Mitigating Credit Risk in Banking:

Predictive Modeling Workshop

Endri Raco

Introduction

All slides can be found in :

https://rpubs.com/DrunkenPhD/bankcreditrisk

Introduction

  • Investopedia webpage defines Credit risk as the possibility of a loss resulting from a borrower’s failure to repay a loan or meet contractual obligations.[@investopedia2019]

Introduction

  • By this definition, we understand that subject who gives the loan (in our case the bank) takes a risk during this process, because it is not known a priori if client who applies and takes the loan will not default (fail to return the money).

Introduction

  • Nowadays, banks and other financial institutions are taking advantange of machine learning methods to predict credit risks (refuse loans to risky customers).

Our Project

  • The aim of this project is to use machine learning techniques such as logistic regression and decision trees for building a predictive model based on bank client’s features.

  • This model should help on understanding which client can be considered as a risky one.

Dataset

Dataset

  • German credit dataset from the UCI Machine Learning Repository, donated by the German professor Hans Hofmann via the European Statlog project, comes with an incorrect code table.

  • Many variables are wrongly represented, which implies that the data cannot be adequately used for experimenting with methods for interpretable machine learning.

Dataset

  • The South German Credit data are meaningful, credit scoring data from southern Germany In dataset, each entry represents a person who takes a credit by a bank.[@gromping2019]

  • Each person is classified as good or bad credit risks according to the set of attributes.

Methods and Analysis

Libraries to be used:

library(rpart)
library(gmodels)
library(ROCR)
library(epiDisplay)
library(dataCompareR)
library(tidyverse)
library(caret)
library(data.table)

Importing data

  • For importing data in our project data folder we will use readr library, which is part of the tidyverse [@R-tidyverse]package:

Importing data

# South German Credit Dataset: Code for creating
# SouthGermanCredit.asc as described in @gromping2019
temp <- read_table("https://data.ub.uni-muenchen.de/23/2/kredit.asc")
### recode pers and gastarb to the stated P2 coding
temp$pers <- 3 - temp$pers
temp$gastarb <- 3 - temp$gastarb
### put credit_risk is last
temp <- cbind(temp[, -1], kredit = temp$kredit)
write.table(temp, file = "./data/SouthGermanCredit.asc", row.names = FALSE,
    quote = FALSE)
## save dataset in our data folder
data_credit <- read.table("./data/SouthGermanCredit.asc")
# remove temp file
remove(temp)

Data intro

  • Let’s have a look in the structure of downloaded dataset:

Data intro

# Check structure of downloaded dataset
str(data_credit)
'data.frame':   1000 obs. of  21 variables:
 $ laufkont: int  1 1 2 1 1 1 1 1 4 2 ...
 $ laufzeit: int  18 9 12 12 12 10 8 6 18 24 ...
 $ moral   : int  4 4 2 4 4 4 4 4 4 2 ...
 $ verw    : int  2 0 9 0 0 0 0 0 3 3 ...
 $ hoehe   : int  1049 2799 841 2122 2171 2241 3398 1361 1098 3758 ...
 $ sparkont: int  1 1 2 1 1 1 1 1 1 3 ...
 $ beszeit : int  2 3 4 3 3 2 4 2 1 1 ...
 $ rate    : int  4 2 2 3 4 1 1 2 4 1 ...
 $ famges  : int  2 3 2 3 3 3 3 3 2 2 ...
 $ buerge  : int  1 1 1 1 1 1 1 1 1 1 ...
 $ wohnzeit: int  4 2 4 2 4 3 4 4 4 4 ...
 $ verm    : int  2 1 1 1 2 1 1 1 3 4 ...
 $ alter   : int  21 36 23 39 38 48 39 40 65 23 ...
 $ weitkred: int  3 3 3 3 1 3 3 3 3 3 ...
 $ wohn    : int  1 1 1 1 2 1 2 2 2 1 ...
 $ bishkred: int  1 2 1 2 2 2 2 1 2 1 ...
 $ beruf   : int  3 3 2 2 2 2 2 2 1 1 ...
 $ pers    : int  2 1 2 1 2 1 2 1 2 2 ...
 $ telef   : int  1 1 1 1 1 1 1 1 1 1 ...
 $ gastarb : int  2 2 2 1 1 1 1 1 2 2 ...
 $ kredit  : int  1 1 1 1 1 1 1 1 1 1 ...

Data intro

  • We are dealing with an object of class dataframe containing 21 columns(variables) and 1000 rows(observations).

Data Processing

  • Since variables are missing names, it is hard initially to understand information represented in dataframe.

  • According to Table 1: Distribution of categorical predictor variables for the South German Credit data, separately for good and bad credit risks in [gromping2019] http://www1.beuth-hochschule.de/FB_II/reports/Report-2019-004.pdf we have all variables as numeric variables. Let’s check this fact in the downloaded dataframe.

Data Processing

split(names(data_credit), sapply(data_credit, function(x) paste(class(x),
    collapse = " ")))
$integer
 [1] "laufkont" "laufzeit" "moral"    "verw"     "hoehe"    "sparkont"
 [7] "beszeit"  "rate"     "famges"   "buerge"   "wohnzeit" "verm"    
[13] "alter"    "weitkred" "wohn"     "bishkred" "beruf"    "pers"    
[19] "telef"    "gastarb"  "kredit"

Data Processing

  • Everything seems OK.

  • In the results we see numeric variable credit.

Data Processing

  • This column contains bank evaluation for customer (1 = Good, 0 = Bad).

  • We will rename first 21 columns with more understandable english names so we can work them easily in the future analysis.

Data Processing

# Rename columns
data_credit <- setNames(data_credit, c("account_status", "duration_month",
    "credit_history", "credit_purpose", "credit_amount", "savings_account",
    "employment_present", "installment_rate_pct", "status_sex",
    "other_debtors_guar", "residence_duration", "property", "age_years",
    "other_install_plans", "housing", "exist_credits_nr", "job",
    "dependents_nr", "telephone_nr", "foreign_worker", "customer_good_bad"))

Data Processing

Let’s print again the structure of dataset:

Data Processing

# Check structure of downloaded dataset
str(data_credit)
'data.frame':   1000 obs. of  21 variables:
 $ account_status      : int  1 1 2 1 1 1 1 1 4 2 ...
 $ duration_month      : int  18 9 12 12 12 10 8 6 18 24 ...
 $ credit_history      : int  4 4 2 4 4 4 4 4 4 2 ...
 $ credit_purpose      : int  2 0 9 0 0 0 0 0 3 3 ...
 $ credit_amount       : int  1049 2799 841 2122 2171 2241 3398 1361 1098 3758 ...
 $ savings_account     : int  1 1 2 1 1 1 1 1 1 3 ...
 $ employment_present  : int  2 3 4 3 3 2 4 2 1 1 ...
 $ installment_rate_pct: int  4 2 2 3 4 1 1 2 4 1 ...
 $ status_sex          : int  2 3 2 3 3 3 3 3 2 2 ...
 $ other_debtors_guar  : int  1 1 1 1 1 1 1 1 1 1 ...
 $ residence_duration  : int  4 2 4 2 4 3 4 4 4 4 ...
 $ property            : int  2 1 1 1 2 1 1 1 3 4 ...
 $ age_years           : int  21 36 23 39 38 48 39 40 65 23 ...
 $ other_install_plans : int  3 3 3 3 1 3 3 3 3 3 ...
 $ housing             : int  1 1 1 1 2 1 2 2 2 1 ...
 $ exist_credits_nr    : int  1 2 1 2 2 2 2 1 2 1 ...
 $ job                 : int  3 3 2 2 2 2 2 2 1 1 ...
 $ dependents_nr       : int  2 1 2 1 2 1 2 1 2 2 ...
 $ telephone_nr        : int  1 1 1 1 1 1 1 1 1 1 ...
 $ foreign_worker      : int  2 2 2 1 1 1 1 1 2 2 ...
 $ customer_good_bad   : int  1 1 1 1 1 1 1 1 1 1 ...

Data Processing

  • Now we show the first five lines for columns 1 to 3:

Data Processing

# First five lines using the function head
head(data_credit[, 1:3], 5)
  account_status duration_month credit_history
1              1             18              4
2              1              9              4
3              2             12              2
4              1             12              4
5              1             12              4

Data Processing

  • Now we need to look if there are any missing values in our dataframe.

Data Processing

# Check data_credit dataframe for NA values
sapply(data_credit, function(x) sum(is.na(x)))
      account_status       duration_month       credit_history 
                   0                    0                    0 
      credit_purpose        credit_amount      savings_account 
                   0                    0                    0 
  employment_present installment_rate_pct           status_sex 
                   0                    0                    0 
  other_debtors_guar   residence_duration             property 
                   0                    0                    0 
           age_years  other_install_plans              housing 
                   0                    0                    0 
    exist_credits_nr                  job        dependents_nr 
                   0                    0                    0 
        telephone_nr       foreign_worker    customer_good_bad 
                   0                    0                    0

Data Processing

  • Our dataset has no missing values.

  • From attribute information provided in According to Table 1: Distribution of categorical predictor variables for the South German Credit data, separately for good and bad credit risks in gromping2019 we see that our variables duration_month, credit_amount, age_years are numeric and others are qualitative (categorical).

Data Processing

  • Let’s transform our categorical variables to class factor for using them in our analysis.

Data Processing

# convert variables to class factor
variables <- c("account_status", "credit_history", "credit_purpose",
    "savings_account", "employment_present", "installment_rate_pct",
    "status_sex", "other_debtors_guar", "residence_duration",
    "property", "other_install_plans", "housing", "exist_credits_nr",
    "job", "dependents_nr", "telephone_nr", "foreign_worker",
    "customer_good_bad")
data_credit[, variables] <- lapply(data_credit[, variables],
    factor)

Data Processing

Result of transformations shown below:

Data Processing

# Factors vs numeric variables
split(names(data_credit), sapply(data_credit, function(x) paste(class(x),
    collapse = " ")))
$factor
 [1] "account_status"       "credit_history"       "credit_purpose"      
 [4] "savings_account"      "employment_present"   "installment_rate_pct"
 [7] "status_sex"           "other_debtors_guar"   "residence_duration"  
[10] "property"             "other_install_plans"  "housing"             
[13] "exist_credits_nr"     "job"                  "dependents_nr"       
[16] "telephone_nr"         "foreign_worker"       "customer_good_bad"   

$integer
[1] "duration_month" "credit_amount"  "age_years"

Data Processing

Let’s view our dataset on which we will work further

# Transformed dataset
str(data_credit)
'data.frame':   1000 obs. of  21 variables:
 $ account_status      : Factor w/ 4 levels "1","2","3","4": 1 1 2 1 1 1 1 1 4 2 ...
 $ duration_month      : int  18 9 12 12 12 10 8 6 18 24 ...
 $ credit_history      : Factor w/ 5 levels "0","1","2","3",..: 5 5 3 5 5 5 5 5 5 3 ...
 $ credit_purpose      : Factor w/ 10 levels "0","1","2","3",..: 3 1 9 1 1 1 1 1 4 4 ...
 $ credit_amount       : int  1049 2799 841 2122 2171 2241 3398 1361 1098 3758 ...
 $ savings_account     : Factor w/ 5 levels "1","2","3","4",..: 1 1 2 1 1 1 1 1 1 3 ...
 $ employment_present  : Factor w/ 5 levels "1","2","3","4",..: 2 3 4 3 3 2 4 2 1 1 ...
 $ installment_rate_pct: Factor w/ 4 levels "1","2","3","4": 4 2 2 3 4 1 1 2 4 1 ...
 $ status_sex          : Factor w/ 4 levels "1","2","3","4": 2 3 2 3 3 3 3 3 2 2 ...
 $ other_debtors_guar  : Factor w/ 3 levels "1","2","3": 1 1 1 1 1 1 1 1 1 1 ...
 $ residence_duration  : Factor w/ 4 levels "1","2","3","4": 4 2 4 2 4 3 4 4 4 4 ...
 $ property            : Factor w/ 4 levels "1","2","3","4": 2 1 1 1 2 1 1 1 3 4 ...
 $ age_years           : int  21 36 23 39 38 48 39 40 65 23 ...
 $ other_install_plans : Factor w/ 3 levels "1","2","3": 3 3 3 3 1 3 3 3 3 3 ...
 $ housing             : Factor w/ 3 levels "1","2","3": 1 1 1 1 2 1 2 2 2 1 ...
 $ exist_credits_nr    : Factor w/ 4 levels "1","2","3","4": 1 2 1 2 2 2 2 1 2 1 ...
 $ job                 : Factor w/ 4 levels "1","2","3","4": 3 3 2 2 2 2 2 2 1 1 ...
 $ dependents_nr       : Factor w/ 2 levels "1","2": 2 1 2 1 2 1 2 1 2 2 ...
 $ telephone_nr        : Factor w/ 2 levels "1","2": 1 1 1 1 1 1 1 1 1 1 ...
 $ foreign_worker      : Factor w/ 2 levels "1","2": 2 2 2 1 1 1 1 1 2 2 ...
 $ customer_good_bad   : Factor w/ 2 levels "0","1": 2 2 2 2 2 2 2 2 2 2 ...

Exploring Data

Data Summary

  • Our main goal is to build a model which should help on understanding which client can be considered as a risky one.

  • As prof. Rafael Irizarry mentions[@irizarry2019] : In Machine Learning, data comes in the form of:

    1. the outcome we want to predict and

    2. the features that we will use to predict the outcome

Data Summary

  • For us outcome we want to predict is variable customer_good_bad.

  • This outcome depends on the features that we will use to predict so we need to take a look in all the features and identify the ones of interest for the model.

Data Summary

  • Let’s first have a thorough look at summary statistics for our outcome customer_good_bad.

  • We will use function tab1 from epiDisplay library [@R-epiDisplay].

Data Summary

# summary customer_good_bad
tab1(data_credit$customer_good_bad, sort.group = "decreasing",
    cum.percent = FALSE, bar.values = "frequency", cex = 1, cex.names = 1,
    main = "Distribution of data", xlab = "customer_good_bad",
    ylab = "count", col = c("red", "yellow", "blue"))

Data Summary

data_credit$customer_good_bad : 
        Frequency Percent
1             700      70
0             300      30
  Total      1000     100

Data Summary

  • We see that our outcome variable contains values of 0s and 1s.

  • As mentioned in attribute information value 1 corrensponds to customer status Good (reliable) and value 2 corrensponds to status Bad(non reliable).

Data Summary

  • As we can see from barchart customers with status Good are 700 much more than customers with status Bad 300.

Data Summary

  • Now we go on by taking some insights on other features and see which of them can be potential candidates on affecting our output.

  • First, we start with account_status

Data Summary

# summary account_status
tab1(data_credit$account_status, sort.group = "decreasing", cum.percent = FALSE,
    bar.values = "percent", cex = 1, cex.names = 1, main = "Distribution of data",
    xlab = "account_status", ylab = "count", col = c("red", "yellow",
        "blue"), horiz = TRUE)

Data Summary

data_credit$account_status : 
        Frequency Percent
4             394    39.4
1             274    27.4
2             269    26.9
3              63     6.3
  Total      1000   100.0

Data Summary

  • We see that categorical variable account_status has 4 levels. According to Table 1: Distribution of categorical predictor variables for the South German Credit data, separately for good and bad credit risks in gromping2019 , We notice that largest percent of customers (39.4 %) have account status >= 200 DM.

Data Summary

  • Beside that only 6.3 % have balance less than 200 DM. This level has very few observations comparing to other levels so maybe it is a good idea to recode variable account_status in only 3 levels and giving to these labels some more informative labels.

Data Summary

  • Based on attribute meaning our 3 new levels for this variable will be: no_account, no_money_acc, positive_acc

Data Summary

# recode account_status
account_status_temp <- recode(data_credit$account_status, `3` = "positive_acc",
    `4` = "positive_acc", `1` = "no_account", `2` = "no_money_acc")
data_credit$account_status <- account_status_temp

Data Summary

The results of recoding:

Data Summary

# recoding results
tab1(data_credit$account_status, sort.group = "decreasing", cum.percent = FALSE,
    bar.values = "percent", cex = 1, cex.names = 1, main = "Distribution of data",
    xlab = "account_status", ylab = "count", col = c("red", "yellow",
        "blue"), horiz = TRUE)

Data Summary

data_credit$account_status : 
             Frequency Percent
positive_acc       457    45.7
no_account         274    27.4
no_money_acc       269    26.9
  Total           1000   100.0

Data Summary

  • Now let’s continue with our numeric variable duration_month .

Data Summary

# summary duration_month
summary(data_credit$duration_month)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
    4.0    12.0    18.0    20.9    24.0    72.0 
data_credit %>%
    ggplot(aes(duration_month)) + geom_density()

Data Summary

  • We notice that credits have a minimum duration of 4 months and a maximum duration of 72 months.

  • The average duration of credits is 20.9 months.

Data Summary

  • Next feature is credit_history.

Data Summary

# summary credit_history
tab1(data_credit$credit_history, sort.group = "decreasing", cum.percent = FALSE,
    bar.values = "frequency", cex = 1, cex.names = 1, main = "Distribution of data",
    xlab = "credit_history", ylab = "count", col = c("red", "yellow",
        "blue"), horiz = TRUE)

Data Summary

data_credit$credit_history : 
        Frequency Percent
2             530    53.0
4             293    29.3
3              88     8.8
1              49     4.9
0              40     4.0
  Total      1000   100.0

Data Summary

  • We see that credit_history variable has 5 levels.

  • According to Table 1: Distribution of categorical predictor variables for the South German Credit data, separately for good and bad credit risks in gromping2019 , We see that most clients have no credits taken/all credits paid back duly (53 % of customers). Levels 0, 1, 3 (4% , 4.9%, 8.8%) has very few observations comparing to other levels so maybe it is a good idea to recode this variable in only 3 levels : pay_problems, all_paid, no_prob_currbank

Data Summary

# recode credit_history
credit_history_temp <- recode(data_credit$credit_history, `0` = "pay_problems",
    `1` = "pay_problems", `2` = "all_paid", `3` = "no_prob_currbank",
    `4` = "no_prob_currbank")
data_credit$credit_history <- credit_history_temp

Data Summary

The results of recoding:

Data Summary

# recoding results
tab1(data_credit$credit_history, sort.group = "decreasing", cum.percent = FALSE,
    bar.values = "percent", cex = 1, cex.names = 1, main = "Distribution of data",
    xlab = "credit_history", ylab = "count", col = c("red", "yellow",
        "blue"), horiz = TRUE)
data_credit$credit_history : 
                 Frequency Percent
all_paid               530    53.0
no_prob_currbank       381    38.1
pay_problems            89     8.9
  Total               1000   100.0

Data Summary

Next we move to feature credit_purpose.

Data Summary

# summary credit_purpose
tab1(data_credit$credit_purpose, sort.group = "decreasing", cum.percent = FALSE,
    bar.values = "percent", main = "Distribution of data", xlab = "credit_purpose",
    ylab = "count", col = c("red", "yellow", "blue"), horiz = TRUE)

Data Summary

data_credit$credit_purpose : 
        Frequency Percent
3             280    28.0
0             234    23.4
2             181    18.1
1             103    10.3
9              97     9.7
6              50     5.0
5              22     2.2
4              12     1.2
10             12     1.2
8               9     0.9
  Total      1000   100.0

Data Summary

According to Table 1: Distribution of categorical predictor variables for the South German Credit data, separately for good and bad credit risks in gromping2019 ,, this categorical variable should have 11 levels, but in our plot we see only 10.

Data Summary

Level 7(education) has no observations. Levels 8, 10, 4, 5, 6 have very few observations comparing to other levels so maybe it is a good idea to recode this variable in 4 levels: new_car, used_car, domestic, services.

Data Summary

# recode credit_purpose
credit_purpose_temp <- recode(data_credit$credit_purpose, `0` = "services",
    `1` = "new_car", `2` = "used_car", `3` = "domestic", `4` = "domestic",
    `5` = "domestic", `6` = "services", `7` = "services", `8` = "services",
    `9` = "services", `10` = "services")
data_credit$credit_purpose <- credit_purpose_temp

Data Summary

The results of recoding:

Data Summary

# recoding results
tab1(data_credit$credit_purpose, sort.group = "decreasing", cum.percent = FALSE,
    bar.values = "percent", cex = 1, cex.names = 1, main = "Distribution of data",
    xlab = "credit_purpose", ylab = "count", col = c("red", "yellow",
        "blue"), horiz = TRUE)

Data Summary

data_credit$credit_purpose : 
         Frequency Percent
services       402    40.2
domestic       314    31.4
used_car       181    18.1
new_car        103    10.3
  Total       1000   100.0

Data Summary

Now let’s continue with our numeric variable credit_amount .

Data Summary

# summary of credit_amount
summary(data_credit$credit_amount)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
    250    1366    2320    3271    3972   18424 
data_credit %>%
    ggplot(aes(credit_amount)) + geom_density()

Data Summary

We notice that credits required by bank clients have an average of 3271 DM.

Data Summary

Next we move to feature savings_account.

Data Summary

# summary savings_account
tab1(data_credit$savings_account, sort.group = "decreasing",
    cum.percent = FALSE, bar.values = "percent", main = "Distribution of data",
    xlab = "savings_account", ylab = "count", col = c("red",
        "yellow", "blue"), horiz = TRUE)

Data Summary

data_credit$savings_account : 
        Frequency Percent
1             603    60.3
5             183    18.3
2             103    10.3
3              63     6.3
4              48     4.8
  Total      1000   100.0

Data Summary

  • According to Table 1: Distribution of categorical predictor variables for the South German Credit data, separately for good and bad credit risks in gromping2019 ,, this categorical variable has 5 levels. Levels 3 and 4 have very few observations comparing to other levels so maybe it is a good idea to recode this variable in 4 new levels no_sav, less100, 100to1000, over1000

Data Summary

# recode savings_account
savings_account_temp <- recode(data_credit$savings_account, `1` = "no_sav",
    `2` = "less100", `3` = "100to1000", `4` = "100to1000", `5` = "over1000")
data_credit$savings_account <- savings_account_temp

Data Summary

The results of recoding:

Data Summary

# recoding results
tab1(data_credit$savings_account, sort.group = "decreasing",
    cum.percent = FALSE, bar.values = "percent", cex = 1, cex.names = 1,
    main = "Distribution of data", xlab = "savings_account",
    ylab = "count", col = c("red", "yellow", "blue"), horiz = TRUE)

Data Summary

data_credit$savings_account : 
          Frequency Percent
no_sav          603    60.3
over1000        183    18.3
100to1000       111    11.1
less100         103    10.3
  Total        1000   100.0

Data Summary

Now let’s see feature employment_present.

Data Summary

# summary employment_present
tab1(data_credit$employment_present, sort.group = "decreasing",
    cum.percent = FALSE, bar.values = "percent", main = "Distribution of data",
    xlab = "employment_present", ylab = "count", col = c("red",
        "yellow", "blue"), horiz = TRUE)

Data Summary

data_credit$employment_present : 
        Frequency Percent
3             339    33.9
5             253    25.3
4             174    17.4
2             172    17.2
1              62     6.2
  Total      1000   100.0

Data Summary

According to Table 1: Distribution of categorical predictor variables for the South German Credit data, separately for good and bad credit risks in gromping2019 ,, this categorical variable has 5 levels. Levels 1 has very few observations comparing to other levels so maybe it is a good idea to recode this variable in 4 levels: unemp_less1year, 1to4, 4to7, 7plus

Data Summary

# recode employment_present
employment_present_temp <- recode(data_credit$employment_present,
    `1` = "unemp_less1year", `2` = "unemp_less1year", `3` = "1to4",
    `4` = "4to7", `5` = "7plus")
data_credit$employment_present <- employment_present_temp

Data Summary

The results of recoding:

Data Summary

# recoding results
tab1(data_credit$employment_present, sort.group = "decreasing",
    cum.percent = FALSE, bar.values = "percent", cex = 1, cex.names = 1,
    main = "Distribution of data", xlab = "employment_present",
    ylab = "count", col = c("red", "yellow", "blue"), horiz = TRUE)

Data Summary

data_credit$employment_present : 
                Frequency Percent
1to4                  339    33.9
7plus                 253    25.3
unemp_less1year       234    23.4
4to7                  174    17.4
  Total              1000   100.0

Data Summary

Now let’s see feature installment_rate_pct.

Data Summary

# summary installment_rate_pct
tab1(data_credit$installment_rate_pct, sort.group = "decreasing",
    cum.percent = FALSE, bar.values = "percent", main = "Distribution of data",
    xlab = "installment_rate_pct", ylab = "count", col = c("red",
        "yellow", "blue"), horiz = TRUE)

Data Summary

data_credit$installment_rate_pct : 
        Frequency Percent
4             476    47.6
2             231    23.1
3             157    15.7
1             136    13.6
  Total      1000   100.0

Data Summary

According to Table 1: Distribution of categorical predictor variables for the South German Credit data, separately for good and bad credit risks in gromping2019 , this categorical variable has 4 levels.

Data Summary

This levels are shown also in plot and frequency table. We will not transform this variable.

Data Summary

Now let’s see feature status_sex.

Data Summary

# summary status_sex
tab1(data_credit$status_sex, sort.group = "decreasing", cum.percent = FALSE,
    bar.values = "percent", main = "Distribution of data", xlab = "status_sex",
    ylab = "count", col = c("red", "yellow", "blue"), horiz = TRUE)

Data Summary

data_credit$status_sex : 
        Frequency Percent
3             548    54.8
2             310    31.0
4              92     9.2
1              50     5.0
  Total      1000   100.0

Data Summary

According to Table 1: Distribution of categorical predictor variables for the South German Credit data, separately for good and bad credit risks in gromping2019 , this categorical variable has 4 levels.

Data Summary

We see that largest part of bank customers fall in group 3 (Male married or widowed). Levels 1 and 4 have very few observations comparing to other levels so maybe it is a good idea to recode this variable in 3 levels m_single_divorced, m_married_wid, female

Data Summary

# recode status_sex
status_sex_temp <- recode(data_credit$status_sex, `1` = "m_single_divorced",
    `2` = "m_single_divorced", `3` = "m_married_wid", `4` = "female")
data_credit$status_sex <- status_sex_temp

Data Summary

The results of recoding:

Data Summary

# recoding results
tab1(data_credit$status_sex, sort.group = "decreasing", cum.percent = FALSE,
    bar.values = "percent", cex = 1, cex.names = 1, main = "Distribution of data",
    xlab = "status_sex", ylab = "count", col = c("red", "yellow",
        "blue"), horiz = TRUE)

Data Summary

data_credit$status_sex : 
                  Frequency Percent
m_married_wid           548    54.8
m_single_divorced       360    36.0
female                   92     9.2
  Total                1000   100.0

Data Summary

Now let’s see feature other_debtors_guar.

Data Summary

# summary other_debtors_guar
tab1(data_credit$other_debtors_guar, sort.group = "decreasing",
    cum.percent = FALSE, bar.values = "percent", main = "Distribution of data",
    xlab = "other_debtors_guar", ylab = "count", col = c("red",
        "yellow", "blue"), horiz = TRUE)

Data Summary

data_credit$other_debtors_guar : 
        Frequency Percent
1             907    90.7
3              52     5.2
2              41     4.1
  Total      1000   100.0

Data Summary

According to Table 1: Distribution of categorical predictor variables for the South German Credit data, separately for good and bad credit risks in gromping2019 , this categorical variable has 3 levels.

Data Summary

This variable has data about known debtors / guarantors so we can recode this variable to 2 levels yes, no because levels 2 and 3 have very few observations comparing to other levels.

Data Summary

# recode other_debtors_guar
other_debtors_guar_temp <- recode(data_credit$other_debtors_guar,
    `1` = "no", `2` = "yes", `3` = "yes")
data_credit$other_debtors_guar <- other_debtors_guar_temp

Data Summary

The results of recoding:

Data Summary

# recoding results
tab1(data_credit$other_debtors_guar, sort.group = "decreasing",
    cum.percent = FALSE, bar.values = "percent", cex = 1, cex.names = 1,
    main = "Distribution of data", xlab = "other_debtors_guar",
    ylab = "count", col = c("red", "yellow", "blue"), horiz = TRUE)

Data Summary

data_credit$other_debtors_guar : 
        Frequency Percent
no            907    90.7
yes            93     9.3
  Total      1000   100.0

Data Summary

Now let’s see feature residence_duration.

