Annual Mileage")
Comparing the two exhibits, we conclude that we’ll reject drivers with <10 years of driving experience who are married with 0-7500 annual mileage. This may be a group of high-risk (early marriage), low-experience drivers (low mileage) who we cannot charge adequate premiums to cover losses, at least in this aggregation of data.
compare.pp.capped %>% filter(`Driver Age` < 30) %>% ggplot(aes(x = `Territory`, y = `Predicted Pure Premium`)) + geom_bar(aes(fill = `Territory`), stat = "summary", fun.y = "mean", show.legend = TRUE) + facet_grid(. ~ `Marital Status`) + theme(legend.position = "bottom", axis.text.x = element_blank(), axis.ticks.x = element_blank()) + ggtitle("Total Rejection: Average Projected Loss by Territory")
rej %>% filter(`Driver Age` < 30) %>% ggplot(aes(x = `Territory`, y = `Predicted Pure Premium`)) + geom_bar(aes(fill = `Territory`), stat = "summary", fun.y = "mean", show.legend = TRUE) + facet_grid(. ~ `Marital Status`) + theme(legend.position = "bottom", axis.text.x = element_blank(), axis.ticks.x = element_blank()) + ggtitle("Desired Rejection: Average Projected Loss by Territory")
Territory 4 has the lowest charged premiums of all territories, yet the expected losses in this territory among married policyholders is very high. In general, Territory 4 has the lowest average loss, so we’ll reject young (<15 years driver experience) married drivers from territory 4.
compare.pp.capped %>% filter(`Driver Age` > 60) %>% ggplot(aes(x = `Territory`, y = `Predicted Pure Premium`)) + geom_bar(aes(fill = `Territory`), stat = "summary", fun.y = "mean", show.legend = TRUE) + facet_grid(. ~ `Marital Status`) + theme(legend.position = "bottom", axis.text.x = element_blank(), axis.ticks.x = element_blank()) + ggtitle("Total Rejection: Average Projected Loss by Territory")
rej %>% filter(`Driver Age` > 60) %>% ggplot(aes(x = `Territory`, y = `Predicted Pure Premium`)) + geom_bar(aes(fill = `Territory`), stat = "summary", fun.y = "mean", show.legend = TRUE) + facet_grid(. ~ `Marital Status`) + theme(legend.position = "bottom", axis.text.x = element_blank(), axis.ticks.x = element_blank()) + ggtitle("Desired Rejection: Average Projected Loss by Territory")
We’ll reject older (45+ years driver experience) from Territory 2.
Accident Points:
compare.pp.capped %>% filter(`Driver Age` > 10) %>% ggplot(aes(x = `Accident Points (AP) last 3 years`, y = `Predicted Pure Premium`)) + geom_bar(aes(fill = `Accident Points (AP) last 3 years`), stat = "summary", fun.y = "mean", show.legend = TRUE) + facet_grid(. ~ `Liability Limit`) + theme(legend.position = "bottom", axis.text.x = element_blank(), axis.ticks.x = element_blank()) + ggtitle("Total Rejection: Average Projected Loss by Accident Points (AP) last 3 years")
rej %>% filter(`Driver Age` > 10) %>% ggplot(aes(x = `Accident Points (AP) last 3 years`, y = `Predicted Pure Premium`)) + geom_bar(aes(fill = `Accident Points (AP) last 3 years`), stat = "summary", fun.y = "mean", show.legend = TRUE) + facet_grid(. ~ `Liability Limit`) + theme(legend.position = "bottom", axis.text.x = element_blank(), axis.ticks.x = element_blank()) + ggtitle("Desired Rejection: Average Projected Loss by Accident Points (AP) last 3 years")
Unfortunately it seems as though we cannot safely perform rejections via this aggregation without inadvertently rejecting desired drivers. To be conservative here, we’ll reject all drivers with 2+ accident points in the last 3 years for all coverage of $100,000 liability and above. However, we can offer coverage at the $50,000 limit with a much higher AP relativity. We’ll try conviction points instead of accident points.
Conviction Points:
compare.pp.capped %>% filter(`Driver Age` > 10) %>% ggplot(aes(x = `Conviction Points (CP) last 3 years`, y = `Predicted Pure Premium`)) + geom_bar(aes(fill = `Conviction Points (CP) last 3 years`), stat = "summary", fun.y = "mean", show.legend = TRUE) + facet_grid(. ~ `Liability Limit`) + theme(legend.position = "bottom", axis.text.x = element_blank(), axis.ticks.x = element_blank()) + ggtitle("Total Rejection: Average Projected Loss by Conviction Points (CP) last 3 years")
rej %>% filter(`Driver Age` > 10) %>% ggplot(aes(x = `Conviction Points (CP) last 3 years`, y = `Predicted Pure Premium`)) + geom_bar(aes(fill = `Conviction Points (CP) last 3 years`), stat = "summary", fun.y = "mean", show.legend = TRUE) + facet_grid(. ~ `Liability Limit`) + theme(legend.position = "bottom", axis.text.x = element_blank(), axis.ticks.x = element_blank()) + ggtitle("Desired Rejection: Average Projected Loss by Conviction Points (CP) last 3 years")
From this exhibit, we choose to reject drivers with 3+ conviction points in the last 3 years. However, we want to allow them the least coverage at $50,000 liability, albeit with a high premium relativity.
However, in the present case, we cannot change the rating structure and must take judgement regarding this. We choose to not place restrictions on policyholders with 3+ conviction points in the past 3 years, because we will be making the assumption that no drivers have conviction points in any of policy years (PY) 2016, 2017, 2018.
Recall that we cannot use Credit Score in our underwriting guideline or rating mechanism. Interestingly, our observed and projected pure premiums both show the monotonic trend that lower credit scores incur higher losses.
