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Data/
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Customer Segmentation.Rproj
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customer segmentation.Rmd
customer-segmentation.html
styles.css

# Shopping-Mall-Customer-Segmentation Customer segmentation of shopping mall customers with k-means clustering # Overview This repository contains codes in form of a Rmarkdown file and resources for performing customer segmentation on a shopping mall customer database. The segmentation is done using the K-means clustering algorithm. The dataset used in this project is sourced from Kaggle. ## Introduction Customer segmentation is a crucial aspect of marketing strategy, enabling businesses to better understand their customers and tailor their approach to different segments. In this project, we apply K-means clustering to group shopping mall customers based on various features, such as age, gender, annual income, and spending score. ## Dataset The dataset used for this project is obtained from Kaggle and includes information about shopping mall customers. The data is preprocessed and utilized for K-means clustering to identify distinct customer segments. Dataset Source: [Kaggle](https://www.kaggle.com/datasets/vjchoudhary7/customer-segmentation-tutorial-in-python) ## Methodology The project follows the CRISP-DM (Cross-Industry Standard Process for Data Mining) methodology. The process involves stages such as business understanding, data understanding, data preparation, modeling, evaluation, and deployment. K-means clustering is applied to identify clusters within the customer data. ## Results The results are visualized and interpreted using plots and interactive tools. A summary of the clusters and their characteristics are provided. ## Dependencies Ensure you have the following dependencies installed: - RStudio - tidyverse - tidyquant - broom - umap - plotly - DT ## License This project is licensed under the terms of the MIT license.

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