Telemarketing Bank
Yohanes Partogi
2022-11-07
1 Intro
1.1 Telemarketing Bank Portugal
The data is related with direct marketing campaigns of a Portuguese banking institution. The marketing campaigns were based on phone calls. Often, more than one contact to the same client was required, in order to access if the product (bank term deposit) would be (‘yes’) or not (‘no’) subscribed.
1.2 Brief
library(dplyr)
library(tidyverse)
library(ggplot2)2 Get the Data
bank <- read.csv('bank-additional-full.csv', sep = ";")dim(bank)#> [1] 41188 21Our telemarketing dataset contains 41188 rows and 21 coloumns.
head(bank)#> age job marital education default housing loan contact month
#> 1 56 housemaid married basic.4y no no no telephone may
#> 2 57 services married high.school unknown no no telephone may
#> 3 37 services married high.school no yes no telephone may
#> 4 40 admin. married basic.6y no no no telephone may
#> 5 56 services married high.school no no yes telephone may
#> 6 45 services married basic.9y unknown no no telephone may
#> day_of_week duration campaign pdays previous poutcome emp.var.rate
#> 1 mon 261 1 999 0 nonexistent 1.1
#> 2 mon 149 1 999 0 nonexistent 1.1
#> 3 mon 226 1 999 0 nonexistent 1.1
#> 4 mon 151 1 999 0 nonexistent 1.1
#> 5 mon 307 1 999 0 nonexistent 1.1
#> 6 mon 198 1 999 0 nonexistent 1.1
#> cons.price.idx cons.conf.idx euribor3m nr.employed y
#> 1 93.994 -36.4 4.857 5191 no
#> 2 93.994 -36.4 4.857 5191 no
#> 3 93.994 -36.4 4.857 5191 no
#> 4 93.994 -36.4 4.857 5191 no
#> 5 93.994 -36.4 4.857 5191 no
#> 6 93.994 -36.4 4.857 5191 noColumn explanation
- age (numeric)
- job : type of job (categorical: ‘admin.’,‘blue-collar’,‘entrepreneur’,‘housemaid’,‘management’,‘retired’,‘self-employed’,‘services’,‘student’,‘technician’,‘unemployed’,‘unknown’)
- marital : marital status (categorical: ‘divorced’,‘married’,‘single’,‘unknown’; note: ‘divorced’ means divorced or widowed)
- education (categorical: ‘basic.4y’,‘basic.6y’,‘basic.9y’,‘high.school’,‘illiterate’,‘professional.course’,‘university.degree’,‘unknown’)
- default: has credit in default? (categorical: ‘no’,‘yes’,‘unknown’)
- housing: has housing loan? (categorical: ‘no’,‘yes’,‘unknown’)
- loan: has personal loan? (categorical: ‘no’,‘yes’,‘unknown’)
- contact: contact communication type (categorical: ‘cellular’,‘telephone’)
- month: last contact month of year (categorical: ‘jan’, ‘feb’, ‘mar’, …, ‘nov’, ‘dec’)
- day_of_week: last contact day of the week (categorical: ‘mon’,‘tue’,‘wed’,‘thu’,‘fri’)
- duration: last contact duration, in seconds (numeric). Important note: this attribute highly affects the output target (e.g., if duration=0 then y=‘no’).
- campaign: number of contacts performed during this campaign and for this client (numeric, includes last contact)
- pdays: number of days that passed by after the client was last contacted from a previous campaign (numeric; 999 means client was not previously contacted)
- previous: number of contacts performed before this campaign and for this client (numeric)
- poutcome: outcome of the previous marketing campaign (categorical: ‘failure’,‘nonexistent’,‘success’)
- emp.var.rate: employment variation rate - quarterly indicator (numeric)
- cons.price.idx: consumer price index - monthly indicator (numeric)
- cons.conf.idx: consumer confidence index - monthly indicator (numeric)
- euribor3m: euribor 3 month rate - daily indicator (numeric)
- nr.employed: number of employees - quarterly indicator (numeric)
3 Data Cleansing
3.1 Check the Data Type
str(bank)#> 'data.frame': 41188 obs. of 21 variables:
#> $ age : int 56 57 37 40 56 45 59 41 24 25 ...
#> $ job : chr "housemaid" "services" "services" "admin." ...
#> $ marital : chr "married" "married" "married" "married" ...
#> $ education : chr "basic.4y" "high.school" "high.school" "basic.6y" ...
#> $ default : chr "no" "unknown" "no" "no" ...
#> $ housing : chr "no" "no" "yes" "no" ...
#> $ loan : chr "no" "no" "no" "no" ...
#> $ contact : chr "telephone" "telephone" "telephone" "telephone" ...
#> $ month : chr "may" "may" "may" "may" ...
#> $ day_of_week : chr "mon" "mon" "mon" "mon" ...
#> $ duration : int 261 149 226 151 307 198 139 217 380 50 ...
#> $ campaign : int 1 1 1 1 1 1 1 1 1 1 ...
#> $ pdays : int 999 999 999 999 999 999 999 999 999 999 ...