Data Summary

# summary residence_duration
tab1(data_credit$residence_duration, sort.group = "decreasing",
    cum.percent = FALSE, bar.values = "percent", main = "Distribution of data",
    xlab = "residence_duration", ylab = "count", col = c("red",
        "yellow", "blue"), horiz = TRUE)

Data Summary

data_credit$residence_duration : 
        Frequency Percent
4             413    41.3
2             308    30.8
3             149    14.9
1             130    13.0
  Total      1000   100.0

Data Summary

According to Table 1: Distribution of categorical predictor variables for the South German Credit data, separately for good and bad credit risks in gromping2019 , this categorical variable has 4 levels.

Data Summary

  • This variable will stay as it is.

  • Now let’s see feature property.

Data Summary

# summary property
tab1(data_credit$property, sort.group = "decreasing", cum.percent = FALSE,
    bar.values = "percent", main = "Distribution of data", xlab = "property",
    ylab = "count", col = c("red", "yellow", "blue"), horiz = TRUE)

Data Summary

data_credit$property : 
        Frequency Percent
3             332    33.2
1             282    28.2
2             232    23.2
4             154    15.4
  Total      1000   100.0

Data Summary

According to Table 1: Distribution of categorical predictor variables for the South German Credit data, separately for good and bad credit risks in gromping2019 , this categorical variable has 4 levels.

Data Summary

We will not change this variable.

Data Summary

We will continue with numeric feature age_years.

Data Summary

# summary age_years
summary(data_credit$age_years)
data_credit %>%
    ggplot(aes(age_years)) + geom_density()

Data Summary

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
   19.0    27.0    33.0    35.5    42.0    75.0

Data Summary

We notice that youngest customer is of age 19 and in the average bank customers are between 35-36 years old.

Data Summary

Now let’s see feature other_install_plans.

Data Summary

# summary other_install_plans
tab1(data_credit$other_install_plans, sort.group = "decreasing",
    cum.percent = FALSE, bar.values = "percent", main = "Distribution of data",
    xlab = "other_install_plans", ylab = "count", col = c("red",
        "yellow", "blue"), horiz = TRUE)

Data Summary

data_credit$other_install_plans : 
        Frequency Percent
3             814    81.4
1             139    13.9
2              47     4.7
  Total      1000   100.0

Data Summary

According to Table 1: Distribution of categorical predictor variables for the South German Credit data, separately for good and bad credit risks in gromping2019 , this categorical variable has 3 levels and has data about other credits that customer has in other banks or stores .

Data Summary

We will recode this variable to 2 levels yes, no because level 2 has very few observations comparing to other levels.

Data Summary

# recode other_install_plans
other_install_plans_temp <- recode(data_credit$other_install_plans,
    `1` = "yes", `2` = "yes", `3` = "no")
data_credit$other_install_plans <- other_install_plans_temp

Data Summary

The results of recoding:

Data Summary

# recoding results
tab1(data_credit$other_install_plans, sort.group = "decreasing",
    cum.percent = FALSE, bar.values = "percent", cex = 1, cex.names = 1,
    main = "Distribution of data", xlab = "other_install_plans",
    ylab = "count", col = c("red", "yellow", "blue"), horiz = TRUE)

Data Summary

data_credit$other_install_plans : 
        Frequency Percent
no            814    81.4
yes           186    18.6
  Total      1000   100.0

Data Summary

Next feature is housing.

Data Summary

# summary housing
tab1(data_credit$housing, sort.group = "decreasing", cum.percent = FALSE,
    bar.values = "percent", main = "Distribution of data", xlab = "housing",
    ylab = "count", col = c("red", "yellow", "blue"), horiz = TRUE)

Data Summary

data_credit$housing : 
        Frequency Percent
2             714    71.4
1             179    17.9
3             107    10.7
  Total      1000   100.0

Data Summary

According to Table 1: Distribution of categorical predictor variables for the South German Credit data, separately for good and bad credit risks in gromping2019 , this categorical variable has 3 levels.

Data Summary

This variable will not be subject of transformation.

Data Summary

Now let’s see feature exist_credits_nr.

Data Summary

# summary exist_credits_nr
tab1(data_credit$exist_credits_nr, sort.group = "decreasing",
    cum.percent = FALSE, bar.values = "percent", main = "Distribution of data",
    xlab = "exist_credits_nr", ylab = "count", col = c("red",
        "yellow", "blue"), horiz = TRUE)

Data Summary

data_credit$exist_credits_nr : 
        Frequency Percent
1             633    63.3
2             333    33.3
3              28     2.8
4               6     0.6
  Total      1000   100.0

Data Summary

According to Table 1: Distribution of categorical predictor variables for the South German Credit data, separately for good and bad credit risks in gromping2019 , this categorical variable has 4 levels.

Data Summary

This variable has data about other known credits inside bank where customer is applying for a new credit. We will recode this variable to 2 levels one_credit, morethan1

Data Summary

# recode exist_credits_nr
exist_credits_nr_temp <- recode(data_credit$exist_credits_nr,
    `1` = "one", `2` = "morethan1", `3` = "morethan1", `4` = "morethan1")
data_credit$exist_credits_nr <- exist_credits_nr_temp

Data Summary

The results of recoding:

Data Summary

# recoding results
tab1(data_credit$exist_credits_nr, sort.group = "decreasing",
    cum.percent = FALSE, bar.values = "percent", cex = 1, cex.names = 1,
    main = "Distribution of data", xlab = "exist_credits_nr",
    ylab = "count", col = c("red", "yellow", "blue"), horiz = TRUE)

Data Summary

data_credit$exist_credits_nr : 
          Frequency Percent
one             633    63.3
morethan1       367    36.7
  Total        1000   100.0

Data Summary

Now let’s see feature job.

Data Summary

# summary job
tab1(data_credit$job, sort.group = "decreasing", cum.percent = FALSE,
    bar.values = "percent", main = "Distribution of data", xlab = "job",
    ylab = "count", col = c("red", "yellow", "blue"), horiz = TRUE)

Data Summary

data_credit$job : 
        Frequency Percent
3             630    63.0
2             200    20.0
4             148    14.8
1              22     2.2
  Total      1000   100.0

Data Summary

According to Table 1: Distribution of categorical predictor variables for the South German Credit data, separately for good and bad credit risks in gromping2019 , this categorical variable has 4 levels.

Data Summary

  • This variable has data about job skills level of bank customer who is applying for a new credit.

  • We will not recode this variable.

Data Summary

Next feature is dependents_nr.

Data Summary

# summary dependents_nr
tab1(data_credit$dependents_nr, sort.group = "decreasing", cum.percent = FALSE,
    bar.values = "percent", main = "Distribution of data", xlab = "dependents_nr",
    ylab = "count", col = c("red", "yellow", "blue"), horiz = TRUE)

Data Summary

data_credit$dependents_nr : 
        Frequency Percent
2             845    84.5
1             155    15.5
  Total      1000   100.0

Data Summary

This categorical variable is binary and we will not transform it.

Data Summary

Now let’s see feature telephone_nr.

Data Summary

# summary telephone_nr
tab1(data_credit$telephone_nr, sort.group = "decreasing", cum.percent = FALSE,
    bar.values = "percent", main = "Distribution of data", xlab = "telephone_nr",
    ylab = "count", col = c("red", "yellow", "blue"), horiz = TRUE)

Data Summary

data_credit$telephone_nr : 
        Frequency Percent
1             596    59.6
2             404    40.4
  Total      1000   100.0

Data Summary

This categorical variable is binary and we will not transform it.

Data Summary

Last feature is foreign_worker.

Data Summary

# summary foreign_worker
tab1(data_credit$foreign_worker, sort.group = "decreasing", cum.percent = FALSE,
    bar.values = "percent", main = "Distribution of data", xlab = "foreign_worker",
    ylab = "count", col = c("red", "yellow", "blue"), horiz = TRUE)

Data Summary

data_credit$foreign_worker : 
        Frequency Percent
2             963    96.3
1              37     3.7
  Total      1000   100.0

Data Summary

This categorical variable is binary and we will not transform it.

Relationship between variables

Now we will study relationships between our outcome customer_good_bad and other features.

Data Summary

  • For this we will build crosstables, also perform chi-square test for each pair (outcome,feature).

  • We will use function CrossTable from gmodels library [@R-gmodels]

Data Summary

# Crosstables of outcome vs account_status
CrossTable(data_credit$customer_good_bad, data_credit$account_status,
    digits = 1, prop.r = F, prop.t = F, prop.chisq = F, chisq = T,
    dnn = c("customer_good_bad", "account_status"))
# Crosstables of outcome vs credit_history
CrossTable(data_credit$customer_good_bad, data_credit$credit_history,
    digits = 1, prop.r = F, prop.t = F, prop.chisq = F, chisq = T,
    dnn = c("customer_good_bad", "credit_history"))
# Crosstables of outcome vs credit_purpose
CrossTable(data_credit$customer_good_bad, data_credit$credit_purpose,
    digits = 1, prop.r = F, prop.t = F, prop.chisq = F, chisq = T,
    dnn = c("customer_good_bad", "credit_purpose"))
# Crosstables of outcome vs savings_account
CrossTable(data_credit$customer_good_bad, data_credit$savings_account,
    digits = 1, prop.r = F, prop.t = F, prop.chisq = F, chisq = T,
    dnn = c("customer_good_bad", "savings_account"))
# Crosstables of outcome vs employment_present
CrossTable(data_credit$customer_good_bad, data_credit$employment_present,
    digits = 1, prop.r = F, prop.t = F, prop.chisq = F, chisq = T,
    dnn = c("customer_good_bad", "employment_present"))
# Crosstables of outcome vs installment_rate_pct
CrossTable(data_credit$customer_good_bad, data_credit$installment_rate_pct,
    digits = 1, prop.r = F, prop.t = F, prop.chisq = F, chisq = T,
    dnn = c("customer_good_bad", "installment_rate_pct"))
# Crosstables of outcome vs status_sex
CrossTable(data_credit$customer_good_bad, data_credit$status_sex,
    digits = 1, prop.r = F, prop.t = F, prop.chisq = F, chisq = T,
    dnn = c("customer_good_bad", "status_sex"))
# Crosstables of outcome vs other_debtors_guar
CrossTable(data_credit$customer_good_bad, data_credit$other_debtors_guar,
    digits = 1, prop.r = F, prop.t = F, prop.chisq = F, chisq = T,
    dnn = c("customer_good_bad", "other_debtors_guar"))
# Crosstables of outcome vs residence_duration
CrossTable(data_credit$customer_good_bad, data_credit$residence_duration,
    digits = 1, prop.r = F, prop.t = F, prop.chisq = F, chisq = T,
    dnn = c("customer_good_bad", "residence_duration"))
# Crosstables of outcome vs property
CrossTable(data_credit$customer_good_bad, data_credit$property,
    digits = 1, prop.r = F, prop.t = F, prop.chisq = F, chisq = T,
    dnn = c("customer_good_bad", "property"))
# Crosstables of outcome vs other_install_plans
CrossTable(data_credit$customer_good_bad, data_credit$other_install_plans,
    digits = 1, prop.r = F, prop.t = F, prop.chisq = F, chisq = T,
    dnn = c("customer_good_bad", "other_install_plans"))
# Crosstables of outcome vs housing
CrossTable(data_credit$customer_good_bad, data_credit$housing,
    digits = 1, prop.r = F, prop.t = F, prop.chisq = F, chisq = T,
    dnn = c("customer_good_bad", "housing"))
# Crosstables of outcome vs exist_credits_nr
CrossTable(data_credit$customer_good_bad, data_credit$exist_credits_nr,
    digits = 1, prop.r = F, prop.t = F, prop.chisq = F, chisq = T,
    dnn = c("customer_good_bad", "exist_credits_nr"))
# Crosstables of outcome vs job
CrossTable(data_credit$customer_good_bad, data_credit$job, digits = 1,
    prop.r = F, prop.t = F, prop.chisq = F, chisq = T, dnn = c("customer_good_bad",
        "job"))
# Crosstables of outcome vs dependents_nr
CrossTable(data_credit$customer_good_bad, data_credit$dependents_nr,
    digits = 1, prop.r = F, prop.t = F, prop.chisq = F, chisq = T,
    dnn = c("customer_good_bad", "dependents_nr"))
# Crosstables of outcome vs telephone_nr
CrossTable(data_credit$customer_good_bad, data_credit$telephone_nr,
    digits = 1, prop.r = F, prop.t = F, prop.chisq = F, chisq = T,
    dnn = c("customer_good_bad", "telephone_nr"))
# Crosstables of outcome vs foreign_worker
CrossTable(data_credit$customer_good_bad, data_credit$foreign_worker,
    digits = 1, prop.r = F, prop.t = F, prop.chisq = F, chisq = T,
    dnn = c("customer_good_bad", "foreign_worker"))

Data Summary

  • The results of code are shown in Appendix A.

  • The null hypothesis for statistical test is that there is no relationship between outcome and chosen feature.

Data Summary

  • In the case of customer_good_bad and account_status test shows a p-value smaller than 0.05 so we have reject null hypothesis and keep alternative hypothesis there is some relationship between outcome and chosen feature.

Data Summary

  • Also we can check from the table that 90% of people who have a possitive account balance are listed as good creditors, which fits to intutive.

Data Summary

  • In the case of customer_good_bad and credit_history test shows a p-value smaller than 0.05 so we have reject null hypothesis and keep alternative hypothesis there is some relationship between outcome and chosen feature.

Data Summary

  • From the table we notice that percentage of customers who have payed their debts are listed as good creditors, which fits to intutive.

Data Summary

  • In the case of customer_good_bad and savings_account test shows a p-value smaller than 0.05 so~ we have reject null hypothesis and keep alternative hypothesis there is some relationship between outcome and chosen feature.

Data Summary

  • From the table we notice that percentage of customers who have no money in their accounts are listed as bad creditors, which fits to intutive.

Data Summary

  • In the case of customer_good_bad and employment_present test shows a p-value smaller than 0.05 so we have reject null hypothesis and keep alternative hypothesis there is some relationship between outcome and chosen feature.

Data Summary

  • From the table we notice that percentage of customers who are unemployed or have less than 1 year of work and bad creditors are 93 out of 300 ( 31 %) and higher than 141 out of 700 (20 %) who are who are unemployed or have less than 1 year of work and good creditors.

Data Summary

  • In the case of customer_good_bad and credit_purpose test shows a p-value smaller than 0.05 so we have reject null hypothesis and keep alternative hypothesis there is some relationship between outcome and chosen feature.

Data Summary

  • From the table we notice that 75 % of bad creditors (225 out of 300) have taken credit for domestic or other purposes.

  • In the case of customer_good_bad and installment_rate_pct test shows a p-value bigger than 0.05 so we keep null hypothesis there is no relationship between outcome and chosen feature.

Data Summary

  • In the case of customer_good_bad and status_sex test shows a p-value smaller than 0.05 so we have reject null hypothesis and keep alternative hypothesis there is some relationship between outcome and chosen feature.

Data Summary

  • We also see that married men are twice more than single when it comes to good creditors.

Data Summary

  • In the case of customer_good_bad and other_debtors_guar test shows a p-value bigger than 0.05 so we keep null hypothesis there is no relationship between outcome and chosen feature.

Data Summary

  • In the case of customer_good_bad and residence_duration test shows a p-value bigger than 0.05 so we keep null hypothesis there is no relationship between outcome and chosen feature.

Data Summary

  • In the case of customer_good_bad and property test shows a p-value smaller than 0.05 so we have reject null hypothesis and keep alternative hypothesis there is some relationship between outcome and chosen feature.

Data Summary

  • In the case of customer_good_bad and other_install_plans test shows a p-value smaller than 0.05 so we have reject null hypothesis and keep alternative hypothesis there is some relationship between outcome and chosen feature.

Data Summary

  • In the case of customer_good_bad and exist_credits_nr test shows a p-value bigger than 0.05 so we keep null hypothesis there is no relationship between outcome and chosen feature.

Data Summary

  • In the case of customer_good_bad and housing test shows a p-value smaller than 0.05 so we have reject null hypothesis and keep alternative hypothesis there is some relationship between outcome and chosen feature.

Data Summary

  • In the case of customer_good_bad and job test shows a p-value bigger than 0.05 so we keep null hypothesis there is no relationship between outcome and chosen feature.

Data Summary

  • In the case of customer_good_bad and telephone_nr test shows a p-value bigger than 0.05 so we keep null hypothesis there is no relationship between outcome and chosen feature.

Data Summary

  • In the case of customer_good_bad and dependents_nr test shows a p-value bigger than 0.05 so we keep null hypothesis there is no relationship between outcome and chosen feature.

Data Summary

  • In the case of customer_good_bad and foreign_worker test shows a p-value smaller than 0.05 so we have reject null hypothesis and keep alternative hypothesis there is some relationship between outcome and chosen feature.

Building models

Scaling numerical variables

  • We have in our data_credit 3 numerical variables duration_month, credit_amount, age_years.

  • Before starting modeling process we need to apply on these variables z-score normalization (from each feature value we substract mean and result divide by standart deviation). We will use function scale.