# compare.pp.capped %>% filter(`Driver Age` > 85) %>% ggplot(aes(x = `Annual Mileage`, y = `Pure Premium`)) + geom_bar(fill = `Annual Mileage`, stat = "summary", fun.y = "mean") + facet_grid(. ~ `Marital Status`)
compare.pp.capped %>% filter(`Driver Age` > 10) %>% ggplot(aes(x = `Credit Score`, y = `Pure Premium`)) + geom_bar(aes(fill = `Credit Score`), stat = "summary", fun.y = "mean", show.legend = TRUE) + facet_grid(. ~ `Marital Status`) + theme(legend.position = "bottom", axis.text.x = element_blank(), axis.ticks.x = element_blank()) + ggtitle("Observed Pure Premium by Credit Score")
compare.pp.capped %>% filter(`Driver Age` > 10) %>% ggplot(aes(x = `Credit Score`, y = `Predicted Pure Premium`)) + geom_bar(aes(fill = `Credit Score`), stat = "summary", fun.y = "mean", show.legend = TRUE) + facet_grid(. ~ `Marital Status`) + theme(legend.position = "bottom", axis.text.x = element_blank(), axis.ticks.x = element_blank()) + ggtitle("Predicted Pure Premium by Credit Score")
Comparing the above two figures, we filtered down to drivers only ages 60+ and find that single drivers of age 60+ with low credit scores on average incur high losses. This is not caught in our model, so we need to reject this category somehow.
First we note that there is no overlap in vehicle numbers or policy numbers. In particular, no existing policy is attempting to add another vehicle.
apps <- read_excel("Dropbox/Actuary/proj/case-comp/GA/ga-csv/GA_Data - Applications.xlsx")
max(tbl$`Policy Number`)
## [1] 10000
# [1] 10000
min(apps$`Policy Number`)
## [1] 10001
# [1] 10001
max(tbl$`Vehicle Number`)
## [1] 15000
# [1] 15000
min(apps$`Policy Number`)
## [1] 10001
# [1] 15001
In summary, our underwriting guidelines are to reject any applicant who satisfies one of the following rows:
Ideally, we’ll allow the following but only at the lowest liability (with higher relativities to compensate). But for now, we’ll wholly still accept the following, but with increased rates (as the premiums will be naturally adjusted via relativities: 1/1.2/1.4/1.6)
Now for #6, we would like to reject all. However, in a realistic setting, rejecting longterm loyal customers due to a sudden restriction in risk appetite reflects poorly on Cal Insurance. In terms of public relations and trust, it is much preferrable to retain customers to not have to provide introductory discounts for new customers with Cal Insurance (further analysis required to support this).
# implement UW guidelines as logic check to assign 0: reject, 1: accept
apps <- apps %>% mutate(`Driver Experience` = `Driver Age` - 15) # adjust age into driver experience
apps <- apps %>% mutate(`Decision` = 1) # default decisions to accept and reject in specific scenarios
for (i in 1:15000) {
# 1: reject 45+ years driver experience + single
if (apps$`Driver Experience`[i] >= 45 & apps$`Marital Status`[i] == "Single") {
apps$Decision[i] <- 0
}
# 2: reject 35+ yrs driver exp + single + either low or mid-high annual mileage
if (apps$`Driver Experience`[i] >= 35 & apps$`Marital Status`[i] == "Single" & (apps$`Annual Mileage`[i] == "0-7500" || apps$`Annual Mileage`[i] == "10000-15000") ) {
apps$Decision[i] <- 0
}
# 3: reject <10 yrs driver exp + married + low mileage
if (apps$`Driver Experience`[i] <= 10 & apps$`Marital Status`[i] == "Married" & apps$`Annual Mileage`[i] == "0-7500") {
apps$Decision[i] <- 0
}
# 4: reject <15 yrs driver exp + married + territory 4
if (apps$`Driver Experience`[i] <= 15 & apps$`Marital Status`[i] == "Married" & apps$Territory[i] == 4) {
