Auto Finannce Limeted
Sameer Mathur
Importing and Reviewing the Data
REVIEW OF THE DATA
List of Data Columns
DEFAULT
- Defaulter Flag
1: Customer has delayed paying at least once
0: Otherwise
- Defaulter Type
0: Never Delayed Paying (Good Customer)
1: At least one delay, but always paid before 90 days (OK Customer)
2: At least one delay and did not pay even after 90 days (Bad Customer)
DEMOGRAPHIC VARIABLES
Gender
AGE
Education
QUALHSC
QUAL_PG
Income
Monthly Income in Thousands (MTHINCTH)
Owns a Fridge (FRICODE)
Owns a Washing Machine (WASHCODE)
Profession
PROFBUS
No. of Dependents
NOOFDEPE
Region
LOAN-RELATED VARIABLES
Loan Tenor (TENORYR)
Fraction of Loan in Down Payment (DWNPMFR)
Gave Post-Dated Cheques (FULLPDC)
Branch
Importing Data
library(data.table)
# reading data
autoF.dt <- fread("AutoFinanaceData.csv")
attach(autoF.dt)
# dimension of the dataset
dim(autoF.dt)
[1] 28906 21
Data Structure
# structure of the data table
str(autoF.dt)
Classes 'data.table' and 'data.frame': 28906 obs. of 21 variables:
$ Agmt No : chr "AP18100057" "AP18100140" "AP18100198" "AP18100217" ...
$ ContractStatus: chr "Closed" "Closed" "Closed" "Closed" ...
$ StartDate : chr "19-01-01" "10-05-01" "05-08-01" "03-09-01" ...
$ AGE : int 26 28 32 31 36 33 41 47 43 27 ...
$ NOOFDEPE : int 2 2 2 0 2 2 2 0 0 0 ...
$ MTHINCTH : num 4.5 5.59 8.8 5 12 ...
$ SALDATFR : num 1 1 1 1 1 1 1 1 0.97 1 ...
$ TENORYR : num 1.5 2 1 1 1 2 1 2 1.5 2 ...
$ DWNPMFR : num 0.27 0.25 0.51 0.66 0.17 0.18 0.37 0.42 0.27 0.47 ...
$ PROFBUS : int 0 0 0 0 0 0 0 0 0 0 ...
$ QUALHSC : int 0 0 0 0 0 0 1 0 0 0 ...
$ QUAL_PG : int 0 0 0 0 0 0 0 0 0 0 ...
$ SEXCODE : int 1 1 1 1 1 1 1 1 1 1 ...
$ FULLPDC : int 1 1 1 1 1 0 0 1 1 1 ...
$ FRICODE : int 0 1 1 1 1 0 0 0 0 0 ...
$ WASHCODE : int 0 0 1 1 0 0 0 0 0 0 ...
$ Region : chr "AP2" "AP2" "AP2" "AP2" ...
$ Branch : chr "Vizag" "Vizag" "Vizag" "Vizag" ...
$ DefaulterFlag : int 0 0 0 0 0 0 0 0 0 0 ...
$ DefaulterType : int 0 0 0 0 0 0 0 0 0 0 ...
$ DATASET : chr "" "BUILD" "BUILD" "BUILD" ...
- attr(*, ".internal.selfref")=<externalptr>
Convert catgorical variables to `factor`
# convert 'PROFBUS' to a factor
autoF.dt[, PROFBUS := factor(PROFBUS)]
# convert 'QUALHSC' to a factor
autoF.dt[, QUALHSC := factor(QUALHSC)]
# convert 'QUAL_PG' to a factor
autoF.dt[, QUAL_PG := factor(QUAL_PG)]
# convert 'SEXCODE' to a factor
autoF.dt[, SEXCODE := factor(SEXCODE)]
# convert 'FULLPDC' to a factor
autoF.dt[, FULLPDC := factor(FULLPDC)]
# convert 'FRICODE' to a factor
autoF.dt[, FRICODE := factor(FRICODE)]
# convert 'WASHCODE' to a factor
autoF.dt[, WASHCODE := factor(WASHCODE)]
# convert 'DefaulterFlag' to a factor
autoF.dt[, DefaulterFlag := factor(DefaulterFlag)]
# convert 'DefaulterType' to a factor
autoF.dt[, DefaulterType := factor(DefaulterType)]
# convert 'Region' to a factor
autoF.dt[, Region := factor(Region)]
# convert 'Branch' to a factor
autoF.dt[, Branch := factor(Branch)]
# verify conversion
str(autoF.dt)
Classes 'data.table' and 'data.frame': 28906 obs. of 21 variables:
$ Agmt No : chr "AP18100057" "AP18100140" "AP18100198" "AP18100217" ...
