Clustering Project: Customer Segmentation
Mehmet Fatih AYIK_465768
30-01-2024
Introduction
Clustering is the separation of data showing similar characteristics into groups in a data set. There are many similarities within the same cluster and few similarities between clusters.
K-Means, K-Medoid and Hierarchical Clustering are widely used clustering algorithms. These algorithms are frequently used in areas such as customer segmentation, market segmentation, and computer vision.
We will now consider an example of customer segmentation.To put it simply our aim, we will cluster customers customers based on personal informations by using K-Means and K-Medoid clustering algorithms.
Exploratory Data Analysis
In this part, you can observe Exploratory Data Analysis of the given data with some visualization parts to get an insight about the distribution of the data. We will analyze the data by using specific R codes. We will add visualization parts as well to observe data deeply.
Review of data set
The data set was found on Kaggle. It describes basic personal informations of the customers
The data set consists of 2000 samples and 8 variables.The columns of the Dataset are as following;
Customer ID:
A unique identifier for each customer.
Gender:
The gender of the customer.
Age:
The age of the customer.
Annual Income:
The annual income of the customer.
Spending Score:
Score assigned by the shop, based on customer behavior and spending nature.
Profession:
The profession of the customer.
Work Experience:
The number of years of work experience of the customer.
Family Size:
The size of the customer's family.
Loading Packages
Firstly, we should install the necessary libraries to our environment.
requiredPackages = c( "tidyverse", "dplyr", "ggplot2","cluster","factoextra","knitr","gridExtra","corrplot")
for(i in requiredPackages){if(!require(i,character.only = TRUE)) install.packages(i)}
for(i in requiredPackages){if(!require(i,character.only = TRUE)) library(i,character.only = TRUE) } Statistical Analysis of Data
customer <- read.csv("C:\\Users\\mfati\\Desktop\\Master\\Semester1\\UL\\Projects\\Clustering\\Customer.csv", sep = ";", header=TRUE)
customer <- customer %>% mutate_if(is.character, ~na_if(., ''))
#empty strings changed to NA
colnames(customer)[4] <- "Annual_Income"
colnames(customer)[5] <- "Spending_Score"
colnames(customer)[7] <- "Work_Experience"
colnames(customer)[8] <- "Family_Size"We read our data, and assigned it to the “customer” variable. But in the data file there were inappropriate column names and inappropriate data(have cells which have empty string).
We had to handle these inappropriate column names and data. And also, for getting more meaningful data, I applied “distinct” function for eliminating duplicate data If It has exists
head(customer)
kable(head(customer,10))| CustomerID | Gender | Age | Annual_Income | Spending_Score | Profession | Work_Experience | Family_Size |
|---|---|---|---|---|---|---|---|
| 1 | Male | 19 | 17065 | 19 | Healthcare | 2 | 4 |
| 2 | Male | 21 | 22032 | 15 | Engineer | 1 | 3 |
| 3 | Female | 20 | 17987 | 23 | Engineer | 1 | 1 |
| 4 | Female | 23 | 27887 | 35 | Lawyer | 3 | 2 |
| 5 | Female | 31 | 61048 | 70 | Entertainment | 13 | 6 |
| 6 | Female | 22 | 32372 | 57 | Artist | 3 | 2 |
| 7 | Female | 35 | 47768 | 34 | Healthcare | 14 | 3 |
| 8 | Female | 23 | 46852 | 69 | Healthcare | 2 | 3 |
| 9 | Male | 64 | 132854 | 99 | Engineer | 33 | 3 |
| 10 | Female | 30 | 60445 | 54 | Artist | 8 | 4 |
Summary(customer)
summary(customer)## CustomerID Gender Age Annual_Income
## Min. : 1.0 Length:2000 Min. : 0.00 Min. : 197
## 1st Qu.: 500.8 Class :character 1st Qu.:25.00 1st Qu.: 59347
## Median :1000.5 Mode :character Median :48.00 Median : 94007
## Mean :1000.5 Mean :48.96 Mean : 98407
## 3rd Qu.:1500.2 3rd Qu.:73.00 3rd Qu.:140257
## Max. :2000.0 Max. :99.00 Max. :189709
## Spending_Score Profession Work_Experience Family_Size
## Min. : 5.00 Length:2000 Min. : 0.00 Min. :1.000
## 1st Qu.: 51.00 Class :character 1st Qu.: 8.00 1st Qu.:2.000
## Median : 78.00 Mode :character Median :17.00 Median :4.000
## Mean : 70.28 Mean :19.07 Mean :3.768
## 3rd Qu.: 89.00 3rd Qu.:31.00 3rd Qu.:5.000
## Max. :100.00 Max. :45.00 Max. :9.000Str(customer)
str(customer)## 'data.frame': 2000 obs. of 8 variables:
## $ CustomerID : int 1 2 3 4 5 6 7 8 9 10 ...
## $ Gender : chr "Male" "Male" "Female" "Female" ...
## $ Age : int 19 21 20 23 31 22 35 23 64 30 ...
## $ Annual_Income : int 17065 22032 17987 27887 61048 32372 47768 46852 132854 60445 ...
## $ Spending_Score : int 19 15 23 35 70 57 34 69 99 54 ...
## $ Profession : chr "Healthcare" "Engineer" "Engineer" "Lawyer" ...
## $ Work_Experience: int 2 1 1 3 13 3 14 2 33 8 ...
## $ Family_Size : int 4 3 1 2 6 2 3 3 3 4 ...Checking for missing values
sapply(customer, function(x) sum(is.na(x)))## CustomerID Gender Age Annual_Income Spending_Score
## 0 0 0 0 0
## Profession Work_Experience Family_Size
## 35 0 0removing duplicate values If It has exists
customer %>% distinct()## CustomerID Gender Age Annual_Income Spending_Score Profession
## 1 1 Male 19 17065 19 Healthcare
## 2 2 Male 21 22032 15 Engineer
## 3 3 Female 20 17987 23 Engineer
## 4 4 Female 23 27887 35 Lawyer
## 5 5 Female 31 61048 70 Entertainment
## 6 6 Female 22 32372 57 Artist
## 7 7 Female 35 47768 34 Healthcare
## 8 8 Female 23 46852 69 Healthcare
## 9 9 Male 64 132854 99 Engineer
## 10 10 Female 30 60445 54 Artist
## 11 11 Male 67 63505 42 Engineer
## 12 12 Female 35 54211 64 Healthcare
## 13 13 Female 58 117274 88 Executive
## 14 14 Female 24 52024 22 Lawyer
## 15 15 Male 37 74270 20 Doctor
## 16 16 Male 22 38356 34 Healthcare
## 17 17 Female 35 44591 40 Homemaker
## 18 18 Male 20 21893 28 Healthcare
## 19 19 Male 52 98967 71 Entertainment
## 20 20 Female 35 62448 28 Artist
## 21 21 Male 35 71731 68 Homemaker
## 22 22 Male 25 58846 40 Healthcare
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## 25 25 Female 54 81688 70 Executive
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## 2000 21 2Filter for greater than 18 age and smaller than 65 age and who has profession
customer_v2 <- customer %>%
filter(Age >= 18,Age <= 65, Profession != "NA") %>%
select(Gender,Age,Annual_Income,Spending_Score,Profession,Work_Experience,Family_Size)
# and view the filtered dataset
head(customer_v2)## Gender Age Annual_Income Spending_Score Profession Work_Experience
## 1 Male 19 17065 19 Healthcare 2
## 2 Male 21 22032 15 Engineer 1
## 3 Female 20 17987 23 Engineer 1
## 4 Female 23 27887 35 Lawyer 3
## 5 Female 31 61048 70 Entertainment 13
## 6 Female 22 32372 57 Artist 3
## Family_Size
## 1 4
## 2 3
## 3 1
## 4 2
## 5 6
## 6 2# summarize gender counts by Profession
GenderByProfession <- aggregate(Gender ~ Profession, customer_v2, function(x) sum(!is.na(x)))
GenderByProfession <- GenderByProfession[order(GenderByProfession$Gender, decreasing = TRUE),]
head(GenderByProfession)## Profession Gender
## 1 Artist 325
## 6 Healthcare 182
## 4 Entertainment 110
## 3 Engineer 84
## 2 Doctor 81
## 5 Executive 81This time a filter was applied to increase the meaningfulness of the data.
Histogram for Age distribution
hist(customer_v2$Age, breaks = 20, col = "green", main = "Distribution of Customers Age", xlab = "Age")
Above histogram is the distribution of the our data which is before the implementation of the K-Means clustering method.
