Shopping Mall Data

Name: Venetia Polyzou

mydata <- read.table("./Shopping Mall Customer Segmentation Data2 .csv",
                     header = TRUE,
                     sep = ",",
                     dec = ",")

head(mydata)

##                            Customer.ID Age Gender Annual.Income Spending.Score
## 1 d410ea53-6661-42a9-ad3a-f554b05fd2a7  30   Male        151479             89
## 2 1770b26f-493f-46b6-837f-4237fb5a314e  58 Female        185088             95
## 3 e81aa8eb-1767-4b77-87ce-1620dc732c5e  62 Female         70912             76
## 4 9795712a-ad19-47bf-8886-4f997d6046e3  23   Male         55460             57
## 5 64139426-2226-4cd6-bf09-91bce4b4db5e  24   Male        153752             76
## 6 7e211337-e92f-4140-8231-5c9ac7a2aa12  42   Male        158335             40

General:

• Unit of observation:an individual customer at a shopping mall
• Sample size:200 observations(customers)

Variables:

1.ID:

• A unique identifier for each customer
• Type:Categorical-nominal

2.Age:

• The age of the costumers.(in years)
• Type:Numeric-ratio

3.Gender:

• Gender of the costumer, categories:male/female
• Type:Categorical-nominal

4.Annual Income:

• Annual Income(money) of the costumer in United States Dollar(USD)
• Type:Numeric-ratio

5.Spending Score:

• A score assigned by the mall (typically from 1 to 100) based on customer behavior and spending patterns.
• Type:numerical-ratio

Source of the data: kaggle.com (https://www.kaggle.com/datasets/zubairmustafa/shopping-mall-customer-segmentation-data)

mydata$GenderF <- factor(mydata$Gender, 
                         levels = c("Male", "Female"),
                         labels = c("Male", "Female"))

library(dplyr)

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

library(tidyr)
mydata <- mydata %>%
  rename(annual.income = Annual.Income) %>%
  drop_na()

mydataF <- mydata[mydata$annual.income > 150000 , ]

library(dplyr)
mydata2F <- mydata %>%
  filter(Gender == "Male" )

mydata3 <- mydata[mydata$Spending.Score >= 60 & mydata$Spending.Score <= 80 , ]

summary(mydata[ ,c(-1,-3,-6)])

##       Age        annual.income    Spending.Score  
##  Min.   :18.00   Min.   : 22655   Min.   :  1.00  
##  1st Qu.:32.50   1st Qu.: 69202   1st Qu.: 27.00  
##  Median :48.00   Median :111526   Median : 45.00  
##  Mean   :49.57   Mean   :112493   Mean   : 48.38  
##  3rd Qu.:65.00   3rd Qu.:157317   3rd Qu.: 73.50  
##  Max.   :90.00   Max.   :199879   Max.   :100.00

• The mean of the age is 49.57, which means that the average age of a consumer of the shopping mall is 49.57 years old. Also, the minimum is 18.00, which means that the minimum age of a consumer(the youngest) is 18 years old.
• The median annual income is 111526, which means that half of the consumers of the shopping mall have an annual income of 111526 or less and the other half have more.
• The 3rd Quartile is 73.50, which means that 75% have a spending score of 73.5 or lower(so they are spending at a moderate to lower level) and only 25% of the costumers have a spending score greater than 73.5(the top 25% of costumers are big spenders with scores above 73.5).
• The maximum of the annual income is 199879, which means that the wealthiest costumer of the dataset of this shopping mall earns 199879 per year.

library(ggplot2)
ggplot(mydata, aes(x = Age)) +
  geom_histogram(binwidth = 4, fill = "skyblue", color = "black") 

• The tallest bars are at the lower age (18-25), indicating many costumers are young.After about age of 30, the bars start decreasing in height, meaning we have fewer older costumers. The distribution could be described as slightly right-skewed.So, the majority of mall consumers seem to be younger adults.

ggplot(mydata, aes(x = annual.income, y = Spending.Score)) +
  geom_point(color = "purple") +
  labs(title = "Annual Income vs Spending Score", x = "Annual Income", y = "Spending Score")

• The points are scattered widely across the plot.High earners (e.g., incomes above 100000) are not constantly high spenders and similarly, low earners (e.g., incomes below 50000) are spread across both low and high spending scores. The mall has different costumers types. Some customers spend a lot even with average or lower incomes and some high-income costumers are conservative with spending(low scores). As a result, spending is not strictly tied to income in this mall. Other factors might affect spending more than income.

library(ggplot2)
ggplot(mydata, aes(x = GenderF)) +
  geom_bar()

-The female bar is taller than the male bar, showing more female consumers than male consumers. There are slightly over 100 female and just below 100 male costumers . While there is a small difference, the dataset appears to have a relatively balanced mix of male and female customers.

library(ggplot2)
ggplot(mydata, aes(x = Spending.Score, y = GenderF)) +
  geom_boxplot()

• The median spending score for the males is lower than that of females(around 40).So, half of the male consumers have a spending score of 40 or less and the other half has more. On the other hand, half of the female consumers have a spending score of 55 or less and the other half has more. The whiskers of both genders are almost the same. The males appear to have a little higher spending score as a maximum than the females. Lastly, the interquartile range of both genders are almost the same, showing about the same spending behavior between the two genders.