https://github.com/lucaso21/customer-segmentation
A data analysis project segmenting customers based on certain characteristics.
https://github.com/lucaso21/customer-segmentation
dataanalytics datascience ggplot2 kmeans-clustering r tidyverse
Last synced: 11 months ago
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A data analysis project segmenting customers based on certain characteristics.
- Host: GitHub
- URL: https://github.com/lucaso21/customer-segmentation
- Owner: LucasO21
- Created: 2021-08-14T19:41:41.000Z (over 4 years ago)
- Default Branch: main
- Last Pushed: 2022-12-29T12:34:22.000Z (about 3 years ago)
- Last Synced: 2025-01-22T16:11:17.668Z (about 1 year ago)
- Topics: dataanalytics, datascience, ggplot2, kmeans-clustering, r, tidyverse
- Language: HTML
- Homepage: https://rpubs.com/LucasO/799240
- Size: 4.45 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Customer Segmentation
---
## Description:
A data analysis project segmenting customers based on multiple characteristics such as Age, Annual Income and Spending Score.
## Data Source:
[Kaggle](https://www.kaggle.com/datasets/vjchoudhary7/customer-segmentation-tutorial-in-python).
## Segmentation Method:
KMEANS.
## Tools / Pacakages:
+ R was used to analyze the data
+ Packages used include tidyverse (data wrangling), broom(for working with KMeans objects), patchwork (for data visualization).
## Steps:
+ Exploratory Data Analysis to understand the data better.
+ KMeans clustering was implemented.
+ Clusters were further analyzed.
## Outcome:
Customers were segmented by Age and Income, Age and Spending Score and Income and Spending Score. 4 unique clusters were determined for Age and Income and Age and Spending Score. 5 unique clusters were determined for Income and Spending Score.
Age and Income clusters were further analyzed to provide an additional within cluster binning of income into low, medium and high income.
See final article [here](https://rpubs.com/LucasO/799240).