R Markdown

This is an R Markdown document. Markdown is a simple formatting syntax for authoring HTML, PDF, and MS Word documents. For more details on using R Markdown see http://rmarkdown.rstudio.com.

When you click the Knit button a document will be generated that includes both content as well as the output of any embedded R code chunks within the document. You can embed an R code chunk like this:

library(readr)
credit <- read_csv("C:/Users/otuata4438/Downloads/credit.csv")
## Rows: 1000 Columns: 21
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (13): checking_balance, credit_history, purpose, savings_balance, employ...
## dbl  (8): months_loan_duration, amount, installment_rate, residence_history,...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
View(credit)

stringsAsFactors=TRUE 

options(repos = c(CRAN = "https://cloud.r-project.org"))

install.packages("C50")
## Installing package into 'C:/Users/otuata4438/AppData/Local/R/win-library/4.4'
## (as 'lib' is unspecified)
## package 'C50' successfully unpacked and MD5 sums checked
## 
## The downloaded binary packages are in
##  C:\Users\otuata4438\AppData\Local\Temp\Rtmpqsj7Hi\downloaded_packages
library(C50)
## Warning: package 'C50' was built under R version 4.4.3
options(repos = c(CRAN = "https://cloud.r-project.org"))

install.packages("gmodels")
## Installing package into 'C:/Users/otuata4438/AppData/Local/R/win-library/4.4'
## (as 'lib' is unspecified)
## package 'gmodels' successfully unpacked and MD5 sums checked
## 
## The downloaded binary packages are in
##  C:\Users\otuata4438\AppData\Local\Temp\Rtmpqsj7Hi\downloaded_packages
library(gmodels)
## Warning: package 'gmodels' was built under R version 4.4.3
set.seed(12345)
credit_rand <- credit[order(runif(1000)), ]