apps$Decision[i] <- 0
}
}
print(paste0("The acceptance rate is: ", sum(apps$Decision)/length(apps$Decision) * 100, "%" ) ) # only reject 171 people
## [1] "The acceptance rate is: 98.86%"
# write into .csv
apps %>% select(`Vehicle Number`, `Decision`) %>% write.csv(file = "decisions.csv", )
# apps %>% select(`Vehicle Number`, `Decision`) %>% qkable()
We only reject a very few applications here because we opt to not use incredible data (conviction points) or reject
Here’s a quick look at the applications we reject:
app.rej <- apps %>% filter(`Decision` == 0)
app.acc <- apps %>% filter(`Decision` == 1) # accepted applications
app.rej %>% slice(1:100) %>% qkable(height = "500px") # excerpt of people we reject
Vehicle Number | Policy Year | Policy Number | Marital Status | Driver Age | Driver Age Band | Annual Mileage | Territory | Credit Score | Car Value | Car Value Band | Accident Points | Liability Limit | Physical Damage Deductible | Conviction Points | Base_PP | Rel_Age | Rel_Mileage | Rel_Territory | Rel_Car | Rel_Accident | Rel_Conviction | Rel_Limit | Rel_Deductible | Charged Premium | Driver Experience | Decision |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
15011 | 2019 | 11950 | Single | 55 | 50-59 | 10000-15000 | 3 | High | 39110 | 30000-40000 | 0 | 3e+05 | 500 | 0 | 1500 | 0.8 | 1.0 | 0.9 | 1.045 | 1.0 | 1.0 | 1.00 | 1.000 | 1128.6000 | 40 | 0 |
15143 | 2019 | 14769 | Single | 77 | 70-79 | 15000+ | 3 | High | 20000 | 20000-30000 | 0 | 5e+04 | 100 | 0 | 1500 | 0.8 | 1.2 | 0.9 | 1.000 | 1.0 | 1.0 | 0.60 | 1.080 | 839.8080 | 62 | 0 |
15381 | 2019 | 19695 | Married | 24 | 20-24 | 0-7500 | 2 | Medium | 10550 | 10000-20000 | 0 | 3e+05 | 500 | 1 | 1500 | 2.0 | 0.7 | 1.1 | 0.880 | 1.0 | 1.2 | 1.00 | 1.000 | 2439.3600 | 9 | 0 |
15386 | 2019 | 11282 | Single | 51 | 50-59 | 0-7500 | 4 | Medium | 12590 | 10000-20000 | 0 | 5e+04 | 250 | 0 | 1500 | 0.8 | 0.7 | 0.8 | 0.880 | 1.0 | 1.0 | 0.60 | 1.045 | 370.7827 | 36 | 0 |
15401 | 2019 | 14122 | Married | 30 | 30-39 | 15000+ | 4 | Low | 29340 | 20000-30000 | 1 | 1e+05 | 250 | 0 | 1500 | 1.0 | 1.2 | 0.8 | 1.000 | 1.2 | 1.0 | 0.88 | 1.045 | 1589.0688 | 15 | 0 |
15408 | 2019 | 15895 | Single | 51 | 50-59 | 10000-15000 | 4 | Medium | 56350 | 40000+ | 1 | 5e+05 | 250 | 0 | 1500 | 0.8 | 1.0 | 0.8 | 1.080 | 1.2 | 1.0 | 1.12 | 1.045 | 1456.1649 | 36 | 0 |
15508 | 2019 | 18825 | Married | 29 | 25-29 | 0-7500 | 4 | Low | 20090 | 20000-30000 | 0 | 3e+05 | 500 | 0 | 1500 | 1.5 | 0.7 | 0.8 | 1.000 | 1.0 | 1.0 | 1.00 | 1.000 | 1260.0000 | 14 | 0 |
15532 | 2019 | 10767 | Single | 96 | 90+ | 0-7500 | 1 | Medium | 29440 | 20000-30000 | 0 | 1e+05 | 100 | 0 | 1500 | 0.8 | 0.7 | 1.2 | 1.000 | 1.0 | 1.0 | 0.88 | 1.080 | 958.0032 | 81 | 0 |
15570 | 2019 | 10491 | Married | 28 | 25-29 | 15000+ | 4 | Medium | 10940 | 10000-20000 | 1 | 5e+05 | 1000 | 0 | 1500 | 1.5 | 1.2 | 0.8 | 0.880 | 1.2 | 1.0 | 1.12 | 0.935 | 2388.6213 | 13 | 0 |
15784 | 2019 | 17651 | Single | 50 | 50-59 | 0-7500 | 2 | High | 39330 | 30000-40000 | 0 | 1e+05 | 250 | 0 | 1500 | 0.8 | 0.7 | 1.1 | 1.045 | 1.0 | 1.0 | 0.88 | 1.045 | 887.9474 | 35 | 0 |
15831 | 2019 | 15278 | Married | 26 | 25-29 | 15000+ | 4 | High | 22570 | 20000-30000 | 0 | 5e+04 | 100 | 1 | 1500 | 1.5 | 1.2 | 0.8 | 1.000 | 1.0 | 1.2 | 0.60 | 1.080 | 1679.6160 | 11 | 0 |
15836 | 2019 | 13190 | Single | 51 | 50-59 | 0-7500 | 3 | High | 27880 | 20000-30000 | 0 | 1e+05 | 1000 | 0 | 1500 | 0.8 | 0.7 | 0.9 | 1.000 | 1.0 | 1.0 | 0.88 | 0.935 | 622.0368 | 36 | 0 |
