Customer Segmentation Cluster Analysis in R

• Author: Amelia Tang

About

This project aims to identify customer segments using a subset of the customer information dataset from kaggle.com. The dataset contains 6718 rows of non-null data. To conduct the customer segmentation analysis, I utilized the k-means clustering algorithm and implemented the algorithm in R.

K-means is a popular unsupervised machine learning algorithm used for clustering data points into K distinct clusters based on their similarity. It's one of the most popular algorithms for customer segmentation analysis. The below diagram illustrates how the algorithm works step by step:

After clustering the data, the clusters are visualized using clusplot. Clusplot shows first two principal components to explain the clusters.

Reports

EDA Report using ggplot2
Customer Segmentation using K-means Clustering Project Report

Usage

To replicate the analysis, clone this GitHub repository, install the dependencies listed below, and run the following command at the command line/terminal from the root directory of this project:

make all

To reset the repo to a clean state, with no intermediate or results files, run the following command at the command line/terminal from the root directory of this project:

make clean

Dependencies

R version 4.2.3 (2023-03-15) and R packages:

• readr
• tidyr
• dplyr
• ggplot2
• magrittr
• fastDummies
• factoextra
• cluster
• knitr
• ggthemes
• kableExtra