rm()
ls()character(0)# clearing environment variables before running codeMarket Segmentation Report 1
Market Segmentation
1. Introduction
This document will perform Market Segmentation Analysis on the data provided by KTC. We will be looking into importing and performing cluster analysis on the data to find useful patterns in Customer data.
2. Descriptive Mining
We are going to explore the data and find the patterns and do the segmentation.
2.1. Data Exploration
We have information regarding 30 customers of KTC Company. We have details of their Age, Income, Marital Status, No. of Children and their financial status in the means of whether they have a mortgage loan and other loans.
# A tibble: 30 × 7
Age Female Income Married Children Loan Mortgage
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 48 1 17546 0 1 0 0
2 40 0 30085. 1 3 1 1
3 51 1 16575. 1 0 1 0
4 23 1 20375. 1 3 0 0
5 57 1 50576. 1 0 0 0
6 57 1 37870. 1 2 0 0
7 22 0 8877. 0 0 0 0
8 58 0 24947. 1 0 1 0
9 37 1 25304. 1 2 1 0
10 54 0 24212. 1 2 1 0
# ℹ 20 more rows
Data frame:crs$dataset[, c(crs$input, crs$risk, crs$target)] 30 observations and 7 variables Maximum # NAs:0
Storage
Age double
Female double
Income double
Married double
Children double
Loan double
Mortgage double Age Female Income Married
Min. :22.00 Min. :0.0000 Min. : 8877 Min. :0.0
1st Qu.:37.25 1st Qu.:0.0000 1st Qu.:18166 1st Qu.:1.0
Median :47.00 Median :1.0000 Median :24241 Median :1.0
Mean :45.97 Mean :0.5667 Mean :28012 Mean :0.8
3rd Qu.:56.75 3rd Qu.:1.0000 3rd Qu.:35923 3rd Qu.:1.0
Max. :66.00 Max. :1.0000 Max. :59804 Max. :1.0
Children Loan Mortgage
Min. :0.0000 Min. :0.0000 Min. :0.0
1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0
Median :0.5000 Median :0.0000 Median :0.0
Mean :0.9333 Mean :0.4333 Mean :0.4
3rd Qu.:2.0000 3rd Qu.:1.0000 3rd Qu.:1.0
Max. :3.0000 Max. :1.0000 Max. :1.0 2.1.1. Age
crs$dataset["Age"]
1 Variables 30 Observations
--------------------------------------------------------------------------------
Age
n missing distinct Info Mean pMedian Gmd .05
30 0 23 0.998 45.97 46.5 15.12 22.45
.10 .25 .50 .75 .90 .95
26.60 37.25 47.00 56.75 61.10 64.20
lowest : 22 23 27 31 36, highest: 57 58 61 62 66
--------------------------------------------------------------------------------We obs
2.1.2 Female (Gender Column
crs$dataset["Female"]
1 Variables 30 Observations
--------------------------------------------------------------------------------
Female
n missing distinct Info Sum Mean
30 0 2 0.737 17 0.5667
--------------------------------------------------------------------------------Insert Description of Gender Column here
2.1.3 Income
crs$dataset["Income"]
1 Variables 30 Observations
--------------------------------------------------------------------------------
Income
n missing distinct Info Mean pMedian Gmd .05
30 0 30 1 28012 25590 14919 13945
.10 .25 .50 .75 .90 .95
15716 18166 24241 35923 51039 56676
lowest : 8877.07 12640.3 15538.8 15735.8 16497.3
highest: 41034 50576.3 55204.7 57880.7 59803.9
--------------------------------------------------------------------------------Insert Description of Income Column here
2.1.4 Marriage
crs$dataset["Married"]
1 Variables 30 Observations
--------------------------------------------------------------------------------
Married
n missing distinct Info Sum Mean
30 0 2 0.481 24 0.8
--------------------------------------------------------------------------------Insert Description of Married Column here
2.1.5 Children
crs$dataset["Children"]
1 Variables 30 Observations
--------------------------------------------------------------------------------
Children
n missing distinct Info Mean pMedian Gmd
30 0 4 0.858 0.9333 1 1.163
Value 0 1 2 3
Frequency 15 5 7 3
Proportion 0.500 0.167 0.233 0.100
For the frequency table, variable is rounded to the nearest 0
--------------------------------------------------------------------------------Insert Description of Children Column here
2.1.6 Loan
crs$dataset["Loan"]
1 Variables 30 Observations
--------------------------------------------------------------------------------
Loan
n missing distinct Info Sum Mean
30 0 2 0.737 13 0.4333
--------------------------------------------------------------------------------Insert Description of Loan Column here
2.1.7 Mortgage
crs$dataset["Mortgage"]
1 Variables 30 Observations
--------------------------------------------------------------------------------
Mortgage
n missing distinct Info Sum Mean
30 0 2 0.721 12 0.4
--------------------------------------------------------------------------------Insert Description of Mortgage Column here
2.1.8. Distributions
# Generate the plot.
p01 <- crs %>%
with(dataset[,]) %>%
dplyr::select(Age) %>%
ggplot2::ggplot(ggplot2::aes(x=Age)) +
ggplot2::geom_density(lty=3) +
ggplot2::xlab("Age\n\nRattle 2025-Jul-19 09:48:10 sakth") +
ggplot2::ggtitle("Distribution of Age") +
ggplot2::labs(y="Density")