15971 | 2019 | 17901 | Single | 52 | 50-59 | 0-7500 | 3 | High | 95950 | 40000+ | 0 | 5e+04 | 100 | 0 | 1500 | 0.8 | 0.7 | 0.9 | 1.080 | 1.0 | 1.0 | 0.60 | 1.080 | 529.0790 | 37 | 0 |
16076 | 2019 | 18996 | Single | 50 | 50-59 | 0-7500 | 3 | High | 28240 | 20000-30000 | 0 | 5e+05 | 500 | 0 | 1500 | 0.8 | 0.7 | 0.9 | 1.000 | 1.0 | 1.0 | 1.12 | 1.000 | 846.7200 | 35 | 0 |
16097 | 2019 | 13917 | Single | 56 | 50-59 | 10000-15000 | 1 | Low | 22500 | 20000-30000 | 0 | 3e+05 | 500 | 0 | 1500 | 0.8 | 1.0 | 1.2 | 1.000 | 1.0 | 1.0 | 1.00 | 1.000 | 1440.0000 | 41 | 0 |
16285 | 2019 | 16536 | Single | 51 | 50-59 | 10000-15000 | 1 | Low | 35950 | 30000-40000 | 1 | 1e+05 | 250 | 0 | 1500 | 0.8 | 1.0 | 1.2 | 1.045 | 1.2 | 1.0 | 0.88 | 1.045 | 1660.5769 | 36 | 0 |
16383 | 2019 | 13714 | Single | 55 | 50-59 | 0-7500 | 4 | High | 31360 | 30000-40000 | 0 | 1e+05 | 100 | 1 | 1500 | 0.8 | 0.7 | 0.8 | 1.045 | 1.0 | 1.2 | 0.88 | 1.080 | 800.8907 | 40 | 0 |
16448 | 2019 | 18604 | Single | 74 | 70-79 | 0-7500 | 4 | High | 17420 | 10000-20000 | 0 | 5e+04 | 100 | 0 | 1500 | 0.8 | 0.7 | 0.8 | 0.880 | 1.0 | 1.0 | 0.60 | 1.080 | 383.2013 | 59 | 0 |
16502 | 2019 | 12951 | Single | 50 | 50-59 | 10000-15000 | 4 | High | 75490 | 40000+ | 0 | 5e+05 | 1000 | 0 | 1500 | 0.8 | 1.0 | 0.8 | 1.080 | 1.0 | 1.0 | 1.12 | 0.935 | 1085.7370 | 35 | 0 |
16563 | 2019 | 14487 | Single | 52 | 50-59 | 0-7500 | 2 | High | 20980 | 20000-30000 | 0 | 5e+05 | 500 | 1 | 1500 | 0.8 | 0.7 | 1.1 | 1.000 | 1.0 | 1.2 | 1.12 | 1.000 | 1241.8560 | 37 | 0 |
16566 | 2019 | 12270 | Married | 28 | 25-29 | 0-7500 | 4 | High | 38470 | 30000-40000 | 0 | 5e+04 | 100 | 0 | 1500 | 1.5 | 0.7 | 0.8 | 1.045 | 1.0 | 1.0 | 0.60 | 1.080 | 853.2216 | 13 | 0 |
16593 | 2019 | 12976 | Single | 54 | 50-59 | 0-7500 | 2 | High | 78260 | 40000+ | 0 | 5e+05 | 100 | 0 | 1500 | 0.8 | 0.7 | 1.1 | 1.080 | 1.0 | 1.0 | 1.12 | 1.080 | 1207.0840 | 39 | 0 |
16597 | 2019 | 11754 | Single | 63 | 60-69 | 0-7500 | 4 | High | 94850 | 40000+ | 0 | 5e+05 | 100 | 0 | 1500 | 0.8 | 0.7 | 0.8 | 1.080 | 1.0 | 1.0 | 1.12 | 1.080 | 877.8793 | 48 | 0 |
16730 | 2019 | 17163 | Single | 59 | 50-59 | 0-7500 | 2 | Medium | 47950 | 40000+ | 0 | 5e+05 | 500 | 0 | 1500 | 0.8 | 0.7 | 1.1 | 1.080 | 1.0 | 1.0 | 1.12 | 1.000 | 1117.6704 | 44 | 0 |
16745 | 2019 | 12495 | Single | 51 | 50-59 | 10000-15000 | 2 | Low | 25740 | 20000-30000 | 0 | 5e+04 | 100 | 0 | 1500 | 0.8 | 1.0 | 1.1 | 1.000 | 1.0 | 1.0 | 0.60 | 1.080 | 855.3600 | 36 | 0 |
17033 | 2019 | 16611 | Single | 50 | 50-59 | 0-7500 | 4 | High | 23440 | 20000-30000 | 0 | 5e+05 | 1000 | 0 | 1500 | 0.8 | 0.7 | 0.8 | 1.000 | 1.0 | 1.0 | 1.12 | 0.935 | 703.7184 | 35 | 0 |
17095 | 2019 | 14138 | Single | 53 | 50-59 | 10000-15000 | 4 | Medium | 86160 | 40000+ | 0 | 1e+05 | 500 | 0 | 1500 | 0.8 | 1.0 | 0.8 | 1.080 | 1.0 | 1.0 | 0.88 | 1.000 | 912.3840 | 38 | 0 |
17188 | 2019 | 14888 | Single | 51 | 50-59 | 0-7500 | 2 | Low | 36320 | 30000-40000 | 1 | 5e+04 | 100 | 0 | 1500 | 0.8 | 0.7 | 1.1 | 1.045 | 1.2 | 1.0 | 0.60 | 1.080 | 750.8350 | 36 | 0 |
17330 | 2019 | 14570 | Married | 30 | 30-39 | 10000-15000 | 4 | High | 29170 | 20000-30000 | 0 | 1e+05 | 250 | 0 | 1500 | 1.0 | 1.0 | 0.8 | 1.000 | 1.0 | 1.0 | 0.88 | 1.045 | 1103.5200 | 15 | 0 |
17381 | 2019 | 18039 | Single | 50 | 50-59 | 0-7500 | 1 | High | 13290 | 10000-20000 | 0 | 5e+04 | 1000 | 0 | 1500 | 0.8 | 0.7 | 1.2 | 0.880 | 1.0 | 1.0 | 0.60 | 0.935 | 497.6294 | 35 | 0 |
