Mall-Customers-Segmentation - Customer Segmentation Using K-Means Clustering

Overview

Overview

Customer Segmentation is one the most important applications of unsupervised learning. Using clustering techniques, companies can identify the several segments of customers allowing them to target the potential user base. In this machine learning project, we will make use of K-means clustering which is the essential algorithm for clustering unlabeled dataset. Before ahead in this project, learn what actually customer segmentation is.
seg

What is Customer Segmentation

Customer Segmentation is the process of division of customer base into several groups of individuals that share a similarity in different ways that are relevant to marketing such as gender, age, interests, and miscellaneous spending habits.

Companies that deploy customer segmentation are under the notion that every customer has different requirements and require a specific marketing effort to address them appropriately. Companies aim to gain a deeper approach of the customer they are targeting. Therefore, their aim has to be specific and should be tailored to address the requirements of each and every individual customer. Furthermore, through the data collected, companies can gain a deeper understanding of customer preferences as well as the requirements for discovering valuable segments that would reap them maximum profit. This way, they can strategize their marketing techniques more efficiently and minimize the possibility of risk to their investment.

The technique of customer segmentation is dependent on several key differentiators that divide customers into groups to be targeted. Data related to demographics, geography, economic status as well as behavioral patterns play a crucial role in determining the company direction towards addressing the various segments

What is K-Means Algorithm

While using the k-means clustering algorithm, the first step is to indicate the number of clusters (k) that we wish to produce in the final output. The algorithm starts by selecting k objects from dataset randomly that will serve as the initial centers for our clusters. These selected objects are the cluster means, also known as centroids. Then, the remaining objects have an assignment of the closest centroid. This centroid is defined by the Euclidean Distance present between the object and the cluster mean. We refer to this step as “cluster assignment”. When the assignment is complete, the algorithm proceeds to calculate new mean value of each cluster present in the data. After the recalculation of the centers, the observations are checked if they are closer to a different cluster. Using the updated cluster mean, the objects undergo reassignment. This goes on repeatedly through several iterations until the cluster assignments stop altering. The clusters that are present in the current iteration are the same as the ones obtained in the previous iteration.

Dataset

The dataset is aquired from kaggle and the link is given below :

https://www.kaggle.com/nelakurthisudheer/mall-customer-segmentation

The dataset consists of following five features of 200 customers:

  • CustomerID: Unique ID assigned to the customer

  • Gender: Gender of the customer

  • Age: Age of the customer

  • Annual Income (k$): Annual Income of the customer

  • Spending Score (1-100): Score assigned by the mall based on customer behavior and spending nature.

Steps for implementation

  • Import all neccessary packages
import ----- from ------
import -----
  • Data Exploration
customer_data=read.csv("/home/dataflair/Mall_Customers.csv")
str(customer_data)
names(customer_data)

head(customer_data)
summary(customer_data$Age)
  • Statistical Analysis
sd(customer_data$Age)
summary(customer_data$Annual.Income..k..)
sd(customer_data$Annual.Income..k..)
summary(customer_data$Age)
  • Visualizations
Bar Plot

a=table(customer_data$Gender)
barplot(a,main="Using BarPlot to display Gender Comparision",
       ylab="Count",
       xlab="Gender",
       col=rainbow(2),
       legend=rownames(a))
       

Pie Chart
pct=round(a/sum(a)*100)
lbs=paste(c("Female","Male")," ",pct,"%",sep=" ")
library(plotrix)
pie3D(a,labels=lbs,
   main="Pie Chart Depicting Ratio of Female and Male")
Histogram
hist(customer_data$Age,
    col="blue",
    main="Histogram to Show Count of Age Class",
    xlab="Age Class",
    ylab="Frequency",
    labels=TRUE)
Box Plot
boxplot(customer_data$Age,
       col="ff0066",
       main="Boxplot for Descriptive Analysis of Age")
  • Analysis
Analyzing the annual income of the customers through the Histogram
summary(customer_data$Annual.Income..k..)
hist(customer_data$Annual.Income..k..,
  col="#660033",
  main="Histogram for Annual Income",
  xlab="Annual Income Class",
  ylab="Frequency",
  labels=TRUE)
Density Plot
plot(density(customer_data$Annual.Income..k..),
    col="yellow",
    main="Density Plot for Annual Income",
    xlab="Annual Income Class",
    ylab="Density")
polygon(density(customer_data$Annual.Income..k..),
        col="#ccff66")
Analyzing Spending Score of the Customers with the help of BoxPlot
summary(customer_data$Spending.Score..1.100.)