Scaling numerical variables

# normalization of numeric features
data_credit %>%
    mutate_if(is.numeric, scale)
     account_status duration_month   credit_history credit_purpose
1        no_account      -0.240737 no_prob_currbank       used_car
2        no_account      -0.987079 no_prob_currbank       services
3      no_money_acc      -0.738298         all_paid       services
4        no_account      -0.738298 no_prob_currbank       services
5        no_account      -0.738298 no_prob_currbank       services
6        no_account      -0.904152 no_prob_currbank       services
7        no_account      -1.070006 no_prob_currbank       services
8        no_account      -1.235859 no_prob_currbank       services
9      positive_acc      -0.240737 no_prob_currbank       domestic
10     no_money_acc       0.256825         all_paid       domestic
11       no_account      -0.821225 no_prob_currbank       services
12       no_account       0.754386 no_prob_currbank        new_car
13       no_account      -1.235859 no_prob_currbank       domestic
14     no_money_acc       2.247070 no_prob_currbank       services
15       no_account      -0.240737         all_paid       domestic
16       no_account      -1.235859         all_paid       domestic
17       no_account      -0.821225 no_prob_currbank       services
18     no_money_acc      -0.240737         all_paid       domestic
19     no_money_acc       1.251947 no_prob_currbank       domestic
20     positive_acc      -0.821225 no_prob_currbank       services
21       no_account      -1.235859 no_prob_currbank       services
22     no_money_acc      -0.738298 no_prob_currbank       services
23     no_money_acc       1.251947         all_paid       domestic
24     no_money_acc      -0.738298 no_prob_currbank       domestic
25       no_account      -1.235859 no_prob_currbank       services
26     no_money_acc      -0.821225 no_prob_currbank       domestic
27       no_account      -0.738298         all_paid       used_car
28     no_money_acc      -0.987079 no_prob_currbank       domestic
29     positive_acc      -0.489517         all_paid       services
30     positive_acc       1.749509 no_prob_currbank        new_car
31     positive_acc       0.754386 no_prob_currbank       domestic
32     positive_acc       1.251947 no_prob_currbank       services
33     positive_acc       1.251947 no_prob_currbank       services
34     positive_acc       0.256825         all_paid       domestic
35       no_account      -0.489517         all_paid       services
36       no_account      -1.235859 no_prob_currbank       services
37     positive_acc      -0.738298 no_prob_currbank       services
38     positive_acc      -0.738298 no_prob_currbank       domestic
39     positive_acc      -0.240737         all_paid        new_car
40     positive_acc       0.256825 no_prob_currbank        new_car
41     positive_acc      -0.738298 no_prob_currbank       domestic
42       no_account       0.256825         all_paid       services
43     positive_acc      -0.240737 no_prob_currbank       services
44     no_money_acc       0.256825 no_prob_currbank       services
45     no_money_acc      -0.240737         all_paid       services
46       no_account      -0.240737         all_paid       services
47     positive_acc       0.256825         all_paid       services
48     positive_acc      -0.240737 no_prob_currbank       services
49     positive_acc       0.256825         all_paid       services
50       no_account       0.256825 no_prob_currbank       services
51     positive_acc      -0.738298         all_paid       services
52     positive_acc       1.251947         all_paid       domestic
53     positive_acc      -0.987079 no_prob_currbank       services
54     positive_acc      -0.738298 no_prob_currbank       domestic
55     positive_acc       0.256825         all_paid        new_car
56       no_account      -0.738298 no_prob_currbank       domestic
57     positive_acc      -0.738298 no_prob_currbank       domestic
58       no_account      -0.489517         all_paid       domestic
59     positive_acc       0.008044         all_paid       domestic
60     positive_acc       1.251947         all_paid       services
61     positive_acc      -0.738298 no_prob_currbank       services
62     positive_acc       0.256825         all_paid        new_car
63     positive_acc       2.247070 no_prob_currbank       domestic
64     positive_acc       1.251947 no_prob_currbank       services
65       no_account      -0.738298 no_prob_currbank       services
66     positive_acc      -1.235859 no_prob_currbank       domestic
67     positive_acc      -0.738298         all_paid       domestic
68     positive_acc       0.256825         all_paid       domestic
69     no_money_acc      -0.738298         all_paid       domestic
70     positive_acc       0.256825 no_prob_currbank       services
71     no_money_acc       0.008044 no_prob_currbank       used_car
72     positive_acc       0.754386         all_paid       domestic
73     positive_acc       1.251947         all_paid       domestic
74     positive_acc       0.256825         all_paid       used_car
75     positive_acc      -0.738298         all_paid       domestic
76     no_money_acc      -0.987079         all_paid       used_car
77     positive_acc       0.008044 no_prob_currbank        new_car
78     positive_acc       0.256825 no_prob_currbank       services
79       no_account       0.008044 no_prob_currbank       services
80     positive_acc      -0.489517         all_paid       domestic
81     positive_acc       0.256825 no_prob_currbank       services
82     positive_acc       1.251947 no_prob_currbank       domestic
83     no_money_acc      -0.240737         all_paid       services
84     positive_acc      -0.489517 no_prob_currbank        new_car
85     positive_acc      -0.489517 no_prob_currbank       used_car
86       no_account      -0.738298         all_paid       services
87     positive_acc       0.256825 no_prob_currbank        new_car
88     positive_acc       1.251947 no_prob_currbank       domestic
89       no_account      -1.235859 no_prob_currbank       services
90     no_money_acc       0.256825 no_prob_currbank       services
91     no_money_acc      -0.738298 no_prob_currbank       services
92     no_money_acc       0.754386         all_paid       domestic
93     no_money_acc       0.754386     pay_problems       services
94       no_account      -0.987079         all_paid       domestic
95     no_money_acc      -0.240737 no_prob_currbank       used_car
96     positive_acc       0.505605 no_prob_currbank       used_car
97     no_money_acc      -0.738298 no_prob_currbank       domestic
98     no_money_acc       1.251947 no_prob_currbank       services
99       no_account      -0.987079         all_paid       used_car
100    no_money_acc       1.749509 no_prob_currbank       services
101    positive_acc       0.256825 no_prob_currbank       used_car
102      no_account      -0.489517         all_paid       services
103    no_money_acc       0.256825 no_prob_currbank       services
104    no_money_acc       1.251947 no_prob_currbank       services
105    positive_acc       0.256825 no_prob_currbank       domestic
106    positive_acc       0.256825         all_paid       services
107    positive_acc      -0.738298 no_prob_currbank       services
108    positive_acc      -0.240737         all_paid       domestic
109    positive_acc       0.256825         all_paid       domestic
110    positive_acc      -0.738298 no_prob_currbank       services
111      no_account      -0.489517         all_paid       services
112      no_account      -1.235859         all_paid       used_car
113    no_money_acc      -0.738298 no_prob_currbank       services
114    positive_acc      -0.738298         all_paid       used_car
115    no_money_acc       0.008044         all_paid       used_car
116    no_money_acc      -0.240737         all_paid       services
117    no_money_acc      -0.987079 no_prob_currbank       used_car
118    positive_acc       0.256825         all_paid        new_car
119    positive_acc      -0.987079 no_prob_currbank       services
120    positive_acc       0.256825         all_paid       used_car
121    no_money_acc       1.251947         all_paid       domestic
122    positive_acc       1.251947 no_prob_currbank        new_car
123    no_money_acc      -1.235859         all_paid       domestic
124    positive_acc      -0.489517 no_prob_currbank       domestic
125    positive_acc      -0.489517         all_paid       domestic
126    positive_acc       0.256825 no_prob_currbank       domestic
127    positive_acc      -0.489517         all_paid       domestic
128    positive_acc      -1.235859         all_paid       used_car
129    positive_acc      -0.240737         all_paid       services
130    positive_acc       0.256825 no_prob_currbank       domestic
131    positive_acc       0.256825         all_paid       used_car
132    positive_acc      -0.240737         all_paid       used_car
133    positive_acc      -1.235859         all_paid       used_car
134    positive_acc      -0.240737         all_paid       domestic
135    positive_acc      -0.904152         all_paid       services
136    positive_acc      -1.235859         all_paid       used_car
137    no_money_acc      -0.738298         all_paid       services
138      no_account       0.256825     pay_problems       services
139    no_money_acc      -1.235859 no_prob_currbank       services
140    positive_acc       0.256825 no_prob_currbank       domestic
141    positive_acc       1.251947         all_paid       domestic
142    positive_acc      -0.987079         all_paid       services
143    no_money_acc       0.256825         all_paid       domestic
144    positive_acc      -0.987079 no_prob_currbank       used_car
145    positive_acc       1.251947         all_paid       domestic
146    positive_acc       0.008044         all_paid        new_car
147      no_account       0.256825         all_paid        new_car
148      no_account      -0.738298         all_paid       used_car
149    positive_acc      -0.738298         all_paid       services
150    no_money_acc       0.256825 no_prob_currbank       domestic
151    positive_acc      -0.738298     pay_problems       domestic
152    positive_acc      -0.738298         all_paid       domestic
153    positive_acc       2.247070 no_prob_currbank        new_car
154    no_money_acc       0.256825         all_paid       services
155    no_money_acc      -0.738298         all_paid       domestic
156    positive_acc       0.256825 no_prob_currbank       domestic
157      no_account      -0.738298         all_paid       domestic
158    positive_acc       0.754386 no_prob_currbank       domestic
159    no_money_acc       2.247070     pay_problems       services
160    no_money_acc      -0.738298 no_prob_currbank       used_car
161    positive_acc      -0.738298 no_prob_currbank       domestic
162    positive_acc       0.256825 no_prob_currbank       services
163    positive_acc       0.256825         all_paid       used_car
164    positive_acc      -0.489517 no_prob_currbank       used_car
165    positive_acc      -0.489517         all_paid       services
166    positive_acc      -0.987079 no_prob_currbank       services
167    positive_acc       0.754386 no_prob_currbank       domestic
168    positive_acc       0.256825 no_prob_currbank       services
169    positive_acc      -0.489517 no_prob_currbank       used_car
170    positive_acc      -1.235859 no_prob_currbank       domestic
171    positive_acc       1.251947         all_paid       domestic
172      no_account      -0.738298         all_paid       services
173    positive_acc      -0.738298         all_paid       domestic
174    positive_acc       0.256825         all_paid       domestic
175    positive_acc      -0.738298         all_paid       domestic
176    positive_acc       0.256825         all_paid       domestic
177    positive_acc       0.256825 no_prob_currbank       services
178    no_money_acc      -0.738298 no_prob_currbank        new_car
179    positive_acc      -0.240737         all_paid       services
180    positive_acc       2.247070 no_prob_currbank       domestic
181    positive_acc      -0.987079         all_paid       domestic
182    positive_acc      -0.240737 no_prob_currbank       services
183      no_account       0.754386     pay_problems       services
184    positive_acc       0.754386 no_prob_currbank       domestic
185    positive_acc      -0.987079         all_paid       services
186    positive_acc       1.251947         all_paid       services
187    positive_acc      -0.738298         all_paid       services
188    positive_acc       0.256825         all_paid       domestic
189    no_money_acc       1.251947         all_paid       services
190    positive_acc       0.008044         all_paid       services
191    positive_acc       0.256825         all_paid       domestic
192    no_money_acc       1.251947         all_paid       services
193    no_money_acc       2.247070         all_paid       services
194    positive_acc       0.256825         all_paid       services
195    positive_acc      -0.738298         all_paid       domestic
196    positive_acc       1.003167 no_prob_currbank        new_car
197    positive_acc      -1.235859 no_prob_currbank       services
198    positive_acc      -0.240737 no_prob_currbank       used_car
199    positive_acc      -0.738298         all_paid       domestic
200    positive_acc       2.247070     pay_problems       services
201    positive_acc       0.754386         all_paid       domestic
202    positive_acc       0.588532     pay_problems        new_car
203    positive_acc      -0.240737     pay_problems       domestic
204      no_account       0.256825         all_paid       used_car
205      no_account       0.008044         all_paid       services
206    no_money_acc      -0.240737         all_paid       services
207      no_account      -0.738298         all_paid       domestic
208    no_money_acc      -0.489517         all_paid       domestic
209    no_money_acc       2.247070     pay_problems       services
210    positive_acc      -0.987079         all_paid       domestic
211    positive_acc      -1.401713         all_paid       services
212      no_account       0.256825     pay_problems       used_car
213      no_account       0.256825     pay_problems       used_car
214    positive_acc      -1.235859 no_prob_currbank       services
215    no_money_acc      -0.987079         all_paid       services
216    positive_acc      -0.904152         all_paid       services
217    positive_acc      -0.904152         all_paid        new_car
218    positive_acc      -1.235859         all_paid       services
219      no_account       2.164143         all_paid       services
220    positive_acc      -0.904152         all_paid       services
221      no_account      -0.240737     pay_problems       domestic
222    positive_acc       0.754386 no_prob_currbank       domestic
223    positive_acc       0.256825 no_prob_currbank        new_car
224    positive_acc       0.505605 no_prob_currbank        new_car
225      no_account      -0.240737         all_paid       used_car
226    positive_acc       0.256825 no_prob_currbank       domestic
227      no_account      -0.240737         all_paid       services
228    positive_acc      -0.572444 no_prob_currbank       services
229    positive_acc       0.008044         all_paid       services
230    no_money_acc       2.247070     pay_problems       services
231    positive_acc       0.256825         all_paid       domestic
232    positive_acc      -1.235859     pay_problems       domestic
233    positive_acc       1.500728         all_paid        new_car
234      no_account       0.754386         all_paid        new_car
235    no_money_acc      -0.738298         all_paid       services
236      no_account       0.256825         all_paid       domestic
237    positive_acc       0.008044         all_paid       used_car
238      no_account       0.256825         all_paid       domestic
239    positive_acc      -1.401713         all_paid       used_car
240    positive_acc       1.500728         all_paid        new_car
241    positive_acc      -0.489517 no_prob_currbank       domestic
242    positive_acc       3.242193         all_paid       services
243    positive_acc      -0.240737         all_paid       services
244    positive_acc      -0.240737 no_prob_currbank       domestic
245    positive_acc      -1.235859     pay_problems       domestic
246    positive_acc       0.256825         all_paid        new_car
247    positive_acc      -0.240737 no_prob_currbank       domestic
248    positive_acc       0.256825 no_prob_currbank       used_car
249    no_money_acc      -0.489517         all_paid       domestic
250    positive_acc       0.008044         all_paid        new_car
251    no_money_acc      -0.240737 no_prob_currbank       services
252    no_money_acc      -0.240737 no_prob_currbank       domestic
253    positive_acc      -0.738298         all_paid       domestic
254    positive_acc       1.251947         all_paid       domestic
255    positive_acc       1.251947 no_prob_currbank        new_car
256    positive_acc      -0.489517         all_paid       services
257      no_account      -0.489517 no_prob_currbank       used_car
258    positive_acc      -0.904152 no_prob_currbank       used_car
259      no_account      -0.904152         all_paid       domestic
260    positive_acc      -1.318786         all_paid       services
261    positive_acc      -0.489517         all_paid       used_car
262    positive_acc      -0.821225 no_prob_currbank       services
263    positive_acc      -0.904152         all_paid       used_car
264    positive_acc      -0.987079         all_paid       used_car
265    positive_acc      -0.738298         all_paid       domestic
266    positive_acc       0.090971         all_paid       domestic
267    no_money_acc      -0.987079         all_paid       domestic
268    positive_acc       1.251947         all_paid       services
269    positive_acc      -0.738298 no_prob_currbank       used_car
270    positive_acc      -0.489517     pay_problems       domestic
271    positive_acc      -1.235859         all_paid        new_car
272    positive_acc       1.251947 no_prob_currbank       used_car
273    positive_acc      -1.235859         all_paid       domestic
274    positive_acc       0.256825         all_paid       used_car
275    positive_acc       0.256825         all_paid        new_car
276    positive_acc      -0.738298     pay_problems       services
277    positive_acc      -0.489517         all_paid       domestic
278    no_money_acc       0.008044 no_prob_currbank       services
279      no_account       0.256825         all_paid       domestic
280    positive_acc      -0.987079         all_paid       domestic
281    positive_acc      -1.235859 no_prob_currbank       domestic
282    positive_acc      -0.738298         all_paid       used_car
283    positive_acc      -0.738298         all_paid        new_car
284    no_money_acc       0.008044 no_prob_currbank       services
285      no_account       0.008044         all_paid       domestic
286    positive_acc      -1.235859         all_paid       services
287      no_account      -1.235859 no_prob_currbank       domestic
288    positive_acc      -0.987079         all_paid       domestic
289    positive_acc      -0.987079         all_paid       services
290    positive_acc      -0.489517 no_prob_currbank       domestic
291    positive_acc      -0.738298 no_prob_currbank       services
292    positive_acc      -0.489517         all_paid       used_car
293    positive_acc      -1.235859 no_prob_currbank       domestic
294    no_money_acc      -1.235859 no_prob_currbank       services
295    positive_acc      -0.489517 no_prob_currbank       used_car
296    positive_acc       1.251947         all_paid        new_car
297    positive_acc      -0.987079         all_paid       used_car
298    positive_acc      -1.235859 no_prob_currbank       services
299    no_money_acc      -0.738298         all_paid       used_car
300    positive_acc      -0.240737 no_prob_currbank       domestic
301      no_account      -0.738298     pay_problems       services
302    positive_acc       0.256825         all_paid       domestic
303    positive_acc       0.256825         all_paid       used_car
304    positive_acc      -0.987079 no_prob_currbank       domestic
305    positive_acc      -0.987079         all_paid       domestic
306    positive_acc       0.256825         all_paid       services
307      no_account      -1.235859         all_paid       used_car
308    positive_acc      -1.235859     pay_problems       services
309      no_account       0.008044         all_paid       domestic
310    positive_acc       2.744631     pay_problems        new_car
311    positive_acc      -0.738298 no_prob_currbank       domestic
312    positive_acc       2.247070 no_prob_currbank        new_car
313    positive_acc      -1.235859 no_prob_currbank       services
314    no_money_acc       0.256825         all_paid       services
315      no_account      -1.235859         all_paid       services
316    positive_acc      -0.489517         all_paid        new_car
317    positive_acc       0.256825     pay_problems       services
318    positive_acc      -0.738298         all_paid       domestic
319    positive_acc      -0.489517 no_prob_currbank       domestic
320    positive_acc      -1.235859     pay_problems       services
321    positive_acc      -0.904152         all_paid       services
322    positive_acc      -0.738298         all_paid       domestic
323    positive_acc      -0.821225         all_paid       services
324      no_account      -0.904152 no_prob_currbank       used_car
325    positive_acc      -0.904152         all_paid       services
326    positive_acc       0.256825 no_prob_currbank       services
327    positive_acc      -0.904152         all_paid       services
328    positive_acc      -1.235859 no_prob_currbank       services
329    positive_acc      -0.240737         all_paid       services
330    positive_acc      -0.240737 no_prob_currbank       domestic
331    positive_acc       0.256825 no_prob_currbank       services
332    positive_acc      -0.240737 no_prob_currbank       domestic
333    positive_acc      -1.235859 no_prob_currbank       domestic
334    positive_acc       0.090971         all_paid       services
335    positive_acc       0.256825 no_prob_currbank       services
336      no_account       0.256825         all_paid       used_car
337      no_account      -0.987079 no_prob_currbank       domestic
338      no_account      -0.904152 no_prob_currbank       services
339    positive_acc      -0.904152         all_paid        new_car
340    positive_acc      -0.738298         all_paid        new_car
341    positive_acc       0.754386 no_prob_currbank       domestic
342      no_account       0.256825     pay_problems        new_car
343    positive_acc      -0.240737 no_prob_currbank        new_car
344    positive_acc      -0.987079         all_paid       services
345    positive_acc      -0.738298 no_prob_currbank       services
346    positive_acc      -0.904152         all_paid       domestic
347    positive_acc      -0.904152         all_paid       services
348    positive_acc      -0.904152 no_prob_currbank       services
349      no_account      -0.738298 no_prob_currbank       services
350    positive_acc      -0.738298 no_prob_currbank       services
351    positive_acc      -0.904152 no_prob_currbank       used_car
352    positive_acc       0.256825         all_paid       services
353    positive_acc       2.247070 no_prob_currbank       services
354      no_account      -0.738298 no_prob_currbank       services
355    positive_acc      -1.235859         all_paid       domestic
356    positive_acc       1.251947 no_prob_currbank        new_car
357      no_account       0.256825         all_paid        new_car
358    positive_acc      -0.904152 no_prob_currbank       services
359    positive_acc      -0.240737         all_paid       services
360    positive_acc      -0.489517 no_prob_currbank       services
361    positive_acc      -0.738298 no_prob_currbank       services
362      no_account      -0.738298         all_paid       used_car
363    positive_acc      -1.235859 no_prob_currbank       used_car
364    positive_acc      -0.987079         all_paid       used_car
365    positive_acc      -0.738298         all_paid       domestic
366    positive_acc      -0.738298     pay_problems       used_car
367    positive_acc       0.256825         all_paid       domestic
368    no_money_acc      -0.738298     pay_problems       services
369      no_account      -0.489517         all_paid       services
370      no_account       0.256825         all_paid       used_car
371      no_account       0.256825         all_paid       services
372    positive_acc       0.256825 no_prob_currbank       domestic
373    no_money_acc       0.256825 no_prob_currbank        new_car
374      no_account      -1.235859         all_paid       domestic
375    positive_acc       0.008044 no_prob_currbank       used_car
376      no_account       0.505605         all_paid       domestic
377      no_account      -0.738298     pay_problems       domestic
378    no_money_acc       0.505605 no_prob_currbank        new_car
379    no_money_acc      -0.489517         all_paid       domestic
380    positive_acc       0.754386 no_prob_currbank       domestic
381      no_account      -0.240737         all_paid       used_car
382      no_account       0.008044         all_paid       used_car
383    positive_acc       3.242193 no_prob_currbank       services
384    no_money_acc      -0.987079         all_paid       services
385    positive_acc       1.749509 no_prob_currbank       used_car
386    no_money_acc      -0.987079         all_paid       domestic
387    no_money_acc      -0.987079         all_paid       used_car
388    positive_acc       0.256825         all_paid       services
389    positive_acc      -0.904152         all_paid       domestic
390      no_account       0.256825         all_paid        new_car
391    positive_acc      -0.489517         all_paid        new_car
392    positive_acc      -0.738298 no_prob_currbank       services
393    positive_acc      -1.235859 no_prob_currbank       services
394    positive_acc      -0.489517 no_prob_currbank       domestic
395    positive_acc       0.256825         all_paid       used_car
396    no_money_acc      -0.987079         all_paid       domestic
397    no_money_acc       0.256825 no_prob_currbank       domestic
398    positive_acc      -0.738298         all_paid       domestic
399    positive_acc      -0.738298 no_prob_currbank        new_car
400    no_money_acc      -0.738298         all_paid       services
401    no_money_acc       0.505605         all_paid       services
402    no_money_acc      -0.738298         all_paid       services
403      no_account      -1.235859         all_paid        new_car
404    positive_acc       0.256825         all_paid       domestic
405    positive_acc      -0.240737         all_paid       domestic
406    no_money_acc       2.247070         all_paid       services
407    positive_acc      -0.489517         all_paid        new_car
408    no_money_acc      -0.987079         all_paid       services
409    no_money_acc      -0.240737         all_paid       services
410    positive_acc       1.749509         all_paid       domestic
411    positive_acc      -0.655371         all_paid       domestic
412    positive_acc       0.256825 no_prob_currbank       services
413    positive_acc      -0.738298 no_prob_currbank       domestic
414    positive_acc       0.256825 no_prob_currbank       services
415    positive_acc      -0.738298 no_prob_currbank       domestic
416    positive_acc       0.588532 no_prob_currbank       domestic
417    positive_acc      -0.821225 no_prob_currbank       domestic
418    positive_acc       0.256825 no_prob_currbank        new_car
419    positive_acc      -0.240737 no_prob_currbank       domestic
420      no_account      -0.489517 no_prob_currbank       used_car
421    positive_acc      -0.738298         all_paid       domestic
422    no_money_acc      -0.821225 no_prob_currbank       services
423    no_money_acc      -0.406591 no_prob_currbank       services
424    positive_acc      -0.738298         all_paid       services
425    positive_acc      -0.489517 no_prob_currbank       domestic
426    positive_acc      -0.738298 no_prob_currbank       domestic
427    no_money_acc       1.500728 no_prob_currbank       services
428    no_money_acc      -0.987079 no_prob_currbank       services
429      no_account      -0.738298         all_paid       services
430    no_money_acc      -0.987079         all_paid       services
431    positive_acc       0.754386 no_prob_currbank       domestic
432    positive_acc      -0.738298 no_prob_currbank       domestic
433    no_money_acc      -0.489517 no_prob_currbank       services
434    no_money_acc      -0.240737 no_prob_currbank       services
435    positive_acc      -0.738298 no_prob_currbank       services
436    positive_acc      -1.152933 no_prob_currbank       domestic
437    positive_acc      -0.489517 no_prob_currbank       services
438    positive_acc      -1.235859 no_prob_currbank       domestic
439      no_account       0.505605 no_prob_currbank       services
440    no_money_acc      -0.240737 no_prob_currbank       services
441    positive_acc       0.008044 no_prob_currbank       used_car
442    positive_acc       0.505605 no_prob_currbank       services
443      no_account       1.500728 no_prob_currbank       used_car
444    positive_acc      -0.489517         all_paid       domestic
445    positive_acc      -0.738298 no_prob_currbank       domestic
446    positive_acc      -0.738298         all_paid       used_car
447    positive_acc      -1.235859 no_prob_currbank       domestic
448    positive_acc      -0.738298         all_paid       domestic
449    positive_acc       1.251947         all_paid        new_car
450    no_money_acc      -0.987079         all_paid       domestic
451    no_money_acc      -0.738298         all_paid       domestic
452      no_account      -0.074883         all_paid       used_car
453    no_money_acc      -0.904152         all_paid       services
454    no_money_acc       0.256825 no_prob_currbank       services
455      no_account      -0.572444         all_paid       services
456    no_money_acc       3.242193 no_prob_currbank       domestic
457    no_money_acc      -0.074883 no_prob_currbank       services
458    no_money_acc      -0.240737         all_paid       services
459    no_money_acc       0.256825 no_prob_currbank       used_car
460    positive_acc      -0.738298         all_paid        new_car
461    no_money_acc      -0.074883         all_paid        new_car
462    no_money_acc      -0.240737 no_prob_currbank       used_car
463    positive_acc      -0.489517         all_paid        new_car
464      no_account       0.588532         all_paid       services
465    no_money_acc      -0.738298         all_paid       services
466    positive_acc      -0.738298         all_paid       used_car
467    no_money_acc      -0.738298         all_paid        new_car
468    positive_acc      -0.489517         all_paid       services
469    no_money_acc       1.251947         all_paid       domestic
470    positive_acc      -0.738298         all_paid       services
471    positive_acc      -0.240737         all_paid       used_car
472    positive_acc       0.256825         all_paid       domestic
473    positive_acc       1.251947         all_paid       services
474    positive_acc      -0.489517         all_paid       used_car
475    positive_acc       1.251947         all_paid       domestic
476    positive_acc       0.256825         all_paid       services
477    positive_acc      -0.240737         all_paid       used_car
478    positive_acc       0.256825         all_paid       domestic
479    positive_acc      -0.738298 no_prob_currbank       services
480    no_money_acc      -0.240737         all_paid       domestic
481    positive_acc       0.505605         all_paid       domestic
482    no_money_acc      -0.240737         all_paid       used_car
483    positive_acc       0.256825 no_prob_currbank       services
484    positive_acc      -0.738298 no_prob_currbank       used_car
485    positive_acc      -0.738298 no_prob_currbank       used_car
486    positive_acc      -0.738298 no_prob_currbank       services
487    positive_acc      -0.240737 no_prob_currbank       used_car
488    no_money_acc      -0.240737 no_prob_currbank       used_car
489    positive_acc      -0.738298         all_paid       services
490    positive_acc      -0.738298         all_paid       services
491    positive_acc      -0.240737         all_paid       domestic
492    positive_acc      -0.738298 no_prob_currbank       used_car
493      no_account      -0.240737     pay_problems       services
494    positive_acc       0.256825 no_prob_currbank       domestic
495      no_account      -0.738298 no_prob_currbank        new_car
496    positive_acc       0.256825         all_paid       domestic
497    positive_acc      -1.235859         all_paid       domestic
498    positive_acc       0.256825 no_prob_currbank        new_car
499    positive_acc      -0.738298 no_prob_currbank       services
500      no_account      -0.738298         all_paid       domestic
501    positive_acc       0.256825         all_paid       services
502    positive_acc       0.256825 no_prob_currbank       services
503    positive_acc      -1.235859         all_paid       domestic
504    positive_acc       0.256825 no_prob_currbank       domestic
505    positive_acc       0.256825         all_paid        new_car
506    positive_acc      -1.235859 no_prob_currbank       domestic
507    positive_acc      -0.738298 no_prob_currbank       domestic
508    positive_acc       0.256825 no_prob_currbank        new_car
509    positive_acc       0.256825 no_prob_currbank       used_car
510      no_account      -1.235859         all_paid       services
511    positive_acc      -1.235859 no_prob_currbank       services
512    positive_acc      -1.235859         all_paid       services
513    no_money_acc       0.256825 no_prob_currbank       services
514    no_money_acc       0.754386         all_paid       used_car
515    positive_acc      -0.489517         all_paid       services
516    no_money_acc       0.256825         all_paid       domestic
517    no_money_acc       0.256825 no_prob_currbank       services
518    positive_acc       1.251947         all_paid       services
519    positive_acc       0.256825         all_paid       domestic
520    positive_acc       1.251947 no_prob_currbank       used_car
521    positive_acc       1.251947         all_paid       used_car
522    positive_acc       1.251947         all_paid       used_car
523    positive_acc      -1.235859         all_paid       domestic
524    positive_acc      -0.240737         all_paid       domestic
525    no_money_acc      -0.738298 no_prob_currbank        new_car
526    positive_acc       1.251947 no_prob_currbank       domestic
527      no_account      -0.738298         all_paid       domestic
528    no_money_acc      -0.738298         all_paid       domestic
529    positive_acc      -0.240737         all_paid       domestic
530    positive_acc      -0.240737 no_prob_currbank       domestic
531    positive_acc      -1.401713 no_prob_currbank       domestic
532    positive_acc       0.256825 no_prob_currbank        new_car
533    positive_acc      -0.738298         all_paid       domestic
534      no_account      -0.240737 no_prob_currbank       domestic
535      no_account      -0.240737         all_paid       domestic
536    no_money_acc       0.256825         all_paid       domestic
537    positive_acc      -1.401713 no_prob_currbank       services
538    positive_acc      -1.401713 no_prob_currbank       services
539    positive_acc      -1.152933 no_prob_currbank       domestic
540    no_money_acc      -0.240737     pay_problems       used_car
541    no_money_acc      -0.987079         all_paid       domestic
542    no_money_acc       2.247070         all_paid       domestic
543    no_money_acc      -0.738298         all_paid       used_car
544    no_money_acc      -0.240737 no_prob_currbank       used_car
545    positive_acc      -1.401713 no_prob_currbank       domestic
546    no_money_acc      -1.070006         all_paid       services
547    no_money_acc       0.754386         all_paid       domestic
548    positive_acc      -0.904152 no_prob_currbank       domestic
549    no_money_acc      -0.738298         all_paid       services
550    positive_acc      -0.738298 no_prob_currbank       used_car
551    no_money_acc      -0.738298         all_paid       services
552    positive_acc      -0.738298 no_prob_currbank       services
553    positive_acc      -0.738298         all_paid       services
554    positive_acc      -0.904152         all_paid       services
555    no_money_acc      -0.821225         all_paid       used_car
556    positive_acc       1.003167 no_prob_currbank       services
557    no_money_acc       2.247070     pay_problems       services
558    positive_acc      -0.738298         all_paid       used_car
559    positive_acc       0.256825         all_paid        new_car
560    no_money_acc      -0.572444         all_paid       services
561    positive_acc      -0.074883 no_prob_currbank       services
562    positive_acc      -0.240737 no_prob_currbank        new_car
563    no_money_acc       3.242193         all_paid       services
564    positive_acc       0.256825         all_paid       domestic
565    positive_acc       0.754386 no_prob_currbank       services
566    no_money_acc       0.256825 no_prob_currbank       domestic
567    no_money_acc       1.251947 no_prob_currbank       services
568    positive_acc      -1.235859 no_prob_currbank       services
569    positive_acc      -0.738298 no_prob_currbank       used_car
570    positive_acc       2.247070         all_paid       domestic
571    positive_acc      -0.738298         all_paid       services
572    positive_acc       1.251947         all_paid       services
573    no_money_acc       0.422678         all_paid        new_car
574    no_money_acc       0.754386     pay_problems       used_car
575    no_money_acc       1.251947         all_paid        new_car
576    no_money_acc       2.247070     pay_problems       services
577      no_account       1.251947         all_paid       used_car
578      no_account      -0.738298         all_paid       services
579      no_account       1.003167 no_prob_currbank       used_car
580      no_account       1.749509         all_paid       domestic
581      no_account       0.256825     pay_problems       domestic
582      no_account       0.256825         all_paid       used_car
583    positive_acc       0.256825         all_paid       domestic
584      no_account      -0.738298         all_paid       domestic
585      no_account      -0.738298         all_paid       used_car
586      no_account       0.256825         all_paid        new_car
587      no_account      -0.240737 no_prob_currbank       services
588      no_account       1.251947         all_paid       used_car
589      no_account      -0.240737         all_paid       domestic
590    no_money_acc      -0.738298         all_paid       used_car
591      no_account      -0.738298         all_paid       used_car
592      no_account      -1.070006 no_prob_currbank       services
593      no_account       1.251947 no_prob_currbank       used_car
594      no_account       0.256825     pay_problems       services
595      no_account       0.754386     pay_problems       used_car
596      no_account       1.251947 no_prob_currbank       used_car
597      no_account      -0.738298         all_paid       used_car
598      no_account       0.008044 no_prob_currbank       services
599      no_account       0.754386         all_paid       domestic
600      no_account       1.251947         all_paid       used_car
601      no_account       1.251947         all_paid        new_car
602    positive_acc       0.256825 no_prob_currbank       used_car
603      no_account      -1.235859         all_paid       services
604      no_account      -0.738298         all_paid       used_car
605      no_account       1.251947         all_paid       used_car
606      no_account      -0.489517 no_prob_currbank       used_car
607    no_money_acc      -1.235859 no_prob_currbank       used_car
608      no_account      -1.235859 no_prob_currbank       services
609      no_account       2.247070         all_paid        new_car
610    no_money_acc       0.256825         all_paid       used_car
611    no_money_acc      -0.738298         all_paid       services
612      no_account      -0.738298         all_paid       used_car
613      no_account      -0.738298         all_paid       used_car
614      no_account      -0.489517         all_paid       used_car
615      no_account       0.256825         all_paid        new_car
616      no_account      -0.738298         all_paid       services
617      no_account       0.754386 no_prob_currbank        new_car
618      no_account      -0.240737         all_paid       services
619      no_account       0.754386         all_paid       used_car
620    no_money_acc      -0.738298 no_prob_currbank       services
621    positive_acc       0.256825         all_paid        new_car
622    positive_acc       0.256825 no_prob_currbank       services
623      no_account      -0.738298 no_prob_currbank        new_car
624      no_account      -0.738298         all_paid       used_car
625      no_account       0.256825 no_prob_currbank        new_car
626      no_account      -1.235859 no_prob_currbank       used_car
627    positive_acc      -0.738298         all_paid       services
628    positive_acc      -0.240737 no_prob_currbank       domestic
629    positive_acc      -1.235859         all_paid       services
630      no_account      -0.738298         all_paid       used_car
631      no_account      -0.489517 no_prob_currbank       used_car
632      no_account       1.749509         all_paid       used_car
633      no_account       0.256825         all_paid       used_car
634      no_account       1.251947         all_paid       used_car
635      no_account       1.251947         all_paid       services
636      no_account       0.256825         all_paid       used_car
637      no_account      -0.738298 no_prob_currbank       used_car
638      no_account      -0.655371 no_prob_currbank       services
639      no_account      -0.074883 no_prob_currbank       used_car
640      no_account       0.256825 no_prob_currbank        new_car
641      no_account       1.251947 no_prob_currbank       used_car
642    positive_acc       1.749509     pay_problems       services
643      no_account       0.256825 no_prob_currbank        new_car
644      no_account       2.247070 no_prob_currbank        new_car
645    positive_acc       0.256825 no_prob_currbank       services
646      no_account       1.251947         all_paid       services
647    positive_acc       0.256825 no_prob_currbank       domestic
648    positive_acc       2.247070 no_prob_currbank       domestic
649    no_money_acc       0.754386 no_prob_currbank       services
650    no_money_acc      -0.240737         all_paid       services
651    no_money_acc       0.256825         all_paid       used_car
652    no_money_acc      -1.235859         all_paid       domestic
653    no_money_acc      -0.738298         all_paid       services
654    positive_acc       0.256825 no_prob_currbank       domestic
655    no_money_acc       2.247070 no_prob_currbank       services
656    no_money_acc      -0.240737 no_prob_currbank       services
657    no_money_acc       0.256825 no_prob_currbank       domestic
658    no_money_acc      -1.235859         all_paid       domestic
659    no_money_acc      -1.152933         all_paid       domestic
660    no_money_acc      -1.235859         all_paid       domestic
661    no_money_acc      -1.070006         all_paid       domestic
662    no_money_acc      -0.738298         all_paid       domestic
663    no_money_acc      -0.738298 no_prob_currbank       domestic
664    no_money_acc      -1.235859         all_paid       domestic
665      no_account      -1.070006 no_prob_currbank       services
666    no_money_acc      -0.738298         all_paid       domestic
667    no_money_acc      -1.152933         all_paid       domestic
668    no_money_acc      -0.655371 no_prob_currbank       domestic
669    no_money_acc      -0.240737         all_paid       domestic
670    no_money_acc      -1.152933         all_paid       domestic
671    no_money_acc      -0.655371         all_paid       domestic
672    no_money_acc      -0.240737         all_paid       domestic
673    no_money_acc      -0.240737         all_paid       domestic
674    no_money_acc      -1.070006         all_paid       domestic
675    no_money_acc      -0.738298         all_paid       domestic
676    positive_acc      -1.235859 no_prob_currbank       services
677      no_account      -0.987079 no_prob_currbank       domestic
678    no_money_acc      -0.240737 no_prob_currbank       domestic
679    no_money_acc      -0.489517 no_prob_currbank       domestic
680    no_money_acc      -1.235859         all_paid       domestic
681    no_money_acc      -0.904152     pay_problems       domestic
682    no_money_acc      -0.738298         all_paid       domestic
683    no_money_acc      -0.074883 no_prob_currbank        new_car
684    no_money_acc      -0.489517 no_prob_currbank       domestic
685      no_account      -0.738298         all_paid       domestic
686    positive_acc       0.256825 no_prob_currbank       used_car
687    no_money_acc      -0.904152         all_paid       used_car