17391 | 2019 | 13963 | Single | 51 | 50-59 | 10000-15000 | 3 | High | 15490 | 10000-20000 | 1 | 3e+05 | 500 | 0 | 1500 | 0.8 | 1.0 | 0.9 | 0.880 | 1.2 | 1.0 | 1.00 | 1.000 | 1140.4800 | 36 | 0 |
17408 | 2019 | 18332 | Single | 55 | 50-59 | 0-7500 | 3 | High | 16590 | 10000-20000 | 0 | 3e+05 | 500 | 0 | 1500 | 0.8 | 0.7 | 0.9 | 0.880 | 1.0 | 1.0 | 1.00 | 1.000 | 665.2800 | 40 | 0 |
17475 | 2019 | 16397 | Single | 58 | 50-59 | 10000-15000 | 1 | High | 32580 | 30000-40000 | 1 | 3e+05 | 250 | 0 | 1500 | 0.8 | 1.0 | 1.2 | 1.045 | 1.2 | 1.0 | 1.00 | 1.045 | 1887.0192 | 43 | 0 |
17726 | 2019 | 10859 | Single | 50 | 50-59 | 10000-15000 | 2 | Low | 28360 | 20000-30000 | 0 | 3e+05 | 500 | 0 | 1500 | 0.8 | 1.0 | 1.1 | 1.000 | 1.0 | 1.0 | 1.00 | 1.000 | 1320.0000 | 35 | 0 |
17740 | 2019 | 19902 | Single | 50 | 50-59 | 10000-15000 | 4 | High | 14590 | 10000-20000 | 0 | 3e+05 | 250 | 0 | 1500 | 0.8 | 1.0 | 0.8 | 0.880 | 1.0 | 1.0 | 1.00 | 1.045 | 882.8160 | 35 | 0 |
17748 | 2019 | 11324 | Single | 51 | 50-59 | 0-7500 | 4 | High | 48720 | 40000+ | 0 | 5e+05 | 500 | 0 | 1500 | 0.8 | 0.7 | 0.8 | 1.080 | 1.0 | 1.0 | 1.12 | 1.000 | 812.8512 | 36 | 0 |
17811 | 2019 | 12670 | Single | 54 | 50-59 | 0-7500 | 4 | High | 22730 | 20000-30000 | 0 | 1e+05 | 250 | 0 | 1500 | 0.8 | 0.7 | 0.8 | 1.000 | 1.0 | 1.0 | 0.88 | 1.045 | 617.9712 | 39 | 0 |
17896 | 2019 | 16212 | Married | 29 | 25-29 | 10000-15000 | 4 | Medium | 14360 | 10000-20000 | 0 | 3e+05 | 500 | 1 | 1500 | 1.5 | 1.0 | 0.8 | 0.880 | 1.0 | 1.2 | 1.00 | 1.000 | 1900.8000 | 14 | 0 |
17921 | 2019 | 15497 | Single | 52 | 50-59 | 10000-15000 | 3 | High | 95150 | 40000+ | 0 | 3e+05 | 100 | 0 | 1500 | 0.8 | 1.0 | 0.9 | 1.080 | 1.0 | 1.0 | 1.00 | 1.080 | 1259.7120 | 37 | 0 |
17933 | 2019 | 12202 | Single | 51 | 50-59 | 0-7500 | 4 | Low | 27230 | 20000-30000 | 0 | 1e+05 | 250 | 0 | 1500 | 0.8 | 0.7 | 0.8 | 1.000 | 1.0 | 1.0 | 0.88 | 1.045 | 617.9712 | 36 | 0 |
18033 | 2019 | 13837 | Single | 50 | 50-59 | 0-7500 | 1 | High | 21280 | 20000-30000 | 0 | 1e+05 | 500 | 0 | 1500 | 0.8 | 0.7 | 1.2 | 1.000 | 1.0 | 1.0 | 0.88 | 1.000 | 887.0400 | 35 | 0 |
18084 | 2019 | 17627 | Single | 53 | 50-59 | 10000-15000 | 3 | High | 28990 | 20000-30000 | 0 | 1e+05 | 500 | 0 | 1500 | 0.8 | 1.0 | 0.9 | 1.000 | 1.0 | 1.0 | 0.88 | 1.000 | 950.4000 | 38 | 0 |
18164 | 2019 | 11406 | Single | 52 | 50-59 | 0-7500 | 2 | High | 83140 | 40000+ | 0 | 5e+05 | 1000 | 1 | 1500 | 0.8 | 0.7 | 1.1 | 1.080 | 1.0 | 1.2 | 1.12 | 0.935 | 1254.0262 | 37 | 0 |
18179 | 2019 | 16759 | Married | 24 | 20-24 | 0-7500 | 4 | Low | 32350 | 30000-40000 | 1 | 5e+05 | 100 | 0 | 1500 | 2.0 | 0.7 | 0.8 | 1.045 | 1.2 | 1.0 | 1.12 | 1.080 | 2548.2885 | 9 | 0 |
18189 | 2019 | 13544 | Single | 52 | 50-59 | 10000-15000 | 3 | High | 73850 | 40000+ | 1 | 1e+05 | 1000 | 0 | 1500 | 0.8 | 1.0 | 0.9 | 1.080 | 1.2 | 1.0 | 0.88 | 0.935 | 1151.6567 | 37 | 0 |
18237 | 2019 | 18602 | Married | 27 | 25-29 | 0-7500 | 4 | Medium | 34620 | 30000-40000 | 0 | 1e+05 | 500 | 0 | 1500 | 1.5 | 0.7 | 0.8 | 1.045 | 1.0 | 1.0 | 0.88 | 1.000 | 1158.6960 | 12 | 0 |
18573 | 2019 | 10850 | Married | 28 | 25-29 | 7500-10000 | 4 | High | 7770 | 0-10000 | 0 | 5e+04 | 100 | 0 | 1500 | 1.5 | 0.9 | 0.8 | 0.720 | 1.0 | 1.0 | 0.60 | 1.080 | 755.8272 | 13 | 0 |
18600 | 2019 | 16671 | Married | 30 | 30-39 | 10000-15000 | 4 | High | 21190 | 20000-30000 | 0 | 1e+05 | 1000 | 0 | 1500 | 1.0 | 1.0 | 0.8 | 1.000 | 1.0 | 1.0 | 0.88 | 0.935 | 987.3600 | 15 | 0 |
18681 | 2019 | 10736 | Single | 51 | 50-59 | 0-7500 | 4 | High | 35290 | 30000-40000 | 0 | 5e+05 | 1000 | 0 | 1500 | 0.8 | 0.7 | 0.8 | 1.045 | 1.0 | 1.0 | 1.12 | 0.935 | 735.3857 | 36 | 0 |