summary(credit)
##  checking_balance   months_loan_duration credit_history       purpose         
##  Length:1000        Min.   : 4.0         Length:1000        Length:1000       
##  Class :character   1st Qu.:12.0         Class :character   Class :character  
##  Mode  :character   Median :18.0         Mode  :character   Mode  :character  
##                     Mean   :20.9                                              
##                     3rd Qu.:24.0                                              
##                     Max.   :72.0                                              
##      amount      savings_balance    employment_length  installment_rate
##  Min.   :  250   Length:1000        Length:1000        Min.   :1.000   
##  1st Qu.: 1366   Class :character   Class :character   1st Qu.:2.000   
##  Median : 2320   Mode  :character   Mode  :character   Median :3.000   
##  Mean   : 3271                                         Mean   :2.973   
##  3rd Qu.: 3972                                         3rd Qu.:4.000   
##  Max.   :18424                                         Max.   :4.000   
##  personal_status    other_debtors      residence_history   property        
##  Length:1000        Length:1000        Min.   :1.000     Length:1000       
##  Class :character   Class :character   1st Qu.:2.000     Class :character  
##  Mode  :character   Mode  :character   Median :3.000     Mode  :character  
##                                        Mean   :2.845                       
##                                        3rd Qu.:4.000                       
##                                        Max.   :4.000                       
##       age        installment_plan     housing          existing_credits
##  Min.   :19.00   Length:1000        Length:1000        Min.   :1.000   
##  1st Qu.:27.00   Class :character   Class :character   1st Qu.:1.000   
##  Median :33.00   Mode  :character   Mode  :character   Median :1.000   
##  Mean   :35.55                                         Mean   :1.407   
##  3rd Qu.:42.00                                         3rd Qu.:2.000   
##  Max.   :75.00                                         Max.   :4.000   
##     default      dependents     telephone         foreign_worker    
##  Min.   :1.0   Min.   :1.000   Length:1000        Length:1000       
##  1st Qu.:1.0   1st Qu.:1.000   Class :character   Class :character  
##  Median :1.0   Median :1.000   Mode  :character   Mode  :character  
##  Mean   :1.3   Mean   :1.155                                        
##  3rd Qu.:2.0   3rd Qu.:1.000                                        
##  Max.   :2.0   Max.   :2.000                                        
##      job           
##  Length:1000       
##  Class :character  
##  Mode  :character  
##                    
##                    
## 
summary(credit_rand)
##  checking_balance   months_loan_duration credit_history       purpose         
##  Length:1000        Min.   : 4.0         Length:1000        Length:1000       
##  Class :character   1st Qu.:12.0         Class :character   Class :character  
##  Mode  :character   Median :18.0         Mode  :character   Mode  :character  
##                     Mean   :20.9                                              
##                     3rd Qu.:24.0                                              
##                     Max.   :72.0                                              
##      amount      savings_balance    employment_length  installment_rate
##  Min.   :  250   Length:1000        Length:1000        Min.   :1.000   
##  1st Qu.: 1366   Class :character   Class :character   1st Qu.:2.000   
##  Median : 2320   Mode  :character   Mode  :character   Median :3.000   
##  Mean   : 3271                                         Mean   :2.973   
##  3rd Qu.: 3972                                         3rd Qu.:4.000   
##  Max.   :18424                                         Max.   :4.000   
##  personal_status    other_debtors      residence_history   property        
##  Length:1000        Length:1000        Min.   :1.000     Length:1000       
##  Class :character   Class :character   1st Qu.:2.000     Class :character  
##  Mode  :character   Mode  :character   Median :3.000     Mode  :character  
##                                        Mean   :2.845                       
##                                        3rd Qu.:4.000                       
##                                        Max.   :4.000                       
##       age        installment_plan     housing          existing_credits
##  Min.   :19.00   Length:1000        Length:1000        Min.   :1.000   
##  1st Qu.:27.00   Class :character   Class :character   1st Qu.:1.000   
##  Median :33.00   Mode  :character   Mode  :character   Median :1.000   
##  Mean   :35.55                                         Mean   :1.407   
##  3rd Qu.:42.00                                         3rd Qu.:2.000   
##  Max.   :75.00                                         Max.   :4.000   
##     default      dependents     telephone         foreign_worker    
##  Min.   :1.0   Min.   :1.000   Length:1000        Length:1000       
##  1st Qu.:1.0   1st Qu.:1.000   Class :character   Class :character  
##  Median :1.0   Median :1.000   Mode  :character   Mode  :character  
##  Mean   :1.3   Mean   :1.155                                        
##  3rd Qu.:2.0   3rd Qu.:1.000                                        
##  Max.   :2.0   Max.   :2.000                                        
##      job           
##  Length:1000       
##  Class :character  
##  Mode  :character  
##                    
##                    
## 
head(credit$amount, 10)
##  [1] 1169 5951 2096 7882 4870 9055 2835 6948 3059 5234
head(credit_rand$amount, 10)
##  [1] 1199 2576 1103 4020 1501 1568 4281  918 2629 1845
credit_train <- credit_rand[1:900, ]
credit_test <- credit_rand[901:1000, ]

prop.table(table(credit_train$default))
## 
##         1         2 
## 0.7022222 0.2977778
prop.table(table(credit_test$default))
## 
##    1    2 
## 0.68 0.32
credit_train$default <- as.factor(credit_train$default)
credit_test$default <- as.factor(credit_test$default)