688    no_money_acc       1.251947     pay_problems       domestic
689    positive_acc      -0.240737         all_paid       used_car
690      no_account       0.256825         all_paid       domestic
691    positive_acc       3.242193         all_paid       domestic
692      no_account      -0.738298         all_paid       services
693    no_money_acc      -1.235859         all_paid       domestic
694    positive_acc      -0.489517 no_prob_currbank        new_car
695    positive_acc      -0.738298         all_paid       used_car
696    positive_acc       3.242193 no_prob_currbank       domestic
697    positive_acc      -0.738298 no_prob_currbank       services
698    positive_acc       0.256825         all_paid       domestic
699    no_money_acc       1.998289 no_prob_currbank        new_car
700      no_account       0.256825 no_prob_currbank       domestic
701      no_account       1.251947         all_paid        new_car
702    no_money_acc       1.251947 no_prob_currbank        new_car
703      no_account      -0.240737 no_prob_currbank       services
704      no_account       0.754386         all_paid       used_car
705      no_account      -1.235859 no_prob_currbank       domestic
706    positive_acc      -0.489517         all_paid       domestic
707    positive_acc       0.256825 no_prob_currbank       used_car
708    no_money_acc       0.754386 no_prob_currbank       services
709      no_account       2.247070     pay_problems       services
710    positive_acc       2.247070 no_prob_currbank       services
711    positive_acc       0.256825         all_paid       services
712      no_account       1.749509 no_prob_currbank       domestic
713    positive_acc       1.251947 no_prob_currbank       services
714    positive_acc      -0.987079 no_prob_currbank       services
715    positive_acc      -1.235859         all_paid       services
716    positive_acc       1.251947     pay_problems       domestic
717      no_account       2.247070         all_paid       services
718    positive_acc       0.256825         all_paid       services
719    no_money_acc      -0.240737         all_paid       used_car
720    no_money_acc       4.237315         all_paid       domestic
721    no_money_acc       2.247070 no_prob_currbank       services
722    positive_acc      -0.489517         all_paid       services
723    positive_acc       0.008044 no_prob_currbank        new_car
724    positive_acc       1.251947 no_prob_currbank        new_car
725      no_account       0.256825         all_paid       used_car
726    no_money_acc      -0.240737         all_paid        new_car
727    positive_acc       0.008044         all_paid       services
728    positive_acc      -0.738298         all_paid       services
729    positive_acc       1.251947 no_prob_currbank        new_car
730    no_money_acc       3.242193 no_prob_currbank       domestic
731    no_money_acc       1.749509     pay_problems        new_car
732    positive_acc       3.242193         all_paid       services
733    positive_acc      -0.489517 no_prob_currbank        new_car
734      no_account      -0.489517         all_paid       services
735      no_account       1.251947         all_paid        new_car
736    positive_acc      -1.235859         all_paid       domestic
737    no_money_acc      -0.987079         all_paid       domestic
738    positive_acc      -0.240737 no_prob_currbank       domestic
739    no_money_acc      -0.489517         all_paid       domestic
740    no_money_acc      -0.074883     pay_problems        new_car
741    positive_acc      -0.738298         all_paid       used_car
742    positive_acc      -0.738298         all_paid       domestic
743      no_account       0.256825         all_paid       used_car
744    positive_acc       0.754386 no_prob_currbank        new_car
745    positive_acc       0.754386         all_paid        new_car
746    positive_acc      -1.235859         all_paid       used_car
747    no_money_acc       0.256825         all_paid        new_car
748    positive_acc       0.256825 no_prob_currbank       domestic
749    no_money_acc      -0.987079         all_paid       services
750    positive_acc       0.256825         all_paid       used_car
751    no_money_acc       1.251947 no_prob_currbank       services
752      no_account      -0.987079         all_paid       services
753    no_money_acc      -0.987079 no_prob_currbank       services
754    no_money_acc       0.256825         all_paid        new_car
755    no_money_acc      -1.235859         all_paid       services
756      no_account      -0.489517     pay_problems       services
757      no_account       0.256825 no_prob_currbank       services
758      no_account      -0.738298     pay_problems       services
759    no_money_acc       0.754386         all_paid       services
760    no_money_acc       1.251947 no_prob_currbank       services
761    no_money_acc       2.247070         all_paid       domestic
762      no_account      -0.240737         all_paid       domestic
763    positive_acc       0.008044     pay_problems       services
764    no_money_acc       3.242193         all_paid       services
765    positive_acc       0.256825 no_prob_currbank       services
766    positive_acc      -0.987079         all_paid       domestic
767    no_money_acc       1.500728 no_prob_currbank       domestic
768    positive_acc      -0.240737 no_prob_currbank       services
769    no_money_acc      -0.987079     pay_problems       services
770      no_account      -0.489517         all_paid       domestic
771      no_account       0.256825         all_paid       domestic
772      no_account      -0.987079         all_paid       services
773      no_account      -0.240737         all_paid       domestic
774      no_account       1.251947         all_paid       services
775      no_account       1.251947 no_prob_currbank       services
776      no_account       1.251947         all_paid       services
777    no_money_acc      -0.240737 no_prob_currbank       used_car
778      no_account      -1.235859 no_prob_currbank       used_car
779    no_money_acc      -0.240737 no_prob_currbank       domestic
780    positive_acc      -0.489517         all_paid       services
781    no_money_acc       0.754386 no_prob_currbank       used_car
782      no_account       0.256825 no_prob_currbank       domestic
783    no_money_acc       1.251947         all_paid       services
784    no_money_acc      -1.235859     pay_problems       services
785      no_account      -0.738298     pay_problems       domestic
786    positive_acc       0.256825         all_paid       domestic
787    no_money_acc      -1.235859     pay_problems       services
788    no_money_acc      -0.489517 no_prob_currbank       domestic
789    no_money_acc       0.256825     pay_problems       services
790    no_money_acc       0.256825 no_prob_currbank       used_car
791    no_money_acc      -0.489517     pay_problems       services
792    positive_acc      -0.489517         all_paid       domestic
793      no_account       0.256825     pay_problems       used_car
794      no_account      -0.738298         all_paid       services
795    positive_acc       0.754386 no_prob_currbank       services
796    no_money_acc       1.251947     pay_problems       services
797    positive_acc      -0.240737         all_paid       used_car
798    positive_acc       0.008044 no_prob_currbank       services
799      no_account       0.256825         all_paid       services
800    positive_acc       0.256825         all_paid       services
801      no_account       0.256825         all_paid       services
802      no_account       0.256825         all_paid       services
803      no_account       0.256825         all_paid       services
804    no_money_acc      -0.240737         all_paid       services
805      no_account      -0.738298         all_paid       services
806      no_account       0.256825         all_paid       services
807    no_money_acc      -0.240737     pay_problems       services
808      no_account       3.242193 no_prob_currbank       services
809      no_account       0.256825         all_paid       used_car
810      no_account      -1.235859     pay_problems       services
811    no_money_acc       2.247070         all_paid       services
812      no_account       3.242193         all_paid       services
813    positive_acc      -0.240737         all_paid       domestic
814      no_account      -0.240737         all_paid       domestic
815    no_money_acc      -0.987079     pay_problems        new_car
816    positive_acc      -0.240737 no_prob_currbank       used_car
817    positive_acc      -0.904152         all_paid       services
818      no_account      -0.738298         all_paid       services
819    no_money_acc      -1.070006         all_paid       used_car
820    positive_acc      -0.987079         all_paid       used_car
821    no_money_acc       2.247070         all_paid       domestic
822      no_account       1.251947 no_prob_currbank       services
823      no_account       0.256825         all_paid       domestic
824      no_account       0.008044         all_paid       domestic
825      no_account       0.256825 no_prob_currbank       domestic
826    no_money_acc      -1.235859 no_prob_currbank       services
827    no_money_acc       2.247070     pay_problems       services
828      no_account      -0.738298 no_prob_currbank       services
829    no_money_acc      -0.738298         all_paid       domestic
830    no_money_acc       2.247070         all_paid       domestic
831    no_money_acc       1.251947     pay_problems       domestic
832    positive_acc       1.251947 no_prob_currbank       domestic
833    positive_acc       1.251947 no_prob_currbank       services
834    positive_acc       1.251947         all_paid       domestic
835    positive_acc      -1.235859         all_paid       used_car
836    no_money_acc       0.256825         all_paid        new_car
837    positive_acc      -0.240737     pay_problems       services
838    no_money_acc       0.256825         all_paid       used_car
839    positive_acc      -0.240737     pay_problems       services
840    positive_acc      -0.738298         all_paid       services
841      no_account       1.251947         all_paid       services
842    no_money_acc      -0.240737 no_prob_currbank       used_car
843    positive_acc      -0.738298         all_paid       used_car
844    no_money_acc       0.256825         all_paid       services
845    no_money_acc       0.256825 no_prob_currbank       services
846    no_money_acc       0.505605 no_prob_currbank       domestic
847    no_money_acc       0.754386 no_prob_currbank       domestic
848    no_money_acc       3.242193     pay_problems       services
849    no_money_acc       1.251947         all_paid       domestic
850    no_money_acc       1.251947         all_paid       services
851    no_money_acc       1.998289         all_paid       domestic
852      no_account      -0.738298     pay_problems       domestic
853    no_money_acc       0.256825 no_prob_currbank       services
854    positive_acc       0.256825 no_prob_currbank       services
855    positive_acc      -0.240737         all_paid       used_car
856      no_account       2.247070         all_paid       services
857      no_account       0.256825     pay_problems       used_car
858      no_account       1.251947         all_paid       domestic
859      no_account       1.749509         all_paid       domestic
860      no_account      -0.738298         all_paid       domestic
861    positive_acc       2.247070         all_paid       services
862      no_account       2.247070         all_paid       services
863    no_money_acc      -0.738298         all_paid       domestic
864    positive_acc      -0.240737 no_prob_currbank       services
865      no_account       1.583655 no_prob_currbank       services
866    positive_acc      -0.489517 no_prob_currbank       domestic
867    no_money_acc      -0.738298         all_paid       services
868    no_money_acc       1.251947         all_paid        new_car
869    no_money_acc      -0.738298         all_paid       used_car
870    no_money_acc       0.256825         all_paid       services
871      no_account       2.247070         all_paid       domestic
872    positive_acc       1.251947 no_prob_currbank       services
873    positive_acc      -0.240737         all_paid       domestic
874    positive_acc       1.251947 no_prob_currbank       services
875    no_money_acc       0.256825     pay_problems       services
876    no_money_acc       0.754386 no_prob_currbank       services
877    no_money_acc       0.754386 no_prob_currbank       services
878      no_account       2.247070         all_paid       domestic
879      no_account      -0.987079         all_paid       domestic
880      no_account       0.256825     pay_problems       services
881      no_account      -0.240737         all_paid       used_car
882    positive_acc      -0.987079     pay_problems       domestic
883      no_account       2.247070         all_paid       services
884    no_money_acc      -0.489517     pay_problems       services
885    no_money_acc      -0.489517         all_paid       services
886    no_money_acc       2.247070         all_paid       services
887      no_account       0.256825         all_paid       services
888      no_account       1.251947 no_prob_currbank       services
889      no_account       0.256825         all_paid       domestic
890    no_money_acc       1.251947         all_paid       used_car
891    no_money_acc       3.242193         all_paid       services
892      no_account       1.251947 no_prob_currbank        new_car
893    no_money_acc      -0.738298         all_paid       domestic
894      no_account      -0.240737         all_paid       used_car
895    no_money_acc       0.256825 no_prob_currbank       used_car
896    no_money_acc      -0.240737         all_paid        new_car
897    positive_acc       0.008044 no_prob_currbank       services
898    positive_acc       0.505605         all_paid       services
899    no_money_acc       0.505605         all_paid       services
900    positive_acc      -0.904152         all_paid       services
901      no_account       0.256825         all_paid       services
902      no_account      -0.738298         all_paid       services
903    no_money_acc       1.251947 no_prob_currbank       services
904      no_account      -0.240737         all_paid       used_car
905      no_account      -0.240737     pay_problems       used_car
906      no_account      -0.240737 no_prob_currbank       used_car
907      no_account      -0.240737     pay_problems       used_car
908      no_account       0.754386         all_paid       used_car
909      no_account       0.256825 no_prob_currbank       services
910      no_account       2.247070     pay_problems       used_car
911      no_account       0.256825 no_prob_currbank       services
912      no_account      -0.738298 no_prob_currbank       services
913      no_account       1.749509 no_prob_currbank       domestic
914      no_account       1.251947     pay_problems       used_car
915      no_account       0.256825     pay_problems       used_car
916      no_account      -0.240737         all_paid       used_car
917      no_account      -0.738298         all_paid       used_car
918    no_money_acc      -0.738298     pay_problems       used_car
919      no_account       2.247070     pay_problems        new_car
920      no_account       2.247070 no_prob_currbank        new_car
921      no_account       0.256825     pay_problems       used_car
922      no_account      -0.738298     pay_problems       services
923      no_account       0.256825         all_paid       services
924      no_account       0.505605     pay_problems       services
925      no_account       0.008044 no_prob_currbank       services
926      no_account      -0.240737         all_paid       used_car
927      no_account       0.256825     pay_problems       services
928      no_account      -0.738298         all_paid       services
929      no_account       2.247070         all_paid        new_car
930    no_money_acc       2.247070     pay_problems       services
931    no_money_acc      -0.240737 no_prob_currbank       services
932      no_account      -0.738298         all_paid       services
933    no_money_acc      -0.738298         all_paid       services
934      no_account      -0.738298         all_paid       services
935      no_account      -0.406591 no_prob_currbank       services
936      no_account      -0.074883 no_prob_currbank       services
937    no_money_acc      -0.987079         all_paid       used_car
938    no_money_acc      -0.240737 no_prob_currbank       services
939    no_money_acc       0.256825         all_paid       services
940    no_money_acc       1.251947 no_prob_currbank       services
941    positive_acc       2.247070 no_prob_currbank       services
942    no_money_acc      -0.738298         all_paid       services
943    positive_acc      -0.738298         all_paid       services
944    no_money_acc       1.251947         all_paid       services
945      no_account      -0.738298         all_paid       domestic
946    no_money_acc      -0.489517     pay_problems       services
947    positive_acc      -0.904152         all_paid       used_car
948    positive_acc       0.754386     pay_problems        new_car
949      no_account      -0.240737     pay_problems       services
950    positive_acc      -0.738298 no_prob_currbank       domestic
951    no_money_acc       1.998289 no_prob_currbank       domestic
952    positive_acc      -0.738298 no_prob_currbank       services
953    no_money_acc       0.008044         all_paid       services
954    positive_acc       2.247070 no_prob_currbank        new_car
955    positive_acc      -0.738298     pay_problems       services
956      no_account      -0.240737 no_prob_currbank       domestic
957    no_money_acc       0.008044         all_paid       services
958    no_money_acc       0.754386         all_paid       used_car
959    no_money_acc       0.754386     pay_problems       services
960    no_money_acc       0.256825         all_paid       domestic
961    positive_acc      -0.240737         all_paid       services
962    no_money_acc      -0.738298         all_paid       domestic
963    no_money_acc       2.744631     pay_problems       services
964      no_account       0.256825         all_paid       used_car
965    no_money_acc      -0.489517         all_paid       domestic
966    no_money_acc       2.247070         all_paid       used_car
967    positive_acc       0.256825 no_prob_currbank       services
968      no_account      -0.240737         all_paid       domestic
969      no_account       1.998289         all_paid       domestic
970      no_account       0.008044     pay_problems       services
971    positive_acc       2.247070         all_paid       services
972    no_money_acc       0.505605     pay_problems       services
973    positive_acc      -0.240737         all_paid       domestic
974      no_account       1.998289     pay_problems       services
975      no_account      -1.235859         all_paid       services
976      no_account       0.754386         all_paid       domestic
977    no_money_acc       2.247070     pay_problems       services
978      no_account      -1.235859         all_paid       services
979    no_money_acc      -0.738298         all_paid       used_car
980      no_account      -0.738298         all_paid        new_car
981    no_money_acc       0.256825         all_paid       domestic
982    positive_acc      -0.240737 no_prob_currbank       services
983    no_money_acc       2.247070 no_prob_currbank       used_car
984      no_account      -0.240737         all_paid       domestic
985      no_account       2.247070         all_paid       domestic
986    positive_acc      -0.738298 no_prob_currbank       services
987      no_account      -0.572444         all_paid       services
988      no_account      -0.738298         all_paid       domestic
989      no_account      -0.240737         all_paid       services
990    no_money_acc       0.256825         all_paid       services
991      no_account      -0.240737         all_paid       services
992    no_money_acc       0.256825         all_paid        new_car
993      no_account      -0.240737         all_paid        new_car
994      no_account      -0.240737 no_prob_currbank       services
995      no_account      -0.738298     pay_problems       domestic
996      no_account       0.256825         all_paid       domestic
997      no_account       0.256825         all_paid       services
998    positive_acc       0.008044 no_prob_currbank       services
999    no_money_acc      -0.738298         all_paid       domestic
1000     no_account       0.754386         all_paid       used_car
     credit_amount savings_account employment_present installment_rate_pct
1        -0.787263          no_sav    unemp_less1year                    4
2        -0.167301          no_sav               1to4                    2
3        -0.860950         less100               4to7                    2
4        -0.407137          no_sav               1to4                    3
5        -0.389779          no_sav               1to4                    4
6        -0.364980          no_sav    unemp_less1year                    1
7         0.044904          no_sav               4to7                    1
8        -0.676733          no_sav    unemp_less1year                    2
9        -0.769904          no_sav    unemp_less1year                    4
10        0.172439       100to1000    unemp_less1year                    1
11        0.224516          no_sav               1to4                    2
12        1.032947         less100               4to7                    1
13       -0.465591          no_sav               4to7                    1
14        1.527145         less100    unemp_less1year                    2
15       -0.473031        over1000               4to7                    2
16       -0.221149       100to1000               1to4                    2
17        0.236561          no_sav               1to4                    1
18       -0.020635       100to1000    unemp_less1year                    1
19       -0.330971          no_sav              7plus                    4
20        1.401736          no_sav               1to4                    1
21        0.143389          no_sav               1to4                    1
22       -0.052165          no_sav    unemp_less1year                    1
23       -0.314320          no_sav    unemp_less1year                    4
24       -0.654414          no_sav               4to7                    4
25        0.511824        over1000    unemp_less1year                    1
26        0.531308          no_sav               4to7                    2
27       -0.927906          no_sav              7plus                    4
28       -0.750065          no_sav              7plus                    2
29        0.100877        over1000               1to4                    3
30        0.540165          no_sav              7plus                    4
31       -0.090071          no_sav              7plus                    4
32        0.093438          no_sav               4to7                    4
33        1.184217          no_sav              7plus                    4
34       -0.671419       100to1000               4to7                    4
35       -0.549197          no_sav    unemp_less1year                    2
36       -0.854219          no_sav              7plus                    1
37       -0.629261          no_sav              7plus                    4
38       -0.473739          no_sav              7plus                    2
39        0.037818        over1000               1to4                    2
40        0.211408          no_sav              7plus                    4
41       -0.806039        over1000               4to7                    4
42       -0.537152          no_sav              7plus                    4
43       -0.794703          no_sav               1to4                    4
44       -0.158090        over1000               4to7                    4
45       -0.719953        over1000               1to4                    4
46       -0.728101          no_sav    unemp_less1year                    4
47       -0.713222          no_sav               4to7                    4
48       -0.498538         less100               1to4                    4
49       -0.636701         less100    unemp_less1year                    4
50       -0.669293         less100               4to7                    4
51       -0.932157          no_sav               1to4                    4
52        0.229475          no_sav               1to4                    2
53       -0.725267          no_sav               1to4                    3
54       -0.333096        over1000              7plus                    1
55        1.077584        over1000              7plus                    3
56       -1.022494          no_sav               4to7                    4
57       -0.572579          no_sav              7plus                    2
58       -0.785846          no_sav    unemp_less1year                    4
59       -0.039411        over1000              7plus                    4
60       -0.068107        over1000               1to4                    4
61       -0.746877       100to1000               1to4                    4
62       -0.209812          no_sav    unemp_less1year                    4
63        0.108671        over1000              7plus                    4
64        2.693737          no_sav              7plus                    2
65       -0.682755          no_sav               1to4                    4
66       -0.720661         less100               1to4                    1
67       -0.068815          no_sav               1to4                    2
68       -0.349747          no_sav               4to7                    4
69       -0.603754          no_sav               1to4                    1
70       -0.439021          no_sav              7plus                    4
71       -0.186431       100to1000               4to7                    3
72       -0.497475        over1000              7plus                    4
73       -0.344433       100to1000              7plus                    4
74       -0.829775        over1000               4to7                    4
75        0.045258        over1000              7plus                    2
76       -0.439730        over1000               4to7                    2
77        0.001329          no_sav              7plus                    1
78       -0.471614       100to1000              7plus                    4
79       -0.591355          no_sav              7plus                    4
80       -0.457797        over1000              7plus                    4
81       -0.442564          no_sav               1to4                    4
82        0.025065        over1000              7plus                    4
83        0.919228         less100               1to4                    2
84       -0.322823       100to1000               1to4                    2
85       -0.620405        over1000              7plus                    4
86        0.134533       100to1000               1to4                    1
87       -0.327782          no_sav               4to7                    4
88        0.419007          no_sav               1to4                    4
89       -0.922946       100to1000               4to7                    3
90       -0.462757        over1000               1to4                    4
91       -0.452129         less100    unemp_less1year                    4
92       -0.099282        over1000              7plus                    2
93        0.336463          no_sav               1to4                    2
94       -0.675670          no_sav               4to7                    3
95        1.094589          no_sav              7plus                    2
96        0.444514       100to1000    unemp_less1year                    4
97        0.106900          no_sav               1to4                    1
98        0.419361          no_sav               1to4                    2
99       -0.402178          no_sav               1to4                    3
100       0.950403          no_sav               4to7                    2
101       0.179170       100to1000               1to4                    4
102      -0.873349          no_sav               1to4                    4
103       0.510407        over1000               1to4                    4
104       1.474006          no_sav               1to4                    2
105      -0.503143          no_sav               4to7                    4
106      -0.665396          no_sav               1to4                    2
107      -0.658665          no_sav               1to4                    4
108      -0.637055          no_sav    unemp_less1year                    3
109      -0.615799          no_sav    unemp_less1year                    4
110      -0.446107        over1000               4to7                    4
111       0.243646          no_sav               1to4                    3
112      -1.007261          no_sav              7plus                    2
113      -0.320697       100to1000               4to7                    3
114      -0.888583          no_sav               1to4                    4
115       0.249668        over1000               4to7                    2
116       1.058808          no_sav               4to7                    3
117      -0.479053          no_sav               4to7                    4
118      -0.236736       100to1000               1to4                    2
119      -0.827295       100to1000              7plus                    4
120      -0.074129       100to1000              7plus                    4
121       0.539811          no_sav    unemp_less1year                    4
122       0.910725          no_sav              7plus                    2
123      -0.428039          no_sav    unemp_less1year                    4
124      -0.642015          no_sav               1to4                    4
125      -0.729164       100to1000              7plus                    4
126       0.648924          no_sav    unemp_less1year                    3
127      -0.849259        over1000    unemp_less1year                    4
128      -0.103887       100to1000               1to4                    1
129      -0.514125          no_sav               1to4                    2
130      -0.141439         less100              7plus                    3
131      -0.476928          no_sav               1to4                    2
132      -0.267912          no_sav               1to4                    3
133      -0.409263          no_sav               1to4                    2
134      -0.644140          no_sav    unemp_less1year                    3
135      -0.675670          no_sav               1to4                    2
136      -0.612256       100to1000               1to4                    4
137      -0.691966       100to1000              7plus                    4
138      -0.335222         less100               4to7                    2
139      -0.828712        over1000               4to7                    1
140      -0.043662        over1000               1to4                    3
141       0.199717        over1000              7plus                    2
142       0.198654        over1000              7plus                    1
143       0.642193        over1000              7plus                    2
144      -0.306526          no_sav    unemp_less1year                    2
145      -0.310778        over1000               1to4                    4
146      -0.281728        over1000              7plus                    4
147      -0.108847        over1000              7plus                    4
148      -0.711805        over1000              7plus                    2
149      -0.612611          no_sav               4to7                    2
150      -0.541404          no_sav              7plus                    4
151      -1.013992       100to1000               1to4                    3
152      -0.389779          no_sav    unemp_less1year                    2
153       1.979186        over1000               4to7                    2
154       0.085290         less100               4to7                    2
155      -0.748648       100to1000               1to4                    3
156      -0.208041          no_sav               1to4                    4
157      -0.628198          no_sav               1to4                    4
158       0.950403          no_sav               4to7                    3
159       1.114073          no_sav              7plus                    4
160       0.122488          no_sav              7plus                    1
161      -0.701531          no_sav               1to4                    4
162      -0.707199       100to1000               1to4                    2
163       0.248251          no_sav               4to7                    2
164       0.025419          no_sav               1to4                    4
165      -1.020015          no_sav    unemp_less1year                    4
166      -0.402886          no_sav               1to4                    4
167       0.885573          no_sav               4to7                    4
168       0.444514          no_sav               1to4                    3
169      -0.171197          no_sav               4to7                    2
170      -0.669293          no_sav               1to4                    1
171       0.912851          no_sav               1to4                    4
172      -0.723850          no_sav               1to4                    4
173      -0.699405          no_sav               1to4                    3
174      -0.609068          no_sav               4to7                    3
175      -0.463465          no_sav               4to7                    4
176      -0.012841       100to1000              7plus                    3
177       0.307414         less100               1to4                    3
178      -0.519793         less100    unemp_less1year                    3
179      -0.468071          no_sav               4to7                    4
180       3.357629       100to1000               4to7                    4
181      -0.721016          no_sav    unemp_less1year                    1
182      -0.785137          no_sav    unemp_less1year                    4
183       1.700735        over1000    unemp_less1year                    2
184      -0.155964          no_sav               1to4                    4
185      -0.645557          no_sav               4to7                    3
186       0.875299         less100               4to7                    2
187      -0.312195        over1000              7plus                    4
188       0.056240       100to1000              7plus                    3
189      -0.353644          no_sav               4to7                    3
190      -0.123372         less100               1to4                    1
191      -0.485430         less100               1to4                    4
192       0.155788        over1000               1to4                    2
193       1.847754        over1000               4to7                    1
194      -0.360020        over1000    unemp_less1year                    4
195      -0.711805          no_sav               1to4                    3
196       1.410592          no_sav               4to7                    3
197       1.236294          no_sav               4to7                    1
198      -0.515188          no_sav               1to4                    4
199      -0.400406         less100               4to7                    3
200       0.119653          no_sav               1to4                    1
201      -0.332388       100to1000              7plus                    4
202       1.612877        over1000    unemp_less1year                    3
203      -0.646974        over1000               4to7                    4
204       1.576388        over1000    unemp_less1year                    1
205       0.174210        over1000               4to7                    2
206       0.413693          no_sav              7plus                    1
207      -0.766716          no_sav               1to4                    2
208      -0.647329        over1000    unemp_less1year                    4
209       3.152155        over1000    unemp_less1year                    4
210      -0.183597         less100              7plus                    3
211      -0.629615        over1000    unemp_less1year                    1
212      -0.157027       100to1000               1to4                    4
213      -0.279248       100to1000               1to4                    4
214      -0.698697          no_sav               1to4                    1
215      -0.610131        over1000    unemp_less1year                    4
216       0.240103          no_sav    unemp_less1year                    1
217      -0.131166        over1000    unemp_less1year                    1
218      -0.907713       100to1000    unemp_less1year                    2
219       2.639535          no_sav    unemp_less1year                    1
220      -0.702948        over1000              7plus                    4
221      -0.471614          no_sav    unemp_less1year                    3
222       0.136304        over1000              7plus                    4
223       0.498716          no_sav               4to7                    3
224       1.892392       100to1000               1to4                    2
225      -0.216898       100to1000               1to4                    4
226      -0.621822       100to1000               1to4                    4
227       0.392791         less100               1to4                    3
228      -0.874766          no_sav               1to4                    4
229      -0.601983       100to1000              7plus                    4
230       0.104420         less100               4to7                    4
231      -0.706136          no_sav              7plus                    4
232      -1.007970          no_sav              7plus                    4
233       1.883535         less100              7plus                    4
234       0.207511          no_sav               1to4                    4
235      -0.916215          no_sav               4to7                    2
236      -0.591001          no_sav              7plus                    4
237      -0.364980          no_sav              7plus                    4
238      -0.314320          no_sav              7plus                    4
239      -0.945973          no_sav    unemp_less1year                    1
240      -0.248781       100to1000               1to4                    4
241      -0.692674       100to1000               1to4                    2
242       2.513417          no_sav              7plus                    2
243      -0.603400         less100               1to4                    3
244      -0.936054       100to1000              7plus                    4
245      -0.538924       100to1000              7plus                    2
246       0.076787         less100               4to7                    3
247      -0.521211          no_sav               1to4                    4
248       0.311665         less100               1to4                    2
249      -0.226817         less100               1to4                    3
250       0.700293        over1000               1to4                    1
251      -0.131874        over1000              7plus                    4
252       1.038969          no_sav               1to4                    2
253      -0.874058          no_sav              7plus                    4
254       0.114694          no_sav              7plus                    4
255       0.864317       100to1000              7plus                    4
256      -0.206978          no_sav               4to7                    2
257       0.131698          no_sav              7plus                    1
258      -0.398635          no_sav    unemp_less1year                    1
259      -0.338764          no_sav              7plus                    3
260       0.062617          no_sav               4to7                    1
261      -0.199539          no_sav    unemp_less1year                    2
262      -0.665396          no_sav    unemp_less1year                    4
263      -0.707199          no_sav    unemp_less1year                    4
264      -0.693737          no_sav              7plus                    1
265      -0.629970          no_sav    unemp_less1year                    4
266      -0.211229       100to1000              7plus                    3
267      -0.408555          no_sav               1to4                    2
268      -0.836860       100to1000              7plus                    4
269      -0.713222          no_sav    unemp_less1year                    2
270      -0.603046         less100              7plus                    4
271      -0.721016       100to1000               1to4                    2
272       1.561155       100to1000               4to7                    2
273      -0.925072       100to1000               4to7                    2
274      -0.154547       100to1000              7plus                    3
275      -0.213001          no_sav              7plus                    4
276       0.062263       100to1000               1to4                    4
277       0.105129          no_sav              7plus                    4
278       0.134887          no_sav               4to7                    2
279       0.137721          no_sav               1to4                    2
280      -0.759985         less100              7plus                    2
281      -0.916924          no_sav    unemp_less1year                    2
282      -0.361437          no_sav               1to4                    1
283       0.497299        over1000    unemp_less1year                    1
284      -0.325302          no_sav               1to4                    1
285       0.030379       100to1000    unemp_less1year                    4
286      -0.920821          no_sav    unemp_less1year                    1
287      -1.039145       100to1000              7plus                    4
288      -0.203436          no_sav               1to4                    1
289      -0.270746       100to1000              7plus                    2
290      -0.635284          no_sav               1to4                    4
291       0.104066        over1000    unemp_less1year                    2
292      -0.372065       100to1000               1to4                    2
293      -0.486493        over1000               1to4                    1
294      -0.645557         less100              7plus                    1
295      -0.818793       100to1000               4to7                    3
296       1.722345          no_sav               1to4                    1
297      -0.343724         less100    unemp_less1year                    2
298      -0.541404         less100               1to4                    1
299      -0.810644       100to1000    unemp_less1year                    1
300      -0.336993          no_sav    unemp_less1year                    2
301      -1.038791          no_sav              7plus                    4
302       0.666283          no_sav               4to7                    4
303       0.169250          no_sav    unemp_less1year                    2
304      -0.069878        over1000               1to4                    1
305      -0.894959          no_sav               1to4                    3
306      -0.638472         less100              7plus                    4
307      -0.672127          no_sav               1to4                    1
308      -0.881497        over1000               1to4                    1
309      -0.235674          no_sav    unemp_less1year                    4
310       2.183951        over1000               1to4                    2
311      -0.829420        over1000              7plus                    4
312      -0.184305        over1000              7plus                    4
313      -1.070320       100to1000               1to4                    2
314      -0.733415          no_sav    unemp_less1year                    4
315      -0.924363          no_sav    unemp_less1year                    3
316      -0.698343        over1000              7plus                    4
317      -0.606588          no_sav               4to7                    4
318      -0.090425          no_sav               1to4                    3
319      -0.677087          no_sav               1to4                    4
320      -0.732352         less100               1to4                    4
321      -0.593126       100to1000               1to4                    3
322      -0.424496         less100               1to4                    4
323      -0.400052       100to1000              7plus                    1
324      -0.403595        over1000    unemp_less1year                    2
325      -0.611194          no_sav               1to4                    3
326      -0.702948       100to1000              7plus                    4
327      -0.656539         less100               1to4                    3
328      -0.683109          no_sav              7plus                    1
329      -0.215835        over1000               4to7                    4
330       0.991498          no_sav              7plus                    3
331      -0.476219        over1000               1to4                    3
332      -0.307235          no_sav               1to4                    2
333      -0.608360          no_sav               4to7                    1
334      -0.704365        over1000               4to7                    4
335      -0.904879        over1000              7plus                    4
