EDA with R - Bank Marketing

This dataset consists of direct marketing campaigns by a Portuguese banking institution using phone calls. The campaigns aimed to sell subscriptions to a bank term deposit (see variable y).

Source of dataset.

Management has asked to analysis following:

🗺️ Task 1 - Explore: What are the jobs of the people most likely to subscribe to a term deposit?
📊 Task 2 - Visualize: Create a plot to visualize the number of people subscribing to a term deposit by month.
🔎 Task 3 - Analyze: What impact does the number of contacts performed during the last campaign have on the likelihood that a customer subscribes to a term deposit?

Lastly, any additional information is welcomed and use of machine learning algorithms. The bank want to use more algorithms and management wants to know what is the best way to use them. Ideally, compare various machine learning tehcniques.

Background:

You work for a financial services firm. The past few campaigns have not gone as well as the firm would have hoped, and they are looking for ways to optimize their marketing efforts.

They have supplied you with data from a previous campaign and some additional metrics such as the consumer price index and consumer confidence index. They want to know whether you can predict the likelihood of subscribing to a term deposit. The manager would also like to know what factors are most likely to increase a customer's probability of subscribing.

You will need to prepare a report which include raw data, codes as well as used techniques. Later, the results will be cleaned for the nicer report to a broad audience.

Step 1. Import data

#Import dataset

library(tidyverse)

bank <- read_delim('data/bank-marketing.csv', delim=";", show_col_types = FALSE)

Data Dictionary

Column	Variable	Class
age	age of customer
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	highest degree of customer	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"
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
y	has the client subscribed a term deposit?	binary: "yes","no"

Step 1. Explore and investigate data

head(bank)

str(bank)

summary(bank)

nrow(bank)

dim(bank)

Exploring task 1 - What are the jobs of the people most likely to subscribe to a term deposit?

# Filter the data where the client has subscribed a term deposit
subscribed_data <- bank %>% filter(y == "yes")

# Check the count of each job category
table(subscribed_data$job)

Visualizing task 2 - Create a plot to visualize the number of people subscribing to a term deposit by month

# Create the bar plot
ggplot(data = subscribed_data, aes(x = month)) +
  geom_bar(fill = "steelblue") +
  labs(x = "Month", y = "Number of Subscriptions", title = "Subscriptions by Month")

Analyzing task 3 - What impact does the number of contacts performed during the last campaign have on the likelihood that a customer subscribes to a term deposit?

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