#> $ previous : int 0 0 0 0 0 0 0 0 0 0 ...
#> $ poutcome : chr "nonexistent" "nonexistent" "nonexistent" "nonexistent" ...
#> $ emp.var.rate : num 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 ...
#> $ cons.price.idx: num 94 94 94 94 94 ...
#> $ cons.conf.idx : num -36.4 -36.4 -36.4 -36.4 -36.4 -36.4 -36.4 -36.4 -36.4 -36.4 ...
#> $ euribor3m : num 4.86 4.86 4.86 4.86 4.86 ...
#> $ nr.employed : num 5191 5191 5191 5191 5191 ...
#> $ y : chr "no" "no" "no" "no" ...From the result above, we find some of data type not in the corect type. We need to convert it into corect type.
bank <- bank %>%
mutate_if(is.character, as.factor)
str(bank)#> 'data.frame': 41188 obs. of 21 variables:
#> $ age : int 56 57 37 40 56 45 59 41 24 25 ...
#> $ job : Factor w/ 12 levels "admin.","blue-collar",..: 4 8 8 1 8 8 1 2 10 8 ...
#> $ marital : Factor w/ 4 levels "divorced","married",..: 2 2 2 2 2 2 2 2 3 3 ...
#> $ education : Factor w/ 8 levels "basic.4y","basic.6y",..: 1 4 4 2 4 3 6 8 6 4 ...
#> $ default : Factor w/ 3 levels "no","unknown",..: 1 2 1 1 1 2 1 2 1 1 ...
#> $ housing : Factor w/ 3 levels "no","unknown",..: 1 1 3 1 1 1 1 1 3 3 ...
#> $ loan : Factor w/ 3 levels "no","unknown",..: 1 1 1 1 3 1 1 1 1 1 ...
#> $ contact : Factor w/ 2 levels "cellular","telephone": 2 2 2 2 2 2 2 2 2 2 ...
#> $ month : Factor w/ 10 levels "apr","aug","dec",..: 7 7 7 7 7 7 7 7 7 7 ...
#> $ day_of_week : Factor w/ 5 levels "fri","mon","thu",..: 2 2 2 2 2 2 2 2 2 2 ...
#> $ duration : int 261 149 226 151 307 198 139 217 380 50 ...
#> $ campaign : int 1 1 1 1 1 1 1 1 1 1 ...
#> $ pdays : int 999 999 999 999 999 999 999 999 999 999 ...
#> $ previous : int 0 0 0 0 0 0 0 0 0 0 ...
#> $ poutcome : Factor w/ 3 levels "failure","nonexistent",..: 2 2 2 2 2 2 2 2 2 2 ...
#> $ emp.var.rate : num 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.1 ...
#> $ cons.price.idx: num 94 94 94 94 94 ...
#> $ cons.conf.idx : num -36.4 -36.4 -36.4 -36.4 -36.4 -36.4 -36.4 -36.4 -36.4 -36.4 ...
#> $ euribor3m : num 4.86 4.86 4.86 4.86 4.86 ...
#> $ nr.employed : num 5191 5191 5191 5191 5191 ...
#> $ y : Factor w/ 2 levels "no","yes": 1 1 1 1 1 1 1 1 1 1 ...Each of column already changed into desired data type.
3.2 Check Whether There is Missing Values
colSums(is.na(bank))#> age job marital education default
#> 0 0 0 0 0
#> housing loan contact month day_of_week
#> 0 0 0 0 0
#> duration campaign pdays previous poutcome
#> 0 0 0 0 0
#> emp.var.rate cons.price.idx cons.conf.idx euribor3m nr.employed
#> 0 0 0 0 0
#> y
#> 0There is no missing value on the data.
4 Plotting
4.1 Last Contact Duration by Marital
ggplot(bank, aes(marital, duration))+
geom_boxplot(aes(fill = marital))+
labs(title = "Duration Call by Marital",
x = "Marital",
y = "Duration")+
theme(plot.title = element_text(hjust=0.5))+
theme(legend.position = "None")
4.2 Average Duration in Each Month
duration_month <- bank %>%
group_by(month) %>%
summarise(avg = mean(duration))
duration_month$month <- factor(duration_month$month, levels = c("apr","may","jun","jul","aug", "sep", "oct", "nov", "dec"))
ggplot(duration_month, aes(x = as.factor(month), y = avg))+
geom_line(group = 1, color = "#16d9e8") +
geom_point() +
labs(x = 'Month',
y = NULL,
title = 'Average Duration') +
theme_minimal() +
theme(plot.title = element_text(hjust=0.5))
4.3 Job Frequency
job <- bank %>%
group_by(job) %>%
summarise(freq = n()) %>%
arrange(-freq)
ggplot(data = job, mapping = aes(x = freq,
y = reorder(job, freq))) +
geom_col(mapping = aes(fill = freq)) +
scale_fill_gradient(low = "#5e7496", high = "#16d9e8")+
labs(title = 'Most Job in Direct Marketing Campaigns',
x = 'Total',
y = NULL) +
theme_minimal() +
theme(plot.title = element_text(hjust=0.5))