18816 | 2019 | 16241 | Single | 55 | 50-59 | 0-7500 | 1 | Low | 87800 | 40000+ | 0 | 3e+05 | 500 | 0 | 1500 | 0.8 | 0.7 | 1.2 | 1.080 | 1.0 | 1.0 | 1.00 | 1.000 | 1088.6400 | 40 | 0 |
18900 | 2019 | 15798 | Single | 62 | 60-69 | 15000+ | 1 | High | 12250 | 10000-20000 | 0 | 3e+05 | 500 | 0 | 1500 | 0.8 | 1.2 | 1.2 | 0.880 | 1.0 | 1.0 | 1.00 | 1.000 | 1520.6400 | 47 | 0 |
19075 | 2019 | 16890 | Single | 51 | 50-59 | 0-7500 | 2 | High | 33690 | 30000-40000 | 0 | 5e+04 | 100 | 0 | 1500 | 0.8 | 0.7 | 1.1 | 1.045 | 1.0 | 1.0 | 0.60 | 1.080 | 625.6958 | 36 | 0 |
19173 | 2019 | 13588 | Single | 50 | 50-59 | 0-7500 | 2 | High | 18410 | 10000-20000 | 0 | 5e+04 | 250 | 1 | 1500 | 0.8 | 0.7 | 1.1 | 0.880 | 1.0 | 1.2 | 0.60 | 1.045 | 611.7915 | 35 | 0 |
19271 | 2019 | 16704 | Married | 25 | 25-29 | 0-7500 | 3 | High | 15780 | 10000-20000 | 0 | 5e+05 | 1000 | 0 | 1500 | 1.5 | 0.7 | 0.9 | 0.880 | 1.0 | 1.0 | 1.12 | 0.935 | 1306.2773 | 10 | 0 |
19361 | 2019 | 18789 | Single | 50 | 50-59 | 10000-15000 | 3 | Medium | 61480 | 40000+ | 0 | 5e+05 | 1000 | 0 | 1500 | 0.8 | 1.0 | 0.9 | 1.080 | 1.0 | 1.0 | 1.12 | 0.935 | 1221.4541 | 35 | 0 |
19428 | 2019 | 19903 | Single | 53 | 50-59 | 10000-15000 | 2 | High | 24910 | 20000-30000 | 0 | 5e+04 | 100 | 0 | 1500 | 0.8 | 1.0 | 1.1 | 1.000 | 1.0 | 1.0 | 0.60 | 1.080 | 855.3600 | 38 | 0 |
19490 | 2019 | 15786 | Single | 57 | 50-59 | 0-7500 | 3 | Medium | 11750 | 10000-20000 | 0 | 5e+04 | 250 | 0 | 1500 | 0.8 | 0.7 | 0.9 | 0.880 | 1.0 | 1.0 | 0.60 | 1.045 | 417.1306 | 42 | 0 |
19504 | 2019 | 17946 | Single | 63 | 60-69 | 10000-15000 | 2 | Medium | 33110 | 30000-40000 | 0 | 3e+05 | 500 | 0 | 1500 | 0.8 | 1.0 | 1.1 | 1.045 | 1.0 | 1.0 | 1.00 | 1.000 | 1379.4000 | 48 | 0 |
19531 | 2019 | 10611 | Single | 51 | 50-59 | 10000-15000 | 4 | High | 21450 | 20000-30000 | 0 | 5e+05 | 1000 | 0 | 1500 | 0.8 | 1.0 | 0.8 | 1.000 | 1.0 | 1.0 | 1.12 | 0.935 | 1005.3120 | 36 | 0 |
19615 | 2019 | 19559 | Married | 29 | 25-29 | 15000+ | 4 | Medium | 18170 | 10000-20000 | 0 | 3e+05 | 100 | 1 | 1500 | 1.5 | 1.2 | 0.8 | 0.880 | 1.0 | 1.2 | 1.00 | 1.080 | 2463.4368 | 14 | 0 |
19637 | 2019 | 14221 | Single | 75 | 70-79 | 15000+ | 4 | High | 19250 | 10000-20000 | 0 | 5e+05 | 250 | 1 | 1500 | 0.8 | 1.2 | 0.8 | 0.880 | 1.0 | 1.2 | 1.12 | 1.045 | 1423.8056 | 60 | 0 |
19656 | 2019 | 12538 | Single | 67 | 60-69 | 10000-15000 | 1 | Medium | 2650 | 0-10000 | 1 | 5e+05 | 500 | 0 | 1500 | 0.8 | 1.0 | 1.2 | 0.720 | 1.2 | 1.0 | 1.12 | 1.000 | 1393.4592 | 52 | 0 |
19708 | 2019 | 18824 | Single | 62 | 60-69 | 10000-15000 | 3 | High | 8970 | 0-10000 | 0 | 5e+05 | 250 | 0 | 1500 | 0.8 | 1.0 | 0.9 | 0.720 | 1.0 | 1.0 | 1.12 | 1.045 | 910.1030 | 47 | 0 |
19832 | 2019 | 12178 | Single | 51 | 50-59 | 0-7500 | 3 | Medium | 19100 | 10000-20000 | 0 | 5e+05 | 250 | 1 | 1500 | 0.8 | 0.7 | 0.9 | 0.880 | 1.0 | 1.2 | 1.12 | 1.045 | 934.3725 | 36 | 0 |
19931 | 2019 | 11860 | Single | 56 | 50-59 | 0-7500 | 4 | Medium | 71450 | 40000+ | 0 | 5e+05 | 1000 | 0 | 1500 | 0.8 | 0.7 | 0.8 | 1.080 | 1.0 | 1.0 | 1.12 | 0.935 | 760.0159 | 41 | 0 |
19963 | 2019 | 19380 | Single | 50 | 50-59 | 0-7500 | 2 | Low | 23930 | 20000-30000 | 0 | 5e+05 | 1000 | 0 | 1500 | 0.8 | 0.7 | 1.1 | 1.000 | 1.0 | 1.0 | 1.12 | 0.935 | 967.6128 | 35 | 0 |
20070 | 2019 | 12634 | Single | 65 | 60-69 | 15000+ | 1 | Medium | 36560 | 30000-40000 | 0 | 5e+05 | 1000 | 1 | 1500 | 0.8 | 1.2 | 1.2 | 1.045 | 1.0 | 1.2 | 1.12 | 0.935 | 2269.1902 | 50 | 0 |