Correlation Matrix
corrplot(cor(customer_v2 %>% dplyr::select(where(is.numeric))), type = 'upper', method = 'number', cl.cex = 0.9)
Box plot of Annual_Income and Spending_Score by Profession
library(ggplot2)
ggplot(customer_v2, aes(x = Profession, y = Annual_Income)) +
geom_boxplot() +
ggtitle("Annual_Income Distribution by Profession")
library(ggplot2)
ggplot(customer_v2, aes(x = Profession, y = Spending_Score)) +
geom_boxplot() +
ggtitle("Spending_Score Distribution by Profession")
Clustering
In this part K-Means and PAM clustering methods was used for clustering the customers.
K-Means
There are many clustering algorithms, each has its advantages and disadvantages. A popular algorithm for clustering is k-means, which aims to identify the best k cluster centers in an iterative manner. Cluster centers are served as “representative” of the objects associated with the cluster.
K-Means With Different Center Points
Let’s compare k-means with k = [2,3,4,5]
clusterdata <- customer_v2[, c("Age", "Work_Experience", "Spending_Score")]
set.seed(123)
cluster2 <- kmeans(clusterdata, centers = 2, nstart = 25)
cluster3 <- kmeans(clusterdata, centers = 3, nstart = 25)
cluster4 <- kmeans(clusterdata, centers = 4, nstart = 25)
cluster5 <- kmeans(clusterdata, centers = 5, nstart = 25)
cl1 <- fviz_cluster(cluster2, geom = "point", ellipse.type = "convex",data = clusterdata) + ggtitle(" K = 2")
cl2 <- fviz_cluster(cluster3, geom = "point", ellipse.type = "convex",data = clusterdata) + ggtitle(" K = 3")
cl3 <- fviz_cluster(cluster4, geom = "point", ellipse.type = "convex",data = clusterdata) + ggtitle(" K = 4")
cl4 <- fviz_cluster(cluster5, geom = "point", ellipse.type = "convex",data = clusterdata) + ggtitle(" K = 5")
grid.arrange(cl1, cl2, cl3, cl4, nrow = 2)
Find Optimal Number of Clusters K
Let’s compare number of clusters with Silhouette and
Elbow Graphs
fonc1 <- fviz_nbclust(clusterdata, FUNcluster = kmeans, method = "silhouette") +
ggtitle("Optimal number of clusters \n K-means")
fonc2 <- fviz_nbclust(clusterdata, FUNcluster = kmeans, method = "wss") +
ggtitle("Optimal number of clusters \n K-means")
grid.arrange(fonc1, fonc2, ncol=2)
From Silhouette and Elbow graphs, we can observe that there are 4 candidate for number of clusters 2,3,4 and 5. Let’s deep dive into the number of clusters.
K-Means With 2,3,4 and 5 Number of Clusters
Let’s compare k-means with number of clusters
2,3,4 and 5.
kmean2 <- eclust(clusterdata, k=2, FUNcluster="kmeans", hc_metric="euclidean", graph=F)
cus2 <- fviz_cluster(kmean2, data=clusterdata, elipse.type="convex", geom=c("point")) + ggtitle("K-means with 2 clusters")
sil2 <- fviz_silhouette(kmean2)## cluster size ave.sil.width
## 1 1 430 0.48
## 2 2 581 0.56grid.arrange(cus2, sil2, ncol=2)
kmean3 <- eclust(clusterdata, k=3, FUNcluster="kmeans", hc_metric="euclidean", graph=F)
cus3 <- fviz_cluster(kmean3, data=clusterdata, elipse.type="convex", geom=c("point")) + ggtitle("K-means with 3 clusters")
sil3 <- fviz_silhouette(kmean3)## cluster size ave.sil.width
## 1 1 253 0.16
## 2 2 491 0.50
## 3 3 267 0.54grid.arrange(cus3, sil3, ncol=2)
kmean4 <- eclust(clusterdata, k=4, FUNcluster="kmeans", hc_metric="euclidean", graph=F)
cus4 <- fviz_cluster(kmean4, data=clusterdata, elipse.type="convex", geom=c("point")) + ggtitle("K-means with 4 clusters")
sil4 <- fviz_silhouette(kmean4)## cluster size ave.sil.width
## 1 1 98 0.46
## 2 2 310 0.46
## 3 3 310 0.52
## 4 4 293 0.35grid.arrange(cus4, sil4, ncol=2)
kmean5 <- eclust(clusterdata, k=5, FUNcluster="kmeans", hc_metric="euclidean", graph=F)
cus5 <- fviz_cluster(kmean5, data=clusterdata, elipse.type="convex", geom=c("point")) + ggtitle("K-means with 5 clusters")
sil5 <- fviz_silhouette(kmean5)## cluster size ave.sil.width
## 1 1 93 0.48
## 2 2 231 0.43
## 3 3 232 0.50
## 4 4 180 0.38
## 5 5 275 0.41grid.arrange(cus5, sil5, ncol=2)
PAM
The PAM algorithm is based on the search for k representative objects or medoids among the observations of the data set.