credit_model <- C5.0(credit_train[-17], credit_train$default)
summary(credit_model)
## 
## Call:
## C5.0.default(x = credit_train[-17], y = credit_train$default)
## 
## 
## C5.0 [Release 2.07 GPL Edition]      Thu Apr 10 14:16:24 2025
## -------------------------------
## 
## Class specified by attribute `outcome'
## 
## Read 900 cases (21 attributes) from undefined.data
## 
## Decision tree:
## 
## checking_balance = unknown: 1 (358/44)
## checking_balance in {< 0 DM,1 - 200 DM,> 200 DM}:
## :...foreign_worker = no:
##     :...installment_plan in {none,stores}: 1 (17/1)
##     :   installment_plan = bank:
##     :   :...residence_history <= 3: 2 (2)
##     :       residence_history > 3: 1 (2)
##     foreign_worker = yes:
##     :...credit_history in {fully repaid,fully repaid this bank}: 2 (61/20)
##         credit_history in {critical,repaid,delayed}:
##         :...months_loan_duration <= 11: 1 (76/13)
##             months_loan_duration > 11:
##             :...savings_balance = > 1000 DM: 1 (13)
##                 savings_balance in {< 100 DM,101 - 500 DM,501 - 1000 DM,
##                 :                   unknown}:
##                 :...checking_balance = > 200 DM:
##                     :...dependents > 1: 2 (3)
##                     :   dependents <= 1:
##                     :   :...credit_history in {repaid,delayed}: 1 (23/3)
##                     :       credit_history = critical:
##                     :       :...amount <= 2337: 2 (3)
##                     :           amount > 2337: 1 (6)
##                     checking_balance = < 0 DM:
##                     :...other_debtors = guarantor:
##                     :   :...credit_history = critical: 2 (1)
##                     :   :   credit_history in {repaid,delayed}: 1 (11/1)
##                     :   other_debtors in {none,co-applicant}:
##                     :   :...job = mangement self-employed: 1 (26/6)
##                     :       job in {unskilled resident,skilled employee,
##                     :       :       unemployed non-resident}:
##                     :       :...purpose in {radio/tv,others,repairs,
##                     :           :           domestic appliances,
##                     :           :           retraining}: 2 (33/10)
##                     :           purpose = education: [S1]
##                     :           purpose = business:
##                     :           :...job in {unskilled resident,
##                     :           :   :       unemployed non-resident}: 1 (3)
##                     :           :   job = skilled employee: 2 (3)
##                     :           purpose = car (new): [S2]
##                     :           purpose = car (used):
##                     :           :...amount > 6229: 2 (5)
##                     :           :   amount <= 6229: [S3]
##                     :           purpose = furniture:
##                     :           :...months_loan_duration > 27: 2 (9/1)
##                     :               months_loan_duration <= 27: [S4]
##                     checking_balance = 1 - 200 DM:
##                     :...savings_balance = unknown: 1 (34/6)
##                         savings_balance in {< 100 DM,101 - 500 DM,
##                         :                   501 - 1000 DM}:
##                         :...months_loan_duration > 45: 2 (11/1)
##                             months_loan_duration <= 45:
##                             :...installment_plan = stores:
##                                 :...age <= 35: 2 (4)
##                                 :   age > 35: 1 (2)
##                                 installment_plan = bank:
##                                 :...residence_history <= 1: 1 (3)
##                                 :   residence_history > 1:
##                                 :   :...existing_credits <= 