20103 | 2019 | 14470 | Married | 28 | 25-29 | 15000+ | 4 | Low | 36360 | 30000-40000 | 0 | 5e+05 | 500 | 0 | 1500 | 1.5 | 1.2 | 0.8 | 1.045 | 1.0 | 1.0 | 1.12 | 1.000 | 2528.0640 | 13 | 0 |
20132 | 2019 | 17793 | Single | 57 | 50-59 | 10000-15000 | 1 | High | 67350 | 40000+ | 1 | 1e+05 | 100 | 1 | 1500 | 0.8 | 1.0 | 1.2 | 1.080 | 1.2 | 1.2 | 0.88 | 1.080 | 2128.4094 | 42 | 0 |
20454 | 2019 | 13536 | Single | 51 | 50-59 | 10000-15000 | 3 | High | 39340 | 30000-40000 | 0 | 3e+05 | 500 | 0 | 1500 | 0.8 | 1.0 | 0.9 | 1.045 | 1.0 | 1.0 | 1.00 | 1.000 | 1128.6000 | 36 | 0 |
20677 | 2019 | 18112 | Single | 55 | 50-59 | 10000-15000 | 1 | High | 18580 | 10000-20000 | 0 | 3e+05 | 1000 | 0 | 1500 | 0.8 | 1.0 | 1.2 | 0.880 | 1.0 | 1.0 | 1.00 | 0.935 | 1184.8320 | 40 | 0 |
20813 | 2019 | 18737 | Single | 57 | 50-59 | 10000-15000 | 3 | High | 12040 | 10000-20000 | 0 | 1e+05 | 100 | 0 | 1500 | 0.8 | 1.0 | 0.9 | 0.880 | 1.0 | 1.0 | 0.88 | 1.080 | 903.2602 | 42 | 0 |
20923 | 2019 | 12578 | Single | 51 | 50-59 | 10000-15000 | 2 | Medium | 24350 | 20000-30000 | 0 | 1e+05 | 500 | 0 | 1500 | 0.8 | 1.0 | 1.1 | 1.000 | 1.0 | 1.0 | 0.88 | 1.000 | 1161.6000 | 36 | 0 |
21213 | 2019 | 19589 | Married | 27 | 25-29 | 10000-15000 | 4 | Medium | 13020 | 10000-20000 | 0 | 5e+04 | 100 | 0 | 1500 | 1.5 | 1.0 | 0.8 | 0.880 | 1.0 | 1.0 | 0.60 | 1.080 | 1026.4320 | 12 | 0 |
21253 | 2019 | 18425 | Single | 52 | 50-59 | 0-7500 | 3 | Low | 22190 | 20000-30000 | 0 | 5e+04 | 100 | 0 | 1500 | 0.8 | 0.7 | 0.9 | 1.000 | 1.0 | 1.0 | 0.60 | 1.080 | 489.8880 | 37 | 0 |
21258 | 2019 | 11584 | Single | 54 | 50-59 | 0-7500 | 3 | High | 26000 | 20000-30000 | 0 | 5e+05 | 500 | 0 | 1500 | 0.8 | 0.7 | 0.9 | 1.000 | 1.0 | 1.0 | 1.12 | 1.000 | 846.7200 | 39 | 0 |
21354 | 2019 | 18700 | Single | 54 | 50-59 | 10000-15000 | 4 | High | 13700 | 10000-20000 | 0 | 5e+04 | 250 | 0 | 1500 | 0.8 | 1.0 | 0.8 | 0.880 | 1.0 | 1.0 | 0.60 | 1.045 | 529.6896 | 39 | 0 |
21425 | 2019 | 18333 | Single | 53 | 50-59 | 10000-15000 | 4 | Low | 59790 | 40000+ | 0 | 3e+05 | 500 | 0 | 1500 | 0.8 | 1.0 | 0.8 | 1.080 | 1.0 | 1.0 | 1.00 | 1.000 | 1036.8000 | 38 | 0 |
21446 | 2019 | 19821 | Single | 66 | 60-69 | 0-7500 | 3 | High | 96860 | 40000+ | 1 | 5e+05 | 1000 | 0 | 1500 | 0.8 | 0.7 | 0.9 | 1.080 | 1.2 | 1.0 | 1.12 | 0.935 | 1026.0214 | 51 | 0 |
21454 | 2019 | 10966 | Single | 50 | 50-59 | 10000-15000 | 3 | High | 25120 | 20000-30000 | 0 | 5e+05 | 1000 | 0 | 1500 | 0.8 | 1.0 | 0.9 | 1.000 | 1.0 | 1.0 | 1.12 | 0.935 | 1130.9760 | 35 | 0 |
21642 | 2019 | 14574 | Single | 51 | 50-59 | 10000-15000 | 3 | High | 19030 | 10000-20000 | 0 | 5e+05 | 500 | 0 | 1500 | 0.8 | 1.0 | 0.9 | 0.880 | 1.0 | 1.0 | 1.12 | 1.000 | 1064.4480 | 36 | 0 |
21724 | 2019 | 18296 | Single | 52 | 50-59 | 0-7500 | 3 | High | 39550 | 30000-40000 | 0 | 5e+05 | 500 | 0 | 1500 | 0.8 | 0.7 | 0.9 | 1.045 | 1.0 | 1.0 | 1.12 | 1.000 | 884.8224 | 37 | 0 |
21844 | 2019 | 19932 | Single | 53 | 50-59 | 10000-15000 | 3 | Low | 48240 | 40000+ | 0 | 5e+04 | 100 | 0 | 1500 | 0.8 | 1.0 | 0.9 | 1.080 | 1.0 | 1.0 | 0.60 | 1.080 | 755.8272 | 38 | 0 |
22023 | 2019 | 10929 | Single | 50 | 50-59 | 10000-15000 | 1 | High | 32350 | 30000-40000 | 0 | 5e+05 | 1000 | 0 | 1500 | 0.8 | 1.0 | 1.2 | 1.045 | 1.0 | 1.0 | 1.12 | 0.935 | 1575.8266 | 35 | 0 |
22080 | 2019 | 11807 | Single | 50 | 50-59 | 10000-15000 | 1 | Low | 4470 | 0-10000 | 0 | 3e+05 | 500 | 0 | 1500 | 0.8 | 1.0 | 1.2 | 0.720 | 1.0 | 1.0 | 1.00 | 1.000 | 1036.8000 | 35 | 0 |