336      -0.539987          no_sav    unemp_less1year                    4
337      -0.702594         less100              7plus                    3
338      -0.791160          no_sav               4to7                    4
339      -0.149942         less100               1to4                    1
340      -0.658311       100to1000               4to7                    3
341      -0.068815        over1000              7plus                    3
342       0.127802          no_sav               1to4                    1
343      -0.014967        over1000    unemp_less1year                    2
344       0.108317         less100               1to4                    1
345      -0.917278         less100               4to7                    4
346      -0.477282          no_sav               1to4                    1
347      -0.901336       100to1000              7plus                    4
348      -0.882206          no_sav              7plus                    4
349      -0.407492          no_sav               1to4                    4
350      -0.910547          no_sav               1to4                    4
351      -0.425913        over1000               1to4                    2
352      -0.618633       100to1000               4to7                    4
353       1.543796        over1000              7plus                    4
354       0.080684          no_sav               1to4                    3
355      -0.682047         less100              7plus                    2
356       2.552740        over1000              7plus                    2
357      -0.123018          no_sav               1to4                    3
358      -0.722787          no_sav              7plus                    3
359      -0.464174          no_sav              7plus                    3
360       0.628377        over1000              7plus                    1
361      -0.714285          no_sav              7plus                    4
362      -0.500663          no_sav    unemp_less1year                    4
363      -0.726330        over1000               1to4                    1
364      -0.667167          no_sav               1to4                    4
365      -0.351518        over1000               1to4                    4
366      -0.181471          no_sav              7plus                    2
367      -0.713222       100to1000               1to4                    3
368      -0.659374          no_sav               1to4                    2
369      -0.661853          no_sav               1to4                    2
370      -0.088654          no_sav               1to4                    2
371       1.167921          no_sav               1to4                    2
372      -0.245593       100to1000              7plus                    2
373       1.589496       100to1000              7plus                    2
374      -1.037374          no_sav    unemp_less1year                    4
375      -0.595252         less100               4to7                    4
376       0.051280          no_sav               1to4                    3
377      -0.766361          no_sav               4to7                    4
378       0.954300          no_sav              7plus                    1
379      -0.622530         less100               1to4                    4
380       1.229563        over1000               4to7                    2
381       0.134178          no_sav    unemp_less1year                    1
382       0.116111          no_sav               4to7                    1
383       3.714373        over1000              7plus                    2
384      -1.061109          no_sav               1to4                    4
385       0.272696       100to1000               1to4                    4
386      -0.996633          no_sav               1to4                    4
387      -0.833672          no_sav               1to4                    4
388       1.460189          no_sav               1to4                    1
389      -0.724912          no_sav               1to4                    2
390      -0.162695        over1000              7plus                    2
391      -0.085820          no_sav               4to7                    2
392      -0.634575       100to1000    unemp_less1year                    2
393      -0.787972          no_sav               1to4                    2
394      -0.637763          no_sav               1to4                    4
395       0.793464         less100               1to4                    4
396      -0.731644          no_sav              7plus                    4
397       1.109468          no_sav    unemp_less1year                    1
398      -0.908421          no_sav               1to4                    4
399      -0.626427          no_sav               1to4                    4
400       0.994332          no_sav               4to7                    2
401      -0.263306          no_sav    unemp_less1year                    4
402      -0.791514         less100               4to7                    3
403      -0.679921       100to1000    unemp_less1year                    1
404      -0.031972          no_sav    unemp_less1year                    4
405       0.468604          no_sav    unemp_less1year                    3
406       0.747410        over1000    unemp_less1year                    3
407       0.490922          no_sav               1to4                    3
408      -0.666105          no_sav               1to4                    2
409      -0.481179       100to1000    unemp_less1year                    3
410       1.379771        over1000               4to7                    2
411      -0.659728         less100    unemp_less1year                    2
412      -0.103887        over1000               1to4                    4
413      -0.813124        over1000              7plus                    4
414      -0.317509       100to1000               1to4                    4
415      -0.973960       100to1000              7plus                    4
416      -0.187139          no_sav              7plus                    4
417      -0.750065         less100    unemp_less1year                    4
418       0.897263       100to1000               1to4                    4
419      -0.744751        over1000               1to4                    4
420      -0.635284          no_sav              7plus                    4
421      -0.883977          no_sav               1to4                    4
422      -0.690549       100to1000               1to4                    4
423      -0.742626          no_sav    unemp_less1year                    2
424      -0.403241        over1000              7plus                    4
425      -0.510937          no_sav              7plus                    4
426      -0.904879          no_sav              7plus                    4
427       3.007261         less100               4to7                    2
428      -0.627136          no_sav              7plus                    2
429      -0.733769        over1000               1to4                    4
430      -0.027012        over1000               1to4                    1
431       0.445931          no_sav               4to7                    4
432      -0.608005       100to1000              7plus                    4
433      -0.334868       100to1000               1to4                    2
434      -0.490390        over1000               1to4                    4
435      -0.711096        over1000              7plus                    4
436      -0.859179        over1000              7plus                    3
437      -0.616153         less100               1to4                    4
438      -0.827649          no_sav               1to4                    3
439      -0.293773          no_sav              7plus                    4
440       0.112922          no_sav    unemp_less1year                    3
441      -0.348330          no_sav    unemp_less1year                    4
442       0.653884          no_sav               4to7                    3
443       3.864226        over1000               4to7                    4
444      -0.667876        over1000               1to4                    4
445      -0.939951          no_sav              7plus                    4
446      -0.601274          no_sav               1to4                    4
447      -0.910901        over1000              7plus                    4
448      -0.845008        over1000               1to4                    4
449       0.501196          no_sav               1to4                    2
450      -0.879017       100to1000               1to4                    4
451      -0.887520       100to1000               1to4                    4
452      -0.375254        over1000               4to7                    4
453       1.430077          no_sav    unemp_less1year                    2
454       0.875653          no_sav    unemp_less1year                    2
455       0.248606          no_sav    unemp_less1year                    1
456       1.469046        over1000               1to4                    1
457      -0.227525          no_sav               1to4                    2
458      -0.471259       100to1000               1to4                    4
459      -0.332388        over1000    unemp_less1year                    4
460      -0.292710        over1000    unemp_less1year                    2
461       1.132495        over1000    unemp_less1year                    1
462       1.453458          no_sav    unemp_less1year                    4
463       0.191569         less100    unemp_less1year                    1
464       0.260296          no_sav               1to4                    3
465       1.488176        over1000    unemp_less1year                    1
466      -0.654414        over1000              7plus                    3
467      -0.440438        over1000               1to4                    4
468       0.727217       100to1000              7plus                    1
469      -0.335930          no_sav               4to7                    4
470      -0.665396          no_sav              7plus                    4
471      -0.456026          no_sav               1to4                    4
472      -0.804976        over1000              7plus                    4
473       1.465858        over1000              7plus                    3
474      -0.384465        over1000               4to7                    1
475       0.425738          no_sav              7plus                    4
476      -0.826941          no_sav    unemp_less1year                    4
477       0.053406          no_sav              7plus                    4
478      -0.058896        over1000    unemp_less1year                    4
479      -0.185368          no_sav              7plus                    2
480       0.212825          no_sav    unemp_less1year                    2
481       0.679745        over1000              7plus                    4
482      -0.095739          no_sav               4to7                    2
483       0.209637          no_sav               1to4                    1
484       0.896201        over1000              7plus                    2
485      -0.594898       100to1000               4to7                    3
486      -0.739083          no_sav               1to4                    3
487       0.180233          no_sav    unemp_less1year                    3
488       0.120716          no_sav              7plus                    3
489      -0.777698          no_sav               1to4                    2
490       0.090604        over1000    unemp_less1year                    2
491      -0.432290          no_sav    unemp_less1year                    4
492       0.021168          no_sav              7plus                    2
493      -0.059250          no_sav               4to7                    3
494      -0.233902          no_sav              7plus                    4
495      -0.659728          no_sav              7plus                    4
496      -0.694446         less100               4to7                    4
497      -0.412097          no_sav               4to7                    2
498       0.273050        over1000               4to7                    3
499      -0.830837          no_sav    unemp_less1year                    1
500      -0.563722       100to1000              7plus                    3
501      -0.716410          no_sav    unemp_less1year                    4
502      -0.286333         less100               4to7                    4
503      -0.593835          no_sav               4to7                    3
504      -0.429810          no_sav               1to4                    4
505       1.609335          no_sav               4to7                    3
506      -0.542466          no_sav              7plus                    2
507      -0.719599        over1000              7plus                    4
508       1.264990        over1000               1to4                    2
509       0.665575          no_sav              7plus                    4
510      -0.732706         less100              7plus                    3
511      -0.422017       100to1000               1to4                    1
512      -0.614028          no_sav    unemp_less1year                    1
513       0.214951         less100    unemp_less1year                    4
514       0.198654          no_sav    unemp_less1year                    2
515      -0.030200       100to1000               4to7                    2
516      -0.132937         less100    unemp_less1year                    2
517       1.309273         less100               4to7                    4
518      -0.514480          no_sav               1to4                    4
519       0.946506        over1000    unemp_less1year                    1
520       1.365955          no_sav    unemp_less1year                    2
521       0.027545          no_sav               1to4                    4
522       2.728810          no_sav    unemp_less1year                    4
523      -0.975377          no_sav               1to4                    3
524      -0.759985        over1000    unemp_less1year                    4
525      -0.499955          no_sav    unemp_less1year                    4
526       2.230006          no_sav               1to4                    2
527      -0.910547          no_sav               1to4                    4
528      -0.120892          no_sav               4to7                    2
529      -0.625719          no_sav               1to4                    4
530      -0.366043          no_sav               1to4                    2
531      -0.626427          no_sav               4to7                    2
532      -0.380568        over1000               4to7                    4
533      -0.492515          no_sav               1to4                    2
534      -0.492869          no_sav               4to7                    4
535      -0.312549          no_sav    unemp_less1year                    4
536      -0.462048          no_sav              7plus                    4
537       0.038527          no_sav               4to7                    1
538      -0.643432          no_sav               4to7                    2
539      -0.900273        over1000              7plus                    4
540      -0.009653          no_sav               1to4                    1
541      -0.567265          no_sav    unemp_less1year                    4
542       0.250731        over1000               4to7                    4
543      -0.477990          no_sav               1to4                    4
544      -0.700114          no_sav    unemp_less1year                    4
545      -0.611902          no_sav               4to7                    2
546      -0.837569          no_sav    unemp_less1year                    3
547      -0.551323        over1000               1to4                    4
548      -0.681692        over1000               4to7                    4
549      -0.802142       100to1000               1to4                    4
550      -0.662208       100to1000               4to7                    3
551      -0.449649          no_sav               4to7                    3
552      -0.416348          no_sav               4to7                    2
553      -0.768841          no_sav               1to4                    3
554      -0.842174        over1000               4to7                    4
555      -0.600211       100to1000    unemp_less1year                    4
556      -0.179700          no_sav               1to4                    2
557       1.802054       100to1000    unemp_less1year                    1
558      -0.636701          no_sav    unemp_less1year                    4
559       0.765831        over1000    unemp_less1year                    2
560      -0.659374       100to1000              7plus                    1
561       0.075725        over1000    unemp_less1year                    2
562       0.205031          no_sav               4to7                    3
563       1.465503         less100    unemp_less1year                    4
564      -0.671064         less100              7plus                    4
565       0.354531         less100               1to4                    2
566      -0.608714         less100               4to7                    3
567       2.333096         less100               4to7                    1
568      -1.030643         less100               1to4                    4
569      -0.473385          no_sav              7plus                    4
570       2.462403        over1000               4to7                    4
571      -0.687715          no_sav    unemp_less1year                    4
572       2.048976        over1000               1to4                    2
573       1.663183          no_sav    unemp_less1year                    2
574       0.079622       100to1000               1to4                    4
575       1.302542          no_sav               1to4                    2
576       3.164555        over1000               1to4                    2
577       0.061908          no_sav              7plus                    4
578      -0.916569          no_sav               1to4                    4
579       0.357719       100to1000               1to4                    1
580       1.382605        over1000               4to7                    4
581      -0.611194          no_sav               4to7                    4
582      -0.323177         less100    unemp_less1year                    1
583       0.123905         less100              7plus                    2
584      -0.896376         less100    unemp_less1year                    4
585       1.627402          no_sav              7plus                    4
586      -0.127977          no_sav               4to7                    2
587       0.719423          no_sav              7plus                    2
588       0.123550          no_sav               1to4                    1
589       0.084227          no_sav               4to7                    4
590      -0.090071          no_sav    unemp_less1year                    3
591      -0.571870          no_sav               1to4                    2
592      -0.746523          no_sav              7plus                    3
593       1.047826          no_sav    unemp_less1year                    4
594      -0.736249          no_sav    unemp_less1year                    1
595       0.464707          no_sav               1to4                    2
596       0.743867          no_sav               1to4                    3
597      -0.908067          no_sav               1to4                    2
598      -0.956601          no_sav              7plus                    4
599      -0.265432          no_sav              7plus                    1
600       0.675848          no_sav               4to7                    4
601       1.756354          no_sav               1to4                    2
602      -0.440438          no_sav               4to7                    2
603      -0.672127        over1000    unemp_less1year                    4
604      -0.702240          no_sav               1to4                    4
605      -0.198122          no_sav              7plus                    2
606      -0.813479          no_sav               1to4                    2
607      -0.786909          no_sav    unemp_less1year                    4
608      -0.943139          no_sav               4to7                    4
609       0.537331          no_sav               4to7                    4
610      -0.071649         less100              7plus                    4
611      -0.862721         less100    unemp_less1year                    4
612      -0.245947          no_sav               1to4                    2
613      -0.584978          no_sav               1to4                    2
614      -0.505269          no_sav    unemp_less1year                    4
615       1.171818          no_sav    unemp_less1year                    4
616      -0.488264          no_sav               1to4                    4
617       2.604463          no_sav              7plus                    3
618      -0.362146         less100               4to7                    4
619      -0.057833          no_sav    unemp_less1year                    2
620      -0.819501          no_sav               4to7                    2
621       2.127623        over1000               1to4                    2
622       1.077938          no_sav    unemp_less1year                    4
623      -0.618279          no_sav              7plus                    4
624       0.112922          no_sav               1to4                    2
625       1.184572          no_sav    unemp_less1year                    2
626      -0.495704          no_sav    unemp_less1year                    4
627      -0.146045        over1000    unemp_less1year                    4
628      -0.598440       100to1000              7plus                    4
629      -0.720307        over1000    unemp_less1year                    4
630      -0.245593          no_sav    unemp_less1year                    3
631      -0.651226          no_sav               1to4                    4
632       1.633425          no_sav               4to7                    2
633       0.318041          no_sav               1to4                    4
634       0.243646          no_sav    unemp_less1year                    4
635      -0.007882          no_sav               4to7                    2
636      -0.043308          no_sav    unemp_less1year                    4
637      -0.363209          no_sav              7plus                    3
638      -0.522273          no_sav    unemp_less1year                    3
639       0.354531          no_sav              7plus                    1
640      -0.111327          no_sav              7plus                    4
641      -0.327074          no_sav               1to4                    3
642       1.069082          no_sav    unemp_less1year                    2
643       1.115136          no_sav              7plus                    2
644       1.017359          no_sav              7plus                    4
645      -0.593126          no_sav              7plus                    4
646       4.458682          no_sav    unemp_less1year                    2
647      -0.371357         less100              7plus                    4
648       1.405278        over1000              7plus                    3
649      -0.272163         less100              7plus                    4
650      -0.230005         less100               1to4                    4
651       0.382518        over1000               1to4                    1
652      -1.028517        over1000              7plus                    4
653      -0.891771        over1000              7plus                    4
654      -0.300150        over1000              7plus                    4
655       1.207953        over1000               1to4                    4
656      -0.299087        over1000              7plus                    4
657      -0.728101         less100    unemp_less1year                    4
658      -0.892125          no_sav               1to4                    2
659      -0.246302          no_sav               1to4                    2
660      -0.949870          no_sav    unemp_less1year                    3
661      -0.657957          no_sav               1to4                    4
662      -0.768133          no_sav               4to7                    4
663      -0.951642          no_sav               1to4                    4
664      -0.780532          no_sav              7plus                    4
665      -0.906296          no_sav              7plus                    4
666      -0.772030          no_sav               1to4                    4
667      -0.333805          no_sav    unemp_less1year                    1
668      -0.846425          no_sav    unemp_less1year                    4
669      -0.852093          no_sav               1to4                    4
670      -0.303338          no_sav               1to4                    3
671      -0.414577          no_sav    unemp_less1year                    2
672      -0.697988          no_sav              7plus                    4
673      -0.764590          no_sav               1to4                    4
674      -0.889645          no_sav               4to7                    4
675      -0.937471          no_sav    unemp_less1year                    4
676      -0.690195         less100              7plus                    2
677      -0.755733          no_sav               1to4                    4
678      -0.522982          no_sav              7plus                    3
679      -0.192453        over1000               4to7                    4
680      -0.987422          no_sav               4to7                    3
681      -0.787617          no_sav               1to4                    4
682      -0.749711          no_sav              7plus                    3
683       1.341157        over1000               4to7                    3
684      -0.614382        over1000              7plus                    4
685      -0.374545          no_sav               1to4                    4
686      -0.597377          no_sav               4to7                    4
687      -0.620050          no_sav               1to4                    4
688       0.254628        over1000    unemp_less1year                    3
689      -0.078735          no_sav    unemp_less1year                    1
690      -0.704719         less100               1to4                    4
691       2.434770         less100               4to7                    2
692      -0.745106          no_sav               1to4                    4
693      -0.998050          no_sav    unemp_less1year                    3
694       0.114339          no_sav    unemp_less1year                    1
695      -0.532547          no_sav               1to4                    3
696       4.386412          no_sav               4to7                    2
697      -0.362854          no_sav               1to4                    2
698      -0.658311          no_sav               1to4                    4
699       0.462227         less100    unemp_less1year                    3
700      -0.722787       100to1000              7plus                    4
701       1.793906        over1000              7plus                    3
702       0.895846          no_sav               1to4                    3
703       1.842086        over1000               1to4                    1
704       0.124259       100to1000              7plus                    4
705      -0.744751        over1000              7plus                    4
706      -0.711805       100to1000               4to7                    4
707       0.122488        over1000              7plus                    4
708      -0.386236        over1000              7plus                    4
709       1.563634          no_sav               4to7                    2
710       1.005668          no_sav               1to4                    1
711       0.172085          no_sav              7plus                    4
712       0.043487          no_sav    unemp_less1year                    4
713       1.074396        over1000              7plus                    4
714      -0.718181        over1000              7plus                    4
715       0.087415          no_sav               1to4                    2
716      -0.233194          no_sav               1to4                    4
717       1.489593          no_sav               4to7                    4
718       0.467541       100to1000               1to4                    2
719      -0.477282        over1000    unemp_less1year                    4
720       0.823222         less100               1to4                    2
721       1.046054          no_sav              7plus                    4
722      -0.484013          no_sav              7plus                    4
723      -0.098573          no_sav               1to4                    3
724       2.010716        over1000               4to7                    3
725       0.265256          no_sav               1to4                    2
726      -0.174386          no_sav               1to4                    1
727      -0.173323       100to1000               4to7                    1
728      -0.491452          no_sav              7plus                    4
729       2.757151        over1000               1to4                    4
730       2.085111        over1000               1to4                    2
731       2.129749          no_sav    unemp_less1year                    1
732       1.153396        over1000               1to4                    4
733       0.034276       100to1000              7plus                    3
734      -0.269329          no_sav    unemp_less1year                    1
735       0.787087          no_sav              7plus                    2
736      -0.684881       100to1000               1to4                    1
737      -0.775572          no_sav              7plus                    4
738      -0.751837       100to1000               1to4                    4
739      -0.695509          no_sav              7plus                    4
740       1.019130         less100              7plus                    3
741      -0.543883          no_sav               4to7                    3
742      -0.075192       100to1000               4to7                    2
743      -0.097511        over1000               1to4                    2
744       1.532105        over1000              7plus                    1
745       0.545479        over1000               4to7                    2
746      -0.533256          no_sav               1to4                    1
747      -0.181117        over1000              7plus                    4
748       0.792047          no_sav              7plus                    3
749      -0.734123          no_sav               4to7                    4
750      -0.134354          no_sav              7plus                    3
751      -0.144982         less100              7plus                    4
752      -0.927197          no_sav               1to4                    4
753      -0.756442       100to1000              7plus                    4
754       0.298203       100to1000    unemp_less1year                    3
755       3.997430        over1000    unemp_less1year                    1
756      -0.822335          no_sav              7plus                    4
757      -0.734123          no_sav              7plus                    4
758      -0.775572          no_sav               1to4                    4
759      -0.397218          no_sav               1to4                    4
760      -0.159861          no_sav    unemp_less1year                    4
761      -0.074838          no_sav               4to7                    4
762      -0.237799          no_sav               1to4                    4
763       0.613498        over1000               1to4                    1
764       1.068727          no_sav               1to4                    4
765      -0.259764          no_sav              7plus                    4
766      -0.635284          no_sav               4to7                    4
767       0.588699          no_sav               4to7                    2
768      -0.616862          no_sav               1to4                    3
769      -0.649808         less100               4to7                    2
770      -0.707199        over1000               1to4                    4
771      -0.513062          no_sav    unemp_less1year                    4
772      -0.655122          no_sav    unemp_less1year                    3
773      -0.727747          no_sav               1to4                    4
774       2.125498          no_sav               4to7                    2
775      -0.398989          no_sav               4to7                    2
776      -0.506331          no_sav    unemp_less1year                    4
777       0.363387          no_sav              7plus                    4
778       0.039944          no_sav               1to4                    1
779      -0.717827          no_sav               1to4                    4
780       0.478877         less100               1to4                    3
781       1.811974          no_sav               4to7                    2
782      -0.796120          no_sav    unemp_less1year                    4
783       3.913469          no_sav              7plus                    4
784      -1.005490       100to1000    unemp_less1year                    4
785      -0.397572          no_sav               1to4                    4
786      -0.309715       100to1000              7plus                    3
787      -0.829066         less100    unemp_less1year                    1
788      -0.623239       100to1000               1to4                    3
789       0.343548          no_sav               1to4                    1
790       0.518909          no_sav    unemp_less1year                    2
791      -0.529004          no_sav    unemp_less1year                    2
792      -0.334513          no_sav    unemp_less1year                    2
793       1.275618          no_sav    unemp_less1year                    2
794      -0.877246          no_sav    unemp_less1year                    4
795      -0.482950          no_sav              7plus                    4
796      -0.467008          no_sav              7plus                    4
797      -0.144273          no_sav               1to4                    2
798      -0.337347          no_sav    unemp_less1year                    2
799      -0.834734        over1000              7plus                    4
800      -0.823398          no_sav               4to7                    4
801      -0.669647        over1000               1to4                    4
802      -0.703657        over1000               4to7                    4
803      -0.673190        over1000               1to4                    4
804      -0.789743        over1000               1to4                    4
805      -0.840048        over1000               1to4                    4
806      -0.731289          no_sav    unemp_less1year                    4
807      -0.351872         less100    unemp_less1year                    3
808       1.262864          no_sav              7plus                    3
809       0.026128          no_sav              7plus                    4
810      -0.734478          no_sav              7plus                    4
811       4.393143          no_sav               1to4                    2
812       1.426180          no_sav              7plus                    4
813      -0.470551          no_sav    unemp_less1year                    4
814      -0.028783          no_sav               1to4                    2
815       0.658135          no_sav              7plus                    2
816      -0.518376          no_sav               4to7                    4
817      -0.719599         less100              7plus                    1
818      -0.890000          no_sav               4to7                    4
819      -0.720661          no_sav               1to4                    3
820      -0.457443          no_sav    unemp_less1year                    2
821       2.724204       100to1000               4to7                    1
822       1.280932          no_sav               1to4                    4
823      -0.472322          no_sav    unemp_less1year                    4
824      -0.508811          no_sav               1to4                    3
825      -0.571162          no_sav    unemp_less1year                    4
826      -0.730581          no_sav    unemp_less1year                    4
827       0.202906         less100               4to7                    4
828       0.556816          no_sav              7plus                    3
829      -0.932511          no_sav               1to4                    4
830       0.949340          no_sav               1to4                    2
831       0.188735          no_sav               1to4                    4
832       0.422195          no_sav               1to4                    4
833       1.668142        over1000    unemp_less1year                    4
834       0.332566          no_sav               1to4                    4
835       0.474626          no_sav    unemp_less1year                    1
836       2.936408          no_sav               1to4                    1
837       0.316624          no_sav               1to4                    2
838       0.278364          no_sav               4to7                    3
839       1.128952          no_sav              7plus                    2
840      -0.667876       100to1000               1to4                    2
841      -0.458506        over1000              7plus                    4
842      -0.475865          no_sav    unemp_less1year                    2
843      -0.761047       100to1000               1to4                    4
844       2.854219          no_sav               1to4                    2
845       3.070320          no_sav               1to4                    2
846      -0.266140       100to1000               1to4                    4
847      -0.479053         less100    unemp_less1year                    4
848       4.077848         less100              7plus                    3
849      -0.212646         less100               1to4                    4
850       3.309094         less100               1to4                    1
851      -0.085111         less100               1to4                    4
852      -0.937117          no_sav               1to4                    4
853      -0.473385          no_sav              7plus                    4
854      -0.682755        over1000               4to7                    4
855      -0.615799          no_sav    unemp_less1year                    4
856       0.233727          no_sav               4to7                    4
857       0.027545       100to1000    unemp_less1year                    4
858      -0.343370          no_sav               1to4                    4
859       0.245772          no_sav    unemp_less1year                    4
860      -0.901336         less100    unemp_less1year                    4
861       0.227704        over1000               1to4                    4
862       0.367284          no_sav    unemp_less1year                    3
863      -0.615445          no_sav    unemp_less1year                    1
864      -0.175803          no_sav               4to7                    2
865       0.965991          no_sav               1to4                    4
866      -0.708616        over1000               1to4                    3
867      -0.700114          no_sav    unemp_less1year                    3
868       2.170489          no_sav    unemp_less1year                    1
869      -0.821981         less100    unemp_less1year                    4
870      -0.678858          no_sav    unemp_less1year                    3
871      -0.078026          no_sav               1to4                    3
872       1.623859          no_sav               1to4                    4
873      -1.005490          no_sav    unemp_less1year                    3
874       2.232131          no_sav    unemp_less1year                    1
875      -0.508103          no_sav               4to7                    4
876       0.346383          no_sav    unemp_less1year                    4
877       0.695333          no_sav    unemp_less1year                    4
878       1.235232          no_sav               1to4                    3
879      -0.674961          no_sav    unemp_less1year                    3
880      -0.677795        over1000              7plus                    4
881      -0.282791          no_sav    unemp_less1year                    4
882      -0.685235          no_sav    unemp_less1year                    4
883       1.591267          no_sav              7plus                    4
884      -0.711096         less100               1to4                    2
885      -0.226817         less100               1to4                    2
886       1.165087         less100               4to7                    3
887      -0.052519          no_sav    unemp_less1year                    4
888       1.698255          no_sav               1to4                    3
889      -0.294836          no_sav    unemp_less1year                    4
890       2.041537         less100    unemp_less1year                    4
891       3.810378          no_sav               4to7                    4
892       2.252324          no_sav               4to7                    4
893      -0.633158        over1000               1to4                    2
894      -0.758213          no_sav    unemp_less1year                    4
895      -0.427685          no_sav    unemp_less1year                    3
896       3.438047          no_sav    unemp_less1year                    3
897      -0.244884       100to1000    unemp_less1year                    4
898      -0.248427          no_sav               1to4                    3
899       0.228058          no_sav               1to4                    4
900      -0.695154        over1000               1to4                    4
901       0.547605          no_sav               4to7                    2
902      -0.245239          no_sav    unemp_less1year                    4
903      -0.370648          no_sav              7plus                    4
904       0.312373          no_sav               1to4                    2
905      -0.055707          no_sav    unemp_less1year                    1
906      -0.406429          no_sav               1to4                    4
907      -0.608714          no_sav               1to4                    4
908      -0.306526          no_sav               4to7                    4
909      -0.686652          no_sav    unemp_less1year                    4
910       1.363121          no_sav               1to4                    3
911       0.566381          no_sav               1to4                    3
912      -0.914090          no_sav              7plus                    4
913       0.389249          no_sav               4to7                    3
914      -0.186077          no_sav              7plus                    4
915       0.297140          no_sav              7plus                    3
916      -0.286688          no_sav               1to4                    2
917      -0.704719          no_sav               1to4                    2
918      -0.107076          no_sav    unemp_less1year                    4
919       0.472501          no_sav              7plus                    3
920       1.083961          no_sav              7plus                    4
921       0.099460          no_sav               4to7                    3
922      -0.911964          no_sav    unemp_less1year                    4
923      -0.648037          no_sav               4to7                    4
924       0.716234          no_sav    unemp_less1year                    2
925       0.050572          no_sav    unemp_less1year                    2
926      -0.436541          no_sav               1to4                    1
927      -0.039057          no_sav               1to4                    4
928      -0.839340          no_sav               4to7                    4
929       2.488973          no_sav               4to7                    4
930       3.949958          no_sav               1to4                    2
931      -0.784783          no_sav              7plus                    3
932      -0.707554          no_sav    unemp_less1year                    3
933      -0.725621          no_sav              7plus                    1
934      -0.672836          no_sav               4to7                    2
935      -0.228943          no_sav              7plus                    2
936      -0.367106          no_sav               1to4                    4
937      -0.819147          no_sav               1to4                    1
938      -0.845717          no_sav              7plus                    4
939      -0.717473          no_sav    unemp_less1year                    4
940       1.705694         less100              7plus                    2
941       2.428748       100to1000               1to4                    2
942      -0.844300          no_sav              7plus                    4
943      -0.904170          no_sav              7plus                    4
944       3.230093        over1000               1to4                    1
945      -0.907713          no_sav              7plus                    4
946       1.267824         less100    unemp_less1year                    1
947      -0.375962          no_sav               1to4                    2
948       1.492782        over1000    unemp_less1year                    4
949      -0.648037          no_sav               4to7                    4
950      -0.876538        over1000              7plus                    4
951       0.522452          no_sav    unemp_less1year                    4
952      -0.826232       100to1000               4to7                    4