PAM With 2,3,4 and 5 Number of Clusters
Let’s compare PAM with number of clusters
2,3,4 and 5.
pam2 <- eclust(clusterdata, k=2 , FUNcluster="pam", hc_metric="euclidean", graph=F)
cuspam2 <- fviz_cluster(pam2, data=clusterdata, elipse.type="convex", geom=c("point")) + ggtitle("PAM with 2 clusters")
silpam2 <- fviz_silhouette(pam2)## cluster size ave.sil.width
## 1 1 433 0.48
## 2 2 578 0.56grid.arrange(cuspam2, silpam2, ncol=2)
pam3 <- eclust(clusterdata, k=3 , FUNcluster="pam", hc_metric="euclidean", graph=F)
cuspam3 <- fviz_cluster(pam3, data=clusterdata, elipse.type="convex", geom=c("point")) + ggtitle("PAM with 3 clusters")
silpam3 <- fviz_silhouette(pam3)## cluster size ave.sil.width
## 1 1 377 0.48
## 2 2 311 0.41
## 3 3 323 0.36grid.arrange(cuspam3, silpam3, ncol=2)
pam4 <- eclust(clusterdata, k=4 , FUNcluster="pam", hc_metric="euclidean", graph=F)
cuspam4 <- fviz_cluster(pam4, data=clusterdata, elipse.type="convex", geom=c("point")) + ggtitle("PAM with 4 clusters")
silpam4 <- fviz_silhouette(pam4)## cluster size ave.sil.width
## 1 1 317 0.52
## 2 2 329 0.32
## 3 3 277 0.49
## 4 4 88 0.53grid.arrange(cuspam4, silpam4, ncol=2)
pam5 <- eclust(clusterdata, k=5 , FUNcluster="pam", hc_metric="euclidean", graph=F)
cuspam5 <- fviz_cluster(pam5, data=clusterdata, elipse.type="convex", geom=c("point")) + ggtitle("PAM with 5 clusters")
silpam5 <- fviz_silhouette(pam5)## cluster size ave.sil.width
## 1 1 244 0.49
## 2 2 174 0.39
## 3 3 272 0.41
## 4 4 225 0.44
## 5 5 96 0.46grid.arrange(cuspam5, silpam5, ncol=2)
Conclusion
Both algorithms is giving similar results. Therefore, although we cannot make a clear comment. I think we can say according to results ;
Optimal Number of Cluster and Clustering Method:2 and K-Means Clustering
cluster2## K-means clustering with 2 clusters of sizes 430, 581
##
## Cluster means:
## Age Work_Experience Spending_Score
## 1 31.25814 11.05116 38.26279
## 2 48.16695 25.79174 83.19277
##
## Clustering vector:
## [1] 1 1 1 1 2 1 1 1 2 1 1 2 1 1 1 1 1 2 1 2 1 2 1 2 1 2 2 2 1 1 1 2 1 1 1 2 1
## [38] 1 1 2 1 2 2 2 1 1 1 1 1 2 1 2 2 2 2 2 1 2 1 2 2 1 1 1 1 2 2 1 2 1 2 2 1 2
## [75] 2 2 1 2 1 1 1 2 1 2 2 1 1 2 1 2 1 1 2 1 2 1 1 2 1 2 1 1 1 2 1 1 1 1 2 1 1
## [112] 2 1 1 2 2 2 1 1 1 1 1 2 2 1 1 1 1 1 1 1 1 2 2 1 1 2 2 2 2 2 2 2 1 2 1 2 1
## [149] 1 2 2 2 1 2 1 2 1 2 1 1 2 1 2 1 2 2 1 2 2 1 2 2 1 1 1 1 2 1 1 2 1 2 