1: 2 (5)
##                                 :       existing_credits > 1:
##                                 :       :...installment_rate > 2: 2 (3)
##                                 :           installment_rate <= 2: [S5]
##                                 installment_plan = none:
##                                 :...other_debtors = guarantor: 1 (7/1)
##                                     other_debtors = co-applicant: 2 (3/1)
##                                     other_debtors = none:
##                                     :...employment_length = 4 - 7 yrs:
##                                         :...age <= 41: 1 (16)
##                                         :   age > 41: 2 (3/1)
##                                         employment_length in {> 7 yrs,
##                                         :                     1 - 4 yrs,
##                                         :                     0 - 1 yrs,
##                                         :                     unemployed}:
##                                         :...amount > 7980: 2 (7)
##                                             amount <= 7980:
##                                             :...amount > 4746: 1 (10)
##                                                 amount <= 4746: [S6]
## 
## SubTree [S1]
## 
## savings_balance in {< 100 DM,101 - 500 DM,501 - 1000 DM}: 2 (6)
## savings_balance = unknown: 1 (2)
## 
## SubTree [S2]
## 
## savings_balance = 101 - 500 DM: 1 (1)
## savings_balance in {501 - 1000 DM,unknown}: 2 (4)
## savings_balance = < 100 DM:
## :...personal_status in {single male,female,divorced male}: 2 (29/6)
##     personal_status = married male: 1 (2)
## 
## SubTree [S3]
## 
## job = unskilled resident: 2 (1)
## job in {skilled employee,unemployed non-resident}: 1 (8/1)
## 
## SubTree [S4]
## 
## employment_length in {> 7 yrs,4 - 7 yrs}: 1 (7/1)
## employment_length = unemployed: 2 (2)
## employment_length = 0 - 1 yrs:
## :...job = unskilled resident: 2 (1)
## :   job in {skilled employee,unemployed non-resident}: 1 (4)
## employment_length = 1 - 4 yrs:
## :...property in {building society savings,unknown/none}: 1 (5)
##     property in {other,real estate}:
##     :...residence_history <= 2: 1 (4/1)
##         residence_history > 2: 2 (5)
## 
## SubTree [S5]
## 
## other_debtors in {none,guarantor}: 1 (3)
## other_debtors = co-applicant: 2 (1)
## 
## SubTree [S6]
## 
## housing = for free: 1 (2)
## housing = rent:
## :...credit_history = critical: 1 (1)
## :   credit_history in {repaid,delayed}: 2 (10/2)
## housing = own:
## :...savings_balance = 101 - 500 DM: 1 (6)
##     savings_balance in {< 100 DM,501 - 1000 DM}:
##     :...residence_history <= 1: 1 (8/1)
##         residence_history > 1:
##         :...installment_rate <= 1: 1 (2)
##             installment_rate > 1:
##             :...employment_length in {> 7 yrs,unemployed}: 1 (13/6)
##                 employment_length in {1 - 4 yrs,0 - 1 yrs}: 2 (10)
## 
## 
## Evaluation on training data (900 cases):
## 
##      Decision Tree   
##    ----------------  
##    Size      Errors  
## 
##      57  127(14.1%)   <<
## 
## 
##     (a)   (b)    <-classified as
##    ----  ----
##     590    42    (a): class 1
##      85   183    (b): class 2
## 
## 
##  Attribute usage:
## 
##  100.00% checking_balance
##   60.22% foreign_worker
##   57.89% credit_history
##   51.11% months_loan_duration
##   42.67% savings_balance
##   30.44% other_debtors
##   17.78% job
##   15.56% installment_plan
##   14.89% purpose
##   12.89% employment_length
##   10.22% amount
##    6.78% residence_history
##    5.78% housing
##    3.89% dependents
##    3.56% installment_rate
##    3.44% personal_status
##    2.78% age
##    1.56% property
##    1.33% existing_credits
## 
## 
## Time: 0.0 secs
credit_pred <- predict(credit_model, credit_test[-17])