953      -0.738020          no_sav              7plus                    2
954       2.947036         less100               1to4                    2
955      -0.943139          no_sav    unemp_less1year                    4
956      -0.737312          no_sav    unemp_less1year                    2
957      -0.178637         less100              7plus                    4
958       0.060137         less100               1to4                    2
959       0.357365         less100               1to4                    4
960      -0.063501         less100    unemp_less1year                    3
961       1.236294        over1000               1to4                    2
962      -0.687360          no_sav    unemp_less1year                    2
963       4.489857          no_sav    unemp_less1year                    3
964      -0.013196          no_sav    unemp_less1year                    4
965      -0.874766          no_sav              7plus                    4
966       2.369586          no_sav    unemp_less1year                    1
967       1.904791          no_sav    unemp_less1year                    2
968      -0.682401          no_sav               1to4                    4
969      -0.505269          no_sav               1to4                    4
970      -0.575413        over1000               1to4                    4
971       0.557170          no_sav    unemp_less1year                    3
972       1.787884          no_sav              7plus                    2
973      -0.414931          no_sav               1to4                    4
974       3.027100          no_sav              7plus                    2
975      -1.000176          no_sav    unemp_less1year                    4
976       3.091576          no_sav    unemp_less1year                    1
977       5.368078          no_sav               1to4                    1
978       4.118234          no_sav              7plus                    1
979      -0.180408        over1000              7plus                    1
980       0.040653          no_sav              7plus                    3
981      -0.436541          no_sav    unemp_less1year                    1
982      -0.390487          no_sav               1to4                    4
983       0.646444          no_sav               1to4                    2
984      -0.492161          no_sav               1to4                    4
985       1.320609          no_sav               4to7                    1
986      -0.346913          no_sav    unemp_less1year                    4
987       2.021698          no_sav              7plus                    1
988      -0.920112         less100               4to7                    4
989      -0.813124          no_sav    unemp_less1year                    1
990      -0.195996          no_sav               1to4                    3
991      -0.893188          no_sav    unemp_less1year                    4
992       3.297404          no_sav              7plus                    4
993       1.501993        over1000              7plus                    1
994       0.246126          no_sav              7plus                    1
995       1.037198          no_sav               1to4                    4
996      -0.454963          no_sav               1to4                    2
997      -0.343016          no_sav              7plus                    4
998       3.333184        over1000              7plus                    4
999       1.132495        over1000    unemp_less1year                    2
1000      1.090692        over1000              7plus                    4
            status_sex other_debtors_guar residence_duration property age_years
1    m_single_divorced                 no                  4        2  -1.28093
2        m_married_wid                 no                  2        1   0.04034
3    m_single_divorced                 no                  4        1  -1.10476
4        m_married_wid                 no                  2        1   0.30460
5        m_married_wid                 no                  4        2   0.21651
6        m_married_wid                 no                  3        1   1.09736
7        m_married_wid                 no                  4        1   0.30460
8        m_married_wid                 no                  4        1   0.39268
9    m_single_divorced                 no                  4        3   2.59481
10   m_single_divorced                 no                  4        4  -1.10476
11       m_married_wid                 no                  2        1   0.04034
12              female                 no                  4        3  -1.01668
13   m_single_divorced                 no                  4        3  -0.40008
14       m_married_wid                 no                  4        4  -0.40008
15              female                 no                  4        3  -1.10476
16       m_married_wid                 no                  3        1   0.74502
17       m_married_wid                 no                  2        1   0.39268
18              female                 no                  3        1  -0.92859
19       m_married_wid                 no                  4        1   0.04034
20       m_married_wid                 no                  4        2   0.30460
21       m_married_wid                 no                  3        1   0.12843
22       m_married_wid                 no                  3        1   1.18545
23       m_married_wid                 no                  1        4  -0.22391
24       m_married_wid                 no                  3        2  -0.84051
25       m_married_wid                 no                  3        1   0.74502
26       m_married_wid                 no                  4        2   1.36162
27   m_single_divorced                 no                  4        2  -1.01668
28       m_married_wid                 no                  4        1   0.12843
29       m_married_wid                 no                  2        4  -0.57625
30       m_married_wid                 no                  4        4   1.80204
31       m_married_wid                 no                  4        2   1.00928
32       m_married_wid                 no                  4        3   0.12843
33       m_married_wid                 no                  4        3  -0.13583
34   m_single_divorced                 no                  1        3  -0.66434
35       m_married_wid                 no                  3        1   0.04034
36   m_single_divorced                 no                  4        4   0.30460
37       m_married_wid                 no                  1        1   0.21651
38       m_married_wid                 no                  2        4  -0.84051
39       m_married_wid                 no                  1        2  -0.40008
40   m_single_divorced                 no                  2        3   0.48077
41   m_single_divorced                 no                  4        1  -1.10476
42   m_single_divorced                yes                  4        1   1.97821
43   m_single_divorced                 no                  3        1   0.04034
44       m_married_wid                 no                  3        4  -0.13583
45       m_married_wid                 no                  4        4   2.24247
46   m_single_divorced                 no                  3        3  -1.10476
47       m_married_wid                 no                  1        1  -0.92859
48   m_single_divorced                 no                  2        1  -0.48817
49              female                 no                  3        1  -0.22391
50       m_married_wid                 no                  1        1  -0.84051
51   m_single_divorced                 no                  2        1   1.18545
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822      m_married_wid                 no                  3        2  -0.57625
823  m_single_divorced                 no                  3        2  -0.31200
824  m_single_divorced                 no                  2        1  -0.92859
825  m_single_divorced                 no                  2        3  -0.57625
826      m_married_wid                 no                  4        2   1.00928
827      m_married_wid                 no                  4        4  -0.13583
828      m_married_wid                yes                  4        2   0.65694
829      m_married_wid                 no                  2        3  -0.48817
830  m_single_divorced                 no                  2        1  -1.19285
831  m_single_divorced                 no                  1        3   0.56885
832      m_married_wid                 no                  2        3  -0.84051
833      m_married_wid                 no                  4        3  -0.75242
834      m_married_wid                 no                  2        3  -0.84051
835  m_single_divorced                 no                  4        2  -0.31200
836  m_single_divorced                 no                  4        3  -1.10476
837      m_married_wid                 no                  2        3   0.04034
838  m_single_divorced                 no                  3        3   0.65694
839      m_married_wid                 no                  4        4   0.30460
840  m_single_divorced                 no                  2        2  -0.84051
841      m_married_wid                 no                  4        4   0.39268
842      m_married_wid                 no                  2        1  -0.40008
843  m_single_divorced                 no                  4        3  -0.57625
844      m_married_wid                yes                  3        3  -0.57625
845      m_married_wid                yes                  3        3   0.30460
846      m_married_wid                 no                  2        2  -1.10476
847      m_married_wid                 no                  3        4  -0.48817
848  m_single_divorced                 no                  4        4   2.15438
849  m_single_divorced                yes                  4        4   1.27353
850      m_married_wid                 no                  4        4   1.00928
851      m_married_wid                yes                  4        2  -1.28093
852  m_single_divorced                 no                  4        1  -1.01668
853  m_single_divorced                 no                  4        1  -0.40008
854      m_married_wid                 no                  2        1   0.12843
855             female                yes                  1        2   0.65694
856      m_married_wid                 no                  4        4   0.92119
857      m_married_wid                 no                  4        4  -0.48817
858  m_single_divorced                 no                  4        3  -0.40008
859      m_married_wid                 no                  3        3  -0.13583
860             female                 no                  3        4  -0.22391
861  m_single_divorced                 no                  2        1   0.21651
862  m_single_divorced                 no                  4        2  -1.01668
863             female                 no                  1        1  -1.10476
864      m_married_wid                 no                  2        2  -0.40008
865      m_married_wid                 no                  3        4  -0.75242
866      m_married_wid                 no                  4        4   0.30460
867  m_single_divorced                 no                  1        3  -0.92859
868             female                 no                  4        3  -0.66434
869  m_single_divorced                 no                  4        3  -0.75242
870  m_single_divorced                 no                  4        3  -0.92859
871      m_married_wid                 no                  4        3   1.62587
872  m_single_divorced                 no                  2        1  -0.92859
873  m_single_divorced                yes                  4        1  -1.19285
874  m_single_divorced                 no                  1        3  -0.66434
875  m_single_divorced                 no                  4        4  -0.13583
876             female                 no                  2        3  -0.66434
877             female                 no                  2        3  -0.66434
878  m_single_divorced                 no                  2        3  -0.40008
879  m_single_divorced                 no                  4        2  -1.19285
880      m_married_wid                 no                  3        3   0.39268
881      m_married_wid                 no                  1        3  -0.92859
882      m_married_wid                 no                  2        3  -0.13583
883      m_married_wid                 no                  4        4   0.56885
884             female                 no                  2        2  -0.92859
885  m_single_divorced                 no                  4        3  -0.66434
886      m_married_wid                 no                  2        2  -1.01668
887  m_single_divorced                 no                  1        2  -0.75242
888  m_single_divorced                 no                  2        4  -0.92859
889  m_single_divorced                 no                  4        1  -0.04774
890      m_married_wid                yes                  1        4  -0.57625
891      m_married_wid                 no                  2        4  -0.75242
892      m_married_wid                 no                  4        3  -1.01668
893             female                 no                  1        1  -0.92859
894  m_single_divorced                 no                  2        3  -0.22391
895  m_single_divorced                 no                  2        2  -0.13583
896  m_single_divorced                 no                  4        4   0.21651
897      m_married_wid                 no                  2        1   0.48077
898  m_single_divorced                 no                  3        1  -1.28093
899      m_married_wid                 no                  2        3   0.04034
900      m_married_wid                yes                  4        2  -0.75242
901      m_married_wid                yes                  3        2  -0.40008
902      m_married_wid                 no                  1        1  -0.22391
903      m_married_wid                 no                  4        4   1.89013
904      m_married_wid                yes                  3        3   0.56885
905  m_single_divorced                 no                  4        2  -0.84051
906  m_single_divorced                 no                  4        1  -1.01668
907      m_married_wid                 no                  3        3   0.74502
908  m_single_divorced                 no                  4        1  -1.10476
909      m_married_wid                 no                  2        1   0.65694
910      m_married_wid                 no                  4        4   1.53779
911      m_married_wid                 no                  4        4   1.53779
912      m_married_wid                 no                  3        2  -0.04774
913      m_married_wid                 no                  2        2  -0.84051
914      m_married_wid                 no                  4        3  -0.40008
915      m_married_wid                 no                  4        4  -1.10476
916      m_married_wid                 no                  2        3  -1.19285
917  m_single_divorced                 no                  4        3  -1.36902
918  m_single_divorced                 no                  3        2  -0.92859
919      m_married_wid                 no                  4        4  -1.01668
920      m_married_wid                 no                  4        4   0.92119
921      m_married_wid                 no                  4        3  -0.75242
922      m_married_wid                 no                  2        3   0.92119
923  m_single_divorced                 no                  4        3  -1.10476
924      m_married_wid                 no                  4        2   1.27353
925      m_married_wid                 no                  1        2  -0.84051
926  m_single_divorced                 no                  4        1  -1.36902
927      m_married_wid                 no                  2        2  -0.40008
928             female                 no                  4        2  -1.28093
929      m_married_wid                 no                  4        4   0.30460
930      m_married_wid                 no                  2        3  -0.92859
931      m_married_wid                yes                  3        1  -0.48817
932  m_single_divorced                 no                  1        1   0.12843
933  m_single_divorced                 no                  1        1   0.92119
934  m_single_divorced                 no                  3        3   0.04034
935      m_married_wid                yes                  4        2   0.65694
936             female                yes                  2        2  -0.22391
937  m_single_divorced                 no                  2        3  -0.57625
938      m_married_wid                 no                  4        3   0.04034
939      m_married_wid                 no                  2        1  -1.10476
940      m_married_wid                 no                  4        3   0.56885
941      m_married_wid                 no                  2        4   0.74502
942      m_married_wid                 no                  4        3   0.48077
943      m_married_wid                 no                  4        3   0.48077
944      m_married_wid                 no                  4        4   0.12843
945      m_married_wid                 no                  4        1   1.89013
946      m_married_wid                 no                  2        2  -0.13583
947      m_married_wid                 no                  2        1  -0.92859
948  m_single_divorced                 no                  1        1   1.53779
949      m_married_wid                 no                  4        4  -0.31200
950  m_single_divorced                 no                  3        2  -0.22391
951      m_married_wid                 no                  2        2  -1.01668
952             female                 no                  2        1  -0.66434
953  m_single_divorced                 no                  4        2   0.30460
954  m_single_divorced                 no                  4        3  -1.01668
955  m_single_divorced                 no                  1        1  -0.84051
956  m_single_divorced                 no                  4        4   1.71396
957  m_single_divorced                 no                  2        3   2.24247
958  m_single_divorced                yes                  4        3  -1.28093
959  m_single_divorced                 no                  4        3  -0.84051
960             female                 no                  2        3  -1.19285
961      m_married_wid                 no                  4        3   2.85906
962      m_married_wid                 no                  1        3  -1.19285
963      m_married_wid                 no                  4        4   1.97821
964  m_single_divorced                 no                  4        1  -1.10476
965      m_married_wid                 no                  3        3   0.12843
966  m_single_divorced                 no                  2        3  -0.84051
967      m_married_wid                 no                  2        3  -0.75242
968             female                 no                  3        1  -0.84051
969      m_married_wid                 no                  4        4  -1.10476
970      m_married_wid                 no                  2        2   0.39268
971      m_married_wid                 no                  2        3  -0.22391
972  m_single_divorced                 no                  4        4   0.56885
973      m_married_wid                yes                  2        1   0.12843
974      m_married_wid                 no                  4        3  -0.57625
975  m_single_divorced                 no                  4        2  -1.10476
976  m_single_divorced                 no                  1        4  -0.13583
977  m_single_divorced                 no                  2        2  -0.31200
978      m_married_wid                 no                  4        4   2.85906
979  m_single_divorced                 no                  2        2  -0.92859
980      m_married_wid                 no                  4        4  -0.04774
981             female                 no                  1        2  -1.19285
982             female                 no                  2        3  -0.66434
983  m_single_divorced                 no                  3        3  -0.48817
984  m_single_divorced                 no                  4        3  -0.92859
985             female                yes                  1        1  -0.13583
986      m_married_wid                 no                  2        3   0.56885
987  m_single_divorced                 no                  4        2   0.83311
988             female                 no                  1        2  -1.36902
989  m_single_divorced                 no                  2        3  -1.10476
990  m_single_divorced                 no                  4        2  -1.36902
991  m_single_divorced                 no                  1        1  -0.75242
992  m_single_divorced                 no                  2        4   0.74502
993      m_married_wid                 no                  4        2   1.36162
994  m_single_divorced                 no                  4        1  -0.22391
995      m_married_wid                 no                  2        2  -0.66434
996      m_married_wid                 no                  4        1  -1.28093
997      m_married_wid                yes                  1        1   0.83311
998      m_married_wid                 no                  4        4  -0.48817
999      m_married_wid                 no                  1        4   1.44970
1000     m_married_wid                 no                  4        2  -0.40008
     other_install_plans housing exist_credits_nr job dependents_nr
1                     no       1              one   3             2
2                     no       1        morethan1   3             1
3                     no       1              one   2             2
4                     no       1        morethan1   2             1
5                    yes       2        morethan1   2             2
6                     no       1        morethan1   2             1
7                     no       2        morethan1   2             2
8                     no       2              one   2             1
9                     no       2        morethan1   1             2
10                    no       1              one   1             2
11                    no       1        morethan1   3             1
12                    no       1        morethan1   3             2
13                    no       2              one   3             2
14                    no       2              one   4             2
15                    no       1        morethan1   2             2
16                    no       1              one   3             1
17                    no       2        morethan1   2             1
18                    no       1              one   3             2
19                    no       2              one   3             2
20                    no       2        morethan1   2             2
21                    no       1        morethan1   3             1
22                   yes       2        morethan1   2             1
23                    no       1              one   2             2
24                    no       2              one   3             2
25                    no       2        morethan1   2             1
26                    no       2              one   3             2
27                    no       1              one   3             2
28                    no       2        morethan1   2             2
29                    no       2              one   3             2
30                    no       3              one   3             2
31                    no       2              one   3             2
32                    no       2        morethan1   3             2
33                    no       2        morethan1   4             2
34                    no       2              one   3             2
35                    no       2              one   3             2
36                    no       2        morethan1   3             2
37                    no       2        morethan1   2             1
38                    no       2        morethan1   3             2
39                    no       2              one   3             2
40                    no       1        morethan1   4             2
41                    no       2        morethan1   3             2
42                    no       2              one   2             2
43                    no       2        morethan1   3             2
44                    no       2        morethan1   3             1
45                    no       3              one   3             2
46                    no       1              one   3             2
47                    no       2              one   3             2
48                    no       2        morethan1   3             2
49                    no       2              one   3             2
50                    no       2        morethan1   3             2
51                    no       2              one   2             2
52                    no       2              one   3             2
53                    no       2        morethan1   3             2
54                    no       2              one   3             2
55                    no       2              one   4             1
56                    no       2        morethan1   2             2
57                    no       2        morethan1   2             2
58                    no       2              one   3             2
59                    no       2              one   3             2
60                    no       2              one   3             2
61                    no       2              one   3             2
62                    no       2              one   4             2
63                    no       2              one   3             2
64                    no       2        morethan1   3             1
65                    no       2        morethan1   2             1
66                    no       2        morethan1   3             2
67                    no       2              one   3             2
68                    no       2              one   3             2
69                    no       2              one   3             2
70                    no       3        morethan1   3             2
71                    no       2        morethan1   3             2
72                    no       2              one   3             2
73                    no       2              one   3             2
74                   yes       2              one   3             2
75                    no       2              one   4             2
76                    no       2              one   3             2
77                    no       2              one   4             2
78                    no       2              one   3             2
79                    no       2        morethan1   3             2
80                    no       2              one   3             2
81                    no       2              one   3             2
82                    no       2              one   3             2
83                    no       2        morethan1   3             2
84                    no       2              one   3             2
85                    no       2              one   3             2
86                    no       2              one   3             1
87                    no       2        morethan1   3             2
88                    no       2        morethan1   3             2
89                    no       2        morethan1   2             2
90                    no       1        morethan1   3             2
91                    no       2              one   3             2
92                    no       2              one   3             2
93                    no       2        morethan1   3             2
94                    no       2              one   3             2
95                    no       2              one   3             2
96                   yes       2        morethan1   2             1
97                    no       2              one   2             2
98                   yes       2        morethan1   4             2
99                    no       2              one   3             2
100                  yes       2        morethan1   2             2
101                   no       2              one   3             2
102                   no       2              one   2             2
103                  yes       2        morethan1   4             2
104                   no       1              one   3             2
105                   no       2        morethan1   3             2
106                   no       2              one   3             2
107                   no       2        morethan1   4             2
108                   no       2              one   3             2
109                  yes       2              one   3             2
110                   no       2              one   3             2
111                   no       2              one   3             2
112                  yes       2              one   3             2
113                   no       2              one   4             2
114                   no       2              one   3             2
115                   no       2              one   3             2
116                   no       1              one   2             2
117                   no       1              one   3             2
118                   no       1              one   3             2
119                   no       2        morethan1   3             2
120                   no       1              one   3             2
121                   no       2              one   4             2
122                   no       2        morethan1   3             1
123                   no       1              one   4             2
124                   no       2              one   2             2
125                  yes       2              one   3             2
126                   no       3        morethan1   3             2
127                   no       2              one   3             2
128                   no       2              one   3             2
129                   no       2              one   4             2
130                   no       2              one   3             1
131                   no       2              one   3             2
132                   no       2              one   3             2
133                   no       2              one   3             2
134                   no       2              one   3             2
135                   no       2              one   3             2
136                   no       2              one   3             2
137                   no       2              one   3             2
138                  yes       2              one   3             2
139                   no       2        morethan1   2             2
140                   no       2        morethan1   3             2
141                   no       2              one   2             2
142                   no       2              one   2             2
143                   no       2              one   3             2
144                   no       2              one   4             2
145                   no       2              one   3             2
146                   no       2              one   4             2
147                  yes       3              one   3             1
148                   no       2              one   2             2
149                   no       2              one   3             2
150                   no       2        morethan1   2             2
151                   no       1        morethan1   3             2
152                  yes       2              one   3             2
153                   no       3        morethan1   3             2
154                  yes       2        morethan1   3             2
155                   no       2              one   3             2
156                   no       2        morethan1   2             2
157                  yes       2              one   3             2
158                   no       2              one   3             2
159                   no       1              one   3             2
160                   no       1        morethan1   3             2
161                   no       2        morethan1   3             2
162                   no       2        morethan1   3             2
163                   no       1              one   3             2
164                   no       3              one   3             2
165                   no       1              one   3             2
166                   no       2        morethan1   3             2
167                   no       2        morethan1   3             2
168                   no       2              one   4             2
169                  yes       2        morethan1   3             2
170                   no       2        morethan1   3             2
171                   no       2              one   3             2
172                   no       2              one   2             2
173                   no       1              one   3             2
174                  yes       2              one   3             1
175                   no       1        morethan1   4             1
176                   no       2              one   4             2
177                   no       2        morethan1   2             2
178                   no       2              one   3             2
179                  yes       2        morethan1   3             2
180                   no       2              one   4             2
181                   no       1              one   3             2
182                   no       2        morethan1   3             2
183                  yes       2        morethan1   3             2
184                   no       2              one   3             2
185                   no       2        morethan1   3             2
186                   no       2        morethan1   3             2
187                   no       2              one   3             2
188                   no       2              one   3             1
189                   no       2        morethan1   3             1
190                  yes       2              one   4             2
191                   no       1              one   4             2
192                   no       2              one   3             2
193                   no       2              one   3             2
194                   no       2              one   3             2
195                   no       2              one   3             2
196                   no       2        morethan1   4             2
197                   no       2        morethan1   4             1
198                   no       2        morethan1   3             2
199                   no       2              one   3             2
200                  yes       2              one   3             2
201                  yes       2              one   4             2
202                  yes       1        morethan1   3             1
203                  yes       2              one   2             2
204                   no       2              one   3             2
205                   no       2              one   2             2
206                  yes       2              one   4             2
207                   no       1              one   4             1
208                   no       2              one   3             2
209                   no       3              one   4             2
210                   no       2              one   3             2
211                   no       2              one   2             1
212                  yes       2              one   3             2
213                  yes       2              one   3             2
214                   no       2        morethan1   1             1
215                   no       2              one   1             2
216                   no       2              one   2             1
217                   no       1              one   3             2
218                   no       2              one   1             2
219                   no       2              one   2             2
220                   no       2              one   2             2
221                  yes       3              one   4             2
222                  yes       2        morethan1   2             2
223                   no       2        morethan1   2             2
224                   no       2        morethan1   3             2
225                   no       2              one   3             2
226                   no       2        morethan1   2             2
227                   no       2              one   2             1
228                   no       2        morethan1   2             2
229                  yes       2              one   2             2
230                   no       2              one   3             2
231                   no       2              one   4             2
232                   no       2              one   2             2
233                   no       2              one   4             2
234                   no       2              one   4             2
235                  yes       2              one   2             2
236                   no       2              one   3             2
237                   no       2        morethan1   3             2
238                  yes       1              one   2             2
239                   no       1              one   2             1
240                   no       2              one   3             2
241                   no       2        morethan1   2             2
242                   no       2              one   4             2
243                   no       1              one   2             2
244                  yes       2        morethan1   4             2
245                  yes       2              one   2             1
246                   no       2              one   3             2
247                   no       2        morethan1   3             2
248                   no       2        morethan1   3             2
249                   no       2              one   2             2
250                   no       2              one   3             2
251                   no       2              one   3             1
252                   no       2              one   2             1
253                   no       2              one   3             2
254                   no       2              one   3             2
255                   no       2        morethan1   4             2
256                   no       1              one   3             2
257                   no       2        morethan1   2             2
258                   no       1        morethan1   3             2
259                   no       2              one   2             2
260                   no       2              one   2             2
261                  yes       2        morethan1   2             2
262                   no       2        morethan1   4             2
263                   no       2              one   3             2
264                   no       2              one   3             2
265                   no       2              one   3             1
266                   no       2              one   3             2
267                   no       2              one   2             1
268                   no       2              one   3             2
269                   no       1        morethan1   2             2
270                  yes       2              one   2             1
271                   no       1              one   3             2
272                   no       2        morethan1   3             2
273                   no       1              one   2             2
274                   no       2              one   3             2
275                   no       2              one   4             2
276                   no       2              one   2             1
277                  yes       1              one   4             2
278                   no       2        morethan1   3             2
279                   no       2              one   3             2
280                   no       2              one   3             2
281                  yes       2              one   3             2
282                   no       2              one   2             2
283                   no       1              one   3             2
284                   no       2        morethan1   3             2
285                  yes       2              one   3             2
286                   no       2              one   1             2
287                   no       2        morethan1   3             2
288                   no       2              one   3             1
289                   no       3              one   2             2
290                  yes       2        morethan1   3             2
291                   no       2        morethan1   2             1
292                   no       1              one   3             2
293                   no       2        morethan1   2             1
294                  yes       2        morethan1   3             1
295                   no       2        morethan1   3             2
296                  yes       2              one   3             2
297                   no       1              one   3             2
298                   no       2        morethan1   2             2
299                   no       1              one   2             2
300                   no       2        morethan1   3             2
301                  yes       2              one   2             2
302                  yes       2              one   3             2
303                   no       2              one   3             2
304                   no       2        morethan1   3             1
305                   no       2              one   2             2
306                   no       1              one   2             2
307                  yes       2              one   2             2
308                  yes       2              one   2             1
309                   no       1              one   4             2
310                   no       2              one   2             1
311                   no       2        morethan1   3             2
312                   no       2        morethan1   3             1
313                  yes       2        morethan1   2             2
314                   no       2              one   3             2
315                   no       2              one   2             1
316                  yes       3              one   3             1
317                  yes       2              one   3             2
318                   no       2              one   3             2
319                   no       2        morethan1   3             2
320                  yes       1              one   3             2
321                   no       1              one   2             1
322                   no       2              one   3             2
323                   no       2              one   3             2
324                   no       1        morethan1   3             2
325                   no       2              one   2             1
326                   no       2        morethan1   3             2
327                   no       1              one   2             2
328                   no       2        morethan1   3             1
329                   no       2              one   3             2
330                   no       2        morethan1   3             2
331                   no       2        morethan1   3             2
332                   no       2        morethan1   3             2
333                   no       1        morethan1   3             2
334                   no       1              one   3             2
335                   no       2        morethan1   3             2
336                   no       2              one   2             2
337                   no       2        morethan1   3             1
338                   no       2        morethan1   3             2
339                   no       2              one   3             1
340                   no       2              one   3             2
341                   no       2        morethan1   3             1
342                  yes       1              one   3             2
343                   no       2              one   4             2
344                   no       1              one   3             1
345                   no       2        morethan1   3             2
346                   no       2              one   3             2
347                   no       3              one   3             2
348                   no       3        morethan1   3             2
349                   no       2        morethan1   3             2
350                   no       2        morethan1   3             2
351                   no       2        morethan1   3             2
352                   no       2              one   3             1