22259 | 2019 | 19451 | Married | 30 | 30-39 | 7500-10000 | 4 | High | 39660 | 30000-40000 | 0 | 5e+05 | 500 | 0 | 1500 | 1.0 | 0.9 | 0.8 | 1.045 | 1.0 | 1.0 | 1.12 | 1.000 | 1264.0320 | 15 | 0 |
22402 | 2019 | 18196 | Single | 54 | 50-59 | 0-7500 | 2 | High | 15600 | 10000-20000 | 0 | 1e+05 | 500 | 1 | 1500 | 0.8 | 0.7 | 1.1 | 0.880 | 1.0 | 1.2 | 0.88 | 1.000 | 858.6547 | 39 | 0 |
22436 | 2019 | 13613 | Single | 50 | 50-59 | 0-7500 | 1 | Low | 2440 | 0-10000 | 0 | 3e+05 | 500 | 0 | 1500 | 0.8 | 0.7 | 1.2 | 0.720 | 1.0 | 1.0 | 1.00 | 1.000 | 725.7600 | 35 | 0 |
22529 | 2019 | 18387 | Single | 55 | 50-59 | 0-7500 | 4 | Low | 20240 | 20000-30000 | 0 | 3e+05 | 250 | 1 | 1500 | 0.8 | 0.7 | 0.8 | 1.000 | 1.0 | 1.2 | 1.00 | 1.045 | 842.6880 | 40 | 0 |
22645 | 2019 | 19620 | Single | 88 | 80-89 | 15000+ | 2 | High | 28750 | 20000-30000 | 0 | 5e+04 | 100 | 0 | 1500 | 0.8 | 1.2 | 1.1 | 1.000 | 1.0 | 1.0 | 0.60 | 1.080 | 1026.4320 | 73 | 0 |
22951 | 2019 | 15405 | Married | 29 | 25-29 | 10000-15000 | 4 | Low | 20390 | 20000-30000 | 0 | 1e+05 | 1000 | 0 | 1500 | 1.5 | 1.0 | 0.8 | 1.000 | 1.0 | 1.0 | 0.88 | 0.935 | 1481.0400 | 14 | 0 |
23083 | 2019 | 14385 | Single | 52 | 50-59 | 0-7500 | 2 | Low | 35600 | 30000-40000 | 0 | 5e+04 | 250 | 0 | 1500 | 0.8 | 0.7 | 1.1 | 1.045 | 1.0 | 1.0 | 0.60 | 1.045 | 605.4187 | 37 | 0 |
23229 | 2019 | 12391 | Single | 50 | 50-59 | 10000-15000 | 2 | High | 28630 | 20000-30000 | 1 | 5e+05 | 1000 | 0 | 1500 | 0.8 | 1.0 | 1.1 | 1.000 | 1.2 | 1.0 | 1.12 | 0.935 | 1658.7648 | 35 | 0 |
23287 | 2019 | 10301 | Single | 52 | 50-59 | 10000-15000 | 1 | High | 23660 | 20000-30000 | 0 | 3e+05 | 1000 | 0 | 1500 | 0.8 | 1.0 | 1.2 | 1.000 | 1.0 | 1.0 | 1.00 | 0.935 | 1346.4000 | 37 | 0 |
23335 | 2019 | 12440 | Married | 23 | 20-24 | 0-7500 | 3 | Low | 17050 | 10000-20000 | 0 | 1e+05 | 250 | 1 | 1500 | 2.0 | 0.7 | 0.9 | 0.880 | 1.0 | 1.2 | 0.88 | 1.045 | 1835.3745 | 8 | 0 |
23340 | 2019 | 15861 | Single | 54 | 50-59 | 10000-15000 | 3 | Medium | 22440 | 20000-30000 | 1 | 1e+05 | 250 | 0 | 1500 | 0.8 | 1.0 | 0.9 | 1.000 | 1.2 | 1.0 | 0.88 | 1.045 | 1191.8016 | 39 | 0 |
23366 | 2019 | 16578 | Single | 52 | 50-59 | 10000-15000 | 2 | High | 62070 | 40000+ | 1 | 5e+04 | 100 | 0 | 1500 | 0.8 | 1.0 | 1.1 | 1.080 | 1.2 | 1.0 | 0.60 | 1.080 | 1108.5466 | 37 | 0 |
23473 | 2019 | 18110 | Single | 50 | 50-59 | 0-7500 | 1 | Low | 22940 | 20000-30000 | 0 | 5e+04 | 100 | 0 | 1500 | 0.8 | 0.7 | 1.2 | 1.000 | 1.0 | 1.0 | 0.60 | 1.080 | 653.1840 | 35 | 0 |
23485 | 2019 | 11138 | Single | 52 | 50-59 | 10000-15000 | 3 | High | 8970 | 0-10000 | 1 | 5e+04 | 100 | 0 | 1500 | 0.8 | 1.0 | 0.9 | 0.720 | 1.2 | 1.0 | 0.60 | 1.080 | 604.6618 | 37 | 0 |
23499 | 2019 | 10589 | Single | 53 | 50-59 | 10000-15000 | 2 | Medium | 11930 | 10000-20000 | 0 | 1e+05 | 500 | 0 | 1500 | 0.8 | 1.0 | 1.1 | 0.880 | 1.0 | 1.0 | 0.88 | 1.000 | 1022.2080 | 38 | 0 |
Here are a few exhibits of their structures.
app.rej %>% ggplot(aes(x = `Annual Mileage`)) + geom_bar( aes(fill = `Annual Mileage`), stat = "count") + facet_grid(. ~ `Driver Age Band`) + theme(legend.position = "bottom", axis.text.x = element_blank(), axis.ticks.x = element_blank()) + ggtitle("Distribution of Annual Mileage of Rejected Applicants by Driver Age Band")
app.rej %>% ggplot(aes(x = `Driver Age Band`)) + geom_bar( aes(fill = `Driver Age Band`), stat = "count") + facet_grid(. ~ `Car Value Band`) + theme(legend.position = "bottom", axis.text.x = element_blank(), axis.ticks.x = element_blank()) + ggtitle("Distribution of Driver Age of Rejected Applicants by Car Value")