CrossTable(credit_test$default, credit_pred,
           prop.chisq = FALSE, prop.c = FALSE, prop.r = FALSE,
           dnn = c('Actual Default', 'Predicted Default'))
## 
##  
##    Cell Contents
## |-------------------------|
## |                       N |
## |         N / Table Total |
## |-------------------------|
## 
##  
## Total Observations in Table:  100 
## 
##  
##                | Predicted Default 
## Actual Default |         1 |         2 | Row Total | 
## ---------------|-----------|-----------|-----------|
##              1 |        54 |        14 |        68 | 
##                |     0.540 |     0.140 |           | 
## ---------------|-----------|-----------|-----------|
##              2 |        11 |        21 |        32 | 
##                |     0.110 |     0.210 |           | 
## ---------------|-----------|-----------|-----------|
##   Column Total |        65 |        35 |       100 | 
## ---------------|-----------|-----------|-----------|
## 
## 
credit_boost10 <- C5.0(credit_train[-17], credit_train$default, trials = 10)
credit_boost_pred10 <- predict(credit_boost10, credit_test[-17])

CrossTable(credit_test$default, credit_boost_pred10,
           prop.chisq = FALSE, prop.c = FALSE, prop.r = FALSE,
           dnn = c('Actual Default', 'Predicted Default'))
## 
##  
##    Cell Contents
## |-------------------------|
## |                       N |
## |         N / Table Total |
## |-------------------------|
## 
##  
## Total Observations in Table:  100 
## 
##  
##                | Predicted Default 
## Actual Default |         1 |         2 | Row Total | 
## ---------------|-----------|-----------|-----------|
##              1 |        63 |         5 |        68 | 
##                |     0.630 |     0.050 |           | 
## ---------------|-----------|-----------|-----------|
##              2 |        16 |        16 |        32 | 
##                |     0.160 |     0.160 |           | 
## ---------------|-----------|-----------|-----------|
##   Column Total |        79 |        21 |       100 | 
## ---------------|-----------|-----------|-----------|
## 
## 
matrix_dimensions <- list(c("no", "yes"), c("no", "yes"))
names(matrix_dimensions) <- c("predicted", "actual")

error_cost <- matrix(c(0, 1, 4, 0), nrow = 2)

credit_cost <- C5.0(credit_train[-17], credit_train$default, costs = error_cost)
## Warning: no dimnames were given for the cost matrix; the factor levels will be
## used
credit_cost_pred <- predict(credit_cost, credit_test[-17])

CrossTable(credit_test$default, credit_cost_pred,
           prop.chisq = FALSE, prop.c = FALSE, prop.r = FALSE,
           dnn = c('Actual Default', 'Predicted Default'))
## 
##  
##    Cell Contents
## |-------------------------|
## |                       N |
## |         N / Table Total |
## |-------------------------|
## 
##  
## Total Observations in Table:  100 
## 
##  
##                | Predicted Default 
## Actual Default |         1 |         2 | Row Total | 
## ---------------|-----------|-----------|-----------|
##              1 |        38 |        30 |        68 | 
##                |     0.380 |     0.300 |           | 
## ---------------|-----------|-----------|-----------|
##              2 |         5 |        27 |        32 | 
##                |     0.050 |     0.270 |           | 
## ---------------|-----------|-----------|-----------|
##   Column Total |        43 |        57 |       100 | 
## ---------------|-----------|-----------|-----------|
## 
## 
matrix_dimensions <- list(c("no", "yes"), c("no", "yes"))
names(matrix_dimensions) <- c("predicted", "actual")

error_cost <- matrix(c(0, 1, 4, 0), nrow = 2)

credit_cost <- C5.0(credit_train[-17], credit_train$default, costs = error_cost)
## Warning: no dimnames were given for the cost matrix; the factor levels will be
## used
credit_cost_pred <- predict(credit_cost, credit_test[-17])