Min. 1st Qu. Median Mean 3rd Qu. Max. 
## 1.00 34.75 50.00 50.20 73.00 99.00

boxplot(customer_data$Spending.Score..1.100.,
   horizontal=TRUE,
   col="#990000",
   main="BoxPlot for Descriptive Analysis of Spending Score")

K-means Algorithm

  • We specify the number of clusters that we need to create.
  • The algorithm selects k objects at random from the dataset. This object is the initial cluster or mean.
  • The closest centroid obtains the assignment of a new observation. We base this assignment on the Euclidean Distance between object and the centroid.
  • k clusters in the data points update the centroid through calculation of the new mean values present in all the data points of the cluster. The kth cluster’s centroid has a - - Length of p that contains means of all variables for observations in the k-th cluster. We denote the number of variables with p.
  • Iterative minimization of the total within the sum of squares. Then through the iterative minimization of the total sum of the square, the assignment stop wavering when we - - Achieve maximum iteration. The default value is 10 that the R software uses for the maximum iterations.

Determining Optimal Clusters

While working with clusters, you need to specify the number of clusters to use. You would like to utilize the optimal number of clusters. To help you in determining the optimal clusters, there are three popular methods –

  • Elbow method The main goal behind cluster partitioning methods like k-means is to define the clusters such that the intra-cluster variation stays minimum.

minimize(sum W(Ck)), k=1…k

Where Ck represents the kth cluster and W(Ck) denotes the intra-cluster variation. With the measurement of the total intra-cluster variation, one can evaluate the compactness of the clustering boundary. We can then proceed to define the optimal clusters as follows –

First, we calculate the clustering algorithm for several values of k. This can be done by creating a variation within k from 1 to 10 clusters. We then calculate the total intra-cluster sum of square (iss). Then, we proceed to plot iss based on the number of k clusters. This plot denotes the appropriate number of clusters required in our model. In the plot, the location of a bend or a knee is the indication of the optimum number of clusters.

minimize(sum W(Ck)), k=1…k
library(purrr)
set.seed(123)
# function to calculate total intra-cluster sum of square 
iss <- function(k) {
  kmeans(customer_data[,3:5],k,iter.max=100,nstart=100,algorithm="Lloyd" )$tot.withinss
}

k.values <- 1:10


iss_values <- map_dbl(k.values, iss)

plot(k.values, iss_values,
    type="b", pch = 19, frame = FALSE, 
    xlab="Number of clusters K",
    ylab="Total intra-clusters sum of squares")

K-Means-Elbow-graph-in-R From the above graph, we conclude that 4 is the appropriate number of clusters since it seems to be appearing at the bend in the elbow plot.

  • Average Silhouette method With the help of the average silhouette method, we can measure the quality of our clustering operation. With this, we can determine how well within the cluster is the data object. If we obtain a high average silhouette width, it means that we have good clustering. The average silhouette method calculates the mean of silhouette observations for different k values. With the optimal number of k clusters, one can maximize the average silhouette over significant values for k clusters.

Using the silhouette function in the cluster package, we can compute the average silhouette width using the kmean function. Here, the optimal cluster will possess highest average.

library(cluster) 
library(gridExtra)
library(grid)


k2<-kmeans(customer_data[,3:5],2,iter.max=100,nstart=50,algorithm="Lloyd")
s2<-plot(silhouette(k2$cluster,dist(customer_data[,3:5],"euclidean")))

np-function-graph-in-data-science-clustering

  • Gap statistic In 2001, researchers at Stanford University – R. Tibshirani, G.Walther and T. Hastie published the Gap Statistic Method. We can use this method to any of the clustering method like K-means, hierarchical clustering etc. Using the gap statistic, one can compare the total intracluster variation for different values of k along with their expected values under the null reference distribution of data. With the help of Monte Carlo simulations, one can produce the sample dataset. For each variable in the dataset, we can calculate the range between min(xi) and max (xj) through which we can produce values uniformly from interval lower bound to upper bound.

For computing the gap statistics method we can utilize the clusGap function for providing gap statistic as well as standard error for a given output.

set.seed(125)
stat_gap <- clusGap(customer_data[,3:5], FUN = kmeans, nstart = 25,
            K.max = 10, B = 50)
fviz_gap_stat(stat_gap)

fviz_gap_stat-function-graph-in-ml

By Using these three methods in k-means clustering we have to find out which is giving the best minimum number optimal clusters. PCA-Cluster-Graph-in-ML-1 PCA-Cluster-Graph-in-data-science From the above segemented graph:

  • Cluster 4 and 1 – These two clusters consist of customers with medium PCA1 and medium PCA2 score.

  • Cluster 6 – This cluster represents customers having a high PCA2 and a low PCA1.

  • Cluster 5 – In this cluster, there are customers with a medium PCA1 and a low PCA2 score.

  • Cluster 3 – This cluster comprises of customers with a high PCA1 income and a high PCA2.

  • Cluster 2 – This comprises of customers with a high PCA2 and a medium annual spend of income.

With the help of clustering, we can understand the variables much better, prompting us to take careful decisions. With the identification of customers, companies can release products and services that target customers based on several parameters like income, age, spending patterns, etc. Furthermore, more complex patterns like product reviews are taken into consideration for better segmentation

Owner
NelakurthiSudheer
Data Science | Kaggle Participant | Web Developer | MachineHack
NelakurthiSudheer
Code release for BlockGAN: Learning 3D Object-aware Scene Representations from Unlabelled Images

BlockGAN Code release for BlockGAN: Learning 3D Object-aware Scene Representations from Unlabelled Images BlockGAN: Learning 3D Object-aware Scene Rep

41 May 18, 2022
Minimal implementation and experiments of "No-Transaction Band Network: A Neural Network Architecture for Efficient Deep Hedging".