353                  yes       2        morethan1   4             1
354                   no       2        morethan1   3             2
355                  yes       3              one   3             1
356                   no       3        morethan1   3             2
357                  yes       2              one   3             1
358                   no       2        morethan1   2             1
359                   no       2              one   4             2
360                   no       2              one   3             2
361                   no       2        morethan1   2             2
362                   no       1              one   3             2
363                   no       2        morethan1   3             2
364                   no       1              one   3             2
365                   no       3              one   3             2
366                   no       2        morethan1   3             2
367                   no       2              one   2             2
368                   no       2              one   2             2
369                   no       1              one   3             2
370                   no       1              one   2             2
371                  yes       2              one   2             2
372                   no       2              one   3             2
373                   no       1              one   3             2
374                   no       2              one   3             2
375                   no       2        morethan1   4             2
376                   no       2              one   4             2
377                   no       2        morethan1   3             2
378                   no       2        morethan1   4             2
379                   no       2              one   3             2
380                   no       2        morethan1   3             2
381                   no       1              one   3             2
382                   no       1              one   2             2
383                  yes       3              one   4             2
384                   no       1              one   2             2
385                   no       2        morethan1   3             2
386                   no       2              one   3             2
387                   no       2              one   3             2
388                   no       2              one   2             1
389                   no       2              one   3             2
390                   no       1              one   3             2
391                   no       2              one   3             2
392                  yes       3        morethan1   1             2
393                   no       2              one   2             2
394                   no       3        morethan1   3             2
395                  yes       2              one   3             2
396                   no       2              one   3             2
397                   no       2              one   3             2
398                  yes       2        morethan1   3             2
399                   no       1              one   3             2
400                   no       2              one   3             2
401                   no       2              one   3             1
402                   no       2              one   2             2
403                   no       1              one   1             2
404                   no       2              one   3             2
405                   no       2              one   3             2
406                  yes       3              one   1             2
407                   no       2              one   3             2
408                  yes       2              one   3             2
409                  yes       2              one   3             2
410                   no       1              one   3             2
411                   no       2              one   3             2
412                   no       2        morethan1   3             1
413                   no       2        morethan1   3             2
414                   no       2        morethan1   3             1
415                   no       2        morethan1   3             1
416                   no       2        morethan1   3             2
417                   no       2        morethan1   2             2
418                   no       2        morethan1   3             2
419                   no       2        morethan1   3             2
420                   no       2        morethan1   3             1
421                   no       2              one   3             2
422                   no       2        morethan1   3             2
423                   no       3        morethan1   1             2
424                   no       3              one   4             2
425                   no       2        morethan1   3             2
426                   no       2        morethan1   3             2
427                   no       1              one   3             2
428                   no       2        morethan1   4             2
429                  yes       1              one   3             2
430                   no       2              one   2             2
431                   no       1              one   4             2
432                   no       3        morethan1   3             1
433                  yes       2              one   3             2
434                  yes       2        morethan1   3             2
435                   no       1              one   2             2
436                   no       3              one   3             2
437                   no       2              one   3             2
438                   no       2              one   3             2
439                  yes       2        morethan1   4             1
440                   no       2        morethan1   1             1
441                   no       2              one   3             2
442                   no       2        morethan1   3             2
443                   no       2        morethan1   4             2
444                   no       1              one   3             2
445                   no       2              one   3             2
446                   no       2              one   3             2
447                   no       3        morethan1   3             2
448                   no       2              one   3             2
449                   no       3              one   4             2
450                   no       2              one   2             2
451                   no       2              one   2             2
452                   no       2              one   3             2
453                  yes       3              one   4             2
454                   no       3        morethan1   3             2
455                   no       3              one   3             2
456                   no       2              one   2             2
457                  yes       2        morethan1   3             2
458                   no       2              one   2             2
459                  yes       2              one   2             2
460                   no       1              one   3             2
461                   no       2              one   4             2
462                  yes       2        morethan1   4             2
463                   no       2              one   3             2
464                   no       2              one   2             2
465                   no       1              one   1             2
466                   no       2              one   4             2
467                   no       2              one   3             2
468                   no       3              one   3             2
469                   no       1              one   3             2
470                  yes       2        morethan1   3             1
471                  yes       3        morethan1   3             2
472                   no       2        morethan1   3             2
473                   no       2        morethan1   3             2
474                  yes       1              one   2             2
475                   no       2              one   3             2
476                   no       2        morethan1   2             2
477                  yes       2        morethan1   3             1
478                   no       2        morethan1   3             2
479                  yes       3        morethan1   2             2
480                   no       2              one   3             2
481                   no       2        morethan1   3             1
482                   no       1              one   3             2
483                   no       3              one   3             2
484                   no       1              one   3             2
485                   no       2              one   3             2
486                   no       2        morethan1   3             2
487                   no       2        morethan1   4             2
488                   no       2              one   3             2
489                   no       2              one   3             2
490                   no       2              one   4             1
491                   no       2              one   3             2
492                  yes       2              one   3             2
493                  yes       2              one   3             2
494                   no       2        morethan1   3             2
495                   no       2              one   3             2
496                   no       2              one   3             2
497                   no       1              one   3             2
498                   no       2        morethan1   3             2
499                   no       2              one   1             2
500                   no       2              one   3             2
501                   no       2              one   3             2
502                   no       2        morethan1   3             2
503                   no       2              one   3             1
504                   no       2        morethan1   3             2
505                   no       2              one   4             2
506                   no       1        morethan1   3             2
507                   no       2        morethan1   3             2
508                   no       2        morethan1   4             1
509                   no       2              one   3             2
510                   no       2              one   3             2
511                   no       2              one   3             2
512                   no       2              one   3             2
513                   no       2              one   3             2
514                   no       2              one   3             2
515                   no       1              one   3             2
516                   no       2              one   3             2
517                   no       1              one   4             2
518                  yes       3              one   3             2
519                   no       2        morethan1   3             2
520                   no       1        morethan1   3             2
521                   no       2              one   4             2
522                   no       2        morethan1   4             2
523                   no       2              one   3             2
524                   no       1              one   3             2
525                   no       2        morethan1   4             2
526                  yes       2        morethan1   3             2
527                   no       2              one   2             2
528                   no       2              one   3             2
529                   no       3              one   4             2
530                   no       2        morethan1   3             2
531                   no       2        morethan1   2             1
532                   no       2        morethan1   3             1
533                   no       1              one   2             2
534                   no       2        morethan1   4             2
535                  yes       2              one   3             2
536                   no       2              one   3             2
537                   no       2              one   3             1
538                   no       2        morethan1   2             1
539                   no       1        morethan1   2             2
540                  yes       2        morethan1   3             2
541                   no       2              one   3             2
542                   no       2        morethan1   3             1
543                   no       2              one   2             2
544                   no       2        morethan1   3             2
545                   no       2        morethan1   2             1
546                   no       2              one   3             2
547                   no       2              one   3             2
548                   no       2        morethan1   3             2
549                   no       2              one   3             2
550                   no       1              one   3             2
551                   no       1              one   3             1
552                   no       2              one   2             1
553                   no       2        morethan1   3             2
554                   no       2              one   3             2
555                   no       2              one   3             2
556                   no       2        morethan1   3             2
557                   no       2        morethan1   3             2
558                  yes       2              one   4             2
559                   no       1              one   4             2
560                   no       2              one   3             2
561                   no       2        morethan1   3             2
562                   no       2        morethan1   3             2
563                   no       2              one   4             2
564                   no       3              one   3             2
565                   no       2        morethan1   2             2
566                   no       1        morethan1   3             2
567                   no       2        morethan1   2             1
568                   no       2        morethan1   2             2
569                   no       2        morethan1   3             2
570                  yes       2              one   3             2
571                   no       2              one   3             2
572                   no       3              one   2             1
573                   no       2        morethan1   3             2
574                  yes       2              one   3             1
575                   no       1              one   4             2
576                  yes       2              one   4             2
577                   no       2              one   3             1
578                   no       1              one   2             1
579                   no       2        morethan1   3             2
580                   no       2              one   4             2
581                  yes       1              one   2             2
582                   no       2              one   3             2
583                   no       2        morethan1   3             2
584                   no       2              one   3             2
585                   no       3              one   4             2
586                   no       3              one   4             2
587                   no       3        morethan1   4             2
588                   no       2              one   3             1
589                   no       2              one   3             2
590                   no       1              one   4             2
591                   no       2              one   3             2
592                  yes       3        morethan1   4             1
593                   no       1        morethan1   2             2
594                   no       1        morethan1   1             2
595                   no       2        morethan1   3             2
596                   no       2        morethan1   3             2
597                   no       2              one   2             1
598                   no       2        morethan1   3             2
599                   no       2              one   3             1
600                   no       2              one   3             2
601                   no       2              one   3             1
602                   no       2        morethan1   2             2
603                   no       2              one   4             2
604                   no       2              one   2             2
605                  yes       2              one   3             1
606                   no       2        morethan1   3             2
607                  yes       2        morethan1   4             2
608                   no       2        morethan1   3             2
609                   no       2              one   3             1
610                   no       3              one   3             2
611                  yes       2              one   2             2
612                   no       2              one   3             2
613                   no       2              one   3             2
614                   no       1              one   3             2
615                   no       3              one   4             2
616                   no       2              one   3             2
617                   no       3        morethan1   4             1
618                   no       2              one   4             1
619                   no       2              one   2             2
620                   no       2        morethan1   2             1
621                   no       3              one   3             2
622                  yes       2        morethan1   4             2
623                   no       3        morethan1   4             2
624                   no       2              one   2             1
625                   no       3        morethan1   4             2
626                   no       3        morethan1   4             2
627                   no       2              one   4             2
628                   no       2        morethan1   3             2
629                   no       2              one   4             1
630                   no       3              one   4             2
631                   no       1        morethan1   3             2
632                   no       3              one   3             1
633                   no       2              one   3             2
634                   no       2              one   4             2
635                  yes       3              one   4             1
636                  yes       3              one   3             2
637                   no       2        morethan1   3             2
638                  yes       2        morethan1   2             2
639                   no       2        morethan1   3             2
640                   no       2        morethan1   3             2
641                   no       2        morethan1   3             2
642                   no       2        morethan1   3             2
643                   no       3        morethan1   4             1
644                  yes       3        morethan1   2             2
645                   no       3        morethan1   3             1
646                   no       2              one   4             2
647                  yes       2        morethan1   3             2
648                  yes       2        morethan1   3             1
649                  yes       2        morethan1   3             2
650                   no       2              one   3             2
651                   no       2              one   2             2
652                   no       2              one   3             2
653                   no       2        morethan1   3             2
654                   no       2        morethan1   3             2
655                   no       3              one   3             1
656                   no       2        morethan1   3             2
657                  yes       2        morethan1   3             1
658                   no       2              one   3             2
659                   no       2              one   3             2
660                   no       2              one   2             2
661                   no       2              one   3             2
662                   no       2        morethan1   3             2
663                   no       1        morethan1   3             2
664                   no       2              one   3             1
665                   no       2        morethan1   2             2
666                   no       2        morethan1   3             2
667                   no       2              one   3             2
668                   no       2        morethan1   3             2
669                   no       2              one   2             2
670                   no       2              one   3             2
671                   no       2              one   2             2
672                   no       2              one   2             2
673                   no       2              one   2             1
674                   no       2              one   2             2
675                  yes       2              one   2             2
676                   no       2        morethan1   3             1
677                   no       2        morethan1   2             2
678                  yes       1        morethan1   2             2
679                  yes       2        morethan1   3             2
680                  yes       2              one   2             2
681                  yes       2              one   2             2
682                  yes       2        morethan1   2             2
683                  yes       1        morethan1   4             1
684                   no       2        morethan1   3             2
685                   no       2              one   2             2
686                   no       2        morethan1   3             2
687                   no       2              one   2             2
688                  yes       2              one   1             2
689                  yes       2              one   2             2
690                   no       2              one   2             2
691                   no       2              one   3             2
692                   no       2              one   2             2
693                   no       2              one   2             2
694                   no       2        morethan1   2             2
695                   no       1              one   2             2
696                   no       2        morethan1   3             2
697                  yes       2        morethan1   3             2
698                   no       2              one   3             2
699                   no       2              one   3             2
700                   no       1        morethan1   4             2
701                   no       3              one   3             2
702                   no       2        morethan1   3             2
703                   no       1        morethan1   3             2
704                   no       1        morethan1   3             2
705                   no       2        morethan1   3             2
706                   no       2        morethan1   3             2
707                   no       1        morethan1   3             2
708                   no       2        morethan1   3             2
709                   no       1              one   3             2
710                  yes       3              one   3             2
711                   no       3              one   3             2
712                   no       2        morethan1   1             2
713                   no       2        morethan1   3             2
714                   no       1        morethan1   2             2
715                   no       1              one   3             2
716                   no       2        morethan1   3             2
717                   no       3              one   4             2
718                   no       2        morethan1   4             2
719                   no       1              one   3             2
720                   no       2              one   3             2
721                   no       3              one   3             2
722                   no       1              one   4             2
723                  yes       2        morethan1   2             2
724                  yes       2              one   4             1
725                  yes       2              one   3             2
726                   no       1              one   3             2
727                  yes       2              one   4             2
728                   no       2              one   4             2
729                   no       2              one   4             2
730                   no       3              one   4             2
731                  yes       3              one   4             2
732                   no       3              one   3             1
733                   no       1        morethan1   3             2
734                   no       1              one   3             2
735                   no       3              one   3             1
736                   no       2              one   3             2
737                   no       2        morethan1   2             2
738                   no       2        morethan1   3             2
739                   no       2        morethan1   2             2
740                  yes       2        morethan1   3             2
741                   no       2              one   2             2
742                   no       2              one   2             2
743                   no       2              one   3             2
744                   no       2        morethan1   3             2
745                  yes       1              one   2             2
746                   no       1              one   3             2
747                  yes       3              one   3             2
748                   no       3        morethan1   3             2
749                   no       2        morethan1   4             2
750                   no       3              one   3             2
751                   no       3              one   3             2
752                   no       2              one   2             2
753                   no       3        morethan1   3             1
754                   no       1              one   3             2
755                   no       2              one   1             2
756                   no       1        morethan1   3             1
757                   no       2        morethan1   2             2
758                  yes       2        morethan1   3             2
759                  yes       2              one   3             2
760                   no       2        morethan1   3             2
761                   no       2        morethan1   3             2
762                   no       3        morethan1   3             2
763                  yes       2        morethan1   3             2
764                   no       3              one   3             2
765                   no       2        morethan1   2             1
766                   no       2              one   3             2
767                   no       2        morethan1   3             2
768                  yes       2        morethan1   3             2
769                   no       2              one   3             2
770                   no       1              one   3             2
771                  yes       2              one   4             1
772                   no       3              one   4             2
773                   no       2              one   2             2
774                   no       2              one   3             2
775                   no       2        morethan1   3             2
776                   no       2              one   3             2
777                   no       2              one   4             2
778                   no       1              one   4             2
779                   no       2              one   3             2
780                   no       2              one   4             2
781                   no       2              one   3             2
782                  yes       2              one   3             2
783                   no       3              one   4             2
784                  yes       1              one   3             1
785                   no       3              one   3             2
786                  yes       2        morethan1   3             2
787                  yes       2              one   2             2
788                  yes       2        morethan1   3             2
789                   no       2        morethan1   2             2
790                  yes       2              one   2             2
791                   no       1        morethan1   1             2
792                   no       2              one   2             2
793                  yes       2              one   3             2
794                   no       2              one   3             2
795                   no       2              one   4             2
796                   no       3              one   4             2
797                   no       2              one   2             1
798                   no       1              one   3             2
799                  yes       2              one   3             2
800                  yes       3              one   3             1
801                   no       2              one   3             2
802                   no       1              one   3             2
803                   no       1              one   3             2
804                   no       2              one   3             2
805                   no       2              one   3             2
806                   no       1              one   3             2
807                   no       2        morethan1   3             2
808                   no       2        morethan1   3             2
809                   no       1              one   4             2
810                   no       3              one   3             2
811                   no       2              one   3             2
812                   no       1              one   3             2
813                   no       2              one   3             2
814                   no       2              one   3             2
815                  yes       3              one   4             1
816                   no       2              one   3             2
817                   no       3              one   2             1
818                   no       2              one   3             2
819                   no       2              one   3             2
820                   no       1        morethan1   3             2
821                  yes       2        morethan1   3             2
822                  yes       2              one   3             2
823                   no       2              one   3             2
824                   no       2        morethan1   3             2
825                   no       1              one   2             2
826                   no       2              one   4             2
827                   no       3              one   2             1
828                   no       1        morethan1   3             2
829                   no       2              one   3             2
830                   no       2              one   3             2
831                   no       2              one   3             2
832                   no       2        morethan1   4             2
833                   no       1        morethan1   3             2
834                   no       2              one   3             2
835                   no       2              one   3             2
836                   no       1        morethan1   4             2
837                  yes       2        morethan1   3             1
838                   no       2              one   3             2
839                  yes       2        morethan1   4             1
840                   no       2              one   3             2
841                   no       2              one   4             2
842                   no       2        morethan1   2             2
843                   no       1              one   2             2
844                  yes       2        morethan1   4             2
845                   no       2        morethan1   4             1
846                   no       2        morethan1   2             2
847                  yes       2        morethan1   4             2
848                  yes       3        morethan1   4             2
849                   no       3              one   3             2
850                   no       3              one   3             1
851                   no       1              one   3             2
852                  yes       2              one   2             2
853                   no       2        morethan1   3             2
854                  yes       2        morethan1   2             1
855                   no       2              one   2             1
856                   no       3              one   3             1
857                   no       3              one   3             1
858                   no       1              one   3             2
859                   no       2              one   3             2
860                   no       2              one   2             2
861                  yes       2              one   3             2
862                   no       1              one   3             2
863                   no       1              one   3             2
864                  yes       2        morethan1   3             2
865                  yes       2              one   3             2
866                   no       3        morethan1   3             2
867                   no       1              one   3             2
868                   no       1              one   4             2
869                  yes       1        morethan1   3             2
870                   no       2              one   2             2
871                   no       2              one   3             2
872                  yes       2        morethan1   3             2
873                   no       1              one   3             2
874                   no       2        morethan1   3             2
875                  yes       3              one   2             2
876                   no       2        morethan1   4             2
877                   no       2        morethan1   4             2
878                   no       2              one   3             2
879                   no       1              one   3             2
880                  yes       2              one   4             2
881                   no       2              one   1             2
882                   no       2        morethan1   4             2
883                  yes       3              one   4             2
884                   no       1              one   3             2
885                   no       1        morethan1   3             2
886                   no       2              one   3             2
887                   no       2              one   3             2
888                   no       2        morethan1   4             2
889                   no       2              one   3             2
890                   no       1              one   4             2
891                   no       2              one   4             2
892                   no       2        morethan1   3             2
893                   no       2              one   3             2
894                   no       2              one   3             2
895                   no       2              one   4             2
896                   no       3              one   4             2
897                  yes       2              one   2             1
898                   no       1              one   3             2
899                   no       2              one   3             1
900                   no       2              one   2             2
901                   no       2              one   3             2
902                   no       2              one   2             1
903                  yes       3        morethan1   3             2
904                   no       2              one   3             2
905                   no       1              one   3             2
906                   no       1        morethan1   3             2
907                  yes       2              one   3             2
908                   no       1              one   3             2
909                   no       3        morethan1   3             1
910                   no       3        morethan1   3             1
911                   no       3        morethan1   3             1
912                   no       2        morethan1   3             2
913                  yes       2        morethan1   3             1
914                  yes       2              one   3             2
915                  yes       1        morethan1   3             1
916                   no       2              one   3             2
917                   no       1              one   3             2
918                   no       1        morethan1   3             2
919                   no       3        morethan1   3             1
920                   no       3        morethan1   3             2
921                  yes       2              one   3             2
922                  yes       2        morethan1   3             2
923                   no       1        morethan1   3             2
924                   no       2        morethan1   3             2
925                  yes       2        morethan1   3             2
926                   no       1              one   3             2
927                  yes       1              one   3             2
928                   no       1              one   3             2
929                   no       3        morethan1   3             1
930                  yes       2              one   3             2
931                   no       2        morethan1   3             2
932                  yes       2              one   2             2
933                   no       1        morethan1   3             2
934                   no       2              one   3             2
935                   no       1              one   3             2
936                  yes       1        morethan1   3             2
937                  yes       2              one   3             2
938                   no       2              one   3             1
939                  yes       2              one   2             2
940                  yes       2        morethan1   4             2
941                   no       3              one   3             2
942                  yes       2              one   2             1
943                  yes       2              one   2             1
944                  yes       3              one   3             2
945                   no       2              one   2             2
946                  yes       2              one   4             1
947                   no       1              one   2             2
948                  yes       2              one   4             2
949                  yes       3        morethan1   2             1
950                   no       2              one   2             1
951                  yes       2        morethan1   2             2
952                   no       2        morethan1   3             2
953                   no       2              one   3             1
954                   no       1        morethan1   2             2
955                  yes       2              one   1             2
956                   no       3        morethan1   1             1
957                   no       1        morethan1   2             2
958                  yes       1              one   3             2
959                   no       1        morethan1   2             2
960                   no       1              one   3             2
961                  yes       1        morethan1   3             2
962                   no       2              one   3             2
963                   no       1              one   3             2
964                   no       1              one   2             2
965                   no       2              one   3             1
966                  yes       2              one   3             2
967                  yes       2        morethan1   3             2
968                   no       2              one   3             2
969                   no       3              one   3             2
970                  yes       2        morethan1   2             1
971                   no       1              one   4             2
972                  yes       3        morethan1   4             2
973                   no       2              one   3             2
974                   no       1        morethan1   3             2
975                   no       2              one   3             2
976                  yes       2              one   2             2
977                  yes       2              one   4             2
978                  yes       2              one   4             2
979                   no       2              one   3             2
980                   no       3              one   3             2
981                   no       2              one   3             2
982                   no       2              one   3             2
983                   no       2              one   4             2
984                  yes       1        morethan1   3             2
985                   no       2        morethan1   3             2
986                  yes       2        morethan1   4             2
987                   no       2              one   4             2
988                   no       2              one   3             2
989                   no       2              one   2             2
990                   no       1              one   2             2
991                   no       2              one   1             2
992                   no       3              one   4             2
993                   no       3              one   3             1
994                  yes       1        morethan1   3             2
995                   no       1        morethan1   3             2
996                   no       1              one   2             1
997                   no       2              one   3             2
998                   no       3              one   4             2
999                   no       2              one   4             2
1000                  no       2              one   3             2
     telephone_nr foreign_worker customer_good_bad
1               1              2                 1
2               1              2                 1
3               1              2                 1
4               1              1                 1
5               1              1                 1
6               1              1                 1
7               1              1                 1
8               1              1                 1
9               1              2                 1
10              1              2                 1
11              1              2                 1
12              1              2                 1
13              1              2                 1
14              2              2                 1
15              1              2                 1
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17              1              2                 1
18              1              2                 1
19              1              2                 1
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21              1              2                 1
22              1              2                 1
23              1              2                 0
24              1              2                 1
25              1              2                 1
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27              1              2                 1
28              1              2                 1
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46              2              2                 0
47              2              2                 1
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55              2              2                 1
56              2              2                 1
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63              2              2                 1
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93              1              2                 1
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140             2              2                 1
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Data splitting