CrossTable(credit_test$default, credit_cost_pred,
           prop.chisq = FALSE, prop.c = FALSE, prop.r = FALSE,
           dnn = c('Actual Default', 'Predicted Default'))
## 
##  
##    Cell Contents
## |-------------------------|
## |                       N |
## |         N / Table Total |
## |-------------------------|
## 
##  
## Total Observations in Table:  100 
## 
##  
##                | Predicted Default 
## Actual Default |         1 |         2 | Row Total | 
## ---------------|-----------|-----------|-----------|
##              1 |        38 |        30 |        68 | 
##                |     0.380 |     0.300 |           | 
## ---------------|-----------|-----------|-----------|
##              2 |         5 |        27 |        32 | 
##                |     0.050 |     0.270 |           | 
## ---------------|-----------|-----------|-----------|
##   Column Total |        43 |        57 |       100 | 
## ---------------|-----------|-----------|-----------|
## 
## 
library(partykit )
## Warning: package 'partykit' was built under R version 4.4.3
## Loading required package: grid
## Loading required package: libcoin
## Warning: package 'libcoin' was built under R version 4.4.3
## Loading required package: mvtnorm
## Warning: package 'mvtnorm' was built under R version 4.4.3
plot(credit_model)
## Warning in partysplit(varid = as.integer(i), breaks = as.numeric(j[1]), : NAs
## introduced by coercion
## Warning in partysplit(varid = as.integer(i), breaks = as.numeric(j[1]), : NAs
## introduced by coercion
## Warning in partysplit(varid = as.integer(i), breaks = as.numeric(j[1]), : NAs
## introduced by coercion
## Warning in partysplit(varid = as.integer(i), breaks = as.numeric(j[1]), : NAs
## introduced by coercion
## Warning in partysplit(varid = as.integer(i), breaks = as.numeric(j[1]), : NAs
## introduced by coercion
## Warning in partysplit(varid = as.integer(i), breaks = as.numeric(j[1]), : NAs
## introduced by coercion
## Warning in partysplit(varid = as.integer(i), breaks = as.numeric(j[1]), : NAs
## introduced by coercion
## Warning in partysplit(varid = as.integer(i), breaks = as.numeric(j[1]), : NAs
## introduced by coercion
## Warning in partysplit(varid = as.integer(i), breaks = as.numeric(j[1]), : NAs
## introduced by coercion
## Warning in partysplit(varid = as.integer(i), breaks = as.numeric(j[1]), : NAs
## introduced by coercion
## Warning in partysplit(varid = as.integer(i), breaks = as.numeric(j[1]), : NAs
## introduced by coercion
## Warning in partysplit(varid = as.integer(i), breaks = as.numeric(j[1]), : NAs
## introduced by coercion
## Warning in partysplit(varid = as.integer(i), breaks = as.numeric(j[1]), : NAs
## introduced by coercion
## Warning in partysplit(varid = as.integer(i), breaks = as.numeric(j[1]), : NAs
## introduced by coercion
## Warning in partysplit(varid = as.integer(i), breaks = as.numeric(j[1]), : NAs
## introduced by coercion
## Warning in partysplit(varid = as.integer(i), breaks = as.numeric(j[1]), : NAs
## introduced by coercion
## Warning in partysplit(varid = as.integer(i), breaks = as.numeric(j[1]), : NAs
## introduced by coercion
## Warning in partysplit(varid = as.integer(i), breaks = as.numeric(j[1]), : NAs
## introduced by coercion
## Warning in partysplit(varid = as.integer(i), breaks = as.numeric(j[1]), : NAs
## introduced by coercion
## Warning in partysplit(varid = as.integer(i), breaks = as.numeric(j[1]), : NAs
## introduced by coercion
## Warning in partysplit(varid = as.integer(i), breaks = as.numeric(j[1]), : NAs
## introduced by coercion
## Warning in partysplit(varid = as.integer(i), breaks = as.numeric(j[1]), : NAs
## introduced by coercion
## Warning in partysplit(varid = as.integer(i), breaks = as.numeric(j[1]), : NAs
## introduced by coercion
## Warning in partysplit(varid = as.integer(i), breaks = as.numeric(j[1]), : NAs
## introduced by coercion
## Warning in partysplit(varid = as.integer(i), breaks = as.numeric(j[1]), : NAs
## introduced by coercion
## Warning in .bincode(as.numeric(x), breaks = unique(c(-Inf, breaks_split(split),
## : NAs introduced by coercion
## Warning in .bincode(as.numeric(x), breaks = unique(c(-Inf, breaks_split(split),
## : NAs introduced by coercion
## Warning in .bincode(as.numeric(x), breaks = unique(c(-Inf, breaks_split(split),
## : NAs introduced by coercion
## Warning in .bincode(as.numeric(x), breaks = unique(c(-Inf, breaks_split(split),
## : NAs introduced by coercion
## Warning in .bincode(as.numeric(x), breaks = unique(c(-Inf, breaks_split(split),
## : NAs introduced by coercion
## Warning in .bincode(as.numeric(x), breaks = unique(c(-Inf, breaks_split(split),
## : NAs introduced by coercion
## Warning in .bincode(as.numeric(x), breaks = unique(c(-Inf, breaks_split(split),
## : NAs introduced by coercion
## Warning in .bincode(as.numeric(x), breaks = unique(c(-Inf, breaks_split(split),
## : NAs introduced by coercion
## Warning in .bincode(as.numeric(x), breaks = unique(c(-Inf, breaks_split(split),
## : NAs introduced by coercion
## Warning in .bincode(as.numeric(x), breaks = unique(c(-Inf, breaks_split(split),
## : NAs introduced by coercion
## Warning in .bincode(as.numeric(x), breaks = unique(c(-Inf, breaks_split(split),
## : NAs introduced by coercion

Including Plots

You can also embed plots, for example:

Note that the echo = FALSE parameter was added to the code chunk to prevent printing of the R code that generated the plot.