Bank Data Project
Summary
summary(bank)## age age.group eligible job salary
## Min. :18.00 Min. :1.000 N: 1831 blue-collar:9732 Min. : 0
## 1st Qu.:33.00 1st Qu.:3.000 Y:43380 management :9458 1st Qu.: 20000
## Median :39.00 Median :3.000 technician :7597 Median : 60000
## Mean :40.94 Mean :3.646 admin. :5171 Mean : 57006
## 3rd Qu.:48.00 3rd Qu.:4.000 services :4154 3rd Qu.: 70000
## Max. :95.00 Max. :9.000 retired :2264 Max. :120000
## (Other) :6835
## marital education marital.education targeted
## divorced: 5207 primary : 6851 married-secondary :13770 no : 8120
## married :27214 secondary:23202 married-tertiary : 7038 yes:37091
## single :12790 tertiary :13301 single-secondary : 6617
## unknown : 1857 married-primary : 5246
## single-tertiary : 4792
## divorced-secondary: 2815
## (Other) : 4933
## default balance housing loan contact
## no :44396 Min. : -8019 no :20081 no :37967 cellular :29285
## yes: 815 1st Qu.: 72 yes:25130 yes: 7244 telephone: 2906
## Median : 448 unknown :13020
## Mean : 1362
## 3rd Qu.: 1428
## Max. :102127
##
## day month duration campaign
## Min. : 1.00 may :13766 Min. : 0.0 Min. : 1.000
## 1st Qu.: 8.00 jul : 6895 1st Qu.: 103.0 1st Qu.: 1.000
## Median :16.00 aug : 6247 Median : 180.0 Median : 2.000
## Mean :15.81 jun : 5341 Mean : 258.2 Mean : 2.764
## 3rd Qu.:21.00 nov : 3970 3rd Qu.: 319.0 3rd Qu.: 3.000
## Max. :31.00 apr : 2932 Max. :4918.0 Max. :63.000
## (Other): 6060
## pdays previous poutcome y response
## Min. : -1.0 Min. : 0.0000 failure: 4901 no :39922 Min. :0.000
## 1st Qu.: -1.0 1st Qu.: 0.0000 other : 1840 yes: 5289 1st Qu.:0.000
## Median : -1.0 Median : 0.0000 success: 1511 Median :0.000
## Mean : 40.2 Mean : 0.5803 unknown:36959 Mean :0.117
## 3rd Qu.: -1.0 3rd Qu.: 0.0000 3rd Qu.:0.000
## Max. :871.0 Max. :275.0000 Max. :1.000
## Structure
str(bank)## 'data.frame': 45211 obs. of 23 variables:
## $ age : int 58 44 33 47 33 35 28 42 58 43 ...
## $ age.group : int 5 4 3 4 3 3 2 4 5 4 ...
## $ eligible : Factor w/ 2 levels "N","Y": 2 2 2 2 2 2 2 2 2 2 ...
## $ job : Factor w/ 12 levels "admin.","blue-collar",..: 5 10 3 2 12 5 5 3 6 10 ...
## $ salary : int 100000 60000 120000 20000 0 100000 100000 120000 55000 60000 ...
## $ marital : Factor w/ 3 levels "divorced","married",..: 2 3 2 2 3 2 3 1 2 3 ...
## $ education : Factor w/ 4 levels "primary","secondary",..: 3 2 2 4 4 3 3 3 1 2 ...
## $ marital.education: Factor w/ 12 levels "divorced-primary",..: 7 10 6 8 12 7 11 3 5 10 ...
## $ targeted : Factor w/ 2 levels "no","yes": 2 2 2 1 1 2 1 1 2 2 ...
## $ default : Factor w/ 2 levels "no","yes": 1 1 1 1 1 1 1 2 1 1 ...
## $ balance : int 2143 29 2 1506 1 231 447 2 121 593 ...
## $ housing : Factor w/ 2 levels "no","yes": 2 2 2 2 1 2 2 2 2 2 ...
## $ loan : Factor w/ 2 levels "no","yes": 1 1 2 1 1 1 2 1 1 1 ...
## $ contact : Factor w/ 3 levels "cellular","telephone",..: 3 3 3 3 3 3 3 3 3 3 ...
## $ day : int 5 5 5 5 5 5 5 5 5 5 ...
## $ month : Factor w/ 12 levels "apr","aug","dec",..: 9 9 9 9 9 9 9 9 9 9 ...
## $ duration : int 261 151 76 92 198 139 217 380 50 55 ...
## $ campaign : int 1 1 1 1 1 1 1 1 1 1 ...
## $ pdays : int -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 ...
## $ previous : int 0 0 0 0 0 0 0 0 0 0 ...
## $ poutcome : Factor w/ 4 levels "failure","other",..: 4 4 4 4 4 4 4 4 4 4 ...
## $ y : Factor w/ 2 levels "no","yes": 1 1 1 1 1 1 1 1 1 1 ...
## $ response : int 0 0 0 0 0 0 0 0 0 0 ...Marital-Salary Plot
plot(bank$marital, bank$salary)
##Marital Plot
plot(bank$marital)
Job-Salary Plot
plot(bank$job, bank$salary)
Salary-Balance Plot
ggplot(bank, aes(x=salary, y= balance, col= as.factor(education)))+ geom_point()
Job Plot