$ ContractStatus: chr "Closed" "Closed" "Closed" "Closed" ...
$ StartDate : chr "19-01-01" "10-05-01" "05-08-01" "03-09-01" ...
$ AGE : int 26 28 32 31 36 33 41 47 43 27 ...
$ NOOFDEPE : int 2 2 2 0 2 2 2 0 0 0 ...
$ MTHINCTH : num 4.5 5.59 8.8 5 12 ...
$ SALDATFR : num 1 1 1 1 1 1 1 1 0.97 1 ...
$ TENORYR : num 1.5 2 1 1 1 2 1 2 1.5 2 ...
$ DWNPMFR : num 0.27 0.25 0.51 0.66 0.17 0.18 0.37 0.42 0.27 0.47 ...
$ PROFBUS : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
$ QUALHSC : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 2 1 1 1 ...
$ QUAL_PG : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
$ SEXCODE : Factor w/ 2 levels "0","1": 2 2 2 2 2 2 2 2 2 2 ...
$ FULLPDC : Factor w/ 2 levels "0","1": 2 2 2 2 2 1 1 2 2 2 ...
$ FRICODE : Factor w/ 2 levels "0","1": 1 2 2 2 2 1 1 1 1 1 ...
$ WASHCODE : Factor w/ 2 levels "0","1": 1 1 2 2 1 1 1 1 1 1 ...
$ Region : Factor w/ 8 levels "AP1","AP2","Chennai",..: 2 2 2 2 2 2 2 2 2 2 ...
$ Branch : Factor w/ 14 levels "Bangalore","Chennai",..: 14 14 14 14 14 14 14 14 14 14 ...
$ DefaulterFlag : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
$ DefaulterType : Factor w/ 3 levels "0","1","2": 1 1 1 1 1 1 1 1 1 1 ...
$ DATASET : chr "" "BUILD" "BUILD" "BUILD" ...
- attr(*, ".internal.selfref")=<externalptr>
DEFAULT
Defaulters (based on DefaulterFlag)
DefaulterFlag
0 1
28.82 71.18
Pie Chart
Defaulters (based on DefaulterType)
DefaulterType
0 1 2
28.82 57.65 13.53
Pie Chart
DEMOGRAPHIC VARIABLES
Defaulters based on Gender
SEXCODE
DefaulterFlag 0 1
0 9.17 90.83
1 7.06 92.94
SEXCODE
DefaulterFlag 0 1
0 34.48 28.35
1 65.52 71.65
Sum 100.00 100.00
SEXCODE = 1 (Male) SEXCODE = 0 (Female)
Barplot
Defaulters based on their Profession
PROFBUS
DefaulterFlag 0 1
0 84.59 15.41
1 85.39 14.61
PROFBUS
DefaulterFlag 0 1
0 28.63 29.93
1 71.37 70.07
Sum 100.00 100.00
PROFBUS = 1 (BUSINESS)
PROFBUS = 0 (PROFESSIONAL)
Barplot
Education
Defaulters based on their Education (HSC)
QUALHSC
DefaulterFlag 0 1
0 79.05 20.95
1 75.92 24.08
Barplot
Defaulters based on their Education (QUAL_PG)
QUAL_PG
DefaulterFlag 0 1
0 94.48 5.52
1 96.56 3.44
Income
Defaulters based on Monthly Income
DefaulterFlag AvgIncome
1: 0 9.5
2: 1 8.7
Defaulters based on whether they gave post-dated cheques FULLPDC
FULLPDC
DefaulterFlag 0 1
0 38.40 61.60
1 70.03 29.97
Defaulters based on whether they own a Fridge
FRICODE
DefaulterFlag 0 1
0 49.51 50.49
1 61.31 38.69
Defaulters based on whether they own a Washing Machine
WASHCODE
DefaulterFlag 0 1
0 74.78 25.22
1 83.52 16.48