The caret package [@R-caret] includes the function createDataPartition that helps us generates indexes for randomly splitting the data into training and test sets: We will split our data in half:

Data splitting

# Validation set will be 50% of South German Credit data
# Set seed as a starting point
set.seed(1, sample.kind = "Rounding")
# Store row numbers for train set: test_index
test_index <- createDataPartition(y = data_credit$customer_good_bad,
    times = 1, p = 0.5, list = FALSE)

Data splitting

We use the result of the createDataPartition function call to define the training and test sets like this:

Data splitting

# Create the train set
train <- data_credit[-test_index, ]
# Create the validation set
validation <- data_credit[test_index, ]

Data splitting

  • Now, we can save the result of steps above (train and validation dataframes ) as R objects, so we can reload the final version of the data into the session for further analysis without repeating the process.

Data splitting

# Save our data as R objects
save(train, file = "./data/train.RData")
save(validation, file = "./data/validation.RData")

Data splitting

  • We see that train data frame has 500 rows and 21 variables, while validation data frame has 500 rows and 21.

Data splitting

  • Now let’s print features of both data frames train and validation together to reassure ourselves that both contain the same features. We will use function rCompare from dataCompareR library[@dataCompareR]

Data splitting

# compare train and validation
library(dataCompareR)
comp_train_val <- rCompare(train, validation)
comp_summ <- summary(comp_train_val)
comp_summ[c("datasetSummary", "ncolInAOnly", "ncolInBOnly", "ncolCommon",
    "rowsInAOnly", "rowsInBOnly", "nrowCommon")]
$datasetSummary
  Dataset Name Number of Rows Number of Columns
1        train            500                21
2   validation            500                21

$ncolInAOnly
[1] 0

$ncolInBOnly
[1] 0

$ncolCommon
[1] 21

$rowsInAOnly
[1] indices_removed
<0 rows> (or 0-length row.names)

$rowsInBOnly
[1] indices_removed
<0 rows> (or 0-length row.names)

$nrowCommon
[1] 500

Logistic Regression model

  • We will start with a Logistic Regression model. Logistic regression is a specific case of a set of generalized linear models.

  • For logistic regression, outcome is a categorical variable which fits very well to our case since we have outcome customer_good_bad as categorical variable with 2 levels. In R, we can fit the logistic regression model with the function glm: generalized linear models.

Logistic Regression model

This function is more general than logistic regression so we need to specify the model we want through the family parameter [@irizarry2019]:

Logistic Regression model

# Fitting initial model
glm_model <- glm(customer_good_bad ~ ., family = "binomial",
    data = train)
# Obtain significance levels using summary()
summary(glm_model)

Call:
glm(formula = customer_good_bad ~ ., family = "binomial", data = train)

Coefficients:
                                 Estimate Std. Error z value    Pr(>|z|)    
(Intercept)                     0.2060292  1.3176866    0.16      0.8758    
account_statusno_money_acc      0.4714934  0.3220657    1.46      0.1432    
account_statuspositive_acc      1.9214381  0.3302976    5.82 0.000000006 ***
duration_month                 -0.0070307  0.0138640   -0.51      0.6121    
credit_historyall_paid          0.2335696  0.4548991    0.51      0.6076    
credit_historyno_prob_currbank  1.2112308  0.4739822    2.56      0.0106 *  
credit_purposenew_car           1.5122902  0.5787822    2.61      0.0090 ** 
credit_purposeused_car          0.9620207  0.3710509    2.59      0.0095 ** 
credit_purposedomestic          0.6204252  0.3115121    1.99      0.0464 *  
credit_amount                  -0.0002192  0.0000702   -3.12      0.0018 ** 
savings_accountless100          0.5326944  0.4047966    1.32      0.1882    
savings_account100to1000        1.0745236  0.5189242    2.07      0.0384 *  
savings_accountover1000         1.1834529  0.4010053    2.95      0.0032 ** 
employment_present1to4          0.5107393  0.3435275    1.49      0.1371    
employment_present4to7          0.6485676  0.4261576    1.52      0.1280    
employment_present7plus         0.4108147  0.4049936    1.01      0.3104    
installment_rate_pct2          -0.0652359  0.4927730   -0.13      0.8947    
installment_rate_pct3          -0.7409467  0.5453258   -1.36      0.1742    
installment_rate_pct4          -1.3321188  0.4836422   -2.75      0.0059 ** 
status_sexm_married_wid         0.8414334  0.2934204    2.87      0.0041 ** 
status_sexfemale                0.4499305  0.4812451    0.93      0.3498    
other_debtors_guaryes           0.0921798  0.4416947    0.21      0.8347    
residence_duration2            -1.0934504  0.4421509   -2.47      0.0134 *  
residence_duration3            -1.3719931  0.4915116   -2.79      0.0052 ** 
residence_duration4            -0.8004271  0.4332062   -1.85      0.0646 .  
property2                      -0.4495755  0.3497790   -1.29      0.1987    
property3                       0.1024474  0.3503562    0.29      0.7700    
property4                      -0.7351560  0.6027972   -1.22      0.2226    
age_years                       0.0224649  0.0136187    1.65      0.0990 .  
other_install_plansno           0.4197510  0.3412602    1.23      0.2187    
housing2                       -0.0380901  0.3541866   -0.11      0.9144    
housing3                       -0.0565226  0.6904937   -0.08      0.9348    
exist_credits_nrmorethan1      -0.3824053  0.3450054   -1.11      0.2677    
job2                           -0.3649171  0.7592653   -0.48      0.6308    
job3                           -0.1728530  0.7284976   -0.24      0.8124    
job4                           -0.1171575  0.7845059   -0.15      0.8813    
dependents_nr2                  0.1155544  0.3816709    0.30      0.7621    
telephone_nr2                   0.4937540  0.2992690    1.65      0.0990 .  
foreign_worker2                -0.6551446  0.8111144   -0.81      0.4193    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 610.86  on 499  degrees of freedom
Residual deviance: 425.59  on 461  degrees of freedom
AIC: 503.6

Number of Fisher Scoring iterations: 5

Logistic Regression model

  • We see that some of variables have 3 stars beside them.

  • This variables are statistically significant(have p-values < 0.05). Le’ s filter them:

Logistic Regression model

# Filter significant values
sig <- summary(glm_model)$coeff[-1, 4] < 0.05
names(sig)[sig == T]
 [1] "account_statuspositive_acc"     "credit_historyno_prob_currbank"
 [3] "credit_purposenew_car"          "credit_purposeused_car"        
 [5] "credit_purposedomestic"         "credit_amount"                 
 [7] "savings_account100to1000"       "savings_accountover1000"       
 [9] "installment_rate_pct4"          "status_sexm_married_wid"       
[11] "residence_duration2"            "residence_duration3"

Logistic Regression model

Next let’s obtain prediction using the predict function:

Logistic Regression model

# Predictions
pred_logit <- predict(glm_model, newdata = validation, type = "response")

Logistic Regression model

  • To form a prediction, we define a decision rule: predict good_creditor if pred_logit > 0.5.

  • Let’s evaluate the accuracy of our model using function confusionMatrix from caret library[@R-caret]:

Logistic Regression model

# convert pred_logit to a vector of binary values : put as
# cut pred_logit > 0.5
y_hat_glm <- factor(ifelse(pred_logit > 0.5, 1, 0))
# print confusion matrix
confusionMatrix(y_hat_glm, reference = validation$customer_good_bad,
    positive = "1")
Confusion Matrix and Statistics

          Reference
Prediction   0   1
         0  66  48
         1  84 302
                                        
               Accuracy : 0.736         
                 95% CI : (0.695, 0.774)
    No Information Rate : 0.7           
    P-Value [Acc > NIR] : 0.04259       
                                        
                  Kappa : 0.325         
                                        
 Mcnemar's Test P-Value : 0.00232       
                                        
            Sensitivity : 0.863         
            Specificity : 0.440         
         Pos Pred Value : 0.782         
         Neg Pred Value : 0.579         
             Prevalence : 0.700         
         Detection Rate : 0.604         
   Detection Prevalence : 0.772         
      Balanced Accuracy : 0.651         
                                        
       'Positive' Class : 1

Logistic Regression model

Since the output contains too much information, let’s print only value of accuracy :

Logistic Regression model

# Print only accuracy
confusionMatrix(y_hat_glm, reference = validation$customer_good_bad,
    positive = "1")$overall["Accuracy"]
Accuracy 
   0.736

Logistic Regression model

  • We obtained an overall accuracy of \(73.6 \%\).

  • The prediction for bad creditors is good judged by value of Specificity \(44\%\).

Logistic Regression model

  • Sensitivity is \(86 \%\), which is quite good.

  • Also we notice that NPV is nearly \(58 \%\), and PPV is almost \(78 \%\).

Classification Tree model

  • Let’ build our model will all features included.

  • For building the decision tree we will use function rpart from rpart library[@R-rpart]

Classification Tree model

# Decision Tree model
credit_tree <- rpart(customer_good_bad ~ ., method = "class",
    data = train)
# Plot the decision tree
plot(credit_tree, uniform = TRUE)
# Add labels
text(credit_tree)

Classification Tree model

Classification Tree model

Let’s make our prediction as we did with previous model

Classification Tree model

# Predictions
pred_tree <- predict(credit_tree, newdata = validation, type = "class")

Classification Tree model

Let’s evaluate the accuracy of our model:

Classification Tree model

# print confusion matrix
confusionMatrix(pred_tree, reference = validation$customer_good_bad,
    positive = "1")
Confusion Matrix and Statistics

          Reference
Prediction   0   1
         0  54  53
         1  96 297
                                       
               Accuracy : 0.702        
                 95% CI : (0.66, 0.742)
    No Information Rate : 0.7          
    P-Value [Acc > NIR] : 0.48313      
                                       
                  Kappa : 0.227        
                                       
 Mcnemar's Test P-Value : 0.00058      
                                       
            Sensitivity : 0.849        
            Specificity : 0.360        
         Pos Pred Value : 0.756        
         Neg Pred Value : 0.505        
             Prevalence : 0.700        
         Detection Rate : 0.594        
   Detection Prevalence : 0.786        
      Balanced Accuracy : 0.604        
                                       
       'Positive' Class : 1

Classification Tree model

Since the output contains too much information, let’s print only value of accuracy :

Classification Tree model

# Print only accuracy
confusionMatrix(pred_tree, reference = validation$customer_good_bad,
    positive = "1")$overall["Accuracy"]
Accuracy 
   0.702

Classification Tree model

  • We obtained an overall accuracy of \(70.2 \%\).

  • Sensitivity is \(84.86 \%\), a very good one, and specificity is \(36 \%\).

Results

  • We build a logistic regression model with accuracy of \(73.6 \%\).

  • Specificity of this model was \(44 \%\) and sensitivity \(86 \%\).

Results

  • We build a classification tree model with accuracy of \(70.2 \%\).

  • Sensitivity is 84.86 and specificity is \(36 \%\).

Conclusions

  • In project with real-life credit data, we tried to model credit risk by using logistic regression and decision trees in R.

Conclusions

  • Credit risk models are very important for financial institutions such as banks.

  • The risk of creditor not turning the loan is a parameter which can be modeled and measured apriori in order to minimize loses.

  • Machine learning algorithms and techniques come in help in such problems.

Appendix A


 
   Cell Contents
|-------------------------|
|                       N |
|           N / Col Total |
|-------------------------|

 
Total Observations in Table:  1000 

 
                  | account_status 
customer_good_bad |   no_account | no_money_acc | positive_acc |    Row Total | 
------------------|--------------|--------------|--------------|--------------|
                0 |          135 |          105 |           60 |          300 | 
                  |          0.5 |          0.4 |          0.1 |              | 
------------------|--------------|--------------|--------------|--------------|
                1 |          139 |          164 |          397 |          700 | 
                  |          0.5 |          0.6 |          0.9 |              | 
------------------|--------------|--------------|--------------|--------------|
     Column Total |          274 |          269 |          457 |         1000 | 
                  |          0.3 |          0.3 |          0.5 |              | 
------------------|--------------|--------------|--------------|--------------|

 
Statistics for All Table Factors


Pearson's Chi-squared test 
------------------------------------------------------------
Chi^2 =  120.8     d.f. =  2     p =  0.000000000000000000000000005743 


 

 
   Cell Contents
|-------------------------|
|                       N |
|           N / Col Total |
|-------------------------|

 
Total Observations in Table:  1000 

 
                  | credit_history 
customer_good_bad |     pay_problems |         all_paid | no_prob_currbank |        Row Total | 
------------------|------------------|------------------|------------------|------------------|
                0 |               53 |              169 |               78 |              300 | 
                  |              0.6 |              0.3 |              0.2 |                  | 
------------------|------------------|------------------|------------------|------------------|
                1 |               36 |              361 |              303 |              700 | 
                  |              0.4 |              0.7 |              0.8 |                  | 
------------------|------------------|------------------|------------------|------------------|
     Column Total |               89 |              530 |              381 |             1000 | 
                  |              0.1 |              0.5 |              0.4 |                  | 
------------------|------------------|------------------|------------------|------------------|

 
Statistics for All Table Factors


Pearson's Chi-squared test 
------------------------------------------------------------
Chi^2 =  54.38     d.f. =  2     p =  0.000000000001557 


 

 
   Cell Contents
|-------------------------|
|                       N |
|           N / Col Total |
|-------------------------|

 
Total Observations in Table:  1000 

 
                  | credit_purpose 
customer_good_bad |  services |   new_car |  used_car |  domestic | Row Total | 
------------------|-----------|-----------|-----------|-----------|-----------|
                0 |       151 |        17 |        58 |        74 |       300 | 
                  |       0.4 |       0.2 |       0.3 |       0.2 |           | 
------------------|-----------|-----------|-----------|-----------|-----------|
                1 |       251 |        86 |       123 |       240 |       700 | 
                  |       0.6 |       0.8 |       0.7 |       0.8 |           | 
------------------|-----------|-----------|-----------|-----------|-----------|
     Column Total |       402 |       103 |       181 |       314 |      1000 | 
                  |       0.4 |       0.1 |       0.2 |       0.3 |           | 
------------------|-----------|-----------|-----------|-----------|-----------|

 
Statistics for All Table Factors


Pearson's Chi-squared test 
------------------------------------------------------------
Chi^2 =  26.43     d.f. =  3     p =  0.000007759 


 

 
   Cell Contents
|-------------------------|
|                       N |
|           N / Col Total |
|-------------------------|

 
Total Observations in Table:  1000 

 
                  | savings_account 
customer_good_bad |    no_sav |   less100 | 100to1000 |  over1000 | Row Total | 
------------------|-----------|-----------|-----------|-----------|-----------|
                0 |       217 |        34 |        17 |        32 |       300 | 
                  |       0.4 |       0.3 |       0.2 |       0.2 |           | 
------------------|-----------|-----------|-----------|-----------|-----------|
                1 |       386 |        69 |        94 |       151 |       700 | 
                  |       0.6 |       0.7 |       0.8 |       0.8 |           | 
------------------|-----------|-----------|-----------|-----------|-----------|
     Column Total |       603 |       103 |       111 |       183 |      1000 | 
                  |       0.6 |       0.1 |       0.1 |       0.2 |           | 
------------------|-----------|-----------|-----------|-----------|-----------|

 
Statistics for All Table Factors


Pearson's Chi-squared test 
------------------------------------------------------------
Chi^2 =  35.78     d.f. =  3     p =  0.00000008336 


 

 
   Cell Contents
|-------------------------|
|                       N |
|           N / Col Total |
|-------------------------|

 
Total Observations in Table:  1000 

 
                  | employment_present 
customer_good_bad | unemp_less1year |            1to4 |            4to7 |           7plus |       Row Total | 
------------------|-----------------|-----------------|-----------------|-----------------|-----------------|
                0 |              93 |             104 |              39 |              64 |             300 | 
                  |             0.4 |             0.3 |             0.2 |             0.3 |                 | 
------------------|-----------------|-----------------|-----------------|-----------------|-----------------|
                1 |             141 |             235 |             135 |             189 |             700 | 
                  |             0.6 |             0.7 |             0.8 |             0.7 |                 | 
------------------|-----------------|-----------------|-----------------|-----------------|-----------------|
     Column Total |             234 |             339 |             174 |             253 |            1000 | 
                  |             0.2 |             0.3 |             0.2 |             0.3 |                 | 
------------------|-----------------|-----------------|-----------------|-----------------|-----------------|

 
Statistics for All Table Factors


Pearson's Chi-squared test 
------------------------------------------------------------
Chi^2 =  18.09     d.f. =  3     p =  0.0004221 


 

 
   Cell Contents
|-------------------------|
|                       N |
|           N / Col Total |
|-------------------------|

 
Total Observations in Table:  1000 

 
                  | installment_rate_pct 
customer_good_bad |         1 |         2 |         3 |         4 | Row Total | 
------------------|-----------|-----------|-----------|-----------|-----------|
                0 |        34 |        62 |        45 |       159 |       300 | 
                  |       0.2 |       0.3 |       0.3 |       0.3 |           | 
------------------|-----------|-----------|-----------|-----------|-----------|
                1 |       102 |       169 |       112 |       317 |       700 | 
                  |       0.8 |       0.7 |       0.7 |       0.7 |           | 
------------------|-----------|-----------|-----------|-----------|-----------|
     Column Total |       136 |       231 |       157 |       476 |      1000 | 
                  |       0.1 |       0.2 |       0.2 |       0.5 |           | 
------------------|-----------|-----------|-----------|-----------|-----------|

 
Statistics for All Table Factors


Pearson's Chi-squared test 
------------------------------------------------------------
Chi^2 =  5.477     d.f. =  3     p =  0.14 


 

 
   Cell Contents
|-------------------------|
|                       N |
|           N / Col Total |
|-------------------------|

 
Total Observations in Table:  1000 

 
                  | status_sex 
customer_good_bad | m_single_divorced |     m_married_wid |            female |         Row Total | 
------------------|-------------------|-------------------|-------------------|-------------------|
                0 |               129 |               146 |                25 |               300 | 
                  |               0.4 |               0.3 |               0.3 |                   | 
------------------|-------------------|-------------------|-------------------|-------------------|
                1 |               231 |               402 |                67 |               700 | 
                  |               0.6 |               0.7 |               0.7 |                   | 
------------------|-------------------|-------------------|-------------------|-------------------|
     Column Total |               360 |               548 |                92 |              1000 | 
                  |               0.4 |               0.5 |               0.1 |                   | 
------------------|-------------------|-------------------|-------------------|-------------------|

 
Statistics for All Table Factors


Pearson's Chi-squared test 
------------------------------------------------------------
Chi^2 =  9.125     d.f. =  2     p =  0.01043 


 

 
   Cell Contents
|-------------------------|
|                       N |
|           N / Col Total |
|-------------------------|

 
Total Observations in Table:  1000 

 
                  | other_debtors_guar 
customer_good_bad |        no |       yes | Row Total | 
------------------|-----------|-----------|-----------|
                0 |       272 |        28 |       300 | 
                  |       0.3 |       0.3 |           | 
------------------|-----------|-----------|-----------|
                1 |       635 |        65 |       700 | 
                  |       0.7 |       0.7 |           | 
------------------|-----------|-----------|-----------|
     Column Total |       907 |        93 |      1000 | 
                  |       0.9 |       0.1 |           | 
------------------|-----------|-----------|-----------|

 
Statistics for All Table Factors


Pearson's Chi-squared test 
------------------------------------------------------------
Chi^2 =  0.0005645     d.f. =  1     p =  0.981 

Pearson's Chi-squared test with Yates' continuity correction 
------------------------------------------------------------
Chi^2 =  0.000000000000000000000000000006051     d.f. =  1     p =  1 

 

 
   Cell Contents
|-------------------------|
|                       N |
|           N / Col Total |
|-------------------------|

 
Total Observations in Table:  1000 

 
                  | residence_duration 
customer_good_bad |         1 |         2 |         3 |         4 | Row Total | 
------------------|-----------|-----------|-----------|-----------|-----------|
                0 |        36 |        97 |        43 |       124 |       300 | 
                  |       0.3 |       0.3 |       0.3 |       0.3 |           | 
------------------|-----------|-----------|-----------|-----------|-----------|
                1 |        94 |       211 |       106 |       289 |       700 | 
                  |       0.7 |       0.7 |       0.7 |       0.7 |           | 
------------------|-----------|-----------|-----------|-----------|-----------|
     Column Total |       130 |       308 |       149 |       413 |      1000 | 
                  |       0.1 |       0.3 |       0.1 |       0.4 |           | 
------------------|-----------|-----------|-----------|-----------|-----------|

 
Statistics for All Table Factors


Pearson's Chi-squared test 
------------------------------------------------------------
Chi^2 =  0.7493     d.f. =  3     p =  0.8616 


 

 
   Cell Contents
|-------------------------|
|                       N |
|           N / Col Total |
|-------------------------|

 
Total Observations in Table:  1000 

 
                  | property 
customer_good_bad |         1 |         2 |         3 |         4 | Row Total | 
------------------|-----------|-----------|-----------|-----------|-----------|
                0 |        60 |        71 |       102 |        67 |       300 | 
                  |       0.2 |       0.3 |       0.3 |       0.4 |           | 
------------------|-----------|-----------|-----------|-----------|-----------|
                1 |       222 |       161 |       230 |        87 |       700 | 
                  |       0.8 |       0.7 |       0.7 |       0.6 |           | 
------------------|-----------|-----------|-----------|-----------|-----------|
     Column Total |       282 |       232 |       332 |       154 |      1000 | 
                  |       0.3 |       0.2 |       0.3 |       0.2 |           | 
------------------|-----------|-----------|-----------|-----------|-----------|

 
Statistics for All Table Factors


Pearson's Chi-squared test 
------------------------------------------------------------
Chi^2 =  23.72     d.f. =  3     p =  0.00002858 


 

 
   Cell Contents
|-------------------------|
|                       N |
|           N / Col Total |
|-------------------------|

 
Total Observations in Table:  1000 

 
                  | other_install_plans 
customer_good_bad |       yes |        no | Row Total | 
------------------|-----------|-----------|-----------|
                0 |        76 |       224 |       300 | 
                  |       0.4 |       0.3 |           | 
------------------|-----------|-----------|-----------|
                1 |       110 |       590 |       700 | 
                  |       0.6 |       0.7 |           | 
------------------|-----------|-----------|-----------|
     Column Total |       186 |       814 |      1000 | 
                  |       0.2 |       0.8 |           | 
------------------|-----------|-----------|-----------|

 
Statistics for All Table Factors


Pearson's Chi-squared test 
------------------------------------------------------------
Chi^2 =  12.83     d.f. =  1     p =  0.0003405 

Pearson's Chi-squared test with Yates' continuity correction 
------------------------------------------------------------
Chi^2 =  12.21     d.f. =  1     p =  0.0004763 

 

 
   Cell Contents
|-------------------------|
|                       N |
|           N / Col Total |
|-------------------------|

 
Total Observations in Table:  1000 

 
                  | housing 
customer_good_bad |         1 |         2 |         3 | Row Total | 
------------------|-----------|-----------|-----------|-----------|
                0 |        70 |       186 |        44 |       300 | 
                  |       0.4 |       0.3 |       0.4 |           | 
------------------|-----------|-----------|-----------|-----------|
                1 |       109 |       528 |        63 |       700 | 
                  |       0.6 |       0.7 |       0.6 |           | 
------------------|-----------|-----------|-----------|-----------|
     Column Total |       179 |       714 |       107 |      1000 | 
                  |       0.2 |       0.7 |       0.1 |           | 
------------------|-----------|-----------|-----------|-----------|

 
Statistics for All Table Factors


Pearson's Chi-squared test 
------------------------------------------------------------
Chi^2 =  18.67     d.f. =  2     p =  0.0000881 


 

 
   Cell Contents
|-------------------------|
|                       N |
|           N / Col Total |
|-------------------------|

 
Total Observations in Table:  1000 

 
                  | exist_credits_nr 
customer_good_bad |       one | morethan1 | Row Total | 
------------------|-----------|-----------|-----------|
                0 |       200 |       100 |       300 | 
                  |       0.3 |       0.3 |           | 
------------------|-----------|-----------|-----------|
                1 |       433 |       267 |       700 | 
                  |       0.7 |       0.7 |           | 
------------------|-----------|-----------|-----------|
     Column Total |       633 |       367 |      1000 | 
                  |       0.6 |       0.4 |           | 
------------------|-----------|-----------|-----------|

 
Statistics for All Table Factors


Pearson's Chi-squared test 
------------------------------------------------------------
Chi^2 =  2.091     d.f. =  1     p =  0.1482 

Pearson's Chi-squared test with Yates' continuity correction 
------------------------------------------------------------
Chi^2 =  1.889     d.f. =  1     p =  0.1693 

 

 
   Cell Contents
|-------------------------|
|                       N |
|           N / Col Total |
|-------------------------|

 
Total Observations in Table:  1000 

 
                  | job 
customer_good_bad |         1 |         2 |         3 |         4 | Row Total | 
------------------|-----------|-----------|-----------|-----------|-----------|
                0 |         7 |        56 |       186 |        51 |       300 | 
                  |       0.3 |       0.3 |       0.3 |       0.3 |           | 
------------------|-----------|-----------|-----------|-----------|-----------|
                1 |        15 |       144 |       444 |        97 |       700 | 
                  |       0.7 |       0.7 |       0.7 |       0.7 |           | 
------------------|-----------|-----------|-----------|-----------|-----------|
     Column Total |        22 |       200 |       630 |       148 |      1000 | 
                  |       0.0 |       0.2 |       0.6 |       0.1 |           | 
------------------|-----------|-----------|-----------|-----------|-----------|

 
Statistics for All Table Factors


Pearson's Chi-squared test 
------------------------------------------------------------
Chi^2 =  1.885     d.f. =  3     p =  0.5966 


 

 
   Cell Contents
|-------------------------|
|                       N |
|           N / Col Total |
|-------------------------|

 
Total Observations in Table:  1000 

 
                  | dependents_nr 
customer_good_bad |         1 |         2 | Row Total | 
------------------|-----------|-----------|-----------|
                0 |        46 |       254 |       300 | 
                  |       0.3 |       0.3 |           | 
------------------|-----------|-----------|-----------|
                1 |       109 |       591 |       700 | 
                  |       0.7 |       0.7 |           | 
------------------|-----------|-----------|-----------|
     Column Total |       155 |       845 |      1000 | 
                  |       0.2 |       0.8 |           | 
------------------|-----------|-----------|-----------|

 
Statistics for All Table Factors


Pearson's Chi-squared test 
------------------------------------------------------------
Chi^2 =  0.009089     d.f. =  1     p =  0.924 

Pearson's Chi-squared test with Yates' continuity correction 
------------------------------------------------------------
Chi^2 =  0     d.f. =  1     p =  1 

 

 
   Cell Contents
|-------------------------|
|                       N |
|           N / Col Total |
|-------------------------|

 
Total Observations in Table:  1000 

 
                  | telephone_nr 
customer_good_bad |         1 |         2 | Row Total | 
------------------|-----------|-----------|-----------|
                0 |       187 |       113 |       300 | 
                  |       0.3 |       0.3 |           | 
------------------|-----------|-----------|-----------|
                1 |       409 |       291 |       700 | 
                  |       0.7 |       0.7 |           | 
------------------|-----------|-----------|-----------|
     Column Total |       596 |       404 |      1000 | 
                  |       0.6 |       0.4 |           | 
------------------|-----------|-----------|-----------|

 
Statistics for All Table Factors


Pearson's Chi-squared test 
------------------------------------------------------------
Chi^2 =  1.33     d.f. =  1     p =  0.2488 

Pearson's Chi-squared test with Yates' continuity correction 
------------------------------------------------------------
Chi^2 =  1.173     d.f. =  1     p =  0.2789 

 

 
   Cell Contents
|-------------------------|
|                       N |
|           N / Col Total |
|-------------------------|

 
Total Observations in Table:  1000 

 
                  | foreign_worker 
customer_good_bad |         1 |         2 | Row Total | 
------------------|-----------|-----------|-----------|
                0 |         4 |       296 |       300 | 
                  |       0.1 |       0.3 |           | 
------------------|-----------|-----------|-----------|
                1 |        33 |       667 |       700 | 
                  |       0.9 |       0.7 |           | 
------------------|-----------|-----------|-----------|
     Column Total |        37 |       963 |      1000 | 
                  |       0.0 |       1.0 |           | 
------------------|-----------|-----------|-----------|

 
Statistics for All Table Factors


Pearson's Chi-squared test 
------------------------------------------------------------
Chi^2 =  6.737     d.f. =  1     p =  0.009443 

Pearson's Chi-squared test with Yates' continuity correction 
------------------------------------------------------------
Chi^2 =  5.822     d.f. =  1     p =  0.01583

Thank you