2 2 1
## [186] 1 1 2 2 2 1 1 2 2 2 2 2 2 2 1 1 1 2 2 1 1 1 2 1 2 2 1 2 2 2 2 2 2 1 2 1 1
## [223] 1 2 2 2 1 1 2 2 1 2 2 2 1 2 2 1 2 2 2 2 1 1 2 2 2 2 1 2 1 2 1 1 1 1 1 2 2
## [260] 2 2 2 1 2 1 2 1 1 2 1 1 1 1 1 1 1 2 2 2 2 2 1 1 2 2 1 2 2 2 1 2 2 2 1 1 1
## [297] 1 1 1 2 1 2 1 1 2 2 2 1 1 2 2 1 2 1 2 2 2 2 2 2 1 2 1 1 2 2 1 2 1 2 1 2 2
## [334] 2 2 1 2 2 1 2 2 2 1 1 2 2 2 2 1 2 2 2 1 1 2 1 2 2 2 2 1 2 2 1 1 2 1 2 2 2
## [371] 1 2 2 2 1 2 2 2 2 2 2 2 2 1 2 2 2 1 2 2 2 2 2 1 1 1 2 2 1 1 1 1 2 2 2 1 2
## [408] 2 2 2 2 2 2 2 2 2 1 2 1 2 2 2 2 1 2 1 1 2 1 2 2 2 2 2 1 2 1 2 2 2 2 2 1 2
## [445] 2 2 2 1 2 2 1 1 1 2 1 2 1 2 2 2 2 2 2 1 1 2 2 2 2 2 2 1 1 1 2 1 2 2 2 2 2
## [482] 2 1 1 1 1 1 1 2 2 2 2 2 2 2 1 2 2 1 1 2 2 2 1 1 1 1 1 1 1 1 2 1 2 2 1 2 1
## [519] 2 1 2 1 2 2 1 2 2 2 1 1 2 1 1 2 2 2 1 2 1 2 2 2 1 2 1 1 2 2 1 2 1 1 2 2 2
## [556] 2 2 2 2 1 1 2 2 2 1 1 2 2 2 1 1 1 2 2 2 2 2 1 1 1 2 2 2 2 2 1 2 2 2 1 2 2
## [593] 1 2 2 1 2 1 2 1 1 2 2 2 1 2 2 1 1 2 1 2 2 1 2 2 2 2 2 1 2 2 1 2 2 1 2 2 2
## [630] 2 1 1 1 1 2 1 2 2 2 2 2 2 1 1 1 2 2 1 2 2 1 1 2 1 2 1 2 1 2 1 2 1 1 1 1 2
## [667] 1 1 1 2 2 1 2 2 1 2 2 1 2 2 1 1 1 1 1 2 2 1 1 1 2 1 2 2 1 2 2 2 2 2 1 1 2
## [704] 2 1 1 1 2 2 1 2 1 2 2 1 1 2 2 2 2 1 1 2 1 2 2 1 2 1 1 2 2 2 2 2 2 2 1 1 2
## [741] 2 2 2 2 1 2 1 2 2 1 2 2 1 2 2 2 1 2 1 1 1 2 2 1 2 2 1 2 2 2 2 2 2 1 1 2 2
## [778] 2 1 2 2 1 1 2 1 2 1 1 1 1 2 2 2 2 2 2 1 1 2 1 1 2 2 1 1 1 2 2 1 1 2 2 2 2
## [815] 1 2 1 2 1 2 2 2 2 2 2 2 2 1 1 1 2 2 1 2 1 2 2 1 2 1 2 1 2 2 2 2 2 1 2 1 2
## [852] 2 1 1 2 2 2 2 2 2 1 1 1 2 2 1 1 2 1 2 2 1 2 2 1 2 1 2 1 2 1 2 1 1 1 2 2 1
## [889] 2 2 2 2 2 2 1 1 2 2 2 2 2 1 1 1 2 2 2 2 2 1 2 2 2 2 2 2 2 2 1 2 1 1 1 1 1
## [926] 2 1 2 2 1 1 2 1 2 1 1 1 2 1 2 1 2 2 2 2 2 2 1 1 1 1 2 1 2 1 2 1 1 1 2 1 2
## [963] 2 1 2 2 1 1 2 1 2 1 2 2 1 2 2 2 2 2 1 1 1 2 2 2 2 1 2 2 2 2 2 2 1 2 2 2 2
## [1000] 2 2 2 2 2 1 1 2 2 1 2 1
##
## Within cluster sum of squares by cluster:
## [1] 199842.5 192515.0
## (between_SS / total_SS = 61.4 %)
##
## Available components:
##
## [1] "cluster" "centers" "totss" "withinss" "tot.withinss"
## [6] "betweenss" "size" "iter" "ifault"REFERENCES
Lecture notes of Jacek Lewkowicz - Unsupervised Learning-University of Warsaw.
https://medium.com/@cmukesh8688/silhouette-analysis-in-k-means-clustering-cefa9a7ad111
https://www.r-bloggers.com/2013/09/clustering-search-keywords-using-k-means-clustering/