Retail Customer Segmentation using K-Means Clustering

Overview

This project uses K-Means Clustering to segment retail customers based on their purchasing behaviour. Customers are grouped using Age, Annual Income, and Spending Score, helping businesses identify valuable customer segments and design targeted marketing strategies.

The repository also includes a modern Streamlit dashboard for real-time customer segmentation.

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Business Problem

Retail businesses often use the same marketing strategy for every customer. This project identifies distinct customer groups so businesses can:

• Improve targeted marketing
• Increase customer retention
• Identify high-value customers
• Personalize promotional campaigns

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Dataset

Dataset: Mall_Customers.csv

Features used:

• Age
• Annual Income (k$)
• Spending Score (1-100)

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Technologies

• Python
• Pandas
• NumPy
• Matplotlib
• Seaborn
• Plotly
• Scikit-Learn
• Streamlit

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Machine Learning Workflow

1. Data Cleaning
2. Exploratory Data Analysis
3. Label Encoding
4. Feature Scaling using StandardScaler
5. Elbow Method
6. K-Means Clustering
7. Cluster Analysis
8. Business Recommendations

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Customer Segments

Cluster	Segment	Strategy
0	Senior Value Customers	Loyalty rewards and value offers
1	Premium Customers	VIP services and exclusive rewards
2	Young Active Shoppers	Student offers and social media campaigns
3	Affluent Conservative Customers	Personalized luxury promotions

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Streamlit Dashboard

Features:

• Interactive customer prediction
• Business recommendations
• Plotly visualization
• Random customer generator
• Responsive UI

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Screenshots

Home

Dashboard

Visualization

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Project Structure

Retail-Customer-Segmentation/
│
├── data/
├── models/
├── Final App/
├── Notebook/
├── Graphs/
├── screenshots/
├── requirements.txt
└── README.md

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Installation

git clone <repository-url>

cd Retail-Customer-Segmentation

pip install -r requirements.txt

streamlit run "Final App/app.py"

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Results

• Four meaningful customer segments identified
• Business-driven customer insights
• Interactive prediction dashboard
• Segment-specific recommendations

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Future Improvements

• CSV upload support
• Additional clustering algorithms
• PCA visualization
• Cloud deployment
• Downloadable reports

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Live Demo

Streamlit App

https://customer-clustering-dashboard.streamlit.app/

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Connect

LinkedIn

https://www.linkedin.com/in/rohit-jha-ai/

GitHub

https://github.com/Rohit-coder-py

Author

Rohit Jha

Aspiring AI Engineer | Machine Learning | Data Science

If you found this project useful, consider giving it a star on GitHub.