# Use ggplot2 to generate histogram plot for Female
# Generate the plot.
p02 <- crs %>%
with(dataset[,]) %>%
dplyr::select(Female) %>%
ggplot2::ggplot(ggplot2::aes(x=Female)) +
ggplot2::geom_density(lty=3) +
ggplot2::xlab("Female\n\nRattle 2025-Jul-19 09:48:10 sakth") +
ggplot2::ggtitle("Distribution of Female") +
ggplot2::labs(y="Density")
# Use ggplot2 to generate histogram plot for Income
# Generate the plot.
p03 <- crs %>%
with(dataset[,]) %>%
dplyr::select(Income) %>%
ggplot2::ggplot(ggplot2::aes(x=Income)) +
ggplot2::geom_density(lty=3) +
ggplot2::xlab("Income\n\nRattle 2025-Jul-19 09:48:10 sakth") +
ggplot2::ggtitle("Distribution of Income") +
ggplot2::labs(y="Density")
# Use ggplot2 to generate histogram plot for Married
# Generate the plot.
p04 <- crs %>%
with(dataset[,]) %>%
dplyr::select(Married) %>%
ggplot2::ggplot(ggplot2::aes(x=Married)) +
ggplot2::geom_density(lty=3) +
ggplot2::xlab("Married\n\nRattle 2025-Jul-19 09:48:10 sakth") +
ggplot2::ggtitle("Distribution of Married") +
ggplot2::labs(y="Density")
# Use ggplot2 to generate histogram plot for Children
# Generate the plot.
p05 <- crs %>%
with(dataset[,]) %>%
dplyr::select(Children) %>%
ggplot2::ggplot(ggplot2::aes(x=Children)) +
ggplot2::geom_density(lty=3) +
ggplot2::xlab("Children\n\nRattle 2025-Jul-19 09:48:10 sakth") +
ggplot2::ggtitle("Distribution of Children") +
ggplot2::labs(y="Density")
# Use ggplot2 to generate histogram plot for Loan
# Generate the plot.
p06 <- crs %>%
with(dataset[,]) %>%
dplyr::select(Loan) %>%
ggplot2::ggplot(ggplot2::aes(x=Loan)) +
ggplot2::geom_density(lty=3) +
ggplot2::xlab("Loan\n\nRattle 2025-Jul-19 09:48:10 sakth") +
ggplot2::ggtitle("Distribution of Loan") +
ggplot2::labs(y="Density")
# Use ggplot2 to generate histogram plot for Mortgage
# Generate the plot.
p07 <- crs %>%
with(dataset[,]) %>%
dplyr::select(Mortgage) %>%
ggplot2::ggplot(ggplot2::aes(x=Mortgage)) +
ggplot2::geom_density(lty=3) +
ggplot2::xlab("Mortgage\n\nRattle 2025-Jul-19 09:48:10 sakth") +
ggplot2::ggtitle("Distribution of Mortgage") +
ggplot2::labs(y="Density")
# Display the plots.
gridExtra::grid.arrange(p01, p02, p03, p04, p05, p06, p07)
2.2 Segmentation using Clustering
Clustering is a method of grouping the observation based on their similarities. We use distance measures for assessing the dissimilarity among the observations. There are many measures of distance including Euclidean, Manhattan etc, Similarly we have different types of clustering algorithms such as K Means, Hierarchical, BiClustering etc. We will begin with Hierarchical clustering as part of our data exploration analysis.
2.2.1. Dendrogram
Observing the above dendrogram we can observe that
2.2.2. Elbow Method
# Elbow method for finding the no of clusters
library(factoextra)Welcome! Want to learn more? See two factoextra-related books at https://goo.gl/ve3WBafviz_nbclust(crs$dataset[, c(1:7)], kmeans, method = "wss") +
labs(subtitle = "Elbow Method")
We can observe that when the no. of clusters is 2 there is a sharp change in the total within sum of squares. This shows that 2 is the optimal no. of clusters to have for this dataset
2.2.2. Various Clustering
No. of Clusters = 5
No. of Clusters = 4
No. of Clusters = 3
No. of Clusters = 2
2.3. K-means Clustering
crs$kmeans <- kmeans(na.omit(crs$dataset[, crs$numeric]), 3)
paste(crs$kmeans$size, collapse=' ')[1] "12 10 8"colMeans(na.omit(crs$dataset[, crs$numeric])) Age Female Income Married Children Loan
4.596667e+01 5.666667e-01 2.801187e+04 8.000000e-01 9.333333e-01 4.333333e-01
Mortgage
4.000000e-01 crs$kmeans$centers Age Female Income Married Children Loan Mortgage
1 37.000 0.5833333 16826.18 0.6666667 1.000 0.25 0.4166667
2 47.200 0.5000000 25661.02 0.8000000 1.100 0.80 0.4000000
3 57.875 0.6250000 47728.97 1.0000000 0.625 0.25 0.3750000crs$kmeans$withinss[1] 131352595 61338314 586111857cluster::clusplot(na.omit(crs$dataset[, intersect(crs$input, crs$numeric)]), crs$kmeans$cluster, color=TRUE, shade=TRUE, main='K-Means Clustering with 3 Clusters')