No-Transaction Band Network: A Neural Network Architecture for Efficient Deep Hedging Minimal implementation and experiments of "No-Transaction Band N

19 Jan 03, 2023
Label Studio is a multi-type data labeling and annotation tool with standardized output format

Website • Docs • Twitter • Join Slack Community What is Label Studio? Label Studio is an open source data labeling tool. It lets you label data types

Heartex 11.7k Jan 09, 2023
This project is for a Twitter bot that monitors a bird feeder in my backyard. Any detected birds are identified and posted to Twitter.

Backyard Birdbot Introduction This is a silly hobby project to use existing ML models to: Detect any birds sighted by a webcam Identify whic

Chi Young Moon 71 Dec 25, 2022
Refactoring dalle-pytorch and taming-transformers for TPU VM

Text-to-Image Translation (DALL-E) for TPU in Pytorch Refactoring Taming Transformers and DALLE-pytorch for TPU VM with Pytorch Lightning Requirements

Kim, Taehoon 61 Nov 07, 2022
TensorFlow CNN for fast style transfer

Fast Style Transfer in TensorFlow Add styles from famous paintings to any photo in a fraction of a second! It takes 100ms on a 2015 Titan X to style t

1 Dec 14, 2021
StarGAN - Official PyTorch Implementation (CVPR 2018)

StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation

Yunjey Choi 5.1k Dec 30, 2022
PyTorch Implementation of PortaSpeech: Portable and High-Quality Generative Text-to-Speech

PortaSpeech - PyTorch Implementation PyTorch Implementation of PortaSpeech: Portable and High-Quality Generative Text-to-Speech. Model Size Module Nor

Keon Lee 279 Jan 04, 2023
Bidimensional Leaderboards: Generate and Evaluate Language Hand in Hand

Bidimensional Leaderboards: Generate and Evaluate Language Hand in Hand Introduction We propose a generalization of leaderboards, bidimensional leader

4 Dec 03, 2022
A Python library that provides a simplified alternative to DBAPI 2

A Python library that provides a simplified alternative to DBAPI 2. It provides a facade in front of DBAPI 2 drivers.

Tony Locke 44 Nov 17, 2021
OpenIPDM is a MATLAB open-source platform that stands for infrastructures probabilistic deterioration model

Open-Source Toolbox for Infrastructures Probabilistic Deterioration Modelling OpenIPDM is a MATLAB open-source platform that stands for infrastructure

CIVML 0 Jan 20, 2022
1st-in-MICCAI2020-CPM - Combined Radiology and Pathology Classification

Combined Radiology and Pathology Classification MICCAI 2020 Combined Radiology a

22 Dec 08, 2022
Synthetic LiDAR sequential point cloud dataset with point-wise annotations

SynLiDAR dataset: Learning From Synthetic LiDAR Sequential Point Cloud This is official repository of the SynLiDAR dataset. For technical details, ple

78 Dec 27, 2022
nfelo: a power ranking, prediction, and betting model for the NFL

nfelo nfelo is a power ranking, prediction, and betting model for the NFL. Nfelo take's 538's Elo framework and further adapts it for the NFL, hence t

6 Nov 22, 2022
Kohei's 5th place solution for xview3 challenge

xview3-kohei-solution Usage This repository assumes that the given data set is stored in the following locations: $ ls data/input/xview3/*.csv data/in

Kohei Ozaki 2 Jan 17, 2022
Txt2Xml tool will help you convert from txt COCO format to VOC xml format in Object Detection Problem.

TXT 2 XML All codes assume running from root directory. Please update the sys path at the beginning of the codes before running. Over View Txt2Xml too

Nguyễn Trường Lâu 4 Nov 24, 2022
Deep Occlusion-Aware Instance Segmentation with Overlapping BiLayers [CVPR 2021]

Deep Occlusion-Aware Instance Segmentation with Overlapping BiLayers [BCNet, CVPR 2021] This is the official pytorch implementation of BCNet built on

Lei Ke 434 Dec 01, 2022
A set of tests for evaluating large-scale algorithms for Wasserstein-2 transport maps computation.

Continuous Wasserstein-2 Benchmark This is the official Python implementation of the NeurIPS 2021 paper Do Neural Optimal Transport Solvers Work? A Co

Alexander 22 Dec 12, 2022
Pseudo-rng-app - whos needs science to make a random number when you have pseudoscience?

Pseudo-random numbers with pseudoscience rng is so complicated! Why cant we have a horoscopic, vibe-y way of calculating a random number? Why cant rng

Andrew Blance 1 Dec 27, 2021
BankNote-Net: Open dataset and encoder model for assistive currency recognition

BankNote-Net: Open Dataset for Assistive Currency Recognition Millions of people around the world have low or no vision. Assistive software applicatio

Microsoft 13 Oct 28, 2022