Trends for Characteristics of Subscribing Customers in Response to Bank Marketing Campaigns

Introduction

With this data set, the goal is to be able to predict which customers will subscribe to a term deposit based upon specific qualities and characteristics of the customer and methods used by the campaign. The data set is made up of 45,211 observations and contains 17 variables. Among all of the variables, there were no missing values or null values found. The variables and their descriptions are listed below:

1 - age of customer(numeric)

2 - job : type of job (categorical: “admin.”,“unknown”,“unemployed”,“management”,“housemaid”,“entrepreneur”,“student”, “blue-collar”, “self-employed”, “retired”, “technician”, “services”)

3 - marital : marital status (categorical: “married”, “divorced”, “single”; note: “divorced” means divorced or widowed)

4 - education (categorical: “unknown”, “secondary”, “primary”, “tertiary”)

5 - default: has credit in default? (binary: “yes”, “no”)

6 - balance: average yearly balance, in euros (numeric)

7 - housing: has housing loan? (binary: “yes”, “no”)

8 - loan: has personal loan? (binary: “yes”, “no”) # related with the last contact of the current campaign:

9 - contact: contact communication type (categorical: “unknown”, “telephone”, “cellular”)

10 - day: last contact day of the month (numeric)

11 - month: last contact month of year (categorical: “jan”, “feb”, “mar”, …, “nov”, “dec”)

12 - duration: last contact duration, in seconds (numeric) # other attributes:

13 - campaign: number of contacts performed during this campaign and for this client (numeric, includes last contact)

14 - pdays: number of days that passed by after the client was last contacted from a previous campaign (numeric, -1 means client was not previously contacted)

15 - previous: number of contacts performed before this campaign and for this client (numeric)

16 - poutcome: outcome of the previous marketing campaign (categorical: “unknown”, “other”, “failure”, “success”)

Output variable:

17 – y - has the client subscribed a term deposit? (binary: “yes”, “no”)

Exploratory Data Analysis for Numeric Variables

After finding no missing values or null values, the variables distributions were individually looked at to find outliers. After creating boxplots for the numerical variables, outliers were found in age, balance, duration, campaign, pdays, and previous.

Boxplots for EDA

With each variable, the normality was checked by looking at a histogram and the p-values for normality tests. For the variable age, the histogram was skewed right due to the outliers, so a log transformation was computed to create a more normal distribution and minimize the outliers. The variable balance also had a right skew to its histogram, but as some of the values were negative or zero, the log transformation could not be used. Therefore, the cube root was taken of each value to get a more normal histogram and minimize outliers. After the cube root transformation, the distribution was still skewed right, but was improved from before. The duration variable was found to be right skewed as well, but after the log transformation was performed, the distribution came to look more normal and minimized its outliers as well.

Histograms for EDA

## 
## Winter 
##  45211

## 
##         admin.    blue-collar   entrepreneur      housemaid     management 
##           5171           9732           1487           1240           9458 
##        retired  self-employed       services        student     technician 
##           2264           1579           4154            938           7597 
##     unemployed        unknown 
##           1303            288

## 
##     1     2     3  4-63 
## 17544 12505  5521  9641

Histograms for Transformed Features

The campaign variable was more difficult because none of the transformations like the log, square root, and cube root transformations normalized the distribution. So, instead the variable was grouped into categories to get rid of the sparse values. Both variables pdays and previous were 80% made up of customers that were not previously contacted, so I grouped both variables with two groups, not contacted or contacted. This was done to get rid of the sparse groups as well.

## 
##     0    1+ 
## 36954  8257

## 
##    -1     0 
## 36954  8257

## 
##     1     2     3  4-63 
## 17544 12505  5521  9641

Next, pairwise comparison was performed with a pairwise scatterplot for each numeric variable, excluding the newly grouped variables campaign, previous, and pdays. The scatterplots all showed a similar trend with the red curve differing from the blue curve, showing only a small correlation between the compared numerical variables. A low correlation value for each comparison means they should all be included in further subsequential models and algorithms.

## Warning: Removed 31 rows containing non-finite values (`stat_density()`).

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 31 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 31 rows containing missing values

## Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
## Removed 31 rows containing missing values

## Warning: Removed 31 rows containing missing values (`geom_point()`).
## Removed 31 rows containing missing values (`geom_point()`).
## Removed 31 rows containing missing values (`geom_point()`).

Pairwise Plots

Exploratory Data Analysis for Categorical Variables

None of the categorical variables included any missing values or null values, so next, variables with many different categories were grouped to minimize the number of different levels. Both job and month had over 10 different categories, so month was grouped by season rather then month, and job was split by less specific category titles. One of the new category titles was called unknown/unemployed which included students, retired, unemployed, and unknown. Another was called Blue-collar/Services which included services, housemaid, and technician. The last category was called Business, which included admin., entreprenuer, management, and self-employed.

## 
## Winter 
##  45211

## 
##         admin.    blue-collar   entrepreneur      housemaid     management 
##           5171           9732           1487           1240           9458 
##        retired  self-employed       services        student     technician 
##           2264           1579           4154            938           7597 
##     unemployed        unknown 
##           1303            288

pairwise comparison was performed. Each variable was compared to the output variable y in a mosaic plot. The new grouped variables previous and pdays were included in this comparison due to their newly grouped values. Most of the plots showed that the outcome of whether the customer subscribed to the deposit was dependent on the different variables because the plots individual categories were not equal. The only variable that showed itself to be independent was the variable default, so this means this variable should not be involved in any subsequential models and algorithms.

Pairwise Comparison